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"Data Cleaning Using R" by Nafiseh Sedagha #172

brunogrande opened this Issue Jul 20, 2017 · 0 comments


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brunogrande commented Jul 20, 2017


It is said that cleaning/cleansing data takes 80% of data analysis process. Data cleaning must be repeated for every new data in every project. Typically, data sets obtained from a real world problems violate the standards of clean data in different ways and analyzing data without cleaning is impossible. In the process of cleaning data, we try to remove every possible problem in data and organize the values in a standard manner.

In this workshop, we aim to focus on small but main aspects of data cleaning including:

  1. Importing and exporting data without having problems like:
    • Column headers are values, not variable names
    • Changing type of data
    • Multiple variables are stored in one column
  2. Detection and localization of errors like:
    • Missing values and imputation
    • Special values
    • Outliers
    • Duplicates

Time and Place

Where: Room 7010, Library Research Commons, SFU Burnaby Campus

When: Tuesday, November 7th, 2017 at 3:00 PM



Required Preparation

Assumed Knowledge

To participate in this workshop, having basic knowledge of R is necessary

Software Dependencies

Ensure that you have R and RStudio installed. You will also need to install the following packages:

install.packages(c("tidyr", "impute"))


Lessons Notes: TBA

Etherpad: TBA

@brunogrande brunogrande added this to the Fall 2017 milestone Jul 20, 2017

@brunogrande brunogrande changed the title from "Data tidying/cleaning in R" by Nafiseh Sedagha to "Data Cleaning Using R" by Nafiseh Sedagha Jul 27, 2017

@brunogrande brunogrande removed the workshop label Nov 29, 2017

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