Importing Data in R Script

We can read external datasets and operate with them in our R environment by importing data into an R script. R programming language offers a number of functions for importing data from various file formats. For this demonstration, we will use two examples of a single dataset, one in .csv form and another .txt

You can download the files from here.

1. Reading a Comma-Separated Value(CSV) File

CSV files are a widely used format for tabular data and R offers built-in functions to read them easily.

Method 1: Using read.csv() Function Read CSV Files into R

The function has two parameters: 

• file.choose(): It opens a menu to choose a CSV file from the desktop.
• header: It is to indicate whether the first row of the dataset is a variable name or not. Apply T/True if the variable name is present else put F/False.

Example: 

data1 <- read.csv(file.choose(), header=T)

data1

Output: 

Method 2: Using read.table() Function

This function specifies how the dataset is separated, in this case we take sep=", " as an argument.

Example: 

data2 <- read.table(file.choose(), header=T, sep=", ")

data2

Output: 

2. Reading a Tab-Delimited(txt) File

Tab-delimited files are commonly used for data exchange and R supports reading them with built-in methods.

Method 1: Using read.delim() Function

The function has two parameters: 

• file.choose(): It opens a menu to choose a csv file from the desktop.
• header: It is to indicate whether the first row of the dataset is a variable name or not. Apply T/True if the variable name is present else put F/False.

Example: 

data3 <- read.delim(file.choose(), header=T)

data3

Output: 

Method 2: Using read.table() Function

This function specifies how the dataset is separated, in this case we take sep="\t" as the argument.

Example: 

data4 <- read.table(file.choose(), header=T, sep="\t")

data4

Output: 

3. Using R-Studio

Here we are going to import data through R studio with the following steps.

Steps: 

• From the Environment tab click on the Import Dataset Menu.

• Select the file extension from the option.

• In the third step, a pop-up box will appear, either enter the file name or browse the desktop.
• The selected file will be displayed on a new window with its dimensions.
• In order to see the output on the console, type the filename.

4. Reading JSON Files in R

In order to work with JSON files in R, one needs to install the “rjson” package.

• fromJSON(): This function reads and parses a JSON file into an R object.

JSON file for demonstration:

{
"ID":["1","2","3","4","5"],
"Name":["Mithuna","Tanushree","Parnasha","Arjun","Pankaj"],
"Salary":["722.5","815.2","1611","2829","843.25"],
"StartDate":["6/17/2014","1/1/2012","11/15/2014","9/23/2013","5/21/2013"],
"Dept":["IT","IT","HR","Operations","Finance"]
}

library("rjson")

result <- fromJSON(file = "/content/example.json")

print(result)

Output:

The output shows the JSON data successfully read into R, with fields such as ID, Name, Salary, StartDate and Dept stored as vectors. This data can now be converted into a data frame for further analysis or manipulation.