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fonnesbeck/Dataset Statistics.ipynb

Created April 22, 2015 18:24
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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": [
"0101-2003-0101\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",
"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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>11679</th>\n",
" <td> 1147-2010-0064</td>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2010-2011</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td>...</td>\n",
" <td> 3</td>\n",
" <td> 6</td>\n",
" <td> 65</td>\n",
" <td> 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>11680</th>\n",
" <td> 1147-2010-0064</td>\n",
" <td> year_1_complete_71_arm_1</td>\n",
" <td> 2011-2012</td>\n",
" <td> 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</td>\n",
" <td> 5</td>\n",
" <td> 77</td>\n",
" <td> 2</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>11681</th>\n",
" <td> 1147-2010-0064</td>\n",
" <td> year_2_complete_71_arm_1</td>\n",
" <td> 2012-2013</td>\n",
" <td> 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</td>\n",
" <td> 5</td>\n",
" <td> 89</td>\n",
" <td> 2</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>11682</th>\n",
" <td> 1147-2010-0064</td>\n",
" <td> year_3_complete_71_arm_1</td>\n",
" <td> 2013-2014</td>\n",
" <td> 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</td>\n",
" <td> 5</td>\n",
" <td> 101</td>\n",
" <td> 2</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",
"11679 1147-2010-0064 initial_assessment_arm_1 2010-2011 0 0 \n",
"11680 1147-2010-0064 year_1_complete_71_arm_1 2011-2012 0 NaN \n",
"11681 1147-2010-0064 year_2_complete_71_arm_1 2012-2013 0 NaN \n",
"11682 1147-2010-0064 year_3_complete_71_arm_1 2013-2014 0 NaN \n",
"\n",
" race prim_lang sib mother_ed father_ed ... sle_fo a_fo \\\n",
"11679 0 0 1 3 3 ... 3 6 \n",
"11680 NaN NaN NaN NaN NaN ... 3 5 \n",
"11681 NaN NaN NaN NaN NaN ... 3 5 \n",
"11682 NaN NaN NaN NaN NaN ... 4 5 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"11679 65 0 NaN NaN NaN \n",
"11680 77 2 NaN NaN NaN \n",
"11681 89 2 NaN NaN NaN \n",
"11682 101 2 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid \n",
"11679 NaN NaN NaN \n",
"11680 NaN NaN NaN \n",
"11681 NaN NaN NaN \n",
"11682 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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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> 0101-2003-0101</td>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2002-2003</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td>...</td>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" <td> 54</td>\n",
" <td> 2</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> 0101-2003-0101</td>\n",
" <td> year_1_complete_71_arm_1</td>\n",
" <td> 2003-2004</td>\n",
" <td> 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</td>\n",
" <td> 4</td>\n",
" <td> 80</td>\n",
" <td> 1</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> 0101-2003-0101</td>\n",
" <td> year_2_complete_71_arm_1</td>\n",
" <td> 2004-2005</td>\n",
" <td> 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</td>\n",
" <td> 4</td>\n",
" <td> 80</td>\n",
" <td> 2</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> 0101-2003-0101</td>\n",
" <td> year_3_complete_71_arm_1</td>\n",
" <td> 2005-2006</td>\n",
" <td> 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</td>\n",
" <td> 5</td>\n",
" <td> 96</td>\n",
" <td> 3</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> 0101-2003-0101</td>\n",
" <td> year_4_complete_71_arm_1</td>\n",
" <td> 2006-2007</td>\n",
" <td> 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</td>\n",
" <td> 5</td>\n",
" <td> 109</td>\n",
" <td> 2</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 0101-2003-0101 initial_assessment_arm_1 2002-2003 0 0 0 \n",
"1 0101-2003-0101 year_1_complete_71_arm_1 2003-2004 0 NaN NaN \n",
"2 0101-2003-0101 year_2_complete_71_arm_1 2004-2005 0 NaN NaN \n",
"3 0101-2003-0101 year_3_complete_71_arm_1 2005-2006 0 NaN NaN \n",
"4 0101-2003-0101 year_4_complete_71_arm_1 2006-2007 0 NaN NaN \n",
"\n",
" prim_lang sib mother_ed father_ed ... sle_fo a_fo fam_age \\\n",
"0 0 1 6 6 ... 2 2 54 \n",
"1 NaN NaN NaN NaN ... 4 4 80 \n",
"2 NaN NaN NaN NaN ... 4 4 80 \n",
"3 NaN NaN NaN NaN ... 5 5 96 \n",
"4 NaN NaN NaN NaN ... 5 5 109 \n",
"\n",
" family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"0 2 NaN NaN NaN NaN \n",
"1 1 NaN NaN NaN NaN \n",
"2 2 NaN NaN NaN NaN \n",
"3 3 NaN NaN NaN NaN \n",
"4 2 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": [],
"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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>11679</th>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2010-2011</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 6</td>\n",
" <td> 65</td>\n",
" <td> 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>11680</th>\n",
" <td> year_1_complete_71_arm_1</td>\n",
" <td> 2011-2012</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 5</td>\n",
" <td> 77</td>\n",
" <td> 2</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>11681</th>\n",
" <td> year_2_complete_71_arm_1</td>\n",
" <td> 2012-2013</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 5</td>\n",
" <td> 89</td>\n",
" <td> 2</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>11682</th>\n",
" <td> year_3_complete_71_arm_1</td>\n",
" <td> 2013-2014</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 5</td>\n",
" <td> 101</td>\n",
" <td> 2</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",
"11679 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"11680 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"11681 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"11682 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo \\\n",
"11679 1 3 3 8 ... 6 \n",
"11680 1 3 3 8 ... 5 \n",
"11681 1 3 3 8 ... 5 \n",
"11682 1 3 3 8 ... 5 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"11679 65 0 NaN NaN NaN \n",
"11680 77 2 NaN NaN NaN \n",
"11681 89 2 NaN NaN NaN \n",
"11682 101 2 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid study_id \n",
"11679 NaN NaN NaN 1147-2010-0064 \n",
"11680 NaN NaN NaN 1147-2010-0064 \n",
"11681 NaN NaN NaN 1147-2010-0064 \n",
"11682 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 style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>8319</th>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2012-2013</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td> 3</td>\n",
" <td>...</td>\n",
" <td> 1</td>\n",
" <td> 9</td>\n",
" <td> 1</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> 0735-2012-0008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5035</th>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2007-2008</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td> 2</td>\n",
" <td>...</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 0522-2008-0011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8314</th>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2011-2012</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" <td> 2</td>\n",
" <td>...</td>\n",
" <td> 1</td>\n",
" <td> 7</td>\n",
" <td> 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> 0735-2012-0006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8310</th>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2011-2012</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 8</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 1</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> 0735-2012-0005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5038</th>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2008-2009</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 9</td>\n",
" <td>...</td>\n",
" <td> 3</td>\n",
" <td> 62</td>\n",
" <td> 4</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> 0522-2008-0012</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",
"8319 initial_assessment_arm_1 2012-2013 0 0 6 0 \n",
"5035 initial_assessment_arm_1 2007-2008 0 0 0 0 \n",
"8314 initial_assessment_arm_1 2011-2012 0 1 0 0 \n",
"8310 initial_assessment_arm_1 2011-2012 0 1 8 0 \n",
"5038 initial_assessment_arm_1 2008-2009 0 0 0 0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo fam_age \\\n",
"8319 0 6 6 3 ... 1 9 \n",
"5035 2 6 6 2 ... NaN NaN \n",
"8314 1 4 6 2 ... 1 7 \n",
"8310 2 6 6 8 ... 1 2 \n",
"5038 1 2 4 9 ... 3 62 \n",
"\n",
" family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"8319 1 NaN NaN NaN NaN \n",
"5035 NaN NaN NaN NaN NaN \n",
"8314 0 NaN NaN NaN NaN \n",
"8310 1 NaN NaN NaN NaN \n",
"5038 4 NaN NaN NaN NaN \n",
"\n",
" school_lunch medicaid study_id \n",
"8319 NaN NaN 0735-2012-0008 \n",
"5035 NaN NaN 0522-2008-0011 \n",
"8314 NaN NaN 0735-2012-0006 \n",
"8310 NaN NaN 0735-2012-0005 \n",
"5038 NaN NaN 0522-2008-0012 \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-4 539 489\n",
"CELF-P2 1170 1176\n",
"OWLS 871 877\n",
"PLS 2887 2896\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": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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> 0101-2003-0101</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> 0101-2003-0101</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 0101-2003-0101 initial_assessment_arm_1 51 receptive PLS \n",
"5 0101-2003-0101 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": 19,
"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": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Goldman 4254\n",
"Arizonia 490\n",
"Arizonia and Goldman 49\n",
"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": 21,
"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": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 4790.000000\n",
"mean 69.175365\n",
"std 31.206700\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": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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> 0101-2003-0101</td>\n",
" <td> year_1_complete_71_arm_1</td>\n",
" <td> Goldman</td>\n",
" <td> 78</td>\n",
" <td> 0101</td>\n",
" <td> 80</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</td>\n",
" <td> 0101</td>\n",
" <td> 44</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</td>\n",
" <td> 0101</td>\n",
" <td> 54</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</td>\n",
" <td> 0101</td>\n",
" <td> 53</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</td>\n",
" <td> 0101</td>\n",
" <td> 66</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 0101-2003-0101 year_1_complete_71_arm_1 Goldman 78 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 Goldman 72 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 Goldman 97 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 Goldman 75 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 Goldman 80 0101 \n",
"\n",
" age_test domain \n",
"1 80 Articulation \n",
"9 44 Articulation \n",
"10 54 Articulation \n",
"14 53 Articulation \n",
"15 66 Articulation "
]
},
"execution_count": 23,
"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": 24,
"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": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False 9677\n",
"True 2089\n",
"dtype: int64\n",
"There are 714 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": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_mother_ed:\n",
"6 4293\n",
"4 2485\n",
"3 1693\n",
"5 1282\n",
"2 1115\n",
"1 421\n",
"0 156\n",
"dtype: int64\n",
"mother_ed:\n",
"1 2808\n",
"2 2485\n",
"3 1282\n",
"0 577\n",
"dtype: int64\n",
"\n",
"There are 5328 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": 27,
"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": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 9132\n",
"1 2121\n",
"dtype: int64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.secondary_diagnosis.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1884830711810184"
]
},
"execution_count": 29,
"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": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 3173 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": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 7889\n",
"2 486\n",
"4 324\n",
"12 180\n",
"6 159\n",
"10 125\n",
"8 104\n",
"14 38\n",
"16 2\n",
"dtype: int64"
]
},
"execution_count": 31,
"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": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tech_cats = [\"None\", \"Baha\", \"Hearing aid\", \"Cochlear\"]\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.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.tech_left = np.abs(demographic.tech_left - 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Substitute valid missing values for hearing loss:"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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": 34,
"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": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"baha:\n",
"0 3683\n",
"1 132\n",
"2 57\n",
"dtype: int64\n",
"There are 1476 null values for baha\n",
"\n",
"hearing_aid:\n",
"2 1706\n",
"0 1615\n",
"1 529\n",
"dtype: int64\n",
"There are 1516 null values for hearing_aid\n",
"\n",
"cochlear:\n",
"0 2493\n",
"2 805\n",
"1 574\n",
"dtype: int64\n",
"There are 1476 null values for cochlear\n",
"12480\n"
]
}
],
"source": [
"demographic[\"baha\"] = 0\n",
"demographic.baha = demographic.baha.astype(object)\n",
"demographic.loc[(demographic.tech_right==1) | (demographic.tech_left==1), 'baha'] = 1\n",
"demographic.loc[(demographic.tech_right==1) & (demographic.tech_left==1), 'baha'] = 2\n",
"demographic.loc[(demographic.tech_right.isnull()) & (demographic.tech_left.isnull()), 'baha'] = None\n",
"print(\"baha:\")\n",
"print(demographic.drop_duplicates(subset='study_id').baha.value_counts())\n",
"print(\"There are {0} null values for baha\".format(sum(demographic.baha.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": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"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": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2940, 4339, 1219)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.bilateral_ci.sum(), demographic.bilateral_ha.sum(), demographic.bimodal.sum()"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"bilateral_ci 805\n",
"bilateral_ha 1706\n",
"bimodal 334\n",
"dtype: int64"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.drop_duplicates(subset='study_id')[['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": 38,
"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": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 4339\n",
"3 2940\n",
"4 1219\n",
"0 680\n",
"1 470\n",
"2 294\n",
"8 168\n",
"7 19\n",
"5 8\n",
"dtype: int64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[\"implant_category\"] = None\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==0) & (demographic.baha==0), \n",
" 'implant_category'] = 0\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==1) & (demographic.baha==0), \n",
" 'implant_category'] = 1\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==0) & (demographic.baha==1), \n",
" 'implant_category'] = 2\n",
"demographic.loc[(demographic.cochlear==2) & (demographic.hearing_aid==0) & (demographic.baha==0), \n",
" 'implant_category'] = 3\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==1) & (demographic.baha==0), \n",
" 'implant_category'] = 4\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==0) & (demographic.baha==1), \n",
" 'implant_category'] = 5\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==2) & (demographic.baha==0), \n",
" 'implant_category'] = 6\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==1) & (demographic.baha==1), \n",
" 'implant_category'] = 7\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==0) & (demographic.baha==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": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 6. , 0. , 3. , 1. , 25. , 9. , 13. , 26. ,\n",
" nan, 15. , 2. , 23. , 7. , 11. , 24. , 17. ,\n",
" 36. , 28. , 14. , 48. , 12. , 29. , 20. , 27. ,\n",
" 22. , 5. , 4. , 60. , 32. , 19. , 18. , 52. ,\n",
" 42. , 21. , 16. , 30. , 8. , 10. , 140. , 61. ,\n",
" 66. , 44. , 41. , 40. , 49. , 86. , 33. , 126. ,\n",
" 1.5, 85. , 51. , 2.5, 67. , 39. , 62. , 133. ,\n",
" 38. , 103. , 54. , 35. , 43. , 87. , 83. , 76. ,\n",
" 50. , 37. , 116. , 68. , 72. , 92. , 34. , 57. ,\n",
" 97. , 71. , 55. , 46. , 65. , 78. , 45. , 31. ,\n",
" 107. , 64. , 74. , 77. , 88. , 81. , 84. , 80. ,\n",
" 53. , 59. , 0.5, 56. , 98. , 47. , 58. , 75. ,\n",
" 70. , 119. , 63. , 154. , 89. , 152. ])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.onset_1.unique()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3848"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.age_diag.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"demographic['sex'] = demographic.male.replace({0:'Female', 1:'Male'})"
]
},
{
"cell_type": "code",
"execution_count": 44,
"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": 45,
"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": 46,
"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": 47,
"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": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_race:\n",
"0 6523\n",
"2 2011\n",
"1 1156\n",
"3 861\n",
"6 587\n",
"8 463\n",
"7 219\n",
"4 58\n",
"5 25\n",
"dtype: int64\n",
"race:\n",
"0 6523\n",
"2 2011\n",
"1 1156\n",
"4 1133\n",
"3 861\n",
"dtype: int64\n",
"There are 796 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": 49,
"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": 50,
"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": 51,
"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",
" '0000-0000': np.nan})"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1199b3438>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
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"2BXgZHWtEBZwdoDMXJuZ92fm/bSuCb4fOFqHr33lg8B/BYiIN9D6Zv3Vunz9aV3f3QDfz38n8I0a\n",
"5V8DfKPtcVe+782bu9Op73usPwZ8LiIGgJeAw5k5FRF7aP2l7wN2Zub8/v9ZZ+5x4C5gV0R8jNbd\n",
"mr8C/HYNnoMvAV+IiOdo/dv7EPAd4OkaZH89dfr7v4/W13+Y1lHYw7SOTmvx9c/MIxFxb0R8k1au\n",
"7cDfUpP8tH4qpf0nrrryd3/e3J0uSZJ+NPPpdLokSfoRWOKSJBXKEpckqVCWuCRJhbLEJUkqlCUu\n",
"SVKhLHFJkgr1/wG8Gqu3wH1SCAAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1199b3cf8>"
]
},
"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": 54,
"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": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EVT 3113\n",
"EOWPVT 2305\n",
"EOWPVT and EVT 120\n",
"dtype: int64"
]
},
"execution_count": 55,
"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": 56,
"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": 57,
"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": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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> 0101-2003-0101</td>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 58</td>\n",
" <td> EOWPVT</td>\n",
" <td> 0101</td>\n",
" <td> 54</td>\n",
" <td> Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 0101-2003-0101</td>\n",
" <td> year_2_complete_71_arm_1</td>\n",
" <td> 84</td>\n",
" <td> EOWPVT</td>\n",
" <td> 0101</td>\n",
" <td> 80</td>\n",
" <td> Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 0101-2003-0101</td>\n",
" <td> year_5_complete_71_arm_1</td>\n",
" <td> 90</td>\n",
" <td> EOWPVT</td>\n",
" <td> 0101</td>\n",
" <td> 113</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</td>\n",
" <td> EOWPVT</td>\n",
" <td> 0101</td>\n",
" <td> 53</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</td>\n",
" <td> EOWPVT</td>\n",
" <td> 0101</td>\n",
" <td> 66</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 0101-2003-0101 initial_assessment_arm_1 58 EOWPVT 0101 \n",
"2 0101-2003-0101 year_2_complete_71_arm_1 84 EOWPVT 0101 \n",
"5 0101-2003-0101 year_5_complete_71_arm_1 90 EOWPVT 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 90 EOWPVT 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 87 EOWPVT 0101 \n",
"\n",
" age_test domain \n",
"0 54 Expressive Vocabulary \n",
"2 80 Expressive Vocabulary \n",
"5 113 Expressive Vocabulary \n",
"14 53 Expressive Vocabulary \n",
"15 66 Expressive Vocabulary "
]
},
"execution_count": 58,
"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": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['study_id', 'redcap_event_name', 'age_test_ppvt', 'ppvt_ss', 'age_test_rowpvt', 'rowpvt_ss'], dtype='object')"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.columns"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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": 61,
"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": 62,
"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": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 28 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": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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> 0101-2003-0101</td>\n",
" <td> year_2_complete_71_arm_1</td>\n",
" <td> 90</td>\n",
" <td> PPVT</td>\n",
" <td> 0101</td>\n",
" <td> 80</td>\n",
" <td> Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 0101-2003-0101</td>\n",
" <td> year_5_complete_71_arm_1</td>\n",
" <td> 101</td>\n",
" <td> ROWPVT</td>\n",
" <td> 0101</td>\n",
" <td> 113</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</td>\n",
" <td> PPVT</td>\n",
" <td> 0101</td>\n",
" <td> 44</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</td>\n",
" <td> PPVT</td>\n",
" <td> 0101</td>\n",
" <td> 54</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</td>\n",
" <td> PPVT</td>\n",
" <td> 0101</td>\n",
" <td> 68</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 0101-2003-0101 year_2_complete_71_arm_1 90 PPVT 0101 \n",
"5 0101-2003-0101 year_5_complete_71_arm_1 101 ROWPVT 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 55 PPVT 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 80 PPVT 0101 \n",
"11 0101-2003-0102 year_2_complete_71_arm_1 101 PPVT 0101 \n",
"\n",
" age_test domain \n",
"2 80 Receptive Vocabulary \n",
"5 113 Receptive Vocabulary \n",
"9 44 Receptive Vocabulary \n",
"10 54 Receptive Vocabulary \n",
"11 68 Receptive Vocabulary "
]
},
"execution_count": 64,
"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": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2619,)"
]
},
"execution_count": 65,
"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": 66,
"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": 184,
"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": 185,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>known_synd</th>\n",
" <th>synd_or_disab</th>\n",
" <th>race</th>\n",
" <th>academic_year_start</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>31730</th>\n",
" <td> year_9_complete_71_arm_1</td>\n",
" <td> 2012-2013</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 4</td>\n",
" <td> 4</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
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" <td> 104</td>\n",
" <td> Articulation</td>\n",
" <td> 0521</td>\n",
" <td> 100</td>\n",
" <td> NaN</td>\n",
" <td> Goldman</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31731</th>\n",
" <td> year_9_complete_71_arm_1</td>\n",
" <td> 2012-2013</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" <td> 138</td>\n",
" <td> Articulation</td>\n",
" <td> 0310</td>\n",
" <td> 92</td>\n",
" <td> NaN</td>\n",
" <td> Goldman</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31732</th>\n",
" <td> year_9_complete_71_arm_1</td>\n",
" <td> 2012-2013</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" <td> 137</td>\n",
" <td> Receptive Vocabulary</td>\n",
" <td> 0310</td>\n",
" <td> 65</td>\n",
" <td> NaN</td>\n",
" <td> PPVT and ROWPVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31733</th>\n",
" <td> year_9_complete_71_arm_1</td>\n",
" <td> 2011-2012</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" <td> 160</td>\n",
" <td> Expressive Vocabulary</td>\n",
" <td> 0102</td>\n",
" <td> 92</td>\n",
" <td> NaN</td>\n",
" <td> EOWPVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31734</th>\n",
" <td> year_9_complete_71_arm_1</td>\n",
" <td> 2011-2012</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" <td> 8</td>\n",
" <td>...</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" <td> 162</td>\n",
" <td> Receptive Vocabulary</td>\n",
" <td> 0102</td>\n",
" <td> 84</td>\n",
" <td> NaN</td>\n",
" <td> ROWPVT</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",
"31730 year_9_complete_71_arm_1 2012-2013 0 1 0 0 \n",
"31731 year_9_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"31732 year_9_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"31733 year_9_complete_71_arm_1 2011-2012 0 1 0 0 \n",
"31734 year_9_complete_71_arm_1 2011-2012 0 1 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... known_synd \\\n",
"31730 3 4 4 8 ... 0 \n",
"31731 NaN 6 6 8 ... 0 \n",
"31732 NaN 6 6 8 ... 0 \n",
"31733 3 6 6 8 ... 0 \n",
"31734 3 6 6 8 ... 0 \n",
"\n",
" synd_or_disab race academic_year_start age_test \\\n",
"31730 1 0 NaN 104 \n",
"31731 0 0 NaN 138 \n",
"31732 0 0 NaN 137 \n",
"31733 0 0 NaN 160 \n",
"31734 0 0 NaN 162 \n",
"\n",
" domain school score test_name test_type \n",
"31730 Articulation 0521 100 NaN Goldman \n",
"31731 Articulation 0310 92 NaN Goldman \n",
"31732 Receptive Vocabulary 0310 65 NaN PPVT and ROWPVT \n",
"31733 Expressive Vocabulary 0102 92 NaN EOWPVT \n",
"31734 Receptive Vocabulary 0102 84 NaN ROWPVT \n",
"\n",
"[5 rows x 73 columns]"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.tail()"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2013 6742\n",
"2012 6577\n",
"2011 5159\n",
"2010 4418\n",
"nan 3133\n",
"2009 2356\n",
"2014 984\n",
"2008 821\n",
"2007 531\n",
"2006 344\n",
"2005 276\n",
"2004 172\n",
"2003 90\n",
"2002 47\n",
"2001 35\n",
"1998 16\n",
"1999 15\n",
"2000 12\n",
"1997 6\n",
"1995 1\n",
"dtype: int64"
]
},
"execution_count": 186,
"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": 190,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"current_year_only = True\n",
"\n",
"if current_year_only:\n",
" lsl_dr = lsl_dr[lsl_dr.academic_year_start=='2013']"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"QmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11e00a518>"
]
},
"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": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11e00aba8>"
]
},
"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": 194,
"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": 195,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(6742, 73)"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.shape"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2222,)"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4898,)"
]
},
"execution_count": 197,
"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": 198,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr.score = lsl_dr.score.astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plots of Demographic Data"
]
},
{
"cell_type": "code",
"execution_count": 199,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_color = \"#64AAE8\""
]
},
{
"cell_type": "code",
"execution_count": 200,
"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(1)\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": 201,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_students = demographic.drop_duplicates('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4898, 67)"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 4387.000000\n",
"mean 30.953271\n",
"std 28.380353\n",
"min 0.000000\n",
"25% 9.000000\n",
"50% 25.000000\n",
"75% 42.000000\n",
"max 298.000000\n",
"Name: age, dtype: float64"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x119074c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.male, ('Female', 'Male'), label_offset=20, ylim=(0, 2600), color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 205,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc42278>"
]
},
"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": 206,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4304"
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.prim_lang.count()"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"x2fmK62NpW76V+AfIuIH1H5Xftr/u17jdb8vM3OvVgVpFa8tL0lSYTzmLklSYSx3SZIKY7lLklQY\n",
"y12SpMJY7pIkFcZylySpMJa7JEmF+f9SWqWNbc8D9AAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11910d7f0>"
]
},
"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": 208,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3937"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.sib.count()"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 4563 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": 210,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": 211,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x118530748>"
]
},
"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": 212,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2767"
]
},
"execution_count": 212,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_amp_counts.sum()"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"666.0"
]
},
"execution_count": 213,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age_amp.max()"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x119135c50>"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc332b0>"
]
},
"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": 215,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1190d4198>"
]
},
"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": 216,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11c40b358>"
]
},
"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": 217,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11917fd30>"
]
},
"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": 218,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3850"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_right.count()"
]
},
{
"cell_type": "code",
"execution_count": 219,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3836"
]
},
"execution_count": 219,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_left.count()"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11917c588>"
]
},
"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": 221,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"AAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bef2e48>"
]
},
"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": 222,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3755"
]
},
"execution_count": 222,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.degree_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"QrIAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11c3e19b0>"
]
},
"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": 224,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11908f748>"
]
},
"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": 225,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3681"
]
},
"execution_count": 225,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3757"
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 227,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"gAIOAb8BVwDbmwaTJC0cC/x0HQL2V9Ua8DKwB7iD7ka9JEkz4xb9dF1eVSeTLAHbquo4QJI/GueS\n",
"JC0YV/DTtTb53AOswPkhKs6DlyTNlCv46VpJcpTuzP2BJNcDh4H328aSJC0aW9VOWZKbgDNVdXpS\n",
"4G+pqo9a55IkLRYLvCRJI+QZvCRJI2SBlyRphCzwkiSNkAVekqQRssBLkjRCfwGLy7e5c5qAGgAA\n",
"AABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x118c17b00>"
]
},
"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": 228,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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_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",
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" <th>academic_year_start</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>11679</th>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 2010-2011</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>11680</th>\n",
" <td> year_1_complete_71_arm_1</td>\n",
" <td> 2011-2012</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>11681</th>\n",
" <td> year_2_complete_71_arm_1</td>\n",
" <td> 2012-2013</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
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" <td> 51</td>\n",
" <td> Female</td>\n",
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" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11682</th>\n",
" <td> year_3_complete_71_arm_1</td>\n",
" <td> 2013-2014</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
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" <td>...</td>\n",
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" <td> 0</td>\n",
" <td> 6</td>\n",
" <td> 51</td>\n",
" <td> Female</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td>NaN</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",
"11679 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"11680 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"11681 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"11682 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... \\\n",
"11679 1 3 3 8 ... \n",
"11680 1 3 3 8 ... \n",
"11681 1 3 3 8 ... \n",
"11682 1 3 3 8 ... \n",
"\n",
" bilateral_ha bimodal tech implant_category age_diag sex \\\n",
"11679 True False 0 6 51 Female \n",
"11680 True False 0 6 51 Female \n",
"11681 True False 0 6 51 Female \n",
"11682 True False 0 6 51 Female \n",
"\n",
" known_synd synd_or_disab race academic_year_start \n",
"11679 0 0 0 NaN \n",
"11680 0 0 0 NaN \n",
"11681 0 0 0 NaN \n",
"11682 0 0 0 NaN \n",
"\n",
"[4 rows x 67 columns]"
]
},
"execution_count": 228,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[demographic.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 229,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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" <th></th>\n",
" <th>study_id</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11679</th>\n",
" <td> 1147-2010-0064</td>\n",
" <td> initial_assessment_arm_1</td>\n",
" <td> 96</td>\n",
" <td> PPVT</td>\n",
" <td> 1147</td>\n",
" <td> 63</td>\n",
" <td> Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11680</th>\n",
" <td> 1147-2010-0064</td>\n",
" <td> year_1_complete_71_arm_1</td>\n",
" <td> 91</td>\n",
" <td> PPVT</td>\n",
" <td> 1147</td>\n",
" <td> 73</td>\n",
" <td> Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11681</th>\n",
" <td> 1147-2010-0064</td>\n",
" <td> year_2_complete_71_arm_1</td>\n",
" <td> 93</td>\n",
" <td> PPVT</td>\n",
" <td> 1147</td>\n",
" <td> 85</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",
"11679 1147-2010-0064 initial_assessment_arm_1 96 PPVT 1147 \n",
"11680 1147-2010-0064 year_1_complete_71_arm_1 91 PPVT 1147 \n",
"11681 1147-2010-0064 year_2_complete_71_arm_1 93 PPVT 1147 \n",
"\n",
" age_test domain \n",
"11679 63 Receptive Vocabulary \n",
"11680 73 Receptive Vocabulary \n",
"11681 85 Receptive Vocabulary "
]
},
"execution_count": 229,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <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>known_synd</th>\n",
" <th>synd_or_disab</th>\n",
" <th>race</th>\n",
" <th>academic_year_start</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>23329</th>\n",
" <td> year_3_complete_71_arm_1</td>\n",
" <td> 2013-2014</td>\n",
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" <td> 0</td>\n",
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" <td> 8</td>\n",
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" <td> 0</td>\n",
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" <td> 2013</td>\n",
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" <td>NaN</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 73 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang \\\n",
"23329 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... known_synd \\\n",
"23329 1 3 3 8 ... 0 \n",
"\n",
" synd_or_disab race academic_year_start age_test domain school \\\n",
"23329 0 0 2013 NaN NaN NaN \n",
"\n",
" score test_name test_type \n",
"23329 NaN NaN NaN \n",
"\n",
"[1 rows x 73 columns]"
]
},
"execution_count": 230,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3681"
]
},
"execution_count": 231,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2619,)"
]
},
"execution_count": 232,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(4898,)"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2619,)"
]
},
"execution_count": 234,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1178,)"
]
},
"execution_count": 235,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_ids = receptive.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic_ids = demographic.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 238,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[s for s in receptive_ids if s not in demographic_ids]"
]
},
{
"cell_type": "code",
"execution_count": 239,
"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",
" return summary[['Sample Size','Mean','SD','Min','Max']]"
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
" <tr>\n",
" <th>age_test</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>2 </th>\n",
" <td> 67</td>\n",
" <td> 97.641791</td>\n",
" <td> 18.427336</td>\n",
" <td> 44</td>\n",
" <td> 144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 247</td>\n",
" <td> 96.113360</td>\n",
" <td> 17.888865</td>\n",
" <td> 47</td>\n",
" <td> 139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 290</td>\n",
" <td> 92.762069</td>\n",
" <td> 20.776166</td>\n",
" <td> 0</td>\n",
" <td> 140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 201</td>\n",
" <td> 89.457711</td>\n",
" <td> 19.192172</td>\n",
" <td> 0</td>\n",
" <td> 130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 119</td>\n",
" <td> 89.058824</td>\n",
" <td> 16.842387</td>\n",
" <td> 51</td>\n",
" <td> 137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 70</td>\n",
" <td> 84.885714</td>\n",
" <td> 18.698634</td>\n",
" <td> 43</td>\n",
" <td> 124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 51</td>\n",
" <td> 82.078431</td>\n",
" <td> 15.620299</td>\n",
" <td> 46</td>\n",
" <td> 114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 40</td>\n",
" <td> 83.550000</td>\n",
" <td> 17.485452</td>\n",
" <td> 53</td>\n",
" <td> 120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 46</td>\n",
" <td> 76.847826</td>\n",
" <td> 18.447484</td>\n",
" <td> 20</td>\n",
" <td> 109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 138</td>\n",
" <td> 81.920290</td>\n",
" <td> 18.512202</td>\n",
" <td> 29</td>\n",
" <td> 132</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"age_test \n",
"2 67 97.641791 18.427336 44 144\n",
"3 247 96.113360 17.888865 47 139\n",
"4 290 92.762069 20.776166 0 140\n",
"5 201 89.457711 19.192172 0 130\n",
"6 119 89.058824 16.842387 51 137\n",
"7 70 84.885714 18.698634 43 124\n",
"8 51 82.078431 15.620299 46 114\n",
"9 40 83.550000 17.485452 53 120\n",
"10 46 76.847826 18.447484 20 109\n",
"11 138 81.920290 18.512202 29 132"
]
},
"execution_count": 240,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary = score_summary(\"Receptive Vocabulary\")\n",
"receptive_summary"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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> 10.000000</td>\n",
" <td> 10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 126.900000</td>\n",
" <td> 87.431602</td>\n",
" <td> 18.189100</td>\n",
" <td> 33.300000</td>\n",
" <td> 128.900000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 90.298825</td>\n",
" <td> 6.727914</td>\n",
" <td> 1.381796</td>\n",
" <td> 20.199285</td>\n",
" <td> 11.789355</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 40.000000</td>\n",
" <td> 76.847826</td>\n",
" <td> 15.620299</td>\n",
" <td> 0.000000</td>\n",
" <td> 109.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 55.000000</td>\n",
" <td> 82.446324</td>\n",
" <td> 17.586305</td>\n",
" <td> 22.250000</td>\n",
" <td> 121.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 94.500000</td>\n",
" <td> 86.972269</td>\n",
" <td> 18.437410</td>\n",
" <td> 43.500000</td>\n",
" <td> 131.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 185.250000</td>\n",
" <td> 91.935980</td>\n",
" <td> 18.652026</td>\n",
" <td> 46.750000</td>\n",
" <td> 138.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 290.000000</td>\n",
" <td> 97.641791</td>\n",
" <td> 20.776166</td>\n",
" <td> 53.000000</td>\n",
" <td> 144.000000</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 10.000000 10.000000\n",
"mean 126.900000 87.431602 18.189100 33.300000 128.900000\n",
"std 90.298825 6.727914 1.381796 20.199285 11.789355\n",
"min 40.000000 76.847826 15.620299 0.000000 109.000000\n",
"25% 55.000000 82.446324 17.586305 22.250000 121.000000\n",
"50% 94.500000 86.972269 18.437410 43.500000 131.000000\n",
"75% 185.250000 91.935980 18.652026 46.750000 138.500000\n",
"max 290.000000 97.641791 20.776166 53.000000 144.000000"
]
},
"execution_count": 241,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary.describe()"
]
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1269"
]
},
"execution_count": 242,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-0.5, 9.5)"
]
},
"execution_count": 243,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc373c8>"
]
},
"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')\n",
"plt.xlim(-0.5, 9.5)"
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
" <tr>\n",
" <th>age_test</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>2 </th>\n",
" <td> 61</td>\n",
" <td> 99.918033</td>\n",
" <td> 18.484494</td>\n",
" <td> 55</td>\n",
" <td> 134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 244</td>\n",
" <td> 96.434426</td>\n",
" <td> 21.047463</td>\n",
" <td> 42</td>\n",
" <td> 145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 282</td>\n",
" <td> 95.191489</td>\n",
" <td> 21.178084</td>\n",
" <td> 0</td>\n",
" <td> 139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 203</td>\n",
" <td> 91.073892</td>\n",
" <td> 20.455686</td>\n",
" <td> 0</td>\n",
" <td> 133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 117</td>\n",
" <td> 90.034188</td>\n",
" <td> 19.400467</td>\n",
" <td> 35</td>\n",
" <td> 129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 75</td>\n",
" <td> 85.346667</td>\n",
" <td> 15.480147</td>\n",
" <td> 52</td>\n",
" <td> 117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 50</td>\n",
" <td> 85.780000</td>\n",
" <td> 14.044085</td>\n",
" <td> 46</td>\n",
" <td> 115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 40</td>\n",
" <td> 87.575000</td>\n",
" <td> 14.101987</td>\n",
" <td> 55</td>\n",
" <td> 110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 45</td>\n",
" <td> 84.088889</td>\n",
" <td> 15.545859</td>\n",
" <td> 44</td>\n",
" <td> 110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 135</td>\n",
" <td> 88.422222</td>\n",
" <td> 15.986686</td>\n",
" <td> 52</td>\n",
" <td> 137</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"age_test \n",
"2 61 99.918033 18.484494 55 134\n",
"3 244 96.434426 21.047463 42 145\n",
"4 282 95.191489 21.178084 0 139\n",
"5 203 91.073892 20.455686 0 133\n",
"6 117 90.034188 19.400467 35 129\n",
"7 75 85.346667 15.480147 52 117\n",
"8 50 85.780000 14.044085 46 115\n",
"9 40 87.575000 14.101987 55 110\n",
"10 45 84.088889 15.545859 44 110\n",
"11 135 88.422222 15.986686 52 137"
]
},
"execution_count": 244,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary = score_summary(\"Expressive Vocabulary\")\n",
"expressive_summary"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1252"
]
},
"execution_count": 245,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1191002b0>"
]
},
"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, 400)\n",
"else:\n",
" plt.ylim(0, 1400)"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
" <tr>\n",
" <th>age_test</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>2 </th>\n",
" <td> 42</td>\n",
" <td> 88.547619</td>\n",
" <td> 17.935117</td>\n",
" <td> 50</td>\n",
" <td> 122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 217</td>\n",
" <td> 85.244240</td>\n",
" <td> 19.399319</td>\n",
" <td> 40</td>\n",
" <td> 125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 281</td>\n",
" <td> 84.217082</td>\n",
" <td> 22.830568</td>\n",
" <td> 0</td>\n",
" <td> 121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 178</td>\n",
" <td> 83.174157</td>\n",
" <td> 21.460462</td>\n",
" <td> 40</td>\n",
" <td> 116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 121</td>\n",
" <td> 80.685950</td>\n",
" <td> 22.692155</td>\n",
" <td> 40</td>\n",
" <td> 110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 67</td>\n",
" <td> 79.716418</td>\n",
" <td> 22.760150</td>\n",
" <td> 3</td>\n",
" <td> 108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 44</td>\n",
" <td> 77.500000</td>\n",
" <td> 19.553742</td>\n",
" <td> 40</td>\n",
" <td> 107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 27</td>\n",
" <td> 85.185185</td>\n",
" <td> 18.193484</td>\n",
" <td> 40</td>\n",
" <td> 108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 31</td>\n",
" <td> 82.354839</td>\n",
" <td> 19.142533</td>\n",
" <td> 40</td>\n",
" <td> 105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 65</td>\n",
" <td> 83.969231</td>\n",
" <td> 20.182735</td>\n",
" <td> 39</td>\n",
" <td> 105</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"age_test \n",
"2 42 88.547619 17.935117 50 122\n",
"3 217 85.244240 19.399319 40 125\n",
"4 281 84.217082 22.830568 0 121\n",
"5 178 83.174157 21.460462 40 116\n",
"6 121 80.685950 22.692155 40 110\n",
"7 67 79.716418 22.760150 3 108\n",
"8 44 77.500000 19.553742 40 107\n",
"9 27 85.185185 18.193484 40 108\n",
"10 31 82.354839 19.142533 40 105\n",
"11 65 83.969231 20.182735 39 105"
]
},
"execution_count": 247,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary = score_summary(\"Articulation\")\n",
"articulation_summary"
]
},
{
"cell_type": "code",
"execution_count": 248,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1073"
]
},
"execution_count": 248,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 249,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11e030ba8>"
]
},
"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);plt.xlim(-0.5, 9.5);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Language scores"
]
},
{
"cell_type": "code",
"execution_count": 250,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, 'Language', 'Articulation', 'Receptive Vocabulary',\n",
" 'Expressive Vocabulary'], dtype=object)"
]
},
"execution_count": 250,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.unique()"
]
},
{
"cell_type": "code",
"execution_count": 251,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, 'receptive', 'expressive', 'Goldman', 'ROWPVT', 'EOWPVT',\n",
" 'EVT', 'PPVT', 'Arizonia', 'PPVT and ROWPVT',\n",
" 'Arizonia and Goldman', 'EOWPVT and EVT'], dtype=object)"
]
},
"execution_count": 251,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.test_type.unique()"
]
},
{
"cell_type": "code",
"execution_count": 252,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
" <tr>\n",
" <th>age_test</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>2 </th>\n",
" <td> 169</td>\n",
" <td> 89.597633</td>\n",
" <td> 22.966673</td>\n",
" <td> 50</td>\n",
" <td> 136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 234</td>\n",
" <td> 88.807692</td>\n",
" <td> 18.895051</td>\n",
" <td> 50</td>\n",
" <td> 139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 265</td>\n",
" <td> 86.524528</td>\n",
" <td> 19.317139</td>\n",
" <td> 50</td>\n",
" <td> 145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 170</td>\n",
" <td> 85.494118</td>\n",
" <td> 20.003623</td>\n",
" <td> 47</td>\n",
" <td> 140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 92</td>\n",
" <td> 80.108696</td>\n",
" <td> 20.074836</td>\n",
" <td> 40</td>\n",
" <td> 121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 48</td>\n",
" <td> 77.479167</td>\n",
" <td> 18.109583</td>\n",
" <td> 47</td>\n",
" <td> 120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 35</td>\n",
" <td> 74.371429</td>\n",
" <td> 19.395399</td>\n",
" <td> 40</td>\n",
" <td> 115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 10</td>\n",
" <td> 67.600000</td>\n",
" <td> 18.337575</td>\n",
" <td> 40</td>\n",
" <td> 104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 10</td>\n",
" <td> 77.800000</td>\n",
" <td> 16.771669</td>\n",
" <td> 41</td>\n",
" <td> 99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 27</td>\n",
" <td> 77.740741</td>\n",
" <td> 18.289536</td>\n",
" <td> 40</td>\n",
" <td> 107</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"age_test \n",
"2 169 89.597633 22.966673 50 136\n",
"3 234 88.807692 18.895051 50 139\n",
"4 265 86.524528 19.317139 50 145\n",
"5 170 85.494118 20.003623 47 140\n",
"6 92 80.108696 20.074836 40 121\n",
"7 48 77.479167 18.109583 47 120\n",
"8 35 74.371429 19.395399 40 115\n",
"9 10 67.600000 18.337575 40 104\n",
"10 10 77.800000 16.771669 41 99\n",
"11 27 77.740741 18.289536 40 107"
]
},
"execution_count": 252,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary = score_summary(\"Language\", \"receptive\")\n",
"receptive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 253,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1060"
]
},
"execution_count": 253,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb284a8>"
]
},
"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": 255,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
" <tr>\n",
" <th>age_test</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>2 </th>\n",
" <td> 168</td>\n",
" <td> 91.125000</td>\n",
" <td> 18.812401</td>\n",
" <td> 50</td>\n",
" <td> 150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 234</td>\n",
" <td> 84.739316</td>\n",
" <td> 16.472776</td>\n",
" <td> 53</td>\n",
" <td> 139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 265</td>\n",
" <td> 81.535849</td>\n",
" <td> 18.828966</td>\n",
" <td> 48</td>\n",
" <td> 136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 173</td>\n",
" <td> 80.508671</td>\n",
" <td> 19.248261</td>\n",
" <td> 48</td>\n",
" <td> 137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 103</td>\n",
" <td> 76.689320</td>\n",
" <td> 21.347741</td>\n",
" <td> 40</td>\n",
" <td> 140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 58</td>\n",
" <td> 71.120690</td>\n",
" <td> 20.664469</td>\n",
" <td> 40</td>\n",
" <td> 114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 37</td>\n",
" <td> 66.783784</td>\n",
" <td> 19.894353</td>\n",
" <td> 40</td>\n",
" <td> 116</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 10</td>\n",
" <td> 62.700000</td>\n",
" <td> 22.410563</td>\n",
" <td> 40</td>\n",
" <td> 106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 10</td>\n",
" <td> 80.800000</td>\n",
" <td> 20.595577</td>\n",
" <td> 40</td>\n",
" <td> 107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 26</td>\n",
" <td> 73.769231</td>\n",
" <td> 20.465205</td>\n",
" <td> 40</td>\n",
" <td> 112</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"age_test \n",
"2 168 91.125000 18.812401 50 150\n",
"3 234 84.739316 16.472776 53 139\n",
"4 265 81.535849 18.828966 48 136\n",
"5 173 80.508671 19.248261 48 137\n",
"6 103 76.689320 21.347741 40 140\n",
"7 58 71.120690 20.664469 40 114\n",
"8 37 66.783784 19.894353 40 116\n",
"9 10 62.700000 22.410563 40 106\n",
"10 10 80.800000 20.595577 40 107\n",
"11 26 73.769231 20.465205 40 112"
]
},
"execution_count": 255,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary = score_summary(\"Language\", \"expressive\")\n",
"expressive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 256,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1084"
]
},
"execution_count": 256,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"BtoAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x116005438>"
]
},
"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": 258,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11bac7518>"
]
},
"execution_count": 258,
"metadata": {},
"output_type": "execute_result"
},
{
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"AhnwkiQV6P8DFp2QpGA2ebYAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc32b70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(unique_students.age/12.).hist(grid=False, bins=np.sqrt(unique_students.shape[0]))\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age at enrollment')"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 4387.000000\n",
"mean 2.579439\n",
"std 2.365029\n",
"min 0.000000\n",
"25% 0.750000\n",
"50% 2.083333\n",
"75% 3.500000\n",
"max 24.833333\n",
"Name: age, dtype: float64"
]
},
"execution_count": 259,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(unique_students.age/12.).describe()"
]
},
{
"cell_type": "code",
"execution_count": 260,
"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": 261,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"audition = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference).dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11bb42128>"
]
},
"execution_count": 262,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x119061898>"
]
},
"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": 263,
"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": 264,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11e02e5f8>"
]
},
"execution_count": 264,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x119201278>"
]
},
"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": 265,
"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": 266,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11917fef0>"
]
},
"execution_count": 266,
"metadata": {},
"output_type": "execute_result"
},
{
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"TkSuQmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1191248d0>"
]
},
"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": 267,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 2949\n",
"3 814\n",
"4 812\n",
"5 708\n",
"2 378\n",
"0 176\n",
"1 53\n",
"dtype: int64"
]
},
"execution_count": 267,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.degree_hl.dropna().value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 268,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc2ae10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = lsl_dr.degree_hl.hist(bins=7)"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11bf02f28>"
]
},
"execution_count": 269,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"8XgYmOHiv5fv+/3Xi5n1otcP70Edat97wNcz83eAzwHf4/y7FUwCY70oTIvLzB9Q/yMx78LxGgUq\n",
"nP9ePI3jWIRFxu9V4P7GzOxt4EEu/lvq+BUgM9/LzHcjokI9tL9MF95/vQjJU9SL/FUNzW70oSK8\n",
"RT2gycyjwC+AzQvaK8BED+pS+xa+x+bH6xT1PxoXbld5ns3M1+cfA1up/6F3/AoUEVcDLwNPZeb3\n",
"6cL7rxdh7fXD+88fAY8ARMQHqf8P9lJEbG+03wYcWuK1KsPfRsRNjcfz43UYuDEi1kXEGHAtcKRX\n",
"BaqpFxecMvwU9UOljl+BGueiXwS+lJlPNTa/vtz3Xy/OWXv98P6zH/hORByi/gnxbuqz6281FkS8\n",
"CTzdu/LUhi8CTywcr8yci4h9wDj1w3S7M7P5vULVK58DvhkRZ4HjwM7MPO34FekBYCPwlYj4KvX7\n",
"en6e+vh1/P7z2uCSJBXOhV2SJBXOsJYkqXCGtSRJhTOsJUkqnGEtSVLhDGtJkgpnWEuSVLj/B7/N\n",
"NX3ce4WlAAAAAElFTkSuQmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x11babb898>"
]
},
"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": 270,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.22619400771284484"
]
},
"execution_count": 270,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age_int<6).mean()"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.14224265796499555"
]
},
"execution_count": 271,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age<6).mean()"
]
}
],
"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": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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