Week 4 Homework 2

Load in the library, set working directory, and load in the data

My working directory is already set but I like to manually set it each time to make sure there are no errors.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── 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(plotly)

Warning: package 'plotly' was built under R version 4.3.1

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

setwd("C:/Users/andre/OneDrive/Documents/School/Data 110")
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.

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>

Create a new variable in the data set

Added the variable “gdp” to the data set.

nations <- nations %>%
  mutate(gdp = (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 <dbl>

Create the new data sets and check the first

Looks good. Also create a new variable “gdp2” which is just a cleaned version of “gdp” without NA values.

nations1 <- filter(nations, country %in% c("United States", "China", "Japan", "Germany"))
nations2 <- nations %>%
  group_by(region, year) %>%
  summarise(gdp2 = sum(gdp, na.rm = TRUE))

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

head(nations1)

# 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 CN    CHN   China    1992      1260. 1164970000       18.3               29.4
2 CN    CHN   China    2005      5053. 1303720000       12.4               14  
3 CN    CHN   China    2000      2915. 1262645000       14.0               21.2
4 CN    CHN   China    1991      1091. 1150780000       19.7               29.7
5 CN    CHN   China    2013     12219. 1357380000       12.1                6.3
6 CN    CHN   China    1999      2650. 1252735000       14.6               22.2
# ℹ 3 more variables: region <chr>, income <chr>, gdp <dbl>

Check the second new data set

Looks good.

head(nations2)

# A tibble: 6 × 3
# Groups:   region [1]
  region               year  gdp2
  <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

Create the first plot

Using “nations1” I create the first plot.

p1 <- ggplot(nations1, aes(x = year, y = gdp, color = country)) +
  geom_point() +
  geom_line() +
  scale_color_brewer(palette = "Set1") +
  labs(title = "China's Rise to Become the Largest Economy",
       x = "Year",
       y = "GDP (trillions of dollars)") +
  guides(color = guide_legend(title = "Country"))
p1

Create the second plot

Using “nations2” I create the second plot. Making sure to use “fill” in the aes function and “color” in the geom_area function.

p2 <- ggplot(nations2, aes(x = year, y = gdp2, fill = region)) +
  geom_area(color = "white")+
  scale_fill_brewer(palette = "Set2") +
  labs(title = "GDP by Wrold Bank Region",
       x = "Year",
       y = "GDP (trillions of dollars)") +
  guides(fill = guide_legend(title = "Region"))
p2