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Course Outline
Overview of R and R Studio
- R overview
- R Studio Environment Windows
- Script Editor Window
- Data Environment
- Console
- Plots/Help/Packages
Working with Data
- Introduction to vectors and matrices (data.frame)
- Types of variables
- Numeric, Integer, factor, etc.
- Converting variable types
- Importing data via R Studio menu functions
- Removing variables using the ls() command
- Creating variables at the console prompt – single values, vectors, data frames
- Naming vectors and matrices
- Using head and tail commands
- Introduction to dim, length, and class
- Command line import (reading .csv and tab-delimited .txt files)
- Attaching and detaching data (advantages vs data.frame$)
- Merging data using cbind and rbind
Exploratory Data Analysis
- Summarizing data
- Using the summary command on vectors and data frames
- Subsetting data with square brackets
- Summarizing and creating new variables
- Using table and summary commands
- Statistical summary commands
- Mean
- Median
- Standard Deviation
- Variance
- Count & frequencies
- Min & Max
- Quartiles
- Percentiles
- Correlation
Exporting Data
- Writing to a .txt table
- Writing to a .csv file
R Workspace
- Concepts of Working Directories and Projects (menu-driven and code – setwd())
Introduction to R Scripts
- Creating R Scripts
- Saving scripts
- Managing workspace images
Concepts of Packages
- Installing packages
- Loading packages into memory
Plotting Data (using standard default R plot command and ggplot2 package)
- Bar Charts and Histograms
- Boxplots
- Line charts / time series
- Scatter plots
- Stem and leaf
- Mosaic
- Modifying plots
- Titles
- Legends
- Axis
- Plot Area
- Exporting plots to third-party applications
Requirements
- No prior experience with R is required.
- Basic familiarity with programming or data analysis concepts is beneficial but not mandatory.
Target Audience
- Data analysts and statisticians who are new to R.
- Researchers and academics interested in data manipulation and visualization.
- Professionals transitioning into data science roles.
7 Hours
Testimonials (4)
I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.
Anna - Birmingham City University
Course - Foundation R
Background knowledge and 'provenance' of trainer.
Francis McGonigal - Birmingham City University
Course - Foundation R
The trainer was very concern about individual understanding.
Muhammad Surajo Sanusi - Birmingham City University
Course - Foundation R
I genuinely enjoyed the hands passed exercises.
