── 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(ggplot2)library(alluvial)
Warning: package 'alluvial' was built under R version 4.5.2
library(ggalluvial)
Warning: package 'ggalluvial' was built under R version 4.5.2
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.
Creating a New Variable: GDP (in trillions of dollars)
For both charts, you will first need to create a new variable in the data, usingmutate from dplyr, giving the GDP of each country in trillions of dollars, by multiplying gdp_percap by population and dividing by a trillion.
# 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 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
# ℹ 3 more variables: region <chr>, income <chr>, gdp <dbl>
For both charts, you will first need to create a new variable in the data, usingmutate from dplyr, giving the GDP of each country in trillions of dollars, by multiplying gdp_percap by population and dividing by a trillion.
Draw both charts with ggplot2.
For the first chart, you will need to filter the data with dplyr for the four desired countries. When making the chart with ggplot2 you will need to add both geom_point and geom_line layers, and use the Set1 ColorBrewer palette using: scale_color_brewer(palette = “Set1”).
# 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>
p1 <- nations2 |>ggplot(aes(x = year, y = gdp, fill = country, color = country)) +theme_minimal()+geom_point() +geom_line ()+scale_fill_brewer(palette ="Set1") +labs(title ="China's Rise to Become the Largest Economy",y ="GDP ($ trillion)",x ="year")p1
pFor the second chart, using dplyr you will need to group_by region and year, and then summarize on your mutated value for gdp
using summarise(GDP = sum(gdp, na.rm = TRUE)). (There will be null values, or NAs, in this data, so you will need to use na.rm = TRUE).
Each region’s area will be generated by the command geom_area ()
When drawing the chart with ggplot2, you will need to use the Set2 ColorBrewer palette using scale_fill_brewer(palette = “Set2”)
Think about the difference between filland color when making the chart, and where the above fill command needs to go in order for the regions to fill with the different colors when making the chart, and put a very thin white line around each area.
`summarise()` has grouped output by 'region'. You can override using the
`.groups` argument.
p2 <- nations3 |>ggplot(aes(x = year, y = GDP, fill = region)) +geom_area (color ="white") +scale_fill_brewer(palette ="Set2") +labs(title ="GDP by World Bank Region",# \n breaks the long titley ="GDP ($ Trillions)",fill ="World Bank Region")p2
