PII fines

From some time I’ve been following a company that uses data to explain some data.

I want to use some of the data they published from Civio to see if I learn more about personal identifiable information fines.

fines <- read.csv("~/Downloads/multas-aepd.csv", stringsAsFactors = FALSE)
fines$date <- as.Date(fines$date)
library("ggplot2")
library("scales")
library("forcats")

ggplot(fines) +
  geom_point(aes(date, amount, col = fct_relevel(sanction_type, c("Leve", "Grave", "Muy grave")))) +
  scale_y_continuous(labels = dollar_format(suffix = "€", prefix = "")) +
  # from https://stackoverflow.com/a/32265122/2886003
  theme_bw() +
  scale_color_manual(values = c("Leve" = "orange", "Grave" = "orangered", "Muy grave" = "red")) +
  labs(x = "Year", y = "Amount", col = "Sanction type")

library("lubridate")
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library("dplyr")
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
fines %>% 
  mutate(month = month(date),
         year = year(date),
         date2 = as.character(paste0(year(date), "-", month(date), "-", 1))) %>% 
  group_by(date2) %>% 
  count(sector) %>% 
  mutate(date3 = ymd(date2)) %>%
  ggplot() +
  geom_col(aes(date3, n, fill = sector)) +
  labs(x = element_blank(), y = "Fines") +
  scale_x_date(date_breaks = "1 month", date_labels =  "%Y-%m", expand = c(0, 0)) +
  scale_y_continuous(expand = c(0, 0), breaks = seq(0, 120, by = 10)) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

library("dplyr")
ggplot(fines) +
  geom_histogram(aes(amount, fill = fct_relevel(sanction_type, c("Leve", "Grave", "Muy grave")))) +
  scale_fill_manual(values = c("Leve" = "orange", "Grave" = "orangered", "Muy grave" = "red")) +
  scale_x_log10(labels = dollar_format(suffix = "€", prefix = "")) +
  # from https://stackoverflow.com/a/32265122/2886003
  labs(x = "Amount", fill = "Sanction type", y = "Fines") +
  theme_bw()  +
  facet_wrap(~fct_relevel(sanction_type, c("Leve", "Grave", "Muy grave")), scales = "free")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

  
ggplot(fines) +
  geom_histogram(aes(amount, fill = sector)) +
  scale_fill_viridis_d() +
  scale_x_log10(labels = dollar_format(suffix = "€", prefix = "")) +
  # from https://stackoverflow.com/a/32265122/2886003
  labs(x = "Amount", fill = "Sector", y = "Fines") +
  theme_bw()  +
  facet_wrap(~fct_relevel(sanction_type, c("Leve", "Grave", "Muy grave")), scales = "free")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.



fines %>% 
  filter(amount < 200000) %>% 
  ggplot() +
  geom_point(aes(date, amount, col = fct_relevel(sanction_type, c("Leve", "Grave", "Muy grave")))) +
  scale_y_continuous(labels = dollar_format(suffix = "€", prefix = "")) +
  # from https://stackoverflow.com/a/32265122/2886003
  theme_bw() +
  scale_color_manual(values = c("Leve" = "orange", "Grave" = "orangered", "Muy grave" = "red")) +
  labs(x = "Year", y = "Amount", col = "Sanction type")


ggplot(fines) +
  geom_point(aes(date, amount, col = fct_relevel(sanction_type, c("Leve", "Grave", "Muy grave")))) +
  scale_y_continuous(labels = dollar_format(suffix = "€", prefix = ""), 
                     breaks = c(1000, 4000, 10000, 20000, 40000,
                                50000, 500000, 1000000)) +
  coord_trans(y = "log10") +
  # from https://stackoverflow.com/a/32265122/2886003
  theme_bw() +
  scale_color_manual(values = c("Leve" = "orange", "Grave" = "orangered", "Muy grave" = "red")) +
  labs(x = "Year", y = "Amount", col = "Sanction type")

What sectors are more fined?

fines_sector <- fines %>% 
  group_by(sector) %>% 
  count(sort = TRUE) %>% 
  ungroup() %>% 
  mutate(sector = if_else(sector == "", "Unknown/unclassified", sector))
fines_sector %>% 
  ggplot() + 
  geom_col(aes(fct_reorder(sector, -n), n, fill = fct_reorder(sector, -n))) +
  guides(fill = FALSE) +
  labs(x = "Sector", y = "Fines") + 
  # coord_trans(y = "log10") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

We can see that there is a white space between 2017 and 2018

fines %>% 
  count(name, sort = TRUE) %>% 
  arrange(-n) %>% 
  ggplot() + 
  geom_col(aes(fct_reorder(name, -n), n)) +
  labs(x = "Name", y = "Fines") + 
  scale_y_continuous(expand = c(0, 5)) +
  # coord_trans(y = "log10") +
  theme_bw() +
  theme(axis.text.x = element_blank(), axis.line.x = element_blank(), 
        axis.ticks.x = element_blank(),
        panel.grid.major.x = element_blank())

References

Reproducibility

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##  ctype    en_US.UTF-8                 
##  tz       Europe/Madrid               
##  date     2020-08-20                  
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Avatar
Lluís Revilla Sancho
Bioinformatician

Bioinformatician with interests in functional enrichment, data integration and transcriptomics.

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