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def merge(arr, l, mids, r):
    '''
    Merge is a subprocedure of mergeSort, declared below. It 
    merges k sorted subarrays into a sorted array. It does so by
    iterating continuously over all subarrays, finding the smallest key,
    and relocating it to the right position of the outcome array.
    Inputs:
    
        arr (list) The entire array to be sorted.
        

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import pylab as py
import scipy
import random
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline

class TrafficSimulation:

    def __init__(self, road_length=100, traffic_density=0.1, maximum_velocity=5,

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set.seed(2626)

# Create toy dataset

x1 <- rnorm(200, mean = 10, sd =2)
x2 <- rnorm(200, mean = 50, sd =7)
y <- 10 + 4*x1 + 0.5*x2 - 0.1*x1*x2 + rnorm(200, mean = 0, sd = 5)
data <- data.frame(x1, x2, y)

# Spliting the data

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# Cleaning the workspace
rm(list = ls())

# Loading the data
library(Matching)
data(lalonde)
View(lalonde)
str(lalonde)

# Let's look at two individuals. For the purposes of this tutorial, 

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# Importing the data
# I'm just importing the dataset after having transformed the data columns to numeric.
df <- read.csv("C:/Users/Vinic/Downloads/Copy of datasetforCS112 - TA.csv",
           stringsAsFactors = F)
str(df)

# Converting char columns to numeric
df$cumulative.disbursements <-
  as.numeric(df$cumulative.disbursements)
df$undisbursed.amount <-

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# Characteristics of the data set to be generated:
# 
# N(mu, sigma) error term,
# 500 treated units, 500 control units,
# Predictors: treatment (binary variable), and 
# hectares (continuous variable--use a uniform distribution), 
# pre-treatment productivity (continuous variable--use a uniform distribution).
# Outcome variable: post-treatment productivity: (continuous variable).

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set.seed(1234)

##############
# QUESTION 1 #
##############

# Generating 99 observations

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library(haven)
df <- read_dta("C:/Users/Vinic/Downloads/turnout.dta")
View(df)
df[1,1]
df[1,]

lm2 <- glm(turnout ~ ., data = df, family = binomial)
summary(lm2)

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