GDP Change Across Countries: 2007–2017
Jiajie Chen
2026-09-20
Analysis Question
Which major economies experienced the largest increase in GDP between 2007 and 2017?
Import the Data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── 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 errorslibrary(ggplot2)
gdp <- read_csv("gdp.csv")## Rows: 264 Columns: 60
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Country Name, Country Code
## dbl (58): 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, ...
##
## 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(gdp)## # A tibble: 6 × 60
## `Country Name` `Country Code` `1960` `1961` `1962` `1963` `1964` `1965`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba ABW NA NA NA NA NA NA
## 2 Afghanistan AFG 5.38e8 5.49e8 5.47e8 7.51e8 8.00e8 1.01e9
## 3 Angola AGO NA NA NA NA NA NA
## 4 Albania ALB NA NA NA NA NA NA
## 5 Andorra AND NA NA NA NA NA NA
## 6 Arab World ARB NA NA NA NA NA NA
## # ℹ 52 more variables: `1966` <dbl>, `1967` <dbl>, `1968` <dbl>, `1969` <dbl>,
## # `1970` <dbl>, `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>,
## # `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>, `1979` <dbl>,
## # `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>, `1984` <dbl>,
## # `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>, `1989` <dbl>,
## # `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>,
## # `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>, `1999` <dbl>, …I imported the World Bank GDP dataset and viewed the first few rows of the data.
Calculate GDP Change
gdp$diff <- gdp$"2017" - gdp$"2007"
head(gdp)## # A tibble: 6 × 61
## `Country Name` `Country Code` `1960` `1961` `1962` `1963` `1964` `1965`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Aruba ABW NA NA NA NA NA NA
## 2 Afghanistan AFG 5.38e8 5.49e8 5.47e8 7.51e8 8.00e8 1.01e9
## 3 Angola AGO NA NA NA NA NA NA
## 4 Albania ALB NA NA NA NA NA NA
## 5 Andorra AND NA NA NA NA NA NA
## 6 Arab World ARB NA NA NA NA NA NA
## # ℹ 53 more variables: `1966` <dbl>, `1967` <dbl>, `1968` <dbl>, `1969` <dbl>,
## # `1970` <dbl>, `1971` <dbl>, `1972` <dbl>, `1973` <dbl>, `1974` <dbl>,
## # `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>, `1979` <dbl>,
## # `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>, `1984` <dbl>,
## # `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>, `1989` <dbl>,
## # `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>, `1994` <dbl>,
## # `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>, `1999` <dbl>, …I created a new variable called diff to measure the
change in GDP from 2007 to 2017.
Select Major Economies
major_economies <- subset(gdp,
`Country Name` == "United States" |
`Country Name` == "China" |
`Country Name` == "Japan" |
`Country Name` == "Germany" |
`Country Name` == "United Kingdom" |
`Country Name` == "France" |
`Country Name` == "India" |
`Country Name` == "Brazil" |
`Country Name` == "Canada" |
`Country Name` == "Italy" |
`Country Name` == "Australia" |
`Country Name` == "Spain" |
`Country Name` == "Mexico" |
`Country Name` == "Indonesia" |
`Country Name` == "Korea, Rep."
)
major_economies## # A tibble: 15 × 61
## `Country Name` `Country Code` `1960` `1961` `1962` `1963` `1964`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Australia AUS 1.86e10 1.96e10 1.99e10 2.15e10 2.38e10
## 2 Brazil BRA 1.52e10 1.52e10 1.99e10 2.30e10 2.12e10
## 3 Canada CAN 4.11e10 4.08e10 4.20e10 4.47e10 4.89e10
## 4 China CHN 5.97e10 5.01e10 4.72e10 5.07e10 5.97e10
## 5 Germany DEU NA NA NA NA NA
## 6 Spain ESP 1.21e10 1.38e10 1.61e10 1.91e10 2.13e10
## 7 France FRA 6.27e10 6.83e10 7.63e10 8.56e10 9.49e10
## 8 United Kingdom GBR 7.23e10 7.67e10 8.06e10 8.54e10 9.34e10
## 9 Indonesia IDN NA NA NA NA NA
## 10 India IND 3.65e10 3.87e10 4.16e10 4.78e10 5.57e10
## 11 Italy ITA 4.04e10 4.48e10 5.04e10 5.77e10 6.32e10
## 12 Japan JPN 4.43e10 5.35e10 6.07e10 6.95e10 8.17e10
## 13 Korea, Rep. KOR 3.96e 9 2.42e 9 2.81e 9 3.99e 9 3.46e 9
## 14 Mexico MEX 1.30e10 1.42e10 1.52e10 1.70e10 2.01e10
## 15 United States USA 5.43e11 5.63e11 6.05e11 6.39e11 6.86e11
## # ℹ 54 more variables: `1965` <dbl>, `1966` <dbl>, `1967` <dbl>, `1968` <dbl>,
## # `1969` <dbl>, `1970` <dbl>, `1971` <dbl>, `1972` <dbl>, `1973` <dbl>,
## # `1974` <dbl>, `1975` <dbl>, `1976` <dbl>, `1977` <dbl>, `1978` <dbl>,
## # `1979` <dbl>, `1980` <dbl>, `1981` <dbl>, `1982` <dbl>, `1983` <dbl>,
## # `1984` <dbl>, `1985` <dbl>, `1986` <dbl>, `1987` <dbl>, `1988` <dbl>,
## # `1989` <dbl>, `1990` <dbl>, `1991` <dbl>, `1992` <dbl>, `1993` <dbl>,
## # `1994` <dbl>, `1995` <dbl>, `1996` <dbl>, `1997` <dbl>, `1998` <dbl>, …I selected 15 major economies to compare their GDP changes while excluding regional and income-group aggregates in the dataset.
GDP Change in Trillions
major_economies$diff_trillion <- major_economies$diff / 1000000000000
major_economies[, c("Country Name", "diff_trillion")]## # A tibble: 15 × 2
## `Country Name` diff_trillion
## <chr> <dbl>
## 1 Australia 0.471
## 2 Brazil 0.658
## 3 Canada 0.188
## 4 China 8.69
## 5 Germany 0.237
## 6 Spain -0.168
## 7 France -0.0747
## 8 United Kingdom -0.452
## 9 Indonesia 0.583
## 10 India 1.40
## 11 Italy -0.268
## 12 Japan 0.357
## 13 Korea, Rep. 0.408
## 14 Mexico 0.0972
## 15 United States 4.91I converted the GDP change to trillions of US dollars to make the results easier to interpret.
Summary Statistics
summarize(major_economies, average_growth = mean(diff_trillion, na.rm = TRUE), maximum_growth = max(diff_trillion, na.rm = TRUE), minimum_growth = min(diff_trillion, na.rm = TRUE))## # A tibble: 1 × 3
## average_growth maximum_growth minimum_growth
## <dbl> <dbl> <dbl>
## 1 1.14 8.69 -0.452I used summarize() to calculate the average, maximum,
and minimum GDP changes among the selected economies.
GDP Increase Above $1 Trillion
large_growth <- subset(major_economies, diff > 1000000000000)
large_growth[, c("Country Name", "diff_trillion")]## # A tibble: 3 × 2
## `Country Name` diff_trillion
## <chr> <dbl>
## 1 China 8.69
## 2 India 1.40
## 3 United States 4.91nrow(large_growth)## [1] 3I identified the major economies whose GDP increased by more than $1 trillion.
Count the Increase Groups
major_economies$growth_group <- ifelse(major_economies$diff > 1000000000000, "Over $1 Trillion", "Under $1 Trillion")
count(major_economies, growth_group)## # A tibble: 2 × 2
## growth_group n
## <chr> <int>
## 1 Over $1 Trillion 3
## 2 Under $1 Trillion 12I used count() to compare how many major economies had GDP increases above or below $1 trillion.
Grouped Analysis
gdp_grouped <- group_by(major_economies, growth_group)
summarize(gdp_grouped, average_growth = mean(diff_trillion, na.rm = TRUE))## # A tibble: 2 × 2
## growth_group average_growth
## <chr> <dbl>
## 1 Over $1 Trillion 5.00
## 2 Under $1 Trillion 0.170I used group_by() and summarize() to
compare the average GDP change between the two groups.
Rank GDP Increase
sort_growth <- major_economies[ order(major_economies$diff, decreasing = TRUE), ]
sort_growth[, c("Country Name", "diff_trillion")]## # A tibble: 15 × 2
## `Country Name` diff_trillion
## <chr> <dbl>
## 1 China 8.69
## 2 United States 4.91
## 3 India 1.40
## 4 Brazil 0.658
## 5 Indonesia 0.583
## 6 Australia 0.471
## 7 Korea, Rep. 0.408
## 8 Japan 0.357
## 9 Germany 0.237
## 10 Canada 0.188
## 11 Mexico 0.0972
## 12 France -0.0747
## 13 Spain -0.168
## 14 Italy -0.268
## 15 United Kingdom -0.452Visualization
ggplot(major_economies, aes(x = reorder(`Country Name`, diff_trillion), y = diff_trillion)) +
geom_col() +
coord_flip() +
labs(title = "GDP Change Across Major Economies, 2007–2017", x = "Country", y = "GDP Change (Trillion US$)")
Key Insights
China experienced the largest GDP increase among the selected major economies, followed by the United States and India.
Only 3 of the 15 selected major economies (China, the United States, and India) experienced GDP increases of more than $1 trillion between 2007 and 2017.