GDPR Violations
R Markdown
I love this intro photo from the tidyTuesday page.
This week’s tidyTuesday data cover violations of the GDPR (General Data Protection Regulations) regulatory regime for data privacy in the European Union. The Wikipedia entry on GDPR is fairly extensive. The dataset is large and suggests some interesting regulatory arbitrage. Let’s have a look at the data. First, let’s load them.
gdpr_violations <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-21/gdpr_violations.tsv')##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## id = col_double(),
## picture = col_character(),
## name = col_character(),
## price = col_double(),
## authority = col_character(),
## date = col_character(),
## controller = col_character(),
## article_violated = col_character(),
## type = col_character(),
## source = col_character(),
## summary = col_character()
## )gdpr_text <- readr::read_tsv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-21/gdpr_text.tsv')##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## chapter = col_double(),
## chapter_title = col_character(),
## article = col_double(),
## article_title = col_character(),
## sub_article = col_double(),
## gdpr_text = col_character(),
## href = col_character()
## )Summary
library(skimr)
skim(gdpr_violations)| Name | gdpr_violations |
| Number of rows | 250 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| picture | 0 | 1 | 67 | 80 | 0 | 25 | 0 |
| name | 0 | 1 | 5 | 14 | 0 | 25 | 0 |
| authority | 0 | 1 | 14 | 86 | 0 | 40 | 0 |
| date | 0 | 1 | 10 | 10 | 0 | 140 | 0 |
| controller | 0 | 1 | 2 | 93 | 0 | 187 | 0 |
| article_violated | 0 | 1 | 7 | 87 | 0 | 86 | 0 |
| type | 0 | 1 | 7 | 143 | 0 | 22 | 0 |
| source | 0 | 1 | 29 | 209 | 0 | 218 | 0 |
| summary | 0 | 1 | 13 | 1550 | 0 | 238 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1 | 125.5 | 72.31 | 1 | 63.25 | 125.5 | 187.75 | 2.5e+02 | ▇▇▇▇▇ |
| price | 0 | 1 | 613214.0 | 3980371.95 | 0 | 2500.00 | 10500.0 | 60000.00 | 5.0e+07 | ▇▁▁▁▁ |
Violations
library(tidyverse); library(janitor)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.0.6 ✓ dplyr 1.0.4
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()##
## Attaching package: 'janitor'## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.testgdpr_violations %>% tabyl(article_violated)## article_violated
## Art 6 (1) GDPR|Art 58 (2) e) GDPR|Art 83 (5) a) GDPR
## Art. 12 (3) GDPR|Art. 15 (1) GDPR
## Art. 12 (4) GDPR|Art. 15 GDPR
## Art. 12 (4) GDPR|Art. 15 GDPR|Art. 18 (1) c) GDPR|Art. 13 GDPR
## Art. 12 GDPR|Art. 13 GDPR|Art. 5 (1) c) GDPR|Art. 6 GDPR
## Art. 12 GDPR|Art. 15 GDPR
## Art. 12 GDPR|Art. 15 GDPR|Art. 17 GDPR
## Art. 12 GDPR|Art. 17 GDPR
## Art. 13 GDPR
## Art. 13 GDPR|Art. 14 GDPR|Art. 6 GDPR|Art. 4 GDPR|Art. 5 GDPR
## Art. 13 GDPR|Art. 37 GDPR
## Art. 14 GDPR
## Art. 15 (1), (3) GDPR
## Art. 15 GDPR
## Art. 15 GDPR|Art. 17 GDPR|Art. 21 GDPR
## Art. 17 GDPR
## Art. 21 (3) GDPR|Art. 25 GDPR
## Art. 21 GDPR
## Art. 25 (1) GDPR|Art. 5 (1) c) GDPR
## Art. 25 GDPR
## Art. 28 (3) GDPR
## Art. 28 GDPR
## Art. 28 of the GDPR
## Art. 31 GDPR
## Art. 32 GDPR
## Art. 32 GDPR|Art. 33 GDPR
## Art. 33 (1) GDPR|Art. 33 (5) GDPR|Art. 34 (1) GDPR
## Art. 33 GDPR
## Art. 33 GDPR|Art. 34 GDPR
## Art. 37 GDPR
## Art. 5 (1) a) GDPR
## Art. 5 (1) a) GDPR|Art. 5 (1) b) GDPR|Art. 32 (1) GDPR
## Art. 5 (1) a) GDPR|Art. 5 (1) c) GDPR
## Art. 5 (1) a) GDPR|Art. 5 (1) c) GDPR|Art. 6 (1) GDPR|Art. 13 GDPR
## Art. 5 (1) a) GDPR|Art. 6 (1) a) GDPR
## Art. 5 (1) a) GDPR|Art. 6 GDPR
## Art. 5 (1) a) GDPR|Art. 9 (1) GDPR|Art. 9 (2) GDPR|Art. 6 (1) GDPR
## Art. 5 (1) a), (2) GDPR
## Art. 5 (1) a)|Art. 7 (3) GDPR
## Art. 5 (1) b) GDPR|Art. 5 (1) c) GDPR|Art. 13 (3) GDPR|Art. 17 (1) GDPR|Art. 6 (4) GDRP
## Art. 5 (1) b) GDPR|Art. 6 GDPR
## Art. 5 (1) c) GDPR
## Art. 5 (1) c) GDPR|Art. 12 GDPR|Art. 13 GDPR|Art. 32 GDPR
## Art. 5 (1) c) GDPR|Art. 13 GDPR
## Art. 5 (1) c) GDPR|Art. 25 GDPR
## Art. 5 (1) c) GDPR|Art. 5 (1) e) GDPR
## Art. 5 (1) c) GDPR|Art. 9 GDPR|Art. 35 GDPR|Art. 36 GDPR
## Art. 5 (1) d) GDPR
## Art. 5 (1) e) GDPR|Art. 5 (2) GDPR
## Art. 5 (1) f) GDPR
## Art. 5 (1) f) GDPR|Art. 32 GDPR
## Art. 5 (1) GDPR
## Art. 5 (1) GDPR|Art. 5 (2) GDPR|Art. 6 (1) GDPR|Art. 13 (1) c) GDPR|Art. 14 (1) c) GDPR
## Art. 5 (1) GDPR|Art. 6 GDPR|Art. 7 GDPR
## Art. 5 (1) GDPR|Art. 6 GDPR|Art. 7 GDPR|Art. 9 GDPR
## Art. 5 GDPR
## Art. 5 GDPR|Art. 25 GDPR
## Art. 5 GDPR|Art. 25 GDPR|Art. 32 GDPR|Art. 33 GDPR
## Art. 5 GDPR|Art. 32 GDPR
## Art. 5 GDPR|Art. 32 GDPR|Art. 33 GDPR
## Art. 5 GDPR|Art. 6 GDPR
## Art. 5 GDPR|Art. 6 GDPR|Art. 13 GDPR|Art. 14 GDPR|Art. 21 GDPR
## Art. 5 GDPR|Art. 6 GDPR|Art. 17 GDPR
## Art. 5 GDPR|Art. 6 GDPR|Art. 17 GDPR|Art. 21 GDPR
## Art. 5 GDPR|Art. 6 GDPR|Art. 21 GDPR
## Art. 5 GDPR|Art. 6 GDPR|Art. 32 GDPR
## Art. 5 GDPR|Art. 6 GDPR|Art. 7 GDPR|Art. 21 GDPR
## Art. 5(1) e) GDPR
## Art. 58 GDPR
## Art. 58(2) GDPR
## Art. 6 (1) GDPR
## Art. 6 (1) GDPR|Art. 25 (1) GDPR
## Art. 6 GDPR
## Art. 6 GDPR|Art. 12 GDPR|Art. 13 GDPR
## Art. 6 GDPR|Art. 21 GDPR
## Art. 6 GDPR|Art. 5 (1) a) GDPR
## Art. 6 GDPR|Art. 5 (1) b) GDPR|Art. 13 GDPR
## Art. 6 GDPR|Art. 5 GDPR
## Art. 6 GDPR|Art. 7 GDPR
## Art. 6 GDPR|Art. 9 GDPR
## Art. 83 (4) a) GDPR|Art. 33 (1) GDPR|Art. 34 (1) GDPR
## Art.14 GDPR
## Art.14 of the GDPR
## Data Protection Act 2018
## Failure to implement sufficient measures to ensure information security
## Unknown
## n percent
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 7 0.028
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 10 0.040
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 2 0.008
## 41 0.164
## 2 0.008
## 1 0.004
## 1 0.004
## 1 0.004
## 2 0.008
## 2 0.008
## 1 0.004
## 1 0.004
## 1 0.004
## 2 0.008
## 6 0.024
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 2 0.008
## 6 0.024
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 3 0.012
## 1 0.004
## 7 0.028
## 10 0.040
## 1 0.004
## 1 0.004
## 1 0.004
## 1 0.004
## 10 0.040
## 2 0.008
## 1 0.004
## 3 0.012
## 1 0.004
## 20 0.080
## 1 0.004
## 1 0.004
## 1 0.004
## 2 0.008
## 1 0.004
## 1 0.004
## 1 0.004
## 5 0.020
## 1 0.004
## 4 0.016
## 1 0.004
## 33 0.132
## 1 0.004
## 1 0.004
## 2 0.008
## 1 0.004
## 1 0.004
## 1 0.004
## 3 0.012
## 1 0.004
## 2 0.008
## 1 0.004
## 1 0.004
## 1 0.004
## 2 0.008Revenue?
GDPT <- gdpr_violations %>% group_by(name) %>% summarise(Collected = sum(price), Violations = n())
GDPT %>% select(name, Collected) %>% ggplot(., aes(x=fct_reorder(name, Collected), y=Collected/1e7, fill=name)) + geom_col() + scale_fill_viridis_d() + coord_flip() + labs(x="Country", y="Total Fines Collected (in Millions)") + guides(fill=FALSE)
A Map
library(rnaturalearth)
Europe <- ne_countries(scale = 'medium', type = 'map_units', returnclass = 'sf', continent="Europe")
library(sf)## Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1Europe <- sf::st_crop(Europe, xmin = -20, xmax = 45, ymin = 30, ymax = 73)## although coordinates are longitude/latitude, st_intersection assumes that they are planar## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesggplot(Europe) + geom_sf()
Merge Data
Plot some Europe Maps.
Total Collections
Map.Me <- left_join(Europe, GDPT, by = c("sovereignt" = "name"))
ggplot(Map.Me) + aes(fill=Collected) + geom_sf() + scale_fill_viridis_c()
Count of Violations
ggplot(Map.Me) + aes(fill=Violations) + geom_sf() + scale_fill_viridis_c()
Cost per violation
Map.Me %>% mutate(Avg.Fine = Collected / Violations) %>% ggplot(.) + aes(fill=Avg.Fine) + geom_sf() + labs(title="Average Fine per Violation") + scale_fill_viridis_c()