Big Data Manipulation in R Exercises

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.

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• import data into R in several ways while also beeing able to identify a suitable import tool
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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.