R — Statistics

Descriptive statistics

data <- c(12, 15, 18, 22, 25, 28, 30, 35, 40, 45)

mean(data)      # 27
median(data)    # 26.5
sd(data)        # Standard deviation
var(data)       # Variance
range(data)     # 12 45
diff(range(data)) # 33

Summary

summary(data)

# For data frames
summary(mtcars)

Correlation

x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 5, 4, 5)

cor(x, y)  # Correlation coefficient
cor.test(x, y)  # Full test

Hypothesis testing

# t-test
t.test(rnorm(100, mean = 5), mu = 5)

# Chi-squared test
chisq.test(table(c("A", "B", "A", "B", "A")))

# ANOVA
anova(lm(mpg ~ factor(cyl), data = mtcars))

Linear regression

model <- mtcars$mpg ~ mtcars$wt
lm_model <- lm(model)
summary(lm_model)

# Predict
predict(lm_model, data.frame(wt = 3.0))

Mini Practice

Write R code that:

1. Computes descriptive statistics
2. Calculates correlation
3. Performs a t-test
4. Fits a linear regression model

Up Next

In the next lesson, you'll learn about Visualization — creating charts with ggplot2.