##Tidy data

# Compute rate per 10,000
table1 %>% 
  mutate(rate = cases / population * 10000)
## # A tibble: 6 × 5
##   country      year  cases population  rate
##   <chr>       <dbl>  <dbl>      <dbl> <dbl>
## 1 Afghanistan  1999    745   19987071 0.373
## 2 Afghanistan  2000   2666   20595360 1.29 
## 3 Brazil       1999  37737  172006362 2.19 
## 4 Brazil       2000  80488  174504898 4.61 
## 5 China        1999 212258 1272915272 1.67 
## 6 China        2000 213766 1280428583 1.67
# Compute cases per year
table1 %>% 
  count(year, wt = cases)
## # A tibble: 2 × 2
##    year      n
##   <dbl>  <dbl>
## 1  1999 250740
## 2  2000 296920
# Visualize changes over time
library(ggplot2)
ggplot(table1, aes(year, cases)) + 
  geom_line(aes(group = country), colour = "grey50") + 
  geom_point(aes(colour = country))

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

Unit 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

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

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
## # A tibble: 5 × 3
##   bike_model material price
##   <chr>      <chr>    <dbl>
## 1 A          steel      100
## 2 A          aluminum   200
## 3 B          steel      300
## 4 B          aluminum   400
## 5 C          steel      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

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
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