library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(dplyr)
library(ggplot2)
setwd
## function (dir)
## .Internal(setwd(dir))
## <bytecode: 0x7f7f75b19b58>
## <environment: namespace:base>
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.
nations_GDP <- nations %>%
mutate(gdp = gdp_percap * population / 10^12)
Plot 1
nation1 <- nations_GDP %>% filter(country == "India" | country =="China"| country == "United States" | country == "Indonesia")
plot1 <- nation1 %>%
ggplot(aes(x = year, y = gdp, color = country)) +
geom_point() +
geom_line() +
scale_color_brewer(palette = "Set1") +
labs(x = "Year",
y = "GDP ($ Trillions)",
title = "The Rise Of Economy Of the 4 Most Populated Countries in The World")
theme_minimal(base_size = 20)
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plot1
nations2 <- nations_GDP %>%
group_by(region,year) %>%
summarise(GDP = sum(gdp, na.rm = TRUE))
## `summarise()` has grouped output by 'region'. You can override using the
## `.groups` argument.
plot2 <- nations2 %>%
ggplot(aes(x=year,y = GDP, fill = region)) +
geom_area(color = "white") +
scale_fill_brewer(palette = "Set2") +
labs(title = "GDP by World Bank Region") +
xlab("Year")+
ylab("GPD ($ Trillion)")
plot2
