Corona has also affected what I do at work and we have created together with colleagues two separate resources for tracking the effect of corona on Kela benefits.
koronamittarit.kela.fi (corona indicators) provides data and analysis on several Kela benefits at daily, weekly and monthly resolutions in terms of number of applications, number of recipients and on the performance of Kela customer service. Site is refreshed every weekday at 10am.
Purchased prescription medicines in Finland- web app provides a dynamic access to information on reimbursable prescription medicines in terms of costs, number of purchases and number of patients on a weekly basis in 2019 and 2020 at the national level as well as health district level.
Both application provide open data in addition to their simplistic analysis. Data on corona indicators is documented on the website (in Finnish only) and can be doanloaded from Github. Data from medicine app can be accessed also at Github.
There are examples on how to get started with the data both about corona indicators and medicine data. The easiest way to get started is to create an account in rstudio.cloud, copy the code snippets and begin to wonder! Below is one more example!
# A tibble: 6 × 19
etuus vuosi aikat…¹ kuuka…² viikko paiva viiko…³ alue ikalu…⁴ sukup…⁵
<fct> <fct> <chr> <int> <int> <date> <chr> <chr> <fct> <chr>
1 Työttöm… 2020 paiva 1 1 2020-01-01 keskiv… Koko… kaikki kaikki
2 Työttöm… 2020 paiva 1 1 2020-01-02 torstai Koko… kaikki kaikki
3 Työttöm… 2020 paiva 1 1 2020-01-03 perjan… Koko… kaikki kaikki
4 Työttöm… 2020 paiva 1 1 2020-01-04 lauant… Koko… kaikki kaikki
5 Työttöm… 2020 paiva 1 1 2020-01-05 sunnun… Koko… kaikki kaikki
6 Työttöm… 2021 paiva 1 1 2021-01-04 maanan… Koko… kaikki kaikki
# … with 9 more variables: saapuneet_hakemukset <dbl>, data <chr>,
# saajat_kaikki <dbl>, saajat_uudet <dbl>, saajakotitaloudet_kaikki <dbl>,
# saajakotitaloudet_uudet <dbl>, saajaruokakunnat_kaikki <dbl>,
# saajaruokakunnat_uudet <dbl>, updated <dttm>, and abbreviated variable
# names ¹aikatyyppi, ²kuukausi, ³viikonpaiva, ⁴ikaluokka, ⁵sukupuoli
</>
levels(dat$etuus)[levels(dat$etuus) =="Työttömyysturva"] <-"Unemployment benefit"levels(dat$etuus)[levels(dat$etuus) =="Yrittäjien työmarkkinatuki"] <-"Unemployment benefit for self-employed"levels(dat$etuus)[levels(dat$etuus) =="Yleinen asumistuki"] <-"Housing benefit"levels(dat$etuus)[levels(dat$etuus) =="Perustoimeentulotuki"] <-"Social assistance"levels(dat$etuus)[levels(dat$etuus) =="Sairauspäiväraha"] <-"Sickness allowance"levels(dat$etuus)[levels(dat$etuus) =="Tartuntatautipäiväraha"] <-"Infectious disease allowance"levels(dat$etuus)[levels(dat$etuus) =="Epidemiatuki"] <-"Temporary compensation due to an epidemic outbreak for basic social assistance clients"library(ggplot2)datplot <- dat %>% dplyr::filter(aikatyyppi =="viikko", ikaluokka =="kaikki", sukupuoli =="kaikki", alue =="Koko Suomi") %>%mutate(viikko =as.integer(viikko))ggplot(datplot, aes(x = viikko, y = saapuneet_hakemukset, color = vuosi, fill = vuosi)) +geom_line() +geom_point(shape =21, color ="white", size =1.6, show.legend =FALSE) +facet_wrap(~etuus, ncol =1, scales ="free_y") +scale_x_continuous(breaks =1:max(datplot$viikko)) +labs(fill =NULL, color =NULL, y =NULL,title ="Applications per week",subtitle ="number of applications received at Kela per week",x ="Viikko") +theme_light() +theme(legend.position ="right", legend.direction ="vertical",panel.grid.minor =element_blank()) +scale_y_continuous(labels =function(x) format(x, big.mark =" ",scientific =FALSE),limits =c(0,NA))
Uudelleenkäyttö
CC BY 4.0
Viittaus
BibTeX-viittaus:
@online{kainu,
author = {Markus Kainu and Markus Kainu},
title = {Kela corona indicators and a web app on reimbursed medicine
consumption},
date = {},
url = {https://markuskainu.fi/posts/2020-12-13-kela_corona_indicators},
langid = {fi}
}