NationsCharts

library(tidyverse)

Warning: package 'tidyr' was built under R version 4.3.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── 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

setwd("C:/Users/Papad/Documents/MontCommunityCollege/Data110-Summer2024/DataSets")
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.

data(Nations)

Warning in data(Nations): data set 'Nations' not found

#mutate from dplyr, giving the GDP of each country in trillions of dollars, by multiplying gdp_percap by population and dividing by a trillion.

Nations1 <- Nations|>
     mutate(GDP = gdp_percap * population / 10^12)

#Select four countries to work with and filter for them

Nations2 <- Nations1|>
      filter(country %in% c("United Arab Emirates", "Angola", "Zimbabwe", "Zambia"))
Nations2

# A tibble: 100 × 11
   iso2c iso3c country  year gdp_percap population birth_rate neonat_mortal_rate
   <chr> <chr> <chr>   <dbl>      <dbl>      <dbl>      <dbl>              <dbl>
 1 AE    ARE   United…  1991     73037.    1913190       24.6                7.9
 2 AE    ARE   United…  1993     71960.    2127863       22.4                7.3
 3 AE    ARE   United…  2001     83534.    3217865       15.8                5.5
 4 AE    ARE   United…  1992     73154.    2019014       23.5                7.6
 5 AE    ARE   United…  1994     74684.    2238281       21.3                6.9
 6 AE    ARE   United…  2007     75427.    6010100       12.8                4.7
 7 AE    ARE   United…  2004     87844.    3975945       14.2                5.1
 8 AE    ARE   United…  1996     79480.    2467726       19.3                6.4
 9 AE    ARE   United…  2006     82754.    5171255       13.3                4.9
10 AE    ARE   United…  2000     84975.    3050128       16.4                5.6
# ℹ 90 more rows
# ℹ 3 more variables: region <chr>, income <chr>, GDP <dbl>

#Use ggplot 2 to make a chart including geomline and geompoint

Chart1 <- Nations2 |>
  ggplot(aes(x = country, y = GDP))+
  geom_point()+
  geom_line(color = "red")+
  labs(title = "GDP per trillion, for each country's year",
       x = "Country",
       y = "GDP in Trillions")+
  scale_color_brewer(palette = "Set1")
Chart1

#Second Chart #Group by region and year with a summary of GDP

Nations3 <- Nations1 |>
   group_by(region, year)|>
  summarise(GDP = sum(GDP, na.rm = TRUE))

`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.

Nations3

# A tibble: 175 × 3
# Groups:   region [7]
   region               year   GDP
   <chr>               <dbl> <dbl>
 1 East Asia & Pacific  1990  5.52
 2 East Asia & Pacific  1991  6.03
 3 East Asia & Pacific  1992  6.50
 4 East Asia & Pacific  1993  7.04
 5 East Asia & Pacific  1994  7.64
 6 East Asia & Pacific  1995  8.29
 7 East Asia & Pacific  1996  8.96
 8 East Asia & Pacific  1997  9.55
 9 East Asia & Pacific  1998  9.60
10 East Asia & Pacific  1999 10.1 
# ℹ 165 more rows

#Create second chart using Nations3 dataframe.

Chart2 <- Nations3|>
    ggplot(aes(x = GDP, y = region, fill = year, color = GDP))+
    geom_area()+
    scale_fill_brewer(palette = "Set2")