Here’s a cheat sheet for common operations in base R:
Data Structures
Vectors:
vector <- c(1, 2, 3, 4)
Matrices:
matrix_data <- matrix(1:6, nrow = 2, ncol = 3)
Lists:
list_data <- list(1, "a", TRUE)
Data Frames:
df <- data.frame(Name = c("Zach", "Arun", "Charles"), Age = c(25, 30, 22))
Basic Operations
Arithmetic Operations:
result <- 2 + 3 * 4 / 2
Logical Operations:
logical_result <- TRUE & FALSE | TRUE
Data Manipulation
Indexing Elements:
element <- vector[2]
Subset Data Frame:
subset_df <- df[df$Age > 25, ]
Sorting Data Frame:
sorted_df <- df[order(df$Age), ]
Merge Data Frames:
merged_df <- merge(df1, df2, by = "ID", all = TRUE)
Control Structures
if-else Statement:
if (condition) {
# code block
} else {
# code block
}
for Loop:
for (i in 1:5) {
# code block
}
while Loop:
while (condition) {
# code block
}
Functions
Function Definition:
my_function <- function(arg1, arg2) {
# code block
return(result)
}
Apply Functions:
result <- lapply(list_data, my_function)
Data Analysis
Descriptive Statistics:
mean_value <- mean(vector)
Correlation:
correlation <- cor(df$Age, df$Height)
Linear Regression:
lm_model <- lm(Y ~ X1 + X2, data = df)
File I/O
Read CSV File:
data <- read.csv("file.csv")
Write CSV File:
write.csv(df, "output.csv", row.names = FALSE)
Plotting
Scatter Plot:
plot(df$X, df$Y, main = "Scatter Plot", xlab = "X-axis", ylab = "Y-axis")
Histogram:
hist(vector, main = "Histogram", xlab = "Values", col = "lightblue")
Miscellaneous
Help Documentation:
?function_name
Install and Load Packages:
install.packages("package_name")
library(package_name)
This cheat sheet provides a quick reference for common operations in base R. Keep in mind that R has a rich ecosystem of packages, and using specialized packages can enhance your capabilities in data analysis, visualization, and statistical modeling. For more detailed information, refer to the official R documentation.