R Package with Covid19 data
The “covid19kosovo” R package offers data on the Covid-19 situation in
Kosovo, including daily totals of confirmed cases, recovered patients,
and deaths at the national level, as well as by municipality and
cadastral zone. However, please note that this package is no longer
being updated and the data provided is only from March 13th, 2020 to
February 6th, 2022. Additionally, the information is sourced from the
IKSHPK Facebook page page and its
accuracy cannot be guaranteed.
Datasets:
Daily summaries of confirmed, healed, and death cases on the national
level.
Daily summaries of confirmed cases by municipality.
Daily summaries of confirmed cases by cadastral zone (village).
To install the R package ‘covid19kosovo’ from Github, you will need to
have the devtools package installed. If you do not have devtools
installed, you can install it by running the following command:
install.packages("devtools")
Once ‘devtools’ is installed, you can use the ‘install_github()’
function to install the ‘covid19kosovo’ package. The basic syntax for
installing a package from Github using ‘install_github()’ is:
devtools::install_github("Kushtrimvisoka/covid19kosovo")
library(covid19kosovo)
Daily summaries of confirmed, healed, and death cases on the national
level.
data <- covid19kosovo(level = "total")#> Downloading data from#> https://raw.githubusercontent.com/Kushtrimvisoka/datasets/main/covid19kosovo_timeseries.csv...head(data)#> date confirmed healed dead confirmed_cumulative healed_cumulative#> 1 2020-03-13 2 0 0 2 0#> 2 2020-03-14 3 0 0 5 0#> 3 2020-03-15 3 0 0 8 0#> 4 2020-03-16 7 0 0 15 0#> 5 2020-03-17 4 0 0 19 0#> 6 2020-03-18 1 0 0 20 0#> dead_cumulative active#> 1 0 2#> 2 0 5#> 3 0 8#> 4 0 15#> 5 0 19#> 6 0 20
Daily summaries of confirmed cases by municipality.
data <- covid19kosovo(level = "municipality")#> Downloading data from#> https://raw.githubusercontent.com/Kushtrimvisoka/datasets/main/covid19kosovo_timeseries_municipality.csv...head(data)#> date id municipality confirmed#> 1 2020-03-13 8 Klinë 1#> 2 2020-03-13 26 Viti 1#> 3 2020-03-14 30 Malishevë 1#> 4 2020-03-14 26 Viti 2#> 5 2020-03-15 30 Malishevë 3#> 6 2020-03-16 15 Obiliq 1
Daily summaries of confirmed cases by cadastral zone (village).
data <- covid19kosovo(level = "village")#> Downloading data from#> https://raw.githubusercontent.com/Kushtrimvisoka/datasets/main/covid19kosovo_timeseries_cz.csv...head(data)#> date id municipality cadastral_zone confirmed#> 1 2020-03-13 8 Klinë Dranashiq 1#> 2 2020-03-13 26 Viti Stubëll e Poshtme 1#> 3 2020-03-14 30 Malishevë Bubavec 1#> 4 2020-03-14 26 Viti Stubëll e Poshtme 2#> 5 2020-03-15 30 Malishevë Bubavec 1#> 6 2020-03-15 30 Malishevë Llashkadrenoc 1
Daily vaccination process
data <- vaccination()#> Downloading data from#> https://raw.githubusercontent.com/Kushtrimvisoka/datasets/main/kosovo_dailyvaccinations.csv...head(data)#> date daily_vaccinated one_dose two_doses three_doses total_doses#> 1 2021-05-30 1120 NA 0 NA 64776#> 2 2021-05-31 2318 NA 0 NA 67094#> 3 2021-06-01 2595 NA 0 NA 69686#> 4 2021-06-02 2955 NA 0 NA 72637#> 5 2021-06-03 2915 NA 0 NA 75553#> 6 2021-06-04 2922 NA 0 NA 78477
library(tidyverse)#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──#> ✔ ggplot2 3.4.0 ✔ purrr 1.0.0#> ✔ tibble 3.1.8 ✔ dplyr 1.0.10#> ✔ tidyr 1.2.1 ✔ stringr 1.5.0#> ✔ readr 2.1.3 ✔ forcats 0.5.2#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──#> ✖ dplyr::filter() masks stats::filter()#> ✖ dplyr::lag() masks stats::lag()library(sf)#> Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE# devtools::install_github("Kushtrimvisoka/kosovomaps")library(kosovomaps)library(covid19kosovo)rksmap <- mapof("municip")data <- covid19kosovo(level = "municipality") %>%group_by(id, municipality) %>%summarise(confirmed = sum(confirmed))#> Downloading data from https://raw.githubusercontent.com/Kushtrimvisoka/datasets/main/covid19kosovo_timeseries_municipality.csv...#> `summarise()` has grouped output by 'id'. You can override using the `.groups` argument.rksmap <- merge(rksmap, data)p <- ggplot()+geom_sf(data = rksmap, aes(fill = confirmed)) +scale_fill_viridis_c("Confirmed", direction = -1) +theme_void()p
