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x <- 10
x = 10  # also works

x <- 3.14

x <- 5L

name <- "R"

flag <- TRUE

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

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

class(x)
typeof(x)

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

1:10
seq(1, 10, by=2)

rep(1, times=5)
rep(1:3, each=2)

v[1]  # first element (1-indexed)
v[c(1,3)]  # 1st and 3rd

v[2:4]  # elements 2 to 4

v[-1]  # all except first

v * 2  # element-wise
sum(v)
mean(v)
max(v), min(v)

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

df <- data.frame(col1=v1, col2=v2)

str(df)
head(df)
tail(df)

nrow(df)
ncol(df)
dim(df)

names(df)
colnames(df)

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

df[1, ]  # first row

df[1, 2]  # row 1, col 2

df[, c("name", "age")]

df[df$age > 25, ]

subset(df, age > 25)

df$new_col <- values

df$col <- NULL

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

rbind(df1, df2)

cbind(df1, df2)

df[order(df$age), ]

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

ifelse(x > 0, "pos", "neg")

for (i in 1:10) {
  print(i)
}

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

for (i in 1:10) {
  if (i == 5) break
  if (i == 3) next
  print(i)
}

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

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

square <- function(x) x^2

stats <- function(v) {
  list(mean=mean(v), sd=sd(v))
}

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

apply(matrix, 1, sum)  # by row
apply(matrix, 2, sum)  # by col

lapply(list, function)  # returns list

sapply(list, function)  # simplifies

mapply(function, v1, v2)  # parallel

tapply(values, groups, mean)

library(dplyr)
library(tidyverse)

df %>% filter(x > 5) %>% select(a, b)

df %>% filter(age > 25)

df %>% select(name, age)
df %>% select(-id)  # exclude

df %>% mutate(age2 = age * 2)

df %>% arrange(age)
df %>% arrange(desc(age))

df %>% group_by(category)

df %>% summarize(mean_age = mean(age))

left_join(df1, df2, by = "id")
inner_join(df1, df2, by = "id")

df %>% distinct(category)

df %>% count(category)

df %>% rename(new_name = old_name)

df %>% slice(1:10)

df %>% pull(column)

plot(x, y)

plot(x, y, type = "l")

hist(x)

barplot(table(x))

boxplot(y ~ group)

ggplot(df, aes(x=col1, y=col2)) +
  geom_point()

ggplot(df, aes(x, y)) + geom_point()

ggplot(df, aes(x, y)) + geom_line()

ggplot(df, aes(x)) + geom_bar()

ggplot(df, aes(x)) + geom_histogram()

+ facet_wrap(~category)

+ theme_minimal()

+ labs(title="Title", x="X", y="Y")

df <- read.csv("file.csv")
df <- readr::read_csv("file.csv")

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

library(readxl)
df <- read_excel("file.xlsx")

library(writexl)
write_xlsx(df, "file.xlsx")

saveRDS(obj, "file.rds")

obj <- readRDS("file.rds")

summary(df)

mean(x)
median(x)

sd(x)
var(x)

quantile(x, 0.25)

cor(x, y)

t.test(x, y)

chisq.test(table)

model <- lm(y ~ x, data=df)
summary(model)