Download R and RStudio

What is R?

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  • R is a free software environment originally designed for statistical computing and graphics. It has grown considerably in capability and usability over the years. Now it is an excellent “jackknife” for anything that you might want to do with data.
  • R is open-source and open-development, which means that thousands of users contribute improvements and enhancements, without changing the basic functionality.


Why use R?

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  • It’s free!
  • Statistics plus data management/representation/analysis
  • Great graphics
  • Ever-expanding capabilities (just about any statistics under the sun)
  • Can easily import data from most other major programs (including SPSS, SAS, Stata, Excel)
  • Beautifully-formatted output (R notebooks, R bookdown)
  • Not too hard to learn (especially after this workshop!)
  • Comprehensive help resources (internal help;r-bloggers.com Google search)
  • Excellent video tutorials: MarinStatsLectures

Check out the following opinions: Opinion 1. Opinion 2. Opinion 3. Opinion 4.


Capabilities of R:

What is Rstudio?

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RStudio is a graphical user interface for R which includes a set of integrated tools designed to help you be more productive with R. It includes:

  • Console, which displays executed R commands
  • An editor (with syntax highlighting). Code may be executed directly from the editor.
  • History viewer (record of past commands)
  • Environment viewer (displays all variables in your current workspace)
  • Package installer (adds new capabilities to R)
  • Plot viewer / exporter
  • Help window


Downloading and installing R and Rstudio (windows)

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Exercises:

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  1. Install R (either from USB or from the Internet)
  2. Install Rstudio (either from USB or from the Internet)

Note: Once R and Rstudio are installed, it is not necessary to start R, because Rstudio will start it


Overview of this notebook

This notebook covers the following topics:


Histograms and numerical summaries

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A histogram is a visual representation of the distribution of a dataset. The shape of a histogram allows you to easily see where most of the data is situated. In particular, you can see where the middle of distribution is located, how closely the data lie around the middle, and where possible outliers are to be found. As shown in the figures below, a histogram consists of an x-axis, a y-axis and bars of different heights. The x-axis is divided into intervals (called “bins”), and on each bin a vertical bar is constructed whose height represents the number of data values within that bin. Note that histograms (unlike bar charts) don’t have gaps between the bars (if it looks like there’s a gap, that’s because that particular bin has no data in it).


Example: Suppose you are interested in the distribution of ages for employees working in a certain office. The following data is available: 36, 25, 38, 46, 55, 68, 72, 55, 36, 38, 67, 45, 22, 48, 91, 46, 52, 61, 58, 55. We use R to construct a histogram to represent the distribution of the data.

age<-c(36, 25, 38, 46, 55, 68, 72, 55, 36, 38, 67, 45, 22, 48, 91, 46, 52, 61, 58, 55)
hist(age)

The output appears under the ‘Plots’ tab.

Packages

Create a shiny app

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