4.1 geom_point

• x = unemp_youth_t
• y = gdp_gr
• color = emigration_t / immigration_t
• size = inv_per_empl
• Use theme_classic()
• You might realize that the label symbols of size are a bit weird as soon as you add the regression line. This is because the function to create the regression line interacts with the size argument within the ggplot() function. Therefore, as soon as you add the regression function, move the size argument to geom_point. But don’t forget that the size argument has to be put within aes()!
  • Here is an important learning: aes() can be passed to either ggplot() or to a specific layer (e.g. geom_). Aesthetics specified to ggplot() are used as defaults for every layer, while aes() passed to a specific layer a used as default for that layer only.

4.2 geom_col

• use theme_economist()
• x = geo_code
• y = unemp_youth_t
• fill = unemp_youth_t
• Play a bit with the width of the bars.
• Crucially, we add points which relate to unemp_workagepop_t. You need to write a new aes() in the point function and assign the color “red”. Outside of the aes() argument, adjust the size of the points to 3.
• Rename the axes accordingly. Note that although the y axis is actually just related to youth unemployment, we simply rename it as Unemployment because the total unemployment (e.g. the points) are within the same scale.
• Furthermore, add this line + scale_color_manual(values = c("red"), labels = c(" ")). Do you understand what it does?

4.3 geom_hist

Here, we first do some some data management. We reload the data (but save them as eurost2), filter for some countries and the time period 1990-2015. Furthermore, we create two new columns which copy the values of (youth) unemployment ONLY in the year of 2015.

eurost2 <- read_csv2("data/eurostat_data.csv") %>%
  filter(geo_code %in% c("DE", "IT", "EL", "ES", "UK"),
         time >= 1990,
         time <= 2015) %>%
  mutate(
    unemp_tod = if_else(time == 2015, unemp_workagepop_t, NA_real_),
    unemp_youth_tod = if_else(time == 2015, unemp_youth_t, NA_real_)
  )

We use this data for the plot

• As aesthetic in the principle ggplot() function, just use x = geo_code
• What we are doing then is using two seperate geom_violin and two seperate geom_point functions
  • Both violin functions use the argument alpha = 0.5 to increase the transparency of the violin plots
  • One violin function hasy=unemp_youth_t and the othery = unemp_workagepop_t. Use the appropriate colors
  • One point function has y = unemp_tod and the other y = unemp_youth_tod. Use color = "black", size = 3 outside of the aesthetic.
• Use theme_minimal()
• Add this line: scale_fill_manual(values = c("red", "blue"), labels = c("Total Unemployment", "Youth Unemployment"))

5.1 Adjusting colors

First of all, the colors of this main_plot are not so nice.

There are hundreds of ways to change the colors of all the components of a ggplot. You might want to check out his page for more information.

Try to replicate this plot:

• Remember, we just saved the first plot as main_plot. So you do not need to rewrite everything from this plot but only ….
• In this case, use the function scale_color_gradient2() to get the colors.
• Use ?scale_color_gradient2() to understand the arguments you need to use to replicate the plot

5.2 Adding labels to points

We want to have labels with the country codes next to our points. ggplot has a proprietary function for this, but a cleaner and more efficient function comes from the ggrepel package (e.g. install ggrepel).

• Again, use main_plot as the base

• The function from ggrepel we want to use is geom_text_repel. Use geo_code as the label within the aes() argument of geom_text_repel.

5.3 Facets and more

You can add a facets (e.g. the same plotting relationship in many windows representing different variables such as different years) with the function +facet_wrap(~FACETS_VARIABLE_NAME). Make facets using our main graph, using location as a facets variable.

• Use location as the facet variable.