1 + 1[1] 2Assignment 6
Quarto
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Running Code
When you click the Render button a document will be generated that includes both content and the output of embedded code. You can embed code like this:
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[1] 4The echo: false option disables the printing of code (only output is displayed).
library(tidyverse) ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.2.0 ✔ readr 2.1.6
✔ forcats 1.0.1 ✔ stringr 1.6.0
✔ ggplot2 4.0.2 ✔ tibble 3.3.1
✔ lubridate 1.9.5 ✔ tidyr 1.3.2
✔ purrr 1.2.1
── 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 errorslibrary(ggfortify)
library(htmltools)
library(plotly)
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':
layoutlibrary(dplyr)getwd()[1] "C:/Users/angel/OneDrive/Desktop"list.files() [1] "AALAS training"
[2] "AngelPorter_resume_docx (2).docx"
[3] "assignment-6.rmarkdown"
[4] "assignment-6_files"
[5] "assignment 6.html"
[6] "assignment 6.qmd"
[7] "assignment 6.rmarkdown"
[8] "assignment 6_files"
[9] "Data110"
[10] "desktop.ini"
[11] "Discord.lnk"
[12] "Guardian Browser.lnk"
[13] "Myfirstworddocument.docx"
[14] "nations.csv"
[15] "need proof of insurance.pptx"
[16] "Python Bootcamp for Data Analytics"
[17] "rsconnect"
[18] "Sociology 100"
[19] "Training Links"
[20] "Training material on CITI PROGRAM.org"
[21] "Zoom Workplace.lnk" file.choose("nations.csv")[1] "C:\\Users\\angel\\OneDrive\\Desktop\\assignment 6.rmarkdown"nations <- read_csv('C:/Users/angel/OneDrive/Desktop/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.#source: U.S. Census Bureau and Nathan Yau
nations %>%
mutate(gdp_trillions = gdp_percap * population / 1e12)# A tibble: 5,275 × 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
7 AD AND Andorra 2004 NA 78337 10.9 2
8 AD AND Andorra 2010 NA 84419 9.8 1.7
9 AD AND Andorra 2001 NA 67770 11.8 2.1
10 AD AND Andorra 2002 NA 71046 11.2 2.1
# ℹ 5,265 more rows
# ℹ 3 more variables: region <chr>, income <chr>, gdp_trillions <dbl>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>View(nations)
str(nations)spc_tbl_ [5,275 × 10] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ iso2c : chr [1:5275] "AD" "AD" "AD" "AD" ...
$ iso3c : chr [1:5275] "AND" "AND" "AND" "AND" ...
$ country : chr [1:5275] "Andorra" "Andorra" "Andorra" "Andorra" ...
$ year : num [1:5275] 1996 1994 2003 1990 2009 ...
$ gdp_percap : num [1:5275] NA NA NA NA NA NA NA NA NA NA ...
$ population : num [1:5275] 64291 62707 74783 54511 85474 ...
$ birth_rate : num [1:5275] 10.9 10.9 10.3 11.9 9.9 NA 10.9 9.8 11.8 11.2 ...
$ neonat_mortal_rate: num [1:5275] 2.8 3.2 2 4.3 1.7 1.6 2 1.7 2.1 2.1 ...
$ region : chr [1:5275] "Europe & Central Asia" "Europe & Central Asia" "Europe & Central Asia" "Europe & Central Asia" ...
$ income : chr [1:5275] "High income" "High income" "High income" "High income" ...
- attr(*, "spec")=
.. cols(
.. iso2c = col_character(),
.. iso3c = col_character(),
.. country = col_character(),
.. year = col_double(),
.. gdp_percap = col_double(),
.. population = col_double(),
.. birth_rate = col_double(),
.. neonat_mortal_rate = col_double(),
.. region = col_character(),
.. income = col_character()
.. )
- attr(*, "problems")=<externalptr> summary(nations) iso2c iso3c country year
Length:5275 Length:5275 Length:5275 Min. :1990
Class :character Class :character Class :character 1st Qu.:1996
Mode :character Mode :character Mode :character Median :2002
Mean :2002
3rd Qu.:2008
Max. :2014
gdp_percap population birth_rate neonat_mortal_rate
Min. : 239.7 Min. :9.004e+03 Min. : 6.90 Min. : 0.70
1st Qu.: 2263.6 1st Qu.:7.175e+05 1st Qu.:13.40 1st Qu.: 6.70
Median : 6563.2 Median :5.303e+06 Median :21.60 Median :15.00
Mean : 12788.8 Mean :2.958e+07 Mean :24.16 Mean :19.40
3rd Qu.: 17195.0 3rd Qu.:1.757e+07 3rd Qu.:33.88 3rd Qu.:29.48
Max. :141968.1 Max. :1.364e+09 Max. :55.12 Max. :73.10
NA's :766 NA's :14 NA's :295 NA's :525
region income
Length:5275 Length:5275
Class :character Class :character
Mode :character Mode :character
# Step 1: Create the new GDP variable for the entire dataset
# The dataset contains 'gdp_percap' and 'population' columns [1].
nations <- nations %>%
mutate(gdp = (gdp_percap * population) / 1000000000000)
# ---------------------------------------------------------
# Chart 1: Four-Country Trend
# ---------------------------------------------------------
# Filter data for four desired countries
chart1_data <- nations %>%
filter(country %in% c("United States", "China", "Germany", "Japan"))
# Draw Chart 1
ggplot(data = chart1_data, mapping = aes(x = year, y = gdp, color = country)) +
geom_point() +
geom_line() +
scale_color_brewer(palette = "Set1") +
labs(y = "GDP (in Trillions of Dollars)") # Add your custom name here
# Step 1: Create the summarized data using dplyr
chart2_data <- nations %>%
group_by(region, year) %>%
summarise(GDP = sum(gdp, na.rm = TRUE))`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by region and year.
ℹ Output is grouped by region.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(region, year))` for per-operation grouping
(`?dplyr::dplyr_by`) instead.# Step 2: Draw the area chart with ggplot2
ggplot(data = chart2_data, mapping = aes(x = year, y = GDP, fill = region)) +
geom_area(color = "white", linewidth = 0.2) +
scale_fill_brewer(palette = "Set2") +
labs(title = "Total GDP by Region over Time",
y = "GDP (Trillions of Dollars)",
x = "Year")