This is my report on the R Programming Basics for Data Science I did on edX.
The purpose of the project was to practically apply what I’ve learnt throughout the course. The project is summarily about getting data from the web, cleaning the data, and doing some analysis on the cleaned data.
edX Project
The project requires the httr and rvest packages which are used for communicating with websites and extracting data from the webpage, respectively.They are used together in web scraping. The httr package is needed to be able to use the GET function which is used to retrieve a webpage’s content.
library(httr)
library(rvest)get_wiki_covid19_page <- function() {
# Our target COVID-19 wiki page URL is:
#https://en.wikipedia.org/w/index.php?title=Template:COVID-19_testing_by_country
# This URL has two parts; the base URL and the URL parameter, these are separated by question mark ?:
# 1) base URL `https://en.wikipedia.org/w/index.php
# 2) URL parameter: `title=Template:COVID-19_testing_by_country`,
# Wiki page base
wiki_base_url <- "https://en.wikipedia.org/w/index.php"
#I have to create a list;
#which will contain a title specifying the page I want to get from wiki'
#in this case, it'll be the URL parameter
wiki_param <- list(title="Template:COVID-19_testing_by_country")
#use the GET function to get a http response;
#this function takes in the URL and a query(which is basically the URL parameter)
wiki_reponse <- GET(wiki_base_url, query=wiki_param)
#use the return function to return the response
return(wiki_reponse)
}
#the above lines of code creates a function that we use to get an http response;
#the response gives the info we want, and allows R to actually work on it
#To get an http response, we call the function we created above.
#But we have to assign it to an object because we'd need to pass it into the read_html function when we want to read the root html node, and we can't pass a function into it(ie, read_html expects an object)
wiki_covid19_page_response <- get_wiki_covid19_page()
wiki_covid19_page_response## Response [https://en.wikipedia.org/w/index.php?title=Template%3ACOVID-19_testing_by_country]
## Date: 2025-11-19 10:05
## Status: 200
## Content-Type: text/html; charset=UTF-8
## Size: 456 kB
## <!DOCTYPE html>
## <html class="client-nojs vector-feature-language-in-header-enabled vector-fea...
## <head>
## <meta charset="UTF-8">
## <title>Template:COVID-19 testing by country - Wikipedia</title>
## <script>(function(){var className="client-js vector-feature-language-in-heade...
## RLSTATE={"ext.globalCssJs.user.styles":"ready","site.styles":"ready","user.st...
## <script>(RLQ=window.RLQ||[]).push(function(){mw.loader.impl(function(){return...
## }];});});</script>
## <link rel="stylesheet" href="/w/load.php?lang=en&modules=ext.cite.styles%...
## ...#the read_html is used to read the root html node from response.
#reading HTML node helps R to parse(analyze) the entire HTML content into a structured object which can now be easily navigated
wiki_covid19_page_rootnode <- read_html(wiki_covid19_page_response)
wiki_covid19_page_rootnode## {html_document}
## <html class="client-nojs vector-feature-language-in-header-enabled vector-feature-language-in-main-page-header-disabled vector-feature-page-tools-pinned-disabled vector-feature-toc-pinned-clientpref-1 vector-feature-main-menu-pinned-disabled vector-feature-limited-width-clientpref-1 vector-feature-limited-width-content-enabled vector-feature-custom-font-size-clientpref-1 vector-feature-appearance-pinned-clientpref-1 vector-feature-night-mode-enabled skin-theme-clientpref-day vector-sticky-header-enabled vector-toc-available" lang="en" dir="ltr">
## [1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8 ...
## [2] <body class="skin--responsive skin-vector skin-vector-search-vue mediawik ...#to get the table node from the root html node which is the piece of data we want to extract, the html_node is used; it takes in the root node and finds all the table nodes; we use wiki.table to access the table in the wiki page
wiki_covid19_page_table_node <- html_node(wiki_covid19_page_rootnode, "table.wikitable")
#to read the table node as a data frame, we use the html_table function; this function takes in the object that contains the table node; we now have a data frame of the table in the wiki page
wiki_covid19_page_dframe <- html_table(wiki_covid19_page_table_node)
wiki_covid19_page_dframe## # A tibble: 173 × 9
## `Country or region` `Date[a]` Tested `Units[b]` `Confirmed(cases)`
## <chr> <chr> <chr> <chr> <chr>
## 1 Afghanistan 17 Dec 2020 154,767 samples 49,621
## 2 Albania 18 Feb 2021 428,654 samples 96,838
## 3 Algeria 2 Nov 2020 230,553 samples 58,574
## 4 Andorra 23 Feb 2022 300,307 samples 37,958
## 5 Angola 2 Feb 2021 399,228 samples 20,981
## 6 Antigua and Barbuda 6 Mar 2021 15,268 samples 832
## 7 Argentina 16 Apr 2022 35,716,069 samples 9,060,495
## 8 Armenia 29 May 2022 3,099,602 samples 422,963
## 9 Australia 9 Sep 2022 78,548,492 samples 10,112,229
## 10 Austria 1 Feb 2023 205,817,752 samples 5,789,991
## # ℹ 163 more rows
## # ℹ 4 more variables: `Confirmed /tested,%` <chr>,
## # `Tested /population,%` <chr>, `Confirmed /population,%` <chr>, Ref. <chr>The goal of task 3 is to pre-process the extracted data frame from the previous step, and export it as a csv file.
#to see what our data frame entails, we use the summary function; it helps us see if there is need to remove or replace things
summary(wiki_covid19_page_dframe)## Country or region Date[a] Tested Units[b]
## Length:173 Length:173 Length:173 Length:173
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Confirmed(cases) Confirmed /tested,% Tested /population,%
## Length:173 Length:173 Length:173
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## Confirmed /population,% Ref.
## Length:173 Length:173
## Class :character Class :character
## Mode :character Mode :characternames(wiki_covid19_page_dframe) #this tells the column names## [1] "Country or region" "Date[a]"
## [3] "Tested" "Units[b]"
## [5] "Confirmed(cases)" "Confirmed /tested,%"
## [7] "Tested /population,%" "Confirmed /population,%"
## [9] "Ref."wiki_covid19_page_dframe$`Country or region`## [1] "Afghanistan"
## [2] "Albania"
## [3] "Algeria"
## [4] "Andorra"
## [5] "Angola"
## [6] "Antigua and Barbuda"
## [7] "Argentina"
## [8] "Armenia"
## [9] "Australia"
## [10] "Austria"
## [11] "Azerbaijan"
## [12] "Bahamas"
## [13] "Bahrain"
## [14] "Bangladesh"
## [15] "Barbados"
## [16] "Belarus"
## [17] "Belgium"
## [18] "Belize"
## [19] "Benin"
## [20] "Bhutan"
## [21] "Bolivia"
## [22] "Bosnia and Herzegovina"
## [23] "Botswana"
## [24] "Brazil"
## [25] "Brunei"
## [26] "Bulgaria"
## [27] "Burkina Faso"
## [28] "Burundi"
## [29] "Cambodia"
## [30] "Cameroon"
## [31] "Canada"
## [32] "Chad"
## [33] "Chile"
## [34] "China[c]"
## [35] "Colombia"
## [36] "Costa Rica"
## [37] "Croatia"
## [38] "Cuba"
## [39] "Cyprus[d]"
## [40] "Czechia"
## [41] "Denmark[e]"
## [42] "Djibouti"
## [43] "Dominica"
## [44] "Dominican Republic"
## [45] "DR Congo"
## [46] "Ecuador"
## [47] "Egypt"
## [48] "El Salvador"
## [49] "Equatorial Guinea"
## [50] "Estonia"
## [51] "Eswatini"
## [52] "Ethiopia"
## [53] "Faroe Islands"
## [54] "Fiji"
## [55] "Finland"
## [56] "France[f][g]"
## [57] "Gabon"
## [58] "Gambia"
## [59] "Georgia[h]"
## [60] "Germany"
## [61] "Ghana"
## [62] "Greece"
## [63] "Greenland"
## [64] "Grenada"
## [65] "Guatemala"
## [66] "Guinea"
## [67] "Guinea-Bissau"
## [68] "Guyana"
## [69] "Haiti"
## [70] "Honduras"
## [71] "Hungary"
## [72] "Iceland"
## [73] "India"
## [74] "Indonesia"
## [75] "Iran"
## [76] "Iraq"
## [77] "Ireland"
## [78] "Israel"
## [79] "Italy"
## [80] "Ivory Coast"
## [81] "Jamaica"
## [82] "Japan"
## [83] "Jordan"
## [84] "Kazakhstan"
## [85] "Kenya"
## [86] "Kosovo"
## [87] "Kuwait"
## [88] "Kyrgyzstan"
## [89] "Laos"
## [90] "Latvia"
## [91] "Lebanon"
## [92] "Lesotho"
## [93] "Liberia"
## [94] "Libya"
## [95] "Lithuania"
## [96] "Luxembourg[i]"
## [97] "Madagascar"
## [98] "Malawi"
## [99] "Malaysia"
## [100] "Maldives"
## [101] "Mali"
## [102] "Malta"
## [103] "Mauritania"
## [104] "Mauritius"
## [105] "Mexico"
## [106] "Moldova[j]"
## [107] "Mongolia"
## [108] "Montenegro"
## [109] "Morocco"
## [110] "Mozambique"
## [111] "Myanmar"
## [112] "Namibia"
## [113] "Nepal"
## [114] "Netherlands"
## [115] "New Caledonia"
## [116] "New Zealand"
## [117] "Niger"
## [118] "Nigeria"
## [119] "North Korea"
## [120] "North Macedonia"
## [121] "Northern Cyprus[k]"
## [122] "Norway"
## [123] "Oman"
## [124] "Pakistan"
## [125] "Palestine"
## [126] "Panama"
## [127] "Papua New Guinea"
## [128] "Paraguay"
## [129] "Peru"
## [130] "Philippines"
## [131] "Poland"
## [132] "Portugal"
## [133] "Qatar"
## [134] "Romania"
## [135] "Russia"
## [136] "Rwanda"
## [137] "Saint Kitts and Nevis"
## [138] "Saint Lucia"
## [139] "Saint Vincent"
## [140] "San Marino"
## [141] "Saudi Arabia"
## [142] "Senegal"
## [143] "Serbia"
## [144] "Singapore"
## [145] "Slovakia"
## [146] "Slovenia"
## [147] "South Africa"
## [148] "South Korea"
## [149] "South Sudan"
## [150] "Spain"
## [151] "Sri Lanka"
## [152] "Sudan"
## [153] "Sweden"
## [154] "Switzerland[l]"
## [155] "Taiwan[m]"
## [156] "Tanzania"
## [157] "Thailand"
## [158] "Togo"
## [159] "Trinidad and Tobago"
## [160] "Tunisia"
## [161] "Turkey"
## [162] "Uganda"
## [163] "Ukraine"
## [164] "United Arab Emirates"
## [165] "United Kingdom"
## [166] "United States"
## [167] "Uruguay"
## [168] "Uzbekistan"
## [169] "Venezuela"
## [170] "Vietnam"
## [171] "Zambia"
## [172] "Zimbabwe"
## [173] ".mw-parser-output .reflist{margin-bottom:0.5em;list-style-type:decimal}@media screen{.mw-parser-output .reflist{font-size:90%}}.mw-parser-output .reflist .references{font-size:100%;margin-bottom:0;list-style-type:inherit}.mw-parser-output .reflist-columns-2{column-width:30em}.mw-parser-output .reflist-columns-3{column-width:25em}.mw-parser-output .reflist-columns{margin-top:0.3em}.mw-parser-output .reflist-columns ol{margin-top:0}.mw-parser-output .reflist-columns li{page-break-inside:avoid;break-inside:avoid-column}.mw-parser-output .reflist-upper-alpha{list-style-type:upper-alpha}.mw-parser-output .reflist-upper-roman{list-style-type:upper-roman}.mw-parser-output .reflist-lower-alpha{list-style-type:lower-alpha}.mw-parser-output .reflist-lower-greek{list-style-type:lower-greek}.mw-parser-output .reflist-lower-roman{list-style-type:lower-roman}\n^ Local time.\n\n^ For some countries it is unclear whether they report samples or cases. One person tested twice is recorded as one case and two samples.\n\n^ Excluding Taiwan.\n\n^ Excluding Northern Cyprus.\n\n^ Excluding Greenland and the Faroe Islands.\n\n^ Excluding Overseas France.\n\n^ Testing data from 4 May to 12 May is missing because of the transition to the new reporting system SI-DEP.\n\n^ Excluding Abkhazia and South Ossetia.\n\n^ Data for residents only.\n\n^ Excluding Transnistria.\n\n^ Northern Cyprus is not recognized as a sovereign state by any country except Turkey.\n\n^ Includes data for Liechtenstein.\n\n^ Not a United Nations member."#we need to remove some columns and rename the rest; we also need to change the data types, so we create a function that does all of that.
preprocess_covid_data_frame <- function(data_frame) {
shape <- dim(data_frame)
# Remove the last row
data_frame <- data_frame[1:172, ]
# We don't need the Units and Ref columns, we remove them
data_frame["Ref."] <- NULL
data_frame["Units[b]"] <- NULL
# Renaming the columns
names(data_frame) <- c("country", "date", "tested", "confirmed", "confirmed.tested.ratio", "tested.population.ratio", "confirmed.population.ratio")
# Convert column data types
data_frame$country <- as.factor(data_frame$country)
data_frame$date <- as.factor(data_frame$date)
data_frame$tested <- as.numeric(gsub(",","",data_frame$tested))
data_frame$confirmed <- as.numeric(gsub(",","",data_frame$confirmed))
data_frame$'confirmed.tested.ratio' <- as.numeric(gsub(",","",data_frame$`confirmed.tested.ratio`))
data_frame$'tested.population.ratio' <- as.numeric(gsub(",","",data_frame$`tested.population.ratio`))
data_frame$'confirmed.population.ratio' <- as.numeric(gsub(",","",data_frame$`confirmed.population.ratio`))
return(data_frame)
}
#we call the function to preprocess the initial data frame,and assign the result to an object.
new_covid19__dframe <- preprocess_covid_data_frame(wiki_covid19_page_dframe)
new_covid19__dframe## # A tibble: 172 × 7
## country date tested confirmed confirmed.tested.ratio tested.population.ra…¹
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Afghani… 17 D… 1.55e5 49621 32.1 0.4
## 2 Albania 18 F… 4.29e5 96838 22.6 15
## 3 Algeria 2 No… 2.31e5 58574 25.4 0.53
## 4 Andorra 23 F… 3.00e5 37958 12.6 387
## 5 Angola 2 Fe… 3.99e5 20981 5.3 1.3
## 6 Antigua… 6 Ma… 1.53e4 832 5.4 15.9
## 7 Argenti… 16 A… 3.57e7 9060495 25.4 78.3
## 8 Armenia 29 M… 3.10e6 422963 13.6 105
## 9 Austral… 9 Se… 7.85e7 10112229 12.9 313
## 10 Austria 1 Fe… 2.06e8 5789991 2.8 2312
## # ℹ 162 more rows
## # ℹ abbreviated name: ¹tested.population.ratio
## # ℹ 1 more variable: confirmed.population.ratio <dbl>head(new_covid19__dframe) #this gives the first 6 entries## # A tibble: 6 × 7
## country date tested confirmed confirmed.tested.ratio tested.population.ra…¹
## <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Afghanis… 17 D… 154767 49621 32.1 0.4
## 2 Albania 18 F… 428654 96838 22.6 15
## 3 Algeria 2 No… 230553 58574 25.4 0.53
## 4 Andorra 23 F… 300307 37958 12.6 387
## 5 Angola 2 Fe… 399228 20981 5.3 1.3
## 6 Antigua … 6 Ma… 15268 832 5.4 15.9
## # ℹ abbreviated name: ¹tested.population.ratio
## # ℹ 1 more variable: confirmed.population.ratio <dbl># Export the data frame to a csv file
write.csv(new_covid19__dframe, "C:/Users/MODEL24/Documents/Covid19.csv")The goal of task 4 is to get the 5th to 10th rows from the data frame with only country and confirmed columns selected
#to get the 5th to 10th rows from the data frame with only country and confirmed columns selected; we first call back the csv file
Covid19_dframe <- read.csv("C:/Users/MODEL24/Documents/Covid19.csv", header=T, sep=",")
#then we subset the data by specifying the rows and columns we want into square brackets
Covid19_dframe[5:10, c("country","confirmed")]## country confirmed
## 5 Angola 20981
## 6 Antigua and Barbuda 832
## 7 Argentina 9060495
## 8 Armenia 422963
## 9 Australia 10112229
## 10 Austria 5789991The goal of task 5 is to get the total confirmed and tested cases worldwide, and try to figure the overall positive ratio using confirmed cases / tested cases
# Get the total confirmed cases worldwide
total_confirmed_cases <- sum(Covid19_dframe$confirmed)
total_confirmed_cases## [1] 431434555# Get the total tested cases worldwide
total_tested_cases <- sum(Covid19_dframe$tested)
total_tested_cases## [1] 5396881644# Get the positive ratio (confirmed / tested)
positive_ratio <- total_confirmed_cases/total_tested_cases
positive_ratio## [1] 0.07994145The goal of task 6 is to get a catalog or sorted list of countries;who have reported their COVID-19 testing data
# Get the `country` column
country <- Covid19_dframe$country
country## [1] "Afghanistan" "Albania" "Algeria"
## [4] "Andorra" "Angola" "Antigua and Barbuda"
## [7] "Argentina" "Armenia" "Australia"
## [10] "Austria" "Azerbaijan" "Bahamas"
## [13] "Bahrain" "Bangladesh" "Barbados"
## [16] "Belarus" "Belgium" "Belize"
## [19] "Benin" "Bhutan" "Bolivia"
## [22] "Bosnia and Herzegovina" "Botswana" "Brazil"
## [25] "Brunei" "Bulgaria" "Burkina Faso"
## [28] "Burundi" "Cambodia" "Cameroon"
## [31] "Canada" "Chad" "Chile"
## [34] "China[c]" "Colombia" "Costa Rica"
## [37] "Croatia" "Cuba" "Cyprus[d]"
## [40] "Czechia" "Denmark[e]" "Djibouti"
## [43] "Dominica" "Dominican Republic" "DR Congo"
## [46] "Ecuador" "Egypt" "El Salvador"
## [49] "Equatorial Guinea" "Estonia" "Eswatini"
## [52] "Ethiopia" "Faroe Islands" "Fiji"
## [55] "Finland" "France[f][g]" "Gabon"
## [58] "Gambia" "Georgia[h]" "Germany"
## [61] "Ghana" "Greece" "Greenland"
## [64] "Grenada" "Guatemala" "Guinea"
## [67] "Guinea-Bissau" "Guyana" "Haiti"
## [70] "Honduras" "Hungary" "Iceland"
## [73] "India" "Indonesia" "Iran"
## [76] "Iraq" "Ireland" "Israel"
## [79] "Italy" "Ivory Coast" "Jamaica"
## [82] "Japan" "Jordan" "Kazakhstan"
## [85] "Kenya" "Kosovo" "Kuwait"
## [88] "Kyrgyzstan" "Laos" "Latvia"
## [91] "Lebanon" "Lesotho" "Liberia"
## [94] "Libya" "Lithuania" "Luxembourg[i]"
## [97] "Madagascar" "Malawi" "Malaysia"
## [100] "Maldives" "Mali" "Malta"
## [103] "Mauritania" "Mauritius" "Mexico"
## [106] "Moldova[j]" "Mongolia" "Montenegro"
## [109] "Morocco" "Mozambique" "Myanmar"
## [112] "Namibia" "Nepal" "Netherlands"
## [115] "New Caledonia" "New Zealand" "Niger"
## [118] "Nigeria" "North Korea" "North Macedonia"
## [121] "Northern Cyprus[k]" "Norway" "Oman"
## [124] "Pakistan" "Palestine" "Panama"
## [127] "Papua New Guinea" "Paraguay" "Peru"
## [130] "Philippines" "Poland" "Portugal"
## [133] "Qatar" "Romania" "Russia"
## [136] "Rwanda" "Saint Kitts and Nevis" "Saint Lucia"
## [139] "Saint Vincent" "San Marino" "Saudi Arabia"
## [142] "Senegal" "Serbia" "Singapore"
## [145] "Slovakia" "Slovenia" "South Africa"
## [148] "South Korea" "South Sudan" "Spain"
## [151] "Sri Lanka" "Sudan" "Sweden"
## [154] "Switzerland[l]" "Taiwan[m]" "Tanzania"
## [157] "Thailand" "Togo" "Trinidad and Tobago"
## [160] "Tunisia" "Turkey" "Uganda"
## [163] "Ukraine" "United Arab Emirates" "United Kingdom"
## [166] "United States" "Uruguay" "Uzbekistan"
## [169] "Venezuela" "Vietnam" "Zambia"
## [172] "Zimbabwe"# Check its class (should be character)
class(country)## [1] "character"# Sort the countries AtoZ
Covid19_dframe[order(country, decreasing = F),]## X country date tested confirmed
## 1 1 Afghanistan 17 Dec 2020 154767 49621
## 2 2 Albania 18 Feb 2021 428654 96838
## 3 3 Algeria 2 Nov 2020 230553 58574
## 4 4 Andorra 23 Feb 2022 300307 37958
## 5 5 Angola 2 Feb 2021 399228 20981
## 6 6 Antigua and Barbuda 6 Mar 2021 15268 832
## 7 7 Argentina 16 Apr 2022 35716069 9060495
## 8 8 Armenia 29 May 2022 3099602 422963
## 9 9 Australia 9 Sep 2022 78548492 10112229
## 10 10 Austria 1 Feb 2023 205817752 5789991
## 11 11 Azerbaijan 11 May 2022 6838458 792638
## 12 12 Bahamas 28 Nov 2022 259366 37483
## 13 13 Bahrain 3 Dec 2022 10578766 696614
## 14 14 Bangladesh 24 Jul 2021 7417714 1151644
## 15 15 Barbados 14 Oct 2022 770100 103014
## 16 16 Belarus 9 May 2022 13217569 982809
## 17 17 Belgium 24 Jan 2023 36548544 4691499
## 18 18 Belize 8 Jun 2022 572900 60694
## 19 19 Benin 4 May 2021 595112 7884
## 20 20 Bhutan 28 Feb 2022 1736168 12702
## 21 21 Bolivia 5 Jun 2022 4358669 910228
## 22 22 Bosnia and Herzegovina 27 Sep 2022 1872934 399887
## 23 23 Botswana 11 Jan 2022 2026898 232432
## 24 24 Brazil 19 Feb 2021 23561497 10081676
## 25 25 Brunei 2 Aug 2021 153804 338
## 26 26 Bulgaria 2 Feb 2023 10993239 1295524
## 27 27 Burkina Faso 4 Mar 2021 158777 12123
## 28 28 Burundi 5 Jan 2021 90019 884
## 29 29 Cambodia 1 Aug 2021 1812706 77914
## 30 30 Cameroon 18 Feb 2021 942685 32681
## 31 31 Canada 26 Nov 2022 66343123 4423053
## 32 32 Chad 2 Mar 2021 99027 4020
## 33 33 Chile 1 Feb 2023 48154268 5123007
## 34 34 China[c] 31 Jul 2020 160000000 87655
## 35 35 Colombia 24 Nov 2022 36875818 6314769
## 36 36 Costa Rica 2 Nov 2021 2575363 561054
## 37 37 Croatia 2 Feb 2023 5481285 1267798
## 38 38 Cuba 2 Feb 2023 14301394 1112470
## 39 39 Cyprus[d] 29 Jan 2023 27820163 644160
## 40 40 Czechia 1 Feb 2023 22544928 4590529
## 41 41 Denmark[e] 31 Jan 2023 67682707 3399947
## 42 42 Djibouti 28 Apr 2022 305941 15631
## 43 43 Dominica 20 Jun 2022 209803 14821
## 44 44 Dominican Republic 22 Jul 2022 3574665 626030
## 45 45 DR Congo 28 Feb 2021 124838 25961
## 46 46 Ecuador 23 Jul 2021 1627189 480720
## 47 47 Egypt 23 Jul 2021 3137519 283947
## 48 48 El Salvador 18 Mar 2022 1847861 161052
## 49 49 Equatorial Guinea 30 Jan 2023 403773 17113
## 50 50 Estonia 31 Jan 2023 3637908 613954
## 51 51 Eswatini 8 Dec 2021 415110 49253
## 52 52 Ethiopia 24 Jun 2021 2981185 278446
## 53 53 Faroe Islands 27 Feb 2022 774000 34237
## 54 54 Fiji 2 Jan 2023 667953 68848
## 55 55 Finland 14 Jan 2022 9042453 371135
## 56 56 France[f][g] 15 May 2022 272417258 29183646
## 57 57 Gabon 23 Jul 2021 958807 25325
## 58 58 Gambia 15 Feb 2021 43217 4469
## 59 59 Georgia[h] 3 Nov 2021 4888787 732965
## 60 60 Germany 7 Jul 2021 65247345 3733519
## 61 61 Ghana 3 Jul 2021 1305749 96708
## 62 62 Greece 18 Dec 2022 101576831 5548487
## 63 63 Greenland 30 Jan 2022 164573 10662
## 64 64 Grenada 11 May 2021 28684 161
## 65 65 Guatemala 6 Jan 2023 6800560 1230098
## 66 66 Guinea 21 Jul 2021 494898 24878
## 67 67 Guinea-Bissau 7 Jul 2022 145231 8400
## 68 68 Guyana 15 Jun 2022 648569 66129
## 69 69 Haiti 26 Nov 2022 223475 33874
## 70 70 Honduras 26 Nov 2021 1133782 377859
## 71 71 Hungary 10 May 2022 11394556 1909948
## 72 72 Iceland 9 Aug 2022 1988652 203162
## 73 73 India 8 Jul 2022 866177937 43585554
## 74 74 Indonesia 3 Jul 2023 76062770 6812127
## 75 75 Iran 31 May 2022 52269202 7232268
## 76 76 Iraq 3 Aug 2022 19090652 2448484
## 77 77 Ireland 31 Jan 2023 12990476 1700817
## 78 78 Israel 17 Jan 2022 41373364 1792137
## 79 79 Italy 16 Mar 2023 269127054 25651205
## 80 80 Ivory Coast 3 Mar 2021 429177 33285
## 81 81 Jamaica 30 Sep 2022 1184973 151931
## 82 82 Japan 1 Mar 2021 8487288 432773
## 83 83 Jordan 6 Jun 2021 7407053 739847
## 84 84 Kazakhstan 28 May 2021 11575012 385144
## 85 85 Kenya 5 Mar 2021 1322806 107729
## 86 86 Kosovo 31 May 2021 611357 107410
## 87 87 Kuwait 9 Mar 2022 7754247 624573
## 88 88 Kyrgyzstan 10 Feb 2021 695415 85253
## 89 89 Laos 1 Mar 2021 114030 45
## 90 90 Latvia 5 Sep 2021 3630095 144518
## 91 91 Lebanon 14 Jun 2021 4599186 542649
## 92 92 Lesotho 30 Mar 2022 431221 32910
## 93 93 Liberia 17 Jul 2021 128246 5396
## 94 94 Libya 14 Apr 2022 2578215 501862
## 95 95 Lithuania 31 Jan 2023 9046584 1170108
## 96 96 Luxembourg[i] 12 May 2022 4248188 244182
## 97 97 Madagascar 19 Feb 2021 119608 19831
## 98 98 Malawi 29 Nov 2022 624784 88086
## 99 99 Malaysia 7 Sep 2021 23705425 1880734
## 100 100 Maldives 13 Mar 2022 2216560 174658
## 101 101 Mali 7 Jul 2021 322504 14449
## 102 102 Malta 8 Sep 2021 1211456 36606
## 103 103 Mauritania 16 Apr 2021 268093 18103
## 104 104 Mauritius 22 Nov 2020 289552 494
## 105 105 Mexico 15 Oct 2021 10503678 3749860
## 106 106 Moldova[j] 20 Apr 2022 3213594 516864
## 107 107 Mongolia 10 Jul 2021 3354200 136053
## 108 108 Montenegro 10 May 2021 394388 98449
## 109 109 Morocco 6 Jan 2023 14217563 1272299
## 110 110 Mozambique 22 Jul 2021 688570 105866
## 111 111 Myanmar 16 Sep 2021 4047680 440741
## 112 112 Namibia 4 Jul 2022 1062663 166229
## 113 113 Nepal 26 Jul 2022 5804358 984475
## 114 114 Netherlands 6 Jul 2021 14526293 1692834
## 115 115 New Caledonia 3 Sep 2021 41962 136
## 116 116 New Zealand 29 Jan 2023 7757935 2136662
## 117 117 Niger 22 Feb 2021 79321 4740
## 118 118 Nigeria 28 Feb 2021 1544008 155657
## 119 119 North Korea 25 Nov 2020 16914 0
## 120 120 North Macedonia 1 Jul 2021 881870 155689
## 121 121 Northern Cyprus[k] 12 Jul 2022 7096998 103034
## 122 122 Norway 20 Jan 2022 9811888 554778
## 123 123 Oman 28 Oct 2020 509959 114434
## 124 124 Pakistan 5 Mar 2021 9173593 588728
## 125 125 Palestine 5 Feb 2022 3078533 574105
## 126 126 Panama 28 Jan 2023 7475016 1029701
## 127 127 Papua New Guinea 17 Feb 2021 47490 961
## 128 128 Paraguay 27 Mar 2022 2609819 647950
## 129 129 Peru 17 Nov 2022 36073768 4177786
## 130 130 Philippines 7 Jan 2023 34402980 4073980
## 131 131 Poland 27 Apr 2022 36064311 5993861
## 132 132 Portugal 5 Jan 2022 27515490 1499976
## 133 133 Qatar 11 Nov 2022 4061988 473440
## 134 134 Romania 29 Jan 2021 5405393 724250
## 135 135 Russia 6 Jun 2022 295542733 18358459
## 136 136 Rwanda 6 Oct 2021 2885812 98209
## 137 137 Saint Kitts and Nevis 26 Aug 2021 30231 995
## 138 138 Saint Lucia 7 Oct 2022 212132 29550
## 139 139 Saint Vincent 28 Jan 2023 113504 9585
## 140 140 San Marino 29 Jan 2023 192613 23427
## 141 141 Saudi Arabia 26 Apr 2022 41849069 753632
## 142 142 Senegal 12 Jul 2021 624502 46509
## 143 143 Serbia 2 Feb 2023 12185475 2473599
## 144 144 Singapore 3 Aug 2021 16206203 65315
## 145 145 Slovakia 2 Feb 2023 7391882 1861034
## 146 146 Slovenia 2 Feb 2023 2826117 1322282
## 147 147 South Africa 24 May 2021 11378282 1637848
## 148 148 South Korea 1 Mar 2021 6592010 90029
## 149 149 South Sudan 26 May 2021 164472 10688
## 150 150 Spain 1 Jul 2021 54128524 3821305
## 151 151 Sri Lanka 30 Mar 2021 2384745 93128
## 152 152 Sudan 7 Jan 2021 158804 23316
## 153 153 Sweden 24 May 2021 9996795 1074751
## 154 154 Switzerland[l] 7 Nov 2022 23283909 4276836
## 155 155 Taiwan[m] 3 Feb 2023 30275725 8622129
## 156 156 Tanzania 18 Nov 2020 3880 509
## 157 157 Thailand 4 Mar 2021 1579597 26162
## 158 158 Togo 6 Jan 2023 807269 39358
## 159 159 Trinidad and Tobago 3 Jan 2022 512730 92997
## 160 160 Tunisia 23 Aug 2021 2893625 703732
## 161 161 Turkey 2 Jul 2021 61236294 5435831
## 162 162 Uganda 11 Feb 2021 852444 39979
## 163 163 Ukraine 24 Nov 2021 15648456 3367461
## 164 164 United Arab Emirates 1 Feb 2023 198685717 1049537
## 165 165 United Kingdom 19 May 2022 522526476 22232377
## 166 166 United States 29 Jul 2022 929349291 90749469
## 167 167 Uruguay 16 Apr 2022 6089116 895592
## 168 168 Uzbekistan 7 Sep 2020 2630000 43975
## 169 169 Venezuela 30 Mar 2021 3179074 159149
## 170 170 Vietnam 28 Aug 2022 45772571 11403302
## 171 171 Zambia 10 Mar 2022 3301860 314850
## 172 172 Zimbabwe 15 Oct 2022 2529087 257893
## confirmed.tested.ratio tested.population.ratio confirmed.population.ratio
## 1 32.100 0.4000 0.13000
## 2 22.600 15.0000 3.40000
## 3 25.400 0.5300 0.13000
## 4 12.600 387.0000 49.00000
## 5 5.300 1.3000 0.06700
## 6 5.400 15.9000 0.86000
## 7 25.400 78.3000 20.00000
## 8 13.600 105.0000 14.30000
## 9 12.900 313.0000 40.30000
## 10 2.800 2312.0000 65.00000
## 11 11.600 69.1000 8.00000
## 12 14.500 67.3000 9.70000
## 13 6.600 674.0000 44.40000
## 14 15.500 4.5000 0.70000
## 15 13.400 268.0000 35.90000
## 16 7.400 139.0000 10.40000
## 17 12.800 317.0000 40.70000
## 18 10.600 140.0000 14.90000
## 19 1.300 5.1000 0.06700
## 20 0.730 234.0000 1.71000
## 21 20.900 38.1000 8.00000
## 22 21.400 54.7000 11.70000
## 23 11.500 89.9000 10.30000
## 24 42.800 11.2000 4.80000
## 25 0.220 33.5000 0.07400
## 26 11.800 158.0000 18.60000
## 27 7.600 0.7600 0.05800
## 28 0.980 0.7600 0.00740
## 29 4.300 11.2000 0.48000
## 30 3.500 3.6000 0.12000
## 31 6.700 175.0000 11.70000
## 32 4.100 0.7200 0.02900
## 33 10.600 252.0000 26.90000
## 34 0.055 11.1000 0.00610
## 35 17.100 76.4000 13.10000
## 36 21.800 51.5000 11.20000
## 37 23.100 134.0000 31.10000
## 38 7.800 126.0000 9.80000
## 39 2.300 3223.0000 74.40000
## 40 20.400 211.0000 42.90000
## 41 5.000 1162.0000 58.40000
## 42 5.100 33.2000 1.70000
## 43 7.100 293.0000 20.70000
## 44 17.500 32.9000 5.80000
## 45 20.800 0.1400 0.02900
## 46 29.500 9.5000 2.80000
## 47 9.100 3.1000 0.28000
## 48 8.700 28.5000 2.50000
## 49 4.200 30.8000 1.30000
## 50 16.900 274.0000 46.20000
## 51 11.900 36.5000 4.30000
## 52 9.300 2.6000 0.24000
## 53 4.400 1493.0000 65.70000
## 54 10.300 74.5000 7.70000
## 55 4.100 163.0000 6.70000
## 56 10.700 417.0000 44.70000
## 57 2.600 3.1000 0.08200
## 58 10.300 2.0000 0.21000
## 59 15.000 132.0000 19.70000
## 60 5.700 77.8000 4.50000
## 61 7.400 4.2000 0.31000
## 62 5.500 943.0000 51.50000
## 63 6.500 293.0000 19.00000
## 64 0.560 25.7000 0.14000
## 65 18.100 39.4000 7.10000
## 66 5.000 3.8000 0.19000
## 67 5.800 7.7000 0.45000
## 68 10.200 82.5000 8.40000
## 69 15.200 2.0000 0.30000
## 70 33.300 11.8000 3.90000
## 71 16.800 118.0000 19.80000
## 72 10.200 546.0000 55.80000
## 73 5.000 63.0000 31.70000
## 74 9.000 28.2000 2.50000
## 75 13.800 62.8000 8.70000
## 76 12.800 47.5000 6.10000
## 77 13.100 264.0000 34.60000
## 78 4.300 451.0000 19.50000
## 79 9.500 446.0000 42.50000
## 80 7.800 1.6000 0.13000
## 81 12.800 43.5000 5.60000
## 82 5.100 6.7000 0.34000
## 83 10.000 69.5000 6.90000
## 84 3.300 62.1000 2.10000
## 85 8.100 2.8000 0.23000
## 86 17.600 33.8000 5.90000
## 87 8.100 181.0000 14.60000
## 88 12.300 10.7000 1.30000
## 89 0.039 1.6000 0.00063
## 90 4.000 189.0000 7.50000
## 91 11.800 67.4000 8.00000
## 92 7.600 21.5000 1.60000
## 93 4.200 2.5000 0.11000
## 94 19.500 37.6000 7.30000
## 95 12.900 324.0000 41.90000
## 96 5.700 679.0000 39.00000
## 97 16.600 0.4600 0.07600
## 98 14.100 3.3000 0.46000
## 99 7.900 72.3000 5.70000
## 100 7.900 398.0000 31.30000
## 101 4.500 1.6000 0.07100
## 102 3.000 245.0000 7.40000
## 103 6.800 6.1000 0.41000
## 104 0.170 22.9000 0.03900
## 105 35.700 8.2000 2.90000
## 106 16.100 122.0000 19.60000
## 107 4.100 100.0000 4.10000
## 108 25.000 62.5000 15.60000
## 109 8.900 38.5000 3.40000
## 110 15.400 2.2000 0.34000
## 111 10.900 7.4000 0.81000
## 112 15.600 38.7000 6.10000
## 113 17.000 20.7000 3.50000
## 114 11.700 83.4000 9.70000
## 115 0.320 15.7000 0.05000
## 116 27.500 156.0000 42.90000
## 117 6.000 0.3500 0.02100
## 118 10.100 0.7500 0.07600
## 119 0.000 0.0660 0.00000
## 120 17.700 42.5000 7.50000
## 121 1.500 2177.0000 31.60000
## 122 5.700 183.0000 10.30000
## 123 22.400 11.0000 2.50000
## 124 6.400 4.2000 0.27000
## 125 18.600 60.9000 11.40000
## 126 13.800 179.0000 24.70000
## 127 2.000 0.5300 0.01100
## 128 24.800 36.6000 9.10000
## 129 11.600 109.9000 12.70000
## 130 11.800 34.1000 4.00000
## 131 16.600 94.0000 15.60000
## 132 5.500 268.0000 14.60000
## 133 11.700 141.0000 16.40000
## 134 13.400 27.9000 3.70000
## 135 6.200 201.0000 12.50000
## 136 3.400 22.3000 0.76000
## 137 3.300 57.6000 1.90000
## 138 13.900 116.6000 16.20000
## 139 8.400 103.0000 8.70000
## 140 12.200 563.0000 68.40000
## 141 1.800 120.0000 2.20000
## 142 7.400 3.9000 0.29000
## 143 20.300 175.0000 35.50000
## 144 0.400 284.0000 1.10000
## 145 25.200 135.0000 34.10000
## 146 46.800 135.0000 63.10000
## 147 14.400 19.2000 2.80000
## 148 1.400 12.7000 0.17000
## 149 6.500 1.3000 0.08400
## 150 7.100 116.0000 8.20000
## 151 3.900 10.9000 0.43000
## 152 14.700 0.3600 0.05300
## 153 10.800 96.8000 10.40000
## 154 18.400 270.0000 49.70000
## 155 28.480 128.3000 36.52800
## 156 13.100 0.0065 0.00085
## 157 1.700 2.3000 0.03800
## 158 4.900 9.4000 0.46000
## 159 18.100 37.6000 6.80000
## 160 24.300 24.5000 6.00000
## 161 8.900 73.6000 6.50000
## 162 4.700 1.9000 0.08700
## 163 21.500 37.2000 8.00000
## 164 0.530 2070.0000 10.90000
## 165 4.300 774.0000 32.90000
## 166 9.800 281.0000 27.40000
## 167 14.700 175.0000 25.80000
## 168 1.700 7.7000 0.13000
## 169 5.000 11.0000 0.55000
## 170 24.900 46.4000 11.60000
## 171 9.500 19.0000 1.80000
## 172 10.200 17.0000 1.70000# Sort the countries ZtoA
countries_ZtoA <- Covid19_dframe[order(country, decreasing = T),]
# Print the sorted ZtoA list
countries_ZtoA## X country date tested confirmed
## 172 172 Zimbabwe 15 Oct 2022 2529087 257893
## 171 171 Zambia 10 Mar 2022 3301860 314850
## 170 170 Vietnam 28 Aug 2022 45772571 11403302
## 169 169 Venezuela 30 Mar 2021 3179074 159149
## 168 168 Uzbekistan 7 Sep 2020 2630000 43975
## 167 167 Uruguay 16 Apr 2022 6089116 895592
## 166 166 United States 29 Jul 2022 929349291 90749469
## 165 165 United Kingdom 19 May 2022 522526476 22232377
## 164 164 United Arab Emirates 1 Feb 2023 198685717 1049537
## 163 163 Ukraine 24 Nov 2021 15648456 3367461
## 162 162 Uganda 11 Feb 2021 852444 39979
## 161 161 Turkey 2 Jul 2021 61236294 5435831
## 160 160 Tunisia 23 Aug 2021 2893625 703732
## 159 159 Trinidad and Tobago 3 Jan 2022 512730 92997
## 158 158 Togo 6 Jan 2023 807269 39358
## 157 157 Thailand 4 Mar 2021 1579597 26162
## 156 156 Tanzania 18 Nov 2020 3880 509
## 155 155 Taiwan[m] 3 Feb 2023 30275725 8622129
## 154 154 Switzerland[l] 7 Nov 2022 23283909 4276836
## 153 153 Sweden 24 May 2021 9996795 1074751
## 152 152 Sudan 7 Jan 2021 158804 23316
## 151 151 Sri Lanka 30 Mar 2021 2384745 93128
## 150 150 Spain 1 Jul 2021 54128524 3821305
## 149 149 South Sudan 26 May 2021 164472 10688
## 148 148 South Korea 1 Mar 2021 6592010 90029
## 147 147 South Africa 24 May 2021 11378282 1637848
## 146 146 Slovenia 2 Feb 2023 2826117 1322282
## 145 145 Slovakia 2 Feb 2023 7391882 1861034
## 144 144 Singapore 3 Aug 2021 16206203 65315
## 143 143 Serbia 2 Feb 2023 12185475 2473599
## 142 142 Senegal 12 Jul 2021 624502 46509
## 141 141 Saudi Arabia 26 Apr 2022 41849069 753632
## 140 140 San Marino 29 Jan 2023 192613 23427
## 139 139 Saint Vincent 28 Jan 2023 113504 9585
## 138 138 Saint Lucia 7 Oct 2022 212132 29550
## 137 137 Saint Kitts and Nevis 26 Aug 2021 30231 995
## 136 136 Rwanda 6 Oct 2021 2885812 98209
## 135 135 Russia 6 Jun 2022 295542733 18358459
## 134 134 Romania 29 Jan 2021 5405393 724250
## 133 133 Qatar 11 Nov 2022 4061988 473440
## 132 132 Portugal 5 Jan 2022 27515490 1499976
## 131 131 Poland 27 Apr 2022 36064311 5993861
## 130 130 Philippines 7 Jan 2023 34402980 4073980
## 129 129 Peru 17 Nov 2022 36073768 4177786
## 128 128 Paraguay 27 Mar 2022 2609819 647950
## 127 127 Papua New Guinea 17 Feb 2021 47490 961
## 126 126 Panama 28 Jan 2023 7475016 1029701
## 125 125 Palestine 5 Feb 2022 3078533 574105
## 124 124 Pakistan 5 Mar 2021 9173593 588728
## 123 123 Oman 28 Oct 2020 509959 114434
## 122 122 Norway 20 Jan 2022 9811888 554778
## 121 121 Northern Cyprus[k] 12 Jul 2022 7096998 103034
## 120 120 North Macedonia 1 Jul 2021 881870 155689
## 119 119 North Korea 25 Nov 2020 16914 0
## 118 118 Nigeria 28 Feb 2021 1544008 155657
## 117 117 Niger 22 Feb 2021 79321 4740
## 116 116 New Zealand 29 Jan 2023 7757935 2136662
## 115 115 New Caledonia 3 Sep 2021 41962 136
## 114 114 Netherlands 6 Jul 2021 14526293 1692834
## 113 113 Nepal 26 Jul 2022 5804358 984475
## 112 112 Namibia 4 Jul 2022 1062663 166229
## 111 111 Myanmar 16 Sep 2021 4047680 440741
## 110 110 Mozambique 22 Jul 2021 688570 105866
## 109 109 Morocco 6 Jan 2023 14217563 1272299
## 108 108 Montenegro 10 May 2021 394388 98449
## 107 107 Mongolia 10 Jul 2021 3354200 136053
## 106 106 Moldova[j] 20 Apr 2022 3213594 516864
## 105 105 Mexico 15 Oct 2021 10503678 3749860
## 104 104 Mauritius 22 Nov 2020 289552 494
## 103 103 Mauritania 16 Apr 2021 268093 18103
## 102 102 Malta 8 Sep 2021 1211456 36606
## 101 101 Mali 7 Jul 2021 322504 14449
## 100 100 Maldives 13 Mar 2022 2216560 174658
## 99 99 Malaysia 7 Sep 2021 23705425 1880734
## 98 98 Malawi 29 Nov 2022 624784 88086
## 97 97 Madagascar 19 Feb 2021 119608 19831
## 96 96 Luxembourg[i] 12 May 2022 4248188 244182
## 95 95 Lithuania 31 Jan 2023 9046584 1170108
## 94 94 Libya 14 Apr 2022 2578215 501862
## 93 93 Liberia 17 Jul 2021 128246 5396
## 92 92 Lesotho 30 Mar 2022 431221 32910
## 91 91 Lebanon 14 Jun 2021 4599186 542649
## 90 90 Latvia 5 Sep 2021 3630095 144518
## 89 89 Laos 1 Mar 2021 114030 45
## 88 88 Kyrgyzstan 10 Feb 2021 695415 85253
## 87 87 Kuwait 9 Mar 2022 7754247 624573
## 86 86 Kosovo 31 May 2021 611357 107410
## 85 85 Kenya 5 Mar 2021 1322806 107729
## 84 84 Kazakhstan 28 May 2021 11575012 385144
## 83 83 Jordan 6 Jun 2021 7407053 739847
## 82 82 Japan 1 Mar 2021 8487288 432773
## 81 81 Jamaica 30 Sep 2022 1184973 151931
## 80 80 Ivory Coast 3 Mar 2021 429177 33285
## 79 79 Italy 16 Mar 2023 269127054 25651205
## 78 78 Israel 17 Jan 2022 41373364 1792137
## 77 77 Ireland 31 Jan 2023 12990476 1700817
## 76 76 Iraq 3 Aug 2022 19090652 2448484
## 75 75 Iran 31 May 2022 52269202 7232268
## 74 74 Indonesia 3 Jul 2023 76062770 6812127
## 73 73 India 8 Jul 2022 866177937 43585554
## 72 72 Iceland 9 Aug 2022 1988652 203162
## 71 71 Hungary 10 May 2022 11394556 1909948
## 70 70 Honduras 26 Nov 2021 1133782 377859
## 69 69 Haiti 26 Nov 2022 223475 33874
## 68 68 Guyana 15 Jun 2022 648569 66129
## 67 67 Guinea-Bissau 7 Jul 2022 145231 8400
## 66 66 Guinea 21 Jul 2021 494898 24878
## 65 65 Guatemala 6 Jan 2023 6800560 1230098
## 64 64 Grenada 11 May 2021 28684 161
## 63 63 Greenland 30 Jan 2022 164573 10662
## 62 62 Greece 18 Dec 2022 101576831 5548487
## 61 61 Ghana 3 Jul 2021 1305749 96708
## 60 60 Germany 7 Jul 2021 65247345 3733519
## 59 59 Georgia[h] 3 Nov 2021 4888787 732965
## 58 58 Gambia 15 Feb 2021 43217 4469
## 57 57 Gabon 23 Jul 2021 958807 25325
## 56 56 France[f][g] 15 May 2022 272417258 29183646
## 55 55 Finland 14 Jan 2022 9042453 371135
## 54 54 Fiji 2 Jan 2023 667953 68848
## 53 53 Faroe Islands 27 Feb 2022 774000 34237
## 52 52 Ethiopia 24 Jun 2021 2981185 278446
## 51 51 Eswatini 8 Dec 2021 415110 49253
## 50 50 Estonia 31 Jan 2023 3637908 613954
## 49 49 Equatorial Guinea 30 Jan 2023 403773 17113
## 48 48 El Salvador 18 Mar 2022 1847861 161052
## 47 47 Egypt 23 Jul 2021 3137519 283947
## 46 46 Ecuador 23 Jul 2021 1627189 480720
## 45 45 DR Congo 28 Feb 2021 124838 25961
## 44 44 Dominican Republic 22 Jul 2022 3574665 626030
## 43 43 Dominica 20 Jun 2022 209803 14821
## 42 42 Djibouti 28 Apr 2022 305941 15631
## 41 41 Denmark[e] 31 Jan 2023 67682707 3399947
## 40 40 Czechia 1 Feb 2023 22544928 4590529
## 39 39 Cyprus[d] 29 Jan 2023 27820163 644160
## 38 38 Cuba 2 Feb 2023 14301394 1112470
## 37 37 Croatia 2 Feb 2023 5481285 1267798
## 36 36 Costa Rica 2 Nov 2021 2575363 561054
## 35 35 Colombia 24 Nov 2022 36875818 6314769
## 34 34 China[c] 31 Jul 2020 160000000 87655
## 33 33 Chile 1 Feb 2023 48154268 5123007
## 32 32 Chad 2 Mar 2021 99027 4020
## 31 31 Canada 26 Nov 2022 66343123 4423053
## 30 30 Cameroon 18 Feb 2021 942685 32681
## 29 29 Cambodia 1 Aug 2021 1812706 77914
## 28 28 Burundi 5 Jan 2021 90019 884
## 27 27 Burkina Faso 4 Mar 2021 158777 12123
## 26 26 Bulgaria 2 Feb 2023 10993239 1295524
## 25 25 Brunei 2 Aug 2021 153804 338
## 24 24 Brazil 19 Feb 2021 23561497 10081676
## 23 23 Botswana 11 Jan 2022 2026898 232432
## 22 22 Bosnia and Herzegovina 27 Sep 2022 1872934 399887
## 21 21 Bolivia 5 Jun 2022 4358669 910228
## 20 20 Bhutan 28 Feb 2022 1736168 12702
## 19 19 Benin 4 May 2021 595112 7884
## 18 18 Belize 8 Jun 2022 572900 60694
## 17 17 Belgium 24 Jan 2023 36548544 4691499
## 16 16 Belarus 9 May 2022 13217569 982809
## 15 15 Barbados 14 Oct 2022 770100 103014
## 14 14 Bangladesh 24 Jul 2021 7417714 1151644
## 13 13 Bahrain 3 Dec 2022 10578766 696614
## 12 12 Bahamas 28 Nov 2022 259366 37483
## 11 11 Azerbaijan 11 May 2022 6838458 792638
## 10 10 Austria 1 Feb 2023 205817752 5789991
## 9 9 Australia 9 Sep 2022 78548492 10112229
## 8 8 Armenia 29 May 2022 3099602 422963
## 7 7 Argentina 16 Apr 2022 35716069 9060495
## 6 6 Antigua and Barbuda 6 Mar 2021 15268 832
## 5 5 Angola 2 Feb 2021 399228 20981
## 4 4 Andorra 23 Feb 2022 300307 37958
## 3 3 Algeria 2 Nov 2020 230553 58574
## 2 2 Albania 18 Feb 2021 428654 96838
## 1 1 Afghanistan 17 Dec 2020 154767 49621
## confirmed.tested.ratio tested.population.ratio confirmed.population.ratio
## 172 10.200 17.0000 1.70000
## 171 9.500 19.0000 1.80000
## 170 24.900 46.4000 11.60000
## 169 5.000 11.0000 0.55000
## 168 1.700 7.7000 0.13000
## 167 14.700 175.0000 25.80000
## 166 9.800 281.0000 27.40000
## 165 4.300 774.0000 32.90000
## 164 0.530 2070.0000 10.90000
## 163 21.500 37.2000 8.00000
## 162 4.700 1.9000 0.08700
## 161 8.900 73.6000 6.50000
## 160 24.300 24.5000 6.00000
## 159 18.100 37.6000 6.80000
## 158 4.900 9.4000 0.46000
## 157 1.700 2.3000 0.03800
## 156 13.100 0.0065 0.00085
## 155 28.480 128.3000 36.52800
## 154 18.400 270.0000 49.70000
## 153 10.800 96.8000 10.40000
## 152 14.700 0.3600 0.05300
## 151 3.900 10.9000 0.43000
## 150 7.100 116.0000 8.20000
## 149 6.500 1.3000 0.08400
## 148 1.400 12.7000 0.17000
## 147 14.400 19.2000 2.80000
## 146 46.800 135.0000 63.10000
## 145 25.200 135.0000 34.10000
## 144 0.400 284.0000 1.10000
## 143 20.300 175.0000 35.50000
## 142 7.400 3.9000 0.29000
## 141 1.800 120.0000 2.20000
## 140 12.200 563.0000 68.40000
## 139 8.400 103.0000 8.70000
## 138 13.900 116.6000 16.20000
## 137 3.300 57.6000 1.90000
## 136 3.400 22.3000 0.76000
## 135 6.200 201.0000 12.50000
## 134 13.400 27.9000 3.70000
## 133 11.700 141.0000 16.40000
## 132 5.500 268.0000 14.60000
## 131 16.600 94.0000 15.60000
## 130 11.800 34.1000 4.00000
## 129 11.600 109.9000 12.70000
## 128 24.800 36.6000 9.10000
## 127 2.000 0.5300 0.01100
## 126 13.800 179.0000 24.70000
## 125 18.600 60.9000 11.40000
## 124 6.400 4.2000 0.27000
## 123 22.400 11.0000 2.50000
## 122 5.700 183.0000 10.30000
## 121 1.500 2177.0000 31.60000
## 120 17.700 42.5000 7.50000
## 119 0.000 0.0660 0.00000
## 118 10.100 0.7500 0.07600
## 117 6.000 0.3500 0.02100
## 116 27.500 156.0000 42.90000
## 115 0.320 15.7000 0.05000
## 114 11.700 83.4000 9.70000
## 113 17.000 20.7000 3.50000
## 112 15.600 38.7000 6.10000
## 111 10.900 7.4000 0.81000
## 110 15.400 2.2000 0.34000
## 109 8.900 38.5000 3.40000
## 108 25.000 62.5000 15.60000
## 107 4.100 100.0000 4.10000
## 106 16.100 122.0000 19.60000
## 105 35.700 8.2000 2.90000
## 104 0.170 22.9000 0.03900
## 103 6.800 6.1000 0.41000
## 102 3.000 245.0000 7.40000
## 101 4.500 1.6000 0.07100
## 100 7.900 398.0000 31.30000
## 99 7.900 72.3000 5.70000
## 98 14.100 3.3000 0.46000
## 97 16.600 0.4600 0.07600
## 96 5.700 679.0000 39.00000
## 95 12.900 324.0000 41.90000
## 94 19.500 37.6000 7.30000
## 93 4.200 2.5000 0.11000
## 92 7.600 21.5000 1.60000
## 91 11.800 67.4000 8.00000
## 90 4.000 189.0000 7.50000
## 89 0.039 1.6000 0.00063
## 88 12.300 10.7000 1.30000
## 87 8.100 181.0000 14.60000
## 86 17.600 33.8000 5.90000
## 85 8.100 2.8000 0.23000
## 84 3.300 62.1000 2.10000
## 83 10.000 69.5000 6.90000
## 82 5.100 6.7000 0.34000
## 81 12.800 43.5000 5.60000
## 80 7.800 1.6000 0.13000
## 79 9.500 446.0000 42.50000
## 78 4.300 451.0000 19.50000
## 77 13.100 264.0000 34.60000
## 76 12.800 47.5000 6.10000
## 75 13.800 62.8000 8.70000
## 74 9.000 28.2000 2.50000
## 73 5.000 63.0000 31.70000
## 72 10.200 546.0000 55.80000
## 71 16.800 118.0000 19.80000
## 70 33.300 11.8000 3.90000
## 69 15.200 2.0000 0.30000
## 68 10.200 82.5000 8.40000
## 67 5.800 7.7000 0.45000
## 66 5.000 3.8000 0.19000
## 65 18.100 39.4000 7.10000
## 64 0.560 25.7000 0.14000
## 63 6.500 293.0000 19.00000
## 62 5.500 943.0000 51.50000
## 61 7.400 4.2000 0.31000
## 60 5.700 77.8000 4.50000
## 59 15.000 132.0000 19.70000
## 58 10.300 2.0000 0.21000
## 57 2.600 3.1000 0.08200
## 56 10.700 417.0000 44.70000
## 55 4.100 163.0000 6.70000
## 54 10.300 74.5000 7.70000
## 53 4.400 1493.0000 65.70000
## 52 9.300 2.6000 0.24000
## 51 11.900 36.5000 4.30000
## 50 16.900 274.0000 46.20000
## 49 4.200 30.8000 1.30000
## 48 8.700 28.5000 2.50000
## 47 9.100 3.1000 0.28000
## 46 29.500 9.5000 2.80000
## 45 20.800 0.1400 0.02900
## 44 17.500 32.9000 5.80000
## 43 7.100 293.0000 20.70000
## 42 5.100 33.2000 1.70000
## 41 5.000 1162.0000 58.40000
## 40 20.400 211.0000 42.90000
## 39 2.300 3223.0000 74.40000
## 38 7.800 126.0000 9.80000
## 37 23.100 134.0000 31.10000
## 36 21.800 51.5000 11.20000
## 35 17.100 76.4000 13.10000
## 34 0.055 11.1000 0.00610
## 33 10.600 252.0000 26.90000
## 32 4.100 0.7200 0.02900
## 31 6.700 175.0000 11.70000
## 30 3.500 3.6000 0.12000
## 29 4.300 11.2000 0.48000
## 28 0.980 0.7600 0.00740
## 27 7.600 0.7600 0.05800
## 26 11.800 158.0000 18.60000
## 25 0.220 33.5000 0.07400
## 24 42.800 11.2000 4.80000
## 23 11.500 89.9000 10.30000
## 22 21.400 54.7000 11.70000
## 21 20.900 38.1000 8.00000
## 20 0.730 234.0000 1.71000
## 19 1.300 5.1000 0.06700
## 18 10.600 140.0000 14.90000
## 17 12.800 317.0000 40.70000
## 16 7.400 139.0000 10.40000
## 15 13.400 268.0000 35.90000
## 14 15.500 4.5000 0.70000
## 13 6.600 674.0000 44.40000
## 12 14.500 67.3000 9.70000
## 11 11.600 69.1000 8.00000
## 10 2.800 2312.0000 65.00000
## 9 12.900 313.0000 40.30000
## 8 13.600 105.0000 14.30000
## 7 25.400 78.3000 20.00000
## 6 5.400 15.9000 0.86000
## 5 5.300 1.3000 0.06700
## 4 12.600 387.0000 49.00000
## 3 25.400 0.5300 0.13000
## 2 22.600 15.0000 3.40000
## 1 32.100 0.4000 0.13000The goal of task 7 is using a regular expression to find any countries start with “United”
# Use a regular expression `United.+` to find matches
#grep is used to extract out strings that match the specified condition, in this case, any country with "United something" (the .+ reps every other characters after the United)
country_united <- grep("United.+", country, value = T) #value set to be T tells R to give the countries and not the index
# Print the matched country names
country_united## [1] "United Arab Emirates" "United Kingdom" "United States"The goal of task 8 is to compare the COVID-19 test data between two countries
# Select a subset (should be only one row) of data frame based on a selected country name and columns
which(Covid19_dframe$country == "Nigeria") #this gives the index for Nigeria## [1] 118Nigeria <- Covid19_dframe[118,c(2,5,8)]
Nigeria## country confirmed confirmed.population.ratio
## 118 Nigeria 155657 0.076# Select a subset (should be only one row) of data frame based on a selected country name and columns
which(Covid19_dframe$country == "Niger") #this gives the index for Niger## [1] 117Niger <- Covid19_dframe[117,c(2,5,8)]
Niger## country confirmed confirmed.population.ratio
## 117 Niger 4740 0.021#bringing them together
Covid19_dframe[c(117,118),c(2,5,8)]## country confirmed confirmed.population.ratio
## 117 Niger 4740 0.021
## 118 Nigeria 155657 0.076The goal of task 9 is to find out which country you have selected before has larger ratio of; confirmed cases to population, which may indicate that country has higher COVID-19 infection risk
# Use if-else statement
if (Nigeria[,3] > Niger[,3]) {
print( "Going to Nigeria puts you at a high risk of getting Covid")
} else {
print("Niger isn't safe at the moment; reschedule your tour!")
}## [1] "Going to Nigeria puts you at a high risk of getting Covid"The goal of task 10 is to find out which countries have the confirmed to population ratio less than 1%, it may indicate the risk of those countries are relatively low
# Get a subset of any countries with `confirmed.population.ratio` less than the threshold
Covid19_dframe[which(Covid19_dframe$confirmed.population.ratio < 1), 2]## [1] "Afghanistan" "Algeria" "Angola"
## [4] "Antigua and Barbuda" "Bangladesh" "Benin"
## [7] "Brunei" "Burkina Faso" "Burundi"
## [10] "Cambodia" "Cameroon" "Chad"
## [13] "China[c]" "DR Congo" "Egypt"
## [16] "Ethiopia" "Gabon" "Gambia"
## [19] "Ghana" "Grenada" "Guinea"
## [22] "Guinea-Bissau" "Haiti" "Ivory Coast"
## [25] "Japan" "Kenya" "Laos"
## [28] "Liberia" "Madagascar" "Malawi"
## [31] "Mali" "Mauritania" "Mauritius"
## [34] "Mozambique" "Myanmar" "New Caledonia"
## [37] "Niger" "Nigeria" "North Korea"
## [40] "Pakistan" "Papua New Guinea" "Rwanda"
## [43] "Senegal" "South Korea" "South Sudan"
## [46] "Sri Lanka" "Sudan" "Tanzania"
## [49] "Thailand" "Togo" "Uganda"
## [52] "Uzbekistan" "Venezuela"#Trying to give insights from the Covid data; based on the fact that a high CPR means the infection risk is high
results <- ifelse(Covid19_dframe$confirmed.population.ratio < 1,
"This country is a better option",
"Retreat!")
# To add the results as a new column to your dataframe
Covid19_dframe$Advice <- results
#To see the whole data frame
Covid19_dframe## X country date tested confirmed
## 1 1 Afghanistan 17 Dec 2020 154767 49621
## 2 2 Albania 18 Feb 2021 428654 96838
## 3 3 Algeria 2 Nov 2020 230553 58574
## 4 4 Andorra 23 Feb 2022 300307 37958
## 5 5 Angola 2 Feb 2021 399228 20981
## 6 6 Antigua and Barbuda 6 Mar 2021 15268 832
## 7 7 Argentina 16 Apr 2022 35716069 9060495
## 8 8 Armenia 29 May 2022 3099602 422963
## 9 9 Australia 9 Sep 2022 78548492 10112229
## 10 10 Austria 1 Feb 2023 205817752 5789991
## 11 11 Azerbaijan 11 May 2022 6838458 792638
## 12 12 Bahamas 28 Nov 2022 259366 37483
## 13 13 Bahrain 3 Dec 2022 10578766 696614
## 14 14 Bangladesh 24 Jul 2021 7417714 1151644
## 15 15 Barbados 14 Oct 2022 770100 103014
## 16 16 Belarus 9 May 2022 13217569 982809
## 17 17 Belgium 24 Jan 2023 36548544 4691499
## 18 18 Belize 8 Jun 2022 572900 60694
## 19 19 Benin 4 May 2021 595112 7884
## 20 20 Bhutan 28 Feb 2022 1736168 12702
## 21 21 Bolivia 5 Jun 2022 4358669 910228
## 22 22 Bosnia and Herzegovina 27 Sep 2022 1872934 399887
## 23 23 Botswana 11 Jan 2022 2026898 232432
## 24 24 Brazil 19 Feb 2021 23561497 10081676
## 25 25 Brunei 2 Aug 2021 153804 338
## 26 26 Bulgaria 2 Feb 2023 10993239 1295524
## 27 27 Burkina Faso 4 Mar 2021 158777 12123
## 28 28 Burundi 5 Jan 2021 90019 884
## 29 29 Cambodia 1 Aug 2021 1812706 77914
## 30 30 Cameroon 18 Feb 2021 942685 32681
## 31 31 Canada 26 Nov 2022 66343123 4423053
## 32 32 Chad 2 Mar 2021 99027 4020
## 33 33 Chile 1 Feb 2023 48154268 5123007
## 34 34 China[c] 31 Jul 2020 160000000 87655
## 35 35 Colombia 24 Nov 2022 36875818 6314769
## 36 36 Costa Rica 2 Nov 2021 2575363 561054
## 37 37 Croatia 2 Feb 2023 5481285 1267798
## 38 38 Cuba 2 Feb 2023 14301394 1112470
## 39 39 Cyprus[d] 29 Jan 2023 27820163 644160
## 40 40 Czechia 1 Feb 2023 22544928 4590529
## 41 41 Denmark[e] 31 Jan 2023 67682707 3399947
## 42 42 Djibouti 28 Apr 2022 305941 15631
## 43 43 Dominica 20 Jun 2022 209803 14821
## 44 44 Dominican Republic 22 Jul 2022 3574665 626030
## 45 45 DR Congo 28 Feb 2021 124838 25961
## 46 46 Ecuador 23 Jul 2021 1627189 480720
## 47 47 Egypt 23 Jul 2021 3137519 283947
## 48 48 El Salvador 18 Mar 2022 1847861 161052
## 49 49 Equatorial Guinea 30 Jan 2023 403773 17113
## 50 50 Estonia 31 Jan 2023 3637908 613954
## 51 51 Eswatini 8 Dec 2021 415110 49253
## 52 52 Ethiopia 24 Jun 2021 2981185 278446
## 53 53 Faroe Islands 27 Feb 2022 774000 34237
## 54 54 Fiji 2 Jan 2023 667953 68848
## 55 55 Finland 14 Jan 2022 9042453 371135
## 56 56 France[f][g] 15 May 2022 272417258 29183646
## 57 57 Gabon 23 Jul 2021 958807 25325
## 58 58 Gambia 15 Feb 2021 43217 4469
## 59 59 Georgia[h] 3 Nov 2021 4888787 732965
## 60 60 Germany 7 Jul 2021 65247345 3733519
## 61 61 Ghana 3 Jul 2021 1305749 96708
## 62 62 Greece 18 Dec 2022 101576831 5548487
## 63 63 Greenland 30 Jan 2022 164573 10662
## 64 64 Grenada 11 May 2021 28684 161
## 65 65 Guatemala 6 Jan 2023 6800560 1230098
## 66 66 Guinea 21 Jul 2021 494898 24878
## 67 67 Guinea-Bissau 7 Jul 2022 145231 8400
## 68 68 Guyana 15 Jun 2022 648569 66129
## 69 69 Haiti 26 Nov 2022 223475 33874
## 70 70 Honduras 26 Nov 2021 1133782 377859
## 71 71 Hungary 10 May 2022 11394556 1909948
## 72 72 Iceland 9 Aug 2022 1988652 203162
## 73 73 India 8 Jul 2022 866177937 43585554
## 74 74 Indonesia 3 Jul 2023 76062770 6812127
## 75 75 Iran 31 May 2022 52269202 7232268
## 76 76 Iraq 3 Aug 2022 19090652 2448484
## 77 77 Ireland 31 Jan 2023 12990476 1700817
## 78 78 Israel 17 Jan 2022 41373364 1792137
## 79 79 Italy 16 Mar 2023 269127054 25651205
## 80 80 Ivory Coast 3 Mar 2021 429177 33285
## 81 81 Jamaica 30 Sep 2022 1184973 151931
## 82 82 Japan 1 Mar 2021 8487288 432773
## 83 83 Jordan 6 Jun 2021 7407053 739847
## 84 84 Kazakhstan 28 May 2021 11575012 385144
## 85 85 Kenya 5 Mar 2021 1322806 107729
## 86 86 Kosovo 31 May 2021 611357 107410
## 87 87 Kuwait 9 Mar 2022 7754247 624573
## 88 88 Kyrgyzstan 10 Feb 2021 695415 85253
## 89 89 Laos 1 Mar 2021 114030 45
## 90 90 Latvia 5 Sep 2021 3630095 144518
## 91 91 Lebanon 14 Jun 2021 4599186 542649
## 92 92 Lesotho 30 Mar 2022 431221 32910
## 93 93 Liberia 17 Jul 2021 128246 5396
## 94 94 Libya 14 Apr 2022 2578215 501862
## 95 95 Lithuania 31 Jan 2023 9046584 1170108
## 96 96 Luxembourg[i] 12 May 2022 4248188 244182
## 97 97 Madagascar 19 Feb 2021 119608 19831
## 98 98 Malawi 29 Nov 2022 624784 88086
## 99 99 Malaysia 7 Sep 2021 23705425 1880734
## 100 100 Maldives 13 Mar 2022 2216560 174658
## 101 101 Mali 7 Jul 2021 322504 14449
## 102 102 Malta 8 Sep 2021 1211456 36606
## 103 103 Mauritania 16 Apr 2021 268093 18103
## 104 104 Mauritius 22 Nov 2020 289552 494
## 105 105 Mexico 15 Oct 2021 10503678 3749860
## 106 106 Moldova[j] 20 Apr 2022 3213594 516864
## 107 107 Mongolia 10 Jul 2021 3354200 136053
## 108 108 Montenegro 10 May 2021 394388 98449
## 109 109 Morocco 6 Jan 2023 14217563 1272299
## 110 110 Mozambique 22 Jul 2021 688570 105866
## 111 111 Myanmar 16 Sep 2021 4047680 440741
## 112 112 Namibia 4 Jul 2022 1062663 166229
## 113 113 Nepal 26 Jul 2022 5804358 984475
## 114 114 Netherlands 6 Jul 2021 14526293 1692834
## 115 115 New Caledonia 3 Sep 2021 41962 136
## 116 116 New Zealand 29 Jan 2023 7757935 2136662
## 117 117 Niger 22 Feb 2021 79321 4740
## 118 118 Nigeria 28 Feb 2021 1544008 155657
## 119 119 North Korea 25 Nov 2020 16914 0
## 120 120 North Macedonia 1 Jul 2021 881870 155689
## 121 121 Northern Cyprus[k] 12 Jul 2022 7096998 103034
## 122 122 Norway 20 Jan 2022 9811888 554778
## 123 123 Oman 28 Oct 2020 509959 114434
## 124 124 Pakistan 5 Mar 2021 9173593 588728
## 125 125 Palestine 5 Feb 2022 3078533 574105
## 126 126 Panama 28 Jan 2023 7475016 1029701
## 127 127 Papua New Guinea 17 Feb 2021 47490 961
## 128 128 Paraguay 27 Mar 2022 2609819 647950
## 129 129 Peru 17 Nov 2022 36073768 4177786
## 130 130 Philippines 7 Jan 2023 34402980 4073980
## 131 131 Poland 27 Apr 2022 36064311 5993861
## 132 132 Portugal 5 Jan 2022 27515490 1499976
## 133 133 Qatar 11 Nov 2022 4061988 473440
## 134 134 Romania 29 Jan 2021 5405393 724250
## 135 135 Russia 6 Jun 2022 295542733 18358459
## 136 136 Rwanda 6 Oct 2021 2885812 98209
## 137 137 Saint Kitts and Nevis 26 Aug 2021 30231 995
## 138 138 Saint Lucia 7 Oct 2022 212132 29550
## 139 139 Saint Vincent 28 Jan 2023 113504 9585
## 140 140 San Marino 29 Jan 2023 192613 23427
## 141 141 Saudi Arabia 26 Apr 2022 41849069 753632
## 142 142 Senegal 12 Jul 2021 624502 46509
## 143 143 Serbia 2 Feb 2023 12185475 2473599
## 144 144 Singapore 3 Aug 2021 16206203 65315
## 145 145 Slovakia 2 Feb 2023 7391882 1861034
## 146 146 Slovenia 2 Feb 2023 2826117 1322282
## 147 147 South Africa 24 May 2021 11378282 1637848
## 148 148 South Korea 1 Mar 2021 6592010 90029
## 149 149 South Sudan 26 May 2021 164472 10688
## 150 150 Spain 1 Jul 2021 54128524 3821305
## 151 151 Sri Lanka 30 Mar 2021 2384745 93128
## 152 152 Sudan 7 Jan 2021 158804 23316
## 153 153 Sweden 24 May 2021 9996795 1074751
## 154 154 Switzerland[l] 7 Nov 2022 23283909 4276836
## 155 155 Taiwan[m] 3 Feb 2023 30275725 8622129
## 156 156 Tanzania 18 Nov 2020 3880 509
## 157 157 Thailand 4 Mar 2021 1579597 26162
## 158 158 Togo 6 Jan 2023 807269 39358
## 159 159 Trinidad and Tobago 3 Jan 2022 512730 92997
## 160 160 Tunisia 23 Aug 2021 2893625 703732
## 161 161 Turkey 2 Jul 2021 61236294 5435831
## 162 162 Uganda 11 Feb 2021 852444 39979
## 163 163 Ukraine 24 Nov 2021 15648456 3367461
## 164 164 United Arab Emirates 1 Feb 2023 198685717 1049537
## 165 165 United Kingdom 19 May 2022 522526476 22232377
## 166 166 United States 29 Jul 2022 929349291 90749469
## 167 167 Uruguay 16 Apr 2022 6089116 895592
## 168 168 Uzbekistan 7 Sep 2020 2630000 43975
## 169 169 Venezuela 30 Mar 2021 3179074 159149
## 170 170 Vietnam 28 Aug 2022 45772571 11403302
## 171 171 Zambia 10 Mar 2022 3301860 314850
## 172 172 Zimbabwe 15 Oct 2022 2529087 257893
## confirmed.tested.ratio tested.population.ratio confirmed.population.ratio
## 1 32.100 0.4000 0.13000
## 2 22.600 15.0000 3.40000
## 3 25.400 0.5300 0.13000
## 4 12.600 387.0000 49.00000
## 5 5.300 1.3000 0.06700
## 6 5.400 15.9000 0.86000
## 7 25.400 78.3000 20.00000
## 8 13.600 105.0000 14.30000
## 9 12.900 313.0000 40.30000
## 10 2.800 2312.0000 65.00000
## 11 11.600 69.1000 8.00000
## 12 14.500 67.3000 9.70000
## 13 6.600 674.0000 44.40000
## 14 15.500 4.5000 0.70000
## 15 13.400 268.0000 35.90000
## 16 7.400 139.0000 10.40000
## 17 12.800 317.0000 40.70000
## 18 10.600 140.0000 14.90000
## 19 1.300 5.1000 0.06700
## 20 0.730 234.0000 1.71000
## 21 20.900 38.1000 8.00000
## 22 21.400 54.7000 11.70000
## 23 11.500 89.9000 10.30000
## 24 42.800 11.2000 4.80000
## 25 0.220 33.5000 0.07400
## 26 11.800 158.0000 18.60000
## 27 7.600 0.7600 0.05800
## 28 0.980 0.7600 0.00740
## 29 4.300 11.2000 0.48000
## 30 3.500 3.6000 0.12000
## 31 6.700 175.0000 11.70000
## 32 4.100 0.7200 0.02900
## 33 10.600 252.0000 26.90000
## 34 0.055 11.1000 0.00610
## 35 17.100 76.4000 13.10000
## 36 21.800 51.5000 11.20000
## 37 23.100 134.0000 31.10000
## 38 7.800 126.0000 9.80000
## 39 2.300 3223.0000 74.40000
## 40 20.400 211.0000 42.90000
## 41 5.000 1162.0000 58.40000
## 42 5.100 33.2000 1.70000
## 43 7.100 293.0000 20.70000
## 44 17.500 32.9000 5.80000
## 45 20.800 0.1400 0.02900
## 46 29.500 9.5000 2.80000
## 47 9.100 3.1000 0.28000
## 48 8.700 28.5000 2.50000
## 49 4.200 30.8000 1.30000
## 50 16.900 274.0000 46.20000
## 51 11.900 36.5000 4.30000
## 52 9.300 2.6000 0.24000
## 53 4.400 1493.0000 65.70000
## 54 10.300 74.5000 7.70000
## 55 4.100 163.0000 6.70000
## 56 10.700 417.0000 44.70000
## 57 2.600 3.1000 0.08200
## 58 10.300 2.0000 0.21000
## 59 15.000 132.0000 19.70000
## 60 5.700 77.8000 4.50000
## 61 7.400 4.2000 0.31000
## 62 5.500 943.0000 51.50000
## 63 6.500 293.0000 19.00000
## 64 0.560 25.7000 0.14000
## 65 18.100 39.4000 7.10000
## 66 5.000 3.8000 0.19000
## 67 5.800 7.7000 0.45000
## 68 10.200 82.5000 8.40000
## 69 15.200 2.0000 0.30000
## 70 33.300 11.8000 3.90000
## 71 16.800 118.0000 19.80000
## 72 10.200 546.0000 55.80000
## 73 5.000 63.0000 31.70000
## 74 9.000 28.2000 2.50000
## 75 13.800 62.8000 8.70000
## 76 12.800 47.5000 6.10000
## 77 13.100 264.0000 34.60000
## 78 4.300 451.0000 19.50000
## 79 9.500 446.0000 42.50000
## 80 7.800 1.6000 0.13000
## 81 12.800 43.5000 5.60000
## 82 5.100 6.7000 0.34000
## 83 10.000 69.5000 6.90000
## 84 3.300 62.1000 2.10000
## 85 8.100 2.8000 0.23000
## 86 17.600 33.8000 5.90000
## 87 8.100 181.0000 14.60000
## 88 12.300 10.7000 1.30000
## 89 0.039 1.6000 0.00063
## 90 4.000 189.0000 7.50000
## 91 11.800 67.4000 8.00000
## 92 7.600 21.5000 1.60000
## 93 4.200 2.5000 0.11000
## 94 19.500 37.6000 7.30000
## 95 12.900 324.0000 41.90000
## 96 5.700 679.0000 39.00000
## 97 16.600 0.4600 0.07600
## 98 14.100 3.3000 0.46000
## 99 7.900 72.3000 5.70000
## 100 7.900 398.0000 31.30000
## 101 4.500 1.6000 0.07100
## 102 3.000 245.0000 7.40000
## 103 6.800 6.1000 0.41000
## 104 0.170 22.9000 0.03900
## 105 35.700 8.2000 2.90000
## 106 16.100 122.0000 19.60000
## 107 4.100 100.0000 4.10000
## 108 25.000 62.5000 15.60000
## 109 8.900 38.5000 3.40000
## 110 15.400 2.2000 0.34000
## 111 10.900 7.4000 0.81000
## 112 15.600 38.7000 6.10000
## 113 17.000 20.7000 3.50000
## 114 11.700 83.4000 9.70000
## 115 0.320 15.7000 0.05000
## 116 27.500 156.0000 42.90000
## 117 6.000 0.3500 0.02100
## 118 10.100 0.7500 0.07600
## 119 0.000 0.0660 0.00000
## 120 17.700 42.5000 7.50000
## 121 1.500 2177.0000 31.60000
## 122 5.700 183.0000 10.30000
## 123 22.400 11.0000 2.50000
## 124 6.400 4.2000 0.27000
## 125 18.600 60.9000 11.40000
## 126 13.800 179.0000 24.70000
## 127 2.000 0.5300 0.01100
## 128 24.800 36.6000 9.10000
## 129 11.600 109.9000 12.70000
## 130 11.800 34.1000 4.00000
## 131 16.600 94.0000 15.60000
## 132 5.500 268.0000 14.60000
## 133 11.700 141.0000 16.40000
## 134 13.400 27.9000 3.70000
## 135 6.200 201.0000 12.50000
## 136 3.400 22.3000 0.76000
## 137 3.300 57.6000 1.90000
## 138 13.900 116.6000 16.20000
## 139 8.400 103.0000 8.70000
## 140 12.200 563.0000 68.40000
## 141 1.800 120.0000 2.20000
## 142 7.400 3.9000 0.29000
## 143 20.300 175.0000 35.50000
## 144 0.400 284.0000 1.10000
## 145 25.200 135.0000 34.10000
## 146 46.800 135.0000 63.10000
## 147 14.400 19.2000 2.80000
## 148 1.400 12.7000 0.17000
## 149 6.500 1.3000 0.08400
## 150 7.100 116.0000 8.20000
## 151 3.900 10.9000 0.43000
## 152 14.700 0.3600 0.05300
## 153 10.800 96.8000 10.40000
## 154 18.400 270.0000 49.70000
## 155 28.480 128.3000 36.52800
## 156 13.100 0.0065 0.00085
## 157 1.700 2.3000 0.03800
## 158 4.900 9.4000 0.46000
## 159 18.100 37.6000 6.80000
## 160 24.300 24.5000 6.00000
## 161 8.900 73.6000 6.50000
## 162 4.700 1.9000 0.08700
## 163 21.500 37.2000 8.00000
## 164 0.530 2070.0000 10.90000
## 165 4.300 774.0000 32.90000
## 166 9.800 281.0000 27.40000
## 167 14.700 175.0000 25.80000
## 168 1.700 7.7000 0.13000
## 169 5.000 11.0000 0.55000
## 170 24.900 46.4000 11.60000
## 171 9.500 19.0000 1.80000
## 172 10.200 17.0000 1.70000
## Advice
## 1 This country is a better option
## 2 Retreat!
## 3 This country is a better option
## 4 Retreat!
## 5 This country is a better option
## 6 This country is a better option
## 7 Retreat!
## 8 Retreat!
## 9 Retreat!
## 10 Retreat!
## 11 Retreat!
## 12 Retreat!
## 13 Retreat!
## 14 This country is a better option
## 15 Retreat!
## 16 Retreat!
## 17 Retreat!
## 18 Retreat!
## 19 This country is a better option
## 20 Retreat!
## 21 Retreat!
## 22 Retreat!
## 23 Retreat!
## 24 Retreat!
## 25 This country is a better option
## 26 Retreat!
## 27 This country is a better option
## 28 This country is a better option
## 29 This country is a better option
## 30 This country is a better option
## 31 Retreat!
## 32 This country is a better option
## 33 Retreat!
## 34 This country is a better option
## 35 Retreat!
## 36 Retreat!
## 37 Retreat!
## 38 Retreat!
## 39 Retreat!
## 40 Retreat!
## 41 Retreat!
## 42 Retreat!
## 43 Retreat!
## 44 Retreat!
## 45 This country is a better option
## 46 Retreat!
## 47 This country is a better option
## 48 Retreat!
## 49 Retreat!
## 50 Retreat!
## 51 Retreat!
## 52 This country is a better option
## 53 Retreat!
## 54 Retreat!
## 55 Retreat!
## 56 Retreat!
## 57 This country is a better option
## 58 This country is a better option
## 59 Retreat!
## 60 Retreat!
## 61 This country is a better option
## 62 Retreat!
## 63 Retreat!
## 64 This country is a better option
## 65 Retreat!
## 66 This country is a better option
## 67 This country is a better option
## 68 Retreat!
## 69 This country is a better option
## 70 Retreat!
## 71 Retreat!
## 72 Retreat!
## 73 Retreat!
## 74 Retreat!
## 75 Retreat!
## 76 Retreat!
## 77 Retreat!
## 78 Retreat!
## 79 Retreat!
## 80 This country is a better option
## 81 Retreat!
## 82 This country is a better option
## 83 Retreat!
## 84 Retreat!
## 85 This country is a better option
## 86 Retreat!
## 87 Retreat!
## 88 Retreat!
## 89 This country is a better option
## 90 Retreat!
## 91 Retreat!
## 92 Retreat!
## 93 This country is a better option
## 94 Retreat!
## 95 Retreat!
## 96 Retreat!
## 97 This country is a better option
## 98 This country is a better option
## 99 Retreat!
## 100 Retreat!
## 101 This country is a better option
## 102 Retreat!
## 103 This country is a better option
## 104 This country is a better option
## 105 Retreat!
## 106 Retreat!
## 107 Retreat!
## 108 Retreat!
## 109 Retreat!
## 110 This country is a better option
## 111 This country is a better option
## 112 Retreat!
## 113 Retreat!
## 114 Retreat!
## 115 This country is a better option
## 116 Retreat!
## 117 This country is a better option
## 118 This country is a better option
## 119 This country is a better option
## 120 Retreat!
## 121 Retreat!
## 122 Retreat!
## 123 Retreat!
## 124 This country is a better option
## 125 Retreat!
## 126 Retreat!
## 127 This country is a better option
## 128 Retreat!
## 129 Retreat!
## 130 Retreat!
## 131 Retreat!
## 132 Retreat!
## 133 Retreat!
## 134 Retreat!
## 135 Retreat!
## 136 This country is a better option
## 137 Retreat!
## 138 Retreat!
## 139 Retreat!
## 140 Retreat!
## 141 Retreat!
## 142 This country is a better option
## 143 Retreat!
## 144 Retreat!
## 145 Retreat!
## 146 Retreat!
## 147 Retreat!
## 148 This country is a better option
## 149 This country is a better option
## 150 Retreat!
## 151 This country is a better option
## 152 This country is a better option
## 153 Retreat!
## 154 Retreat!
## 155 Retreat!
## 156 This country is a better option
## 157 This country is a better option
## 158 This country is a better option
## 159 Retreat!
## 160 Retreat!
## 161 Retreat!
## 162 This country is a better option
## 163 Retreat!
## 164 Retreat!
## 165 Retreat!
## 166 Retreat!
## 167 Retreat!
## 168 This country is a better option
## 169 This country is a better option
## 170 Retreat!
## 171 Retreat!
## 172 Retreat!#To export this insightful data set
write.csv(Covid19_dframe, "C:/Users/MODEL24/Documents/Covid19_Insight.csv")