Data Handling in R

This three day course is aimed at those wishing to learn how to use R with Tidyverse packages to work with and handle Data. When combined with our Introduction to Data Science course you would be set up well to follow an R learning journey into Data Science, Machine Learning, and Artificial Intelligence.

During the programme you will be introduced to R and specific development environments and packages for working with Data, with a focus on Tidyverse packages including dplyr, tidyr, stringr, ggplot2 and more.

Along the way you will see how to clean and manipulate tabular data, apply simple statistical techniques and create engaging data visualisations.

Throughout the course you will engage with activities and discussions with one of our Data Science technical specialists and complete technical lab activities to practice the techniques you have learnt and develop ideas for further practice.

Prerequisites

No prior experience with R is necessary, though it is assumed that you will be familiar with core data concepts such as simple table structures and data types – all the pre-requisites you need are covered by our Data Essentials (QADESS) course.

• Benefit from the speed and functionality of R and the Tidyverse
• Create and control Data Visualisations using ggplot and related packages
• Use the RStudio environment with R Scripts or Quarto Documents
• Retrieve, clean, and prepare data from multiple types of sources with tidyr, dplyr, and related packages
• Gain a firm grounding in R with Data in order to progress to further study to connect to AI models, Engineer data pipelines, and develop Data Science solutions

Introduction to Programming for Data Handling

• Describe the pros and cons of using programming languages to work with data
• Identify the languages most suitable for data handling
• Explain the challenges of using programming languages versus data analysis tools

Introduction to R, RStudio, and Quarto

• Describe the key attributes of the R programming language.
• Explain the role of RStudio and Quarto for R programming.
• Use RStudio to write a basic R program.
• Write a program which uses string, integer, float and boolean data types.

Data Structures, Functions, and Pipes

• Construct dataframes and tibbles to solve data problems.
• Write reusable functions which can be used to alter data & automate repetitive tasks.
• Use a selection of R’s built-in functions and trustworthy packages along with base R and dplyr’s Pipe.

Data Sources

• Read from csv, excel, and json files.
• Connect to databases using DBI paired with a backend

Data Manipulation

• Create, manipulate, and alter dataframes and tibbles.
• Use base R and tidyverse methods for indexing, slicing, querying, filtering, grouping, pivoting, and merging tables.

Data Cleaning and Preparation

• Identify data quality metrics, missing data and apply techniques to deal with it.
• Deduplicate, transform and replace values.
• Use string methods to manipulate text data.
• Write regular expressions which munge text data.

Methods for Visualising Data

• Construct and tailor data visualisations using ggplot2.
• Meaningfully visualise aggregate data.

Related learning

Data Science Learning Pathways can be selected by choosing either Python or R and a Cloud Platform certification:

• QAIDSDP Introduction to Data Science for Data Professionals
• Sourcing and handling data:
  • QADHPYTHON Data Handling with Python
  • QADHR Data Handling with R
  • QAPDHAI Python Data Handling with AI APIs
• Statistics for Data Analysis:
  • QASDAPY Statistics for Data Analysis with Python
  • QASDAR Statistics for Data Analysis with R
• Programming and Software Development skills:
  • QAPYTH3 Python Programming
  • QARPROG R Programming
• Machine Learning Development:
  • QADSMLP Data Science and Machine Learning with Python
  • QADSMLR Data Science and Machine Learning with R
• Mathematics for Developing Algorithms for AI models, Big Data Mining, and working with Neural Networks:
  • QAMFDS Mathematics for Data Science
• Forecasting:
  • QATSFP Time Series and Forecasting with Python
  • QATSFR Time Series and Forecasting with R

Suggested courses leading to Certification:

• MDP100 Designing and Implementing a Data Science Solution on Azure (DP-100)
• AMWSMLP Machine Learning Pipelines on AWS
• GCPMLGC Machine Learning on Google Cloud