Citizenship, Bees, and Zucchini (ggplot2)

Opendata.swiss currently provides about 8000 open government data sets from agriculture to health to culture. Here, I’ll be looking at a data set from the Federal Statistical Office containing the 500 most successful Swiss films by theater admissions.

This post is mostly about preparing data for ggplot and customizing figures in R.

You can download the Excel file to a working directory and then import — or, as shown below, get it into R directly from the web. Some clean-up / variable renaming is the first step (using dplyr from the tidyverse).

library(openxlsx)
movies <- read.xlsx("https://www.bfs.admin.ch/bfsstatic/dam/assets/21464060/master", startRow = 3, colNames = TRUE, check.names = TRUE)

names(movies)

library(tidyverse)
movies <- movies %>% transmute(X1 = NULL,
                     title = Originaltitel.des.Films,
                     director = Regisseur.Regisseurin,
                     genre = Genre,
                     year = Produktionsjahr,
                     admissions = Kinoeintritte2.) %>% as_tibble()

Load some more packages required for the analysis:

library(ggrepel)
library(scales)
library(grid)
library(gridExtra)

First look at the data

Check the data structure, variables, and distributions:

movies

year_hist <- movies %>% ggplot(aes(year)) +
  geom_histogram()

admissions_hist <- movies %>% ggplot(aes(admissions)) +
  geom_histogram() + labs(y = NULL)

grid.arrange(year_hist, admissions_hist, ncol = 2)
# A tibble: 510 × 5
   title                        director                     genre  year admis…¹
   <chr>                        <chr>                        <chr> <dbl>   <dbl>
 1 SCHWEIZERMACHER, DIE         Lyssy Rolf                   Spie…  1978  942066
 2 HERBSTZEITLOSEN, DIE         Oberli Bettina               Spie…  2006  596272
 3 MEIN NAME IST EUGEN          Steiner Michael              Spie…  2005  580870
 4 ACHTUNG, FERTIG, CHARLIE!    Eschmann Mike                Spie…  2003  560523
 5 SCHELLEN-URSLI               Koller Xavier                Spie…  2014  456159
 6 PETITES FUGUES, LES          Yersin Yves                  Spie…  1979  426649
 7 GROUNDING                    Steiner Michael, Fueter Tob… Spie…  2005  377713
 8 GÖTTLICHE ORDNUNG, DIE       Volpe Petra                  Spie…  2016  357879
 9 SCHWEIZER NAMENS NÖTZLI, EIN Ehmck Gustav                 Spie…  1988  350681
10 PLATZSPITZBABY               Monnard Pierre               Spie…  2019  334076
# … with 500 more rows, and abbreviated variable name ¹​admissions

Admission by year

As seen above already, admissions are very skewed; pull tail end of the distribution up with log:

ad_plot <- movies %>%
  ggplot(aes(x = year, y = admissions, color = genre)) +
  geom_point()

grid.arrange(ad_plot +
               theme(legend.position = c(0.5,0.75)),
             ad_plot +
               scale_y_log10(labels = label_log()) + 
               labs(y = "log(admissions)")+
               theme(legend.position = "none"),
             ncol = 2)  

Most successful films by genre

There are fiction movies, documentaries, and animated films in the list; makes sense to separate these.

There are only 3 animated films in the data:

movies %>% count(genre)
# A tibble: 4 × 2
  genre              n
  <chr>          <int>
1 Animationsfilm     3
2 Dokumentarfilm   240
3 Spielfilm        257
4 <NA>              10
movies %>% filter(genre == "Animationsfilm") %>% 
  ggplot(aes(x = year, y = admissions)) +
  geom_point() +
  geom_text_repel(aes(label = title))

Now, let’s focus on movies and documentaries. Only the top films get labels so it’s not too crowded. For geom_text_repel(), the easiest way is probably to prepare the data such that the title label is empty for those we don’t want labeled in the plot (see the case_when() function below). Some titles are also very long, so we might want to truncate those first using str_trunc(). Finally, the two plots are combined using grid.arrange(), where we can also add some additional text such as the data source.

## Spielfilm (movie) cut-off 300k
spiel <- movies %>% mutate(title0 = title, title = "") %>%
  mutate(title = case_when(admissions > 300000 & genre == "Spielfilm" ~ title0,
                           TRUE ~ title)) %>%
  mutate(title = str_trunc(title, 25)) %>% 
  filter(genre == "Spielfilm") %>% 
  ggplot(aes(x = year, y = admissions)) +
  geom_point(color = "midnightblue", alpha = 0.6) +
  expand_limits(x = c(1975, 2021)) +
  scale_x_continuous(breaks = seq(1975, 2020, by = 5)) +
  geom_text_repel(aes(label = title),
                   size = 3,
                   min.segment.length = 0,
                   box.padding = 0.3,
                   color = "midnightblue")

## Dokumentarfilm (documentary) cut-off 50k
dok <- movies %>% mutate(title0 = title, title = "") %>%
  mutate(title = case_when(admissions > 50000 & genre == "Dokumentarfilm" ~ title0,
                           TRUE ~ title)) %>%
  mutate(title = str_trunc(title, 25)) %>% 
  filter(genre == "Dokumentarfilm") %>% 
    ggplot(aes(x = year, y = admissions)) +
    geom_point(color = "midnightblue", alpha = 0.6) +
    expand_limits(x = c(1975, 2021)) +
    scale_x_continuous(breaks = seq(1975, 2020, by = 5)) +
    geom_text_repel(aes(label = title),
                     size = 3,
                     min.segment.length = 0,
                     box.padding = 0.4,
                     color = "midnightblue")

grid.arrange(spiel + 
               labs(x= NULL, y = NULL, title = "Fiction") + 
               theme(axis.text.x=element_blank()),
             dok + labs(x = NULL, y = NULL, title = "Documentaries"),
             top = textGrob("Most successful Swiss films",
                            gp = gpar(fontsize = 16,font = 2)),
             bottom = "Production year",
             left = "Movie theatre admissions",
             right = textGrob("Data: opendata.swiss",
                              gp = gpar(fontsize = 8), rot = 90))

Now we have quite an informative but not overcrowded figure. Separating the two categories (movies and documentaries) makes sense here because they are at very different levels of the Y-axis (admissions). I chose not to take the log of admissions because the top films are spread out enough and this way it’s more intuitively interpretable.

And here are the winners, all of which can be streamed for free on https://www.playsuisse.ch:

Watch full movie: https://www.playsuisse.ch/de/show/890917
Watch full documentary: https://www.playsuisse.ch/de/show/930523
Watch full animated film: https://www.playsuisse.ch/de/show/808668
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