Big Data Manipulation in R Exercises
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Some times it is necessary to download really big csv files to deliver some analysis. When you hit file sizes in Gigabytes it is useful to use R instead of spreadsheets. This exercise teaches us to manipulate this kind of files.
Answers to the exercises are available here.
Exercise 1
Create a directory canada immigration/Work/Income and put all files related to income then load dplyr.
Download data set from here.
Exercise 2
Create a string vector with file names: 00540002-eng, 00540005-eng, 00540007-eng, 00540009-eng, 00540011-eng, 00540013-eng, 00540015-eng, and 00540017-eng.
Exercise 3
Create a list of data frames and put the data of each file in list position. For example, data[[1]] will contain the first file. To reduce this data size, for each data set select only data from 2014.
Exercise 4
Clean up the first data sets in the list (data[[1]]) and exclude registers that summarizes other like: “Both sexes” to avoid double operations while summarizing.
Exercise 5
Clean up all other data sets in the list and exclude registers the same way discribed at exercise 4. Then, pile up all data in a sigle data set.
- 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 6
Write a csv file with the recent create data set.
Exercise 7
Create a directory canada immigration/Work/Income and put all files related to income then load dplyr.
Download data set from here.
Create a string vector with file names: 00540018-eng, 00540019-eng, 00540020-eng, 00540021-eng, 00540022-eng, 00540023-eng, 00540024-eng, and 00540025-eng.
Create a list of data frames and put the data of each file in list position. For example, data[[1]] will contain the first file. To reduce this data size, for each data set select only data from 2014.
Exercise 8
Clean up the first data sets in the list (data[[1]]) and exclude registers that summarizes other like: “Both sexes” to avoid double operations while summarizing.
Exercise 9
Clean up all other data sets in the list and exclude registers the same way discribed at exercise 8. Then, pile up all data in a sigle data set.
Exercise 10
Write a csv file with the recent create data set.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.