## ----echo=FALSE, message=FALSE, warning = FALSE----------------------------------------
library(tidyverse)
library(mosaic)
library(infer)
library(caret)


## ----echo = FALSE----------------------------------------------------------------------
GSS <- read.csv("GSS2016.csv")


## --------------------------------------------------------------------------------------
table(GSS$GRASS)


## --------------------------------------------------------------------------------------
GSS <- GSS %>% filter(YEAR==2016) %>% 
  filter(GRASS != 'Not applicable')


## --------------------------------------------------------------------------------------
GSS <- GSS %>%
mutate(LEGAL = ifelse(GRASS=='Legal', 'Legal', 'Not legal'))


## --------------------------------------------------------------------------------------
GSS$AGE <- as.numeric(GSS$AGE)


## --------------------------------------------------------------------------------------
set.seed(4)
test_id <- sample(1:nrow(GSS), size=round(0.2*nrow(GSS)))
TEST <- GSS[test_id,]
TRAIN <- GSS[-test_id,]


## --------------------------------------------------------------------------------------
TRAIN %>% group_by(LEGAL) %>% summarize(n=n())


## ----message=FALSE, fig.height=8, fig.width=10, eval = FALSE---------------------------
# 
# #install.packages("rattle")
library(rattle)
tree <- rpart(LEGAL~AGE, data=TRAIN, na.action = na.pass)
fancyRpartPlot(tree)

## ----warning=FALSE, fig.height=6, fig.width=8, fig.align='center'----------------------
TRAIN %>% ggplot(aes(x=LEGAL, y=AGE)) +
geom_boxplot(aes(col=LEGAL)) +
  geom_hline(yintercept=69, col='black') 


## --------------------------------------------------------------------------------------
TRAIN <- TRAIN %>%
  mutate(Legal_Tree = predict(tree, type='class'))

confusion_train <- tally(Legal_Tree~LEGAL, data=TRAIN)
confusion_train


## --------------------------------------------------------------------------------------
TEST <- TEST %>%
  mutate(Legal_Tree = predict(tree, type='class', newdata = TEST))

confusion_test <- tally(Legal_Tree~LEGAL, data=TEST)
confusion_test


## --------------------------------------------------------------------------------------
sum(diag(confusion_train))/nrow(TRAIN)


## --------------------------------------------------------------------------------------
sum(diag(confusion_test))/nrow(TEST)


## ----message=FALSE---------------------------------------------------------------------
printcp(tree)


## ----eval = FALSE----------------------------------------------------------------------
# rpart(LEGAL~AGE, data=TRAIN, na.action = na.pass,
#       control = rpart.control(cp = 0.05))
# 


## ----eval = FALSE----------------------------------------------------------------------
tree2 <- rpart(AGE~POLVIEWS+MARITAL, data=TRAIN)
fancyRpartPlot(tree2)

## --------------------------------------------------------------------------------------
TEST <- TEST %>% filter(MARITAL != "No answer") 

predict(tree2, TEST , method = "anova") %>% head()


## ----message=FALSE---------------------------------------------------------------------
#install.packages('randomForest')
library(randomForest)

forest_grass <- randomForest(as.factor(LEGAL)~NEWSFROM+HAPPY+RELIG+COURTS+
                             ENERGY+EDUC+ENVIR+POLVIEWS+PARTYID+REGION+
                             INCOME+SEX+DEGREE+AGE+MARITAL+BALLOT,data=TRAIN,
                             na.action = na.omit, ntree=201, mtry=4)

forest_grass


## --------------------------------------------------------------------------------------
TEST <- TEST %>%
  mutate(Legal_RF = predict(forest_grass, type='class', 
                            newdata = TEST)) 

TEST$Legal_RF[1:5]

confusion_test <- tally(Legal_RF~LEGAL, data=TEST)
sum(diag(confusion_test))/nrow(TEST)


## --------------------------------------------------------------------------------------
randomForest::importance(forest_grass) %>% as.data.frame() %>% 
  rownames_to_column() %>% arrange(desc(MeanDecreaseGini))


# ----eval = FALSE----------------------------------------------------------------------
tree4 <- rpart(LEGAL~AGE+REGION+POLVIEWS, data=TRAIN)
fancyRpartPlot(tree4)