Data Import, Plotting, and Cleaning in R Programming

R Programming: DataFrames, Cleaning, and Plotting

R Programming: DataFrames, Data Cleaning, and Plotting

This tutorial covers the fundamentals of working with data in R: creating dataframes, importing data, cleaning messy datasets, and creating visualizations using base R functions. All examples use simple, easy-to-understand datasets perfect for beginners.

1. Creating DataFrames in R

A DataFrame is R’s primary data structure for storing tabular data. Think of it as a spreadsheet with rows and columns where each column can contain different data types.

Creating a Simple DataFrame

Let’s create a simple dataset of student information:

Creating a DataFrame
# Create vectors of data student_id <- 1:5 student_names <- c("Alice", "Bob", "Charlie", "Diana", "Eve") math_scores <- c(85, 92, 78, 96, 88) science_scores <- c(90, 85, 92, 79, 95) passed <- c(TRUE, TRUE, TRUE, TRUE, TRUE) # Combine into a dataframe students_df <- data.frame( id = student_id, name = student_names, math = math_scores, science = science_scores, passed = passed ) # View the dataframe print(students_df)

This creates the following dataframe:

id name math science passed
1 Alice 85 90 TRUE
2 Bob 92 85 TRUE
3 Charlie 78 92 TRUE
4 Diana 96 79 TRUE
5 Eve 88 95 TRUE

Tip: Use the str() function to examine the structure of your dataframe, showing data types and a preview of the data.

2. Importing and Exporting Data

In practice, you’ll usually import data from external files rather than creating dataframes manually.

Reading from CSV Files

Reading CSV Files
# Read a CSV file my_data <- read.csv("data_file.csv") # If your CSV has a header row (column names) my_data <- read.csv("data_file.csv", header = TRUE) # If your CSV uses a different separator (like semicolon) my_data <- read.csv("data_file.csv", sep = ";") # Prevent strings from automatically converting to factors my_data <- read.csv("data_file.csv", stringsAsFactors = FALSE)

Writing to CSV Files

Writing to CSV
# Write dataframe to CSV write.csv(students_df, file = "students_data.csv", row.names = FALSE)

Note: Setting row.names = FALSE prevents R from adding an extra column with row numbers, which is usually not needed in exported data.

3. Data Cleaning and Preparation

Real-world data is often messy. Let’s create a dataset with common issues and learn how to fix them.

Creating Messy Data
# Create a dataset with common data issues messy_data <- data.frame( id = 1:6, name = c("Alice", "BOB", "charlie", "Diana", "EVE", "Frank"), age = c(20, 25, NA, 22, 30, 35), score = c("85", "92", "78", "ninety-six", "88", "95"), grade = c("B", "A", "C", "A", "B", "A"), date_joined = c("2023-01-15", "2023-02-20", "2023-01-10", "2023-03-05", "2023-02-28", "2023-04-12") ) print(messy_data)

Common Data Cleaning Tasks

Data Cleaning Steps
# 1. Check for missing values sum(is.na(messy_data)) # Total missing values colSums(is.na(messy_data)) # Missing values by column # 2. Fix inconsistent text (convert to proper case) messy_data$name <- tolower(messy_data$name) # First make all lowercase substr(messy_data$name, 1, 1) <- toupper(substr(messy_data$name, 1, 1)) # Capitalize first letter # 3. Handle missing values # Option A: Remove rows with missing values clean_data <- na.omit(messy_data) # Option B: Fill missing values (with mean for numeric columns) mean_age <- mean(messy_data$age, na.rm = TRUE) messy_data$age[is.na(messy_data$age)] <- mean_age # 4. Fix data types # Convert score from character to numeric (non-numeric becomes NA) messy_data$score <- as.numeric(messy_data$score) # Convert date from character to Date type messy_data$date_joined <- as.Date(messy_data$date_joined) # Convert grade to factor (categorical variable) messy_data$grade <- as.factor(messy_data$grade) # 5. Check the cleaned data str(messy_data) summary(messy_data)

Tip: Always check your data after cleaning using str() and summary() to ensure all transformations worked as expected.

4. Data Manipulation

Once your data is clean, you often need to transform it for analysis.

Data Manipulation Examples
# Create a simple dataset for manipulation sales_data <- data.frame( month = c("Jan", "Feb", "Mar", "Apr", "May", "Jun"), product_a = c(150, 200, 175, 220, 190, 210), product_b = c(180, 160, 195, 170, 205, 185), region = c("North", "South", "North", "South", "North", "South") ) # 1. Add a new column (total sales) sales_data$total_sales <- sales_data$product_a + sales_data$product_b # 2. Create a conditional column sales_data$performance <- ifelse(sales_data$total_sales > 380, "High", "Low") # 3. Subset data (filter rows) high_sales <- sales_data[sales_data$total_sales > 380, ] north_region <- sales_data[sales_data$region == "North", ] # 4. Select specific columns product_data <- sales_data[, c("month", "product_a", "product_b")] # 5. Sort data sales_sorted <- sales_data[order(sales_data$total_sales, decreasing = TRUE), ] print(sales_data)

5. Plotting with Base R

R has powerful built-in plotting functions. Let’s explore the most common types of plots.

Creating Sample Data for Plotting

Sample Data for Plotting
# Create sample data for plotting set.seed(123) # For reproducible random numbers plot_data <- data.frame( category = rep(c("A", "B", "C", "D"), each = 10), value = c(rnorm(10, 50, 10), rnorm(10, 60, 8), rnorm(10, 55, 12), rnorm(10, 65, 9)), group = rep(c("X", "Y"), 20), time = 1:40 ) # Add some relationship for scatter plots plot_data$related_var <- plot_data$value * 1.5 + rnorm(40, 0, 15)

Basic Plot Types

Basic Plot Types
# 1. Histogram - shows distribution of a single variable hist(plot_data$value, main = "Distribution of Values", xlab = "Value", ylab = "Frequency", col = "lightblue", border = "black") # 2. Boxplot - shows distribution by category boxplot(value ~ category, data = plot_data, main = "Values by Category", xlab = "Category", ylab = "Value", col = c("lightcoral", "lightgreen", "lightyellow", "lightblue")) # 3. Scatter plot - shows relationship between two variables plot(plot_data$value, plot_data$related_var, main = "Value vs Related Variable", xlab = "Value", ylab = "Related Variable", pch = 16, # Type of point col = "darkblue") # Add a trend line fit <- lm(related_var ~ value, data = plot_data) abline(fit, lwd = 2) # 4. BARPLOT: counts per Group grp_tab <- table(plot_data$category) barplot(grp_tab, main = "Count by Category", ylab = "Count", xlab = "Category") # 5. LINE PLOT: Value over Time (time-series) # Order by Time first ord <- order(plot_data$time) plot(plot_data$time[ord], plot_data$value[ord], type = "o", main = "Value over Time", xlab = "Time", ylab = "Value") # 6. PAIRS: quick multi-plot to inspect relationships pairs(plot_data[, c("value","related_var")], main = "Pairs plot (Numeric)") # 7. PIE CHART: proportion of Groups pie(table(plot_data$group), main = "Proportion of Groups")

Customizing Plots

Customizing Plots
# Create a customized plot with multiple elements plot(plot_data$value, plot_data$related_var, main = "Customized Scatter Plot", xlab = "Main Variable", ylab = "Related Variable", pch = ifelse(plot_data$group == "X", 16, 17), # Different shapes for groups col = ifelse(plot_data$group == "X", "blue", "red"), # Different colors for groups cex = 1.2) # Point size # Add a legend legend("topleft", legend = c("Group X", "Group Y"), pch = c(16, 17), col = c("blue", "red"), title = "Groups") # Add grid lines grid() # Save plot to file # png("my_plot.png", width = 800, height = 600) # plot(...) # dev.off()

Tip: Use par(mfrow = c(2, 2)) to create a 2×2 grid of plots. This is useful for comparing multiple visualizations. Reset with par(mfrow = c(1, 1)).

6. Putting It All Together: Complete Example

Let’s walk through a complete example from data creation to visualization.

Complete Example
# Step 1: Create sample sales data set.seed(456) months <- month.name[1:6] regions <- c("North", "South", "East", "West") sales <- data.frame( month = rep(months, each = 4), region = rep(regions, 6), revenue = runif(24, 1000, 5000), expenses = runif(24, 500, 3000) ) # Step 2: Calculate profit sales$profit <- sales$revenue - sales$expenses # Step 3: Add a performance indicator sales$performance <- ifelse(sales$profit > 2000, "Excellent", ifelse(sales$profit > 1000, "Good", "Needs Improvement")) # Step 4: Convert to factors sales$month <- factor(sales$month, levels = months) sales$region <- as.factor(sales$region) sales$performance <- factor(sales$performance, levels = c("Needs Improvement", "Good", "Excellent")) # Step 5: Create summary statistics by region region_summary <- aggregate(profit ~ region, data = sales, mean) # Step 6: Visualize the data # Set up a 2x2 plot layout par(mfrow = c(2, 2)) # Plot 1: Bar chart of average profit by region barplot(region_summary$profit, names.arg = region_summary$region, main = "Average Profit by Region", ylab = "Profit ($)", col = "lightgreen") # Plot 2: Boxplot of profit by performance category boxplot(profit ~ performance, data = sales, main = "Profit by Performance", ylab = "Profit ($)", col = c("lightcoral", "lightyellow", "lightgreen")) # Plot 3: Revenue vs Expenses scatter plot plot(sales$revenue, sales$expenses, main = "Revenue vs Expenses", xlab = "Revenue ($)", ylab = "Expenses ($)", pch = 16, col = as.numeric(sales$region)) # Add a reference line for break-even abline(a = 0, b = 1, lty = 2, col = "red") # Plot 4: Profit trend over months (by region) north_data <- sales[sales$region == "North", ] south_data <- sales[sales$region == "South", ] plot(north_data$month, north_data$profit, type = "o", main = "Profit Trend: North vs South", xlab = "Month", ylab = "Profit ($)", col = "blue", ylim = range(sales$profit)) lines(south_data$month, south_data$profit, type = "o", col = "red") legend("topright", legend = c("North", "South"), col = c("blue", "red"), lty = 1) # Reset plot layout par(mfrow = c(1, 1)) # Step 7: Save the cleaned data write.csv(sales, "cleaned_sales_data.csv", row.names = FALSE)

Summary of Key R Functions

Function Purpose Example
data.frame() Create a dataframe df <- data.frame(x=1:3, y=c("a","b","c"))
read.csv() Import CSV file data <- read.csv("file.csv")
write.csv() Export to CSV write.csv(df, "file.csv")
str() Examine structure str(df)
summary() Summary statistics summary(df)
is.na() Find missing values is.na(df$column)
na.omit() Remove rows with NAs clean_df <- na.omit(df)
as.numeric() Convert to numeric df$num <- as.numeric(df$char)
as.factor() Convert to factor df$cat <- as.factor(df$char)
plot() Create various plots plot(x, y)
hist() Create histogram hist(df$values)
boxplot() Create boxplot boxplot(values ~ group, data=df)

Practice Tip: The best way to learn R is by doing. Try modifying the examples above - change the data, adjust the plots, and experiment with different functions. Don't worry about making mistakes; that's how we learn!

R Programming — Data Import, Cleaning & Processing

Clear explanations, examples, and ready-to-run R code (CSV generation → import → clean → process).
R data import and cleaning are core skills. Below we generate sample data (15 rows, 5 columns), save as CSV, show how to import it, then demonstrate common cleaning steps: checking types, handling missing values, renaming, converting factors/numerics, and creating derived columns. Each step is explained with base R functions students will use in real tasks.

The example dataset will simulate a small experiment or sales record: an ID column, a categorical group, two numeric measurements, and a date. After import we'll:
  • inspect structure with str() and head(),
  • treat missing values with simple imputation or removal (is.na()),
  • coerce types (as.numeric(), as.factor(), as.Date()),
  • rename columns, and
  • create derived variables using arithmetic or conditional logic (e.g., categorise scores).
These operations are critical because plotting and analysis need clean, correctly-typed data. Example use-cases: exam scores, sensor readings, or simple sales logs.

Detailed Explanation of Data Generation Code

Below is a clear breakdown of how each column of the dataset was generated in R. These explanations help students understand why each function is used and what type of data it creates.
🔹 1. ID = sprintf("S%02d", 1:n)

The sprintf() function formats text and numbers. The pattern "S%02d" means:
  • Start every ID with the letter S
  • %02d = format numbers so they always have 2 digits (padded with 0 if needed)
Examples produced: S01, S02, S03.

This makes IDs readable and neatly aligned, which is helpful for data management.
🔹 2. Group = sample(c("A","B","C"), n, replace = TRUE)

This line randomly assigns each row to one of three categories: A, B, or C.
  • sample() picks random values from a vector.
  • replace = TRUE allows the same category to appear multiple times.
This is commonly used to simulate group labels in real-world datasets.
🔹 3. Measure1 = round(rnorm(n, mean=50, sd=10), 1)

The rnorm() function generates normally distributed random numbers.
  • mean = 50 → center of distribution
  • sd = 10 → spread/variation
  • round(...,1) → round values to 1 decimal place
Example values: 43.2, 55.7, 49.9.

This is ideal for simulating measurement data such as exam scores or sensor readings.
🔹 4. Measure2 = round(runif(n, 30, 80), 1)

The runif() function generates values from a uniform distribution between 30 and 80.

Example values: 31.4, 72.8, 58.3.

This is often used when values should be equally likely across a range, such as temperature or random test scores.
🔹 5. Date = seq(as.Date("2025-01-01"), by = "7 days", length.out = n)

This creates a sequence of dates:
  • Starting from 2025-01-01
  • Incrementing by 7 days (weekly)
  • Total of n dates
Example sequence:
2025-01-01, 2025-01-08, 2025-01-15, …

This is useful for time-based datasets such as weekly sales, observations, or experimental timelines.
End of data generation explanation — students can now understand how each variable was created.
# 1) GENERATE SAMPLE DATA (5 columns x 15 rows) - run in R set.seed(42) n <- 15 df <- data.frame( ID = sprintf("S%02d", 1:n), # ID: character Group = sample(c("A","B","C"), n, replace = TRUE), # Group: categorical Measure1 = round(rnorm(n, mean=50, sd=10),1), # numeric Measure2 = round(runif(n, 30, 80),1), # numeric Date = seq(as.Date("2025-01-01"), by = "7 days", length.out = n) # Date ) # Introduce some NAs for cleaning examples df$Measure1[c(3,9)] <- NA df$Group[5] <- NA # Write to CSV in working directory write.csv(df, file = "sample_data_rstudy.csv", row.names = FALSE) # Check file created list.files(pattern = "sample_data_rstudy.csv")
Explanation of the R code above:
  • set.seed() ensures reproducible random numbers (important for exercises).
  • We build a data.frame with 5 columns and 15 rows: ID, Group, two numeric measures, and a date column.
  • write.csv(..., row.names = FALSE) writes a CSV without R row numbers — that makes the CSV clean and portable.
  • We intentionally insert a few NAs to show cleaning steps later.
# 2) IMPORT CSV # Use read.csv() which is a base-R function data_in <- read.csv("sample_data_rstudy.csv", stringsAsFactors = FALSE) # Quick checks head(data_in) str(data_in) summary(data_in)
Import notes:
  • read.csv() imports CSVs. Setting stringsAsFactors = FALSE avoids automatic conversion of strings to factors (gives you control).
  • head() shows the first rows. str() reveals column types (character, numeric, etc.). summary() provides min/median/max for numeric columns and counts for character columns.
Next we clean types and missing values so plotting and numeric summaries behave correctly.
# 3) CLEANING & PROCESSING # Convert types data_in$ID <- as.character(data_in$ID) data_in$Group <- as.factor(data_in$Group) # treat as factor (category) data_in$Date <- as.Date(data_in$Date) # convert date column data_in$Measure1 <- as.numeric(data_in$Measure1) # ensure numeric data_in$Measure2 <- as.numeric(data_in$Measure2) # Detect missing values colSums(is.na(data_in)) # shows count of NAs per column # Simple strategies: # a) Remove rows with NAs: data_dropna <- na.omit(data_in) # b) Impute missing numeric values with mean (example for Measure1) mean_m1 <- mean(data_in$Measure1, na.rm = TRUE) data_impute <- data_in data_impute$Measure1[is.na(data_impute$Measure1)] <- round(mean_m1,1) # c) Fill missing Group with "Unknown" data_impute$Group <- as.character(data_impute$Group) data_impute$Group[is.na(data_impute$Group) | data_impute$Group==""] <- "Unknown" data_impute$Group <- as.factor(data_impute$Group) # Derived column: average of measures and a categorical flag data_impute$Avg <- round((data_impute$Measure1 + data_impute$Measure2)/2,1) data_impute$HighAvg <- ifelse(data_impute$Avg >= 55, "High", "Low") data_impute$HighAvg <- as.factor(data_impute$HighAvg) # Final check str(data_impute) head(data_impute)
Cleaning explanation and tips:
  • Always confirm column types with str(). Dates must be Date objects for time series plotting.
  • Handle missing values deliberately: removal (na.omit()) is simple but may bias results; imputation (mean/median or domain-specific) preserves row count.
  • Converting categories to factors (as.factor()) is useful for grouping, table counts, and plotting categories.
  • Creating derived features (like Avg) is commonly needed before plotting or modeling.
End of Topic 1 — data generation, CSV write/read, cleaning, and processing basics.

R Programming — Plotting with Base R (Topic 2)

Plotting is how you explore and present data. This section uses only base R plotting functions (no ggplot2) so students learn the fundamentals that always work in any R environment. We'll produce several common plots using the cleaned dataset created earlier: histogram, boxplot, scatterplot, barplot, line plot/time-series, pairs plot, and pie chart. Each example includes the code and explanation of why and when to use the plot.

Important base functions covered: hist(), boxplot(), plot() (scatter and line), barplot(), pie(), and pairs(). We'll also show how to add titles, axis labels, legends, colors (base R default or simple palettes), and use par() to arrange multiple plots in one display.
# Use the cleaned 'data_impute' from Topic 1 # 1) HISTOGRAM of Avg hist(data_impute$Avg, main = "Histogram of Average Score", xlab = "Average", ylab = "Frequency", breaks = 8) # 2) BOXPLOT of Measure1 by Group boxplot(Measure1 ~ Group, data = data_impute, main = "Measure1 by Group", xlab = "Group", ylab = "Measure1", notch = TRUE) # 3) SCATTER PLOT Measure1 vs Measure2 with regression line plot(data_impute$Measure1, data_impute$Measure2, main = "Measure1 vs Measure2", xlab = "Measure1", ylab = "Measure2", pch = 19) # Add linear fit fit <- lm(Measure2 ~ Measure1, data = data_impute) abline(fit, lwd = 2) # 4) BARPLOT: counts per Group grp_tab <- table(data_impute$Group) barplot(grp_tab, main = "Count by Group", ylab = "Count", xlab = "Group") # 5) LINE PLOT: Avg over Date (time-series) # Order by Date first ord <- order(data_impute$Date) plot(data_impute$Date[ord], data_impute$Avg[ord], type = "o", main = "Avg over Time", xlab = "Date", ylab = "Avg") # 6) PAIRS: quick multi-plot to inspect relationships pairs(data_impute[, c("Measure1","Measure2","Avg")], main = "Pairs plot (Numeric)") # 7) PIE CHART: proportion of HighAvg pie(table(data_impute$HighAvg), main = "Proportion High vs Low Avg")
Plot explanations + examples:
  • Histogram (`hist()`): Good for checking distribution shape (normal, skewed, multimodal). Use `breaks` to control bin width.
  • Boxplot (`boxplot()`): Shows median, quartiles, and outliers. Use formula syntax like `y ~ x` to plot numeric by group.
  • Scatter plot + regression (`plot()` + `lm()` + `abline()`): Visualise relationships between two numeric variables and add a fitted line to judge correlation.
  • Barplot (`barplot()`): For categorical counts (converted by `table()`), e.g., number of samples in each group.
  • Line/time plot (`plot(..., type="o")`): Plot a numeric variable over time; ensure your Date column is of class `Date` and data are ordered by date.
  • Pairs plot (`pairs()`): Quick matrix of scatterplots for several numeric variables — great for exploratory data analysis.
  • Pie chart (`pie()`): Use sparingly — shows proportions. For accessibility prefer barplot or a table.
Example interpretation: if the boxplot shows Group B has higher median `Measure1`, you might inspect Group B rows for experimental differences or verify if a confounder exists.
Tip: In scripts destined for reproducible reports, save plots to files using base functions like png("plot.png", width=800, height=600); ...; dev.off(). For interactive use, run plotting commands in the console or RStudio plot pane.
End of Topic 2 — Base R plotting essentials and examples.

R Code Summary & Helpful Quick Reference

Short cheat-sheet of commands used above (copy/paste friendly). These are base R and work without additional packages.
# Quick reference (base R) read.csv("file.csv", stringsAsFactors = FALSE) write.csv(df, "file.csv", row.names = FALSE) str(df); head(df); summary(df) is.na(df); colSums(is.na(df)) na.omit(df) as.numeric(x); as.factor(x); as.Date(x) hist(x); boxplot(y ~ group, data = df) plot(x,y); abline(lm(y~x, data=df)) barplot(table(df$group)); pie(table(df$group)) pairs(df[c("num1","num2")]) png("file.png", width=800, height=600); plot(...); dev.off()
Final notes for students:
  1. Practice by changing the synthetic data generator (means, sd, groups) and observe how plots change.
  2. Document every cleaning step — keep raw CSV safe and create a cleaned version you use for analysis.
  3. Use base R plotting for fast exploration; later you can learn advanced visualizations (ggplot2) after mastering fundamentals.
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R Programming — Data Cleaning & Analysis Exercises

Practice problems covering data generation, import, cleaning, processing, and visualization.
These exercises will help you practice the R programming concepts covered in the study materials. Work through each problem step by step, testing your code in R to ensure it produces the expected results. The exercises progress from basic data generation to more complex analysis and visualization tasks.

Exercise 1: Data Generation & CSV Export

Create a synthetic dataset with the following specifications:

  • Generate 25 observations (rows)
  • Create these columns:
    • StudentID: Format as "STU001", "STU002", etc.
    • Department: Randomly sample from "Biology", "Chemistry", "Physics", "Mathematics"
    • Test1: Normally distributed scores with mean=75, sd=12
    • Test2: Uniformly distributed scores between 60 and 95
    • EnrollmentDate: Dates starting from "2024-09-01", spaced 3 days apart
  • Introduce 3-4 missing values at random positions in Test1 and Test2 columns
  • Save the dataset as "student_scores.csv" without row names

Verify your work by checking the file exists and examining its structure in R.

Exercise 2: Data Import & Initial Inspection

Import the CSV file you created in Exercise 1 and perform these tasks:

  • Load the data using read.csv() with appropriate parameters
  • Display the first 8 rows of the dataset
  • Check the structure of all variables using str()
  • Generate a statistical summary of all columns
  • Count the number of missing values in each column
  • Identify which specific rows contain missing values in Test1 or Test2

Document any issues you notice with data types or structure.

Exercise 3: Data Cleaning & Type Conversion

Clean the imported dataset by performing these operations:

  • Convert StudentID to character type
  • Convert Department to a factor with appropriate levels
  • Ensure Test1 and Test2 are numeric
  • Convert EnrollmentDate to Date format
  • Handle missing values using two different approaches:
    • Create a version where rows with any missing values are removed
    • Create a version where missing Test scores are imputed with the median of available values
  • Check that all conversions worked correctly using str()

Compare the row counts between the two approaches to missing value handling.

Exercise 4: Data Processing & Derived Variables

Using the cleaned dataset (with imputed missing values), create these derived variables:

  • Calculate the average of Test1 and Test2 for each student
  • Create a categorical variable "Performance" with levels:
    • "Excellent" for averages ≥ 85
    • "Good" for averages between 70 and 84
    • "Needs Improvement" for averages < 70
  • Calculate the difference between Test2 and Test1 scores
  • Create a binary variable "Improved" indicating whether Test2 score is higher than Test1
  • Count how many students are in each Performance category

Verify your calculations by examining a few individual cases.

Exercise 5: Basic Data Visualization

Create the following visualizations using base R plotting functions:

  • A histogram of average test scores with appropriate title and axis labels
  • A boxplot comparing Test1 scores across different Departments
  • A scatter plot of Test1 vs Test2 scores, colored by Department
  • A bar plot showing the count of students in each Performance category
  • A line plot showing the average test score over EnrollmentDate (time series)

For each plot, ensure you include proper titles, axis labels, and legends where appropriate.

Exercise 6: Advanced Analysis & Multi-plot Display

Perform these more advanced analytical tasks:

  • Calculate the mean and standard deviation of Test1 and Test2 for each Department
  • Create a pairs plot (scatterplot matrix) of Test1, Test2, and Average scores
  • Use par(mfrow=...) to display 4 different plots in a single graphics device:
    1. Histogram of Test1 scores
    2. Boxplot of Test2 by Department
    3. Barplot of student counts by Performance category
    4. Scatter plot of Test1 vs Test2 with a regression line
  • Save the multi-plot display as a PNG file
  • Create a summary table showing for each Department:
    • Number of students
    • Mean Test1 and Test2 scores
    • Percentage of students in each Performance category

Exercise 7: Data Export & Process Documentation

Complete your analysis with these final tasks:

  • Save the fully processed dataset (with all derived variables) as a new CSV file
  • Create a text file that documents:
    • The original data issues you identified
    • The cleaning steps you performed
    • Any assumptions you made during data processing
    • Key findings from your analysis
  • Write a function that takes a department name as input and returns:
    • The number of students in that department
    • Their average Test1 and Test2 scores
    • The department's highest performing student
  • Test your function with at least two different department names
Note to Students: These exercises build upon each other. Complete them in order, as later exercises depend on datasets created in earlier ones. Check your work at each step to ensure data integrity throughout the process.
R Programming Exercises - Data Cleaning & Analysis Practice

R Programming — Data Cleaning & Analysis Solutions

Complete solutions for the data generation, cleaning, processing, and visualization exercises.
These solutions demonstrate one approach to solving each exercise. Remember that in R, there are often multiple valid ways to achieve the same result. The key is understanding the concepts and ensuring your code produces the correct output.

Solution 1: Data Generation & CSV Export

# Set seed for reproducibility set.seed(123) # Generate synthetic student data n <- 25 student_data <- data.frame( StudentID = sprintf("STU%03d", 1:n), Department = sample(c("Biology", "Chemistry", "Physics", "Mathematics"), n, replace = TRUE), Test1 = round(rnorm(n, mean = 75, sd = 12), 1), Test2 = round(runif(n, 60, 95), 1), EnrollmentDate = seq(as.Date("2024-09-01"), by = "3 days", length.out = n) ) # Introduce missing values missing_positions <- sample(1:n, 4) student_data$Test1[missing_positions[1:2]] <- NA student_data$Test2[missing_positions[3:4]] <- NA # Save to CSV write.csv(student_data, "student_scores.csv", row.names = FALSE) # Verify file creation file.exists("student_scores.csv")

Expected Output:

> file.exists("student_scores.csv") [1] TRUE > head(student_data) StudentID Department Test1 Test2 EnrollmentDate 1 STU001 Mathematics 80.3 85.7 2024-09-01 2 STU002 Physics 64.2 70.4 2024-09-04 3 STU003 Physics NA 92.8 2024-09-07 4 STU004 Biology 78.9 NA 2024-09-10 5 STU005 Physics 85.6 78.3 2024-09-13 6 STU006 Biology 62.4 63.9 2024-09-16

Solution 2: Data Import & Initial Inspection

# Import the CSV file student_df <- read.csv("student_scores.csv", stringsAsFactors = FALSE) # Display first 8 rows head(student_df, 8) # Check structure str(student_df) # Generate summary summary(student_df) # Count missing values colSums(is.na(student_df)) # Identify rows with missing values missing_rows <- which(rowSums(is.na(student_df[, c("Test1", "Test2")])) > 0) missing_rows

Expected Output:

> str(student_df) 'data.frame': 25 obs. of 5 variables: $ StudentID : chr "STU001" "STU002" "STU003" "STU004" ... $ Department : chr "Mathematics" "Physics" "Physics" "Biology" ... $ Test1 : num 80.3 64.2 NA 78.9 85.6 62.4 72.8 88.1 59.7 NA ... $ Test2 : num 85.7 70.4 92.8 NA 78.3 63.9 84.2 90.1 74.6 68.3 ... $ EnrollmentDate: chr "2024-09-01" "2024-09-04" "2024-09-07" "2024-09-10" ... > colSums(is.na(student_df)) StudentID Department Test1 Test2 EnrollmentDate 0 0 2 2 0 > missing_rows [1] 3 4 10 20

Solution 3: Data Cleaning & Type Conversion

# Convert data types student_clean <- student_df student_clean$StudentID <- as.character(student_clean$StudentID) student_clean$Department <- as.factor(student_clean$Department) student_clean$Test1 <- as.numeric(student_clean$Test1) student_clean$Test2 <- as.numeric(student_clean$Test2) student_clean$EnrollmentDate <- as.Date(student_clean$EnrollmentDate) # Approach 1: Remove rows with missing values student_no_na <- na.omit(student_clean) # Approach 2: Impute missing values with median student_imputed <- student_clean student_imputed$Test1[is.na(student_imputed$Test1)] <- median(student_imputed$Test1, na.rm = TRUE) student_imputed$Test2[is.na(student_imputed$Test2)] <- median(student_imputed$Test2, na.rm = TRUE) # Verify conversions str(student_imputed) cat("Original rows:", nrow(student_clean), "\n") cat("After removing NAs:", nrow(student_no_na), "\n") cat("After imputation:", nrow(student_imputed), "\n")

Expected Output:

> str(student_imputed) 'data.frame': 25 obs. of 5 variables: $ StudentID : chr "STU001" "STU002" "STU003" "STU004" ... $ Department : Factor w/ 4 levels "Biology","Chemistry",..: 3 4 4 1 4 1 2 3 1 2 ... $ Test1 : num 80.3 64.2 74.1 78.9 85.6 62.4 72.8 88.1 59.7 74.1 ... $ Test2 : num 85.7 70.4 92.8 78.9 78.3 63.9 84.2 90.1 74.6 68.3 ... $ EnrollmentDate: Date, format: "2024-09-01" "2024-09-04" ... Original rows: 25 After removing NAs: 21 After imputation: 25

Solution 4: Data Processing & Derived Variables

# Use the imputed dataset analysis_df <- student_imputed # Calculate average score analysis_df$Average <- round((analysis_df$Test1 + analysis_df$Test2) / 2, 1) # Create performance categories analysis_df$Performance <- cut(analysis_df$Average, breaks = c(0, 69.9, 84.9, 100), labels = c("Needs Improvement", "Good", "Excellent")) # Calculate score difference analysis_df$ScoreDiff <- analysis_df$Test2 - analysis_df$Test1 # Create improvement indicator analysis_df$Improved <- ifelse(analysis_df$ScoreDiff > 0, "Yes", "No") analysis_df$Improved <- as.factor(analysis_df$Improved) # Count students by performance category performance_counts <- table(analysis_df$Performance) performance_counts # Display sample of results head(analysis_df[, c("StudentID", "Test1", "Test2", "Average", "Performance", "Improved")])

Expected Output:

> performance_counts Needs Improvement Good Excellent 10 11 4 > head(analysis_df[, c("StudentID", "Test1", "Test2", "Average", "Performance", "Improved")]) StudentID Test1 Test2 Average Performance Improved 1 STU001 80.3 85.7 83.0 Good Yes 2 STU002 64.2 70.4 67.3 Needs Improvement Yes 3 STU003 74.1 92.8 83.5 Good Yes 4 STU004 78.9 78.9 78.9 Good No 5 STU005 85.6 78.3 82.0 Good No 6 STU006 62.4 63.9 63.2 Needs Improvement Yes

Solution 5: Basic Data Visualization

# Set up plotting area par(mfrow = c(2, 3)) # 1. Histogram of average scores hist(analysis_df$Average, main = "Distribution of Average Test Scores", xlab = "Average Score", ylab = "Frequency", col = "lightblue", breaks = 8) # 2. Boxplot by Department boxplot(Test1 ~ Department, data = analysis_df, main = "Test1 Scores by Department", xlab = "Department", ylab = "Test1 Score", col = c("lightgreen", "lightcoral", "lightyellow", "lightblue"), notch = TRUE) # 3. Scatter plot with colors by Department colors <- c("Biology" = "green", "Chemistry" = "red", "Physics" = "blue", "Mathematics" = "purple") plot(analysis_df$Test1, analysis_df$Test2, main = "Test1 vs Test2 Scores", xlab = "Test1 Score", ylab = "Test2 Score", pch = 19, col = colors[analysis_df$Department]) legend("topleft", legend = names(colors), fill = colors) # 4. Bar plot of performance categories barplot(performance_counts, main = "Students by Performance Category", ylab = "Number of Students", xlab = "Performance Level", col = c("red", "yellow", "green")) # 5. Line plot over time (ordered by date) time_ordered <- analysis_df[order(analysis_df$EnrollmentDate), ] plot(time_ordered$EnrollmentDate, time_ordered$Average, type = "o", main = "Average Scores Over Time", xlab = "Enrollment Date", ylab = "Average Score", pch = 16, col = "darkblue") # Reset plotting parameters par(mfrow = c(1, 1))

Expected Output:

Five different plots will be generated showing:

  • Histogram: Bell-shaped distribution of average scores
  • Boxplot: Test1 score distributions across departments
  • Scatter plot: Positive correlation between Test1 and Test2
  • Bar plot: Distribution of performance categories
  • Line plot: Average scores over enrollment dates

Solution 6: Advanced Analysis & Multi-plot Display

# Department-wise statistics dept_stats <- aggregate(cbind(Test1, Test2, Average) ~ Department, data = analysis_df, FUN = function(x) c(Mean = mean(x), SD = sd(x))) dept_stats # Performance by department performance_by_dept <- table(analysis_df$Department, analysis_df$Performance) performance_by_dept # Pairs plot pairs(analysis_df[, c("Test1", "Test2", "Average")], main = "Scatterplot Matrix: Test Scores", pch = 19, col = colors[analysis_df$Department]) # Multi-plot display png("student_analysis_plots.png", width = 1000, height = 800) par(mfrow = c(2, 2)) # Plot 1: Histogram hist(analysis_df$Test1, main = "Test1 Score Distribution", xlab = "Test1 Score", col = "lightblue") # Plot 2: Boxplot by Department boxplot(Test2 ~ Department, data = analysis_df, main = "Test2 by Department", col = "lightgreen") # Plot 3: Barplot of performance barplot(performance_counts, main = "Performance Categories", col = c("red", "gold", "green")) # Plot 4: Scatter with regression plot(analysis_df$Test1, analysis_df$Test2, main = "Test1 vs Test2 with Regression", xlab = "Test1", ylab = "Test2", pch = 19, col = "blue") abline(lm(Test2 ~ Test1, data = analysis_df), col = "red", lwd = 2) dev.off() # Summary table summary_table <- data.frame( Department = levels(analysis_df$Department), N_Students = as.numeric(table(analysis_df$Department)), Mean_Test1 = round(tapply(analysis_df$Test1, analysis_df$Department, mean), 1), Mean_Test2 = round(tapply(analysis_df$Test2, analysis_df$Department, mean), 1) ) # Add performance percentages performance_pct <- prop.table(performance_by_dept, margin = 1) * 100 summary_table <- cbind(summary_table, round(performance_pct, 1)) summary_table

Expected Output:

> dept_stats Department Test1.Mean Test1.SD Test2.Mean Test2.SD Average.Mean Average.SD 1 Biology 70.10 9.63 75.64 9.77 72.87 9.19 2 Chemistry 74.05 8.54 78.45 10.41 76.25 8.85 3 Mathematics 77.83 9.27 81.17 8.64 79.50 8.58 4 Physics 73.43 11.26 79.29 11.30 76.36 10.87 > summary_table Department N_Students Mean_Test1 Mean_Test2 Needs.Improvement Good Excellent 1 Biology 7 70.1 75.6 42.9 42.9 14.3 2 Chemistry 6 74.0 78.4 16.7 66.7 16.7 3 Mathematics 6 77.8 81.2 0.0 83.3 16.7 4 Physics 6 73.4 79.3 33.3 50.0 16.7

Solution 7: Data Export & Process Documentation

# Save processed dataset write.csv(analysis_df, "student_scores_processed.csv", row.names = FALSE) # Create documentation function create_analysis_report <- function(department_name) { dept_data <- analysis_df[analysis_df$Department == department_name, ] if (nrow(dept_data) == 0) { return(paste("No students found in", department_name)) } result <- list( Department = department_name, Number_of_Students = nrow(dept_data), Mean_Test1 = round(mean(dept_data$Test1), 1), Mean_Test2 = round(mean(dept_data$Test2), 1), Top_Student = dept_data[which.max(dept_data$Average), "StudentID"], Top_Score = max(dept_data$Average) ) return(result) } # Test the function bio_results <- create_analysis_report("Biology") math_results <- create_analysis_report("Mathematics") # Display results cat("Biology Department Analysis:\n") print(bio_results) cat("\nMathematics Department Analysis:\n") print(math_results) # Create documentation file doc_content <- paste( "STUDENT PERFORMANCE ANALYSIS REPORT", "====================================", "", "DATA PROCESSING STEPS:", "1. Generated synthetic data for 25 students across 4 departments", "2. Introduced 4 missing values (2 in Test1, 2 in Test2)", "3. Imported data and converted types (character, factor, numeric, Date)", "4. Handled missing values using median imputation", "5. Created derived variables: Average, Performance, ScoreDiff, Improved", "", "KEY FINDINGS:", "- Mathematics department has highest average scores", "- Biology department has highest percentage of 'Needs Improvement' students", "- Overall positive correlation between Test1 and Test2 scores", "- 60% of students showed improvement from Test1 to Test2", "", "ASSUMPTIONS:", "- Missing test scores were imputed using department medians", "- Performance categories based on standard educational thresholds", "- All departments have similar grading standards", sep = "\n" ) writeLines(doc_content, "analysis_documentation.txt") cat("Documentation saved to 'analysis_documentation.txt'\n")

Expected Output:

> bio_results $Department [1] "Biology" $Number_of_Students [1] 7 $Mean_Test1 [1] 70.1 $Mean_Test2 [1] 75.6 $Top_Student [1] "STU016" $Top_Score [1] 86.2 > math_results $Department [1] "Mathematics" $Number_of_Students [1] 6 $Mean_Test1 [1] 77.8 $Mean_Test2 [1] 81.2 $Top_Student [1] "STU024" $Top_Score [1] 91.5
Note: These solutions demonstrate one approach to solving each exercise. Your actual output values may vary slightly due to random number generation, but the structure and patterns should be consistent. The key concepts demonstrated include data manipulation, type conversion, missing value handling, visualization, and analysis techniques.
R Programming Solutions - Complete Data Analysis Workflow

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