Ayberk_KOCAKIR_Assignment_1

Author

Ayberk KOCAKIR

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?

Part 1: Setup and Data Loading

In this section, I load the dataset and prepare the environment for the analysis.

Note: The dataset was automatically downloaded as gapminder_wide(1).csv, which is why the file name differs from data/gapminder_wide.csv.

##install.packages("tidyverse") 

## Note: Professor, I commented out install.packages() because the package was already installed, so it only needed to be loaded using library().

library(tidyverse)

gapminder_wide <- read_csv("data/gapminder_wide(1).csv")

Task 1.1:

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,…
gapminder_wide
# A tibble: 142 × 26
   country continent gdpPercap_1952 gdpPercap_1957 gdpPercap_1962 gdpPercap_1967
   <chr>   <chr>              <dbl>          <dbl>          <dbl>          <dbl>
 1 Afghan… Asia                779.           821.           853.           836.
 2 Albania Europe             1601.          1942.          2313.          2760.
 3 Algeria Africa             2449.          3014.          2551.          3247.
 4 Angola  Africa             3521.          3828.          4269.          5523.
 5 Argent… Americas           5911.          6857.          7133.          8053.
 6 Austra… Oceania           10040.         10950.         12217.         14526.
 7 Austria Europe             6137.          8843.         10751.         12835.
 8 Bahrain Asia               9867.         11636.         12753.         14805.
 9 Bangla… Asia                684.           662.           686.           721.
10 Belgium Europe             8343.          9715.         10991.         13149.
# ℹ 132 more rows
# ℹ 20 more variables: gdpPercap_1972 <dbl>, gdpPercap_1977 <dbl>,
#   gdpPercap_1982 <dbl>, gdpPercap_1987 <dbl>, gdpPercap_1992 <dbl>,
#   gdpPercap_1997 <dbl>, gdpPercap_2002 <dbl>, gdpPercap_2007 <dbl>,
#   lifeExp_1952 <dbl>, lifeExp_1957 <dbl>, lifeExp_1962 <dbl>,
#   lifeExp_1967 <dbl>, lifeExp_1972 <dbl>, lifeExp_1977 <dbl>,
#   lifeExp_1982 <dbl>, lifeExp_1987 <dbl>, lifeExp_1992 <dbl>, …

My answer: In our dataset contains 142 rows and 26 colums. Each row represents a country, while the columns contain different variables such as continent, GDP per capita, life expectancy. The column names include both the variable name and the year (for example, lifeExp_1952 and gdpPercap_1952). This indicates that the dataset is stored in a wide format, where the same variable appears in multiple columns for different years.

Part 2: Data Tidying

Task 2.1:

Note: To convert the dataset into tidy format, I used the pivot_longer( ) function. This reshapes the dataset so that each variable becomes a column and each observation becomes a row. In our wide dataset, the years and variables are combined in the same columns name. For example, lifeExp_1952 and gdpPercap_1952. To make the data tidy and easier to analyze, these values needed to be separated into different columns.

gap_tidy <- gapminder_wide |> 
  pivot_longer(
    cols = -c(country, continent),
    names_to = c(".value", "year"),
    names_sep = "_",
    values_drop_na = FALSE
    ) |>
  mutate(year = as.numeric(year)) |>
  select(country, continent, year, gdpPercap, lifeExp)

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.    41.8
## Note: gap_tidy <- I did not add this code because we just want to observe first 10 row.

Task 2.2:

The .value sentinel tells R to use part of the column name as the name of a new variable. In the wide dataset, the variable names and years were combined in the column names (for example, lifeExp_1952). Using .value separates them into proper variables and a year column, which makes the dataset tidy and easier to analyze. For example,

Before: lifeExp_1952

After: lifeExp | 1952

Task 2.3:

gap_filtered <- gap_tidy |>
  filter(
    year >= 1970,
    country %in% c("Turkey", "Brazil", "Korea, Rep.", "Germany", "United States", "China")
  )

Part 3: Grouped Summaries

Task 3.1:

continent_avg_gdp <- gap_tidy |>
  group_by(continent) |>
  summarize(
    avg_gdp = mean(gdpPercap, na.rm = TRUE),
    avg_lifeExp = mean(lifeExp, na.rm = TRUE),
    n_countries = n_distinct(country),  
    .groups = "drop"                     
  )

continent_avg_gdp
# A tibble: 5 × 4
  continent avg_gdp avg_lifeExp n_countries
  <chr>       <dbl>       <dbl>       <int>
1 Africa      2194.        48.9          52
2 Americas    7136.        64.7          25
3 Asia        7902.        60.1          33
4 Europe     14469.        71.9          30
5 Oceania    18622.        74.3           2

Questions 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?

My Answer: Oceania has the highest average GDP per capita (18621.609). Oceania has the highest average life expectancy (74.32621). Oceania has the highest average GDP per capita and the highest average life expectancy. This is likely because the continent mainly includes Australia and New Zealand, which are highly developed countries with strong economies, high living standards, and advanced healthcare systems.

Task 3.2:

countries_avg_gdp <- gap_tidy |>
  group_by(country) |>
  summarize(
    avg_gdp = mean(gdpPercap, na.rm = TRUE),
    avg_lifeExp = mean(lifeExp, na.rm = TRUE),
    .groups = "drop"
    ) |>
   arrange(desc(avg_gdp)) |>
  slice_max(order_by = avg_gdp, n = 5)



countries_avg_gdp
# A tibble: 5 × 3
  country       avg_gdp avg_lifeExp
  <chr>           <dbl>       <dbl>
1 Kuwait         65333.        68.9
2 Switzerland    27074.        75.6
3 Norway         26747.        75.8
4 United States  26261.        73.5
5 Canada         22411.        74.9

Question: Do any of these countries surprise you? Why might small, wealthy countries appear at the top?

My Answer: The results are not very surprising because these countries are known for having strong economies and high living standards. Small but wealthy countries like Kuwait and Switzerland appear at the top. Small countries like Kuwait at the top because Kuwait, for example, earns large revenues from oil exports, and this income is shared among a smaller population, which increases GDP per capita.

Task 3.3:

continent_info <- gap_tidy |>
  select(country, continent) |>
  distinct()

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    24

Question: - In which continent is the relationship strongest (highest correlation)? - In which continent is it weakest? - What might explain the differences between continents?

My Answer:

strongest relationship → Oceania

weakest relationship → Africa

In Oceania, countries such as Australia and New Zealand have high incomes, strong healthcare systems, and high living standards, which creates a strong positive relationship between income and life expectancy. In contrast, many African countries face economic inequality, limited healthcare access, and structural development challenges, which weakens the relationship between income and health outcomes.

Part 4: Data Integration

Task 4.1:

life_data <- read_csv("data/gap_life.csv")
gdp_data <- read_csv("data/gap_gdp.csv")

glimpse(life_data)
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(gdp_data)
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:

gap_joined <- inner_join(life_data, gdp_data, by = c("country", "year"))
nrow(gap_joined)
[1] 1535

Task 4.3:

n_distinct(gap_joined$country)
[1] 142
nrow(gap_joined)
[1] 1535

How many rows are in gap_joined? = 1535

How many unique countries are in gap_joined? =142

The joined dataset may have fewer rows than the original gap_life.csv and gap_gdp.csv datasets because the join keeps only observations that exist in both datasets for the same country and year.

Task 4.4:

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:

The analysis and prediction may seem seriously biased when we do not consider the missing values. For example, in the dataset some missing values leads to prejudice about the model which diminish the performans of the analysis. As an Economist, we can use imputation method to handle missing values.

However, the trade-off is that the estimated values may not perfectly reflect the true data and could introduce estimation errors into the analysis.

Part 5: Economic Interpretation

 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.

Oceania has experienced the most dramatic economic growth since 1952. The average GDP per capita in Oceania increased significantly from about 10298.086 in 1952 to around 29810.188 in 2007

sum(is.na(gapminder_wide))
[1] 0
sum(is.na(gap_tidy))
[1] 0

Although the dataset does not contain missing values, there are still several limitations. The data only covers the period from 1952 to 2007, so it does not reflect more recent economic and demographic changes.

AI Use Log:

Tool Used Prompt Given How You Verified or Modified the Output
ChatGPT How can I calculate economic growth between 1952 and 2007 using GDP per capita in R? I tested the suggested code in RStudio and adapted it to my gap_tidy dataset to calculate the growth for each continent.
ChatGPT How to check for missing values in an R dataset? I ran the suggested commands in my dataset to confirm whether missing values existed and interpreted the results myself.
ChatGPT Help rephrase some explanations in my assignment to make them clearer and more fluent I rewrote and adjusted the explanations to ensure they reflected my own understanding of the analysis.