Data Handling in R

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