r-statistics.co by Selva Prabhakaran

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R Cheat Sheet: The Ultimate Quick Reference — 200 Functions

A comprehensive, searchable reference of the 200 most important R functions organized by category. Bookmark this page — you'll come back to it every day.

This cheat sheet covers base R and essential package functions across seven major categories. Each entry includes the function signature, a one-line description, and a minimal example. Whether you're a beginner looking up syntax or an experienced user refreshing your memory, this is your go-to quick reference.

Vectors & Basic Operations

Vectors are R's fundamental data structure. These functions create, manipulate, and query vectors.

Function Description Example
c() Combine values into a vector c(1, 2, 3)
seq() Generate a sequence seq(1, 10, by = 2)
seq_len() Sequence from 1 to n seq_len(5)
seq_along() Sequence along an object seq_along(letters)
rep() Repeat elements rep(1:3, times = 2)
length() Number of elements length(1:10)
rev() Reverse a vector rev(1:5)
sort() Sort a vector sort(c(3,1,2))
order() Return sort indices order(c(3,1,2))
rank() Rank elements rank(c(3,1,2))
unique() Remove duplicates unique(c(1,1,2,3))
duplicated() Find duplicate positions duplicated(c(1,1,2))
table() Frequency table table(c("a","b","a"))
which() Indices where TRUE which(c(T,F,T))
which.min() Index of minimum which.min(c(3,1,2))
which.max() Index of maximum which.max(c(3,1,2))
any() Any TRUE? any(c(T,F,F))
all() All TRUE? all(c(T,T,F))
ifelse() Vectorized if-else ifelse(1:5 > 3, "hi", "lo")
match() First match positions match(c("b"), letters)
%in% Element membership test 3 %in% 1:5
setdiff() Set difference setdiff(1:5, 3:7)
intersect() Set intersection intersect(1:5, 3:7)
union() Set union union(1:3, 2:5)
append() Insert into vector append(1:3, 99, after = 2)
head() First n elements head(1:100, 5)
tail() Last n elements tail(1:100, 5)
# Quick demo: creating and querying vectors x <- c(5, 3, 8, 1, 9, 2) cat("Sorted:", sort(x), "\n") cat("Order:", order(x), "\n") cat("Unique:", unique(c(1,1,2,3,3)), "\n") cat("5 in x?", 5 %in% x, "\n")


  






Math & Statistics



Essential mathematical and statistical functions for numeric computation.







Function
Description
Example






sum()
Sum of elements
sum(1:10)




prod()
Product of elements
prod(1:5)




cumsum()
Cumulative sum
cumsum(1:5)




cumprod()
Cumulative product
cumprod(1:5)




cummax()
Cumulative maximum
cummax(c(1,3,2,5))




cummin()
Cumulative minimum
cummin(c(5,3,4,1))




diff()
Lagged differences
diff(c(1,3,6,10))




mean()
Arithmetic mean
mean(1:10)




median()
Median value
median(1:10)




var()
Variance
var(1:10)




sd()
Standard deviation
sd(1:10)




min()
Minimum value
min(c(3,1,2))




max()
Maximum value
max(c(3,1,2))




range()
Min and max
range(1:10)




quantile()
Quantiles
quantile(1:100, 0.75)




IQR()
Interquartile range
IQR(1:100)




cor()
Correlation
cor(1:10, 10:1)




cov()
Covariance
cov(1:10, 10:1)




abs()
Absolute value
abs(-5)




sqrt()
Square root
sqrt(16)




log()
Natural logarithm
log(exp(1))




log10()
Base-10 logarithm
log10(100)




log2()
Base-2 logarithm
log2(8)




exp()
Exponential
exp(1)




ceiling()
Round up
ceiling(3.2)




floor()
Round down
floor(3.8)




round()
Round to n digits
round(3.14159, 2)




trunc()
Truncate decimals
trunc(3.7)




sign()
Sign of number
sign(-3)




factorial()
Factorial
factorial(5)




choose()
Combinations
choose(5, 2)









  


    
# Statistics at a glance
x <- c(12, 15, 18, 22, 25, 30, 35)
cat("Mean:", mean(x), "\n")
cat("SD:", round(sd(x), 2), "\n")
cat("Quantiles:\n")
print(quantile(x))


    


      

      

    


    


  






Data Frames



Functions for creating, inspecting, and manipulating data frames.







Function
Description
Example






data.frame()
Create data frame
data.frame(x = 1:3, y = c("a","b","c"))




str()
Structure overview
str(mtcars)




summary()
Summary statistics
summary(mtcars)




nrow()
Number of rows
nrow(mtcars)




ncol()
Number of columns
ncol(mtcars)




dim()
Dimensions
dim(mtcars)




names()
Column names
names(mtcars)




colnames()
Column names
colnames(mtcars)




rownames()
Row names
rownames(mtcars)




cbind()
Bind columns
cbind(1:3, 4:6)




rbind()
Bind rows
rbind(1:3, 4:6)




merge()
Merge/join data frames
merge(df1, df2, by = "id")




subset()
Filter rows/columns
subset(mtcars, mpg > 25)




transform()
Add/modify columns
transform(mtcars, kpl = mpg * 0.425)




within()
Modify inside data frame
within(mtcars, {kpl <- mpg*0.425})




with()
Evaluate expression
with(mtcars, mean(mpg))




complete.cases()
Rows with no NAs
complete.cases(airquality)




na.omit()
Remove NA rows
na.omit(airquality)




is.na()
Test for NA
is.na(c(1, NA, 3))




reshape()
Reshape wide/long
reshape(df, direction = "long")




stack()
Stack columns
stack(list(a = 1:3, b = 4:6))




expand.grid()
All combinations
expand.grid(x = 1:2, y = c("a","b"))









  


    
# Data frame basics
df <- data.frame(
  name = c("Alice", "Bob", "Carol"),
  score = c(92, 85, 78),
  grade = c("A", "B", "C")
)
cat("Dimensions:", dim(df), "\n")
cat("High scorers:\n")
print(subset(df, score > 80))


    


      

      

    


    


  






Strings



Functions for character manipulation and pattern matching.







Function
Description
Example






paste()
Concatenate with separator
paste("a", "b", sep = "-")




paste0()
Concatenate without separator
paste0("x", 1:3)




sprintf()
Formatted string
sprintf("%.2f", 3.14159)




nchar()
Number of characters
nchar("hello")




substr()
Extract substring
substr("hello", 1, 3)




substring()
Extract/replace substring
substring("hello", 2, 4)




toupper()
Convert to uppercase
toupper("hello")




tolower()
Convert to lowercase
tolower("HELLO")




trimws()
Trim whitespace
trimws("  hello  ")




gsub()
Replace all matches
gsub("o", "0", "foo")




sub()
Replace first match
sub("o", "0", "foo")




grep()
Find pattern (indices)
grep("a", c("apple","banana"))




grepl()
Find pattern (logical)
grepl("a", c("apple","bat"))




regexpr()
First match position
regexpr("o", "hello world")




regmatches()
Extract regex matches
regmatches("abc123", regexpr("[0-9]+", "abc123"))




strsplit()
Split string
strsplit("a,b,c", ",")




startsWith()
Starts with prefix?
startsWith("hello", "he")




endsWith()
Ends with suffix?
endsWith("hello", "lo")




chartr()
Character translation
chartr("abc", "ABC", "abcdef")




format()
Format values as strings
format(3.14, nsmall = 4)




formatC()
C-style formatting
formatC(12345, format = "d", big.mark = ",")









  


    
# String operations
names <- c("Alice Smith", "Bob Jones", "Carol Lee")
cat("Uppercase:", toupper(names[1]), "\n")
cat("Split:", unlist(strsplit(names[1], " ")), "\n")
cat("Contains 'Jones':", grepl("Jones", names), "\n")


    


      

      

    


    


  






Dates & Times



Functions for working with date and time objects.







Function
Description
Example






Sys.Date()
Current date
Sys.Date()




Sys.time()
Current date-time
Sys.time()




as.Date()
Convert to Date
as.Date("2026-03-29")




as.POSIXct()
Convert to datetime
as.POSIXct("2026-03-29 10:30:00")




format()
Format date as string
format(Sys.Date(), "%B %d, %Y")




difftime()
Time difference
difftime(Sys.Date(), as.Date("2026-01-01"))




seq.Date()
Sequence of dates
seq.Date(as.Date("2026-01-01"), by = "month", length.out = 6)




weekdays()
Day of the week
weekdays(Sys.Date())




months()
Month name
months(Sys.Date())




quarters()
Quarter of year
quarters(Sys.Date())




as.numeric()
Date to epoch days
as.numeric(Sys.Date())




strptime()
Parse date string
strptime("29/03/2026", "%d/%m/%Y")




strftime()
Format date/time
strftime(Sys.time(), "%H:%M:%S")









  


    
# Date arithmetic
today <- Sys.Date()
cat("Today:", format(today, "%A, %B %d, %Y"), "\n")
cat("30 days ago:", format(today - 30, "%Y-%m-%d"), "\n")
cat("Day of week:", weekdays(today), "\n")


    


      

      

    


    


  






Input/Output



Functions for reading, writing, and interacting with the file system.







Function
Description
Example






read.csv()
Read CSV file
read.csv("data.csv")




read.table()
Read delimited file
read.table("data.txt", header = TRUE)




read.delim()
Read tab-delimited
read.delim("data.tsv")




readLines()
Read lines of text
readLines("file.txt", n = 5)




scan()
Read data flexibly
scan("nums.txt", what = numeric())




readRDS()
Read R object
readRDS("model.rds")




write.csv()
Write CSV file
write.csv(df, "out.csv", row.names = FALSE)




write.table()
Write delimited file
write.table(df, "out.txt", sep = "\t")




writeLines()
Write lines of text
writeLines(c("a","b"), "out.txt")




saveRDS()
Save R object
saveRDS(model, "model.rds")




save()
Save multiple objects
save(x, y, file = "data.RData")




load()
Load .RData file
load("data.RData")




cat()
Print to console/file
cat("Hello\n")




print()
Print an object
print(1:5)




message()
Print a message
message("Processing...")




warning()
Issue a warning
warning("Check input")




stop()
Throw an error
stop("Invalid argument")




file.exists()
Check file existence
file.exists("data.csv")




dir()
List files in directory
dir(pattern = "\\.csv$")




getwd()
Get working directory
getwd()




setwd()
Set working directory
setwd("~/projects")




download.file()
Download a file
download.file(url, "file.csv")









  


    
# Quick IO demo
df <- data.frame(x = 1:5, y = letters[1:5])
cat("Current directory:", getwd(), "\n")
cat("CSV output would look like:\n")
write.csv(df, stdout(), row.names = FALSE)


    


      

      

    


    


  






Apply Family & Functional Programming



Functions for iteration without explicit loops.







Function
Description
Example






apply()
Apply over rows/cols
apply(matrix(1:6, 2), 2, sum)




lapply()
Apply, return list
lapply(1:3, function(x) x^2)




sapply()
Apply, simplify result
sapply(1:3, function(x) x^2)




vapply()
Apply with type check
vapply(1:3, function(x) x^2, numeric(1))




tapply()
Apply by group
tapply(mtcars$mpg, mtcars$cyl, mean)




mapply()
Multiple-argument apply
mapply(function(x,y) x+y, 1:3, 4:6)




Map()
Like mapply, returns list
Map("+", 1:3, 4:6)




Reduce()
Accumulate from left
Reduce("+", 1:5)




Filter()
Keep matching elements
Filter(is.numeric, list(1,"a",3))




Find()
First match
Find(function(x) x > 3, 1:10)




Position()
Index of first match
Position(function(x) x > 3, 1:10)




do.call()
Call function with arg list
do.call(paste, list("a","b","c"))




Vectorize()
Vectorize a function
vf <- Vectorize(choose); vf(5, 1:5)




tryCatch()
Error handling
tryCatch(log(-1), warning = function(w) NA)




try()
Try without stopping
try(log("a"), silent = TRUE)









  


    
# Apply family in action
m <- matrix(1:12, nrow = 3)
cat("Column sums:", apply(m, 2, sum), "\n")
cat("Squares:", sapply(1:5, function(x) x^2), "\n")
cat("Group means:\n")
print(tapply(mtcars$mpg, mtcars$cyl, mean))


    


      

      

    


    


  






Type Checking & Conversion



Functions for inspecting and converting object types.







Function
Description
Example






class()
Object class
class(1:5)




typeof()
Internal type
typeof(1L)




is.numeric()
Is numeric?
is.numeric(3.14)




is.character()
Is character?
is.character("hi")




is.logical()
Is logical?
is.logical(TRUE)




is.factor()
Is factor?
is.factor(factor("a"))




is.list()
Is list?
is.list(list(1,2))




is.data.frame()
Is data frame?
is.data.frame(mtcars)




is.null()
Is NULL?
is.null(NULL)




as.numeric()
Convert to numeric
as.numeric("42")




as.character()
Convert to character
as.character(42)




as.logical()
Convert to logical
as.logical(0)




as.factor()
Convert to factor
as.factor(c("a","b"))




as.integer()
Convert to integer
as.integer(3.7)




as.list()
Convert to list
as.list(1:3)




unlist()
Flatten list to vector
unlist(list(1:2, 3:4))




identical()
Exact equality test
identical(1L, 1L)




all.equal()
Near-equality test
all.equal(0.1+0.2, 0.3)









  


    
# Type checking
x <- "42"
cat("Class:", class(x), "\n")
cat("Is numeric?", is.numeric(x), "\n")
y <- as.numeric(x)
cat("Converted:", y, "- Class:", class(y), "\n")


    


      

      

    


    


  






Summary: When to Use What







Task
Go-To Function






Create a sequence
seq() or 1:n




Find elements
which(), match(), %in%




Summarize numbers
mean(), median(), summary()




Manipulate strings
paste(), gsub(), strsplit()




Read data
read.csv(), readRDS()




Iterate without loops
sapply(), lapply(), apply()




Check/convert types
is.*(), as.*()




Handle errors
tryCatch(), try()







FAQ



How do I search for a function I don't know the name of? Use ??keyword in R to search all help pages, or apropos("pattern") to find functions matching a pattern. For example, apropos("mean") lists all functions with "mean" in the name.



Should I memorize all 200 functions? No. Focus on the 30-40 you use most often. Bookmark this page for the rest. Fluency comes from practice, not memorization.



Are these functions still relevant with tidyverse? Absolutely. Base R functions underpin everything — even tidyverse packages use them internally. Knowing base R makes you a stronger programmer regardless of which packages you prefer.



What's Next




        


        
      


      

        

        
© 2016- Selva Prabhakaran.
          This work is licensed under the Creative Commons License.