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R Programming for Data Analysis - Intermediate

Length 1 day

Course overview

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Why study this course

R is a programming language specifically developed for the statistical analysis of data and for producing graphical output. We introduced the core elements of R programming in our beginner course. This intermediate course is the second in our R series and focuses on:

data manipulation
basic exploratory data analysis
creating customised data visualisations
basic modelling

Our experienced Data Analyst will guide you through exercises to practice writing R code, use a variety of different functions, produce graphical output, and view the results.

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What you’ll learn

After completing this course, students will be able to:

Perform basic exploratory data analysis
Conduct basic modelling and prediction
Find functions to perform specific tasks
Create and manipulate objects
Work with relational data

R Programming at Lumify Work

Learn R programming to analyse, manipulate, and visualise data more effectively.

View all R Programming courses

Course subjects

Introduction

Review of R data types and structures
Review of common syntax for accessing data in data frames

Importing Data

Importing data in RStudio
Packages and functions to import data into R
Using code to import data
Importing data from text files (csv)
Importing data from Excel

Workflow in R

Creating reusable scripts

Manipulating Data

The tidyverse
Summarising data
Ordering data
Working with dates
Convert character to date
Extract years from dates
Extract months from dates
Extract days from dates
Extract days of the week from dates
Add columns to a data frame
Working with strings
Selecting and reordering columns in a data frame
Selecting rows based on values
Grouping data
Summarising data
Identifying blank values and non-number numbers
Working with data that contains missing values and non-number numbers
Removing missing values from a data set
Replacing values
Concatenate strings
Bin continuous variables into categories

Working with Relational Data

Add new variables to a data frame from another
Mutating joins and merge()
Filtering joins
Exporting data to a file

Basic Exploratory Data Analysis

Choosing the right chart for your goal
Choosing the right chart for your data
Univariate analysis of numeric variables
Univariate analysis of categorical variables
Multivariate analysis of numeric variables
Multivariate analysis of numeric and categorical variables
Multivariate analysis of categorical variables

Univariate Analysis

Exploring the data distribution
Central tendency
Spread
Outliers
Shape of the distribution

Visual Representation of Distributions

Histograms
Boxplots
Dot charts / dot plots
Stem and leaf plots
Bar and column charts

Multivariate Analysis

Scatterplots and scatterplot matrix
Correlations
Bar and column charts
Line charts
Customising charts in R
Other graphics options

Basic Modelling

Modelling for prediction
Create a linear model
How good is the model?
Assumptions
Making predictions from the mode

Prerequisites

You should have attended our R Programming for Data Analysis - Beginner course and have a basic understanding of R syntax.

It is assumed that you are familiar with:

basic R syntax
data types and structures
know how to subset data
know how to install and use contributed packages and their functions
basic familiarity with working in RStudio is helpful

Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.