R for Data Analysis
R is the common language of data science and has long been popular among data scientists. It is highly extensible and provides a wide variety of statistical, machine learning methods and predictive modelling techniques. Recently due to a focus on data, reporting and big data, R is becoming more common place and this introductory course will teach you the basics to start you off on your R journey.
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- There are no public, Worcester-based events for this course scheduled at the moment, but you can contact us to request a date or call 0800 018 5597
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Aimed at beginners this R course is intended to introduce the basics of the R language, but quickly moves on to cover all the key concepts you will need to master it.
Anyone wishing to learn more about R. A background in programming will be helpful but by no means essential.
- Get started with R and RStudio.
- Wrangle messy data into shape.
- Perform Exploratory Data Analysis (EDA) using R.
- Create R Notebooks.
- Start using the power of R for your own needs.
R can deliver huge benefits to your organisation through its reporting & integration capabilities. All you need are highly trained staff to be able to harness those capabilities.
R, once the language of data scientists is becoming far more main stream; or has our changing business world meant we are all becoming data scientists? Regardless, R is here to stay and knowing how to leverage it’s capability is essential.
- Alternatives to R for Data Science
- Compiled vs Interpreted
- REPLs
- R IDEs
- RStudio
- R PAckages
- Updating R
- Turtle Graphics
- A Worked Example
- Sydney Beaches Dataset
- Tidyverse and here Packages
- Clean Up Your Column Names
- Exploring the Rows
- Comparing Councils
- Time Series Plots
- Extracting the Date
- Compare Beaches
- Flip the Axes
- Add Colour
- Display a Third Variable
- Facet Wrapping
- Filter to Omit Outliers
- Saving Your Plots
- The Iris Dataset
- Data Distributions
- Box Plots
- Violin Plots
- Analyse the Data
- Box Plots of Beach Data
- Violin Plots of Beaches Data
- Notebooks
- Markdown
- R Notebooks
- The RStudio Notebook Template
- An Iris Notebook
- HtmlWidgets
- Other Languages
- Reproducible Notebooks
- Share and Publish
- Are Scientific Pages Dead?
None. Although a programming or data analysis background will be helpful.