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fonnesbeck/Dataset Statistics.ipynb

Created April 26, 2016 14:56
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Dataset Statistics.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import modules and set options\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Connect to database to import data for the three test domains and demographic information:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from redcap import Project\n",
"api_url = 'https://redcap.vanderbilt.edu/api/'\n",
"api_key = open(\"/Users/fonnescj/Dropbox/Collaborations/LSL-DR/api_token.txt\").read()\n",
"\n",
"lsl_dr_project = Project(api_url, api_key)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metadata = lsl_dr_project.export_metadata()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# for i,j in zip(lsl_dr_project.field_names, \n",
"# lsl_dr_project.field_labels):\n",
"# print('{0}: \\t{1}'.format(i,j))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import each database from REDCap:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation_fields = ['study_id','redcap_event_name', 'age_test_aaps','aaps_ss','age_test_gf2','gf2_ss']\n",
"articulation = lsl_dr_project.export_records(fields=articulation_fields, format='df', df_kwargs={'index_col':None})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"records = lsl_dr_project.export_records(fields=articulation_fields)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0101-2002-0101\n"
]
}
],
"source": [
"print(records[0]['study_id'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive_fields = ['study_id','redcap_event_name','age_test_eowpvt','eowpvt_ss','age_test_evt','evt_ss']\n",
"expressive = lsl_dr_project.export_records(fields=expressive_fields, format='df', \n",
" df_kwargs={'index_col':None,\n",
" 'na_values':[999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_fields = ['study_id','redcap_event_name','age_test_ppvt','ppvt_ss','age_test_rowpvt','rowpvt_ss']\n",
"receptive = lsl_dr_project.export_records(fields=receptive_fields, format='df', \n",
" df_kwargs={'index_col':None,\n",
" 'na_values':[999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"language_fields = ['study_id','redcap_event_name','pls_ac_ss','pls_ec_ss','pls_choice','age_test_pls',\n",
" 'owls_lc_ss','owls_oe_ss','age_test_owls',\n",
" 'celfp_rl_ss','celfp_el_ss','age_test_celp',\n",
" 'celf_elss','celf_rlss','age_test_celf',\n",
" 'celfp_ss_ss', 'celfp_ws_ss', 'celfp_ev_ss', 'celfp_fd_ss',\n",
" 'celfp_rs_ss', 'celfp_bc_ss', 'celfp_wcr_ss', 'celfp_wce_ss',\n",
" 'celfp_wct_ss']\n",
"language_raw = lsl_dr_project.export_records(fields=language_fields, format='df', \n",
" df_kwargs={'index_col':None, \n",
" 'na_values':[999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic_fields = ['study_id','redcap_event_name','redcap_data_access_group', 'academic_year',\n",
"'hl','prim_lang','mother_ed','father_ed','premature_age', 'synd_cause', 'age_disenrolled', 'race',\n",
"'onset_1','age_int','age','age_amp', 'age_ci', 'age_ci_2', 'degree_hl_ad','type_hl_ad','tech_ad','degree_hl_as',\n",
"'type_hl_as','tech_as','etiology','etiology_2', 'sib', 'gender', 'time', 'ad_250', 'as_250', 'ae',\n",
"'ad_500', 'as_500', 'fam_age', 'family_inv', 'demo_ses', 'school_lunch', 'medicaid', 'hearing_changes',\n",
"'slc_fo', 'sle_fo', 'a_fo', 'funct_out_age',\n",
"'att_days_hr', 'att_days_sch', 'att_days_st2_417']\n",
"demographic_raw = lsl_dr_project.export_records(fields=demographic_fields, format='df', \n",
" df_kwargs={'index_col':None, \n",
" 'na_values':[888, 999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>...</th>\n",
" <th>sle_fo</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14329</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>65.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14330</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>77.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14331</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>89.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14332</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>5.0</td>\n",
" <td>101.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name academic_year hl gender \\\n",
"14329 1147-2010-0064 initial_assessment_arm_1 2010-2011 0.0 0.0 \n",
"14330 1147-2010-0064 year_1_complete_71_arm_1 2011-2012 0.0 NaN \n",
"14331 1147-2010-0064 year_2_complete_71_arm_1 2012-2013 0.0 NaN \n",
"14332 1147-2010-0064 year_3_complete_71_arm_1 2013-2014 0.0 NaN \n",
"\n",
" race prim_lang sib mother_ed father_ed ... sle_fo a_fo \\\n",
"14329 0.0 0.0 1.0 3.0 3.0 ... 3.0 6.0 \n",
"14330 NaN NaN NaN NaN NaN ... 3.0 5.0 \n",
"14331 NaN NaN NaN NaN NaN ... 3.0 5.0 \n",
"14332 NaN NaN NaN NaN NaN ... 4.0 5.0 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"14329 65.0 0.0 NaN NaN NaN \n",
"14330 77.0 2.0 NaN NaN NaN \n",
"14331 89.0 2.0 NaN NaN NaN \n",
"14332 101.0 2.0 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid \n",
"14329 NaN NaN NaN \n",
"14330 NaN NaN NaN \n",
"14331 NaN NaN NaN \n",
"14332 NaN NaN NaN \n",
"\n",
"[4 rows x 46 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic_raw[demographic_raw.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attendance information"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several fields in the demographic data have missing values."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>...</th>\n",
" <th>sle_fo</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2002-2003</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>54.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2003-2004</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>80.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2004-2005</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>80.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2005-2006</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>96.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_4_complete_71_arm_1</td>\n",
" <td>2006-2007</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>109.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name academic_year hl gender race \\\n",
"0 0101-2002-0101 initial_assessment_arm_1 2002-2003 0.0 0.0 0.0 \n",
"1 0101-2002-0101 year_1_complete_71_arm_1 2003-2004 0.0 NaN NaN \n",
"2 0101-2002-0101 year_2_complete_71_arm_1 2004-2005 0.0 NaN NaN \n",
"3 0101-2002-0101 year_3_complete_71_arm_1 2005-2006 0.0 NaN NaN \n",
"4 0101-2002-0101 year_4_complete_71_arm_1 2006-2007 0.0 NaN NaN \n",
"\n",
" prim_lang sib mother_ed father_ed ... sle_fo a_fo fam_age \\\n",
"0 0.0 1.0 6.0 6.0 ... 2.0 2.0 54.0 \n",
"1 NaN NaN NaN NaN ... 4.0 4.0 80.0 \n",
"2 NaN NaN NaN NaN ... 4.0 4.0 80.0 \n",
"3 NaN NaN NaN NaN ... 5.0 5.0 96.0 \n",
"4 NaN NaN NaN NaN ... 5.0 5.0 109.0 \n",
"\n",
" family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"0 2.0 NaN NaN NaN NaN \n",
"1 1.0 NaN NaN NaN NaN \n",
"2 2.0 NaN NaN NaN NaN \n",
"3 3.0 NaN NaN NaN NaN \n",
"4 2.0 NaN NaN NaN NaN \n",
"\n",
" school_lunch medicaid \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic_raw.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can fill missing values forward from previous observation (by `study_id`)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n",
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"demographic = demographic_raw.sort(columns='redcap_event_name').groupby('study_id').transform(\n",
" lambda recs: recs.fillna(method='ffill'))#.reset_index()\n",
"demographic[\"study_id\"] = demographic_raw.sort(columns='redcap_event_name').study_id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Random check to make sure this worked"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" <th>study_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14329</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>65.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14330</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>77.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14331</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>89.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14332</th>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>101.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl gender race prim_lang \\\n",
"14329 initial_assessment_arm_1 2010-2011 0.0 0.0 0.0 0.0 \n",
"14330 year_1_complete_71_arm_1 2011-2012 0.0 0.0 0.0 0.0 \n",
"14331 year_2_complete_71_arm_1 2012-2013 0.0 0.0 0.0 0.0 \n",
"14332 year_3_complete_71_arm_1 2013-2014 0.0 0.0 0.0 0.0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo \\\n",
"14329 1.0 3.0 3.0 8.0 ... 6.0 \n",
"14330 1.0 3.0 3.0 8.0 ... 5.0 \n",
"14331 1.0 3.0 3.0 8.0 ... 5.0 \n",
"14332 1.0 3.0 3.0 8.0 ... 5.0 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"14329 65.0 0.0 NaN NaN NaN \n",
"14330 77.0 2.0 NaN NaN NaN \n",
"14331 89.0 2.0 NaN NaN NaN \n",
"14332 101.0 2.0 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid study_id \n",
"14329 NaN NaN NaN 1147-2010-0064 \n",
"14330 NaN NaN NaN 1147-2010-0064 \n",
"14331 NaN NaN NaN 1147-2010-0064 \n",
"14332 NaN NaN NaN 1147-2010-0064 \n",
"\n",
"[4 rows x 46 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[demographic.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Demographic data without missing values:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" <th>study_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2002-2003</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>54.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0101-2002-0101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7884</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2008-2009</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>7.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>53.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0628-2005-2156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7882</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>48.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0628-2005-2081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7876</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>5.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>86.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0628-2005-1986</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7872</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>94.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0628-2005-1978</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl gender race prim_lang \\\n",
"0 initial_assessment_arm_1 2002-2003 0.0 0.0 0.0 0.0 \n",
"7884 initial_assessment_arm_1 2008-2009 0.0 0.0 0.0 0.0 \n",
"7882 initial_assessment_arm_1 2009-2010 0.0 1.0 2.0 0.0 \n",
"7876 initial_assessment_arm_1 2009-2010 0.0 0.0 2.0 0.0 \n",
"7872 initial_assessment_arm_1 2009-2010 0.0 0.0 2.0 0.0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo fam_age \\\n",
"0 1.0 6.0 6.0 9.0 ... 2.0 54.0 \n",
"7884 2.0 5.0 5.0 7.0 ... 5.0 53.0 \n",
"7882 3.0 6.0 6.0 8.0 ... 3.0 48.0 \n",
"7876 0.0 3.0 6.0 5.0 ... 4.0 86.0 \n",
"7872 0.0 6.0 6.0 8.0 ... 5.0 94.0 \n",
"\n",
" family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"0 2.0 NaN NaN NaN NaN \n",
"7884 0.0 NaN NaN NaN NaN \n",
"7882 2.0 NaN NaN NaN NaN \n",
"7876 2.0 NaN NaN NaN NaN \n",
"7872 0.0 NaN NaN NaN NaN \n",
"\n",
" school_lunch medicaid study_id \n",
"0 NaN NaN 0101-2002-0101 \n",
"7884 NaN NaN 0628-2005-2156 \n",
"7882 NaN NaN 0628-2005-2081 \n",
"7876 NaN NaN 0628-2005-1986 \n",
"7872 NaN NaN 0628-2005-1978 \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning languge dataset\n",
"\n",
"5 language measures:\n",
"\n",
"- 3 versions of CELF\n",
"- PLS\n",
" - pls_ac_rs: \tPLS: Auditory Comprehension Raw Score\n",
" - pls_ac_ss: \tPLS: Auditory Comprehension Standard Score\n",
" - pls_ec_rs: \tPLS: Expressive Communication Raw Score\n",
" - pls_ec_ss: \tPLS: Expressive Communication Standard Score\n",
" - pls_tl_rs: \tPLS: Total Language Score Standard Score Total\n",
" - pls_tl_ss: \tPLS: Total Language Score Standard Score\n",
"- OWLS\n",
" - age_test_owls: \tAge at time of testing (OWLS)\n",
" - owls_lc_rs: \tOWLS: Listening Comprehension Raw Score\n",
" - owls_lc_ss: \tOWLS: Listening Comprehension Standard Score\n",
" - owls_oe_rs: \tOWLS: Oral Expression Raw Score\n",
" - owls_oe_ss: \tOWLS: Oral Expression Standard Score\n",
" - owls_oc_sss: \tOWLS: Oral Composite Sum of Listening Comprehension and Oral Expression Standard Scores\n",
" - owls_oc_ss: \tOWLS: Oral Composite Standard Score\n",
" - owls_wes_trs: \tOWLS: Written Expression Scale Total Raw Score\n",
" - owls_wes_as: \tOWLS: Written Expression Scale Ability Score\n",
" - owls_wes_ss: \tOWLS: Written Expression Scale Standard Score\n",
" - owsl_lc: \tOWLS: Written Expression Scale Language Composite (Sum of written expression age-based standard score, listening comprehension standard score and oral expression standard score)\n",
" - owls_lcss: \tOWLS: Language Composite Standard Score"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test_type expressive receptive\n",
"test_name \n",
"CELF-4 611 537\n",
"CELF-P2 1448 1453\n",
"OWLS 1075 1081\n",
"PLS 3447 3459\n",
"There are 0 null values for score\n"
]
}
],
"source": [
"# Test type\n",
"language_raw[\"test_name\"] = None\n",
"language_raw[\"test_type\"] = None\n",
"language_raw[\"score\"] = None\n",
"CELP = language_raw.age_test_celp.notnull()\n",
"CELF = language_raw.age_test_celf.notnull()\n",
"PLS = language_raw.age_test_pls.notnull()\n",
"OWLS = language_raw.age_test_owls.notnull()\n",
"\n",
"language_raw['age_test'] = None\n",
"language_raw.loc[CELP, 'age_test'] = language_raw.age_test_celp\n",
"language_raw.loc[CELF, 'age_test'] = language_raw.age_test_celf\n",
"language_raw.loc[PLS, 'age_test'] = language_raw.age_test_pls\n",
"language_raw.loc[OWLS, 'age_test'] = language_raw.age_test_owls\n",
"\n",
"language1 = language_raw[CELP | CELF | PLS | OWLS].copy()\n",
"language2 = language1.copy()\n",
"\n",
"language1[\"test_type\"] = \"receptive\"\n",
"\n",
"language1.loc[CELP, \"test_name\"] = \"CELF-P2\"\n",
"language1.loc[CELF, \"test_name\"] = \"CELF-4\"\n",
"language1.loc[PLS, \"test_name\"] = \"PLS\"\n",
"language1.loc[OWLS, \"test_name\"] = \"OWLS\"\n",
"\n",
"language1.loc[CELP, \"score\"] = language1.celfp_rl_ss\n",
"language1.loc[CELF, \"score\"] = language1.celf_rlss\n",
"language1.loc[PLS, \"score\"] = language1.pls_ac_ss\n",
"language1.loc[OWLS, \"score\"] = language1.owls_lc_ss\n",
"\n",
"\n",
"language2[\"test_type\"] = \"expressive\"\n",
"\n",
"language2.loc[CELP, \"test_name\"] = \"CELF-P2\"\n",
"language2.loc[CELF, \"test_name\"] = \"CELF-4\"\n",
"language2.loc[PLS, \"test_name\"] = \"PLS\"\n",
"language2.loc[OWLS, \"test_name\"] = \"OWLS\"\n",
"\n",
"language2.loc[CELP, \"score\"] = language1.celfp_el_ss\n",
"language2.loc[CELF, \"score\"] = language1.celf_elss\n",
"language2.loc[PLS, \"score\"] = language1.pls_ec_ss\n",
"language2.loc[OWLS, \"score\"] = language1.owls_oe_ss\n",
"\n",
"language = pd.concat([language1, language2])\n",
"language = language[language.score.notnull()]\n",
"print(pd.crosstab(language.test_name, language.test_type))\n",
"print(\"There are {0} null values for score\".format(sum(language[\"score\"].isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"language[\"school\"] = language.study_id.str.slice(0,4)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"language_subtest = language[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \n",
" \"test_name\", \"school\", \"age_test\", \n",
" 'celfp_ss_ss', 'celfp_ws_ss', \n",
" 'celfp_ev_ss', 'celfp_fd_ss',\n",
" 'celfp_rs_ss', 'celfp_bc_ss', \n",
" 'celfp_wcr_ss', 'celfp_wce_ss',\n",
" 'celfp_wct_ss']]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>score</th>\n",
" <th>test_type</th>\n",
" <th>test_name</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>51</td>\n",
" <td>receptive</td>\n",
" <td>PLS</td>\n",
" <td>0101</td>\n",
" <td>54</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>61</td>\n",
" <td>receptive</td>\n",
" <td>OWLS</td>\n",
" <td>0101</td>\n",
" <td>113</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>55</td>\n",
" <td>receptive</td>\n",
" <td>PLS</td>\n",
" <td>0101</td>\n",
" <td>44</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>77</td>\n",
" <td>receptive</td>\n",
" <td>PLS</td>\n",
" <td>0101</td>\n",
" <td>54</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>93</td>\n",
" <td>receptive</td>\n",
" <td>CELF-P2</td>\n",
" <td>0101</td>\n",
" <td>68</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name score test_type test_name \\\n",
"0 0101-2002-0101 initial_assessment_arm_1 51 receptive PLS \n",
"5 0101-2002-0101 year_5_complete_71_arm_1 61 receptive OWLS \n",
"9 0101-2003-0102 initial_assessment_arm_1 55 receptive PLS \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 77 receptive PLS \n",
"11 0101-2003-0102 year_2_complete_71_arm_1 93 receptive CELF-P2 \n",
"\n",
" school age_test domain \n",
"0 0101 54 Language \n",
"5 0101 113 Language \n",
"9 0101 44 Language \n",
"10 0101 54 Language \n",
"11 0101 68 Language "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"language = language[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"test_name\", \"school\", \"age_test\"]]\n",
"language[\"domain\"] = \"Language\"\n",
"language.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning articulation dataset\n",
"\n",
"We converted the articulation dataset into a \"long\" format:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Goldman 5286\n",
"Arizonia 502\n",
"Arizonia and Goldman 73\n",
"Name: test_type, dtype: int64\n",
"There are 0 null values for test_type\n"
]
}
],
"source": [
"# Test type\n",
"articulation[\"test_type\"] = None\n",
"ARIZ = articulation.aaps_ss.notnull()\n",
"GF = articulation.gf2_ss.notnull()\n",
"articulation = articulation[ARIZ | GF]\n",
"articulation.loc[(ARIZ & GF), \"test_type\"] = \"Arizonia and Goldman\"\n",
"articulation.loc[(ARIZ & ~GF), \"test_type\"] = \"Arizonia\"\n",
"articulation.loc[(~ARIZ & GF), \"test_type\"] = \"Goldman\"\n",
"\n",
"print(articulation.test_type.value_counts())\n",
"print(\"There are {0} null values for test_type\".format(sum(articulation[\"test_type\"].isnull())))\n",
"\n",
"# Test score (Arizonia if both)\n",
"articulation[\"score\"] = articulation.aaps_ss\n",
"articulation.loc[(~ARIZ & GF), \"score\"] = articulation.gf2_ss[~ARIZ & GF]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation[\"school\"] = articulation.study_id.str.slice(0,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The age was taken to be the Arizonia age if there are both test types:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 5859.000000\n",
"mean 68.853559\n",
"std 30.782839\n",
"min 23.000000\n",
"25% 47.000000\n",
"50% 60.000000\n",
"75% 81.000000\n",
"max 243.000000\n",
"Name: age_test, dtype: float64\n"
]
}
],
"source": [
"articulation[\"age_test\"] = articulation.age_test_aaps\n",
"articulation.loc[articulation.age_test.isnull(), 'age_test'] = articulation.age_test_gf2[articulation.age_test.isnull()]\n",
"print(articulation.age_test.describe())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we dropped unwanted columns and added a domain identification column for merging:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>test_type</th>\n",
" <th>score</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>78.0</td>\n",
" <td>0101</td>\n",
" <td>80.0</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>72.0</td>\n",
" <td>0101</td>\n",
" <td>44.0</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>97.0</td>\n",
" <td>0101</td>\n",
" <td>54.0</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>75.0</td>\n",
" <td>0101</td>\n",
" <td>53.0</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>80.0</td>\n",
" <td>0101</td>\n",
" <td>66.0</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name test_type score school \\\n",
"1 0101-2002-0101 year_1_complete_71_arm_1 Goldman 78.0 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 Goldman 72.0 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 Goldman 97.0 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 Goldman 75.0 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 Goldman 80.0 0101 \n",
"\n",
" age_test domain \n",
"1 80.0 Articulation \n",
"9 44.0 Articulation \n",
"10 54.0 Articulation \n",
"14 53.0 Articulation \n",
"15 66.0 Articulation "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation = articulation.drop([\"age_test_aaps\", \"age_test_gf2\", \"aaps_ss\", \"gf2_ss\"], axis=1)\n",
"articulation[\"domain\"] = \"Articulation\"\n",
"articulation.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning demographic dataset\n",
"\n",
"We excluded unwanted columns and rows for which age, gender or race were missing:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Retain only subset of columns\n",
"#demographic = demographic[demographic.gender.notnull()]\n",
"demographic = demographic.rename(columns={'gender':'male'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Due to sample size considerations, we reduced the non-English primary language variable to English (0) and non-English (1):"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False 11660\n",
"True 2590\n",
"Name: non_english, dtype: int64\n",
"There are 710 null values for non_english\n"
]
}
],
"source": [
"demographic[\"non_english\"] = None\n",
"demographic.loc[demographic.prim_lang.notnull(), 'non_english'] = demographic.prim_lang[demographic.prim_lang.notnull()]>0\n",
"print(demographic.non_english.value_counts())\n",
"print(\"There are {0} null values for non_english\".format(sum(demographic.non_english.isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mother's education (`mother_ed`) and father's education (`father_ed`) were both recoded to: \n",
"\n",
"* 0=no high school diploma\n",
"* 1=high school\n",
"* 2=undergraduate\n",
"* 3=graduate\n",
"\n",
"Category 6 (unknown) was recoded as missing."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_mother_ed:\n",
"6.0 5198\n",
"4.0 3039\n",
"3.0 2053\n",
"5.0 1638\n",
"2.0 1436\n",
"1.0 491\n",
"0.0 215\n",
"Name: _mother_ed, dtype: int64\n",
"mother_ed:\n",
"1.0 3489\n",
"2.0 3039\n",
"3.0 1638\n",
"0.0 706\n",
"Name: mother_ed, dtype: int64\n",
"\n",
"There are 6088 null values for mother_ed\n"
]
}
],
"source": [
"demographic = demographic.rename(columns={\"mother_ed\":\"_mother_ed\"})\n",
"demographic[\"mother_ed\"] = demographic._mother_ed.copy()\n",
"demographic.loc[demographic._mother_ed==1, 'mother_ed'] = 0\n",
"demographic.loc[(demographic._mother_ed==2) | (demographic.mother_ed==3), 'mother_ed'] = 1\n",
"demographic.loc[demographic._mother_ed==4, 'mother_ed'] = 2\n",
"demographic.loc[demographic._mother_ed==5, 'mother_ed'] = 3\n",
"demographic.loc[demographic._mother_ed==6, 'mother_ed'] = None\n",
"print(\"_mother_ed:\")\n",
"print(demographic._mother_ed.value_counts())\n",
"print(\"mother_ed:\")\n",
"print(demographic.mother_ed.value_counts())\n",
"print(\"\\nThere are {0} null values for mother_ed\".format(sum(demographic.mother_ed.isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Secondary diagnosis"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(14960, 48)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic['secondary_diagnosis'] = demographic.etiology==0\n",
"# Suspected or unknown treated as missing\n",
"demographic.loc[demographic.etiology > 1, 'secondary_diagnosis'] = None"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0 10979\n",
"1.0 2485\n",
"Name: secondary_diagnosis, dtype: int64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.secondary_diagnosis.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.18456625074272134"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.secondary_diagnosis.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Premature status was recoded to True (premature) and False (full-term). Here, premature indicates <36 weeks."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 3492 null values for premature_weeks\n"
]
}
],
"source": [
"demographic['premature_weeks'] = demographic.premature_age.copy()\n",
"demographic.loc[demographic.premature_age==9, 'premature_weeks'] = None\n",
"demographic.premature_weeks = abs(demographic.premature_weeks-8)*2\n",
"print(\"There are {0} null values for premature_weeks\".format(sum(demographic.premature_weeks.isnull())))"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0 9803\n",
"2.0 585\n",
"4.0 373\n",
"12.0 205\n",
"6.0 183\n",
"10.0 154\n",
"8.0 120\n",
"14.0 42\n",
"16.0 3\n",
"Name: premature_weeks, dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.premature_weeks.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode impant technology variables for each ear to one of four categories (None, Baha, Hearing aid, Cochlear implant):"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0 5090\n",
"0.0 4379\n",
"7.0 1554\n",
"5.0 1022\n",
"2.0 519\n",
"6.0 426\n",
"8.0 76\n",
"9.0 70\n",
"4.0 29\n",
"3.0 28\n",
"10.0 3\n",
"Name: tech_ad, dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_ad.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tech_cats = [\"None\", \"OAD\", \"Hearing aid\", \"Cochlear\", \"Other\"]\n",
"\n",
"demographic[\"tech_right\"] = 4\n",
"demographic.loc[demographic.tech_ad==7, 'tech_right'] = 0\n",
"demographic.loc[demographic.tech_ad==3, 'tech_right'] = 1\n",
"demographic.loc[demographic.tech_ad.isin([1,2,4,5,10]), 'tech_right'] = 2\n",
"demographic.loc[demographic.tech_ad.isin([0,8,6]), 'tech_right'] = 3\n",
"demographic.loc[demographic.tech_ad.isnull(), 'tech_right'] = None\n",
"\n",
"demographic[\"tech_left\"] = 4\n",
"demographic.loc[demographic.tech_as==7, 'tech_left'] = 0\n",
"demographic.loc[demographic.tech_as==3, 'tech_left'] = 1\n",
"demographic.loc[demographic.tech_as.isin([1,2,4,5,10]), 'tech_left'] = 2\n",
"demographic.loc[demographic.tech_as.isin([0,8,6]), 'tech_left'] = 3\n",
"demographic.loc[demographic.tech_as.isnull(), 'tech_left'] = None"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.0 6754\n",
"3.0 4455\n",
"0.0 1877\n",
"4.0 60\n",
"1.0 20\n",
"Name: tech_left, dtype: int64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_left.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.0 6663\n",
"3.0 4881\n",
"0.0 1554\n",
"4.0 70\n",
"1.0 28\n",
"Name: tech_right, dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_right.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Substitute valid missing values for hearing loss:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[demographic.type_hl_ad==5, 'type_hl_ad'] = None\n",
"demographic.loc[demographic.type_hl_as==5, 'type_hl_ad'] = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create `degree_hl`, which is the maximum level of hearing loss in either ear:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic[\"degree_hl\"] = np.maximum(demographic.degree_hl_ad, demographic.degree_hl_as)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create compound indicator variable for each technology (Baha, Hearing aid, Chochlear implant): \n",
"\n",
"* 0=none\n",
"* 1=one ear\n",
"* 2=both ears."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['redcap_event_name', 'academic_year', 'hl', 'male', 'race', 'prim_lang',\n",
" 'sib', '_mother_ed', 'father_ed', 'premature_age', 'onset_1', 'age_amp',\n",
" 'age_int', 'age', 'synd_cause', 'etiology', 'etiology_2',\n",
" 'hearing_changes', 'ae', 'ad_250', 'ad_500', 'degree_hl_ad',\n",
" 'type_hl_ad', 'tech_ad', 'age_ci', 'as_250', 'as_500', 'degree_hl_as',\n",
" 'type_hl_as', 'tech_as', 'age_ci_2', 'time', 'age_disenrolled',\n",
" 'funct_out_age', 'slc_fo', 'sle_fo', 'a_fo', 'fam_age', 'family_inv',\n",
" 'att_days_sch', 'att_days_st2_417', 'att_days_hr', 'demo_ses',\n",
" 'school_lunch', 'medicaid', 'study_id', 'non_english', 'mother_ed',\n",
" 'secondary_diagnosis', 'premature_weeks', 'tech_right', 'tech_left',\n",
" 'degree_hl'],\n",
" dtype='object')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.columns"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"oad:\n",
"0 4676\n",
"1 4\n",
"2 2\n",
"Name: oad, dtype: int64\n",
"There are 1711 null values for OAD\n",
"\n",
"hearing_aid:\n",
"2 2190\n",
"0 1648\n",
"1 813\n",
"Name: hearing_aid, dtype: int64\n",
"There are 1764 null values for hearing_aid\n",
"\n",
"cochlear:\n",
"0 3120\n",
"2 924\n",
"1 638\n",
"Name: cochlear, dtype: int64\n",
"There are 1711 null values for cochlear\n",
"14960\n"
]
}
],
"source": [
"demographic[\"oad\"] = 0\n",
"demographic.oad = demographic.oad.astype(object)\n",
"demographic.loc[(demographic.tech_right==1) | (demographic.tech_left==1), 'oad'] = 1\n",
"demographic.loc[(demographic.tech_right==1) & (demographic.tech_left==1), 'oad'] = 2\n",
"demographic.loc[(demographic.tech_right.isnull()) & (demographic.tech_left.isnull()), 'oad'] = None\n",
"print(\"oad:\")\n",
"print(demographic.drop_duplicates(subset='study_id').oad.value_counts())\n",
"print(\"There are {0} null values for OAD\".format(sum(demographic.oad.isnull())))\n",
"\n",
"demographic[\"hearing_aid\"] = 0\n",
"demographic.hearing_aid = demographic.hearing_aid.astype(object)\n",
"demographic.loc[(demographic.tech_right==2) | (demographic.tech_left==2), 'hearing_aid'] = 1\n",
"demographic.loc[(demographic.tech_right==2) & (demographic.tech_left==2), 'hearing_aid'] = 2\n",
"demographic.loc[(demographic.tech_right.isnull()) & (demographic.tech_right.isnull()), 'hearing_aid'] = None\n",
"print(\"\\nhearing_aid:\")\n",
"print(demographic.drop_duplicates(subset='study_id').hearing_aid.value_counts())\n",
"print(\"There are {0} null values for hearing_aid\".format(sum(demographic.hearing_aid.isnull())))\n",
"\n",
"demographic[\"cochlear\"] = 0\n",
"demographic.cochlear = demographic.cochlear.astype(object)\n",
"demographic.loc[(demographic.tech_right==3) | (demographic.tech_left==3), 'cochlear'] = 1\n",
"demographic.loc[(demographic.tech_right==3) & (demographic.tech_left==3), 'cochlear'] = 2\n",
"demographic.loc[(demographic.tech_right.isnull()) & (demographic.tech_left.isnull()), 'cochlear'] = None\n",
"print(\"\\ncochlear:\")\n",
"print(demographic.drop_duplicates(subset='study_id').cochlear.value_counts())\n",
"print(\"There are {0} null values for cochlear\".format(sum(demographic.cochlear.isnull())))\n",
"print(len(demographic))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Identify bilateral and bimodal individuals:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic[\"unilateral_ci\"] = demographic.cochlear==1\n",
"demographic[\"bilateral_ci\"] = demographic.cochlear==2\n",
"demographic[\"bilateral_ha\"] = demographic.hearing_aid==2\n",
"demographic[\"bimodal\"] = (demographic.cochlear==1) & (demographic.hearing_aid==1)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3603, 5485, 1423, 2130)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.bilateral_ci.sum(), demographic.bilateral_ha.sum(), demographic.bimodal.sum(), demographic.unilateral_ci.sum()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"unilateral_ci 638\n",
"bilateral_ci 924\n",
"bilateral_ha 2190\n",
"bimodal 385\n",
"dtype: int64"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.drop_duplicates(subset='study_id')[['unilateral_ci','bilateral_ci', \n",
" 'bilateral_ha',\n",
" 'bimodal']].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create variable that identifies bilateral (0), bilateral HA left (1), bilateral HA right (2)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 0 null values for tech\n"
]
}
],
"source": [
"demographic['tech'] = 0\n",
"demographic.loc[(demographic.bimodal) & (demographic.tech_left==2), 'tech'] = 1\n",
"demographic.loc[(demographic.bimodal) & (demographic.tech_right==2), 'tech'] = 2\n",
"print(\"There are {0} null values for tech\".format(sum(demographic.tech.isnull())))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 5485\n",
"3 3603\n",
"4 1423\n",
"1 999\n",
"0 687\n",
"8 15\n",
"2 12\n",
"7 5\n",
"5 1\n",
"Name: implant_category, dtype: int64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[\"implant_category\"] = None\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==0) & (demographic.oad==0), \n",
" 'implant_category'] = 0\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==1) & (demographic.oad==0), \n",
" 'implant_category'] = 1\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==0) & (demographic.oad==1), \n",
" 'implant_category'] = 2\n",
"demographic.loc[(demographic.cochlear==2) & (demographic.hearing_aid==0) & (demographic.oad==0), \n",
" 'implant_category'] = 3\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==1) & (demographic.oad==0), \n",
" 'implant_category'] = 4\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==0) & (demographic.oad==1), \n",
" 'implant_category'] = 5\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==2) & (demographic.oad==0), \n",
" 'implant_category'] = 6\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==1) & (demographic.oad==1), \n",
" 'implant_category'] = 7\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==0) & (demographic.oad==2), \n",
" 'implant_category'] = 8\n",
"demographic.implant_category.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Age when hearing loss diagnosed** Data are entered inconsistently here, so we have to go in and replace non-numeric values."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 15. , 4. , 0. , 26. , 36. , 24. , 80. , 14. ,\n",
" 62. , 2. , 49. , 19. , 23. , 18. , 9. , nan,\n",
" 10. , 12. , 1. , 5. , 30. , 7. , 51. , 8. ,\n",
" 3. , 17. , 50. , 31. , 34. , 28. , 35. , 38. ,\n",
" 95. , 42. , 13. , 16. , 61. , 46. , 22. , 53. ,\n",
" 59. , 88. , 6. , 37. , 96. , 52. , 64. , 65. ,\n",
" 48. , 97. , 25. , 47. , 79. , 107. , 74. , 77. ,\n",
" 84. , 60. , 41. , 33. , 39. , 27. , 11. , 20. ,\n",
" 21. , 45. , 29. , 32. , 81. , 1.5, 55. , 70. ,\n",
" 58. , 154. , 54. , 78. , 43. , 57. , 83. , 44. ,\n",
" 72. , 116. , 40. , 119. , 63. , 66. , 56. , 87. ,\n",
" 76. , 68. , 92. , 140. , 86. , 126. , 85. , 133. ,\n",
" 103. , 67. , 71. , 2.5, 98. , 75. , 0.5, 152. ,\n",
" 89. ])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.onset_1.unique()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Don't need this anymore\n",
"# demographic['age_diag'] = demographic.onset_1.replace({'birth': 0, 'R- Birth L-16mo': 0, 'birth - 3': 0, 'at birth': 0, 'NBHS': 0, \n",
"# 'at Birth': 0, '1-2': 1.5, '2-3': 2.5, '0-3': 1.5}).astype(float)\n",
"demographic['age_diag'] = demographic.onset_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of null values for `age_diag`"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3993"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.age_diag.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"demographic['sex'] = demographic.male.replace({0:'Female', 1:'Male'})"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sb\n",
"\n",
"unique_students = demographic.dropna(subset=['sex']).groupby('study_id').first()\n",
"\n",
"# ag = sb.factorplot(\"sex\", data=unique_students, \n",
"# palette=\"PuBuGn_d\", kind='count')\n",
"# ag.set_xticklabels(['Female ({})'.format((unique_students.male==0).sum()), \n",
"# 'Male ({})'.format((unique_students.male==1).sum())])\n",
"# ag.set_xlabels('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Child has another diagnosed disability"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic['known_synd'] = (demographic.synd_cause == 0)\n",
"# Unknown or suspected\n",
"demographic.loc[demographic.synd_cause > 1, 'known_synd'] = None"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# If either known syndrome or secondary diagnosis\n",
"demographic['synd_or_disab'] = demographic.apply(lambda x: x['secondary_diagnosis'] or x['known_synd'], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Missing sibling counts were properly encoded as `None` (missing)."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[demographic.sib==4, 'sib'] = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We reduced the number of race categories, pooling those that were neither caucasian, black, hispanic or asian to \"other\", due to small sample sizes for these categories. Category 7 (unknown) was recoded as missing."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_race:\n",
"0.0 7801\n",
"2.0 2554\n",
"1.0 1367\n",
"3.0 1044\n",
"6.0 725\n",
"8.0 531\n",
"7.0 239\n",
"4.0 65\n",
"5.0 33\n",
"Name: _race, dtype: int64\n",
"race:\n",
"0.0 7801\n",
"2.0 2554\n",
"1.0 1367\n",
"4.0 1354\n",
"3.0 1044\n",
"Name: race, dtype: int64\n",
"There are 840 null values for race\n"
]
}
],
"source": [
"races = [\"Caucasian\", \"Black or African American\", \"Hispanic or Latino\", \"Asian\", \"Other\"]\n",
"demographic = demographic.rename(columns={\"race\":\"_race\"})\n",
"demographic[\"race\"] = demographic._race.copy()\n",
"demographic.loc[demographic.race==7, 'race'] = None\n",
"demographic.loc[demographic.race>3, 'race'] = 4\n",
"print(\"_race:\")\n",
"print(demographic._race.value_counts())\n",
"print(\"race:\")\n",
"print(demographic.race.value_counts())\n",
"print(\"There are {0} null values for race\".format(sum(demographic.race.isnull())))\n",
"# Replace with recoded column"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode implant technology variables"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tech_cats = [\"None\", \"Baha\", \"Hearing aid\", \"Cochlear\", \"Other\"]\n",
"\n",
"demographic[\"tech_right\"] = demographic.tech_ad.copy()\n",
"demographic.loc[demographic.tech_right==6, 'tech_right'] = 0\n",
"demographic.loc[demographic.tech_right==4, 'tech_right'] = 1\n",
"demographic.loc[demographic.tech_right==5, 'tech_right'] = 1\n",
"demographic.loc[demographic.tech_right==3, 'tech_right'] = 2\n",
"demographic.loc[demographic.tech_right==7, 'tech_right'] = 3\n",
"demographic.loc[demographic.tech_right==8, 'tech_right'] = 3\n",
"demographic.loc[demographic.tech_right==9, 'tech_right'] = 4\n",
"demographic.tech_right = np.abs(demographic.tech_right - 3)\n",
"\n",
"demographic[\"tech_left\"] = demographic.tech_as.copy()\n",
"demographic.loc[demographic.tech_left==6, 'tech_left'] = 0\n",
"demographic.loc[demographic.tech_left==4, 'tech_left'] = 1\n",
"demographic.loc[demographic.tech_left==5, 'tech_left'] = 1\n",
"demographic.loc[demographic.tech_left==3, 'tech_left'] = 2\n",
"demographic.loc[demographic.tech_left==7, 'tech_left'] = 3\n",
"demographic.loc[demographic.tech_left==8, 'tech_left'] = 3\n",
"demographic.loc[demographic.tech_left==9, 'tech_left'] = 4\n",
"demographic.tech_left = np.abs(demographic.tech_left - 3)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Don't need this anymore\n",
"# demographic['age_amp'] = demographic.age_amp.replace({'22 mo': 22, 'unknown': np.nan, 'none': np.nan, \n",
"# 'n/a unilateral loss': np.nan, 'not amplified yet': np.nan,\n",
"# 'not amplified': np.nan, 'n/a': np.nan, '4 months-6 months': 5,\n",
"# '6 weeks': 0.5, '13-18': 15.5, '0-3': 1.5, '24-36': 30}).astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['2002-2003', '2008-2009', '2009-2010', nan, '2009-2011',\n",
" '2006-2007', '2007-2008', '2011-2012', '2015-2016', '2014-2015',\n",
" '2013-2014', '2012-2013', '2010-2011', '2005-2006', '2014', '2012-',\n",
" '2006-2007 ', '2003-2004', '2015-206', '2004-2005',\n",
" ' 2010-2011 2010-2011',\n",
" '2012', '2011', '2010', '2009', '2013', '1995-1996', '1998-1999',\n",
" '2001-2002', '1999-2000', '2000-2001', '1997-1998', '2014-15',\n",
" '2015', '2015-2015', '2014-2015 ', '2041-2015', '2015-2106',\n",
" '22014-2015', '2014-1015', '2012-2013 '], dtype=object)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.academic_year.replace(\n",
" {'12013-2014': '2013-2014', '2010 - 20111': '2010 - 2011', \n",
" '2020-2011': '2010-2011', '2012-20013': '2012-2013', \n",
" '642014-2015': '2014-2015', '20114-2015': '2014-2015',\n",
" '2011-012': '2011-2012',\n",
" '0000-0000': np.nan}).str.replace('*', '-').unique()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic['academic_year'] = demographic.academic_year.replace(\n",
" {'12013-2014': '2013-2014', '2010 - 20111': '2010 - 2011', \n",
" '2020-2011': '2010-2011', '2012-20013': '2012-2013', \n",
" '642014-2015': '2014-2015', '20114-2015': '2014-2015',\n",
" '2011-012': '2011-2012', '2014-2105': '2014-2015', '2005-2004': '2004-2005',\n",
" '2014-205': '2014-2015', '2017-2015': '2014-2015', '2014-1015': '2014-2015',\n",
" '2015-2015': '2014-2015', '2009-2011': '2009-2010',\n",
" '0000-0000': np.nan}).str.replace('*', '-')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removed entries that don't contain dashes"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[~(demographic.academic_year.notnull() & demographic.academic_year.str.contains('-')), \n",
" 'academic_year'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[demographic.academic_year.notnull(), 'academic_year'] = demographic.academic_year[demographic.academic_year.notnull()].apply(lambda x: ''.join(x.split()))"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10fd8e2b0>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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99dZbqq6uNm6OtLQ0ffOb35Qkfetb39Ibb7wht9tt1AyS9Pjjj+v555/Xzp07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\n5AAAGOz/Azi/nschd/zLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fd6ca90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"demographic.age_amp.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning expressive vocabulary dataset\n",
"\n",
"We converted the expressive vocabulary dataset to \"long\" format:"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 0 null values for test_type\n"
]
}
],
"source": [
"# Test type\n",
"expressive[\"test_type\"] = None\n",
"EOWPVT = expressive.eowpvt_ss.notnull()\n",
"EVT = expressive.evt_ss.notnull()\n",
"expressive = expressive[EOWPVT | EVT]\n",
"expressive.loc[EOWPVT & EVT, \"test_type\"] = \"EOWPVT and EVT\"\n",
"expressive.loc[EOWPVT & ~EVT, \"test_type\"] = \"EOWPVT\"\n",
"expressive.loc[~EOWPVT & EVT, \"test_type\"] = \"EVT\"\n",
"print(\"There are {0} null values for test_type\".format(sum(expressive[\"test_type\"].isnull())))\n",
"\n",
"expressive[\"score\"] = expressive.eowpvt_ss\n",
"expressive.loc[~EOWPVT & EVT, \"score\"] = expressive.evt_ss[~EOWPVT & EVT]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EVT 3812\n",
"EOWPVT 2707\n",
"EOWPVT and EVT 148\n",
"Name: test_type, dtype: int64"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive.test_type.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive[\"school\"] = expressive.study_id.str.slice(0,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The age was taken to be the EOWPVT age if there are both test types:"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive[\"age_test\"] = expressive.age_test_eowpvt\n",
"expressive.loc[expressive.age_test.isnull(), 'age_test'] = expressive.age_test_evt[expressive.age_test.isnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we dropped unwanted columns and added a domain identification column for merging:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>score</th>\n",
" <th>test_type</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>58.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>54.0</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>84.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>80.0</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>90.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>113.0</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>90.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>53.0</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>87.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>66.0</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name score test_type school \\\n",
"0 0101-2002-0101 initial_assessment_arm_1 58.0 EOWPVT 0101 \n",
"2 0101-2002-0101 year_2_complete_71_arm_1 84.0 EOWPVT 0101 \n",
"5 0101-2002-0101 year_5_complete_71_arm_1 90.0 EOWPVT 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 90.0 EOWPVT 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 87.0 EOWPVT 0101 \n",
"\n",
" age_test domain \n",
"0 54.0 Expressive Vocabulary \n",
"2 80.0 Expressive Vocabulary \n",
"5 113.0 Expressive Vocabulary \n",
"14 53.0 Expressive Vocabulary \n",
"15 66.0 Expressive Vocabulary "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive = expressive[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"school\", \"age_test\"]]\n",
"expressive[\"domain\"] = \"Expressive Vocabulary\"\n",
"expressive.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning receptive vocabulary dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We converted the receptive vocabulary data table to \"long\" format:"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['study_id', 'redcap_event_name', 'age_test_ppvt', 'ppvt_ss',\n",
" 'age_test_rowpvt', 'rowpvt_ss'],\n",
" dtype='object')"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.columns"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 0 null values for test_type\n"
]
}
],
"source": [
"# Test type\n",
"receptive[\"test_type\"] = None\n",
"PPVT = receptive.ppvt_ss.notnull()\n",
"ROWPVT = receptive.rowpvt_ss.notnull()\n",
"receptive = receptive[PPVT | ROWPVT]\n",
"receptive.loc[PPVT & ROWPVT, \"test_type\"] = \"PPVT and ROWPVT\"\n",
"receptive.loc[PPVT & ~ROWPVT, \"test_type\"] = \"PPVT\"\n",
"receptive.loc[~PPVT & ROWPVT, \"test_type\"] = \"ROWPVT\"\n",
"print(\"There are {0} null values for test_type\".format(sum(receptive[\"test_type\"].isnull())))\n",
"\n",
"receptive[\"score\"] = receptive.ppvt_ss\n",
"receptive.loc[~PPVT & ROWPVT, \"score\"] = receptive.rowpvt_ss[~PPVT & ROWPVT]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive[\"school\"] = receptive.study_id.str.slice(0,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The age was taken to be the PPVT age if there are both test types:"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive[\"age_test\"] = receptive.age_test_ppvt\n",
"receptive.loc[receptive.age_test.isnull(), 'age_test'] = receptive.age_test_rowpvt[receptive.age_test.isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 27 null values for age_test\n"
]
}
],
"source": [
"print(\"There are {0} null values for age_test\".format(sum(receptive.age_test.isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we dropped unwanted columns and added a domain identification column for merging:"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>score</th>\n",
" <th>test_type</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>90.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>80.0</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>101.0</td>\n",
" <td>ROWPVT</td>\n",
" <td>0101</td>\n",
" <td>113.0</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>55.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>44.0</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>80.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>54.0</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>101.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>68.0</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name score test_type school \\\n",
"2 0101-2002-0101 year_2_complete_71_arm_1 90.0 PPVT 0101 \n",
"5 0101-2002-0101 year_5_complete_71_arm_1 101.0 ROWPVT 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 55.0 PPVT 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 80.0 PPVT 0101 \n",
"11 0101-2003-0102 year_2_complete_71_arm_1 101.0 PPVT 0101 \n",
"\n",
" age_test domain \n",
"2 80.0 Receptive Vocabulary \n",
"5 113.0 Receptive Vocabulary \n",
"9 44.0 Receptive Vocabulary \n",
"10 54.0 Receptive Vocabulary \n",
"11 68.0 Receptive Vocabulary "
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive = receptive[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"school\", \"age_test\"]]\n",
"receptive[\"domain\"] = \"Receptive Vocabulary\"\n",
"receptive.head()"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3076,)"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.study_id.unique().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge datasets\n",
"\n",
"The four datasets were mereged into a single table. First, we concatenate the test scores data:"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_scores = pd.concat([articulation, expressive, receptive, language])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we perform a merge between the demographic data and the test scores data:"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr = pd.merge(demographic, test_scores, on=[\"study_id\", \"redcap_event_name\"], how='left')"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>male</th>\n",
" <th>_race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>_mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>sex</th>\n",
" <th>known_synd</th>\n",
" <th>synd_or_disab</th>\n",
" <th>race</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" <th>school</th>\n",
" <th>score</th>\n",
" <th>test_name</th>\n",
" <th>test_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>38196</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38197</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2008-2009</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38198</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>138</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>0310</td>\n",
" <td>89</td>\n",
" <td>NaN</td>\n",
" <td>EOWPVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38199</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>138</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>0310</td>\n",
" <td>82</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38200</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 73 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang \\\n",
"38196 year_9_complete_71_arm_1 2010-2011 0.0 1.0 3.0 2.0 \n",
"38197 year_9_complete_71_arm_1 2008-2009 0.0 1.0 0.0 0.0 \n",
"38198 year_9_complete_71_arm_1 2013-2014 0.0 1.0 2.0 0.0 \n",
"38199 year_9_complete_71_arm_1 2013-2014 0.0 1.0 2.0 0.0 \n",
"38200 year_9_complete_71_arm_1 2011-2012 0.0 1.0 0.0 0.0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... sex known_synd \\\n",
"38196 1.0 4.0 4.0 9.0 ... Male 1.0 \n",
"38197 1.0 3.0 2.0 8.0 ... Male 0.0 \n",
"38198 NaN 6.0 6.0 9.0 ... Male 0.0 \n",
"38199 NaN 6.0 6.0 9.0 ... Male 0.0 \n",
"38200 0.0 3.0 6.0 9.0 ... Male 0.0 \n",
"\n",
" synd_or_disab race age_test domain school score \\\n",
"38196 1.0 3.0 NaN NaN NaN NaN \n",
"38197 0.0 0.0 NaN NaN NaN NaN \n",
"38198 0.0 2.0 138 Expressive Vocabulary 0310 89 \n",
"38199 0.0 2.0 138 Receptive Vocabulary 0310 82 \n",
"38200 1.0 0.0 NaN NaN NaN NaN \n",
"\n",
" test_name test_type \n",
"38196 NaN NaN \n",
"38197 NaN NaN \n",
"38198 NaN EOWPVT \n",
"38199 NaN PPVT \n",
"38200 NaN NaN \n",
"\n",
"[5 rows x 73 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.tail()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2013 6940\n",
"2012 6650\n",
"2014 5821\n",
"2011 5245\n",
"2010 4445\n",
"nan 3077\n",
"2009 2455\n",
"2015 1167\n",
"2008 835\n",
"2007 533\n",
"2006 344\n",
"2005 286\n",
"2004 172\n",
"2003 90\n",
"2002 47\n",
"2001 37\n",
"1999 16\n",
"1998 16\n",
"2000 12\n",
"1997 6\n",
"2201 5\n",
"1995 1\n",
"2041 1\n",
"Name: academic_year_start, dtype: int64"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr['academic_year_start'] = lsl_dr.academic_year.apply(lambda x: str(x).strip()[:4])\n",
"lsl_dr.academic_year_start.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"current_year_only = False\n",
"\n",
"if current_year_only:\n",
" lsl_dr = lsl_dr[lsl_dr.academic_year_start=='2013']"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NuGsDAAAhElDgR0REyOVy+X/v06ePIiICGgoAADqAgI7hp6SkaO3atWpubtbevXv1m9/8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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d90e780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_scores = lsl_dr[(lsl_dr.domain=='Expressive Vocabulary') & (lsl_dr.score>=20)].score\n",
"expressive_scores.hist(bins=25)\n",
"plt.xlabel('Standard scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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KAGuEFPiTJk3SpEmTrK4FAPqUO364PMNG9HcZQJ8IKfCnTZumXbt26eOPP9YFF1ygPXv2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\nAwBgAwQ+AAA28P89YKX9Ihfu5wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d99bcc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_lang_scores = lsl_dr[(lsl_dr.domain=='Language') \n",
" & (lsl_dr.test_type=='expressive')].score\n",
"expressive_lang_scores.hist(bins=25)\n",
"plt.xlabel('Standard scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Export dataset"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if current_year_only:\n",
"\n",
" lsl_dr.to_csv('lsl_dr_current_year.csv')\n",
"\n",
"else:\n",
" lsl_dr.to_csv('lsl_dr.csv')"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(38201, 74)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.shape"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5807,)"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5807,)"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.study_id.unique().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert score to floating-point number"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr.score = lsl_dr.score.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['tech_class'] = 'Bimodal'\n",
"lsl_dr.loc[lsl_dr.bilateral_ci==True, 'tech_class'] = 'Bilateral CI'\n",
"lsl_dr.loc[lsl_dr.bilateral_ha==True, 'tech_class'] = 'Bilateral HA'"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['age_year'] = np.floor(lsl_dr.age/12.)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Expressive Vocabulary', 'Language', 'Articulation',\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.dropna().unique()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tech_class\n",
"Bilateral CI 0.43\n",
"Bilateral HA 0.58\n",
"Bimodal 0.50\n",
"Name: prim_lang, dtype: float64"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').prim_lang.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['non_profound'] = lsl_dr.degree_hl<6"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tech_class\n",
"Bilateral CI 0.08\n",
"Bilateral HA 0.87\n",
"Bimodal 0.31\n",
"Name: non_profound, dtype: float64"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').non_profound.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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CCNF7ST3VMgk2QoguqfS9d6nN3USBRo3H422XbVpyRhP/8+tPuV7j6xbX1NQS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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110cc44e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(2, 2, figsize=(14,10))\n",
"for ax, dom in zip(np.ravel(axes), lsl_dr.domain.dropna().unique()):\n",
" plot_data = lsl_dr[lsl_dr.domain==dom].pivot_table(index='age_year', columns='tech_class', values='score', aggfunc='mean')\n",
" plot_data[(plot_data.index>1) & (plot_data.index<7)].plot(ax=ax)\n",
" ax.set_ylim(40, 120)\n",
" ax.set_xticks(range(2,7))\n",
" ax.set_title(dom)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PPVT"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10d99beb8>"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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URrwBoDDiDQCFEW8AKIx4A0BhxBsACiPeAFAY8QaAwog3ABRGvAGgMOINAIURbwAojHgD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\nP92T+pGMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c8896d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ppvt_only = lsl_dr[lsl_dr.test_type=='PPVT']\n",
"ppvt_only.age_year.hist()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ppvt_345 = ppvt_only[ppvt_only.age_year.isin([3,4,5])]"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1978.000000\n",
"mean 89.923660\n",
"std 21.045408\n",
"min 20.000000\n",
"25% 77.000000\n",
"50% 90.000000\n",
"75% 105.000000\n",
"max 154.000000\n",
"Name: score, dtype: float64"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ppvt_345.score.describe()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">score</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>median</th>\n",
" <th>count_nonzero</th>\n",
" </tr>\n",
" <tr>\n",
" <th>age_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>20.0</td>\n",
" <td>150.0</td>\n",
" <td>95.0</td>\n",
" <td>1196.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>20.0</td>\n",
" <td>154.0</td>\n",
" <td>87.0</td>\n",
" <td>481.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>20.0</td>\n",
" <td>130.0</td>\n",
" <td>81.0</td>\n",
" <td>301.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score \n",
" min max median count_nonzero\n",
"age_year \n",
"3.0 20.0 150.0 95.0 1196.0\n",
"4.0 20.0 154.0 87.0 481.0\n",
"5.0 20.0 130.0 81.0 301.0"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ppvt_345.groupby('age_year').agg({'score':[min, max, np.median, np.count_nonzero]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EVT"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"expressive 6581\n",
"receptive 6530\n",
"Goldman 5286\n",
"PPVT 4366\n",
"EVT 3812\n",
"EOWPVT 2707\n",
"ROWPVT 2272\n",
"Arizonia 502\n",
"PPVT and ROWPVT 197\n",
"EOWPVT and EVT 148\n",
"Arizonia and Goldman 73\n",
"Name: test_type, dtype: int64"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.test_type.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10c6e1550>"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eHocnwrkcPXpU99xzj+68807dcccdKX//zZo6cg90s7344ot6+umnJUnHjh1T\nLBZTXl6ew1MhFYsXL9bBgwclSXv37lVRUZHDEyEVa9eu1Z/+9CdJ0oEDB3T99dc7PBHOZmBgQGvX\nrtVDDz2kO++8U5J03XXXpfT9N2vesFZSUqJ9+/apvLxc0r/fPANz3HXXXXrkkUcUCoWUlZWlbdu2\nceXEMFVVVXrssccUj8eVn5+v0tJSp0dCCrZs2aKtW7dqzpw5ysvL049+9COnR8JZ7NixQ8ePH1dT\nU5MaGxvlcrlUXV2tJ598csrff9zbHAAAw/DSCAAAwxBvAAAMQ7wBADAM8QYAwDDEGwAAwxBvAAAM\nQ7wBADDM/wNYl8vcfq5dAAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c6c2320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"evt_only = lsl_dr[lsl_dr.test_type=='EVT']\n",
"evt_only.age_year.hist()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"evt_345 = evt_only[evt_only.age_year.isin([3,4,5])]"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">score</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>median</th>\n",
" <th>count_nonzero</th>\n",
" </tr>\n",
" <tr>\n",
" <th>age_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>20.0</td>\n",
" <td>146.0</td>\n",
" <td>99.0</td>\n",
" <td>1095.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>20.0</td>\n",
" <td>146.0</td>\n",
" <td>90.0</td>\n",
" <td>415.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>20.0</td>\n",
" <td>130.0</td>\n",
" <td>85.0</td>\n",
" <td>273.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score \n",
" min max median count_nonzero\n",
"age_year \n",
"3.0 20.0 146.0 99.0 1095.0\n",
"4.0 20.0 146.0 90.0 415.0\n",
"5.0 20.0 130.0 85.0 273.0"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evt_345.groupby('age_year').agg({'score':[min, max, np.median, np.count_nonzero]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PLS"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"pls_only = (language[(language.test_name=='PLS')]\n",
" .convert_objects(convert_numeric=True))\n",
"pls_only['age_year'] = np.floor(pls_only.age_test/12).astype(int)\n",
"pls_345 = pls_only[pls_only.age_year.isin([3,4,5])]"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">score</th>\n",
" <th>normal_limits</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>median</th>\n",
" <th>len</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>age_year</th>\n",
" <th>test_type</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">3</th>\n",
" <th>expressive</th>\n",
" <td>50.0</td>\n",
" <td>145.0</td>\n",
" <td>78.0</td>\n",
" <td>795.0</td>\n",
" <td>0.355975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>receptive</th>\n",
" <td>50.0</td>\n",
" <td>140.0</td>\n",
" <td>80.0</td>\n",
" <td>795.0</td>\n",
" <td>0.406289</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4</th>\n",
" <th>expressive</th>\n",
" <td>50.0</td>\n",
" <td>141.0</td>\n",
" <td>73.0</td>\n",
" <td>587.0</td>\n",
" <td>0.287905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>receptive</th>\n",
" <td>50.0</td>\n",
" <td>136.0</td>\n",
" <td>77.0</td>\n",
" <td>591.0</td>\n",
" <td>0.382403</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">5</th>\n",
" <th>expressive</th>\n",
" <td>50.0</td>\n",
" <td>138.0</td>\n",
" <td>68.0</td>\n",
" <td>298.0</td>\n",
" <td>0.265101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>receptive</th>\n",
" <td>50.0</td>\n",
" <td>129.0</td>\n",
" <td>73.0</td>\n",
" <td>300.0</td>\n",
" <td>0.293333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score normal_limits\n",
" min max median len mean\n",
"age_year test_type \n",
"3 expressive 50.0 145.0 78.0 795.0 0.355975\n",
" receptive 50.0 140.0 80.0 795.0 0.406289\n",
"4 expressive 50.0 141.0 73.0 587.0 0.287905\n",
" receptive 50.0 136.0 77.0 591.0 0.382403\n",
"5 expressive 50.0 138.0 68.0 298.0 0.265101\n",
" receptive 50.0 129.0 73.0 300.0 0.293333"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(pls_345.assign(normal_limits=pls_345.score>=85).groupby(['age_year', 'test_type'])\n",
" .agg({'score':[min, max, np.median, len], \n",
" 'normal_limits': np.mean}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CELF"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"celf_only = (language_subtest[(language_subtest.test_name=='CELF-P2')]\n",
" .convert_objects(convert_numeric=True))\n",
"celf_only['age_year'] = np.floor(celf_only.age_test/12).astype(int)\n",
"celf_46 = celf_only[celf_only.age_year.isin([4,6])]"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"subtests = ['celfp_ss_ss', 'celfp_ws_ss', \n",
" 'celfp_ev_ss', 'celfp_fd_ss',\n",
" 'celfp_rs_ss', 'celfp_bc_ss', \n",
" 'celfp_wcr_ss', 'celfp_wce_ss',\n",
" 'celfp_wct_ss']"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>age_year</th>\n",
" <th>4</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>celfp_bc_ss</th>\n",
" <td>9.0</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_wce_ss</th>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_ev_ss</th>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_wcr_ss</th>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_ss_ss</th>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_ws_ss</th>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_wct_ss</th>\n",
" <td>9.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_rs_ss</th>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_fd_ss</th>\n",
" <td>8.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"age_year 4 6\n",
"celfp_bc_ss 9.0 4.5\n",
"celfp_wce_ss 9.0 7.0\n",
"celfp_ev_ss 8.0 5.0\n",
"celfp_wcr_ss 10.0 10.0\n",
"celfp_ss_ss 8.0 5.0\n",
"celfp_ws_ss 6.0 4.0\n",
"celfp_wct_ss 9.0 8.0\n",
"celfp_rs_ss 7.0 4.0\n",
"celfp_fd_ss 8.0 4.0"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(celf_46.groupby('age_year')\n",
" .agg({st:np.median for st in subtests})).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plots of Demographic Data"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_color = \"#64AAE8\""
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_demo_data(series, labels=None, color=plot_color, rot=0, label_offset=20, xlim=None, \n",
" ylim=None, title=None, **kwargs):\n",
" ax = kwargs.get('ax')\n",
" if ax is None:\n",
" f, ax = plt.subplots()\n",
" counts = series.value_counts().sort_index()\n",
" counts.plot(kind='bar', grid=False, rot=rot, color=color, **kwargs)\n",
" if xlim is None:\n",
" ax.set_xlim(-0.5, len(counts)-0.5)\n",
" if ylim is not None:\n",
" ax.set_ylim(*ylim)\n",
" ax.set_ylabel('Count')\n",
" if labels is not None:\n",
" ax.set_xticklabels(labels)\n",
" if title:\n",
" ax.set_title(title)\n",
" for i,x in enumerate(counts):\n",
" ax.annotate('%i' % x, (i, x + label_offset))\n",
" \n",
"# plt.gca().tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_students = demographic.drop_duplicates('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5807, 67)"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 5290.00000\n",
"mean 29.50000\n",
"std 27.68008\n",
"min 0.00000\n",
"25% 8.00000\n",
"50% 24.00000\n",
"75% 40.00000\n",
"max 298.00000\n",
"Name: age, dtype: float64"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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oKA0bNkwWi0WpqalKSUmRz+dTRkaGHA6HkpOTlZmZqZSUFDkcDuXl5QVqVAAAjGTxnfxB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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c6be4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.male, \n",
" ('Female', 'Male'), label_offset=20, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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xKJW8iIiIQankRUREDEolLyIiYlAqeREREYNSyYuIiBiUSl5ERMSgVPIiIiIGpZIXEREx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\nX58mTZpgsVjcHa1GWSwWJk6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"text/plain": [
"<matplotlib.figure.Figure at 0x10dea79b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.prim_lang, \n",
" ('English', 'Spanish', 'Chinese', 'French', 'German', 'Tagalong', 'Other'), \n",
" rot=70, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5242"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.prim_lang.count()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10deb6048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.sib, ('1', '2', '3', '4+'), \n",
" color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4846"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.sib.count()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 4911 null values for age_amp\n"
]
}
],
"source": [
"amp_ages = [\"Birth-3 months\", \"4 months - 6 months\", \"7 months - 9 months\", \"10 months- 12 months\", \n",
"\"13 months - 18 months\", \"19 months - 24 months\", \"2 years 1 day - 3 years\", \n",
"\"3 years 1 day - 4 years\", \"4 years 1 day - 5 years\", \"5 years 1 day - 6 years\", \"6 years\"]\n",
"\n",
"demographic.loc[demographic.age_amp==11, 'age_amp'] = None\n",
"print(\"There are {0} null values for age_amp\".format(sum(demographic.age_amp.isnull())))"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"age_classes = pd.Series(pd.cut(unique_students.sort('age_amp').age_amp.dropna(), [-1,3,6,9,12,18,24,36,48,60,72,1000],\n",
" labels=amp_ages))"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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fy/Fal8vFypVrqFIlFMMwOH78GEWKFM3vyCL/2A2V+f79+3ONfffddzc9jIjIzTJp0lha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\nAdcmp7hJRYVMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110212b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"age_amp_counts = age_classes.value_counts()[amp_ages]\n",
"age_amp_counts.plot(kind='bar', grid=False, rot=90, color=plot_color)\n",
"plt.xlim(-0.5, len(age_amp_counts)-0.5)\n",
"plt.ylabel('Count')\n",
"for i,x in enumerate(age_amp_counts):\n",
" plt.annotate('%i' % x, (i, x + 10))"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3627"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_amp_counts.sum()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"173.0"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age_amp.max()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10df86828>"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10deba7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(unique_students.age_amp/12.).hist(bins=16, grid=False, color=plot_color)\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age at amplification')"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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/PXt2KS4uQQkJA887npubowUL3tTbb/9NISG1NGnSOC1f/jfFxyd44tu4IlWq9IwZM7Rp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\n6v8DLNqOz7ZvHdAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110619400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.tech_left, tech_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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66pqHAQDAmzZv3qTi4mJJUq1atfTQQ7/Wt9/mX/Q5//rXbu3evcu93b17z3O2a5KLlnxe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\nKHkAAAz1/wGHZN/gBMZndwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110423d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.tech_right, tech_cats, rot=90, color=plot_color, ylim=(0, 2500))"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ERCq6Cj2Tv/LseoDZs2dz//33OzmViIhI5VChS15ERERuXYU+XC8iIiK3TiUv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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110429cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(2, 1)\n",
"plot_demo_data(unique_students.tech_right, [\"\"]*len(tech_cats), rot=90, \n",
" ax=axes[0], title='Right ear', color=plot_color, ylim=(0, 2500))\n",
"plot_demo_data(unique_students.tech_left, tech_cats, rot=90, \n",
" ax=axes[1], title='Left ear', color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4651"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_right.count()"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4643"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_left.count()"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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dTlBQkGt5QECAa7nVar3gtSIiInL5PDrwzsfnP6uz2+1UqlQJq9V6QYGfv9xut7uWnf9B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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c8a2e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"degree_hl_cats = 'Normal (0-14)', 'Slight (15-25)', 'Mild (26-40)', \\\n",
"'Moderate (41-55)', 'Moderately Severe (56-70)', 'Severe (71-90)', 'Profound (90+)'\n",
"plot_demo_data(unique_students.degree_hl_ad, degree_hl_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YDHzwwaecO3eWKlWq3LHP1KnvERk5jdWrv8Fshv7936Ju3XpFXbqIiNgZhfxt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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11023c9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, axes = plt.subplots(2, 1)\n",
"plot_demo_data(unique_students.degree_hl_ad, [\"\"]*7, rot=90, \n",
" color=plot_color, ax=axes[0], title='Right ear')\n",
"plot_demo_data(unique_students.degree_hl_as, degree_hl_cats, rot=90, \n",
" color=plot_color, ylim=(0,2000), ax=axes[1], title='Left ear');"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4550"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.degree_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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u3ZUeBgAAVB77lZ7MyMhQeXm54uPjNX/+fLlcLklSWVmZZs+erQ0bNlRJSAAA8PNdseQ/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\nAAAYipIHAMBQ/x9rrf3mVmil9wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11014fb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"type_hl_cats = 'Sensorineural', 'Conductive', 'Mixed', 'Neural', 'Normal', 'Unknown'\n",
"plot_demo_data(unique_students.type_hl_ad, type_hl_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lkhcRETEplbyIiIhJqeRFRERMSiUvIiJiUip5ERERk1LJi4iImJRKXkRExKRU8iIiIial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\n1P8HnaMSdZuL0fMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c8a0e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.type_hl_as, type_hl_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4482"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4575"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lJbmqXBEREbfjsun6gQMHEhcXB0BDQwOenp7s3r2bjz/+mLFjx5KcnIzdbqegoIDw8HA8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\nFHkhhBCigZIiL4QQQjRQ/w85cvKPjPwaNQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110441ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"type_hl_cats = 'Sensorineural', 'Conductive', 'Mixed', 'Neural', 'Normal', 'Unknown'\n",
"f, axes = plt.subplots(2,1)\n",
"plot_demo_data(unique_students.type_hl_ad, [\"\"]*len(type_hl_cats), rot=90, \n",
" title='Right ear', ax=axes[0], color=plot_color)\n",
"plot_demo_data(unique_students.type_hl_as, type_hl_cats, rot=90, \n",
" title='Left ear', ax=axes[1], color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>male</th>\n",
" <th>_race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>_mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>bilateral_ci</th>\n",
" <th>bilateral_ha</th>\n",
" <th>bimodal</th>\n",
" <th>tech</th>\n",
" <th>implant_category</th>\n",
" <th>age_diag</th>\n",
" <th>sex</th>\n",
" <th>known_synd</th>\n",
" <th>synd_or_disab</th>\n",
" <th>race</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>14329</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14330</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14331</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14332</th>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>8.0</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 67 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang \\\n",
"14329 initial_assessment_arm_1 2010-2011 0.0 0.0 0.0 0.0 \n",
"14330 year_1_complete_71_arm_1 2011-2012 0.0 0.0 0.0 0.0 \n",
"14331 year_2_complete_71_arm_1 2012-2013 0.0 0.0 0.0 0.0 \n",
"14332 year_3_complete_71_arm_1 2013-2014 0.0 0.0 0.0 0.0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... bilateral_ci \\\n",
"14329 1.0 3.0 3.0 8.0 ... False \n",
"14330 1.0 3.0 3.0 8.0 ... False \n",
"14331 1.0 3.0 3.0 8.0 ... False \n",
"14332 1.0 3.0 3.0 8.0 ... False \n",
"\n",
" bilateral_ha bimodal tech implant_category age_diag sex \\\n",
"14329 True False 0 6 51.0 Female \n",
"14330 True False 0 6 51.0 Female \n",
"14331 True False 0 6 51.0 Female \n",
"14332 True False 0 6 51.0 Female \n",
"\n",
" known_synd synd_or_disab race \n",
"14329 0.0 0.0 0.0 \n",
"14330 0.0 0.0 0.0 \n",
"14331 0.0 0.0 0.0 \n",
"14332 0.0 0.0 0.0 \n",
"\n",
"[4 rows x 67 columns]"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[demographic.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
" study_id redcap_event_name score test_type school \\\n",
"14329 1147-2010-0064 initial_assessment_arm_1 96.0 PPVT 1147 \n",
"14330 1147-2010-0064 year_1_complete_71_arm_1 91.0 PPVT 1147 \n",
"14331 1147-2010-0064 year_2_complete_71_arm_1 93.0 PPVT 1147 \n",
"\n",
" age_test domain \n",
"14329 63.0 Receptive Vocabulary \n",
"14330 73.0 Receptive Vocabulary \n",
"14331 85.0 Receptive Vocabulary "
]
},
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"metadata": {},
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],
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"receptive[receptive.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
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},
"outputs": [
{
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" <td>Bilateral HA</td>\n",
" <td>4.0</td>\n",
" <td>True</td>\n",
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" <tr>\n",
" <th>14321</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>3.0</td>\n",
" <td>3.0</td>\n",
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" <td>72</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>86.0</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" <td>2011</td>\n",
" <td>Bilateral HA</td>\n",
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" <td>True</td>\n",
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" <th>14322</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0.0</td>\n",
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" <td>Receptive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>91.0</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" <td>2011</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4.0</td>\n",
" <td>True</td>\n",
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" <tr>\n",
" <th>24001</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
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" <td>2013-2014</td>\n",
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" <td>2013</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4.0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9 rows × 77 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang \\\n",
"5902 initial_assessment_arm_1 2010-2011 0.0 0.0 0.0 0.0 \n",
"5903 initial_assessment_arm_1 2010-2011 0.0 0.0 0.0 0.0 \n",
"5904 initial_assessment_arm_1 2010-2011 0.0 0.0 0.0 0.0 \n",
"5905 initial_assessment_arm_1 2010-2011 0.0 0.0 0.0 0.0 \n",
"14321 year_1_complete_71_arm_1 2011-2012 0.0 0.0 0.0 0.0 \n",
"14322 year_1_complete_71_arm_1 2011-2012 0.0 0.0 0.0 0.0 \n",
"24001 year_2_complete_71_arm_1 2012-2013 0.0 0.0 0.0 0.0 \n",
"24002 year_2_complete_71_arm_1 2012-2013 0.0 0.0 0.0 0.0 \n",
"30342 year_3_complete_71_arm_1 2013-2014 0.0 0.0 0.0 0.0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... age_test \\\n",
"5902 1.0 3.0 3.0 8.0 ... 63 \n",
"5903 1.0 3.0 3.0 8.0 ... 63 \n",
"5904 1.0 3.0 3.0 8.0 ... 59 \n",
"5905 1.0 3.0 3.0 8.0 ... 59 \n",
"14321 1.0 3.0 3.0 8.0 ... 72 \n",
"14322 1.0 3.0 3.0 8.0 ... 73 \n",
"24001 1.0 3.0 3.0 8.0 ... 88 \n",
"24002 1.0 3.0 3.0 8.0 ... 85 \n",
"30342 1.0 3.0 3.0 8.0 ... NaN \n",
"\n",
" domain school score test_name test_type \\\n",
"5902 Expressive Vocabulary 1147 91.0 NaN EVT \n",
"5903 Receptive Vocabulary 1147 96.0 NaN PPVT \n",
"5904 Language 1147 101.0 PLS receptive \n",
"5905 Language 1147 87.0 PLS expressive \n",
"14321 Expressive Vocabulary 1147 86.0 NaN EVT \n",
"14322 Receptive Vocabulary 1147 91.0 NaN PPVT \n",
"24001 Expressive Vocabulary 1147 95.0 NaN EVT \n",
"24002 Receptive Vocabulary 1147 93.0 NaN PPVT \n",
"30342 NaN NaN NaN NaN NaN \n",
"\n",
" academic_year_start tech_class age_year non_profound \n",
"5902 2010 Bilateral HA 4.0 True \n",
"5903 2010 Bilateral HA 4.0 True \n",
"5904 2010 Bilateral HA 4.0 True \n",
"5905 2010 Bilateral HA 4.0 True \n",
"14321 2011 Bilateral HA 4.0 True \n",
"14322 2011 Bilateral HA 4.0 True \n",
"24001 2012 Bilateral HA 4.0 True \n",
"24002 2012 Bilateral HA 4.0 True \n",
"30342 2013 Bilateral HA 4.0 True \n",
"\n",
"[9 rows x 77 columns]"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4482"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3076,)"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5807,)"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3076,)"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3076,)"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_ids = receptive.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic_ids = demographic.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[s for s in receptive_ids if s not in demographic_ids]"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def score_summary(domain, test_type=None):\n",
" subset = lsl_dr[lsl_dr.domain==domain].copy()\n",
" if test_type is not None:\n",
" subset = subset[subset.test_type==test_type]\n",
" subset['age_test'] = (subset.age_test/12).dropna().astype(int)\n",
" subset.loc[subset.age_test > 11, 'age_test'] = 11\n",
" subset = subset[subset.age_test>1]\n",
" byage = subset.groupby('age_test')\n",
" n = byage.study_id.count()\n",
" mean = byage.score.mean()\n",
" sd = byage.score.std()\n",
" min = byage.score.min()\n",
" max = byage.score.max()\n",
" summary = pd.DataFrame({'Sample Size':n, 'Mean':mean, \n",
" 'SD':sd, 'Min':min, 'Max':max})\n",
" summary.index = summary.index.values.astype(int)\n",
" return summary[['Sample Size','Mean','SD','Min','Max']]"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>412</td>\n",
" <td>93.546117</td>\n",
" <td>18.140445</td>\n",
" <td>40.0</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1428</td>\n",
" <td>92.067227</td>\n",
" <td>19.347476</td>\n",
" <td>0.0</td>\n",
" <td>150.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1547</td>\n",
" <td>90.796380</td>\n",
" <td>20.277519</td>\n",
" <td>0.0</td>\n",
" <td>149.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1161</td>\n",
" <td>89.919897</td>\n",
" <td>18.110998</td>\n",
" <td>0.0</td>\n",
" <td>142.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>652</td>\n",
" <td>85.914110</td>\n",
" <td>16.302309</td>\n",
" <td>40.0</td>\n",
" <td>154.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>424</td>\n",
" <td>83.169811</td>\n",
" <td>16.066041</td>\n",
" <td>40.0</td>\n",
" <td>130.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>304</td>\n",
" <td>80.700658</td>\n",
" <td>17.624780</td>\n",
" <td>20.0</td>\n",
" <td>132.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>227</td>\n",
" <td>78.193833</td>\n",
" <td>17.638889</td>\n",
" <td>25.0</td>\n",
" <td>160.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>191</td>\n",
" <td>76.324607</td>\n",
" <td>17.481099</td>\n",
" <td>20.0</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>459</td>\n",
" <td>78.588235</td>\n",
" <td>18.949552</td>\n",
" <td>20.0</td>\n",
" <td>134.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 412 93.546117 18.140445 40.0 144.0\n",
"3 1428 92.067227 19.347476 0.0 150.0\n",
"4 1547 90.796380 20.277519 0.0 149.0\n",
"5 1161 89.919897 18.110998 0.0 142.0\n",
"6 652 85.914110 16.302309 40.0 154.0\n",
"7 424 83.169811 16.066041 40.0 130.0\n",
"8 304 80.700658 17.624780 20.0 132.0\n",
"9 227 78.193833 17.638889 25.0 160.0\n",
"10 191 76.324607 17.481099 20.0 123.0\n",
"11 459 78.588235 18.949552 20.0 134.0"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary = score_summary(\"Receptive Vocabulary\")\n",
"receptive_summary"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.0000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>680.500000</td>\n",
" <td>84.922087</td>\n",
" <td>17.993911</td>\n",
" <td>20.5000</td>\n",
" <td>141.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>507.197365</td>\n",
" <td>6.377836</td>\n",
" <td>1.295244</td>\n",
" <td>16.4063</td>\n",
" <td>11.802071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>191.000000</td>\n",
" <td>76.324607</td>\n",
" <td>16.066041</td>\n",
" <td>0.0000</td>\n",
" <td>123.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>331.000000</td>\n",
" <td>79.116341</td>\n",
" <td>17.517019</td>\n",
" <td>5.0000</td>\n",
" <td>132.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>441.500000</td>\n",
" <td>84.541961</td>\n",
" <td>17.874943</td>\n",
" <td>20.0000</td>\n",
" <td>143.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1033.750000</td>\n",
" <td>90.577259</td>\n",
" <td>18.747275</td>\n",
" <td>36.2500</td>\n",
" <td>149.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1547.000000</td>\n",
" <td>93.546117</td>\n",
" <td>20.277519</td>\n",
" <td>40.0000</td>\n",
" <td>160.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"count 10.000000 10.000000 10.000000 10.0000 10.000000\n",
"mean 680.500000 84.922087 17.993911 20.5000 141.800000\n",
"std 507.197365 6.377836 1.295244 16.4063 11.802071\n",
"min 191.000000 76.324607 16.066041 0.0000 123.000000\n",
"25% 331.000000 79.116341 17.517019 5.0000 132.500000\n",
"50% 441.500000 84.541961 17.874943 20.0000 143.000000\n",
"75% 1033.750000 90.577259 18.747275 36.2500 149.750000\n",
"max 1547.000000 93.546117 20.277519 40.0000 160.000000"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary.describe()"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6805"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1105ff320>"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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yjrKyMvbty3HJ3EFBN+Ht7e2SuUVELkQlL3KOfftyGLX0WwLqNa7UefOP7SelKwQHh1Tq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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1106225c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_summary[\"Sample Size\"].plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(receptive_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>390</td>\n",
" <td>92.753846</td>\n",
" <td>22.081898</td>\n",
" <td>23.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1376</td>\n",
" <td>93.390262</td>\n",
" <td>21.591975</td>\n",
" <td>0.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1525</td>\n",
" <td>92.449180</td>\n",
" <td>21.817895</td>\n",
" <td>0.0</td>\n",
" <td>146.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1136</td>\n",
" <td>91.602113</td>\n",
" <td>20.054994</td>\n",
" <td>0.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>650</td>\n",
" <td>87.018462</td>\n",
" <td>18.442505</td>\n",
" <td>20.0</td>\n",
" <td>146.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>425</td>\n",
" <td>84.037647</td>\n",
" <td>15.699522</td>\n",
" <td>38.0</td>\n",
" <td>131.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>295</td>\n",
" <td>84.037288</td>\n",
" <td>16.455319</td>\n",
" <td>34.0</td>\n",
" <td>122.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>213</td>\n",
" <td>81.793427</td>\n",
" <td>16.060750</td>\n",
" <td>36.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>185</td>\n",
" <td>81.816216</td>\n",
" <td>15.279596</td>\n",
" <td>40.0</td>\n",
" <td>122.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>460</td>\n",
" <td>84.821739</td>\n",
" <td>17.366822</td>\n",
" <td>18.0</td>\n",
" <td>146.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 390 92.753846 22.081898 23.0 145.0\n",
"3 1376 93.390262 21.591975 0.0 145.0\n",
"4 1525 92.449180 21.817895 0.0 146.0\n",
"5 1136 91.602113 20.054994 0.0 145.0\n",
"6 650 87.018462 18.442505 20.0 146.0\n",
"7 425 84.037647 15.699522 38.0 131.0\n",
"8 295 84.037288 16.455319 34.0 122.0\n",
"9 213 81.793427 16.060750 36.0 145.0\n",
"10 185 81.816216 15.279596 40.0 122.0\n",
"11 460 84.821739 17.366822 18.0 146.0"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary = score_summary(\"Expressive Vocabulary\")\n",
"expressive_summary"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6655"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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/0bt3b7Zu3QpAQUEBERERhIeHU1hYSGVlJWVlZRQVFREWpotyiIiI1JXbj+T7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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10deb7be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_summary[\"Sample Size\"].plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(expressive_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.xlim(-0.5, 9.5)\n",
"if current_year_only:\n",
" plt.ylim(0, 800)\n",
"else:\n",
" plt.ylim(0, 1800)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>297</td>\n",
" <td>85.225589</td>\n",
" <td>14.870600</td>\n",
" <td>50.0</td>\n",
" <td>122.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1194</td>\n",
" <td>83.618090</td>\n",
" <td>18.348582</td>\n",
" <td>40.0</td>\n",
" <td>126.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1368</td>\n",
" <td>83.526316</td>\n",
" <td>20.745277</td>\n",
" <td>0.0</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1065</td>\n",
" <td>83.881690</td>\n",
" <td>34.906576</td>\n",
" <td>39.0</td>\n",
" <td>999.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>614</td>\n",
" <td>79.534202</td>\n",
" <td>21.707111</td>\n",
" <td>39.0</td>\n",
" <td>115.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>402</td>\n",
" <td>80.402985</td>\n",
" <td>51.064887</td>\n",
" <td>3.0</td>\n",
" <td>999.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>259</td>\n",
" <td>78.876448</td>\n",
" <td>21.283801</td>\n",
" <td>40.0</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>188</td>\n",
" <td>81.617021</td>\n",
" <td>20.547639</td>\n",
" <td>40.0</td>\n",
" <td>109.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>145</td>\n",
" <td>81.317241</td>\n",
" <td>20.068184</td>\n",
" <td>40.0</td>\n",
" <td>105.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>324</td>\n",
" <td>84.632716</td>\n",
" <td>54.558552</td>\n",
" <td>39.0</td>\n",
" <td>999.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 297 85.225589 14.870600 50.0 122.0\n",
"3 1194 83.618090 18.348582 40.0 126.0\n",
"4 1368 83.526316 20.745277 0.0 123.0\n",
"5 1065 83.881690 34.906576 39.0 999.0\n",
"6 614 79.534202 21.707111 39.0 115.0\n",
"7 402 80.402985 51.064887 3.0 999.0\n",
"8 259 78.876448 21.283801 40.0 107.0\n",
"9 188 81.617021 20.547639 40.0 109.0\n",
"10 145 81.317241 20.068184 40.0 105.0\n",
"11 324 84.632716 54.558552 39.0 999.0"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary = score_summary(\"Articulation\")\n",
"articulation_summary"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5856"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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QgNVqJS8v75JxEanYsrIyGbPqEP4165fpdnNPHCapMwQHh5TpdkUqMpeX/MKF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\nMan/B+K1XbH3ZbdfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110f3bc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size = articulation_summary[\"Sample Size\"]\n",
"sample_size.plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(articulation_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.ylim(0, sample_size.max()+50);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Language scores"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Expressive Vocabulary', 'Language', 'Articulation', nan,\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.unique()"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['EOWPVT', 'receptive', 'expressive', 'Goldman', nan, 'EVT', 'PPVT',\n",
" 'Arizonia', 'ROWPVT', 'Arizonia and Goldman', 'EOWPVT and EVT',\n",
" 'PPVT and ROWPVT'], dtype=object)"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.test_type.unique()"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>957</td>\n",
" <td>86.323929</td>\n",
" <td>22.295176</td>\n",
" <td>50.0</td>\n",
" <td>150.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1374</td>\n",
" <td>84.938137</td>\n",
" <td>19.634047</td>\n",
" <td>50.0</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1349</td>\n",
" <td>85.316531</td>\n",
" <td>19.507433</td>\n",
" <td>43.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>962</td>\n",
" <td>83.939709</td>\n",
" <td>18.839663</td>\n",
" <td>47.0</td>\n",
" <td>140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>495</td>\n",
" <td>78.078788</td>\n",
" <td>17.673256</td>\n",
" <td>11.0</td>\n",
" <td>127.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>321</td>\n",
" <td>75.981308</td>\n",
" <td>18.835628</td>\n",
" <td>40.0</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>199</td>\n",
" <td>74.989950</td>\n",
" <td>19.793885</td>\n",
" <td>40.0</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>54</td>\n",
" <td>70.425926</td>\n",
" <td>21.219075</td>\n",
" <td>40.0</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>46</td>\n",
" <td>79.413043</td>\n",
" <td>20.985261</td>\n",
" <td>40.0</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>67</td>\n",
" <td>76.522388</td>\n",
" <td>21.469046</td>\n",
" <td>40.0</td>\n",
" <td>139.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 957 86.323929 22.295176 50.0 150.0\n",
"3 1374 84.938137 19.634047 50.0 144.0\n",
"4 1349 85.316531 19.507433 43.0 145.0\n",
"5 962 83.939709 18.839663 47.0 140.0\n",
"6 495 78.078788 17.673256 11.0 127.0\n",
"7 321 75.981308 18.835628 40.0 123.0\n",
"8 199 74.989950 19.793885 40.0 123.0\n",
"9 54 70.425926 21.219075 40.0 120.0\n",
"10 46 79.413043 20.985261 40.0 120.0\n",
"11 67 76.522388 21.469046 40.0 139.0"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary = score_summary(\"Language\", \"receptive\")\n",
"receptive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5824"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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SZLXa1LlzrP7v/5YpOrqzJGnPnl3Kyzulli2jXJYNnnP69Gk1anS3c+v85MkTmjv3n6pa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\nAJgUJQ8AgElR8gAAmBQlDwCASf0/PSyJLbcGuqYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1117c2b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size = receptive_language_summary[\"Sample Size\"]\n",
"sample_size.plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(receptive_language_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.ylim(0, sample_size.max()+50);plt.xlim(-0.5, 9.5);"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>950</td>\n",
" <td>88.427368</td>\n",
" <td>18.557020</td>\n",
" <td>50.0</td>\n",
" <td>150.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1375</td>\n",
" <td>82.408000</td>\n",
" <td>17.458500</td>\n",
" <td>20.0</td>\n",
" <td>147.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1341</td>\n",
" <td>80.609247</td>\n",
" <td>19.553739</td>\n",
" <td>45.0</td>\n",
" <td>141.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>983</td>\n",
" <td>78.691760</td>\n",
" <td>20.189772</td>\n",
" <td>45.0</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>513</td>\n",
" <td>71.773879</td>\n",
" <td>19.234357</td>\n",
" <td>6.0</td>\n",
" <td>140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>343</td>\n",
" <td>67.128280</td>\n",
" <td>20.948304</td>\n",
" <td>40.0</td>\n",
" <td>124.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>205</td>\n",
" <td>68.014634</td>\n",
" <td>21.506834</td>\n",
" <td>40.0</td>\n",
" <td>118.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>54</td>\n",
" <td>65.629630</td>\n",
" <td>21.286275</td>\n",
" <td>40.0</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>46</td>\n",
" <td>77.217391</td>\n",
" <td>24.107088</td>\n",
" <td>40.0</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>66</td>\n",
" <td>73.939394</td>\n",
" <td>22.574239</td>\n",
" <td>40.0</td>\n",
" <td>132.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 950 88.427368 18.557020 50.0 150.0\n",
"3 1375 82.408000 17.458500 20.0 147.0\n",
"4 1341 80.609247 19.553739 45.0 141.0\n",
"5 983 78.691760 20.189772 45.0 144.0\n",
"6 513 71.773879 19.234357 6.0 140.0\n",
"7 343 67.128280 20.948304 40.0 124.0\n",
"8 205 68.014634 21.506834 40.0 118.0\n",
"9 54 65.629630 21.286275 40.0 108.0\n",
"10 46 77.217391 24.107088 40.0 119.0\n",
"11 66 73.939394 22.574239 40.0 132.0"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary = score_summary(\"Language\", \"expressive\")\n",
"expressive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5876"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Hu7R+/Rf6+ust8vLy0osv9lPv3t01b94cGYahnJwTOnToB919972SpHbtblNBQYG++26v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\nACZFyQMAYFL/B4xUA4bW2h+7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110450e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size = expressive_language_summary[\"Sample Size\"]\n",
"sample_size.plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(expressive_language_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.ylim(0, sample_size.max()+50);plt.xlim(-0.5, 9.5);"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 5290.000000\n",
"mean 2.458333\n",
"std 2.306673\n",
"min 0.000000\n",
"25% 0.666667\n",
"50% 2.000000\n",
"75% 3.333333\n",
"max 24.833333\n",
"Name: age, dtype: float64"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(unique_students.age/12.).describe()"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def calc_difference(x, col='a_fo', jitter=True):\n",
" if (len(x)<2 or x[col].isnull().sum() or x['funct_out_age'].isnull().sum()):\n",
" return None\n",
" diff = x[col][x.funct_out_age.argmax()] - x[col][x.funct_out_age.argmin()]\n",
" if jitter:\n",
" diff += np.random.normal(scale=0.05)\n",
" if (x.funct_out_age.max() - x.funct_out_age.min()) > 1000:\n",
" print(x['funct_out_age'])\n",
" return({'difference':diff, 'months': x.funct_out_age.max() - x.funct_out_age.min()})\n"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"audition = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference).dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10c88fdd8>"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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222/nhz/84aRCWXm570xWK2/lut033FCBxWIhkbCQSjnxeBw4HDYKCzWiUQOH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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110431c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.scatter(audition.months, audition.difference, alpha=0.5)\n",
"plt.xlabel('Months between earliest and latest rating'); plt.ylabel('Progress (levels)')\n",
"plt.title('Audition')"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"slc = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference, col='slc_fo').dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10c89c2e8>"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YZbgjtYQ/mJ+oGxuzu/Wha6XjQFOnZrNkySaSSRd2e4CpUzsflz4G48blUl1d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\nIhIhFMxEREREIoSCmYiIiEiE+P+rVzPO9uAKAgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1117c2ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.scatter(slc.months, slc.difference, alpha=0.5)\n",
"plt.xlabel('Months between earliest and latest rating'); plt.ylabel('Progress (levels)')\n",
"plt.title('Spoken language (comprehension)')"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sle = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference, col='sle_fo').dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11011b6d8>"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2WWcVUF5+6OSc+cJvt+fyrW+NY9688SxZ4kpPRkopysu3AFBY2Ek4bMRgMBAK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\nIiIiUggGMyIiIiKF+H8+nu+RQl7NVAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11012e588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.scatter(sle.months, sle.difference, alpha=0.5)\n",
"plt.xlabel('Months between earliest and latest rating'); plt.ylabel('Progress (levels)')\n",
"plt.title('Spoken language (expression)')"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6.0 17270\n",
"4.0 4580\n",
"3.0 4446\n",
"5.0 4246\n",
"2.0 1740\n",
"0.0 1271\n",
"1.0 301\n",
"Name: degree_hl, dtype: int64"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.degree_hl.dropna().value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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MGWptbb0a5wYAwHXPOdSC0tJSdXd3xx/HYrH4n9PT0xUMBhUKheR2u+PH09LS4scty4qv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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11013de80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = lsl_dr.degree_hl.hist(bins=7)"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11014fda0>"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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pkW/e5bv28RWnAICZZEaGXOJrTgEAyYHXyAEAMBghBwDAYDP2qfWRxHsjXKx9AABMN0kX\n8lhvhJOkq998pXsfXJjgVQEAMD5JF3Ip9hvh+m5eSfBqAAAYP14jBwDAYIQcAACDEXIAAAxGyAEA\nMBghBwDAYKMK+ZkzZ1RWViZJunjxogKBgDZu3Kg333wzepnW1lY9++yz2rBhg44fPy5JCoVCevXV\nV/Xcc89p06ZNun79+uQfAQAASSxuyPfv36/a2lpFIhFJ0q5du1RVVaUDBw5ocHBQx44dU09Pj5qb\nm9XS0qL9+/ersbFRkUhEBw8e1IIFC/TRRx9pzZo1ampqcv2AAABIJnFDPn/+fO3duzf693Pnzqmw\nsFCSVFxcrJMnT+rs2bMqKChQamqqLMuS3+9XZ2enOjo6VFxcHL1se3u7S4cBAEByihvyFStWDPnu\nbsdxon9OT09XMBiUbdvKyMiIbvf5fNHtlmUNuSwAAJg8Yz6zW0rKf9pv27YyMzNlWdaQSN+53bbt\n6LY7Yx/P7NlpUnisq3PfPfdYyskZ/XFMZzPlOKYzZpwYzNl9zHj6GnPIFy1apNOnT+vJJ5/UiRMn\nVFRUpPz8fO3evVvhcFihUEhdXV3Ky8vTkiVL1NbWpvz8fLW1tUWfkh+NUCgy1qUlxLVrQXV39071\nMiYsJydjRhzHdMaME4M5u48Zu28id5TGHPLq6mpt375dkUhEubm5WrlypTwej8rKyhQIBOQ4jqqq\nquT1elVaWqrq6moFAgF5vV41NjaOe6EAAOBuowr5Aw88oI8//liS5Pf71dzcfNdl1q9fr/Xr1w/Z\nNmfOHO3Zs2cSlgkAAIbDCWEAADAYIQcAwGCEHAAAgxFyAAAMRsgBADAYIQcAwGCEHAAAgxFyAAAM\nRsgBADAYIQcAwGCEHAAAgxFyAAAMNuZvP0tmzuCgLl78R8zL+P2PaNasWQlaEQAg2RHyMfiut1uN\nLT3yzbs87P6+m99qz5anlZubl+CVAQCSFSEfI9+8+2RlPzDVywAAQBKvkQMAYDQekSfIwMCALlzo\ninkZXl8HAIwVIU+QCxe6VPneEfnm3Tfsfl5fBwCMByFPIF5fBwBMNl4jBwDAYIQcAACDEXIAAAxG\nyAEAMBghBwDAYLxrfRLFOhd7vHO0AwAwHoR8EsU6F/vVb77SvQ8unIJVAQBmMkI+yUb6rHjfzStT\nsBoAwEzHa+QAABiMkAMAYDBCDgCAwQg5AAAGI+QAABiMkAMAYDBCDgCAwQg5AAAGI+QAABiMkAMA\nYDBCDgCAwQg5AAAGI+QAABiMkAMAYDBCDgCAwQg5AAAGSx3vDz7zzDOyLEuS9OCDD6qiokJbt25V\nSkqK8vLyVFdXJ0lqbW1VS0uL0tLSVFFRoZKSkklZOAAAGGfIw+GwJOkPf/hDdNvLL7+sqqoqFRYW\nqq6uTseOHdMTTzyh5uZmHT58WP39/SotLdWyZcuUlpY2OasHACDJjSvknZ2d6uvrU3l5uQYGBvT6\n66/r/PnzKiwslCQVFxfrs88+U0pKigoKCpSamirLsuT3+/X111/rsccem9SDAAAgWY0r5HPmzFF5\nebnWr1+vCxcu6KWXXpLjONH96enpCgaDsm1bGRkZ0e0+n0+9vb0TXzUAAJA0zpD7/X7Nnz8/+ues\nrCydP38+ut+2bWVmZsqyLAWDwbu2j8bs2WlSeDyrM9c991jKycmIf8FJksjrSlbMODGYs/uY8fQ1\nrpAfOnRIf/vb31RXV6crV64oGAxq2bJlOnXqlJYuXaoTJ06oqKhI+fn52r17t8LhsEKhkLq6upSX\nlzeq6wiFIuNZmtGuXQuquzsxz1jk5GQk7LqSFTNODObsPmbsvoncURpXyNetW6eamhoFAgGlpKSo\noaFBWVlZqq2tVSQSUW5urlauXCmPx6OysjIFAgE5jqOqqip5vd5xLxYAAAw1rpCnpaXp/fffv2t7\nc3PzXdvWr1+v9evXj+dqAABAHJwQBgAAgxFyAAAMRsgBADDYuE/RisnlDA7q4sV/xLyM3/+IZs2a\nlaAVAQBMQMinie96u9XY0iPfvMvD7u+7+a32bHlaubmj+/geACA5EPJpxDfvPlnZDwy7L94jdh6t\nA0ByIuSGiPWInUfrAJC8CLlBYj1iBwAkJ961DgCAwQg5AAAGI+QAABiMkAMAYDBCDgCAwQg5AAAG\nI+QAABiMz5HPAJynHQCSFyGfAThPOwAkL0I+Q3DWNwBITrxGDgCAwQg5AAAGI+QAABiMkAMAYDBC\nDgCAwXjXehIY7nPm169bunYtKInPmAOAyQh5Eoj1OfN4nzEfGBjQhQtdI/5u7gQAwNQi5ElivJ8z\nv3ChS5XvHZFv3n137eNEMwAw9Qg54uJkMwAwffFmNwAADEbIAQAwGCEHAMBghBwAAIPxZrckF++7\nzON9zzkAYGoR8iQX77vMr37zle59cGGCVwUAGC1CjpgfL+u7eSXBqwEAjAWvkQMAYDBCDgCAwQg5\nAAAGI+QAABiMkAMAYDBCDgCAwfj4GVwT77vMJb7PHAAmipBj3EZzVrjGljPDfpe5JNk3/ke/2rBE\n//Vf84fdT+QBID5CjnEb7VnhYp1s5l+hv/vn+25+qz1bnlZubt6Y18UzAQCSieshdxxHO3fu1Ndf\nfy2v16vf/OY3euihh9y+WiTIRM8KF+vnY4kV63jPBMS6k8CdAACmcT3kx44dUzgc1scff6wzZ85o\n165dampqcvtqMcNduNClyveODBvreM8ExHpJYKIvB0juhJ47GABG4nrIOzo69JOf/ESS9Pjjj+vL\nL790+yoxA4zm9feRHs3HeyYg1ksCE3k5QBoa+uvXLV27FozuGxgYkOTRrFnDf1gk1v6pfL9BrDsR\n8Y5pItcd787LTLvjwp01jJfrIQ8Gg8rIyPjPFaamanBwUCkpsT/5NtDfq8HrXwy/s/f/qS9y/4g/\n+13vNUmeMe+brj87Xdfl5jFd++fXqv/wvOZY9wy7/+aVLmXdv2Dc1zs3494R9/fd/HbcP9sfvK76\nD/887LpvXunS7PSsmMc00v5YxxvvevuD11T70oqYzyLEcvHiP8Z9TBO57ljXe+fv/f4dJlPFOl5p\n4v8dJ8LNGY/nfTAYyuM4juPmFTQ0NOiJJ57QypUrJUklJSU6fvy4m1cJAEDScP2EMD/60Y/U1tYm\nSfr888+1YMHIjyoAAMDYuP6I/M53rUvSrl279PDDD7t5lQAAJA3XQw4AANzDudYBADAYIQcAwGCE\nHAAAgxFyAAAMNm2+NIVzsrvrmWeekWVZkqQHH3xQFRUV2rp1q1JSUpSXl6e6uropXqG5zpw5o/ff\nf1/Nzc26ePHisHNtbW1VS0uL0tLSVFFRoZKSkqldtGHunPFXX32lTZs2ye/3S5JKS0u1atUqZjwB\nt2/f1htvvKFLly4pEomooqJCjz76KLflSTTcjO+///7JuS0708Sf/vQnZ+vWrY7jOM7nn3/uvPzy\ny1O8opkjFAo5a9euHbKtoqLCOX36tOM4jrNjxw7nz3/+81QszXgffvihs3r1aufnP/+54zjDz7W7\nu9tZvXq1E4lEnN7eXmf16tVOOByeymUb5fszbm1tdX7/+98PuQwznphDhw4577zzjuM4jnPz5k2n\npKSE2/Iku3PGN27ccEpKSpxPPvlkUm7L0+apdc7J7p7Ozk719fWpvLxcL7zwgs6cOaPz58+rsLBQ\nklRcXKz29vYpXqWZ5s+fr71790b/fu7cuSFzPXnypM6ePauCggKlpqbKsiz5/f7oeRUQ33AzPn78\nuDZu3Kja2lrZts2MJ2jVqlWqrKyU9K9zvs+aNeuufyO4LU/MnTMeHBxUamqqzp07p7/+9a8Tvi1P\nm5CPdE52TNycOXNUXl6u3/3ud9q5c6d+9atfybnj9AHp6enq7e2dwhWaa8WKFUO+xOL7cw0Gg7Jt\ne8ht2+fzMe8x+P6MH3/8cf3617/WgQMH9NBDD+mDDz64698PZjw2c+fOlc/nUzAYVGVlpV5//XVu\ny5Ps+zN+7bXX9MMf/lDV1dUTvi1Pm5BbliXbtqN/H80Xq2B0/H6/nn766eifs7KydPXq1eh+27aV\nmZk5VcubUe68zf57rpZlKRgM3rUd47N8+XItWrQo+ufOzk5lZGQw4wm6fPmyfvGLX2jt2rV66qmn\nuC274Psznqzb8rQpJedkd8+hQ4fU0NAgSbpy5YqCwaCWLVumU6dOSZJOnDihgoKCqVzijLFo0SKd\nPn1a0n/mmp+fr46ODoXDYfX29qqrq0t5eXzj03iVl5friy/+9c2I7e3tWrx4MTOeoJ6eHpWXl2vL\nli1au3atJGnhwoXclifRcDOerNvytHnX+ooVK/TZZ59pw4YNkv51TnZMjnXr1qmmpkaBQEApKSlq\naGhQVlaWamtrFYlElJubG/12OkxMdXW1tm/fPmSuHo9HZWVlCgQCchxHVVVV8nq9U71UY+3cuVNv\nv/220tLSlJOTo7feekvp6enMeAL27dunW7duqampSXv37pXH49G2bdtUX1/PbXmSDDfjmpoavfPO\nOxO+LXOudQAADDZtnloHAABjR8gBADAYIQcAwGCEHAAAgxFyAAAMRsgBADAYIQcAwGD/C7DfBkJw\n1pRFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110150e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = (lsl_dr['age'] - lsl_dr['age_int'])\n",
"diff[diff>0].hist(bins=50)"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.20227219182743908"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age_int<6).mean()"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.13415879165466874"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age<6).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Counts by year"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mhYWFKiwslCR16dJFS5Ys0YgRIzRy5Mgmvy4uLk7dunXzfXzKKadoy5Ytvtu9Xq+ioqLk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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10deb69b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"unique_students = lsl_dr.groupby('study_id').first()\n",
"unique_students.academic_year_start.value_counts().sort_index()[:-1].plot(kind='bar')\n",
"plt.ylabel('Frequency'); plt.xlabel('Academic year');"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10c8b7630>"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Q4A680gmyKttpuVZmk2t9ttNyrcy2Y67H42rX+r17u4LaXntzrcx2Wm57stvSEX/eAhby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\nQCEDAGADFDIAADZAIQMAYAP/D6JMo4+OKaBqAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11064f438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"disab_by_year = unique_students.groupby('academic_year_start')['synd_or_disab'].value_counts().unstack().fillna(0)\n",
"disab_by_year.columns = ['No', 'Yes']\n",
"disab_by_year[disab_by_year.index!='nan'].plot(kind='bar', stacked=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
},
"widgets": {
"state": {},
"version": "1.0.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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