GDPR Violations
R Markdown
I love this intro photo from the tidyTuesday page.
This week’s tidyTuesday data cover violations of the GDPR.
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()
Robert W. Walker
Associate Professor of Quantitative Methods
My research interests include causal inference, statistical computation and data visualization.