title: “ASSIGNMENT_CEMRENURHASCAN” author: “CEMRE NUR HASCAN” date: “2026-03-10” output: html_document — ## The Economic Question
# Load the tidyverse package
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.1 ✔ stringr 1.5.2
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## 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("data/gapminder_wide.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.#I imported the dataTask 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 has 142 rows and 26 columns. When I run this function, a table appears showing the columns and their values. The first columns are country and continent, followed by GDP per capita and life expectancy for different years (for example gdpPercap_1952 to gdpPercap_2007 and lifeExp_1952 to lifeExp_2007). The data looks a bit complex because the information for each year is stored in separate columns.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 (%>%) sends the dataset (gapminder_wide) to the next function.
# pivot_longer() reshapes the data from wide format to a tidy/long format.
# The dataset originally has many columns like gdpPercap_1952, gdpPercap_1957, lifeExp_1987 etc.
# cols = -c(country, continent) means these two columns are excluded from the pivot
# because they do not contain year information.
# names_to = c(".value", "year") splits the column names into two parts.
# For example lifeExp_1987 becomes lifeExp and 1987.
# The .value part keeps lifeExp and gdpPercap as separate column names,
# while the second part becomes the year column.
# names_sep = "_" tells R to split the column names using the underscore.
# values_drop_na = FALSE means missing values are kept in the dataset.
# mutate(year = as.numeric(year)) converts the year column into numeric format.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 keeps the first part of the column names as the new column names. This means variables like *lifeExp* and *gdpPercap* stay as separate columns. With names_to = c(".value", "year") , the first part becomes the variable name and the second part becomes the *year* column. As a result, the dataset will have columns such as *gdpPercap, **lifeExp, and **year*. Using .value with pivot_longer() helps organize the data in a clearer and more readable format, which makes analysis easier.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 satisfy certain conditions.
# country %in% c(...) is used to filter specific countries.
# c() creates a list of countries and %in% checks if the country is in that list.
# As a result, the dataset keeps only these six countries.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 data by continent.
# summarize() creates a summary table where each continent has its own row.
# mean() calculates the average values, and na.rm = TRUE ignores missing values.
# .groups = "drop" removes the grouping and returns 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:
Oceania has the highest average GDP per capita and life expectancy. This result surprised me at first. However, countries like Australia and New Zealand are highly developed economies, which increases the regional average. Australia is one of the world’s largest exporters of minerals and energy, and its relatively small population raises GDP per capita. In addition, these countries have strong healthcare systems and high government spending on public health. Higher income levels often allow governments to invest more in healthcare and social services, which can increase 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 value Look 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 see that Kuwait, Norway, and Switzerland have higher GDP per capita than the United States and Canada. Although the US and Canada have very large economies, GDP per capita is calculated by dividing total GDP by population. Countries like Kuwait, Norway, and Switzerland have much smaller populations, which increases their GDP per capita. Kuwait being at the top does not surprise me because it has large oil reserves and high living standards. Norway also earns significant income from exporting natural gas and crude oil. Switzerland, on the other hand, does not have many natural resources but has a strong economy based on high value-added services such as banking and finance. The US and Canada have high GDP overall, but their large populations lower their GDP per capita.
Task 3.3: Calculate the correlation between GDP per capita and life expectancy for each continent. Use the full tidy dataset.
```{r}} 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
**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 has the highest correlation, while Asia has the lowest. In general, as GDP per capita increases, living standards and life expectancy also increase. Money itself does not buy a longer life, but it can provide better conditions such as healthcare, hygiene, clean water, and infrastructure. A strong correlation suggests that economic growth is being used to improve public welfare. A weaker correlation may indicate that the wealth is not equally distributed or not fully invested in public services. In Asia, some countries like Japan and South Korea show a strong relationship between GDP per capita and life expectancy. However, in countries such as India, life expectancy does not increase at the same rate as GDP per capita. This difference may be explained by income inequality and the very large population of the continent. Oceania has fewer observations, which suggests that countries like Australia and New Zealand dominate the results.
------------------------------------------------------------------------
## 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.
``` r
gap_life <- read.csv("data/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…gap_gdp <- read.csv("data/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 rows 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?
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 has 1,535 rows
and 142 unique countries. In the original datasets, both gap_life and
gap_gdp have 1,618 rows. The difference occurs because inner_join() only
keeps rows that exist in both datasets. Any row missing a match in
either dataset is excluded. To be included, each row must have both a
matching year and country. 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 looked into this and found several ways to handle missing data. Each method has its advantages but also potential drawbacks. The first step is to ask why the data is missing. If it is missing randomly, I might remove those rows, but this can have consequences—for example, a missing year could coincide with an important event like a pandemic. If the data is not missing randomly, I would consider using a proxy method, which involves estimating missing values based on another dataset that is strongly correlated with the original data.
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)) keeps only rows for 1952 and 2007.
# group_by(continent, year) groups the data by continent and year.
# summarize(avg_gdp = mean(gdpPercap, na.rm = TRUE)) calculates the average GDP per capita for each group.
# na.rm = TRUE ignores missing values.
# .groups = "drop" removes the grouping after summarizing.
# pivot_wider(names_from = year, values_from = avg_gdp, names_prefix = "gdp_") reshapes the table to wide format.
# The column names come from the years and values come from avg_gdp.
# names_prefix = "gdp_" adds "gdp_" before each year in the column name.
# mutate(growth = gdp_2007 - gdp_1952) creates a new column showing GDP growth between 1952 and 2007. > I calculated the GDP growth rate with the help of ChatGPT. I
wrote some of the code myself, and AI suggested using the pipe operator
and modified the pivot_wider() part. Oceania shows the
highest growth rate. Between 1952 and 2007, many countries in Oceania
gained independence, experienced high migration, and developed their
service sectors and resource exports. Australia had a major impact on
population growth after World War II, especially through its “populate
or perish” policy. During the same period, Asian countries focused on
industrialization and imported natural resources from countries like
Australia and New Zealand. Oceania also invested in agriculture, health
technologies, and tourism, particularly in the Pacific Islands.
From the correlation I calculated earlier,…
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, mainly due to Australia and New Zealand. When governments invest in public health, hygiene, and other social services, we can expect a strong relationship between income and life expectancy.
However, there are several limitations to this analysis. The data only includes GDP per capita and life expectancy, and does not account for other important factors such as war, migration, health crises, or inequalities. The time range is limited to 1952–2007, and events like World War II, which ended just seven years before 1952, are not reflected. Using inner_join() also removed some observations. While we can see correlations, the data does not reveal the underlying causes. Additionally, the data is recorded in five-year intervals, which makes it difficult to observe short-term trends.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 |