install.packages("tidyverse")
library(tidyverse)

random.number <- rnorm(1000)
mean(random.number)
mean(random.number <- rnorm(1000))

rnorm(1000) %>% mean()

# CLT
NperSample = 10
SampleSize = 300000

# "traditional"
random.number <- runif(NperSample * SampleSize)
dat <- matrix(random.number, nrow=NperSample)
means <- colMeans(dat)
dens <- density(means)
hist(means, breaks = 100, probability = T, main = "Distributionf of Means")
lines(dens, lwd = 3, col = "orange")


runif(NperSample * SampleSize) %>% 
  matrix(nrow=NperSample) %>%
     colMeans() -> means
hist(means, breaks=100,probability = T, main = "Distributionf of Means")
means %>% density() %>% lines(,lwd=3,col='orange')


histWdensity <- function(dat, nbreaks=30, main.txt){
  dens <- density(dat)
  hist(dat, breaks = nbreaks, probability = T, main = main.txt)
  lines(dens, lwd = 3, col = "orange")
}

runif(NperSample * SampleSize) %>% 
  matrix(nrow=NperSample) %>%
  colMeans() %>% 
    histWdensity(nbreaks = 100, main.txt = "Distributionf of Means")

dat <- read.csv("http://www.matsuka.info/data_folder/sampleData2013.txt")
dt <- as_tibble(dat)
dt.la <- filter(dt, affil == "LA")

dt.la2 <- filter(dt, affil == "LA" & grade == "SP")
dt.laNot2 <- filter(dt, affil == "LA" & grade != "SP")

dt.GB <- select(dt, grade, nbooks)
dtWOgender <- select(dt, -gender)

dt.arranged <- arrange(dt, affil, grade)

dt.weekly <- mutate(dt,nbooksWeek = nbooks / 52)

dt.atLeastOneBook <- mutate(dt, atleastOneBook = (nbooks/52) >= 1)
dt.atLeastOneBook <- mutate(dt, atleastOneBook = (nbooks/12) >= 1)

dt.BWindex = mutate(dt, nbooksWeek = nbooks / 52, 
                        idx = nbooksWeek / (12*7-Hworked))

dt.byGrade <- group_by(dt, grade)
summarize(dt.byGrade, ave.books = mean(nbooks,na.rm = TRUE), 
                      ave.Hworked = mean(Hworked, na.rm = TRUE))

dt.byGrAf <- group_by(dt, grade, affil)
dt.summ <- summarize(dt.byGrAf, ave.books = mean(nbooks,na.rm = TRUE),
                     ave.Hworked = mean(Hworked, na.rm = TRUE), N = n())

dt.summ2 <- dt %>% 
  group_by(grade, affil) %>% 
  summarize(ave.books = mean(nbooks,na.rm = TRUE), 
            ave.Hworked = mean(Hworked, na.rm = TRUE), 
            N = n()) %>% filter(N > 2) %>% arrange(desc(ave.books))

plot(x = dt.summ2$ave.books, y = dt.summ2$ave.Hworked, pch=20, cex = 3,
  xlab = "Ave. # books read",ylab = "Ave hours worked")

dt <- read_csv("http://www.matsuka.info/data_folder/sampleData2013.txt",
  col_names = TRUE)

dt.summ3 <- dt %>% 
  group_by(grade, gender) %>% 
  summarize(ave.books = mean(nbooks,na.rm = TRUE), 
    ave.Hworked = mean(Hworked, na.rm = TRUE)) 

dt.summ3G <- dt.summ3 %>% gather('ave.books', 'ave.Hworked', 
  key = 'ave.values', value = "BorW")

dt.summ3SformG <- spread(dt.summ3G, key = ave.values, value =BorW)

dt.sumLA <- dt %>% filter(affil=="LA") %>% group_by(grade) %>% 
   summarize(ave.books = mean(nbooks))

toeic <- tibble(
  grade = factor(c("SP", "JR")),
  score = c(800,830),
)

new.dt1 <- dt.sumLA %>% inner_join(toeic, by = "grade")

dt.sumLA <- add_row(dt.sumLA, grade = c("MS"), ave.books = (13))
toeic2 <- tibble(
  grade = factor(c("SP", "JR","DR")),
  score = c(800,830,900),
)
new.dt3 <- full_join(dt.sumLA, toeic2)

new.dt4 <- left_join(dt.sumLA, toeic2)
new.dt5 <- right_join(dt.sumLA, toeic2)