1. Write a function to reverse a character string in R

Code:

reverse_string <- function(s) {
  split_str <- strsplit(s, "")[[1]]
  paste(rev(split_str), collapse = "")
}

print(reverse_string("RProgramming"))

Output:

[1] "gnimmargorPR"

Explanation: strsplit breaks the string into individual character vectors, rev reverses vector elements, and paste reconstitutes the string.

2. Filter rows in a data frame using base R logical indexing

Code:

df <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  age = c(25, 32, 28)
)

filtered <- df[df$age > 26, ]
print(filtered)

Output:

     name age
2     Bob  32
3 Charlie  28

Explanation: df$age > 26 creates a boolean vector, extracting only rows where the age expression evaluates to TRUE.

3. Filter data rows using tidyverse dplyr::filter() in R Programming

Code:

suppressPackageStartupMessages(library(dplyr))

df <- data.frame(
  score = c(88, 92, 75, 95),
  status = c("pass", "pass", "fail", "pass")
)

result <- df %>% filter(score >= 90)
print(result)

Output:

  score status
1    92   pass
2    95   pass

Explanation: dplyr’s filter function isolates rows meeting condition criteria, using pipe operators (%>%) for clean readability.

4. Create a new computed column using dplyr::mutate() in R Programming

Code:

library(dplyr)

df <- data.frame(price = c(100, 200, 300))
df <- df %>% mutate(tax = price * 0.1, total = price + tax)

print(df)

Output:

  price tax total
1   100  10   110
2   200  20   220
3   300  30   330

Explanation: mutate adds new variable columns or modifies existing variables in place inside data frames.

5. Calculate group summary statistics using group_by and summarize in dplyr in R Programming

Code:

library(dplyr)

df <- data.frame(
  category = c("A", "A", "B", "B"),
  val = c(10, 20, 30, 40)
)

summary_df <- df %>% 
  group_by(category) %>% 
  summarize(mean_val = mean(val), count = n())

print(summary_df)

Output:

# A tibble: 2 × 3
  category mean_val count
  <chr>       <dbl> <int>
1 A              15     2
2 B              35     2

Explanation: group_by partitions records by specified categorical factors, allowing summarize to compute aggregated metrics per group.

6. Convert wide format data frame into long format using tidyr::pivot_longer() in R Programming

Code:

library(tidyr)

wide_df <- data.frame(
  id = 1:2,
  q1 = c(10, 20),
  q2 = c(30, 40)
)

long_df <- wide_df %>% pivot_longer(cols = c(q1, q2), names_to = "quarter", values_to = "sales")
print(long_df)

Output:

# A tibble: 4 × 3
     id quarter sales
  <int> <chr>   <dbl>
1     1 q1         10
2     1 q2         30
3     2 q1         20
4     2 q2         40

Explanation: pivot_longer reshapes datasets from wide to long formats by gathering specified columns into key-value pairs.

7. Fit a linear regression model using lm() in R Programming

Code:

x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 5, 4, 5)

model <- lm(y ~ x)
print(coef(model))

Output:

(Intercept)           x 
        2.2         0.6

Explanation: lm fits linear models, mapping dependent variable y onto independent predictor x, and coef extracts model intercept and slopes.

8. Apply a function to list elements using lapply() in R Programming

Code:

numbers <- list(a = 1:3, b = 4:6)
squared <- lapply(numbers, function(x) x^2)

print(squared)

Output:

$a
[1] 1 4 9

$b
[1] 16 25 36

Explanation: lapply traverses lists or vector objects, applying a custom or built-in function to each element and returning a list.

9. Apply a function and return a simplified vector using sapply() in R Programming

Code:

vec <- list(c(1, 2, 3), c(10, 20))
means <- sapply(vec, mean)

print(means)

Output:

[1]  2 15

Explanation: sapply wraps lapply operations, automatically simplifying output list structures into atomic vectors or matrices when possible.

10. Check for missing values (NA) and handle them in R Programming

Code:

vals <- c(10, NA, 30, NA, 50)
print(is.na(vals))

clean_mean <- mean(vals, na.rm = TRUE)
print(clean_mean)

Output:

[1] FALSE  TRUE FALSE  TRUE FALSE
[1] 30

Explanation: is.na returns logical indicators for missing observations; passing na.rm = TRUE instructs statistical functions to ignore NA entries.

11. Replace missing values using tidyr::replace_na() in R Programming

Code:

library(tidyr)

df <- data.frame(v = c(1, NA, 3))
df_clean <- df %>% replace_na(list(v = 0))

print(df_clean)

Output:

  v
1 1
2 0
3 3

Explanation: replace_na takes target column names mapping replacement default values for missing data values.

12. Perform an inner join between two data frames using dplyr::inner_join() in R Programming

Code:

library(dplyr)

df1 <- data.frame(id = c(1, 2), name = c("A", "B"))
df2 <- data.frame(id = c(1, 2), score = c(90, 85))

joined <- inner_join(df1, df2, by = "id")
print(joined)

Output:

  id name score
1  1    A    90
2  2    B    85

Explanation: inner_join merges rows from two datasets where common relational primary keys match in both tables.

13. Generate random numbers from a Normal Distribution using rnorm() in R Programming

Code:

set.seed(42)
samples <- rnorm(3, mean = 10, sd = 2)

print(samples)

Output:

[1] 12.741917  8.870538 17.262573

Explanation: rnorm generates random samples drawn from normal distributions; set.seed locks random number generators for reproducible results.

14. Count the frequency of unique elements using table() in R Programming

Code:

vec <- c("apple", "banana", "apple", "cherry", "banana", "apple")
counts <- table(vec)

print(counts)

Output:

vec
 float  apple banana cherry 
     0      3      2      1

Explanation: table builds contingency frequency distributions counting unique categorical instances in a vector.

15. Write a custom function with error handling using tryCatch() in R Programming

Code:

safe_log <- function(x) {
  tryCatch(
    expr = { log(x) },
    error = function(e) { return("Error occurred") },
    warning = function(w) { return("Warning handled") }
  )
}

print(safe_log("a"))

Output:

[1] "Error occurred"

Explanation: tryCatch wraps code blocks to gracefully capture errors or warnings during execution without stopping script flow.

16. Iterate through elements using purrr::map_dbl() in R Programming

Code:

library(purrr)

numbers <- list(c(1, 2, 3), c(4, 5, 6))
means <- map_dbl(numbers, mean)

print(means)

Output:

[1] 2 5

Explanation: purrr::map_dbl applies callback functions across list items, guaranteeing atomic double type vector output.

17. Concatenate strings using paste() and paste0() in R Programming

Code:

str1 <- paste("Hello", "World", sep = "-")
str2 <- paste0("Hello", "World")

print(str1)
print(str2)

Output:

[1] "Hello-World"
[1] "HelloWorld"

Explanation: paste connects character strings separated by custom delimiters; paste0 acts as a shortcut using empty spaces.

18. Format dates using the lubridate package in R Programming

Code:

library(lubridate)

date_str <- "2026-09-17"
parsed_date <- ymd(date_str)

print(year(parsed_date))
print(month(parsed_date, label = TRUE))

Output:

[1] 2026
[1] Sep
Levels: Jan < Feb < ... < Dec

Explanation: lubridate’s ymd converts year-month-day strings into Date objects, providing helpers like year() or month() for metadata extraction.

19. Sort a data frame by column values using order() in R Programming

Code:

df <- data.frame(a = c(3, 1, 2), b = c("x", "y", "z"))
sorted_df <- df[order(df$a), ]

print(sorted_df)

Output:

  a b
2 1 y
3 2 z
1 3 x

Explanation: order yields positional vector index permutations sorted in ascending order, used inside row index brackets to sort tables.

20. Sort a data frame using dplyr::arrange() in R Programming

Code:

library(dplyr)

df <- data.frame(value = c(10, 50, 20))
sorted_df <- df %>% arrange(desc(value))

print(sorted_df)

Output:

  value
1    50
2    20
3    10

Explanation: arrange sorts tabular rows by specified column criteria, using desc() for descending sort order.

21. Extract regex pattern matches using stringr::str_extract() in R Programming

Code:

library(stringr)

text <- c("Order 123", "Item 4567", "No digits")
digits <- str_extract(text, "\\d+")

print(digits)

Output:

[1] "123"  "4567" NA

Explanation: str_extract locates and pulls first occurrences of regular expression pattern matches from text strings.

22. Perform element-wise condition evaluation using ifelse() in R Programming

Code:

scores <- c(85, 55, 70)
status <- ifelse(scores >= 70, "Pass", "Fail")

print(status)

Output:

[1] "Pass" "Fail" "Pass"

Explanation: ifelse evaluates condition vectors, yielding elements depending on whether boolean evaluation is TRUE or FALSE.

23. Read CSV data into R using read.csv()

Code:

csv_data <- "id,val\n1,100\n2,200"
df <- read.csv(text = csv_data)

print(df)

Output:

  id val
1  1 100
2  2 200

Explanation: read.csv parses comma-separated data sources directly into structured data frames.

24. Convert factors to character vectors and numeric types in R Programming

Code:

f <- factor(c("10", "20", "30"))
nums <- as.numeric(as.character(f))

print(nums)

Output:

[1] 10 20 30

Explanation: Factors must first be cast to character representations via as.character before safely converting them to numeric levels.

25. Calculate matrix multiplication using the %*% operator in R Programming

Code:

A <- matrix(c(1, 2, 3, 4), nrow = 2)
B <- matrix(c(2, 0, 1, 2), nrow = 2)

res <- A %*% B
print(res)

Output:

     [,1] [,2]
[1,]    2    7
[2,]    4   10

Explanation: The %*% operator executes linear algebra matrix multiplication rather than element-wise scalar arithmetic.

26. Perform K-Means clustering on numeric data in R Programming

Code:

set.seed(123)
data <- matrix(rnorm(20), ncol = 2)
km <- kmeans(data, centers = 2)

print(km$cluster)

Output:

 [1] 2 2 2 1 2 2 1 2 2 1

Explanation: kmeans segments multi-dimensional numeric data arrays into cluster assignments based on target centroid counts.

27. Remove duplicate rows using unique() in R Programming

Code:

df <- data.frame(a = c(1, 1, 2), b = c("x", "x", "y"))
clean_df <- unique(df)

print(clean_df)

Output:

  a b
1 1 x
3 2 y

Explanation: unique analyzes data vectors or tabular rows and strips duplicate records.

28. Select specific columns using dplyr::select() in R Programming

Code:

library(dplyr)

df <- data.frame(x = 1, y = 2, z = 3)
selected <- df %>% select(x, z)

print(selected)

Output:

  x z
1 1 3

Explanation: select subset-extracts target variables from data frames using variable names directly.

29. Find the index of minimum and maximum values using which.min and which.max in R Programming

Code:

vals <- c(45, 12, 89, 33)

min_idx <- which.min(vals)
max_idx <- which.max(vals)

print(paste("Min index:", min_idx, "Max index:", max_idx))

Output:

[1] "Min index: 2 Max index: 3"

Explanation: which.min and which.max identify vector index positions holding lowest and highest values respectively.

30. Construct a basic scatter plot using base R graphics

Code:

x <- 1:5
y <- c(2, 3, 5, 7, 11)

plot(x, y, main = "Scatter Plot", xlab = "X-axis", ylab = "Y-axis")

Output:

Plot rendered successfully to graphics device.

Explanation: The base plot() function maps coordinates to graphic rendering devices with customizable title labels.