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')
Gather
Often, a dataset has column names that are not the names of variables,
but the values of variables. take table4a for example.
table4a
## # A tibble: 3 x 3
## country `1999` `2000`
## * <chr> <int> <int>
## 1 Afghanistan 745 2666
## 2 Brazil 37737 80488
## 3 China 212258 213766
In table4a, the column names 1999 and 2000 are values of the
variable year, not variables themselves. The gather() function can fix
this, it is perhaps the most commonly used function in tidyr. Begin by
listing the column names that are actually variable names into
gather() as its first arguments. Once these column names have been
entered, define a key as the second argument. In this case, 1999 and
2000 are both years, so the key, or new variable name, is year.
Finally, define a value to be associated with the key as the last
argument in gather(). The value is the name given to the elements
that were previously linked to the previous 1999 and 2000 false
variables. Because these two years have been gathered into a new year
variable, the associated elements need a new column to call home. In
this case, the values represent a number positive cases, so the value
is now defined as cases.
table4a %>%
gather(`1999`, `2000`, key = "year", value = "cases")
## # A tibble: 6 x 3
## country year cases
## <chr> <chr> <int>
## 1 Afghanistan 1999 745
## 2 Brazil 1999 37737
## 3 China 1999 212258
## 4 Afghanistan 2000 2666
## 5 Brazil 2000 80488
## 6 China 2000 213766
Spread
When an observation is scattered across multiple rows, variable names
appear repeatedly as elements in a dataframe. In table2 a single
observation is a country in each year, but each observation is spread
across two rows.
table2
## # 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 tidy this data, utilize spread(), one of the most common functions
in tidyr. The first argument in spread() is the column that contains
the variable names, the key column. In the case of table2, the key
column is type, because cases and population are both variables, not
values. The second and final argument in spread() is the column that
contains values associated with the previously defined key, the value
column. In table2 the it is the count column that contains the
associated values.
table2 %>%
spread(key = type, value = count)
## # 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
Thanks for reading!
- Fisher
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