Data Visualization for Business 1 - An Introduction

Level: Introductory, No Experience Required

Keywords: R, Data Visualisation, Business Data

Note: Please see Prerequisite Section Below

View Session Slides - Opens new Tab

Download Session Files - Downloads .zip file

Session Summary:

One of the most important elements in statistical communication is through the use of clear data visualisation. In this session we will look more closely at how to construct clear, concise and interpretable data visualisations in R, using the tidyverse package ggplot2. This session will look specifically at providing you with the skills to visualisation your own projects data, and allow you to begin your own journey to improve your data visualisation skills.

Note:

  • This session is designed to be open and accessible to all, whether a beginner in R and programming, a new starter in the ONS or someone who simply needs to refresh their skills. Worksheets will be provided are these two different skill levels.

Session Objectives:

  • Understand data visualisation good practices
  • Introduction to the tidyverse & ggplot2 in R
  • Compare plotting techniques in R (ggplot vs plot functions)

Transferable Skills:

  • Using the tidyverse, in particular ggplot
  • Core and basic ggplot functions
  • Use of layers, aesthetics, and different plot types in data visualisation

Prerequisite Knowledge:

No experience in Programming is required, however some awareness of programming principles is required.

Prerequisite Content:

Access to R & Rstudio (R’s Graphical User Interface, or RStudio Cloud (Free Online)), Provided ZIP File .zip

The importance of Data Visualisation in Business.

Within any form of statistical driven communication, the clear, concise and accurate use of data visualisation is required to ensure that the conclusions drawn are in line with the messages you wish to express. This within the realm of business, which is so statistically driven in this modern age, is incredibly important.

Why R?

Within the field of Business Informatics, there are multiple statistical and visualisation tools which can be used, including software such as D3.js, Tableau and other more general tools like Python. I personally find R one of the most useful tools for data visualisation and associated data analysis, given its simplistic nature, open-source and genuinely lovely community as well as the diversity of tools which can be easily integrated together.

Note:

This workshop will focus only upon several types of data visualisation technique:

  • Scatter Plots
  • Bar Charts & Histograms
  • Density Plots

In future sessions, it is likely that Time series plots, GIS focused plots (location based data) and others will be focused upon.