Create a JSON file by copying the below data into a text editor like notepad. Save the file with a .json extension and choosing the file type as all files(*.*).

{

"ID":["1","2","3","4","5","6","7","8" ],

"Name":["Rick","Dan","Michelle","Ryan","Gary","Nina","Simon","Guru" ],

"Salary":["623.3","515.2","611","729","843.25","578","632.8","722.5" ],




"StartDate":[ "1/1/2012","9/23/2013","11/15/2014","5/11/2014","3/27/2015","5/21/2013",

"7/30/2013","6/17/2014"],

"Dept":[ "IT","Operations","IT","HR","Finance","IT","Operations","Finance"]

}

Read the JSON File

The JSON file is read by R using the function from JSON(). It is stored as a list in R.

# Load the package required to read JSON files.

library("rjson")

# Give the input file name to the function.

result <- fromJSON(file = "input.json")

# Print the result.

print(result)

When we execute the above code, it produces the following result −

$ID
[1] "1"   "2"   "3"   "4"   "5"   "6"   "7"   "8"

$Name
[1] "Rick"     "Dan"      "Michelle" "Ryan"     "Gary"     "Nina"     "Simon"    "Guru"

$Salary
[1] "623.3"  "515.2"  "611"    "729"    "843.25" "578"    "632.8"  "722.5"
$StartDate

[1] "1/1/2012"   "9/23/2013"  "11/15/2014" "5/11/2014"  "3/27/2015"  "5/21/2013"
"7/30/2013"  "6/17/2014"

$Dept
[1] "IT"         "Operations" "IT"         "HR"         "Finance"    "IT"
"Operations" "Finance"

Convert JSON to a Data Frame

We can convert the extracted data above to a R data frame for further analysis using the as.data.frame() function.

# Load the package required to read JSON files.
library("rjson")
# Give the input file name to the function.
result <- fromJSON(file = "input.json")
# Convert JSON file to a data frame.
json_data_frame <- as.data.frame(result)

print(json_data_frame)

When we execute the above code, it produces the following result −

id,   name,    salary,   start_date,     dept

1      1    Rick     623.30    2012-01-01      IT

2      2    Dan      515.20    2013-09-23      Operations

3      3    Michelle 611.00    2014-11-15      IT

4      4    Ryan     729.00    2014-05-11      HR

5     NA    Gary     843.25    2015-03-27      Finance

6      6    Nina     578.00    2013-05-21      IT

7      7    Simon    632.80    2013-07-30      Operations

8      8    Guru     722.50    2014-06-17      Finance

R – Web Data

Many websites provide data for consumption by its users. For example the World Health Organization(WHO) provides reports on health and medical information in the form of CSV, txt and XML files. Using R programs, we can programmatically extract specific data from such websites. Some packages in R which are used to scrap data form the web are − “RCurl”,XML”, and “stringr”. They are used to connect to the URL’s, identify required links for the files and download them to the local environment.

Install R Packages

The following packages are required for processing the URL’s and links to the files. If they are not available in your R Environment, you can install them using following commands.

install.packages("RCurl")
install.packages("XML")
install.packages("stringr")
install.packages("plyr")

Input Data

We will visit the URL weather data and download the CSV files using R for the year 2015.

Example

We will use the function getHTMLLinks() to gather the URLs of the files. Then we will use the function download.file() to save the files to the local system. As we will be applying the same code again and again for multiple files, we will create a function to be called multiple times. The filenames are passed as parameters in form of a R list object to this function.

# Read the URL.

url <- "http://www.geos.ed.ac.uk/~weather/jcmb_ws/"
# Gather the html links present in the webpage.
links <- getHTMLLinks(url)

# Identify only the links which point to the JCMB 2015 files.
filenames <- links[str_detect(links, "JCMB_2015")]

# Store the file names as a list.

filenames_list <- as.list(filenames)
# Create a function to download the files by passing the URL and filename list.
downloadcsv <- function (mainurl,filename) {
filedetails <- str_c(mainurl,filename)
download.file(filedetails,filename)
}


# Now apply the l_ply function and save the files into the current R working directory.
l_ply(filenames,downloadcsv,mainurl = "http://www.geos.ed.ac.uk/~weather/jcmb_ws/")

Verify the File Download

After running the above code, you can locate the following files in the current R working directory.
"JCMB_2015.csv" "JCMB_2015_Apr.csv" "JCMB_2015_Feb.csv" "JCMB_2015_Jan.csv"
"JCMB_2015_Mar.csv"

So, this brings us to the end of blog. This Tecklearn ‘Handling JSON Files in R’ blog helps you with commonly asked questions if you are looking out for a job in Data Science. If you wish to learn R Language and build a career in Data Science domain, then check out our interactive, Data Science using R Language Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Simple Logistic Regression in R
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