Data wrangling : I/O (Part-1)
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Data wrangling is a task of great importance in data analysis. Data wrangling, is the process of importing, cleaning and transforming raw data into actionable information for analysis. It is a time-consuming process which is estimated to take about 60-80% of analyst’s time. In this series we will go through this process. It will be a brief series with goal to craft the reader’s skills on the data wrangling task. This is the first part of this series and it aims to cover the importing and exporting of data locally.
Please download the data from here
Before proceeding, it might be helpful to look over the help pages for the read.csv
, read.table
, read_excel
, fromJSON
, as.data.frame
, xmlParse
, xmlToList
, write.csv
, write.table
, write.xlsx
, toJSON
, write
, write.xml
.
Moreover please load the following libraries.
install.packages("readxl")
library(readxl)
install.packages("xlsx")
library(xlsx)
install.packages("rjson")
library(rjson)
install.packages("plyr")
library(plyr)
install.packages("XML")
library(XML)
install.packages("kulife")
library(kulife)
Answers to the exercises are available here.
If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page.
Exercise 1
Import the data.csv
file to the csv_file
object.
Exercise 2
Import the data.txt
file to the txt_file
object.
Exercise 3
Import the data.xls
file to the xls_file
object.
Exercise 4
Import the data.xlsx
file to the xlsx_file
object.
- import data into R in several ways while also beeing able to identify a suitable import tool
- use SQL code within R
- And much more
Exercise 5
Import the data.json
file to the json_file
object.
Exercise 6
Import the data.xml
file to the xml_file
object.
Exercise 7
Export the csv_file
as “data.csv” and txt_file
as “data.txt”.
Exercise 8
Export the xls_file
as “data.xls” and xlsx_file
as “data.xlsx”.
Exercise 9
Export the json_file
as “data.json”.
Exercise 10
Export the xml_file
as “data.xml”.
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