From Excel to R: Beginner's Guide

What is R and why?

R is a powerful tool to conduct complicated data analysis. In short, R is an open source programming environment, and RStudio is a common used user interface (IDE) that makes using R easier.

Many people would compare R with Python, to my understanding, both has pros and cons. I chose R simply because it fits my goal better, and I felt the language is easier to read thanks to R’s packages.

I am a heavily Excel user, so this section will be focus on the basic functions that I used to perform in Excel.

Installation and Setup R

Download R from CRAN official website, then install RStudio from RStudio website.

R has many powerful packages and can be installed from RStudio directly. There’re only two packages need to be installed for now: tidyverse and openxlsx. To do it, go to menu bar - Tool - Install Packages, put the name of the package and click Install.

Import csv/xlsx Files

There’s some prep needed to be done before any “actual” code. It is essential for every R script.

First is to import the packages that we just installed.

# Import packages

Second step is to setup the working directory, which is the default folder to import/export documents. R only recognizes forward slash, which is contrary to Windows’ backslash, so you would need to make changes if you are on Windows system.


Then we can import our raw data. Ideally, csv is preferred if it is available.

sale <- read.csv('Sales.csv')
# prod/sale is the assigned name of the imported dataframe

If the file is xlsx, the function would be read.xlsx instead, and we need to specify the name of worksheet when importing.

df <- read.xlsx('sample.xlsx',          # Name of the workbook
                sheet = 'Sheet 1',      # Name of the worksheet
                na.strings = ""         # Convert blank cell to NA in R

Let’s take a glimpse on the imported dataframe.

Filter Columns

Filtering in Excel is not difficult when there’re only a few columns. What if there’re more than 30? Even finding the right column takes time. In such cases, R becomes a handy tool. We put names in the script instead of searching through endless columns, and R has the autocomplete feature when typing the name, making the process even smoother.

# filter Region = Asia
asiaonly <- sale %>% filter(Region == 'Asia')

Note that it should be double equal if it relates to specific value (e.g. strings, integer).

To apply multiple filters, simply add more conditions inside the bracket.

# multiple filters:
# filter 1: Region = Asia
# filter 2: Sales Channel = Online
asiaonly <- sale %>% filter(Region == 'Asia',
                            Sales.Channel == 'Online')

Remove Unwanted Columns

For example, if we only need to review the first and second columns:

# Show only first two clumns, and remove other columns
regioncountry <- sale %>% select(1:2)

Usually, it’s easier to use column names (aka variables in R) when dealing with non-consecutive columns.

# Show Regions and Channel only, and remove other columns
regionchannel <- sale %>% select(Region, Sales.Channel)

If we want to remove a certain column while keep everything else, apply a minus sign in front of variables.

# Remove Channel column
nochannel <- sale %>% select(-Sales.Channel)

Those are basic functions that I’ve been used all the time. Next post I’ll introduce more advanced functions that Excel normally cannot handle.