Johannes Enevoldsen
This document is generated from README.rmd
The OxCGRT stringency index is an attempt to quantify the degree to which a given government enforces restrictions on its citizens with the aim of reducing the spread of COVID-19.
From the official website:
Governments are taking a wide range of measures to tackle the COVID-19 outbreak. We aim to track and compare worldwide government responses to the coronavirus rigorously and consistently. Systematic information on which measures governments take, and when, can help us understand the responses in a consistent way, aiding efforts to fight the pandemic.
The official website provide a lot of information about the different types of restrictions that go into the stringency index. Timeseries about the individual restrictions can also be accessed.
Here we will download an plot the timeseries of the stringency index.
library(tidyverse)
stringency_index_url <- "https://raw.githubusercontent.com/OxCGRT/covid-policy-tracker/master/data/timeseries/stringency_index.csv"
stringency_index <- read_csv(stringency_index_url)
print(stringency_index, n_extra = 10)## # A tibble: 186 x 583
## X1 country_code country_name `01Jan2020` `02Jan2020` `03Jan2020`
## <dbl> <chr> <chr> <dbl> <dbl> <dbl>
## 1 1 ABW Aruba 0 0 0
## 2 2 AFG Afghanistan 0 0 0
## 3 3 AGO Angola 0 0 0
## 4 4 ALB Albania 0 0 0
## 5 5 AND Andorra 0 0 0
## 6 6 ARE United Arab Emirates 0 0 0
## 7 7 ARG Argentina 0 0 0
## 8 8 AUS Australia 0 0 0
## 9 9 AUT Austria 0 0 0
## 10 10 AZE Azerbaijan 0 0 0
## # … with 176 more rows, and 577 more variables: 04Jan2020 <dbl>,
## # 05Jan2020 <dbl>, 06Jan2020 <dbl>, 07Jan2020 <dbl>, 08Jan2020 <dbl>,
## # 09Jan2020 <dbl>, 10Jan2020 <dbl>, 11Jan2020 <dbl>, 12Jan2020 <dbl>,
## # 13Jan2020 <dbl>, …
The data is in a wide format, with dates in column headers. First, we should pivot it longer.
stringency_index_long <- pivot_longer(stringency_index, -c(X1, country_code, country_name),
names_to = "date",
values_to = "stringency") %>%
mutate(date = lubridate::dmy(date))
stringency_index_long## # A tibble: 107,880 x 5
## X1 country_code country_name date stringency
## <dbl> <chr> <chr> <date> <dbl>
## 1 1 ABW Aruba 2020-01-01 0
## 2 1 ABW Aruba 2020-01-02 0
## 3 1 ABW Aruba 2020-01-03 0
## 4 1 ABW Aruba 2020-01-04 0
## 5 1 ABW Aruba 2020-01-05 0
## 6 1 ABW Aruba 2020-01-06 0
## 7 1 ABW Aruba 2020-01-07 0
## 8 1 ABW Aruba 2020-01-08 0
## 9 1 ABW Aruba 2020-01-09 0
## 10 1 ABW Aruba 2020-01-10 0
## # … with 107,870 more rows
Stringency index in Denmark
stringency_index_long %>%
filter(country_name == "Denmark") %>%
ggplot(aes(date, stringency)) +
geom_step() +
scale_x_date()