aschleg

games.howell <- function(grp, obs) {
  
  #Create combinations
  combs <- combn(unique(grp), 2)
  
  # Statistics that will be used throughout the calculations:
  # n = sample size of each group
  # groups = number of groups in data
  # Mean = means of each group sample
  # std = variance of each group sample

aschleg

"""

Simple script for extracting Socrata Open Data Access (SODA) datasets. Compatible with 3+, though one can easily make it 2.7 
compatible by changing the `from urllib.error import HTTPError` import to `from urllib2 import HTTPError`

Parameters
----------
endpoint : string
  SODA API endpoint of the dataset.
count : int, default 1000

aschleg

import requests
import numpy as np
import pandas as pd
from six.moves.urllib.error import HTTPError


def get_soda_api_data(endpoint, count=1000, offset=0, return_df=True):
    params = {'$limit': count, '$offset': offset}
    
    results = []

aschleg

library(caret)

ginzberg <- read.csv('D:/Google_Drive/Google_Drive/Resources/Datasets/Rdata/car/Ginzberg.csv')
ginzberg <- ginzberg[2:4]

train <- createDataPartition(y = ginzberg$depression,
                                p = 0.50,
                                list = FALSE)

training <- ginzberg[ train,]

aschleg

<!--PHP file for adding a basic template portfolio page for Wordpress themes using the Bootstrap framework.-->

<?php
/*
Template Name: Portfolio
*/
?>

<?php get_header(); ?>

aschleg

# Function for performing Lagrangian polynomial interpolation https://en.wikipedia.org/wiki/Lagrange_polynomial. 
# Requires the package rSymPy https://cran.r-project.org/web/packages/rSymPy/index.html. 
# Parameters:
#   x: x values of interpolating points
#   y: values corresponding to x values
# Returns:
#   Simplified interpolated polynomial that passes through the given x and y points


lagrange.poly <- function(x, y) {

aschleg

from xlwings import Workbook, Range
import pandas as pd
import os
import re

# Script to merge a folder containing Excel workbooks into a single workbook.
# The folder should only contain Excel workbooks and must all either be in csv, xls or xlsx format
# To run, open the command prompt and enter the command python Merge_Excel_Workbooks.py

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

aschleg

from xlwings import Workbook, Range, Sheet
import pandas as pd
import os

# Alternative method to split an Excel worksheet into multiple sheets based on a column name.
# The script will prompt four questions to enter in the required information. The workbook will then open and
# split the prompted worksheet into separate worksheets based on the desired column name.
# To run, open the command prompt and enter the command python Split_Excel_Worksheet_v2.py

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""

aschleg

from xlwings import Workbook, Range, Sheet
import pandas as pd

# Split Excel data in one worksheet into multiple worksheets based on column name.

# Copy this file into the same location as the Excel workbook with the worksheet you wish to split.
# Download the zip of the xlwings Github repo here: https://github.com/ZoomerAnalytics/xlwings and copy the
# xlwings.bas file from the xlwings folder. Import the xlwings.bas file into your Excel workbook by entering ALT+F11
# and then going to File, Import File and clicking on the file.
# Import the Split_Excel_Worksheet.bas file and run by going to the Developer tab on the Excel Ribbon, click Macros,

aschleg

# Function for calculating the confidence (mean) and prediction intervals of a fitted linear regression model.
# Replicates the predict() function with arguments 'confidence' and 'prediction'
# Used in post demonstrating the confidence and prediction intervals in linear regression: http://www.aaronschlegel.com/notebook/confidence-prediction-intervals/

# Takes three arguments:
#   x = vector of independent variable data points
#   y = vector of dependent variable data points
#   pred.x = value of x to find predicted y

conf.pred.intervals <- function(x, y, pred.x) {