data <- readxl::read_excel("GDP_vs_PlasticWaste_Analysis.xlsx")united_countries <- data %>%
filter(str_detect(Country, "United"))
print(united_countries)## # A tibble: 3 × 6
## Country `GDP per Capita (USD)` Plastic Waste per Ca…¹ Total Plastic Waste …²
## <chr> <dbl> <dbl> <dbl>
## 1 United A… 57233 0.199 600741
## 2 United K… 36367 0.215 4925590
## 3 United S… 49374 0.335 37825550
## # ℹ abbreviated names: ¹`Plastic Waste per Capita (kg)`,
## # ²`Total Plastic Waste Generation (tonnes)`
## # ℹ 2 more variables: `Mismanaged Plastic Waste (tonnes)` <dbl>,
## # `Managed Plastic Waste (tonnes) (recycled, incinerated, sealed landfills)` <dbl>high_gdp_countries <- data %>%
filter(`GDP per Capita (USD)` > 40000)
print(high_gdp_countries)## # A tibble: 21 × 6
## Country `GDP per Capita (USD)` Plastic Waste per Ca…¹ Total Plastic Waste …²
## <chr> <dbl> <dbl> <dbl>
## 1 Austral… 41464 0.112 900658
## 2 Bahrain 40571 0.132 59785
## 3 Belgium 41086 0.08 318151
## 4 Bermuda 56395 0.252 5990
## 5 Brunei 80553 0.026 3688
## 6 Canada 40699 0.093 1154309
## 7 Cayman … 49903 0.252 5106
## 8 Denmark 43998 0.047 95171
## 9 Germany 40429 0.485 14476561
## 10 Hong Ko… 48108 0.398 1020406
## # ℹ 11 more rows
## # ℹ abbreviated names: ¹`Plastic Waste per Capita (kg)`,
## # ²`Total Plastic Waste Generation (tonnes)`
## # ℹ 2 more variables: `Mismanaged Plastic Waste (tonnes)` <dbl>,
## # `Managed Plastic Waste (tonnes) (recycled, incinerated, sealed landfills)` <dbl>gdp_numeric <- str_extract(data$`GDP per Capita (USD)`, "\\d+")
print(gdp_numeric)## [1] "9927" "12871" "5898" "19213" "18712" "35974" "41464" "29222"
## [9] "40571" "2443" "16418" "41086" "7877" "1819" "56395" "9720"
## [17] "14538" "80553" "15283" "2523" "2930" "40699" "5828" "49903"
## [25] "19442" "9526" "10901" "1413" "5186" "13000" "2690" "20172"
## [33] "33941" "660" "43998" "2705" "10198" "11133" "9352" "9857"
## [41] "6301" "33723" "1416" "22741" "7352" "39848" "36856" "15356"
## [49] "1644" "6734" "40429" "3059" "28726" "11178" "6714" "1574"
## [57] "1400" "5848" "1502" "3971" "48108" "38978" "4405" "8433"
## [65] "17943" "12718" "29743" "36201" "7996" "35750" "9473" "2476"
## [73] "1732" "75204" "18252" "16452" "700" "29630" "21071" "98184"
## [81] "1386" "21107" "12006" "28365" "3479" "3317" "15938" "15716"
## [89] "3298" "14035" "6443" "918" "3721" "8461" "6592" "45525"
## [97] "32119" "4029" "62350" "45336" "4284" "11847" "4173" "15629"
## [105] "3192" "9957" "5597" "21771" "27238" "33924" "125141" "17553"
## [113] "23108" "21412" "12124" "9916" "5400" "2642" "45421" "2184"
## [121] "20365" "1200" "72116" "36327" "28678" "1871" "11888" "30352"
## [129] "32507" "8530" "3366" "14212" "42943" "2091" "13487" "1208"
## [137] "4984" "31261" "10436" "17959" "2986" "7824" "57233" "36367"
## [145] "49374" "17082" "2948" "16545" "4408" "4479"vowel_countries <- data %>%
filter(str_detect(Country, "^[AEIOUaeiou]"))
print(vowel_countries)## # A tibble: 26 × 6
## Country `GDP per Capita (USD)` Plastic Waste per Ca…¹ Total Plastic Waste …²
## <chr> <dbl> <dbl> <dbl>
## 1 Albania 9927 0.069 73364
## 2 Algeria 12871 0.144 1898343
## 3 Angola 5898 0.062 528843
## 4 Antigua… 19213 0.66 2753550
## 5 Argenti… 18712 0.183 2753550
## 6 Aruba 35974 0.252 9352
## 7 Austral… 41464 0.112 900658
## 8 Ecuador 9352 0.147 801321
## 9 Egypt 9857 0.178 5464471
## 10 El Salv… 6301 0.147 330763
## # ℹ 16 more rows
## # ℹ abbreviated names: ¹`Plastic Waste per Capita (kg)`,
## # ²`Total Plastic Waste Generation (tonnes)`
## # ℹ 2 more variables: `Mismanaged Plastic Waste (tonnes)` <dbl>,
## # `Managed Plastic Waste (tonnes) (recycled, incinerated, sealed landfills)` <dbl>data$Country <- str_replace_all(data$Country, " ", "_")
print(data$Country)## [1] "Albania" "Algeria"
## [3] "Angola" "Antigua_and_Barbuda"
## [5] "Argentina" "Aruba"
## [7] "Australia" "Bahamas"
## [9] "Bahrain" "Bangladesh"
## [11] "Barbados" "Belgium"
## [13] "Belize" "Benin"
## [15] "Bermuda" "Bosnia_and_Herzegovina"
## [17] "Brazil" "Brunei"
## [19] "Bulgaria" "Cambodia"
## [21] "Cameroon" "Canada"
## [23] "Cape_Verde" "Cayman_Islands"
## [25] "Chile" "China"
## [27] "Colombia" "Comoros"
## [29] "Congo" "Costa_Rica"
## [31] "Cote_d'Ivoire" "Croatia"
## [33] "Cyprus" "Democratic_Republic_of_Congo"
## [35] "Denmark" "Djibouti"
## [37] "Dominica" "Dominican_Republic"
## [39] "Ecuador" "Egypt"
## [41] "El_Salvador" "Equatorial_Guinea"
## [43] "Eritrea" "Estonia"
## [45] "Fiji" "Finland"
## [47] "France" "Gabon"
## [49] "Gambia" "Georgia"
## [51] "Germany" "Ghana"
## [53] "Greece" "Grenada"
## [55] "Guatemala" "Guinea"
## [57] "Guinea-Bissau" "Guyana"
## [59] "Haiti" "Honduras"
## [61] "Hong_Kong" "Iceland"
## [63] "India" "Indonesia"
## [65] "Iran" "Iraq"
## [67] "Israel" "Italy"
## [69] "Jamaica" "Japan"
## [71] "Jordan" "Kenya"
## [73] "Kiribati" "Kuwait"
## [75] "Latvia" "Lebanon"
## [77] "Liberia" "Libya"
## [79] "Lithuania" "Macao"
## [81] "Madagascar" "Malaysia"
## [83] "Maldives" "Malta"
## [85] "Marshall_Islands" "Mauritania"
## [87] "Mauritius" "Mexico"
## [89] "Micronesia_(country)" "Montenegro"
## [91] "Morocco" "Mozambique"
## [93] "Myanmar" "Namibia"
## [95] "Nauru" "Netherlands"
## [97] "New_Zealand" "Nicaragua"
## [99] "Norway" "Oman"
## [101] "Pakistan" "Palau"
## [103] "Palestine" "Panama"
## [105] "Papua_New_Guinea" "Peru"
## [107] "Philippines" "Poland"
## [109] "Portugal" "Puerto_Rico"
## [111] "Qatar" "Romania"
## [113] "Russia" "Saint_Kitts_and_Nevis"
## [115] "Saint_Lucia" "Saint_Vincent_and_the_Grenadines"
## [117] "Samoa" "Sao_Tome_and_Principe"
## [119] "Saudi_Arabia" "Senegal"
## [121] "Seychelles" "Sierra_Leone"
## [123] "Singapore" "Sint_Maarten_(Dutch_part)"
## [125] "Slovenia" "Solomon_Islands"
## [127] "South_Africa" "South_Korea"
## [129] "Spain" "Sri_Lanka"
## [131] "Sudan" "Suriname"
## [133] "Sweden" "Tanzania"
## [135] "Thailand" "Togo"
## [137] "Tonga" "Trinidad_and_Tobago"
## [139] "Tunisia" "Turkey"
## [141] "Tuvalu" "Ukraine"
## [143] "United_Arab_Emirates" "United_Kingdom"
## [145] "United_States" "Uruguay"
## [147] "Vanuatu" "Venezuela"
## [149] "Vietnam" "Yemen"data$`Plastic Waste per Capita (kg)` <- str_replace_all(data$`Plastic Waste per Capita (kg)`, "\\.", ",")
print(data$`Plastic Waste per Capita (kg)`)## [1] "0,069" "0,144" "0,062" "0,66" "0,183" "0,252" "0,112" "0,39" "0,132"
## [10] "0,034" "0,57" "0,08" "0,172" "0,043" "0,252" "0,144" "0,165" "0,026"
## [19] "0,154" "0,066" "0,046" "0,093" "0,065" "0,252" "0,119" "0,121" "0,144"
## [28] "0,201" "0,069" "0,258" "0,103" "0,252" "0,248" "0,045" "0,047" "0,103"
## [37] "0,149" "0,144" "0,147" "0,178" "0,147" "0,144" "0,045" "0,176" "0,189"
## [46] "0,234" "0,192" "0,054" "0,048" "0,068" "0,485" "0,04" "0,2" "0,325"
## [55] "0,28" "0,03" "0,054" "0,586" "0,09" "0,189" "0,398" "0,281" "0,01"
## [64] "0,057" "0,144" "0,103" "0,297" "0,134" "0,034" "0,171" "0,144" "0,027"
## [73] "0,103" "0,686" "0,124" "0,094" "0,084" "0,144" "0,132" "0,368" "0,016"
## [82] "0,198" "0,322" "0,214" "0,192" "0,045" "0,23" "0,087" "0,103" "0,144"
## [91] "0,073" "0,015" "0,075" "0,144" "0,144" "0,424" "0,331" "0,143" "0,28"
## [100] "0,084" "0,103" "0,144" "0,063" "0,145" "0,103" "0,144" "0,075" "0,097"
## [109] "0,265" "0,252" "0,16" "0,042" "0,112" "0,654" "0,522" "0,221" "0,103"
## [118] "0,103" "0,156" "0,103" "0,358" "0,041" "0,194" "0,252" "0,145" "0,103"
## [127] "0,24" "0,112" "0,277" "0,357" "0,103" "0,163" "0,048" "0,023" "0,144"
## [136] "0,057" "0,223" "0,29" "0,144" "0,212" "0,144" "0,103" "0,199" "0,215"
## [145] "0,335" "0,252" "0,295" "0,252" "0,103" "0,103"