Tidy data

Pivoting

Long to Wide Form

table4a_long <- table4a %>%
    
    pivot_longer(cols = c('1999', '2000'), 
                 names_to = "year",
                 values_to = "cases")

Wide to Long Form

table4a_long %>%
    
    pivot_wider(names_from = year,
                values_from = cases)
## # A tibble: 3 × 3
##   country     `1999` `2000`
##   <chr>        <dbl>  <dbl>
## 1 Afghanistan    745   2666
## 2 Brazil       37737  80488
## 3 China       212258 213766

Separating and Uniting

Separate a Column

table3_sep <- table3 %>%
    
    separate(col = rate, into = c("cases", "population"))

Unite Two Columns

table3_sep %>%
    
    unite(col = "rate", c(cases,population), sep = "/", )
## # A tibble: 6 × 3
##   country      year rate             
##   <chr>       <dbl> <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

bikes <- tibble(
  bike_model = c("A", "A", "B", "B", "C"),
  material   = c("steel", "aluminium", "steel", "aluminium", "carbon"),
  price      = c(100, 200, 300, 400, 500)
)
bikes %>%
    
    pivot_wider(names_from = bike_model, values_from = price)
## # A tibble: 3 × 4
##   material      A     B     C
##   <chr>     <dbl> <dbl> <dbl>
## 1 steel       100   300    NA
## 2 aluminium   200   400    NA
## 3 carbon       NA    NA   500
bikes %>%
    
    complete(bike_model, material)
## # A tibble: 9 × 3
##   bike_model material  price
##   <chr>      <chr>     <dbl>
## 1 A          aluminium   200
## 2 A          carbon       NA
## 3 A          steel       100
## 4 B          aluminium   400
## 5 B          carbon       NA
## 6 B          steel       300
## 7 C          aluminium    NA
## 8 C          carbon      500
## 9 C          steel        NA
treatment <- tribble(
  ~ person,           ~ treatment, ~response,
  "Derrick Whitmore", 1,           7,
  NA,                 2,           10,
  NA,                 3,           9,
  "Katherine Burke",  1,           4
)

treatment %>%
    
    fill(person, .direction = "up")
## # A tibble: 4 × 3
##   person           treatment response
##   <chr>                <dbl>    <dbl>
## 1 Derrick Whitmore         1        7
## 2 Katherine Burke          2       10
## 3 Katherine Burke          3        9
## 4 Katherine Burke          1        4

Non-Tidy Data