Comparisons

Example for Monday, June 26, 2023–Friday, June 30, 2023

For this example, we’re going to use cross-national data, but instead of using the typical gapminder dataset, we’re going to collect data directly from the World Bank’s Open Data portal

If you want to skip the data downloading, you can download the data below (you’ll likely need to right click and choose “Save Link As…”):

wdi_comparisons.csv

Live coding example

Slight differences from the video

This is a slightly cleaned up version of the code from the video.

Load and clean data

First, we load the libraries we’ll be using:

Code

library(tidyverse)  # For ggplot, dplyr, and friends
library(WDI)        # For getting data from the World Bank
library(geofacet)   # For map-shaped facets
library(scales)     # For helpful scale functions like dollar()
library(ggrepel)    # For non-overlapping labels

The World Bank has a ton of country-level data at data.worldbank.org. We can use a package named {WDI} (world development indicators) to access their servers and download the data directly into R.

To do this, we need to find the special World Bank codes for specific variables we want to get. These codes come from the URLs of the World Bank’s website. For instance, if you search for “access to electricity” at the World Bank’s website, you’ll find this page. If you look at the end of the URL, you’ll see a cryptic code: EG.ELC.ACCS.ZS. That’s the World Bank’s ID code for the “Access to electricity (% of population)” indicator.

We can feed a list of ID codes to the WDI() function to download data for those specific indicators. We can feed it a list of just the codes (e.g., c("SP.DYN.LE00.IN", "EG.ELC.ACCS.ZS", ...)), but then the columns will be named with those ugly inscrutable names. Alternatively, we can feed the WDI() function a named list (e.g. c(life_expectancy = "SP.DYN.LE00.IN", ...)), which will create columns using the names we specify there. This is much nicer.

We want data from 1995-2015, so we set the start and end years accordingly. The extra=TRUE argument means that it’ll also include other helpful details like region, aid status, etc. Without it, it would only download the indicators we listed.

Code

indicators <- c(life_expectancy = "SP.DYN.LE00.IN",  # Life expectancy
                access_to_electricity = "EG.ELC.ACCS.ZS",  # Access to electricity
                co2_emissions = "EN.ATM.CO2E.PC",  # CO2 emissions
                gdp_per_cap = "NY.GDP.PCAP.KD")  # GDP per capita

wdi_raw <- WDI(country = "all", indicators, extra = TRUE, 
               start = 1995, end = 2015)

head(wdi_raw)

Downloading data from the World Bank every time you knit will get tedious and take a long time (plus if their servers are temporarily down, you won’t be able to get the data). It’s good practice to save this raw data as a CSV file and then work with that.

Code

write_csv(wdi_raw, "data/wdi_comparisons.csv")

Since we care about reproducibility, we still want to include the code we used to get data from the World Bank, we just don’t want it to actually run. You can include chunks but not run them by setting eval=FALSE in the chunk options. In this little example, we show the code for downloading the data, but we don’t evaluate the chunk. We then include a chunk that loads the data from a CSV file with read_csv(), but we don’t include it (include=FALSE). That way, in the knitted file we see the WDI() code, but in reality it’s loading the data from CSV. Super tricky.

I first download data from the World Bank:

```{r get-wdi-data, eval=FALSE}
wdi_raw <- WDI(...)

write_csv(wdi_raw, "data/wdi_comparisons.csv")
```

```{r load-wdi-data-real, include=FALSE}
wdi_raw <- read_csv("data/wdi_comparisons.csv")
```

Then we clean up the data a little by filtering out rows that aren’t actually countries:

Code

wdi_clean <- wdi_raw %>% 
  filter(region != "Aggregates")

head(wdi_clean)
## # A tibble: 6 × 16
##   country     iso2c iso3c  year status lastupdated life_expectancy access_to_electricity co2_emissions gdp_per_cap region   capital longitude latitude
##   <chr>       <chr> <chr> <dbl> <lgl>  <date>                <dbl>                 <dbl>         <dbl>       <dbl> <chr>    <chr>       <dbl>    <dbl>
## 1 Afghanistan AF    AFG    2015 NA     2023-05-10             62.7                 71.5         0.176         592. South A… Kabul        69.2     34.5
## 2 Afghanistan AF    AFG    1997 NA     2023-05-10             53.6                 NA           0.0617         NA  South A… Kabul        69.2     34.5
## 3 Afghanistan AF    AFG    2014 NA     2023-05-10             62.5                 89.5         0.149         603. South A… Kabul        69.2     34.5
## 4 Afghanistan AF    AFG    2003 NA     2023-05-10             57.3                 14.7         0.0538        363. South A… Kabul        69.2     34.5
## 5 Afghanistan AF    AFG    2000 NA     2023-05-10             55.3                  1.61        0.0389         NA  South A… Kabul        69.2     34.5
## 6 Afghanistan AF    AFG    2002 NA     2023-05-10             56.5                  9.41        0.0492        360. South A… Kabul        69.2     34.5
## # ℹ 2 more variables: income <chr>, lending <chr>

Small multiples

First we can make some small multiples plots and show life expectancy over time for a handful of countries. We’ll make a list of some countries chosen at random while I scrolled through the data, and then filter our data to include only those rows. We then plot life expectancy, faceting by country.

Code

life_expectancy_small <- wdi_clean %>%
  filter(country %in% c("Argentina", "Bolivia", "Brazil", 
                        "Belize", "Canada", "Chile"))

ggplot(data = life_expectancy_small, 
       mapping = aes(x = year, y = life_expectancy)) +
  geom_line(linewidth = 1) +
  facet_wrap(vars(country))

Small multiples! That’s all we need to do.

We can do some fancier things, though. We can make this plot hyper minimalist:

Code

ggplot(data = life_expectancy_small, 
       mapping = aes(x = year, y = life_expectancy)) +
  geom_line(linewidth = 1) +
  facet_wrap(vars(country), scales = "free_y") +
  theme_void() +
  theme(strip.text = element_text(face = "bold"))

We can do a whole part of a continent (poor Iraq and Syria 😞)

Code

life_expectancy_mena <- wdi_clean %>% 
  filter(region == "Middle East & North Africa")

ggplot(data = life_expectancy_mena, 
       mapping = aes(x = year, y = life_expectancy)) +
  geom_line(linewidth = 1) +
  facet_wrap(vars(country), scales = "free_y", nrow = 3) +
  theme_void() +
  theme(strip.text = element_text(face = "bold"))

We can use the {geofacet} package to arrange these facets by geography:

Code

life_expectancy_eu <- wdi_clean %>% 
  filter(region == "Europe & Central Asia")

ggplot(life_expectancy_eu, aes(x = year, y = life_expectancy)) +
  geom_line(linewidth = 1) +
  facet_geo(vars(country), grid = "eu_grid1", scales = "free_y") +
  labs(x = NULL, y = NULL, title = "Life expectancy from 1995–2015",
       caption = "Source: The World Bank (SP.DYN.LE00.IN)") +
  theme_minimal() +
  theme(strip.text = element_text(face = "bold"),
        plot.title = element_text(face = "bold"),
        axis.text.x = element_text(angle = 45, hjust = 1))

Neat!

Sparklines

Sparklines are just line charts (or bar charts) that are really really small.

Code

india_co2 <- wdi_clean %>% 
  filter(country == "India")

plot_india <- ggplot(india_co2, aes(x = year, y = co2_emissions)) +
  geom_line() +
  theme_void()
plot_india

Code

ggsave("india_co2.pdf", plot_india, width = 1, height = 0.15, units = "in")
ggsave("india_co2.png", plot_india, width = 1, height = 0.15, units = "in")

Code

china_co2 <- wdi_clean %>% 
  filter(country == "China")

plot_china <- ggplot(china_co2, aes(x = year, y = co2_emissions)) +
  geom_line() +
  theme_void()
plot_china

Code

ggsave("china_co2.pdf", plot_china, width = 1, heighlt = 0.15, units = "in")
ggsave("china_co2.png", plot_china, width = 1, height = 0.15, units = "in")

You can then use those saved tiny plots in your text.

Both India and China have seen increased CO₂ emissions over the past 20 years.

Slopegraphs

We can make a slopegraph to show changes in GDP per capita between two time periods. We need to first filter our WDI to include only the start and end years (here 1995 and 2015). Then, to make sure that we’re using complete data, we’ll get rid of any country that has missing data for either 1995 or 2015. The group_by(...) %>% filter(...) %>% ungroup() pipeline does this, with the !any(is.na(gdp_per_cap)) test keeping any rows where any of the gdp_per_cap values are not missing for the whole country.

We then add a couple special columns for labels. The paste0() function concatenates strings and variables together, so that paste0("2 + 2 = ", 2 + 2) would show “2 + 2 = 4”. Here we make labels that say either “Country name: $GDP” or “$GDP” depending on the year.

Code

gdp_south_asia <- wdi_clean %>% 
  filter(region == "South Asia") %>% 
  filter(year %in% c(1995, 2015)) %>% 
  # Look at each country individually
  group_by(country) %>%
  # Remove the country if any of its gdp_per_cap values are missing
  filter(!any(is.na(gdp_per_cap))) %>%
  ungroup() %>%
  # Make year a factor
  mutate(year = factor(year)) %>% 
  # Make some nice label columns
  # If the year is 1995, format it like "Country name: $GDP". If the year is
  # 2015, format it like "$GDP"
  mutate(label_first = ifelse(year == 1995, paste0(country, ": ", dollar(round(gdp_per_cap))), NA),
         label_last = ifelse(year == 2015, dollar(round(gdp_per_cap, 0)), NA))

With the data filtered like this, we can plot it by mapping year to the x-axis, GDP per capita to the y-axis, and coloring by country. To make the lines go across the two categorical labels in the x-axis (since we made year a factor/category), we need to also specify the group aesthetic.

Code

ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) +
  geom_line(linewidth = 1.5)

Cool! We’re getting closer. We can definitely see different slopes, but with 7 different colors, it’s hard to see exactly which country is which. Instead, we can directly label each of these lines with geom_text():

Code

ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) +
  geom_line(linewidth = 1.5) +
  geom_text(aes(label = country)) +
  guides(color = "none")

That gets us a little closer, but the country labels are hard to see, and we could include more information, like the actual values. Remember those label_first and label_last columns we made? Let’s use those instead:

Code

ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) +
  geom_line(linewidth = 1.5) +
  geom_text(aes(label = label_first)) +
  geom_text(aes(label = label_last)) +
  guides(color = "none")

Now we have dollar amounts and country names, but the labels are still overlapping and really hard to read. To fix this, we can make the labels repel away from each other and randomly position in a way that makes them not overlap. The {ggrepel} package lets us do this with geom_text_repel()

Code

ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) +
  geom_line(linewidth = 1.5) +
  geom_text_repel(aes(label = label_first)) +
  geom_text_repel(aes(label = label_last)) +
  guides(color = "none")

Now none of the labels are on top of each other, but the labels are still on top of the lines. Also, some of the labels moved inward and outward along the x-axis, but they don’t need to do that—they just need to shift up and down. We can force the labels to only move up and down by setting the direction = "y" argument, and we can move all the labels to the left or right with the nudge_x argument. The seed argument makes sure that the random label placement is the same every time we run this. It can be whatever number you want—it just has to be a number.

Code

ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) +
  geom_line(linewidth = 1.5) +
  geom_text_repel(aes(label = label_first), direction = "y", nudge_x = -1, seed = 1234) +
  geom_text_repel(aes(label = label_last), direction = "y", nudge_x = 1, seed = 1234) +
  guides(color = "none")

That’s it! Let’s take the theme off completely, change the colors a little, and it should be perfect.

Code

ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) +
  geom_line(linewidth = 1.5) +
  geom_text_repel(aes(label = label_first), direction = "y", nudge_x = -1, seed = 1234) +
  geom_text_repel(aes(label = label_last), direction = "y", nudge_x = 1, seed = 1234) +
  guides(color = "none") +
  scale_color_viridis_d(option = "magma", end = 0.9) +
  theme_void()

Bump charts

Finally, we can make a bump chart that shows changes in rankings over time. We’ll look at CO₂ emissions in South Asia. First we need to calculate a new variable that shows the rank of each country within each year. We can do this if we group by year and then use the rank() function to rank countries by the co2_emissions column.

Code

sa_co2 <- wdi_clean %>% 
  filter(region == "South Asia") %>% 
  filter(year >= 2004, year < 2015) %>% 
  group_by(year) %>% 
  mutate(rank = rank(co2_emissions))

We then plot this with points and lines, reversing the y-axis so 1 is at the top:

Code

ggplot(sa_co2, aes(x = year, y = rank, color = country)) +
  geom_line() +
  geom_point() +
  scale_y_reverse(breaks = 1:8)

Afghanistan and Nepal switched around for the number 1 spot, while India dropped from 4 to 6, switching places with Pakistan.

As with the slopegraph, there are 8 different colors in the legend and it’s hard to line them all up with the different lines, so we can plot the text directly instead. We’ll use geom_text() again. We don’t need to repel anything, since the text should fit in each row just fine. We need to change the data argument in geom_text() though and filter the data to only include one year, otherwise we’ll get labels on every point, which is excessive. We can also adjust the theme and colors to make it cleaner.

Code

ggplot(sa_co2, aes(x = year, y = rank, color = country)) +
  geom_line(linewidth = 2) +
  geom_point(size = 4) +
  geom_text(data = filter(sa_co2, year == 2004),
            aes(label = iso2c, x = 2003.25),
            fontface = "bold") +
  geom_text(data = filter(sa_co2, year == 2014),
            aes(label = iso2c, x = 2014.75),
            fontface = "bold") +
  guides(color = "none") +
  scale_y_reverse(breaks = 1:8) +
  scale_x_continuous(breaks = 2004:2014) +
  scale_color_viridis_d(option = "magma", begin = 0.2, end = 0.9) +
  labs(x = NULL, y = "Rank") +
  theme_minimal() +
  theme(panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank())

If you want to be super fancy, you can use flags instead of country codes, but that’s a little more complicated (you need to install the {ggflags} package. See here for an example.

--- title: "Comparisons" date: "2023-06-26" date_end: "2023-06-30" --- ```{r load-targets, include=FALSE} withr::with_dir(here::here(), { wdi_path <- targets::tar_read(data_wdi_comparisons) }) ``` For this example, we're going to use cross-national data, but instead of using the typical `gapminder` dataset, we're going to collect data directly from the [World Bank's Open Data portal](https://data.worldbank.org/) If you want to skip the data downloading, you can download the data below (you'll likely need to right click and choose "Save Link As…"): - [{{< fa file-csv >}} `wdi_comparisons.csv`](/`r wdi_path`) ## Live coding example <div class="ratio ratio-16x9"> <iframe src="https://www.youtube.com/embed/tCnfTLHtMs8" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0"></iframe> </div> ::: {.callout-important} ### Slight differences from the video This is a slightly cleaned up version of the code from the video. ::: ```{r setup, include=FALSE} knitr::opts_chunk$set(fig.width = 6, fig.height = 3.6, fig.align = "center", collapse = TRUE) set.seed(1234) options("digits" = 2, "width" = 150) ``` ## Load and clean data First, we load the libraries we'll be using: ```{r load-libraries, message=FALSE, warning=FALSE} library(tidyverse) # For ggplot, dplyr, and friends library(WDI) # For getting data from the World Bank library(geofacet) # For map-shaped facets library(scales) # For helpful scale functions like dollar() library(ggrepel) # For non-overlapping labels ``` The World Bank has a ton of country-level data at [data.worldbank.org](https://data.worldbank.org/). We can use [a package named {WDI}](https://cran.r-project.org/package=WDI) (**w**orld **d**evelopment **i**ndicators) to access their servers and download the data directly into R. To do this, we need to find the special World Bank codes for specific variables we want to get. These codes come from the URLs of the World Bank's website. For instance, if you search for "access to electricity" at the World Bank's website, you'll find [this page](https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS). If you look at the end of the URL, you'll see a cryptic code: `EG.ELC.ACCS.ZS`. That's the World Bank's ID code for the "Access to electricity (% of population)" indicator. We can feed a list of ID codes to the `WDI()` function to download data for those specific indicators. We can feed it a list of just the codes (e.g., `c("SP.DYN.LE00.IN", "EG.ELC.ACCS.ZS", ...)`), but then the columns will be named with those ugly inscrutable names. Alternatively, we can feed the `WDI()` function a *named* list (e.g. `c(life_expectancy = "SP.DYN.LE00.IN", ...)`), which will create columns using the names we specify there. This is much nicer. We want data from 1995-2015, so we set the start and end years accordingly. The `extra=TRUE` argument means that it'll also include other helpful details like region, aid status, etc. Without it, it would only download the indicators we listed. ```{r get-wdi-data, eval=FALSE} indicators <- c(life_expectancy = "SP.DYN.LE00.IN", # Life expectancy access_to_electricity = "EG.ELC.ACCS.ZS", # Access to electricity co2_emissions = "EN.ATM.CO2E.PC", # CO2 emissions gdp_per_cap = "NY.GDP.PCAP.KD") # GDP per capita wdi_raw <- WDI(country = "all", indicators, extra = TRUE, start = 1995, end = 2015) head(wdi_raw) ``` Downloading data from the World Bank every time you knit will get tedious and take a long time (plus if their servers are temporarily down, you won't be able to get the data). It's good practice to save this raw data as a CSV file and then work with that. ```{r write-csv, eval=FALSE} write_csv(wdi_raw, "data/wdi_comparisons.csv") ``` Since we care about reproducibility, we still want to include the code we used to get data from the World Bank, we just don't want it to actually run. You can include chunks but not run them by setting `eval=FALSE` in the chunk options. In this little example, we show the code for downloading the data, but we don't evaluate the chunk. We then include a chunk that loads the data from a CSV file with `read_csv()`, but we don't include it (`include=FALSE`). That way, in the knitted file we see the `WDI()` code, but in reality it's loading the data from CSV. Super tricky. ````markdown I first download data from the World Bank: ```{r get-wdi-data, eval=FALSE} wdi_raw <- WDI(...) write_csv(wdi_raw, "data/wdi_comparisons.csv") ``` ```{r load-wdi-data-real, include=FALSE} wdi_raw <- read_csv("data/wdi_comparisons.csv") ``` ```` ```{r load-data-real, include=FALSE} wdi_raw <- read_csv(here::here(wdi_path)) ``` Then we clean up the data a little by filtering out rows that aren't actually countries: ```{r clean-wdi-data} wdi_clean <- wdi_raw %>% filter(region != "Aggregates") head(wdi_clean) ``` ## Small multiples First we can make some small multiples plots and show life expectancy over time for a handful of countries. We'll make a list of some countries chosen at random while I scrolled through the data, and then filter our data to include only those rows. We then plot life expectancy, faceting by country. ```{r life-expectancy-small} life_expectancy_small <- wdi_clean %>% filter(country %in% c("Argentina", "Bolivia", "Brazil", "Belize", "Canada", "Chile")) ggplot(data = life_expectancy_small, mapping = aes(x = year, y = life_expectancy)) + geom_line(linewidth = 1) + facet_wrap(vars(country)) ``` Small multiples! That's all we need to do. We can do some fancier things, though. We can make this plot hyper minimalist: ```{r life-expectancy-small-minimalist} ggplot(data = life_expectancy_small, mapping = aes(x = year, y = life_expectancy)) + geom_line(linewidth = 1) + facet_wrap(vars(country), scales = "free_y") + theme_void() + theme(strip.text = element_text(face = "bold")) ``` We can do a whole part of a continent (poor Iraq and Syria `r emoji::emoji("sad")`) ```{r life-expectancy-mena, fig.width=10, fig.height=3.5} life_expectancy_mena <- wdi_clean %>% filter(region == "Middle East & North Africa") ggplot(data = life_expectancy_mena, mapping = aes(x = year, y = life_expectancy)) + geom_line(linewidth = 1) + facet_wrap(vars(country), scales = "free_y", nrow = 3) + theme_void() + theme(strip.text = element_text(face = "bold")) ``` We can use the [{geofacet} package](https://hafen.github.io/geofacet/) to arrange these facets by geography: ```{r life-expectancy-eu, warning=FALSE, message=FALSE, fig.width=10, fig.height=8} life_expectancy_eu <- wdi_clean %>% filter(region == "Europe & Central Asia") ggplot(life_expectancy_eu, aes(x = year, y = life_expectancy)) + geom_line(linewidth = 1) + facet_geo(vars(country), grid = "eu_grid1", scales = "free_y") + labs(x = NULL, y = NULL, title = "Life expectancy from 1995–2015", caption = "Source: The World Bank (SP.DYN.LE00.IN)") + theme_minimal() + theme(strip.text = element_text(face = "bold"), plot.title = element_text(face = "bold"), axis.text.x = element_text(angle = 45, hjust = 1)) ``` Neat! ## Sparklines Sparklines are just line charts (or bar charts) that are really really small. ```{r india-spark, fig.width=1, fig.height=0.15, warning=FALSE} india_co2 <- wdi_clean %>% filter(country == "India") plot_india <- ggplot(india_co2, aes(x = year, y = co2_emissions)) + geom_line() + theme_void() plot_india ``` ```{r save-india-fake, eval=FALSE} ggsave("india_co2.pdf", plot_india, width = 1, height = 0.15, units = "in") ggsave("india_co2.png", plot_india, width = 1, height = 0.15, units = "in") ``` ```{r china-spark, fig.width=1, fig.height=0.15, warning=FALSE} china_co2 <- wdi_clean %>% filter(country == "China") plot_china <- ggplot(china_co2, aes(x = year, y = co2_emissions)) + geom_line() + theme_void() plot_china ``` ```{r save-china-fake, eval=FALSE} ggsave("china_co2.pdf", plot_china, width = 1, heighlt = 0.15, units = "in") ggsave("china_co2.png", plot_china, width = 1, height = 0.15, units = "in") ``` You can then use those saved tiny plots in your text. > Both India <img class="img-inline" src="/example/08-example_files/figure-html/india-spark-1.png" width = "100"/> and China <img class="img-inline" src="/example/08-example_files/figure-html/china-spark-1.png" width = "100"/> have seen increased CO<sub>2</sub> emissions over the past 20 years. ## Slopegraphs We can make a slopegraph to show changes in GDP per capita between two time periods. We need to first filter our WDI to include only the start and end years (here 1995 and 2015). Then, to make sure that we're using complete data, we'll get rid of any country that has missing data for either 1995 or 2015. The `group_by(...) %>% filter(...) %>% ungroup()` pipeline does this, with the `!any(is.na(gdp_per_cap))` test keeping any rows where any of the `gdp_per_cap` values are not missing for the whole country. We then add a couple special columns for labels. The `paste0()` function concatenates strings and variables together, so that `paste0("2 + 2 = ", 2 + 2)` would show "2 + 2 = 4". Here we make labels that say either "Country name: \$GDP" or "\$GDP" depending on the year. ```{r slopegraph-sa-data} gdp_south_asia <- wdi_clean %>% filter(region == "South Asia") %>% filter(year %in% c(1995, 2015)) %>% # Look at each country individually group_by(country) %>% # Remove the country if any of its gdp_per_cap values are missing filter(!any(is.na(gdp_per_cap))) %>% ungroup() %>% # Make year a factor mutate(year = factor(year)) %>% # Make some nice label columns # If the year is 1995, format it like "Country name: $GDP". If the year is # 2015, format it like "$GDP" mutate(label_first = ifelse(year == 1995, paste0(country, ": ", dollar(round(gdp_per_cap))), NA), label_last = ifelse(year == 2015, dollar(round(gdp_per_cap, 0)), NA)) ``` With the data filtered like this, we can plot it by mapping year to the x-axis, GDP per capita to the y-axis, and coloring by country. To make the lines go across the two categorical labels in the x-axis (since we made year a factor/category), we need to also specify the `group` aesthetic. ```{r slopegraph-sa-simple} ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) + geom_line(linewidth = 1.5) ``` Cool! We're getting closer. We can definitely see different slopes, but with 7 different colors, it's hard to see exactly which country is which. Instead, we can directly label each of these lines with `geom_text()`: ```{r slopegraph-sa-simple-text} ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) + geom_line(linewidth = 1.5) + geom_text(aes(label = country)) + guides(color = "none") ``` That gets us a *little* closer, but the country labels are hard to see, and we could include more information, like the actual values. Remember those `label_first` and `label_last` columns we made? Let's use those instead: ```{r slopegraph-sa-simple-text-fancier, warning=FALSE} ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) + geom_line(linewidth = 1.5) + geom_text(aes(label = label_first)) + geom_text(aes(label = label_last)) + guides(color = "none") ``` Now we have dollar amounts and country names, but the labels are still overlapping and really hard to read. To fix this, we can make the labels repel away from each other and randomly position in a way that makes them not overlap. The [{ggrepel} package](https://cran.r-project.org/web/packages/ggrepel/vignettes/ggrepel.html) lets us do this with `geom_text_repel()` ```{r slopegraph-sa-getting-warmer, warning=FALSE} ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) + geom_line(linewidth = 1.5) + geom_text_repel(aes(label = label_first)) + geom_text_repel(aes(label = label_last)) + guides(color = "none") ``` Now none of the labels are on top of each other, but the labels are still on top of the lines. Also, some of the labels moved inward and outward along the x-axis, but they don't need to do that—they just need to shift up and down. We can force the labels to only move up and down by setting the `direction = "y"` argument, and we can move all the labels to the left or right with the `nudge_x` argument. The `seed` argument makes sure that the random label placement is the same every time we run this. It can be whatever number you want—it just has to be a number. ```{r slopegraph-sa-fancier, warning=FALSE} ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) + geom_line(linewidth = 1.5) + geom_text_repel(aes(label = label_first), direction = "y", nudge_x = -1, seed = 1234) + geom_text_repel(aes(label = label_last), direction = "y", nudge_x = 1, seed = 1234) + guides(color = "none") ``` That's it! Let's take the theme off completely, change the colors a little, and it should be perfect. ```{r slopegraph-sa-done, warning=FALSE} ggplot(gdp_south_asia, aes(x = year, y = gdp_per_cap, group = country, color = country)) + geom_line(linewidth = 1.5) + geom_text_repel(aes(label = label_first), direction = "y", nudge_x = -1, seed = 1234) + geom_text_repel(aes(label = label_last), direction = "y", nudge_x = 1, seed = 1234) + guides(color = "none") + scale_color_viridis_d(option = "magma", end = 0.9) + theme_void() ``` ## Bump charts Finally, we can make a bump chart that shows changes in rankings over time. We'll look at CO<sub>2</sub> emissions in South Asia. First we need to calculate a new variable that shows the rank of each country within each year. We can do this if we group by year and then use the `rank()` function to rank countries by the `co2_emissions` column. ```{r filter-bump-data} sa_co2 <- wdi_clean %>% filter(region == "South Asia") %>% filter(year >= 2004, year < 2015) %>% group_by(year) %>% mutate(rank = rank(co2_emissions)) ``` We then plot this with points and lines, reversing the y-axis so 1 is at the top: ```{r make-bump-plot} ggplot(sa_co2, aes(x = year, y = rank, color = country)) + geom_line() + geom_point() + scale_y_reverse(breaks = 1:8) ``` Afghanistan and Nepal switched around for the number 1 spot, while India dropped from 4 to 6, switching places with Pakistan. As with the slopegraph, there are 8 different colors in the legend and it's hard to line them all up with the different lines, so we can plot the text directly instead. We'll use `geom_text()` again. We don't need to repel anything, since the text should fit in each row just fine. We need to change the `data` argument in `geom_text()` though and filter the data to only include one year, otherwise we'll get labels on every point, which is excessive. We can also adjust the theme and colors to make it cleaner. ```{r bump-plot-fancier, fig.width=7, fig.height=4.2} ggplot(sa_co2, aes(x = year, y = rank, color = country)) + geom_line(linewidth = 2) + geom_point(size = 4) + geom_text(data = filter(sa_co2, year == 2004), aes(label = iso2c, x = 2003.25), fontface = "bold") + geom_text(data = filter(sa_co2, year == 2014), aes(label = iso2c, x = 2014.75), fontface = "bold") + guides(color = "none") + scale_y_reverse(breaks = 1:8) + scale_x_continuous(breaks = 2004:2014) + scale_color_viridis_d(option = "magma", begin = 0.2, end = 0.9) + labs(x = NULL, y = "Rank") + theme_minimal() + theme(panel.grid.major.y = element_blank(), panel.grid.minor.y = element_blank(), panel.grid.minor.x = element_blank()) ``` If you want to be *super* fancy, you can use flags instead of country codes, but that's a little more complicated (you need to install the [{ggflags} package](https://github.com/rensa/ggflags). [See here for an example](https://dominikkoch.github.io/Bump-Chart/).