Statistics in R

So far, we have applied statistics using Excel and Python.

R is different. It was designed specifically for statistical analysis and data modeling.

In this lesson, we focus on how R naturally supports statistical thinking.

Why R Is Popular in Statistics

• Built by statisticians for statisticians
• Strong support for statistical tests
• Excellent visualization tools
• Widely used in research and academia

Many advanced statistical methods are available in R before any other language.

Core Strength of R

R treats data as statistical objects.

This means:

• Functions are named after statistical concepts
• Outputs are designed for interpretation
• Minimal setup is required

Basic Descriptive Statistics in R

R provides simple functions for descriptive statistics.

data <- c(10, 12, 15, 18, 20)

mean(data)
median(data)
sd(data)

These directly compute mean, median, and standard deviation.

Summary of Data

The summary() function gives a quick statistical overview.

summary(data)

This output includes minimum, quartiles, median, mean, and maximum.

Visualizing Data in R

Visualization is central to statistics in R.

• Histograms for distributions
• Box plots for outliers
• Scatter plots for relationships

hist(data)
boxplot(data)

Hypothesis Testing in R

R includes built-in functions for hypothesis testing.

t.test(group1, group2)

The output includes:

• Test statistic
• p-value
• Confidence interval

These are the same components you learned conceptually.

Regression in R

Regression modeling in R is concise and powerful.

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

The summary output includes:

• Coefficients
• p-values
• R-squared
• Residual diagnostics

ANOVA in R

ANOVA is handled naturally in R.

anova(model)

R automatically handles degrees of freedom and test statistics.

R vs Python for Statistics

Real-World Use of R

R is commonly used in:

• Academic research
• Clinical trials
• Economics and finance
• Statistical reporting

Many official reports and studies are still produced using R.

Common Mistakes to Avoid

• Memorizing syntax instead of concepts
• Ignoring interpretation of results
• Overlooking assumptions
• Treating R output as final truth

Quick Check

Which function gives a statistical summary in R?

Practice Quiz

Question 1:
Which function is used for linear regression in R?

Question 2:
Is R mainly designed for statistics or general programming?

Question 3:
Does R automatically handle statistical assumptions?

Mini Practice

You want to analyze survey data and produce statistical reports.

• Why might R be a good choice?

What’s Next

In the next lesson, we will apply everything learned in a Mini Project: A/B Testing with Proportions.