This document provides an overview of various data interfaces in R, including CSV, Excel, database, XML, JSON, and web data. It discusses how to read and write files in each format using functions like read.csv(), read.xlsx(), dbConnect(), xmlParse(), fromJSON(), and getHTMLLinks(). It also provides an example of extracting data from Facebook using the Rfacebook package.

1. Presented by: 1) Bhavesh Sarvaiya(115347)
2) Naresh Sarvaiya(115340)
R Data Interfaces

2. Topics
CSV Files
Excel Files
Database
XML Files
JSON Files
Web Data
Get Facebook data

3. CSV Files
How to Read CSV Files in R ?
 The csv file is a text file in which the values in the
columns are separated by a comma.
 Each cell inside such data file is separated by a
special character, which usually is a comma, although
other characters can be used as well.
 Example: student info
"No", "Name", "age"
1,Bhavesh,22
2,Ramesh,20
3,Karmraj,22
• Copy it and paste into notepad and save as mydata.csv

4. Continue…
read.csv()
• read.csv(file, header = TRUE, sep = ",", quote = """, dec = ".",
comment.char = “-", ...)
• The read.csv() function has a few important arguments:
• file, the name of a file, or a connection
• header, logical indicating if the file has a line
• sep, a string indicating how the columns are separated
• comment.char, a character string indicating the comment
character. This defalts to "#". If there
are no commented lines in your file, it’s worth setting this to
be the empty string "".
• skip, the number of lines to skip from the beginning
• mydata = read.csv("mydata.csv")
• print(mydata)
No Name age
1 Bhavesh 22
2 Ramesh 20
3 Karmraj 22

5. Continue…
• print(is.data.frame(data)) = TRUE
• print(ncol(data)) = 3
• print(nrow(data)) = 3
• Get the maximum age
• age<- max(mydata$age)
• print(age)
write.csv()
• write.csv(x, file = "", append = FALSE, quote = TRUE, sep = ",",
eol = "n", na = "NA", dec = ".", row.names = TRUE, col.names =
TRUE)
• write.csv(mydata,file = "t.csv ")
• write.csv(mydata,file = "t.csv",row.names = F)

6. Excel Files
How to Read Excel Files in R ?
 Microsoft Excel is the most widely used spreadsheet program which
stores data in the .xls or .xlsx format.
 install.packages("xlsx")
 Load the library into R workspace
library("xlsx")
 Example: employee info
id name salary start_date dept
1 Rick 623.3 1/1/2012 IT
2 Dan 515.2 9/23/2013 Operations
3 Michelle 611 11/15/2014 IT
4 Ryan 729 5/11/2014 HR
5 Gary 843.25 3/27/2015 Finance
6 Nina 578 5/21/2013 IT
7 Simon 632.8 7/30/2013 Operations
8 Guru 722.5 6/17/2014 Finance
Copy it and open microsoft excel and save as named input.xlsx

7. Continue…
read.xlsx()
• read.xlsx(file, sheetIndex, sheetName = NULL, rowIndex = NULL,
startRow = NULL, endRow=NULL, colIndex=NULL, as.data.frame = TRUE,
header=TRUE, keepFormulas = FALSE)
• data <- read.xlsx("input.xlsx", sheetIndex = 1)
• print(data)
write.xlsx()
• write.table(x, file = "", append = FALSE, quote = TRUE, sep = " ",
eol = "n", na = "NA", dec = ".", row.names = TRUE, col.names =
TRUE, qmethod = c("escape", "double"), fileEncoding = "")
• write.xlsx(data,file = "new.xlsx",sheetName = "s1")

8. Database
 The data is Relational database systems are stored in a
normalized format.
 So, to carry out statistical computing we will need very
advanced and complex Sql queries.
 But R can connect easily to many relational databases like
MySql, Oracle, Sql server etc. and fetch records from them
as a data frame.
 Once the data is available in the R environment, it
becomes a normal R data set and can be manipulated or
analyzed using all the powerful packages and functions.

9. Continue..
RMySQL Package
install.packages("RMySQL")
• Opening and closing connections
• mysqlconnection<-dbConnect(MySQL(),user="root",password
= "mysql", dbname = "sakila", host = "localhost")
• on.exit(dbDisconnect(mysqlconnection))
• Processing query results
• List the tables available in this database.
dbListTables(mysqlconnection)

10. Continue..
• Get record from table
• rs <- dbSendQuery(con, "select * from city limit 10;")
• data <- fetch(rs, n=10)
• Creating Tables in MySql
• dbWriteTable(mysqlconnection,“mtcars",iris[1:10,])
• Inserting Data into the Tables
• dbSendQuery(mysqlconnection, "insert into
mtcars(row_names, mpg, cyl, disp, hp, drat, wt, qsec, vs,
am, gear, carb) values('New Mazda RX4 Wag', 21, 6, 168.5,
110, 3.9, 2.875, 17.02, 0, 1, 4, 4)" )

11. Continue..
• Updating Rows in the Tables
• dbSendQuery(mysqlconnection, "update mtcars set
disp = 168.5 where hp = 110")
• Dropping Tables in MySql
• dbSendQuery(mysqlconnection, 'drop table if exists
mtcars')

12. XML Files
• XML is a file format which shares both the file format
and the data on the World Wide Web, intranets, and
elsewhere using standard ASCII text.
• You can read a xml file in R using the "XML" package.
• install.packages("XML")
 Input Data
• Click below link to open xml file
• Parts.xml
• Then read the file from r

13. Continue…
• Reading XML File
• xmlParse()
• Give the input file name to the function.
• result <- xmlParse(file = "parts.xml")
• Exract the root node from the xml file.
• rootnode <- xmlRoot(result)
• Find number of nodes in the root.
rootsize<-xmlSize(rootnode)
• Get the first element of the first node.
print(rootnode[[1]][[1]])

14. Continue…
• XML to Data Frame
• xmlToDataFrame()
• Convert the input xml file to a data frame.
• xmldataframe <- xmlToDataFrame(“parts.xml")
• print(xmldataframe)

15. JSON Files
• JSON file stores data as text in human-readable format.
• Json stands for JavaScript Object Notation.
• R can read JSON files using the rjson package.
• You can read a Json file in R using the "rjson" package.
• install.packages(“rjson")
Input Data
Copy following data and paste into notepad and save as
rjsonfile.json
{
"ID":["1","2","3","4","5","6","7","8" ], "Name":["bhavesh",
"kiran", "mital", "yograj", "ramesh", "gita", "manan", "sital" ],
"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"]
}

16. Continue….
• Reading JSON File
• FromJSON()
• Give the input file name to the function.
• result <- fromJSON(file = "rjsonfile.xml")
• Print(result)
• Convert JSON to a Data Frame
• We can convert the extracted data above to a R data
frame for further analysis the as.data.frame() function.
• json_data_frame <- as.data.frame(result)
• print(json_data_frame)

17. 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".
• Install R Packages
• install.packages("RCurl")
• install.packages("XML")
• install.packages("stringr")
• install.packages("plyr")

18. Continue…
• Input Data
• 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.
• url <- http://gujaratvidyapith.org/
• Gather the html links present in the webpage.
• links <- getHTMLLinks(url)
• Identify only the links which point to the JCMB 2017 files.
• filenames <- links[str_detect(links, "JCMB_2017")]
• Store the file names as a list.
• filenames_list <- as.list(filenames)

19. Continue…
• Input Data
• Create a function to download the files by passing
the URL and filename list.
downloadcsv <- function (url,filename)
{
filedetails <- str_c(url,filename)
download.file(filedetails,filename)
}
• Now apply downloadcsv function and save the files
into the current R working directory.
downloadcsv(url,filename)

20. Get Facebook data
• Step 1 : Facebook Developer Registration
• Go to https://developers.facebook.com and register yourself
by clicking on Get Started button
• Step 2 : Add a new App
• click on My Apps button. Then select Add a New App from the
drop down.
• Step 3 : Get App ID and App Secret
• Step 4 : OAuth Settings
1. On the left hand side menu, click on Add Product Button
2. Click on Facebook Login link
3. Under Settings, make sure YES is selected in Client OAuth Login
4. Type http://localhost:1410/ in Valid OAuth redirect URIs box
5. Click on Save Changes button

21. Continue…
• Install required packages
• install.packages("Rfacebook")
• install.packages("RCurl")
• Paste your app id and app secret below
• fb_oauth <- fbOAuth(app_id="183xxxxxxxx3748",
app_secret="7bfxxxxxxxxcf0",extended_permissions =
TRUE)
• Check your profile account information
• me <- getUsers("me",token=fb_oauth,
private_info=TRUE)
• me$name

22. Continue…
• List of all the pages you have liked
• likes = getLikes(user="me", token = tt)
• sample(likes$names, 10)
• Extract list of posts from a Facebook page
• page <- getPage(page="bbcnews", token=tt, n=200)
• Which of these posts got maximum likes?
• summary = page[which.max(page$likes_count),]
• summary$message

23. Reference
• https://www.tutorialspoint.com
• http://www.listendata.com/2017/03/facebook-data-mining-
using-r.html

24. THANK YOU