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

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)
)
stocks %>%
    
    pivot_wider(names_from = year, values_from = return)
## # A tibble: 4 × 3
##     qtr `2015` `2016`
##   <dbl>  <dbl>  <dbl>
## 1     1   1.88  NA   
## 2     2   0.59   0.92
## 3     3   0.35   0.17
## 4     4  NA      2.66
bikes <- tibble(
    bike_model = c("A", "A", "B", "B", "C"),
    material = c("steel", "aluminum", "steel",  "aluminum", "steel"),
    price = c(100, 200, 300, 400, 500)
)
bikes %>%
    
    pivot_wider(names_from = bike_model, values_from = price)
## # A tibble: 2 × 4
##   material     A     B     C
##   <chr>    <dbl> <dbl> <dbl>
## 1 steel      100   300   500
## 2 aluminum   200   400    NA
bikes %>% 
    
    complete(bike_model, material)
## # A tibble: 6 × 3
##   bike_model material price
##   <chr>      <chr>    <dbl>
## 1 A          aluminum   200
## 2 A          steel      100
## 3 B          aluminum   400
## 4 B          steel      300
## 5 C          aluminum    NA
## 6 C          steel      500
treatment <- tribble(
  ~person,              ~visit, ~score,
  "Derrick Whitmore",   1,      7,
  "Derrick Whitmore",   2,      10,
  "Derrick Whitmore",   3,      9,
  "Katherine Burke",    1,      4
)

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

Non-Tidy Data