read.csv and write.csv in R Programming - Mini Project: Build & Apply

Reading and Writing CSV Files in R

📖 Scenario: You work in a small shop and keep track of your sales data in a CSV file. You want to load this data into R, make some changes, and then save the updated data back to a new CSV file.

🎯 Goal: Learn how to use read.csv to load data from a CSV file and write.csv to save data back to a CSV file in R.

📋 What You'll Learn

Create a CSV file named sales.csv with given data

Use read.csv to load the CSV file into a variable

Add a new column to the data frame

Use write.csv to save the updated data to a new CSV file

💡 Why This Matters

🌍 Real World

CSV files are a common way to store and share data like sales, inventory, or survey results. Being able to read and write CSV files lets you work with data easily in R.

💼 Career

Many jobs in data analysis, business, and science require handling CSV files. Knowing how to use read.csv and write.csv is a basic but important skill.

1

Create the CSV file sales.csv

Create a CSV file named sales.csv with these exact contents (including header):
Product,Quantity,Price
Apple,10,0.5
Banana,5,0.3
Cherry,20,0.2

R Programming

# Create the CSV file sales.csv with the exact data
# Your code here

Hint

Use write.csv with row.names = FALSE to create the file.

2

Load the CSV file into a variable

Use read.csv to read the file sales.csv into a variable called sales_data.

R Programming

write.csv(data.frame(Product=c("Apple", "Banana", "Cherry"), Quantity=c(10, 5, 20), Price=c(0.5, 0.3, 0.2)), file = "sales.csv", row.names = FALSE)
# Use read.csv to load sales.csv into sales_data
# Your code here

Hint

Use sales_data <- read.csv("sales.csv") to load the data.

3

Add a new column to the data frame

Add a new column called Total to sales_data that multiplies Quantity by Price.

R Programming

write.csv(data.frame(Product=c("Apple", "Banana", "Cherry"), Quantity=c(10, 5, 20), Price=c(0.5, 0.3, 0.2)), file = "sales.csv", row.names = FALSE)
sales_data <- read.csv("sales.csv")
# Add a new column Total to sales_data
# Your code here

Hint

Use sales_data$Total <- sales_data$Quantity * sales_data$Price to create the new column.

4

Save the updated data to a new CSV file

Use write.csv to save sales_data to a new file called updated_sales.csv without row names. Then print the contents of sales_data.

R Programming

write.csv(data.frame(Product=c("Apple", "Banana", "Cherry"), Quantity=c(10, 5, 20), Price=c(0.5, 0.3, 0.2)), file = "sales.csv", row.names = FALSE)
sales_data <- read.csv("sales.csv")
sales_data$Total <- sales_data$Quantity * sales_data$Price
# Save sales_data to updated_sales.csv without row names
# Print sales_data
# Your code here

Hint

Use write.csv(sales_data, file = "updated_sales.csv", row.names = FALSE) and print(sales_data).

Practice

1. What does the read.csv function do in R?

easy

A. Loads data from a CSV file into a data frame

B. Saves a data frame to a CSV file

C. Creates a new CSV file with default data

D. Deletes a CSV file from the disk

Solution

1. Step 1: Understand the purpose of read.csv

   The read.csv function is designed to read data stored in CSV files and load it into R as a data frame.

2. Step 2: Differentiate from write.csv

   write.csv saves data frames to CSV files, so it does the opposite of read.csv.

3. Final Answer:

   Loads data from a CSV file into a data frame -> Option A

4. Quick Check:

   read.csv loads CSV data [OK]

Hint: Remember: read.csv reads files, write.csv writes files [OK]

Common Mistakes:

• Confusing read.csv with write.csv
• Thinking read.csv creates new files
• Assuming read.csv deletes files

2. Which of the following is the correct syntax to save a data frame named df to a CSV file called output.csv?

easy

A. read.csv(df, file = "output.csv")

B. write.csv(df, file = "output.csv")

C. write.csv(file = df, "output.csv")

D. write.csv("output.csv", df)

Solution

1. Step 1: Recall the argument order of write.csv

   The first argument is the data frame to save, the second is the file name with the parameter file=.

2. Step 2: Check each option's argument order

   write.csv(df, file = "output.csv") correctly uses write.csv(df, file = "output.csv"). read.csv(df, file = "output.csv") uses read.csv which is for reading, not writing. Options B and D have wrong argument order.

3. Final Answer:

   write.csv(df, file = "output.csv") -> Option B

4. Quick Check:

   write.csv(data, file=filename) syntax [OK]

Hint: write.csv(data, file=filename) saves data frame [OK]

Common Mistakes:

• Swapping arguments order
• Using read.csv instead of write.csv
• Omitting the file= parameter

3. What will be the output of the following R code?

data <- data.frame(Name = c("Anna", "Bob"), Age = c(25, 30))
write.csv(data, file = "people.csv", row.names = FALSE)
read_data <- read.csv("people.csv")
print(read_data)

medium

A. Error: file not found

B. Name Age
0 Anna 25
1 Bob 30

C. Name Age
1 Anna 25
2 Bob 30
3 NA NA

D. Name Age
1 Anna 25
2 Bob 30

Solution

1. Step 1: Understand the data frame and write.csv call

   The data frame has two rows with names and ages. The write.csv saves it without row names because row.names = FALSE.

2. Step 2: Read the CSV and print

   The read.csv reads the file back, creating a data frame with default row numbers 1 and 2. Printing shows the two rows with correct data.

3. Final Answer:

   Name Age 1 Anna 25 2 Bob 30 -> Option D

4. Quick Check:

   write.csv without row.names keeps data clean [OK]

Hint: Use row.names=FALSE to avoid extra row numbers in CSV [OK]

Common Mistakes:

• Forgetting row.names=FALSE adds extra column
• Expecting zero-based row numbers
• Assuming file not found error

4. Identify the error in this code snippet:

df <- data.frame(x = 1:3, y = 4:6)
write.csv(df, "data.csv")
new_df <- read.csv(data.csv)
print(new_df)

medium

A. print() cannot display data frames

B. write.csv should not have data frame as first argument

C. Missing quotes around file name in read.csv

D. read.csv cannot read CSV files

Solution

1. Step 1: Check the write.csv usage

   write.csv(df, "data.csv") is correct syntax to save the data frame.

2. Step 2: Check the read.csv usage

   The code uses read.csv(data.csv) without quotes around the file name, which causes an error because R treats data.csv as a variable, not a string.

3. Final Answer:

   Missing quotes around file name in read.csv -> Option C

4. Quick Check:

   File names must be strings in quotes [OK]

Hint: Always put file names in quotes in read.csv and write.csv [OK]

Common Mistakes:

• Omitting quotes around file names
• Confusing argument order
• Thinking print() can't show data frames

5. You have a CSV file with missing values represented as empty cells. Which read.csv argument helps you automatically convert these empty cells to NA in R?

hard

A. na.strings = ""

B. stringsAsFactors = TRUE

C. header = FALSE

D. row.names = NULL

Solution

1. Step 1: Understand missing values in CSV

   Empty cells in CSV files often represent missing data, which R can mark as NA if told how to interpret them.

2. Step 2: Use na.strings argument

   The na.strings = "" argument tells read.csv to treat empty strings as NA values automatically.

3. Final Answer:

   na.strings = "" -> Option A

4. Quick Check:

   na.strings="" converts empty cells to NA [OK]

Hint: Use na.strings="" to read blanks as NA [OK]

Common Mistakes:

• Ignoring na.strings argument
• Using stringsAsFactors which affects factor conversion
• Confusing header or row.names with missing data handling