# Load the tidyverse package
library(tidyverse)
# Import the wide Gapminder dataset
gapminder_wide <- read_csv("gapminder_wide(1).csv")Week 3 Assignment: Core Analysis with Gapminder
The Economic Question
How have GDP per capita and life expectancy evolved across different continents since 1952? Which continents have seen the fastest growth, and which countries are outliers?
Assignment Instructions
This assignment is designed to help you practice the data cleaning and transformation skills you learned in Week 3. You will work with the Gapminder dataset to answer the economic question above.
Before You Start:
- Read each part carefully. The questions ask you to explain your thinking, not just provide code.
- Use the lab handout as a reference – it contains all the code patterns you need.
- If you use AI, follow the Academic Integrity Reminder at the end. Document all AI interactions in your AI Use Log.
Part 1: Setup and Data Loading (5 points)
Task 1.1: Use glimpse() to examine the structure of gapminder_wide. In your own words, describe what you see. How many rows and columns are there? What do the column names tell you about the data format?
# Your code here
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: [I am seeing 142 rows and 26 columns when I run the glimpse() function for the “gapminder_wide(1)” CSV file. These column names tell me the data formats: country and continent columns are
Part 2: Data Tidying with .value (20 points)
In the lab, you learned how to use pivot_longer() with the .value sentinel to reshape wide data into tidy format.
Task 2.1: Write code to transform gapminder_wide into a tidy dataset with columns: country, continent, year, gdpPercap, and lifeExp. Show the first 10 rows of your tidy dataset.
# Your code here
gap_tidy <- gapminder_wide |>
pivot_longer(
cols = -c(country, continent), # Pivot all columns except country and continent
names_to = c(".value", "year"), # Split column names into: (new variable name, year)
names_sep = "_", # Split at the underscore
values_drop_na = FALSE # Keep NAs for now
) |>
mutate(year = as.numeric(year)) # Convert year from character to number
# Look at the result
glimpse(gap_tidy)Rows: 1,704
Columns: 5
$ country <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <chr> "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asia", "Asi…
$ year <dbl> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …
$ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…# Look at the result
head(gap_tidy, 10)# A tibble: 10 × 5
country continent year gdpPercap lifeExp
<chr> <chr> <dbl> <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 in 2-3 sentences what the .value sentinel does in your code. Why is it the right tool for this dataset?
Your answer: The “.value” sentinel is essential for making a tidy dataset from a wide dataset when using the pivot_longer() function. It tells the pivot_longer() function to split column names like “gdpPercap_1952”. “gdpPercap_1952” and “lifeExp_1952” are transformed into “year = 1952”, “gdpPercap = xxx value”, and “lifeExp = xxx value”. So, we separate complex column names and values with the “.value” sentinel.
Task 2.3: From your tidy dataset, filter to keep only observations from 1970 onwards for the following countries: "Turkey", "Brazil", "Korea, Rep.", "Germany", "United States", "China". Save this filtered dataset as gap_filtered.
# Your code here
gap_filtered <- gap_tidy |>
filter(
year >= 1970,
country %in% c("Turkey", "Brazil", "Korea, Rep.", "Germany", "United States", "China")
)
# View the result
gap_filtered# A tibble: 48 × 5
country continent year gdpPercap lifeExp
<chr> <chr> <dbl> <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 (25 points)
Now you will use group_by() and summarize() to answer questions about continents and countries.
Task 3.1: Calculate the average GDP per capita and average life expectancy for each continent across all years (use the full tidy dataset, not the filtered one).
# Your code here
continent_avg_gdp <- gap_tidy |>
group_by(continent) |>
summarize(
mean_gdp = mean(gdpPercap, na.rm = TRUE),
avg_lifeExp = mean(lifeExp, na.rm = TRUE),
.groups = "drop" # drop the grouping after summarizing
)
continent_avg_gdp# A tibble: 5 × 3
continent mean_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. 74.3Questions to answer: - Which continent has the highest average GDP per capita? - Which continent has the highest average life expectancy? - Are these the same continent? Why might that be?
Your answer: We can see clearly Oceania is highest average GDP per capita continent and highest average life expectancy continent. So, Oceania has the highest values for both indicators. The reason might be that Oceania has fewer countries when we compared with the other continents and also Oceania’s countries are developed countries such as Australia and New Zealand, which have high living standards and strong healthcare systems.
Task 3.2: Find the 5 countries with the highest average GDP per capita across all years. Show the country name and its average GDP per capita.
# Your code here
# Highest average GDP per capita
top_gdp <- gap_tidy |>
group_by(country) |>
summarize(avg_gdp = mean(gdpPercap, na.rm = TRUE), .groups = "drop") |>
arrange(desc(avg_gdp)) |>
head(5)
top_gdp# 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.Look at your result: Do any of these countries surprise you? Why might small, wealthy countries appear at the top?
Your answer: According to my findings, I am not surprised to see Canada, the US and Norway. Because these countries are developed, strong and economically consistent year by year. But Kuwait and Switzerland are different in terms of population size compared to Canada, the US and Norway. So mathematically, if a country has small population size, of course their GDP per capita will be higher than other countries under the same or close other conditions. Because of that reason at the first I was surprised but then, when I thought about it, I understood.
Task 3.3: Calculate the correlation between GDP per capita and life expectancy for each continent. Use the full tidy dataset.
# Your code here
cor_by_continent <- gap_tidy |>
group_by(continent) |>
summarize(
correlation = cor(gdpPercap, lifeExp, use = "complete.obs"),
n_obs = n(),
.groups = "drop"
)
cor_by_continent# A tibble: 5 × 3
continent correlation n_obs
<chr> <dbl> <int>
1 Africa 0.426 624
2 Americas 0.558 300
3 Asia 0.382 396
4 Europe 0.781 360
5 Oceania 0.956 24Questions to answer: - In which continent is the relationship strongest (highest correlation)? - In which continent is it weakest? - What might explain the differences between continents?
Your answer: The strongest relationship is in Oceania because it has the highest correlation (0.956). The weakest relationship is in Asia with the lowest correlation (0.382). One reason can be population difference. Asia has many countries and a very large population, so the relationship between GDP per capita and life expectancy can be more complex. Oceania has very few countries, so the relationship looks stronger.
Part 4: Data Integration (20 points)
Now you will practice joining two separate datasets: one containing only life expectancy, and one containing only GDP per capita.
Task 4.1: Import gap_life.csv and gap_gdp.csv. Use glimpse() to examine each one.
# Your code here
gap_life <- read_csv("gap_life.csv")
gap_gdp <- read_csv("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: Use inner_join() to combine them into a dataset called gap_joined. Join by the columns they have in common.
# Your code here
gap_joined <- inner_join(gap_life, gap_gdp, by = c("country", "year"))
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: Answer the following: - How many rows are in gap_joined? - How many unique countries are in gap_joined? - Compare this to the original number of rows in gap_life.csv and gap_gdp.csv. Why might the joined dataset have fewer rows?
# Your code for counting
# number of rows
nrow(gap_joined)[1] 1535# number of unique countries
n_distinct(gap_joined$country)[1] 142# compare with original datasets
nrow(gap_life)[1] 1618nrow(gap_gdp)[1] 1618Your answer: The dataset gap_joined has 1535 rows and 142 unique countries. The original datasets gap_life and gap_gdp both have 1618 rows. The joined dataset has fewer rows because inner_join() only keeps rows that exist in both datasets. If some country-year combinations are missing in one dataset, they will not appear in the joined dataset.
Task 4.4: Check for missing values in gap_joined. Are there any rows where lifeExp or gdpPercap is NA? If so, list them.
# Your code here
# Which rows have NA?
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: Propose one way an economist could handle these missing values. What are the trade-offs of your proposed method?
Your answer: We don’t have NA data but if we had, one way an economist can handle missing values is to remove the rows with missing data. The good side is that the dataset becomes cleaner and easier to analyze. But the bad side is that removing rows can reduce the number of observations and some useful information can be lost.
Part 5: Economic Interpretation (15 points)
Write a short paragraph (5‑8 sentences) addressing the following questions. Use evidence from your analysis in Parts 3 and 4 to support your claims.
- Which continent has seen the most dramatic economic growth since 1952? (Look at the numbers – don’t just guess.)
- Is there a clear relationship between GDP per capita and life expectancy across continents? Refer to your correlation results.
- What are the main limitations of this analysis? Consider data quality, missing values, time period, and what the data can’t tell us.
Your paragraph: For the first question i write simple code for comparing gdp growth levels 1952 and 2007
gdp_growth <- gap_tidy |>
filter(year %in% c(1952, 2007)) |>
group_by(continent, year) |>
summarize(avg_gdp = mean(gdpPercap, na.rm = TRUE), .groups = "drop")
gdp_growth# A tibble: 10 × 3
continent year avg_gdp
<chr> <dbl> <dbl>
1 Africa 1952 1253.
2 Africa 2007 3089.
3 Americas 1952 4079.
4 Americas 2007 11003.
5 Asia 1952 5195.
6 Asia 2007 12473.
7 Europe 1952 5661.
8 Europe 2007 25054.
9 Oceania 1952 10298.
10 Oceania 2007 29810.According to my findings, Oceania has seen the most dramatic economic growth since 1952. In 1952 the average GDP per capita was about 10298 and in 2007 it increased to about 29810. This shows a very large increase compared to other continents.
For the second question, yes, there is a clear relationship between GDP per capita and life expectancy. The correlation results show a positive relationship in all continents. For example, Oceania has the highest correlation (0.95) while Asia has the lowest correlation (0.38). This means when GDP per capita increases, life expectancy usually increases too.
Lastly, one limitation of this analysis is that the data only shows numbers. It cannot explain all reasons behind life expectancy. For example, factors like healthcare quality, inequality, education level, or government policies are not included in the data. These factors can also affect life expectancy in countries. Because of this, the dataset cannot show the full economic and social situation of each country. So the results should be interpreted carefully.
Part 6: Reproducibility (5 points)
Before submitting, check that your document meets these requirements:
Academic Integrity Reminder
You are encouraged to discuss concepts with classmates, but your submitted work must be your own. If you use AI assistants (ChatGPT, Copilot, etc.), you must include an AI Use Log at the end of your document documenting:
| Tool Used | Prompt Given | How You Verified or Modified the Output |
|---|---|---|
| ChatGPT | Asked how to calculate economic growth by continent using GDP per capita data (1952 vs 2007) in R | I checked the output from my own dataset and used the numbers in my interpretation instead of copying the explanation directly |
| ChatGPT | Asked how to interpret correlation results between GDP per capita and life expectancy | I compared the explanation with my R output and rewrote the answer using my own words and numbers |
| ChatGPT | Asked for help correcting grammar in my written answers | I kept my original ideas and structure but corrected spelling and grammar mistakes before submitting |
Using AI to generate entire answers without understanding or modification violates academic integrity and will result in a grade of zero.
Submission Checklist
Glossary of Functions Used
| Function | What it does |
|---|---|
select() |
Keeps only specified columns |
filter() |
Keeps rows that meet conditions |
mutate() |
Adds or modifies columns |
pivot_longer() |
Reshapes wide to long |
group_by() |
Groups data for subsequent operations |
summarize() |
Reduces grouped data to summary stats |
inner_join() |
Combines two tables, keeping matching rows |
distinct() |
Keeps unique rows |
slice_max() |
Keeps rows with highest values |
arrange() |
Sorts rows |
contains() |
Helper for selecting columns with a pattern |
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