Best Practices in Preparing Data Files for Importing into R

In the previous chapter we provided the essentials of R programming including installation, launching, basic data types and arithmetic functions. In the next articles you will learn how to import data into R. To avoid errors during the importation of a file into R, you should make sure that your data is well prepared.
In this article we’ll describe some best practices for preparing your data before importing into R.

Open your file

We suppose that you open and prepare your file with Excel as follow.

Excel file

Prepare your file

  1. Row and column names:

  • Use the first row as column headers (or column names). Generally, columns represent variables.
  • Use the first column as row names. Generally rows represent observations.
  • Each row name should be unique, so remove duplicated names.

Column names should be compatible with R naming conventions. As illustrated below, our data contains some issues that should be fixed before importing:

Excel file

  1. Naming conventions:

  • Avoid names with blank spaces. Good column names: Long_jump or Long.jump. Bad column name: Long jump.
  • Avoid names with special symbols: ?, $, *, +, #, (, ), -, /, }, {, |, >, < etc. Only underscore can be used.
  • Avoid beginning variable names with a number. Use letter instead. Good column names: sport_100m or x100m. Bad column name: 100m
  • Column names must be unique. Duplicated names are not allowed.
  • R is case sensitive. This means that Name is different from Name or NAME.
  • Avoid blank rows in your data
  • Delete any comments in your file
  • Replace missing values by NA (for not available)
  • If you have a column containing date, use the four digit format. Good format: 01/01/2016. Bad format: 01/01/16

  1. Final file:

Our finale file should look like this:

Excel file

Save your file

We recommend to save your file into .txt (tab-delimited text file) or .csv (comma separated value file) format.

If you still want to save it into xls|xlsx file formats, you can do it. We’ll learn in the next articles, how to import xls|xlsx files into R.


This analysis has been performed using R (ver. 3.2.3).

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