library(tidyverse)
# Load dataset
gapminder_wide <- read_csv("data/gapminder_wide.csv")Week 3 Assignment: Core Analysis with Gapminder
Setup
Part 1 – Setup and Data Loading
Task 1.1 Examine the structure
glimpse(gapminder_wide)Rows: 142
Columns: 26
$ country <chr> "Afghanistan", "Albania", "Algeria", "Angola", "Argenti…
$ continent <chr> "Asia", "Europe", "Africa", "Africa", "Americas", "Ocea…
$ gdpPercap_1952 <dbl> 779.4453, 1601.0561, 2449.0082, 3520.6103, 5911.3151, 1…
$ gdpPercap_1957 <dbl> 820.8530, 1942.2842, 3013.9760, 3827.9405, 6856.8562, 1…
$ gdpPercap_1962 <dbl> 853.1007, 2312.8890, 2550.8169, 4269.2767, 7133.1660, 1…
$ gdpPercap_1967 <dbl> 836.1971, 2760.1969, 3246.9918, 5522.7764, 8052.9530, 1…
$ gdpPercap_1972 <dbl> 739.9811, 3313.4222, 4182.6638, 5473.2880, 9443.0385, 1…
$ gdpPercap_1977 <dbl> 786.1134, 3533.0039, 4910.4168, 3008.6474, 10079.0267, …
$ gdpPercap_1982 <dbl> 978.0114, 3630.8807, 5745.1602, 2756.9537, 8997.8974, 1…
$ gdpPercap_1987 <dbl> 852.3959, 3738.9327, 5681.3585, 2430.2083, 9139.6714, 2…
$ gdpPercap_1992 <dbl> 649.3414, 2497.4379, 5023.2166, 2627.8457, 9308.4187, 2…
$ gdpPercap_1997 <dbl> 635.3414, 3193.0546, 4797.2951, 2277.1409, 10967.2820, …
$ gdpPercap_2002 <dbl> 726.7341, 4604.2117, 5288.0404, 2773.2873, 8797.6407, 3…
$ gdpPercap_2007 <dbl> 974.5803, 5937.0295, 6223.3675, 4797.2313, 12779.3796, …
$ lifeExp_1952 <dbl> 28.801, 55.230, 43.077, 30.015, 62.485, 69.120, 66.800,…
$ lifeExp_1957 <dbl> 30.33200, 59.28000, 45.68500, 31.99900, 64.39900, 70.33…
$ lifeExp_1962 <dbl> 31.99700, 64.82000, 48.30300, 34.00000, 65.14200, 70.93…
$ lifeExp_1967 <dbl> 34.02000, 66.22000, 51.40700, 35.98500, 65.63400, 71.10…
$ lifeExp_1972 <dbl> 36.08800, 67.69000, 54.51800, 37.92800, 67.06500, 71.93…
$ lifeExp_1977 <dbl> 38.43800, 68.93000, 58.01400, 39.48300, 68.48100, 73.49…
$ lifeExp_1982 <dbl> 39.854, 70.420, 61.368, 39.942, 69.942, 74.740, 73.180,…
$ lifeExp_1987 <dbl> 40.822, 72.000, 65.799, 39.906, 70.774, 76.320, 74.940,…
$ lifeExp_1992 <dbl> 41.674, 71.581, 67.744, 40.647, 71.868, 77.560, 76.040,…
$ lifeExp_1997 <dbl> 41.763, 72.950, 69.152, 40.963, 73.275, 78.830, 77.510,…
$ lifeExp_2002 <dbl> 42.129, 75.651, 70.994, 41.003, 74.340, 80.370, 78.980,…
$ lifeExp_2007 <dbl> 43.828, 76.423, 72.301, 42.731, 75.320, 81.235, 79.829,…Your answer:
The dataset contains country-level information about GDP per capita and life expectancy across different years. Each row represents a country and its continent. The columns contain values for specific years, meaning the dataset is currently in a wide format rather than a tidy format.
Part 2 – Data Tidying
Task 2.1 Convert to tidy format
gap_tidy <- gapminder_wide %>%
pivot_longer(
cols = contains("_"),
names_to = c(".value", "year"),
names_sep = "_"
) %>%
mutate(year = as.integer(year))
head(gap_tidy,10)# A tibble: 10 × 5
country continent year gdpPercap lifeExp
<chr> <chr> <int> <dbl> <dbl>
1 Afghanistan Asia 1952 779. 28.8
2 Afghanistan Asia 1957 821. 30.3
3 Afghanistan Asia 1962 853. 32.0
4 Afghanistan Asia 1967 836. 34.0
5 Afghanistan Asia 1972 740. 36.1
6 Afghanistan Asia 1977 786. 38.4
7 Afghanistan Asia 1982 978. 39.9
8 Afghanistan Asia 1987 852. 40.8
9 Afghanistan Asia 1992 649. 41.7
10 Afghanistan Asia 1997 635. 41.8Task 2.2 Explain .value
Your answer:
The .value sentinel tells pivot_longer() that part of the column names should become the names of new variables. In this dataset, the column names contain both the variable name and the year. Using .value allows us to create separate columns for GDP per capita and life expectancy while placing the year values into a separate column.
Task 2.3 Filter selected countries
gap_filtered <- gap_tidy %>%
filter(
year >= 1970,
country %in% c(
"Turkey",
"Brazil",
"Korea, Rep.",
"Germany",
"United States",
"China"
)
)
gap_filtered# A tibble: 48 × 5
country continent year gdpPercap lifeExp
<chr> <chr> <int> <dbl> <dbl>
1 Brazil Americas 1972 4986. 59.5
2 Brazil Americas 1977 6660. 61.5
3 Brazil Americas 1982 7031. 63.3
4 Brazil Americas 1987 7807. 65.2
5 Brazil Americas 1992 6950. 67.1
6 Brazil Americas 1997 7958. 69.4
7 Brazil Americas 2002 8131. 71.0
8 Brazil Americas 2007 9066. 72.4
9 China Asia 1972 677. 63.1
10 China Asia 1977 741. 64.0
# ℹ 38 more rowsPart 3 – Grouped Summaries
Task 3.1 Average values by continent
continent_summary <- gap_tidy %>%
group_by(continent) %>%
summarize(
avg_gdp = mean(gdpPercap, na.rm = TRUE),
avg_lifeExp = mean(lifeExp, na.rm = TRUE)
)
continent_summary# A tibble: 5 × 3
continent avg_gdp avg_lifeExp
<chr> <dbl> <dbl>
1 Africa 2194. 48.9
2 Americas 7136. 64.7
3 Asia 7902. 60.1
4 Europe 14469. 71.9
5 Oceania 18622. 74.3Your answer:
North America generally has the highest average GDP per capita and one of the highest life expectancies. Higher income levels often allow countries to invest more in healthcare, nutrition, and infrastructure, which contributes to longer life expectancy.
Task 3.2 Countries with highest GDP
top_gdp_countries <- gap_tidy %>%
group_by(country) %>%
summarize(
avg_gdp = mean(gdpPercap, na.rm = TRUE)
) %>%
slice_max(avg_gdp, n = 5)
top_gdp_countries# A tibble: 5 × 2
country avg_gdp
<chr> <dbl>
1 Kuwait 65333.
2 Switzerland 27074.
3 Norway 26747.
4 United States 26261.
5 Canada 22411.Your answer:
Countries with the highest GDP per capita are typically developed economies with strong financial sectors or natural resources. Smaller countries can also appear high on the list because GDP per capita divides total economic output by population.
Task 3.3 Correlation by continent
continent_correlation <- gap_tidy %>%
group_by(continent) %>%
summarize(
correlation = cor(gdpPercap, lifeExp, use = "complete.obs")
)
continent_correlation# A tibble: 5 × 2
continent correlation
<chr> <dbl>
1 Africa 0.426
2 Americas 0.558
3 Asia 0.382
4 Europe 0.781
5 Oceania 0.956Your answer:
Most continents show a positive correlation between GDP per capita and life expectancy. As countries become wealthier, they can invest more in healthcare systems and public health improvements, which increases life expectancy.
Part 4 – Data Integration
Task 4.1 Import datasets
gap_life <- read_csv("data/gap_life.csv")
gap_gdp <- read_csv("data/gap_gdp.csv")
glimpse(gap_life)Rows: 1,618
Columns: 3
$ country <chr> "Mali", "Malaysia", "Zambia", "Greece", "Swaziland", "Iran", "…
$ year <dbl> 1992, 1967, 1987, 2002, 1967, 1997, 2007, 2007, 1957, 2002, 19…
$ lifeExp <dbl> 48.388, 59.371, 50.821, 78.256, 46.633, 68.042, 73.747, 78.098…glimpse(gap_gdp)Rows: 1,618
Columns: 3
$ country <chr> "Bangladesh", "Mongolia", "Taiwan", "Burkina Faso", "Angola"…
$ year <dbl> 1987, 1997, 2002, 1962, 1962, 1977, 2007, 1962, 1992, 1972, …
$ gdpPercap <dbl> 751.9794, 1902.2521, 23235.4233, 722.5120, 4269.2767, 2785.4…Task 4.2 Join datasets
gap_joined <- inner_join(gap_life, gap_gdp)
gap_joined# A tibble: 1,535 × 4
country year lifeExp gdpPercap
<chr> <dbl> <dbl> <dbl>
1 Mali 1992 48.4 739.
2 Malaysia 1967 59.4 2278.
3 Zambia 1987 50.8 1213.
4 Greece 2002 78.3 22514.
5 Swaziland 1967 46.6 2613.
6 Iran 1997 68.0 8264.
7 Venezuela 2007 73.7 11416.
8 Portugal 2007 78.1 20510.
9 Sweden 1957 72.5 9912.
10 Brazil 2002 71.0 8131.
# ℹ 1,525 more rowsTask 4.3 Compare observations
nrow(gap_joined)[1] 1535gap_joined %>%
distinct(country) %>%
nrow()[1] 142nrow(gap_life)[1] 1618nrow(gap_gdp)[1] 1618Your answer:
The joined dataset may contain fewer rows than the original datasets because inner_join() only keeps observations that appear in both datasets. If a country-year combination exists in one dataset but not the other, it will be removed during the join.
Task 4.4 Missing values
gap_joined %>%
filter(is.na(lifeExp) | is.na(gdpPercap))# A tibble: 0 × 4
# ℹ 4 variables: country <chr>, year <dbl>, lifeExp <dbl>, gdpPercap <dbl>Task 4.5 Handling missing values
Your answer:
One way to handle missing values is to remove rows that contain missing data. This simplifies analysis but may reduce the size of the dataset. Another approach is to estimate missing values using interpolation or averages, but this introduces assumptions that may affect accuracy.
Part 5 – Economic Interpretation
Based on the analysis, Asia appears to have experienced some of the most dramatic economic growth since 1952, especially due to the rapid development of countries such as China and South Korea. The results also show a clear positive relationship between GDP per capita and life expectancy. In general, countries with higher income levels tend to have longer life expectancy because they can invest more in healthcare, infrastructure, and living standards. However, the strength of this relationship varies across continents. Some regions already have high life expectancy levels, which weakens the correlation slightly. There are also limitations to this analysis because GDP per capita alone does not capture other important factors such as inequality, healthcare access, or environmental conditions.
AI Use Log
| Tool Used | Prompt | How Output Was Used |
|---|---|---|
| ChatGPT | Asked for help structuring R code and Quarto document | Reviewed the code, tested it in RStudio, and adjusted explanations |