The concept of tidy data is an extremely important one. Often, we spend a lot of our time preparing the data to be analyzed instead of actually conducting the analysis. Hadley Wickham, the creator of tidyr and the tidyverse wrote a foundational paper on the topic in 2014. I suggest giving that paper a read, then coming back to learn about tidyr.
library('tidyverse')
Separate
Sometimes a column contains more than variable worth of information in
each element. In table3 this is exemplified by the rate column.
table3
## # 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
A simple problem to fix, the separate() function splits two variables
apart from a single column. The first argument in separate() is the
column in question, in this case it’s rate. The next argument is a bit
more complex; the into argument needs a concatenated input, c() to
define the names and number of new columns. As for table3, the new
columns are defined as into = c("cases", "population"). Finally, a
separator character can be defined in the third argument. The separator
defaults to any non-alphanumeric character, but can be customized.
table3 %>%
separate(rate, into = c("cases", "population"))
## # A tibble: 6 x 4
## country year cases population
## <chr> <int> <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
Unite
When a single variable is spread between multiple columns, it’s time to
unite them. In table5 the century and year variables should be united
to create a single variable, the full year.
table5
## # A tibble: 6 x 4
## country century year rate
## * <chr> <chr> <chr> <chr>
## 1 Afghanistan 19 99 745/19987071
## 2 Afghanistan 20 00 2666/20595360
## 3 Brazil 19 99 37737/172006362
## 4 Brazil 20 00 80488/174504898
## 5 China 19 99 212258/1272915272
## 6 China 20 00 213766/1280428583
The unite() function works in a similar way to separate(), as it is
it’s inverse. The first argument of unite() is to define the name of
the new, united, variable. In the case of table3, this variable shall
be called full_year. The next arguments define the columns that need
to be united; this can be any number of columns. century and year
need to be united to form full_year in this example. Finally, it’s
sometimes wise to indicate a separator character, just as with
separate(). The separator character in unite() defaults to the
underscore, _.
table5 %>%
unite(full_year, century, year, sep = "")
## # A tibble: 6 x 3
## country full_year rate
## <chr> <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
Thanks for reading!
- Fisher
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