library(tidyverse)
gapminder_wide <- read_csv("data/gapminder_wide.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.
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,…Use glimpse to see the structure of the dataset
glimpse(gapminder_wide)
# The data set has 142 rows and 26 columns. The first two columns represent the country and the continent, while the remaining columns represent the values of the GDP per capita for different years. The year is included in the column names, such as gdpPercap_1952, which means that the data is in a wide format rather than a tidy format.
------------------------------------------------------------------------
## 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.
::: {.cell}
```{.r .cell-code}
# Your code here
gap_tidy <- gapminder_wide |>
pivot_longer(
cols = -c(country, continent),
names_to = c(".value", "year"),
names_sep = "_"
)
head(gap_tidy, 10)# A tibble: 10 × 5
country continent year gdpPercap lifeExp
<chr> <chr> <chr> <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. 41.8:::
Task 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 argument tells the ‘pivot_longer()’ function to create multiple value columns based on the prefixes in the column names. Here, it splits the variables such as ‘gdpPercap’ and ‘lifeExp’, while at the same time extracting the year into a separate column. This is the right function because the data stores multiple variables in the column names based on the years, and this is the typical structure for a wide format.
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"))
gap_filtered# A tibble: 48 × 5
country continent year gdpPercap lifeExp
<chr> <chr> <chr> <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_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. 74.3Questions 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: The continent with the highest average GDP per capita also has the highest average life expectancy. Yes, this is the same continent: Oceania. This could be because, in Oceania, countries such as Australia and New Zealand are economically stable, have good healthcare systems, and high standards of living, so their residents have a high level of income and life expectancy.
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
top_gdp_countries<- gap_tidy |>
group_by(country) |>
summarize(
avg_gdp= mean(gdpPercap, na.rm = TRUE),
.groups= "drop"
) |>
arrange(desc(avg_gdp)) |>
slice_head(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.Look at your result: Do any of these countries surprise you? Why might small, wealthy countries appear at the top?
Your answer: Some of the countries are not surprising because they are rich countries, such as the United States and Switzerland. It is interesting to see that a small country such as Kuwait is at the top. This is because the GDP per capital is per person and therefore even a small country can be at the top.
Task 3.3: Calculate the correlation between GDP per capita and life expectancy for each continent. Use the full tidy dataset.
# Your code here
correlation_continent <- gap_tidy |>
group_by(continent) |>
summarise(
correlation = cor(gdpPercap, lifeExp, use = "complete.obs")
)
correlation_continent# 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 0.956Questions 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 relationship between GDP and life expectancy is strongest in Oceania and Europe and weakest in Asia. This means that in Europe and Oceania, the relationship between GDP and life expectancy is stronger, whereas in Asia it is weaker.
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("/Users/omeryilmaz/ECON465_DataScience/data/gap_life.csv")
gap_gdp <- read_csv("/Users/omeryilmaz/ECON465_DataScience/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…glimpse(gap_gdp)
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)
glimpse(gap_joined)Rows: 1,535
Columns: 4
$ country <chr> "Mali", "Malaysia", "Zambia", "Greece", "Swaziland", "Iran",…
$ year <dbl> 1992, 1967, 1987, 2002, 1967, 1997, 2007, 2007, 1957, 2002, …
$ lifeExp <dbl> 48.388, 59.371, 50.821, 78.256, 46.633, 68.042, 73.747, 78.0…
$ gdpPercap <dbl> 739.0144, 2277.7424, 1213.3151, 22514.2548, 2613.1017, 8263.…Task 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
nrow(gap_joined)
n_distinct(gap_joined$country) Your answer: The gap_joined dataset contains 1535 rows and 142 unique countries. The number of rows in gap_joined may be lower than in the original datasets due to inner_join, which only keeps rows that are present in both datasets. If some rows are missing in one of the datasets, these rows will be omitted 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
gap_joined |> filter(is.na(lifeExp) | is.na(gdpPercap))
Task 4.5: Propose one way an economist could handle these missing values. What are the trade-offs of your proposed method?
Your answer:One way is by removing the rows that contain NA. This makes the data cleaner and easier to analyze. However the disadvantage is that some data set will 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.
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:
#ai use Log
| Tool Used | Prompt Given | How You Verified or Modified the Output |
|---|---|---|
| ChatGPT | I used AI when I encountered errors in my R code, to identify spelling or syntax mistakes, and to better understand what certain functions and commands meant. | I followed the instructor’s guidance while completing the assignment and attempted to solve the tasks myself. I mainly used AI to locate mistakes and clarify concepts. I verified all outputs by running the code in R and adjusted the results when necessary. |
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 |
```