Tidy Data

table4b
## # A tibble: 3 × 3
##   country         `1999`     `2000`
##   <chr>            <dbl>      <dbl>
## 1 Afghanistan   19987071   20595360
## 2 Brazil       172006362  174504898
## 3 China       1272915272 1280428583

Pivoting

Long to Wide Form

table4a %>%
  
  pivot_longer(cols = c('1999' , '2000'),
               names_to = "year",
               values_to = "cases")
## # A tibble: 6 × 3
##   country     year   cases
##   <chr>       <chr>  <dbl>
## 1 Afghanistan 1999     745
## 2 Afghanistan 2000    2666
## 3 Brazil      1999   37737
## 4 Brazil      2000   80488
## 5 China       1999  212258
## 6 China       2000  213766

Separating and Uniting

table3 %>%
  
  separate(col = rate, into = c("cases","population"))
## # A tibble: 6 × 4
##   country      year cases  population
##   <chr>       <dbl> <chr>  <chr>     
## 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

Missing Values

stocks <- tibble(
  year  = c(2015, 2015, 2015, 2015, 2016, 2016, 2016),
  qtr   = c(1,2,3,4,2,3,4),
  return = c(1.88, 0.59, 0.35, NA, 0.92, 0.17, 2.66)
)

Non-Tidy Data

```