R Programming FAQs

Learn essential Data Manipulation Functions in R like with(), by(), subset(), sample() and concatenation functions in this comprehensive Q&A guide. Perfect for students, researchers, and R programmers seeking practical R coding techniques. Struggling with data manipulation in R? This blog post about Data manipulation in R breaks down critical R functions in an easy question-answer format, covering:
✔ with() vs by() – When to use each for efficient data handling.
✔ Concatenation functions (c(), paste(), cbind(), etc.) – Combine data like a pro.
✔ subset() vs sample() – Filter data and generate random samples effortlessly.
The Data manipulation functions in R include practical examples to boost R programming skills for data analysis, research, and machine learning.

Data Manipulation Functions in R

Explain with() and by() functions in R are used for?

In R programming, with() and by() functions are two useful functions for data manipulation and analysis.

• with() Function: allows to evaluate expressions within a specific data environment (such as data.frame, or list) without repeatedly referencing the dataset. The syntax with an example is with(data, expr)
df = data.frame(x = 1:5, y=6:10)
with(df, x + y)
• by() Function: applies a function to subsets of a dataset split by one or more factors (similar to GROUP BY in SQL). The syntax with an example is
  by(data, INDICES, FUN, …)

df <- data.frame(group = c("A", "B", "B"), value = c(10, 20, 30, 40))
by(df$value, df$group, mean) # computes the mean for each group

Use with() to simplify code when working with columns in a data frame.

Use by() (or dplyr/tidyverse alternatives) for group-wise computations.

Both with() and by() functions are base R functions, but modern alternatives like dplyr (mutate(), summarize(), group_by()) are often preferred for readability. The key difference between with() and by() functions are:

Function	Purpose	Input	Output
with()	Evaluate expressions in a data environment	Data frame + expression	Result of expression
by()	Apply a function to groups of data	Data + grouping factor + function	Results

What are the concatenation functions in R?

In the R programming language, concatenation refers to combining values into vectors, lists, or other structures. The following are primary concatenation functions:

• c() Basic Concatenation: is used to combine elements into a vector (atomic or list). It works with numbers, characters, logical values, and lists. The examples are
  x <- c(1, 2, 3)
y <- c("a", "b", "c")
z <- c(TRUE, FALSE, TRUE, TRUE)
• paste() and paste0() String Concatenation: is used to combine strings (character vectors with optional separators. The key difference between paste() and paste0 is the use of a separator. The paste() has a default space separator. The examples are:
  paste("Hello", "world")
paste0("hello", "world")
paste(c("A", "B"), 1:2, sep = "-")
• cat() Print Concatenation: is used to concatenate outputs to the console/file (it is not used for storing results). It is useful for printing messages or writing to files. The example is:
  cat("R Frequently Asked Questions", "https://rfaqs.com", "\n")
• append() Insert into Vectors/ Lists: is used to add elements to an existing vector/ list at a specified position.
  x <- c(1, 2, 3)
append(x, 4, after = 2) # inserts 4 after position 2
• cbind() and rbind() Matrix/ Data Frame Concatenation: is used to combine objects column-wise and row-wise, respectively. It works with vectors, matrices, or data frames. The examples are:
  df1 <- data.frame(A = 1:2, B = c("X", "Y"))
df2 <- data.frame(A = 3:4, B = c("Z", "W"))
rbind(df1, df2) # stacks rows
cbind(df1, C= c(10, 20)) # adds a new column
• list() Concatenate into a list: is used to combine elements into a list (preserves structure, unlike c(). The example is:
  my_list = list(1, "a", TRUE, 10:15) # keeps elements as separate list time

The key differences between these concatenation functions are:

Function	Output Type	Use Case
c()	Atomic vector/list	Simple element concatenation
paste()	Character vector	String merging with separators
cat()	Console output	Printing/writing text
append()	Modified vector/list	Inserting elements at a position
cbind()	Matrix/data frame	Column-wise combination
rbind()	Matrix/data frame	bRow-wise combination
list()	List	Preserves heterogeneous elements

What is the use of subset() function and sample() function in R?

Both subset() and sample() are essential functions in R for data manipulation and random sampling, respectively. One can use subset() when one needs to filter rows or select columns based on logical conditions. One can prefer cleaner syntax over $df[df$age > 25, ]$. Use sample() when one needs random samples (such as for machine learning splits) or one wants to shuffle data or perform bootstrapping.

• subset() function: is used to filter rows and select columns from a data frame based on conditions. It provides a cleaner syntax compared to base R subsetting with []. The syntax and example are:
  subset(data, subset, select)

df <- data.frame(
name = c("Ali", "Usman", "Imdad"),
age = c(25, 30, 22),
score = c(85, 90, 60))
subset(df, age > 25)
subset(df, age > 25, select = c(name, score))
  Note that the subset() function works only with data frames.
• sample() Function: is used for random sampling from a vector or data frame. It helps create train-test splits, bootstrapping, and randomizing data order. The syntax and example are:
  sample(x, size, replace = FALSE, prob = NULL)

sample(1:10, 3) # sample 3 number from 1 to 10 without replacement
sample(1:6, 10, replace = TRUE) # 6 possible outcomes, sampled 10 times with replacement
sample(letters[1:5]) # shuffle letters A to E

The key difference between subset() and sample() are:

Feature	subset()	sample()
Purpose	Filter data based on conditions	Randomly select elements/rows
Input	Data frames	Vectors, data frames
Output	Subsetted data frame	Randomly sampled elements
Use Case	Data cleaning, filtering	Train-test splits, bootstrapping

Statistics and Data Analysis