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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