DSR: Data sets for Data Science with R

# making table data sets
library(dplyr)
library(tidyr)

who <- read.csv("data-raw/who.csv")
population <- read.csv("data-raw/population.csv")

table1 <- 
  who %>%
  filter(country %in% c("Afghanistan", "Brazil", "China"),
         year >= 1999, year <= 2000) %>% 
  gather("code", "value", 5:60) %>% 
  group_by(country, year) %>% 
  summarise(cases = sum(value, na.rm = TRUE)) %>%
  ungroup() %>%
  left_join(population, by = c("country", "year"))

table2 <- 
  table1 %>% 
  gather("key", "value", 3:4) %>% 
  arrange(country, year)

table3 <-
  table1 %>% 
  unite("rate", cases, population, sep = "/")

table4 <-
  table1 %>%
  select(country, year, cases) %>% 
  spread(year, cases)

table5 <-
  table1 %>%
  select(country, year, population) %>% 
  spread(year, population)

table6 <-
  table3 %>% 
  separate(year, into = c("century", "year"), sep = 2)

write.csv(table1, file = "data-raw/table1.csv", row.names = FALSE)
write.csv(table2, file = "data-raw/table2.csv", row.names = FALSE)
write.csv(table3, file = "data-raw/table3.csv", row.names = FALSE)
write.csv(table4, file = "data-raw/table4.csv", row.names = FALSE)
write.csv(table5, file = "data-raw/table5.csv", row.names = FALSE)
write.csv(table6, file = "data-raw/table6.csv", row.names = FALSE)

save(table1, file = "data/table1.rdata")
save(table2, file = "data/table2.rdata")
save(table3, file = "data/table3.rdata")
save(table4, file = "data/table4.rdata")
save(table5, file = "data/table5.rdata")
save(table6, file = "data/table6.rdata")