Separating and uniting

table3

table3 %>%
  separate(rate, into = c("cases", "population"), sep = "/", convert = TRUE)

## # A tibble: 6 x 4
##   country      year  cases population
## * <chr>       <int>  <int>      <int>
## 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 %>%
  separate(
    rate,
    into = c("cases", "population"),
    sep = "/",
    convert = TRUE
  ) %>%
  gather(type, count, -country, -year) %>%
  arrange(country, year, type)

## # A tibble: 12 x 4
##    country      year type            count
##    <chr>       <int> <chr>           <int>
##  1 Afghanistan  1999 cases             745
##  2 Afghanistan  1999 population   19987071
##  3 Afghanistan  2000 cases            2666
##  4 Afghanistan  2000 population   20595360
##  5 Brazil       1999 cases           37737
##  6 Brazil       1999 population  172006362
##  7 Brazil       2000 cases           80488
##  8 Brazil       2000 population  174504898
##  9 China        1999 cases          212258
## 10 China        1999 population 1272915272
## 11 China        2000 cases          213766
## 12 China        2000 population 1280428583

To table3

table2 %>%
  spread(type, count) %>% 
  unite(rate, cases, population, sep = "/")

## # A tibble: 6 x 3
##   country      year rate             
## * <chr>       <int> <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

Hours and minutes

## Use a numeric value for sep

flights %>%
  separate(dep_time, into = c("dep_hour", "dep_minute"), sep = -3, convert = TRUE) %>%
  separate(arr_time, into = c("arr_hour", "arr_minute"), sep = -3, convert = TRUE) %>% 
  select(starts_with("dep_"), starts_with("arr_"))

## # A tibble: 336,776 x 6
##    dep_hour dep_minute dep_delay arr_hour arr_minute arr_delay
##  *    <int>      <int>     <dbl>    <int>      <int>     <dbl>
##  1        5         17      2.00        8         30     11.0 
##  2        5         33      4.00        8         50     20.0 
##  3        5         42      2.00        9         23     33.0 
##  4        5         44     -1.00       10          4    -18.0 
##  5        5         54     -6.00        8         12    -25.0 
##  6        5         54     -4.00        7         40     12.0 
##  7        5         55     -5.00        9         13     19.0 
##  8        5         57     -3.00        7          9    -14.0 
##  9        5         57     -3.00        8         38    - 8.00
## 10        5         58     -2.00        7         53      8.00
## # ... with 336,766 more rows