Identifying data for Final Project
This analysis will combine country level data to see how cultural indicators drive COVID-19 vaccination rates around the world, and how different health systems, and strength of those impact that relationship. I will combine three different datasets at the country level:
1: Cultural indicators from Geert Hofstede’s Cultural Dimensions Theory Data
2: National Vaccination Rates from Open-Source Github Repo: https://github.com/owid/covid-19-data
3: National Health System Classification Data - TBD
4: Global Country Indicators (Income, Income Per Capita, Government Type (indexes?), Population etc…) - TBD
culture_indices <- read.csv2("https://query.data.world/s/c4dbzx65xq3bnkf65gnutajtb2566a", header=TRUE, stringsAsFactors=FALSE) %>%
na_if("#NULL!")
head(df)
1 function (x, df1, df2, ncp, log = FALSE)
2 {
3 if (missing(ncp))
4 .Call(C_df, x, df1, df2, log)
5 else .Call(C_dnf, x, df1, df2, ncp, log)
6 } "https://github.com/owid/covid-19-data/tree/master/public/data/vaccinations/country_data" %>%
read_html() %>%
html_nodes(xpath = '//*[@role="rowheader"]') %>%
html_nodes('span a') %>%
html_attr('href') %>%
sub('blob/', '', .) %>%
paste0('https://raw.githubusercontent.com', .) %>%
purrr::map_df(read.csv) -> vaccine_data
head(vaccine_data)
location date
1 Afghanistan 2021-02-22
2 Afghanistan 2021-02-28
3 Afghanistan 2021-03-16
4 Afghanistan 2021-04-07
5 Afghanistan 2021-04-22
6 Afghanistan 2021-05-11
vaccine
1 Oxford/AstraZeneca
2 Oxford/AstraZeneca
3 Oxford/AstraZeneca
4 Oxford/AstraZeneca
5 Oxford/AstraZeneca
6 Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing
source_url
1 https://tolonews.com/index.php/health-170225
2 https://tolonews.com/index.php/health-170355
3 http://www.xinhuanet.com/english/asiapacific/2021-03/16/c_139814668.htm
4 http://www.xinhuanet.com/english/asiapacific/2021-04/07/c_139864755.htm
5 https://reliefweb.int/report/afghanistan/afghanistan-strategic-situation-report-covid-19-no-95-22-april-2021
6 https://covid19.who.int/
total_vaccinations people_vaccinated people_fully_vaccinated
1 0 0 NA
2 8200 8200 NA
3 54000 54000 NA
4 120000 120000 NA
5 240000 240000 NA
6 504502 448878 55624
total_boosters people_partly_vaccinated
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NAKeep only the most up to date vaccination data
df <- vaccine_data %>%
arrange(desc(date)) %>%
distinct(location, .keep_all= TRUE) %>%
full_join(culture_indices,by=c("location"="country"))
print(head(df))
location date
1 Argentina 2022-02-15
2 Aruba 2022-02-15
3 Australia 2022-02-15
4 Azerbaijan 2022-02-15
5 Brazil 2022-02-15
6 Bulgaria 2022-02-15
vaccine
1 CanSino, Moderna, Oxford/AstraZeneca, Pfizer/BioNTech, Sinopharm/Beijing, Sputnik V
2 Pfizer/BioNTech
3 Moderna, Oxford/AstraZeneca, Pfizer/BioNTech
4 Oxford/AstraZeneca, Pfizer/BioNTech, Sinovac, Sputnik V
5 Johnson&Johnson, Pfizer/BioNTech, Oxford/AstraZeneca, Sinovac
6 Johnson&Johnson, Oxford/AstraZeneca, Moderna, Pfizer/BioNTech
source_url
1 https://www.argentina.gob.ar/coronavirus/vacuna/aplicadas
2 https://www.government.aw
3 https://covidbaseau.com/
4 https://koronavirusinfo.az/storage/app/MvN7udDQpfRpye6gH9GF7aIwLueGHk0PTRK18Rvg.pdf
5 https://coronavirusbra1.github.io
6 https://coronavirus.bg/bg/statistika
total_vaccinations people_vaccinated people_fully_vaccinated
1 91223956 40072715 35528853
2 167315 87115 80200
3 52635184 21933086 20384601
4 12549963 5270596 4766430
5 379776348 174665423 152576040
6 4254776 NA 2030626
total_boosters people_partly_vaccinated ctr pdi idv mas uai
1 15346048 NA ARG 49 46 56 86
2 NA NA <NA> <NA> <NA> <NA> <NA>
3 10317497 NA AUL 38 90 61 51
4 2512937 NA AZE <NA> <NA> <NA> <NA>
5 57563050 NA BRA 69 38 49 76
6 659963 NA BUL 70 30 40 85
ltowvs ivr
1 20 62
2 <NA> <NA>
3 21 71
4 61 22
5 44 59
6 69 16Text and figures are licensed under Creative Commons Attribution CC BY-NC 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Markowitz (2022, Feb. 20). Data Analytics and Computational Social Science: HW3. Retrieved from https://github.com/DACSS/dacss_course_website/posts/httpsrpubscomamarkowitzhw3/
BibTeX citation
@misc{markowitz2022hw3,
author = {Markowitz, Ari},
title = {Data Analytics and Computational Social Science: HW3},
url = {https://github.com/DACSS/dacss_course_website/posts/httpsrpubscomamarkowitzhw3/},
year = {2022}
}