Week 8: Code along 7

R for Data Sience Chap 12

Tidy Data

Pivoting

Long to Wide Form

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

Wide ot 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

Seperating and Uniting

Seperate

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

Unite Two Columns

table3_sep %>%
    unite(col = "rate", 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", "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 Aluminium   200   400    NA

bikes %>%
    complete(bike_model, material)

## # A tibble: 6 × 3
##   bike_model material  price
##   <chr>      <chr>     <dbl>
## 1 A          Aluminium   200
## 2 A          Steel       100
## 3 B          Aluminium   400
## 4 B          Steel       300
## 5 C          Aluminium    NA
## 6 C          Steel       500

treatment <- tribble(
  ~ person,           ~ treatment, ~response,
  "Derrick Whitmore", 1,           7,
  NA,                 2,           10,
  NA,                 3,           9,
  "Katherine Burke",  1,           4
)

treatment %>%
    fill(person, .direction = "down")

## # A tibble: 4 × 3
##   person           treatment response
##   <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