Load libraries and dataset
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(RColorBrewer)
# Load the dataset (adjust filename/path if necessary)
setwd("C:/Users/ryanj/Downloads")
nations <- read_csv("nations.csv")
## Rows: 5275 Columns: 10
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): iso2c, iso3c, country, region, income
## dbl (5): year, gdp_percap, population, birth_rate, neonat_mortal_rate
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Create the new GDP variable in trillions of dollars ($)
nations <- nations |>
mutate(gdp_tn = (gdp_percap * population) / 1e12)
head(nations)
## # A tibble: 6 × 11
## iso2c iso3c country year gdp_percap population birth_rate neonat_mortal_rate
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AD AND Andorra 1996 NA 64291 10.9 2.8
## 2 AD AND Andorra 1994 NA 62707 10.9 3.2
## 3 AD AND Andorra 2003 NA 74783 10.3 2
## 4 AD AND Andorra 1990 NA 54511 11.9 4.3
## 5 AD AND Andorra 2009 NA 85474 9.9 1.7
## 6 AD AND Andorra 2011 NA 82326 NA 1.6
## # ℹ 3 more variables: region <chr>, income <chr>, gdp_tn <dbl>
unique(nations$country)
## [1] "Andorra" "United Arab Emirates"
## [3] "Afghanistan" "Antigua and Barbuda"
## [5] "Albania" "Armenia"
## [7] "Angola" "Argentina"
## [9] "American Samoa" "Austria"
## [11] "Australia" "Aruba"
## [13] "Azerbaijan" "Bosnia and Herzegovina"
## [15] "Barbados" "Bangladesh"
## [17] "Belgium" "Burkina Faso"
## [19] "Bulgaria" "Bahrain"
## [21] "Burundi" "Benin"
## [23] "Bermuda" "Brunei Darussalam"
## [25] "Bolivia" "Brazil"
## [27] "Bahamas, The" "Bhutan"
## [29] "Botswana" "Belarus"
## [31] "Belize" "Canada"
## [33] "Congo, Dem. Rep." "Central African Republic"
## [35] "Congo, Rep." "Switzerland"
## [37] "Cote d'Ivoire" "Chile"
## [39] "Cameroon" "China"
## [41] "Colombia" "Costa Rica"
## [43] "Cuba" "Curacao"
## [45] "Cyprus" "Czech Republic"
## [47] "Germany" "Djibouti"
## [49] "Denmark" "Dominica"
## [51] "Dominican Republic" "Algeria"
## [53] "Ecuador" "Estonia"
## [55] "Egypt, Arab Rep." "Eritrea"
## [57] "Spain" "Ethiopia"
## [59] "Finland" "Fiji"
## [61] "Micronesia, Fed. Sts." "France"
## [63] "Gabon" "United Kingdom"
## [65] "Grenada" "Georgia"
## [67] "Ghana" "Gibraltar"
## [69] "Greenland" "Gambia, The"
## [71] "Guinea" "Equatorial Guinea"
## [73] "Greece" "Guatemala"
## [75] "Guam" "Guinea-Bissau"
## [77] "Guyana" "Hong Kong SAR, China"
## [79] "Honduras" "Croatia"
## [81] "Haiti" "Hungary"
## [83] "Indonesia" "Ireland"
## [85] "Israel" "Isle of Man"
## [87] "India" "Iraq"
## [89] "Iran, Islamic Rep." "Iceland"
## [91] "Italy" "Channel Islands"
## [93] "Jamaica" "Jordan"
## [95] "Japan" "Kenya"
## [97] "Kyrgyz Republic" "Cambodia"
## [99] "Kiribati" "Comoros"
## [101] "St. Kitts and Nevis" "Korea, Rep."
## [103] "Kuwait" "Cayman Islands"
## [105] "Kazakhstan" "Lao PDR"
## [107] "Lebanon" "St. Lucia"
## [109] "Liechtenstein" "Sri Lanka"
## [111] "Liberia" "Lesotho"
## [113] "Lithuania" "Luxembourg"
## [115] "Latvia" "Libya"
## [117] "Morocco" "Monaco"
## [119] "Moldova" "Montenegro"
## [121] "St. Martin (French part)" "Madagascar"
## [123] "Marshall Islands" "Macedonia, FYR"
## [125] "Mali" "Myanmar"
## [127] "Mongolia" "Macao SAR, China"
## [129] "Northern Mariana Islands" "Mauritania"
## [131] "Malta" "Mauritius"
## [133] "Maldives" "Malawi"
## [135] "Mexico" "Malaysia"
## [137] "Mozambique" "Namibia"
## [139] "New Caledonia" "Niger"
## [141] "Nigeria" "Nicaragua"
## [143] "Netherlands" "Norway"
## [145] "Nepal" "New Zealand"
## [147] "Oman" "Panama"
## [149] "Peru" "French Polynesia"
## [151] "Papua New Guinea" "Philippines"
## [153] "Pakistan" "Poland"
## [155] "Puerto Rico" "West Bank and Gaza"
## [157] "Portugal" "Palau"
## [159] "Paraguay" "Qatar"
## [161] "Romania" "Serbia"
## [163] "Russian Federation" "Rwanda"
## [165] "Saudi Arabia" "Solomon Islands"
## [167] "Seychelles" "Sudan"
## [169] "Sweden" "Singapore"
## [171] "Slovenia" "Slovak Republic"
## [173] "Sierra Leone" "San Marino"
## [175] "Senegal" "Somalia"
## [177] "Suriname" "South Sudan"
## [179] "Sao Tome and Principe" "El Salvador"
## [181] "Sint Maarten (Dutch part)" "Syrian Arab Republic"
## [183] "Swaziland" "Turks and Caicos Islands"
## [185] "Chad" "Togo"
## [187] "Thailand" "Tajikistan"
## [189] "Timor-Leste" "Turkmenistan"
## [191] "Tunisia" "Tonga"
## [193] "Turkey" "Trinidad and Tobago"
## [195] "Tuvalu" "Tanzania"
## [197] "Ukraine" "Uganda"
## [199] "United States" "Uruguay"
## [201] "Uzbekistan" "St. Vincent and the Grenadines"
## [203] "Venezuela, RB" "Virgin Islands (U.S.)"
## [205] "Vietnam" "Vanuatu"
## [207] "Samoa" "Yemen, Rep."
## [209] "South Africa" "Zambia"
## [211] "Zimbabwe"
unique(nations$iso3c)
## [1] "AND" "ARE" "AFG" "ATG" "ALB" "ARM" "AGO" "ARG" "ASM" "AUT" "AUS" "ABW"
## [13] "AZE" "BIH" "BRB" "BGD" "BEL" "BFA" "BGR" "BHR" "BDI" "BEN" "BMU" "BRN"
## [25] "BOL" "BRA" "BHS" "BTN" "BWA" "BLR" "BLZ" "CAN" "COD" "CAF" "COG" "CHE"
## [37] "CIV" "CHL" "CMR" "CHN" "COL" "CRI" "CUB" "CUW" "CYP" "CZE" "DEU" "DJI"
## [49] "DNK" "DMA" "DOM" "DZA" "ECU" "EST" "EGY" "ERI" "ESP" "ETH" "FIN" "FJI"
## [61] "FSM" "FRA" "GAB" "GBR" "GRD" "GEO" "GHA" "GIB" "GRL" "GMB" "GIN" "GNQ"
## [73] "GRC" "GTM" "GUM" "GNB" "GUY" "HKG" "HND" "HRV" "HTI" "HUN" "IDN" "IRL"
## [85] "ISR" "IMN" "IND" "IRQ" "IRN" "ISL" "ITA" "CHI" "JAM" "JOR" "JPN" "KEN"
## [97] "KGZ" "KHM" "KIR" "COM" "KNA" "KOR" "KWT" "CYM" "KAZ" "LAO" "LBN" "LCA"
## [109] "LIE" "LKA" "LBR" "LSO" "LTU" "LUX" "LVA" "LBY" "MAR" "MCO" "MDA" "MNE"
## [121] "MAF" "MDG" "MHL" "MKD" "MLI" "MMR" "MNG" "MAC" "MNP" "MRT" "MLT" "MUS"
## [133] "MDV" "MWI" "MEX" "MYS" "MOZ" "NAM" "NCL" "NER" "NGA" "NIC" "NLD" "NOR"
## [145] "NPL" "NZL" "OMN" "PAN" "PER" "PYF" "PNG" "PHL" "PAK" "POL" "PRI" "PSE"
## [157] "PRT" "PLW" "PRY" "QAT" "ROU" "SRB" "RUS" "RWA" "SAU" "SLB" "SYC" "SDN"
## [169] "SWE" "SGP" "SVN" "SVK" "SLE" "SMR" "SEN" "SOM" "SUR" "SSD" "STP" "SLV"
## [181] "SXM" "SYR" "SWZ" "TCA" "TCD" "TGO" "THA" "TJK" "TLS" "TKM" "TUN" "TON"
## [193] "TUR" "TTO" "TUV" "TZA" "UKR" "UGA" "USA" "URY" "UZB" "VCT" "VEN" "VIR"
## [205] "VNM" "VUT" "WSM" "YEM" "ZAF" "ZMB" "ZWE"
Chart 1
# Filter for four specific countries
big4 <- nations |>
filter(iso3c %in% c("PER", "PHL", "COL", "HND"))
# Draw static Chart 1
ggplot(big4, aes(x = year, y = gdp_tn, color = country)) +
geom_line(linewidth = 1) +
geom_point(size = 2) +
scale_color_brewer(palette = "Set1") +
labs(
title = "GDP ($ trillion) for Desired Countries",
x = "Year",
y = "GDP ($ trillion)",
color = "Country"
) +
theme_minimal(base_size = 14)
Chart 2
# Group and summarize data by region and year
regions <- nations |>
group_by(region, year) |>
summarise(GDP = sum(gdp_tn, na.rm = TRUE), .groups = "drop")
# Drawing static Chart 2
ggplot(regions, aes(x = year, y = GDP, fill = region)) +
geom_area(color = "white", linewidth = 0.5) + # Thin white line around areas
scale_fill_brewer(palette = "Set2") +
labs(
title = "Total GDP by Region Over Time",
x = "Year",
y = "GDP ($ trillion)",
fill = "Region"
) +
theme_minimal(base_size = 14)
