R Cheat Sheet

name <- "John"

age <- 25

nums <- c(1, 2, 3, 4, 5)

person <- list(name = "John", age = 30)

mat <- matrix(1:9, nrow = 3, ncol = 3)

df <- data.frame(name = c("A", "B"), age = c(25, 30))

status <- factor(c("low", "high", "medium"))

if (x > 0) {
  print("positive")
} else if (x < 0) {
  print("negative")
} else {
  print("zero")
}

for (i in 1:5) {
  print(i)
}

while (count < 10) {
  count <- count + 1
}

sapply(1:5, function(x) x^2)

result <- ifelse(x > 0, "positive", "non-positive")

greet <- function(name) {
  paste("Hello,", name, "!")
}

greet <- function(name = "World") {
  paste("Hello,", name, "!")
}

add <- function(a, b) {
  return(a + b)
}

function(x) x^2

v <- c(1, 2, 3, 4, 5)

seq(1, 10, by = 2)  # 1, 3, 5, 7, 9

rep(1, 5)  # 1, 1, 1, 1, 1

v[1]  # First element
v[2:4]  # Elements 2 to 4

v[v > 2]  # Elements greater than 2

sum(v)
mean(v)
max(v)
length(v)

df <- data.frame(
  name = c("Alice", "Bob"),
  age = c(25, 30)
)

df$name
df[["name"]]
df[, "name"]

df[1, ]  # First row

df[df$age > 25, ]

df$city <- c("NYC", "LA")

merge(df1, df2, by = "id")

install.packages("dplyr")

library(dplyr)

df %>% filter(age > 25)

df %>% select(name, age)

df %>% mutate(age_plus = age + 1)

df %>% group_by(city) %>% summarise(avg = mean(age))