Data Scientist with R. Prior to joining DataCamp, he earned his master's degree at Johns Hopkins Biostatistics and worked as a data scientist for McKinsey. This course provides a very basic introduction to cleaning data in R using the tidyr, dplyr, and stringr packages. The str (for "structure") function is one of the most versatile and useful functions in the R language because it can be called on any object and will normally provide a useful and compact summary of its internal structure. When passed a data frame, as in this case, str tells us how many rows and columns we have. One of the big issues when it comes to working with data in any context is the issue of data cleaning and merging of datasets, since it is often the case that you will find yourself having to collate data across multiple files, and will need to rely on R to carry out functions that you would normally carry out using.


Author: Maurine Fritsch
Country: Ukraine
Language: English
Genre: Education
Published: 3 August 2016
Pages: 819
PDF File Size: 25.95 Mb
ePub File Size: 30.30 Mb
ISBN: 507-1-96222-896-3
Downloads: 87234
Price: Free
Uploader: Maurine Fritsch


A Data Cleaning Example

We load this into R under the name mydata. This file contains the variables ID, Age, and Country.

We load this into R under the name mydata2. The following are examples data cleaning in r popular techniques employed in R to clean a dataset, along with how to format variables effectively to facilitate analysis. The below functions work particularly well with panel datasets, where we have a mixture of cross-sectional and time series data.

Storing variables in a data frame To start off with a simple example, data cleaning in r us choose the customers dataset. Suppose that we only wish to include the variables ID and Age in our data.

A Data Cleaning Example | R-bloggers

To do this, we define our data cleaning in r frame as follows: MyData is our data frame holding our gaming data, Date is of type factor, and Coinin is an integer.

So, the data frame and the integer should make sense to you, but take note that R sets our dates up for what it calls a factor.

Factors are categorical variables data cleaning in r are beneficial in summary statistics, plots, and regressions, but not so much as date data cleaning in r.

To remedy this, we can use the R functions substr and paste as shown next: We find that this line of script converts our Data field to type character and, finally, we can use them as.

Date function to re-data type our values to an R Date type: With a little trial and error, you can reformat a string or character data point exactly how you want it.

Data Cleaning and Wrangling With R

The source of this additional data could be calculations using information already in the data or added from another source. There are a variety of reasons why a data scientist may take the time to enhance data.

Based upon the purpose or objective at hand, the information the data scientist adds might be used for reference, comparison, contrast, or show tendencies.

Typical use cases include: Derived fact calculation Indicating the use of calendar versus fiscal year Converting time zones Adding current versus previous period indicators Calculating values such as the total units shipped per day Maintaining slowly changing dimensions Note As a data scientist, you should always use scripting to enhance your data, as this approach is much better than editing a data file directly since it is less prone to errors and maintains the data cleaning in r of the data cleaning in r file.

For a working example, let us again go back to our GammingData. Assume we're receiving files of the Coinin amounts by slot machine and our gaming company now runs casinos outside of the continental United States.

How to tackle common data cleaning issues in R

These locations are sending us files to be included in our statistical analysis and we've now discovered that these international files are providing the Coinin amounts in their local currencies.

To be able to correctly model the data, we'll need to convert those amounts to Data cleaning in r dollars.


Here is the scenario: Great Britain Currency used: Not a huge deal, but one might want to experiment with creating a user-defined function that determines the rate to be used, as data cleaning in r next: Finally, to make things better still, save off your function in an R file so that it can always be used: Harmonization With data harmonization, the data scientist converts, translates, or maps data values to other more desirable values, based upon the overall objective or purpose of the analysis to be performed.

The most common examples of this can be data cleaning in r or country code.