# Load the tidyverse package
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# Import the wide Gapminder dataset
gapminder_wide <- read_csv("gapminder_wide(1).csv") ## Rows: 142 Columns: 26
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): country, continent
## dbl (24): gdpPercap_1952, gdpPercap_1957, gdpPercap_1962, gdpPercap_1967, gd...
##
## 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.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?
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 can see that the dataset contains 142 rows and 26 columns. When I
run this function, a table appears showing the columns and their values,
but the structure seems somewhat messy. The first columns are
country and continent, followed by
variables such as gdpPercap_1952 through
gdpPercap_2007, and lifeExp_1952
through lifeExp_2007. This makes the dataset appear
very wide because the information is spread across many years in
separate columns. The country and
continent columns are labeled as
<chr>, meaning they contain character data, while the
remaining columns are labeled as <dbl>, indicating
that they store numeric values.
.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.
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))
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…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# The pipe operator (%>% or |>) takes the dataset (gapminder_wide)
# and passes it to the function on the right.
# The pivot_longer() function helps reorganize the dataset because
# the original data contains many columns such as gdpPercap_1952,
# gdpPercap_1957, gdpPercap_1962 and lifeExp_1987, lifeExp_1992, etc.
# This function converts the dataset from wide format to long format.
# As a result, the number of columns decreases while the number of rows increases.
# cols = -c(country, continent) means these two columns will not be included
# in the pivoting process. The minus (-) sign excludes them because they
# do not contain information about years.
# names_to splits the column names into two parts.
# For example: lifeExp_1987 becomes lifeExp and 1987.
# Normally these parts would go into new columns, but using ".value"
# makes the first part become the new column name.
# Therefore, lifeExp and gdpPercap will become column names.
# names_to = c(".value", "year") means the first part becomes the variable name
# and the second part is stored in a new column called year.
# names_sep = "_" indicates that the column names are separated
# at the underscore (_) character.
# values_drop_na = FALSE means missing values will be kept
# and not removed from the dataset.
# mutate(year = as.numeric(year)) converts the year column
# into numeric format so the years can be treated as numbers.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:
Using .value allows the first part of the original column name to become the new column name. In other words, it keeps the original variable names as column names. For example, lifeExp and gdpPercap become the names of columns in the new dataset.
When we use names_to = c(".value", "year"), the first
part of the column name becomes the variable name, while the second part
is stored in a new column called year. As a result, the
dataset will contain three main columns: gdpPercap,
lifeExp, and year.
The .value argument is used together with the
pivot_longer() function. It helps make the dataset more
organized, easier to read, and simpler to analyze.
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.
gap_filtered <- gap_tidy %>%
filter(country %in% c("Turkey", "Brazil", "Korea, Rep.", "Germany", "United States", "China"),
year >= 1970)
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 rows# filter() keeps only the rows that meet specific conditions.
# country %in% c(...) is used to filter countries.
# c() creates a vector of values, and %in% checks whether
# the country column contains any of those values.
# This means that only the selected 6 countries
# will remain in the dataset.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).
average_gdp_and_lifeExp <- gap_tidy %>%
group_by(continent) %>%
summarize(
average_gdp = mean(gdpPercap, na.rm = TRUE),
average_lifeExp = mean(lifeExp, na.rm = TRUE),
.groups = "drop"
)
average_gdp_and_lifeExp## # A tibble: 5 × 3
## continent average_gdp average_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. 74.3# group_by(continent) groups the dataset by the continent variable,
# meaning the data will be processed separately for each continent.
# summarize() reduces the data into a smaller table by calculating
# summary statistics. Each continent will have its own row in the result.
# The mean() function calculates the average value, and
# na.rm = TRUE removes missing values before calculating the mean.
# .groups = "drop" removes the grouping after the summarization
# so the result is returned as a regular table.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?
Your answer:
Ocenia has the highest gdp per capita and average life expectancy. Actually it suprised me. This will be because of all of the countries here being an island country. They could be doing so much sea trade. Avustralia and New Zeland are the most developed countries in this area. When i researched, i saw that Australia is the worlds biggest ore and energy exporter. With its low population this will increase gdp per capita. Also Australia has strict migration rules, they only accept people that will benefit the country. a country with higher gdp will have a higher life expectancy - this will make sense if the government is spending for the public.- These countries have developped and free healhcare systems. Also another fact that I learned was These countries have the highest budget for early diagnosis and healty life campaigns. Governments spending for the publics health will increase average 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.
highest_avg_gdp_top_5 <- gap_tidy %>%
group_by(country) %>%
summarise(
avg_gdp = mean(gdpPercap, na.rm = TRUE),
.groups = "drop"
) %>%
slice_max(avg_gdp, n=5)
highest_avg_gdp_top_5## # 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.# slice_max() keeps rows with the highest valueLook at your result: Do any of these countries surprise you? Why might small, wealthy countries appear at the top?
Your answer:
When I look at the table, I notice that Kuwait, Norway, and Switzerland have higher GDP per capita than the United States and Canada. Although the United States and Canada have very large economies, GDP per capita is calculated by dividing total GDP by the population. Since Kuwait, Norway, and Switzerland have smaller populations than the United States and Canada, their GDP per capita can appear higher. Kuwait being at the top did not surprise me because it is well known for its large oil reserves and high living standards. The country is also known for benefits such as low or no personal income tax and various government payments to citizens. When I looked into Norway, I found that a large share of its exports comes from natural gas and crude oil. These natural resources generate significant national income, which increases its GDP per capita. Switzerland, on the other hand, does not have many natural resources. However, it has a very strong economy based on high value-added services such as banking, finance, and other advanced industries. In contrast, the United States and Canada still have very high total GDP, but their larger populations reduce their GDP per capita compared with countries that have smaller populations and strong income sources.
Task 3.3: Calculate the correlation between GDP per capita and life expectancy for each continent. Use the full tidy dataset.
continent_info <- gap_tidy %>%
select(country, continent) %>%
distinct()
corelation_by_continent <- gap_tidy |>
group_by(continent) |>
summarize(
correlation = cor(gdpPercap, lifeExp, use = "complete.obs"),
n_obs = n(),
.groups = "drop"
)
corelation_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# distinct() removes duplicate rows from the dataset.
# cor() calculates the correlation between two variables.
# The correlation value ranges from -1 to 1:
# - Close to 1 → strong positive correlation
# - Close to 0 → little or no correlation
# - Close to -1 → strong negative correlation
# use = "complete.obs" tells R to only use rows where both
# variables have non-missing values.
# n_obs = n() counts the number of rows for each continent.Questions 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: Oceania shows the highest correlation between GDP per capita and life expectancy, while Asia has the lowest. Generally, as GDP per capita increases, living standards improve, which tends to increase life expectancy. Money itself does not buy more years of life, but it provides conditions that support longer life, such as better healthcare, sanitation, and infrastructure. A strong correlation indicates that economic growth is being used effectively for public benefits, like clean water, safe food, and hospitals. On the other hand, a weak correlation suggests that wealth may not be distributed to improve public welfare and may reflect higher income inequality. In Asia, we can see that some countries, like Japan and South Korea, have a strong correlation between GDP per capita and life expectancy. However, in countries like India, increases in GDP per capita do not necessarily lead to higher life expectancy. This pattern suggests that income inequality may weaken the correlation.
Population size is another factor. Asia has the largest population in the world, which can affect averages and correlations. Additionally, Asia ranks fourth among continents in terms of income inequality. For Oceania, the number of observations is only 24, which likely means that Australia and New Zealand dominate the data for that continent.
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.
gap_life <- read.csv("gap_life.csv")
glimpse(gap_life)## Rows: 1,618
## Columns: 3
## $ country <chr> "Mali", "Malaysia", "Zambia", "Greece", "Swaziland", "Iran", "…
## $ year <int> 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…getwd()## [1] "C:/Users/KLAB/Downloads"gap_gdp <- read.csv("gap_gdp.csv")
glimpse(gap_gdp)## Rows: 1,618
## Columns: 3
## $ country <chr> "Bangladesh", "Mongolia", "Taiwan", "Burkina Faso", "Angola"…
## $ year <int> 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.
gap_joined <- inner_join(gap_life, gap_gdp, by = c("country", "year"))
# Combines two tables, keeping matching 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?
nrow(gap_joined) #how many rows - 1535## [1] 1535nrow(gap_life) # - 1618 - original dataset## [1] 1618nrow(gap_gdp) # - 1618 - original dataset## [1] 1618unique(gap_joined$country) #unique countries## [1] "Mali" "Malaysia"
## [3] "Zambia" "Greece"
## [5] "Swaziland" "Iran"
## [7] "Venezuela" "Portugal"
## [9] "Sweden" "Brazil"
## [11] "Pakistan" "Algeria"
## [13] "Equatorial Guinea" "Botswana"
## [15] "Haiti" "Saudi Arabia"
## [17] "Korea, Dem. Rep." "Niger"
## [19] "Congo, Dem. Rep." "United States"
## [21] "Eritrea" "Trinidad and Tobago"
## [23] "Colombia" "Panama"
## [25] "Comoros" "Italy"
## [27] "Nicaragua" "Gambia"
## [29] "Iceland" "Bosnia and Herzegovina"
## [31] "Hong Kong, China" "El Salvador"
## [33] "Myanmar" "Croatia"
## [35] "Finland" "South Africa"
## [37] "Ireland" "United Kingdom"
## [39] "Liberia" "Libya"
## [41] "Malawi" "Norway"
## [43] "India" "Guatemala"
## [45] "Netherlands" "Japan"
## [47] "Mauritania" "Ghana"
## [49] "Taiwan" "Paraguay"
## [51] "Morocco" "Cuba"
## [53] "Guinea" "Denmark"
## [55] "Chad" "Zimbabwe"
## [57] "Yemen, Rep." "Austria"
## [59] "Bahrain" "Egypt"
## [61] "Angola" "Reunion"
## [63] "Senegal" "Gabon"
## [65] "Albania" "Serbia"
## [67] "Lebanon" "Germany"
## [69] "Jamaica" "Canada"
## [71] "Montenegro" "Rwanda"
## [73] "New Zealand" "Syria"
## [75] "Spain" "Slovak Republic"
## [77] "Kenya" "Guinea-Bissau"
## [79] "Cote d'Ivoire" "Sri Lanka"
## [81] "Switzerland" "Afghanistan"
## [83] "Mozambique" "Togo"
## [85] "Namibia" "Tunisia"
## [87] "Uganda" "Mongolia"
## [89] "Bulgaria" "Sao Tome and Principe"
## [91] "Uruguay" "Nepal"
## [93] "West Bank and Gaza" "Iraq"
## [95] "Oman" "Burkina Faso"
## [97] "Cameroon" "Philippines"
## [99] "Kuwait" "Vietnam"
## [101] "Benin" "Dominican Republic"
## [103] "Turkey" "Somalia"
## [105] "Tanzania" "Puerto Rico"
## [107] "Jordan" "Peru"
## [109] "Cambodia" "Chile"
## [111] "Burundi" "China"
## [113] "Israel" "Australia"
## [115] "Mexico" "Lesotho"
## [117] "Madagascar" "Sierra Leone"
## [119] "Korea, Rep." "Ecuador"
## [121] "Slovenia" "Honduras"
## [123] "France" "Belgium"
## [125] "Indonesia" "Romania"
## [127] "Hungary" "Thailand"
## [129] "Central African Republic" "Argentina"
## [131] "Congo, Rep." "Poland"
## [133] "Singapore" "Bangladesh"
## [135] "Bolivia" "Sudan"
## [137] "Mauritius" "Nigeria"
## [139] "Djibouti" "Costa Rica"
## [141] "Ethiopia" "Czech Republic"Your answer: The gap_joined dataset
contains 1,535 rows and 142 unique countries. In the original datasets,
gap_life and gap_gdp each had 1,618 rows. The
difference occurs because the join function only keeps
observations that exist in both datasets. If a row is missing in either
dataset, it is excluded. To ensure a row is included in the joined
dataset, both the year and country
must match between the two datasets. .
Task 4.4: Check for missing values in
gap_joined. Are there any rows where lifeExp
or gdpPercap is NA? If so, list them.
gap_joined %>%
filter(is.na(lifeExp) | is.na(gdpPercap)) # there is no NA data## [1] country year lifeExp gdpPercap
## <0 rows> (or 0-length row.names)Task 4.5: Propose one way an economist could handle these missing values. What are the trade-offs of your proposed method?
Your answer:
I researched this issue and found that there are several ways to handle missing data. Each method has its advantages, but they also come with potential drawbacks. The first step is to ask: why is the data missing? If the missing data is random or coincidental, it might be acceptable to remove those rows. However, this can have consequences—for example, the missing year might coincide with a major event, like a pandemic, which could affect the analysis. If the missing data is not random, a better approach is to use a proxy method, where another dataset that is highly correlated with the original one is used to estimate the missing values. This approach helps preserve important patterns in the data while filling the gaps.
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.
Your paragraph:
growth_rate <- gap_tidy %>%
filter(year%in% c(1952, 2007)) %>%
group_by(continent, year) %>%
summarize(avg_gdp = mean(gdpPercap, na.rm = TRUE),
.groups = "drop") %>%
pivot_wider(names_from = year, values_from = avg_gdp, names_prefix = "gdp_") %>%
mutate(growth= gdp_2007 - gdp_1952 / gdp_1952)
growth_rate## # A tibble: 5 × 4
## continent gdp_1952 gdp_2007 growth
## <chr> <dbl> <dbl> <dbl>
## 1 Africa 1253. 3089. 3088.
## 2 Americas 4079. 11003. 11002.
## 3 Asia 5195. 12473. 12472.
## 4 Europe 5661. 25054. 25053.
## 5 Oceania 10298. 29810. 29809.# filter(year%in% c(1952, 2007)) only gets the years 1952 and 2007
# group_by(continent, year) grouping by continent and year
# summarize(avg_gdp = mean(gdpPercap, na.rm = TRUE) calculates average gdp per capita for each continent and year.
# na.rm = TRUE removes missing variables
# creates a new column called avg_gdp
# .groups = "drop" ends grouping
# pivot_wider(names_from = year, values_from = avg_gdp, names_prefix = "gdp_") makes the table wide
# names_from = year makes new column name year
# values_from = avg_gdp assingns the values to that new column
# names_prefix = "gdp_" adds gdp_ in the columns name (left handed side)
# mutate(growth= gdp_2007 - gdp_1952) creates a column named growthI calculated the growth rate with the help of ChatGPT. I wrote some
of the code, and AI added the pipe operator and redesigned the
pivot_wider() part. According to the results,
Oceania has the highest growth rate. Between 1950 and
2007, many Oceania countries gained independence, experienced high
migration, exported natural resources, and developed their service
sectors. Australia, in particular, boosted its population after World
War II through its “populate or perish” policy. During this period, Asia
focused on industrialization, often trading for natural resources from
Australia and New Zealand, while also developing agriculture, health
technologies, and tourism in the Pacific Islands.
This belongs the correlation i did previously,
continent_info <- gap_tidy %>%
select(country, continent) %>%
distinct()
corelation_by_continent <- gap_tidy |>
group_by(continent) |>
summarize(
correlation = cor(gdpPercap, lifeExp, use = "complete.obs"),
n_obs = n(),
.groups = "drop"
)
corelation_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 24Oceania has the highest correlation between GDP per capita and life expectancy, largely due to Australia and New Zealand. High GDP combined with government spending on public health, hygiene, and infrastructure helps explain this strong relationship.
The main limitations are that the data only includes GDP per capita
and life expectancy, ignoring factors like war, migration, health
issues, and inequalities. It covers a limited period (1952–2007), and
using inner_join() dropped some observations. Also, since
the data is recorded every five years, short-term trends cannot be
observed, and correlation shows association but not causation.
Before submitting, check that your document meets these requirements:
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 |
|---|
| Tool Used:Gemini |
| Prompt Given: what happened in oceania between 1952 and 2007 |
Tool Used: ChatGpt
How You Verified or Modified the Output: growth_rate <- gap_tidy %>% filter(year %in% c(1952, 2007)) %>% group_by(continent, year) %>% summarize(avg_gdp = mean(gdpPercap, na.rm = TRUE), .groups = “drop”) %>% pivot_wider(names_from = year, values_from = avg_gdp, names_prefix = “gdp_”) %>% mutate( absolute_growth = gdp_2007 - gdp_1952, growth_rate = ((gdp_2007 - gdp_1952)) ) ————————————————————————————————————————————-
| 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 |