library(readr)
library(dplyr)
library(tidyr)
who <- as_tibble(
read_csv("data-raw/who.csv", col_types = list())
)
population <- as_tibble(
read_csv("data-raw/population.csv", col_types = list())
)
table1 <-
who %>%
filter(
country %in% c("Afghanistan", "Brazil", "China"),
year >= 1999, year <= 2000
) %>%
gather("code", "value", 5:60) %>%
summarise(cases = sum(value, na.rm = TRUE), .by = c(country, year)) %>%
left_join(population, by = c("country", "year"))
table2 <-
table1 %>%
gather("type", "count", 3:4) %>%
arrange(country, year)
table3 <-
table1 %>%
unite("rate", cases, population, sep = "/")
table4a <-
table1 %>%
select(country, year, cases) %>%
spread(year, cases)
table4b <-
table1 %>%
select(country, year, population) %>%
spread(year, population)
table5 <-
table3 %>%
separate(year, into = c("century", "year"), sep = 2)
write_csv(table1, "data-raw/table1.csv")
write_csv(table2, "data-raw/table2.csv")
write_csv(table3, "data-raw/table3.csv")
write_csv(table4a, "data-raw/table4a.csv")
write_csv(table4b, "data-raw/table4b.csv")
write_csv(table5, "data-raw/table6.csv")
usethis::use_data(table1, overwrite = TRUE)
usethis::use_data(table2, overwrite = TRUE)
usethis::use_data(table3, overwrite = TRUE)
usethis::use_data(table4a, overwrite = TRUE)
usethis::use_data(table4b, overwrite = TRUE)
usethis::use_data(table5, overwrite = TRUE)
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