library(tidyverse)
library(ggplot2)
library(dplyr)Nations Dataset Charts Assignment
Load Libraries and Dataset
Load libraries
Set working directory and load dataset
setwd("C:/Users/Administrator/Documents/Data110/Datasets")
nations <- read_csv("nations.csv")Clean headers and view data
names(nations) <- tolower(names(nations))
names(nations) <- gsub(" ","",names(nations))
head(nations)# A tibble: 6 × 10
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 2 more variables: region <chr>, income <chr>Show countries to select from
unique_countries <- sort(unique(nations$country))
print(unique_countries) [1] "Afghanistan" "Albania"
[3] "Algeria" "American Samoa"
[5] "Andorra" "Angola"
[7] "Antigua and Barbuda" "Argentina"
[9] "Armenia" "Aruba"
[11] "Australia" "Austria"
[13] "Azerbaijan" "Bahamas, The"
[15] "Bahrain" "Bangladesh"
[17] "Barbados" "Belarus"
[19] "Belgium" "Belize"
[21] "Benin" "Bermuda"
[23] "Bhutan" "Bolivia"
[25] "Bosnia and Herzegovina" "Botswana"
[27] "Brazil" "Brunei Darussalam"
[29] "Bulgaria" "Burkina Faso"
[31] "Burundi" "Cambodia"
[33] "Cameroon" "Canada"
[35] "Cayman Islands" "Central African Republic"
[37] "Chad" "Channel Islands"
[39] "Chile" "China"
[41] "Colombia" "Comoros"
[43] "Congo, Dem. Rep." "Congo, Rep."
[45] "Costa Rica" "Cote d'Ivoire"
[47] "Croatia" "Cuba"
[49] "Curacao" "Cyprus"
[51] "Czech Republic" "Denmark"
[53] "Djibouti" "Dominica"
[55] "Dominican Republic" "Ecuador"
[57] "Egypt, Arab Rep." "El Salvador"
[59] "Equatorial Guinea" "Eritrea"
[61] "Estonia" "Ethiopia"
[63] "Fiji" "Finland"
[65] "France" "French Polynesia"
[67] "Gabon" "Gambia, The"
[69] "Georgia" "Germany"
[71] "Ghana" "Gibraltar"
[73] "Greece" "Greenland"
[75] "Grenada" "Guam"
[77] "Guatemala" "Guinea"
[79] "Guinea-Bissau" "Guyana"
[81] "Haiti" "Honduras"
[83] "Hong Kong SAR, China" "Hungary"
[85] "Iceland" "India"
[87] "Indonesia" "Iran, Islamic Rep."
[89] "Iraq" "Ireland"
[91] "Isle of Man" "Israel"
[93] "Italy" "Jamaica"
[95] "Japan" "Jordan"
[97] "Kazakhstan" "Kenya"
[99] "Kiribati" "Korea, Rep."
[101] "Kuwait" "Kyrgyz Republic"
[103] "Lao PDR" "Latvia"
[105] "Lebanon" "Lesotho"
[107] "Liberia" "Libya"
[109] "Liechtenstein" "Lithuania"
[111] "Luxembourg" "Macao SAR, China"
[113] "Macedonia, FYR" "Madagascar"
[115] "Malawi" "Malaysia"
[117] "Maldives" "Mali"
[119] "Malta" "Marshall Islands"
[121] "Mauritania" "Mauritius"
[123] "Mexico" "Micronesia, Fed. Sts."
[125] "Moldova" "Monaco"
[127] "Mongolia" "Montenegro"
[129] "Morocco" "Mozambique"
[131] "Myanmar" "Namibia"
[133] "Nepal" "Netherlands"
[135] "New Caledonia" "New Zealand"
[137] "Nicaragua" "Niger"
[139] "Nigeria" "Northern Mariana Islands"
[141] "Norway" "Oman"
[143] "Pakistan" "Palau"
[145] "Panama" "Papua New Guinea"
[147] "Paraguay" "Peru"
[149] "Philippines" "Poland"
[151] "Portugal" "Puerto Rico"
[153] "Qatar" "Romania"
[155] "Russian Federation" "Rwanda"
[157] "Samoa" "San Marino"
[159] "Sao Tome and Principe" "Saudi Arabia"
[161] "Senegal" "Serbia"
[163] "Seychelles" "Sierra Leone"
[165] "Singapore" "Sint Maarten (Dutch part)"
[167] "Slovak Republic" "Slovenia"
[169] "Solomon Islands" "Somalia"
[171] "South Africa" "South Sudan"
[173] "Spain" "Sri Lanka"
[175] "St. Kitts and Nevis" "St. Lucia"
[177] "St. Martin (French part)" "St. Vincent and the Grenadines"
[179] "Sudan" "Suriname"
[181] "Swaziland" "Sweden"
[183] "Switzerland" "Syrian Arab Republic"
[185] "Tajikistan" "Tanzania"
[187] "Thailand" "Timor-Leste"
[189] "Togo" "Tonga"
[191] "Trinidad and Tobago" "Tunisia"
[193] "Turkey" "Turkmenistan"
[195] "Turks and Caicos Islands" "Tuvalu"
[197] "Uganda" "Ukraine"
[199] "United Arab Emirates" "United Kingdom"
[201] "United States" "Uruguay"
[203] "Uzbekistan" "Vanuatu"
[205] "Venezuela, RB" "Vietnam"
[207] "Virgin Islands (U.S.)" "West Bank and Gaza"
[209] "Yemen, Rep." "Zambia"
[211] "Zimbabwe" First Chart
# Filter 4 Countries
countries_to_filter <- c("United States", "Russian Federation", "China", "Germany")
# Create a new variable for GDP in trillions
chart1 <- nations |>
filter(country %in% countries_to_filter) |>
mutate(gdp_trillions = (gdp_percap * population) / 1e12)
# Create a line graph
ggplot(chart1, aes(x = year, y = gdp_trillions, color = country)) +
geom_line() +
geom_point() +
scale_color_brewer(palette = "Set1") +
labs(x = "year", y = "GDP ($ trillion)", color = NULL,title = "China's Rise to Become the Largest Economy")
Second Chart
# Create variable, group, and summarize
chart2 <- nations |>
mutate(gdp_trillions = (gdp_percap * population) / 1e12) |>
group_by(region, year) |>
summarise(GDP = sum(gdp_trillions, na.rm = TRUE))
# Create chart2
ggplot(chart2, aes(x = year, y = GDP, fill = region)) +
geom_area() +
scale_fill_brewer(palette = "Set2") +
labs(x = "year", y = "GDP ($ trillion)", fill = "region",title = "GDP by World Bank Region") 