# Importing Data Into R from Different Sources

December 6, 2012
By

(This article was first published on Statistical Research » R, and kindly contributed to R-bloggers)

I have found that I get data from many different sources.  These sources range from simple .csv files to more complex relational databases, to structure XML or JSON files.  I have compiled the different approaches that one can use to easily access these datasets.

Local Column Delimited Files

This is probably the most common and easiest approach to load data into R.  It simply requires one line to do everything that is needed to set up the data.  Then a couple additional lines to tidy up the dataset.


file <- "c:\\my_folder\\my_file.txt"
raw_data <- read.csv(file, sep=",");  ##'sep' can be a number of options including \t for tab delimited
names(raw_data) <- c("VAR1","VAR2","RESPONSE1")



Text File From the Internet

I find this very useful when I need to get datasets from a Web site.  This is particularly useful if I need to rerun the script and the Web site continually updates their data.  This save me from having to download the dataset into a csv file each time I need to run an update.  In this example I use one of my favorite data sources which comes from the National Data Buoy Center.  This example pulls data from a buoy (buoy #44025) off the coast of New Jersey.  Conveniently you can use the same read.csv() function that you would use if read the file from you own computer.  You simply replace the file location with the URL of the data.


file <- "<a href="http://www.ndbc.noaa.gov/view_text_file.php?filename=44025h2011.txt.gz&dir=data/historical/stdmet/">http://www.ndbc.noaa.gov/view_text_file.php?filename=44025h2011.txt.gz&dir=data/historical/stdmet/</a>"



Files From Other Software

Often I will have Excel files, SPSS files, or SAS dataset set to me.  Once again I can either export the data as a csv file and then import using the read.csv function.  However, taking that approach every time means that there is an additional step.  By adding unnecessary steps to a process increases the risk that the data might get corrupted due to human error.  Furthermore, if the data is updated from time to time then the data that you downloaded last week may not have the most current data.

SPSS


library(foreign)
file <- "C:\\my_folder\\my_file.sav"



Microsoft Excel


library(XLConnect)

file <- "C:\\my_folder\\my_file.xlsx"
raw <- as.data.frame( readWorksheet(raw_wb, sheet='Sheet1') )



Data From Relational Databases

There is the RMySQL library which is very useful.  However, I have generally been in the habit of using the RODBC library.  The reason for this is that I will often jump between databases (e.g. Oracle, MSSQL, MySQL).  By using the RODBC library I can keep all of my connections in one location and use the same functions regardless of the databases.  This example below will work on any standard SQL database.  You just need to make sure you set up an ODBC connection call (in this example) MY_DATABASE.


library(RODBC)

raw <- sqlQuery(channel, "SELECT * FROM Table1");



Data from Non-Relational Databases

R has the capability to pull data from non-relational databases.  These include Hadoop (rhbase), Cassandra (RCassandra), MongoDB (rmongodb).  I personally have not used RCassandra but here is the documentation.  The example here uses MongoDB using an example provided by MongoDB.


library(rmongodb)
MyMongodb <- "test"
ns <- "articles"
mongo <- mongo.create(db=MyMmongodb)

list.d <- mongo.bson.from.list(list(
"_id"="wes",
name=list(first="Wesley", last=""),
sex="M",
age=40,
value=c("7", "5","8","2")
))
mongo.insert(mongo, "test.MyPeople", list.d)
list.d2 <- mongo.bson.from.list(list(
"_id"="Article1",
when=mongo.timestamp.create(strptime("2012-10-01 01:30:00",
"%Y-%m-%d %H:%M:%s"), increment=1),
author="wes",
title="Importing Data Into R from Different Sources",
text="Provides R code on how to import data into R from different sources.",
tags=c("R", "MongoDB", "Cassandra","MySQL","Excel","SPSS"),
list(
who="wes",
when=mongo.timestamp.create(strptime("2012-10-01 01:35:00",
"%Y-%m-%d %H:%M:%s"), increment=1),
comment="I'm open to comments or suggestions on other data sources to include."
)
)
)
)
list.d2
mongo.insert(mongo, "test.MyArticles", list.d2)

res <- mongo.find(mongo, "test.MyArticles", query=list(author="wes"), fields=list(title=1L))
out <- NULL
while (mongo.cursor.next(res)){
out <- c(out, list(mongo.bson.to.list(mongo.cursor.value(res))))

}

out



Copied and Pasted Text


raw_txt <- "
AL 36 36
AK 5 8
AZ 15 16
AR 21 27
CA 43 43
CT 56 68
DE 22 22
DC 7 7
FL 130 132
GA 53 54
HI 11 16
ID 11 11
IL 24 24
IN 65 77
IA 125 130
KS 22 26
KY 34 34
LA 27 34
ME 94 96
MD 25 26
MA 82 92
Mi 119 126
MN 69 80
MS 43 43
MO 74 82
MT 34 40
NE 9 13
NV 64 64
NM 120 137
NY 60 62
NJ 29 33
NH 44 45
ND 116 135
NC 29 33
OH 114 130
OK 19 22
PA 101 131
RI 32 32
Sc 35 45
SD 25 25
TN 30 34
TX 14 25
UT 11 11
VT 33 49
VA 108 124
WV 27 36
WI 122 125
WY 12 14
"
raw_data <- textConnection(raw_txt)
close.connection(raw_data)

raw

###Or the following line can be used



Structured Local or Remote Data

One feature that I find quite useful is when there is a Web site with a table that I want to analyze.  R has the capability to read through the HTML and import the table that you want.  This example uses the XML library and pulls down the population by country in the world.  Once the data is brought into R it may need to be cleaned up a bit removing unnecessary columns and other stray characters.  The examples here use remote data from other Web sites.  If the data is available as a local file then it can be imported in a similar fashion just using filename rather than the URL.


library(XML)

url <- "http://en.wikipedia.org/wiki/List_of_countries_by_population"
population



Or you can use the feature to simple grab XML content.  I have found this particularly useful when I need geospatial data and need to get the latitude/longitude of a location (this example uses Open Street Maps API provided by MapQuest).  This example obtains the results for the coordinates of the United States White House.


mygeo <- xmlToDataFrame(url)
mygeo\$result



An alternate approach is to use a JSON format.  I generally find that JSON is a better format and it can be readily used in most programming languages.


library(rjson)

raw_json <- scan(url, "", sep="\n")

mygeo <- fromJSON(raw_json)