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",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(2020, 2020, 2020, 2020, 2021, 2021, 2021),
  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 `2020` `2021`
##   <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", "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,         NA,
  "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       NA
## 4 Katherine Burke          1        4

Importing data from excel into R

data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-01-25/details.csv')
## Rows: 21631 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): primary, description, boardgamecategory, boardgamemechanic, boardg...
## dbl (13): num, id, yearpublished, minplayers, maxplayers, playingtime, minpl...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.