Tidy Data

library("tidyverse")

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

t2_cases <- filter(table2, type == "cases") %>%
  rename(cases = count) %>%
  arrange(country, year)
t2_population <- filter(table2, type == "population") %>%
  rename(population = count) %>%
  arrange(country, year)
t2_cases_per_cap <- tibble(
  year = t2_cases$year,
  country = t2_cases$country,
  cases = t2_cases$cases,
  population = t2_population$population
) %>%
  mutate(cases_per_cap = (cases / population) * 10000) %>%
  select(country, year, cases_per_cap)
t2_cases_per_cap <- t2_cases_per_cap %>%
  mutate(type = "cases_per_cap") %>%
  rename(count = cases_per_cap)
bind_rows(table2, t2_cases_per_cap) %>%
  arrange(country, year, type, count)

## # A tibble: 18 x 4
##    country      year type            count
##    <chr>       <int> <chr>           <dbl>
##  1 Afghanistan  1999 cases         7.45e+2
##  2 Afghanistan  1999 cases_per_cap 3.73e-1
##  3 Afghanistan  1999 population    2.00e+7
##  4 Afghanistan  2000 cases         2.67e+3
##  5 Afghanistan  2000 cases_per_cap 1.29e+0
##  6 Afghanistan  2000 population    2.06e+7
##  7 Brazil       1999 cases         3.77e+4
##  8 Brazil       1999 cases_per_cap 2.19e+0
##  9 Brazil       1999 population    1.72e+8
## 10 Brazil       2000 cases         8.05e+4
## 11 Brazil       2000 cases_per_cap 4.61e+0
## 12 Brazil       2000 population    1.75e+8
## 13 China        1999 cases         2.12e+5
## 14 China        1999 cases_per_cap 1.67e+0
## 15 China        1999 population    1.27e+9
## 16 China        2000 cases         2.14e+5
## 17 China        2000 cases_per_cap 1.67e+0
## 18 China        2000 population    1.28e+9