Regular Price: £480 Batch Price: From £420 📅 View Dates & Prices Group Booking Discount: From £300 per person 🧮 Calculate & Book 1-to-1 Training: £600 📋 Booking Request Form Course Overview Data Analysis with R is a practical 12-hour instructor-led course that teaches learners to prepare, analyse, visualise, and interpret data using R. The course follows a practical data analysis workflow, covering data frames, importing and cleaning data, transformation with dplyr, exploratory data analysis, grouping and aggregation, joining datasets, and creating professional visualisations with ggplot2. Learners will also be introduced to simple Linear Regression and Logistic Regression to understand how R can be used for statistical modelling and prediction. Requirements Participants should have basic knowledge of R programming before joining this course. They should have completed our R Programming for Beginners course or have equivalent experience. Learners should be comfortable with basic R concepts such as variables, data types, operators, decision-making, loops, functions and vectors. No advanced statistical knowledge is required. Basic familiarity with concepts such as averages, percentages and relationships between variables is helpful but not essential. Learners should have access to R and an appropriate development environment such as RStudio. Required R packages will be installed and used during the course. Course Content Introduction to Data Analysis with R Understanding the data analysis workflow Using R and RStudio for data analysis Working with R packages Loading and exploring datasets Understanding rows, columns and observations Identifying numerical and categorical data Understanding features and variables Working with Data Frames Understanding data frames and tibbles Creating data frames Viewing the structure and summary of a dataset Selecting rows and columns Accessing individual values Adding and removing columns Filtering data Sorting data Importing, Cleaning & Preparing Data Importing CSV files Importing Excel and other common data sources Checking data types Identifying missing values Handling missing data Finding and removing duplicate records Renaming columns Converting data types Preparing data for reliable analysis Data Transformation with dplyr Introduction to the dplyr package Selecting columns with select() Filtering rows with filter() Sorting data with arrange() Creating and transforming columns with mutate() Creating calculated values Using multiple conditions Building clear data transformation workflows Grouping, Aggregation & Combining Data Calculating totals, averages, counts, minimum and maximum values Grouping data with group_by() Summarising data with summarise() Analysing data by category Working with multiple grouping variables Introduction to joining datasets Combining related data from multiple data frames Exploratory Data Analysis Understanding descriptive statistics Calculating mean, median, minimum and maximum values Understanding variance and standard deviation Exploring distributions Identifying unusual values and outliers Exploring relationships between variables Introduction to correlation Identifying trends, patterns and possible insights Data Visualisation with ggplot2 Introduction to ggplot2 Understanding the grammar of graphics Creating bar charts Creating line charts Creating histograms Creating scatter plots Creating box plots Adding titles, labels and legends Customising charts for clear communication Selecting appropriate visualisations for different types of data Introduction to Regression & Predictive Analysis Understanding relationships between variables Introduction to Linear Regression Building a simple Linear Regression model in R Understanding coefficients and predictions Introduction to Logistic Regression Understanding binary classification Building a simple Logistic Regression model Interpreting predicted probabilities and results Practical Data Analysis Project Import a realistic dataset Inspect and understand the data Clean and prepare the dataset Transform data using dplyr Group and summarise data Perform exploratory data analysis Create visualisations using ggplot2 Identify trends, patterns and key findings Present and interpret the results Course Dates, Prices & Enrolment All Training Physical Classes Virtual Classes UTC British Time (UK, Ireland, Iceland) Central European Time (France, Germany, Sweden) Eastern European Time (Finland, Cyprus) Eastern Time (New York, Toronto, Montreal) Central Time (Chicago, Houston, Winnipeg) Mountain Time (Calgary, Denver, Edmonton) Mountain Time (Phoenix) Pacific Time (Los Angeles, Seattle, Vancouver) Singapore Time Arabic Standard Time (Qatar, Saudi Arabia) Gulf Standard Time (UAE, Oman) Australian Eastern Time (Sydney, Melbourne) Australian Central Time (Adelaide) Western Australia Time (Perth) New Zealand Time China Standard Time (China, Taiwan, Hong Kong) Scroll right for more details Online Training using Zoom 14 Dec 2026 - 15 Dec 2026 Monday to Tuesday 10:00 AM - 04:00 PM BT £420 £480 Enrol Now Classroom Training 14 Dec 2026 - 15 Dec 2026 Monday to Tuesday 10:00 AM - 04:00 PM BT £420 £480 Enrol Now Classroom Training 28 Jan 2027 - 29 Jan 2027 Thursday to Friday 10:00 AM - 04:00 PM BT £480 Enrol Now Online Training using Zoom 28 Jan 2027 - 29 Jan 2027 Thursday to Friday 10:00 AM - 04:00 PM BT £420 £480 Enrol Now Price Calculator & Booking Request Form Calculate prices for Corporate, 1-on-1 or group training and request a booking. 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