##1) library(tidyverse) pew <- tbl_df(read.csv(‘pew.csv’, stringsAsFactors = FALSE, check.names = FALSE)) pew2 <- pew %>% gather(key = religion, value = frequency, ‘<$10k’:‘$10k-20’:‘$20k-30’:‘$30k-40’:$40k-50’:‘50k-75’) pew2

library(tidyverse) table1 ## # A tibble: 6 x 4 ## country year cases population ## ## 1 Afghanistan 1999 745 19987071 ## 2 Afghanistan 2000 2666 20595360 ## 3 Brazil 1999 37737 172006362 ## 4 Brazil 2000 80488 174504898 ## 5 China 1999 212258 1272915272 ## 6 China 2000 213766 1280428583

table2 ## # A tibble: 12 x 4 ## country year type count ## ## 1 Afghanistan 1999 cases 745 ## 2 Afghanistan 1999 population 19987071 ## 3 Afghanistan 2000 cases 2666 ## 4 Afghanistan 2000 population 20595360 ## 5 Brazil 1999 cases 37737 ## 6 Brazil 1999 population 172006362 ## 7 Brazil 2000 cases 80488 ## 8 Brazil 2000 population 174504898 ## 9 China 1999 cases 212258 ## 10 China 1999 population 1272915272 ## 11 China 2000 cases 213766 ## 12 China 2000 population 1280428583

table3 ## # A tibble: 6 x 3 ## country year rate
## *
## 1 Afghanistan 1999 745/19987071
## 2 Afghanistan 2000 2666/20595360
## 3 Brazil 1999 37737/172006362
## 4 Brazil 2000 80488/174504898
## 5 China 1999 212258/1272915272 ## 6 China 2000 213766/1280428583

table4a ## # A tibble: 3 x 3 ## country 1999 2000 ## * ## 1 Afghanistan 745 2666 ## 2 Brazil 37737 80488 ## 3 China 212258 213766

table4b ## # A tibble: 3 x 3 ## country 1999 2000 ## * ## 1 Afghanistan 19987071 20595360 ## 2 Brazil 172006362 174504898 ## 3 China 1272915272 1280428583

table5 ## # A tibble: 6 x 4 ## country century year rate
## *
## 1 Afghanistan 19 99 745/19987071
## 2 Afghanistan 20 00 2666/20595360
## 3 Brazil 19 99 37737/172006362
## 4 Brazil 20 00 80488/174504898
## 5 China 19 99 212258/1272915272 ## 6 China 20 00 213766/1280428583

##2) tidy4b <- table4b %>% gather(key = year, value = population, 1999:2000) tidy4b

tidy4a <- table4a %>% gather(key = “year”, value = “cases”, 1999:2000) tidy4a

##3) full_join(tidy4a, tidy4b, by = “country”)

##4) stocks <- tibble( year = c(2015, 2015, 2016, 2016), half = c( 1, 2, 1, 2), return = c(1.88, 0.59, 0.92, 0.17) ) stocks

# A tibble: 4 x 3

year half return

1 2015 1 1.88

2 2015 2 0.59

3 2016 1 0.92

4 2016 2 0.17

spread(stocks, key = year, value = half)

##5) pregnant <- tribble( ~pregnant, ~male, ~female, “yes”, NA, 10, “no”, 20, 12 ) pregnant

# A tibble: 2 x 3

pregnant male female

1 yes NA 10

2 no 20 12

pregnant2 <- pregnant %>% gather(pregnant, key = pregnant, value = pregnant) pregnant2

##6) tidy5 <- table5 %>% unite(year, century, year, sep = ““)

# A tibble: 6 x 3

country year rate

1 Afghanistan 1999 745/19987071

2 Afghanistan 2000 2666/20595360

3 Brazil 1999 37737/172006362

4 Brazil 2000 80488/174504898

5 China 1999 212258/1272915272

6 China 2000 213766/1280428583

tidy5 %>% separate(rate, into = c(“cases”, “population”), sep = “/”)