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library(Matching)

data(lalonde)

#### EASY QUANTILE EFFECTS FOR RCTs...
## MEDIAN EFFECT
quantile(lalonde$re78[lalonde$treat == 1], probs = 0.5) -
quantile(lalonde$re78[lalonde$treat == 0], probs = 0.5)

#### 0.9 QUANTILE EFFECT

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# DATA PREPROCESSING
foo <- read.csv("https://tinyurl.com/y2zt2hyc")
foo <- foo[, c(6:8, 11:16, 99, 50, 114, 49, 63, 136, 109, 126, 48, 160, 142, 10)]
foo <- foo[c(-19, -47), ]
which(is.na(foo) == TRUE)
head(foo)

# What is the 3 digit country code associated with the first row of the data set?
foo$clust2[1]
foo$yrbeg[1]

diamonaj

## -----------------------------------------------------------------------------------------------------------
library(maps)
data(us.cities)
head(us.cities)

map(database = "usa") 
capitals <- subset(us.cities, capital == 2) # subset state capitals

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dd <- read.csv("progresa.csv")
dim(dd)

dd <- na.omit(dd)
dim(dd)

### QUESTION 1
# An example of what I'm looking for

par(mfrow = c(2,1))

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install.packages("tree")
library(MASS)
library(tree)
head(Pima.tr)

#############

set.seed(1234)
# enter your code here
# fit a tree in the training set

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### This exercise requires installing a bunch of packages---
### Unfortunately, the precise sequence and rules for installing may vary
### depending upon your computer and configuration.

## ***Taken from Chapter 5 in Kosuke Imai's "Quantitative Social Science"
## Transcribed by Alexis Diamond, all errors my own...

##########################################################################

diamonaj

# assuming you have downloaded the data (Data1.csv) correctly,
# as discussed here: https://piazza.com/class/l7oq25mqbrz1nd/post/110


# you may need to change the file location in quotes below, to suit where your file is
apple <- read.csv("~/Documents/Data1.csv", stringsAsFactors = F,  encoding="UTF-8")

str(apple)

install.packages("tm")

diamonaj

# assuming you have downloaded the data (Data1.csv) correctly,
# as discussed here: https://piazza.com/class/l7oq25mqbrz1nd/post/110


# you may need to change the file location in quotes below, to suit where your file is
apple <- read.csv("~/Documents/Data1.csv", stringsAsFactors = F,  encoding="UTF-8")

str(apple)

library(tm)

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## We're going to be running regressions...
## If a predicted value is positive, we're going to say it's a prediction for hamilton authorship.
## If a predicted value is negative, we're going to say it's a prediction for madison authorship.

author <- rep(NA, nrow(dtm1)) # a vector with a missing value
author[hamilton] <- 1 # 1 if Hamilton
author[madison] <- -1 # -1 if Madison

## data frame for regression
author.data <- data.frame(author = author[c(hamilton, madison)],

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## Authorship prediction
## authorship of some Federalist Papers is unknown
## We use the 66 essays attributed to either Hamilton or Madison to 
## predict the authorship of the 11 disputed papers.

## Since each paper deals with a different topic, we focus on usage of articles, 
## prepositions, and conjuctions. We analyze the frequency of the following
## 10 words: although, always, commonly, consequently, considerable, enough, there, upon, while, 
## and whilst.