## 4.3 Case study: Democracy in Guatemala

In this section, we’ll show you another example of how to convert a data frame that isn’t in “tidy” format (“wide” format) to a data frame that is in “tidy” format (“long/narrow” format). We’ll do this using the pivot_longer() function from the tidyr package again.

Furthermore, we’ll make use of functions from the ggplot2 and dplyr packages to produce a time-series plot showing how the democracy scores have changed over the 40 years from 1952 to 1992 for Guatemala. Recall that we saw time-series plots in Section 2.4 on creating linegraphs using geom_line().

Let’s use the dem_score data frame we imported in Section 4.1, but focus on only data corresponding to Guatemala.

guat_dem <- dem_score %>%
filter(country == "Guatemala")
guat_dem
# A tibble: 1 x 10
country   1952 1957 1962 1967 1972 1977 1982 1987 1992
<chr>      <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1 Guatemala      2     -6     -5      3      1     -3     -7      3      3

Let’s lay out the grammar of graphics we saw in Section 2.1.

First we know we need to set data = guat_dem and use a geom_line() layer, but what is the aesthetic mapping of variables? We’d like to see how the democracy score has changed over the years, so we need to map:

• year to the x-position aesthetic and
• democracy_score to the y-position aesthetic

Now we are stuck in a predicament, much like with our drinks_smaller example in Section 4.2. We see that we have a variable named country, but its only value is "Guatemala". We have other variables denoted by different year values. Unfortunately, the guat_dem data frame is not “tidy” and hence is not in the appropriate format to apply the grammar of graphics, and thus we cannot use the ggplot2 package just yet.

We need to take the values of the columns corresponding to years in guat_dem and convert them into a new “names” variable called year. Furthermore, we need to take the democracy score values in the inside of the data frame and turn them into a new “values” variable called democracy_score. Our resulting data frame will have three columns: country, year, and democracy_score. Recall that the pivot_longer() function in the tidyr package does this for us:

guat_dem_tidy <- guat_dem %>%
pivot_longer(names_to = "year",
values_to = "democracy_score",
cols = -country,
names_transform = list(year = as.integer))
guat_dem_tidy
# A tibble: 9 x 3
country    year democracy_score
<chr>     <int>           <dbl>
1 Guatemala  1952               2
2 Guatemala  1957              -6
3 Guatemala  1962              -5
4 Guatemala  1967               3
5 Guatemala  1972               1
6 Guatemala  1977              -3
7 Guatemala  1982              -7
8 Guatemala  1987               3
9 Guatemala  1992               3

(Note this code differs slightly from our print edition due to an update of the tidyr package to version 1.1.0.) We set the arguments to pivot_longer() as follows:

1. names_to is the name of the variable in the new “tidy” data frame that will contain the column names of the original data. Observe how we set names_to = "year". In the resulting guat_dem_tidy, the column year contains the years where Guatemala’s democracy scores were measured.
2. values_to is the name of the variable in the new “tidy” data frame that will contain the values of the original data. Observe how we set values_to = "democracy_score". In the resulting guat_dem_tidy the column democracy_score contains the 1 $$\times$$ 9 = 9 democracy scores as numeric values.
3. The third argument is the columns you either want to or don’t want to “tidy.” Observe how we set this to cols = -country indicating that we don’t want to “tidy” the country variable in guat_dem and rather only variables 1952 through 1992.
4. The last argument of names_transform tells R what type of variable year should be set to. Without specifying that it is an integer as we’ve done here, pivot_longer() will set it to be a character value by default.

We can now create the time-series plot in Figure 4.5 to visualize how democracy scores in Guatemala have changed from 1952 to 1992 using a geom_line(). Furthermore, we’ll use the labs() function in the ggplot2 package to add informative labels to all the aes()thetic attributes of our plot, in this case the x and y positions.

ggplot(guat_dem_tidy, aes(x = year, y = democracy_score)) +
geom_line() +
labs(x = "Year", y = "Democracy Score")

Note that if we forgot to include the names_transform argument specifying that year was not of character format, we would have gotten an error here since geom_line() wouldn’t have known how to sort the character values in year in the right order.

Learning check

(LC4.4) Convert the dem_score data frame into a “tidy” data frame and assign the name of dem_score_tidy to the resulting long-formatted data frame.

(LC4.5) Read in the life expectancy data stored at https://moderndive.com/data/le_mess.csv and convert it to a “tidy” data frame.