How to Approach Assignments on Data Manipulation with dplyr in R

Why dplyr and tidyverse are Essential in Assignments

Before diving into specific assignment-solving strategies, it is important to understand why dplyr matters in statistics coursework:

• Readable Syntax: dplyr verbs (like filter(), select(), mutate(), arrange(), summarize()) make your code intuitive and closer to natural language.
• Efficiency: dplyr is optimized for performance, allowing manipulation of large datasets faster than base R.
• Chaining Operations: Using the pipe operator (|> or %>%), you can link multiple operations into a single clear workflow.
• Reproducibility: Assignments that use dplyr are easier to follow and replicate, which is critical in both academic and professional settings.
• Integration: dplyr works seamlessly with other tidyverse packages like ggplot2 for visualization and tidyr for reshaping data.

Thus, if your assignment asks for data wrangling, exploratory analysis, or preparing datasets for modeling, dplyr is the toolkit to rely on.

The Gapminder Dataset: A Perfect Learning Case

Most assignments using dplyr often rely on datasets like gapminder, which contains information about life expectancy, GDP per capita, and population across countries and years.

Here’s what makes it suitable for assignments:

• It has continuous variables (GDP per capita, life expectancy).
• It includes categorical variables (continent, country).
• It spans multiple time periods, making it perfect for longitudinal analysis.
• It provides realistic global data, allowing for meaningful statistical insights.

You can load the dataset by installing the required packages:

install.packages("gapminder") install.packages("tidyverse") library(gapminder) library(dplyr)

Once loaded, you can view it with:

head(gapminder)

Step 1: Understanding the Basics of dplyr Verbs

Your assignment will usually require specific transformations.

Here are the core dplyr verbs you should master:

select() – choose specific columns.

gapminder %>% select(country, year, lifeExp)

filter() – pick rows that meet conditions.

gapminder %>% filter(year == 2007, continent == "Asia")

arrange() – reorder rows.

gapminder %>% arrange(desc(lifeExp))

mutate() – create new columns.

gapminder %>% mutate(gdp = gdpPercap * pop)

summarize() (or summarise()) – compute summary statistics.

gapminder %>% summarize(mean_life = mean(lifeExp))

group_by() – split data into groups for grouped operations.

gapminder %>% group_by(continent) %>% summarize(avg_life = mean(lifeExp))

Assignments typically require combining these verbs to filter, compute, and interpret results.

Step 2: Filtering Data for Assignments

One of the first tasks in assignments is subsetting data.

For example, suppose you are asked:

"Find the countries in Asia with life expectancy greater than 70 in the year 2007."

gapminder %>% filter(continent == "Asia", year == 2007, lifeExp > 70)

This code applies logical conditions, producing a smaller dataset you can interpret.

• Statistical Skill Practiced: Identifying relevant subsets of data for hypothesis testing or descriptive summaries.

Step 3: Creating New Variables with mutate()

Assignments often require creating derived variables.

Suppose you need to calculate the total GDP of each country:

gapminder %>% mutate(total_gdp = gdpPercap * pop)

This creates a new variable while keeping the dataset intact.

• Statistical Skill Practiced: Understanding relationships between variables (population × per-capita GDP = total GDP).

Step 4: Summarizing Data

Summarization is a key component of exploratory data analysis (EDA).

Assignments may ask:

"What is the average life expectancy by continent in 2007?"

gapminder %>% filter(year == 2007) %>% group_by(continent) %>% summarize(avg_life = mean(lifeExp), .groups = 'drop')

This produces continent-level statistics, a common requirement for comparative analysis.

• Statistical Skill Practiced: Aggregation, descriptive statistics, and interpretation across groups.

Step 5: Combining Verbs with Pipes

Assignments rarely stop at one step. They often require chaining operations to get the final result.

Example question:

"Find the top 5 countries with the highest life expectancy in 2007 across all continents."

gapminder %>% filter(year == 2007) %>% arrange(desc(lifeExp)) %>% head(5)

This combines filtering, arranging, and subsetting.

• Statistical Skill Practiced: Designing multi-step workflows and interpreting results.

Step 6: Comparative and Grouped Analysis

Assignments often require comparisons over time or groups.

Example:

"Compare the average GDP per capita between Africa and Europe in 1952 and 2007."

filter(year %in% c(1952, 2007), continent %in% c("Africa", "Europe")) %>% group_by(continent, year) %>% summarize(avg_gdpPercap = mean(gdpPercap), .groups = 'drop')

This produces a summary table showing economic growth trends.

• Statistical Skill Practiced: Grouped comparison and interpretation of trends.

Step 7: Preparing Data for Statistical Modeling

Assignments may not stop at descriptive statistics—they may ask you to prepare the dataset for regression modeling or time-series analysis.

For instance, you might need to:

• Subset only certain countries.
• Create new predictors like log(GDP per capita).
• Aggregate yearly data into decades.

Example:

gapminder %>% filter(country %in% c("India", "China")) %>% mutate(log_gdpPercap = log(gdpPercap), decade = floor(year / 10) * 10) %>% group_by(country, decade) %>% summarize(avg_life = mean(lifeExp), avg_log_gdp = mean(log_gdpPercap), .groups = 'drop')

This transforms the dataset into a form ready for regression or trend analysis.

Statistical Skill Practiced: Feature engineering, transformation, and preparing for inferential statistics.

Step 8: Exploratory Data Analysis with dplyr

Assignments often combine data wrangling with EDA. Using dplyr with ggplot2, you can create meaningful plots.

For instance:

library(ggplot2) gapminder %>% filter(year == 2007) %>% ggplot(aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) + geom_point(alpha = 0.7) + scale_x_log10() + theme_minimal()

This visualization shows the relationship between wealth and life expectancy across continents.

• Statistical Skill Practiced: Linking data wrangling with visualization for storytelling.

Step 9: Common Assignment Pitfalls and How to Avoid Them

• Forgetting Grouping Behavior: After group_by(), remember to use .groups = 'drop' in summarize() if you want to reset grouping.
• Confusing mutate() and summarize(): mutate() adds new columns for each observation, while summarize() collapses groups into summaries.
• Data Type Issues: Always check variable types (str()) before filtering or summarizing.
• Overusing Base R: Many students mix base R functions with dplyr unnecessarily, leading to messy code. Stick to dplyr when the assignment requires it.

Step 10: Interpreting Results in a Statistical Context

The biggest mistake students make is focusing only on code, without providing statistical interpretation in assignments.

For example:

If you compute:

gapminder %>% group_by(continent) %>% summarize(avg_life = mean(lifeExp))

Don’t just present the table.

Explain what it means:

• Europe has the highest average life expectancy, suggesting better healthcare and living standards.
• Africa lags behind, showing global health inequality.

Assignments are graded not only on code correctness but also on interpretation.

Conclusion

Assignments that involve data manipulation with dplyr in R are not just coding tasks—they test your ability to think statistically, clean and structure data, and provide interpretations backed by evidence. Using the gapminder dataset as an example, we have walked through how to use dplyr verbs (filter, select, mutate, arrange, summarize, group_by) to answer assignment-style questions.

The key to excelling is combining these verbs into clear workflows, avoiding common mistakes, and always explaining your results in a broader statistical context.

Whether your goal is to score high on assignments or build practical skills for research and industry, mastering dplyr and tidyverse is an essential step.