Data analysis with R



Data analysis with R is a powerful approach, making it a popular choice among statisticians and data scientists. Example of data analysis using R, utilizing a dataset of student exam scores.


Firstly, we need to import the dataset into R. Assuming our dataset is in a CSV file named "scores.csv," we can use the `read.csv` function:


# Read the dataset

data <- read.csv("exam_scores.csv")


# Display the first few rows of the dataset

head(data)


# Summarize the dataset

summary(data)


# Calculate the average score

average_score <- mean(data$Score)


To visualize the distribution of scores, we can create a histogram:

# Create a histogram

hist(data$Score, main = "Exam Score Distribution", xlab = "Score")


We can create a scatter plot:

# Create a scatter plot

plot(data$StudyHours, data$Score, main = "Study Hours vs. Exam Score", xlab = "Study Hours", ylab = "Score")


These are just the basics; R offers a wide range of packages and functions for more advanced analyses, including statistical tests, machine learning, and data visualization. Learning R can empower us to gain valuable insights from data and make informed decisions.

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