**Course Title:** Mastering R Programming: Data Analysis, Visualization, and Beyond **Section Title:** Data Import and Export in R **Topic:** Import data from CSV and Excel files, perform basic data cleaning, and export the cleaned data.(Lab topic) **Objective:** In this lab topic, you will learn how to import data from CSV and Excel files into R, perform basic data cleaning operations, and export the cleaned data. You will gain practical experience working with real-world datasets and applying data cleaning techniques using R. **Importing Data from CSV Files** To import data from a CSV file, you can use the `read.csv()` function in R. This function takes the file path as an argument and returns a data frame. ```R # Import data from a CSV file data <- read.csv("data.csv") # Check the structure of the data str(data) ``` Alternatively, you can use the `readr` package, which provides faster and more efficient data import functions. ```R # Install the readr package install.packages("readr") # Load the readr package library(readr) # Import data from a CSV file using readr data <- read_csv("data.csv") # Check the structure of the data str(data) ``` The `read_csv()` function from the `readr` package is generally faster and more efficient than the built-in `read.csv()` function. **Importing Data from Excel Files** To import data from an Excel file, you can use the `readxl` package in R. This package provides functions for importing data from Excel files (.xls and .xlsx) into R. ```R # Install the readxl package install.packages("readxl") # Load the readxl package library(readxl) # Import data from an Excel file data <- read_excel("data.xlsx") # Check the structure of the data str(data) ``` The `read_excel()` function can import data from both .xls and .xlsx files. **Basic Data Cleaning** Data cleaning is an essential step in data analysis. It involves checking for and correcting errors, inconsistencies, and inaccuracies in the data. Here are some basic data cleaning operations you can perform in R: * Check for missing values: Use the `summary()` function to check for missing values in the data. ```R # Check for missing values summary(data) ``` * Remove duplicates: Use the `duplicated()` function to identify and remove duplicate rows. ```R # Remove duplicates data <- data[!duplicated(data), ] ``` * Handle missing values: Use the `is.na()` function to identify missing values and the `ifelse()` function to replace them with a specified value. ```R # Replace missing values with 0 data$column_name <- ifelse(is.na(data$column_name), 0, data$column_name) ``` **Exporting Data** Once you have cleaned the data, you can export it to a CSV or Excel file. Use the `write.csv()` function to export the data to a CSV file. ```R # Export data to a CSV file write.csv(data, "cleaned_data.csv") ``` Alternatively, you can use the `readr` package to export the data to a CSV file. ```R # Export data to a CSV file using readr write_csv(data, "cleaned_data.csv") ``` To export the data to an Excel file, use the `openxlsx` package. ```R # Install the openxlsx package install.packages("openxlsx") # Load the openxlsx package library(openxlsx) # Export data to an Excel file write.xlsx(data, "cleaned_data.xlsx") ``` **Conclusion:** In this lab topic, you learned how to import data from CSV and Excel files, perform basic data cleaning operations, and export the cleaned data. You gained practical experience working with real-world datasets and applying data cleaning techniques using R. **External Resources:** * `readr` package: [https://cran.r-project.org/web/packages/readr/index.html](https://cran.r-project.org/web/packages/readr/index.html) * `readxl` package: [https://cran.r-project.org/web/packages/readxl/index.html](https://cran.r-project.org/web/packages/readxl/index.html) * `openxlsx` package: [https://cran.r-project.org/web/packages/openxlsx/index.html](https://cran.r-project.org/web/packages/openxlsx/index.html) **Practical Exercise:** Use the `mtcars` dataset and perform the following operations: * Import the dataset into R. * Check for missing values and remove duplicates. * Replace missing values with 0. * Export the cleaned data to a CSV file. **Next Topic:** In the next topic, we will cover the `dplyr` package and learn how to perform data manipulation operations using R.