r-statistics.co by Selva Prabhakaran

R Basics Exercises: 15 Practice Problems for Beginners (With Solutions)

The best way to learn R is to write R. These 15 exercises cover variables, operators, functions, vectors, and type conversions — everything from the R Syntax 101 tutorial. Each problem has an interactive code block where you write your solution, plus a collapsible answer.

Try each exercise yourself before looking at the solution. Writing code from scratch — even struggling with it — builds muscle memory that reading alone can't.

How to Use These Exercises

  1. Read the problem description
  2. Write your solution in the interactive code block
  3. Click Run to test it
  4. If stuck, check the hint
  5. Compare with the solution (click to reveal)

Exercises are grouped by difficulty: Easy (1-5), Medium (6-10), Hard (11-15).

Easy (1-5): Variables and Basic Operations

Exercise 1: Variable Assignment

Create three variables: name (your name as text), age (your age as a number), and is_student (TRUE or FALSE). Print all three using cat().

# Exercise 1: Create and print three variables # Expected output (example): # Name: Alice # Age: 25 # Student: TRUE # Write your code below: 

  






Click to reveal solution



  

    
# Solution
name <- "Alice"
age <- 25
is_student <- TRUE

cat("Name:", name, "\n")
cat("Age:", age, "\n")
cat("Student:", is_student, "\n")

    

      
      
    

    

  





Key concept: Use <- for assignment. Character values need quotes. Logical values are TRUE/FALSE (uppercase, no quotes).






Exercise 2: Arithmetic Calculator



A pizza costs $12.99 and you're ordering for 7 people. Calculate: the subtotal, the tax (8.5%), the tip (18% of subtotal), and the total. Print each value rounded to 2 decimal places.




  

    
# Exercise 2: Pizza order calculator
# Pizza price: $12.99 per person
# People: 7
# Tax rate: 8.5%
# Tip: 18% of subtotal (before tax)

# Write your code below:


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
price <- 12.99
people <- 7
tax_rate <- 0.085
tip_rate <- 0.18

subtotal <- price * people
tax <- subtotal * tax_rate
tip <- subtotal * tip_rate
total <- subtotal + tax + tip

cat("Subtotal: $", round(subtotal, 2), "\n")
cat("Tax:      $", round(tax, 2), "\n")
cat("Tip:      $", round(tip, 2), "\n")
cat("Total:    $", round(total, 2), "\n")
cat("Per person: $", round(total / people, 2), "\n")

    

      
      
    

    

  





Key concept: Store values in variables, then compute from variables. round(x, 2) gives 2 decimal places.






Exercise 3: Comparison Operators



Given temperature <- 72, write expressions that check: Is it above 80? Is it between 60 and 80 (inclusive)? Is it exactly 72? Is it NOT equal to 100?




  

    
# Exercise 3: Temperature checks
temperature <- 72

# Write 4 comparisons and print results:


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
temperature <- 72

cat("Above 80:", temperature > 80, "\n")
cat("Between 60-80:", temperature >= 60 & temperature <= 80, "\n")
cat("Exactly 72:", temperature == 72, "\n")
cat("Not 100:", temperature != 100, "\n")

    

      
      
    

    

  





Key concept: == for equality (not =). & for AND. Use >= and <= for inclusive ranges.






Exercise 4: Built-in Functions



Given numbers <- c(23, 7, 45, 12, 89, 34, 56), use built-in functions to find: the count, sum, mean, min, max, and sorted order.




  

    
# Exercise 4: Use built-in functions
numbers <- c(23, 7, 45, 12, 89, 34, 56)

# Find and print: count, sum, mean, min, max, sorted


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
numbers <- c(23, 7, 45, 12, 89, 34, 56)

cat("Count:", length(numbers), "\n")
cat("Sum:", sum(numbers), "\n")
cat("Mean:", round(mean(numbers), 2), "\n")
cat("Min:", min(numbers), "\n")
cat("Max:", max(numbers), "\n")
cat("Sorted:", sort(numbers), "\n")
cat("Sorted (desc):", sort(numbers, decreasing = TRUE), "\n")

    

      
      
    

    

  





Key concept: R has built-in functions for all common statistics. length() counts elements, not count() or len().






Exercise 5: String Operations



Create a first name and last name variable. Combine them into a full name using paste(). Then print the full name in uppercase and its character count.




  

    
# Exercise 5: String operations
# Create first_name and last_name
# Combine into full_name
# Print uppercase version and character count


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
first_name <- "Jane"
last_name <- "Doe"
full_name <- paste(first_name, last_name)

cat("Full name:", full_name, "\n")
cat("Uppercase:", toupper(full_name), "\n")
cat("Characters:", nchar(full_name), "\n")

# Bonus: paste0 (no space separator)
username <- paste0(tolower(first_name), ".", tolower(last_name))
cat("Username:", username, "\n")

    

      
      
    

    

  





Key concept: paste() joins strings with a space. paste0() joins with no separator. nchar() counts characters (including spaces).






Medium (6-10): Vectors and Logic



Exercise 6: Vector Filtering



Given exam scores, find: how many students passed (score >= 70), the average of passing scores, and the names of failing students.




  

    
# Exercise 6: Exam score analysis
students <- c("Alice", "Bob", "Carol", "David", "Eve", "Frank")
scores <- c(85, 62, 91, 58, 77, 44)

# 1. How many passed (>= 70)?
# 2. Average of passing scores?
# 3. Names of failing students?


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
students <- c("Alice", "Bob", "Carol", "David", "Eve", "Frank")
scores <- c(85, 62, 91, 58, 77, 44)

passed <- scores >= 70
cat("Passed:", sum(passed), "of", length(scores), "\n")
cat("Pass rate:", round(mean(passed) * 100, 1), "%\n")
cat("Average passing score:", round(mean(scores[passed]), 1), "\n")
cat("Failing students:", students[!passed], "\n")
cat("Their scores:", scores[!passed], "\n")

    

      
      
    

    

  





Key concept: scores >= 70 creates a logical vector. Use it to filter both scores and students. !passed flips TRUE/FALSE.






Exercise 7: Sequence Generation



Create these sequences without typing every number:


    

  1. 1 to 20
    2. 

  2. Even numbers from 2 to 20
    3. 

  3. 5, 10, 15, ..., 100
    4. 

  4. 1 repeated 10 times
    5. 

  5. The pattern 1, 2, 3, 1, 2, 3, 1, 2, 3
    6. 





  

    
# Exercise 7: Create sequences efficiently
# Don't type out every number — use :, seq(), and rep()


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
cat("1 to 20:", 1:20, "\n\n")
cat("Even 2-20:", seq(2, 20, by = 2), "\n\n")
cat("5 to 100 by 5:", seq(5, 100, by = 5), "\n\n")
cat("1 repeated 10x:", rep(1, 10), "\n\n")
cat("1,2,3 pattern:", rep(1:3, times = 3), "\n")

    

      
      
    

    

  





Key concept: : for simple integer sequences. seq() for custom step sizes. rep() for repetition — times repeats the whole vector, each repeats each element.






Exercise 8: Named Vectors as Lookup Tables



Create a named vector mapping country codes to country names (US, UK, DE, JP, BR). Then look up the full name for "DE" and "JP".




  

    
# Exercise 8: Country code lookup
# Create a named vector: codes → full names
# Look up "DE" and "JP"


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
countries <- c(
  US = "United States",
  UK = "United Kingdom",
  DE = "Germany",
  JP = "Japan",
  BR = "Brazil"
)

cat("DE:", countries["DE"], "\n")
cat("JP:", countries["JP"], "\n")

# Bonus: look up multiple at once
codes_to_find <- c("US", "BR", "JP")
cat("Multiple:", countries[codes_to_find], "\n")

    

      
      
    

    

  





Key concept: Named vectors work as key-value lookup tables. countries["DE"] returns the value associated with the name "DE".






Exercise 9: Type Detective



Predict the type of each value, then verify with class(). Some are tricky!




  

    
# Exercise 9: Predict the type, then verify
a <- 42
b <- 42L
c <- "42"
d <- TRUE
e <- 4 + 2i
f <- c(1, 2, "3")
g <- c(TRUE, FALSE, 0)

# Predict each type, then uncomment to check:
# cat("a:", class(a), "\n")
# cat("b:", class(b), "\n")
# cat("c:", class(c), "\n")
# cat("d:", class(d), "\n")
# cat("e:", class(e), "\n")
# cat("f:", class(f), "\n")
# cat("g:", class(g), "\n")


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
a <- 42          # numeric (not integer — no L)
b <- 42L         # integer (L suffix)
c <- "42"        # character (quotes)
d <- TRUE        # logical
e <- 4 + 2i      # complex
f <- c(1, 2, "3") # character (coercion: string wins)
g <- c(TRUE, FALSE, 0) # numeric (coercion: numeric wins over logical)

cat("a (42):", class(a), "\n")
cat("b (42L):", class(b), "\n")
cat("c ('42'):", class(c), "\n")
cat("d (TRUE):", class(d), "\n")
cat("e (4+2i):", class(e), "\n")
cat("f (1,2,'3'):", class(f), "← string coerces everything\n")
cat("g (T,F,0):", class(g), "← numeric coerces logical\n")

    

      
      
    

    

  





Key concept: When mixing types in c(), R coerces to the most flexible: logical → integer → numeric → character.






Exercise 10: Vector Math



Convert temperatures from Fahrenheit to Celsius for an entire week, then find the hottest and coldest days.




  

    
# Exercise 10: Temperature conversion
days <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
temps_f <- c(72, 68, 75, 80, 85, 70, 65)

# 1. Convert to Celsius: C = (F - 32) * 5/9
# 2. Find hottest and coldest day (by name)
# 3. How many days were above 25°C?


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
days <- c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")
temps_f <- c(72, 68, 75, 80, 85, 70, 65)

# 1. Vectorized conversion (no loop needed!)
temps_c <- round((temps_f - 32) * 5/9, 1)
names(temps_c) <- days

cat("Fahrenheit:", temps_f, "\n")
cat("Celsius:   ", temps_c, "\n\n")

# 2. Hottest and coldest
cat("Hottest:", days[which.max(temps_c)], "at", max(temps_c), "°C\n")
cat("Coldest:", days[which.min(temps_c)], "at", min(temps_c), "°C\n\n")

# 3. Days above 25°C
above_25 <- temps_c > 25
cat("Days above 25°C:", sum(above_25), "\n")
cat("Which days:", days[above_25], "\n")

    

      
      
    

    

  





Key concept: (temps_f - 32) * 5/9 converts ALL temperatures at once — R applies math to every vector element automatically.






Hard (11-15): Combined Skills



Exercise 11: Grade Calculator Function



Write a function that takes a numeric score (0-100) and returns a letter grade: A (90+), B (80+), C (70+), D (60+), F (<60). Test it on a vector of scores.




  

    
# Exercise 11: Grade calculator
# Write a function get_grade(score) that returns "A", "B", "C", "D", or "F"
# Then apply it to: scores <- c(95, 82, 73, 61, 55, 88, 77, 100, 42, 90)


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
get_grade <- function(score) {
  ifelse(score >= 90, "A",
  ifelse(score >= 80, "B",
  ifelse(score >= 70, "C",
  ifelse(score >= 60, "D", "F"))))
}

scores <- c(95, 82, 73, 61, 55, 88, 77, 100, 42, 90)
grades <- get_grade(scores)

# Results
cat("Scores:", scores, "\n")
cat("Grades:", grades, "\n\n")

# Summary
cat("Grade distribution:\n")
print(table(grades))
cat("\nAverage score:", round(mean(scores), 1), "\n")
cat("Pass rate (D or above):", round(mean(scores >= 60) * 100, 1), "%\n")

    

      
      
    

    

  





Key concept: ifelse() is vectorized — it works on entire vectors. Nesting ifelse() handles multiple conditions. table() counts occurrences.






Exercise 12: Data Cleaning Challenge



A dataset has messy price values. Clean them and compute statistics.




  

    
# Exercise 12: Clean messy price data
prices_raw <- c("$24.99", "$8.50", "N/A", "$15.00", "$42", "missing", "$7.99", "$31.50")

# 1. Remove "$" from all values
# 2. Replace "N/A" and "missing" with NA
# 3. Convert to numeric
# 4. Calculate mean, median, and count of valid prices
# Hint: gsub() removes characters, as.numeric() converts


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
prices_raw <- c("$24.99", "$8.50", "N/A", "$15.00", "$42", "missing", "$7.99", "$31.50")

# Step 1: Remove $
step1 <- gsub("\\$", "", prices_raw)
cat("After removing $:", step1, "\n")

# Step 2: Replace text NA values with real NA
step2 <- ifelse(step1 %in% c("N/A", "missing"), NA, step1)
cat("After NA handling:", step2, "\n")

# Step 3: Convert to numeric
prices <- as.numeric(step2)
cat("Numeric:", prices, "\n\n")

# Step 4: Statistics
cat("Valid prices:", sum(!is.na(prices)), "of", length(prices), "\n")
cat("Mean: $", round(mean(prices, na.rm = TRUE), 2), "\n")
cat("Median: $", round(median(prices, na.rm = TRUE), 2), "\n")
cat("Total: $", round(sum(prices, na.rm = TRUE), 2), "\n")

    

      
      
    

    

  





Key concept: gsub("\\$", "", x) removes dollar signs ($ is special in regex, so escape with \\). %in% checks membership. na.rm = TRUE ignores NAs in calculations.






Exercise 13: BMI Calculator with Validation



Write a BMI calculator function that validates inputs, handles edge cases, and categorizes the result.




  

    
# Exercise 13: BMI calculator
# Write bmi_report(weight_kg, height_m) that:
# 1. Validates: both must be positive numbers
# 2. Calculates BMI = weight / height^2
# 3. Categorizes: Underweight(<18.5), Normal(18.5-24.9), Overweight(25-29.9), Obese(30+)
# 4. Returns a formatted message
# Test: bmi_report(70, 1.75), bmi_report(95, 1.80), bmi_report(-5, 1.70)


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
bmi_report <- function(weight_kg, height_m) {
  # Validate
  if (!is.numeric(weight_kg) || !is.numeric(height_m)) {
    return("Error: weight and height must be numbers")
  }
  if (weight_kg <= 0 || height_m <= 0) {
    return("Error: weight and height must be positive")
  }
  if (height_m > 3) {
    return("Warning: height seems too large. Did you use cm instead of m?")
  }

  # Calculate
  bmi <- weight_kg / height_m^2

  # Categorize
  category <- ifelse(bmi < 18.5, "Underweight",
              ifelse(bmi < 25, "Normal weight",
              ifelse(bmi < 30, "Overweight", "Obese")))

  # Report
  sprintf("Weight: %.1f kg | Height: %.2f m | BMI: %.1f (%s)",
          weight_kg, height_m, round(bmi, 1), category)
}

cat(bmi_report(70, 1.75), "\n")
cat(bmi_report(95, 1.80), "\n")
cat(bmi_report(50, 1.65), "\n")
cat(bmi_report(-5, 1.70), "\n")
cat(bmi_report(70, 175), "\n")

    

      
      
    

    

  





Key concept: Good functions validate inputs before computing. sprintf() creates formatted strings. return() for early exits on errors.






Exercise 14: Fibonacci Generator



Write a function that generates the first n Fibonacci numbers. Use a pre-allocated vector (not growing with c()).




  

    
# Exercise 14: Fibonacci sequence
# Write fibonacci(n) that returns the first n Fibonacci numbers
# Rules: F(1)=1, F(2)=1, F(n) = F(n-1) + F(n-2)
# Use pre-allocation for efficiency
# Test: fibonacci(10) should give: 1 1 2 3 5 8 13 21 34 55


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
fibonacci <- function(n) {
  if (n <= 0) return(integer(0))
  if (n == 1) return(1)

  # Pre-allocate (important for large n!)
  fib <- numeric(n)
  fib[1:2] <- 1

  for (i in 3:n) {
    fib[i] <- fib[i-1] + fib[i-2]
  }
  return(fib)
}

cat("First 10:", fibonacci(10), "\n")
cat("First 20:", fibonacci(20), "\n")
cat("F(30):", fibonacci(30)[30], "\n")

# Bonus: verify the golden ratio
fib20 <- fibonacci(20)
ratios <- fib20[-1] / fib20[-length(fib20)]
cat("Ratio converges to golden ratio:", round(tail(ratios, 1), 6), "\n")
cat("Actual golden ratio:", round((1 + sqrt(5))/2, 6), "\n")

    

      
      
    

    

  





Key concept: Pre-allocate with numeric(n) instead of growing with c(). Edge cases (n=0, n=1) need special handling. Fibonacci ratios converge to the golden ratio (1.618...).






Exercise 15: Mini Data Analysis



Perform a complete analysis on this dataset: find summary statistics, identify outliers, and create a report.




  

    
# Exercise 15: Complete data analysis
# Daily step counts for a month (30 days)
set.seed(42)
steps <- c(round(rnorm(25, mean = 8000, sd = 2500)),
           NA, 450, 25000, NA, 15)

# 1. Basic stats (handle NAs)
# 2. How many NAs? What percentage?
# 3. Find outliers (values < 1000 or > 20000 are likely errors)
# 4. Replace outliers with NA
# 5. Report: mean, median, days above 10000, most active day


    

      
      
    

    

  






Click to reveal solution



  

    
# Solution
set.seed(42)
steps <- c(round(rnorm(25, mean = 8000, sd = 2500)),
           NA, 450, 25000, NA, 15)

cat("=== Raw Data Report ===\n")
cat("Days:", length(steps), "\n")
cat("NAs:", sum(is.na(steps)), "(", round(mean(is.na(steps))*100, 1), "%)\n")
cat("Raw mean:", round(mean(steps, na.rm = TRUE), 0), "\n\n")

# Find outliers
outliers <- !is.na(steps) & (steps < 1000 | steps > 20000)
cat("Outliers found:", sum(outliers), "\n")
cat("Outlier values:", steps[outliers], "\n\n")

# Clean: replace outliers with NA
clean_steps <- steps
clean_steps[outliers] <- NA

# Final report
valid <- clean_steps[!is.na(clean_steps)]
cat("=== Clean Data Report ===\n")
cat("Valid days:", length(valid), "of", length(steps), "\n")
cat("Mean steps:", round(mean(valid), 0), "\n")
cat("Median steps:", round(median(valid), 0), "\n")
cat("Std dev:", round(sd(valid), 0), "\n")
cat("Days above 10k:", sum(valid > 10000), "\n")
cat("Most active day:", max(valid), "steps (day",
    which.max(clean_steps), ")\n")
cat("Least active day:", min(valid), "steps (day",
    which.min(clean_steps), ")\n")

    

      
      
    

    

  





Key concept: Real data analysis workflow: explore → identify problems → clean → analyze. Always handle NAs with na.rm = TRUE. Define outlier rules clearly. Report both raw and cleaned statistics.






Summary: What You Practiced






ExercisesSkills Covered




1-5 (Easy)Variables, arithmetic, comparisons, functions, strings


6-10 (Medium)Vector filtering, sequences, named vectors, types, vectorized math


11-15 (Hard)Functions, data cleaning, validation, loops, complete analysis






If you got 1-5 right: You've got the basics. Move on to R Data Types and R Vectors tutorials.



If you got 6-10 right: You understand R's vector-first approach. Move on to R Data Frames.



If you got 11-15 right: You're ready for real data analysis. Try the Data Wrangling with dplyr tutorial.



What's Next?



More practice exercises for specific topics:



    

  1. R Vectors Exercises — 12 problems focused on vector operations
    2. 

  2. R Data Frames Exercises — 15 problems with tabular data
    3. 

  3. R Control Flow Exercises — if/else, loops, and pattern matching
    4. 




Or continue learning with the next tutorial in the series: R Data Types.



        


        
      


      

        

        
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