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Installing R on your computer

From: R Statistics Essential Training

Video: Installing R on your computer

The first thing we need to do is we need to install R onto your own computer. Now R is a free download. It's available for Windows, Mac, Linux computers and installation is a simple process. The first thing you need to do is you need to go to the website r-project.org. You can actually download it from a number of places, but this is the central location. Now if you scroll down a little bit, you'll see that they have version 3.0.1 is currently available. I want to make a brief note about versions and R.

Installing R on your computer

The first thing we need to do is we need to install R onto your own computer. Now R is a free download. It's available for Windows, Mac, Linux computers and installation is a simple process. The first thing you need to do is you need to go to the website r-project.org. You can actually download it from a number of places, but this is the central location. Now if you scroll down a little bit, you'll see that they have version 3.0.1 is currently available. I want to make a brief note about versions and R.

Version one was released in February of 2000 and version two was released in October of 2004, four and a half years later. Version three was released in April of 2013 so that's a nine year gap. On the other hand, the versions are almost identical. The functionality, the interface are almost identical. Really, the biggest difference between three and two is that three is an accumulation of things, the most important of which is 64-bit support for all platforms in which case, you also support for parallel processing and other things.

But functionally. They're identical. In the day to day, you would not notice the difference. So, you don't have to worry about getting a new version that completely out dates all of your existing work. Anyhow, we're going to go and download version 3.0.1. So just click on Download R. And then you get to pick a mirror. And by the way, CRAN stands for the comprehensive R archive network. And all of the information is duplicated at all of these locations. So pick one that's close to you or not. I'll go to U.S.A I am close to Berkeley. And so, just click the one that you want.

If you're on a Mac, you just want to click and choose this top one. That's the package, the binary. If you're on a Windows computer, choose the base. And if you're on Linux, it's going to depend a little bit about which distribution you're using, but, if you're a Linux user, then you know what this all means and you know how to deal with it. But choose the one that you want and install it on your computer. From there, you can launch it. To launch it, just double-click on the icon, and this is what you get. I'm going to make this bigger. R is a command line application and well technically, R is a programming languages and this environment is a way of working with the language.

Now, it's all command line and you type one thing after another. It is possible to add drop-down menus. But mostly, you're typing out here. And it's going to look mostly the same from one platform to another. We're going to talk about one particular program that we're going to lay on top of it called RStudio. Which will give it a completely consistent interface from one operating system to another. As well as provide a number of things that make it easier to work with R. But this fundamentally is the program. We've downloaded it, we've installed it. And we've got it up and running now.

Now, I thought I would just mention one other thing before we go and it's about the name of the program, R. R is an implementation of an earlier programming language called S, which was for statistics so from S to R. Also the creators, Robert Gentleman and Ross Ihaka are both R's in their name, so that's where we get R. It's just a little bit of an origin story for the program. If all went well then you should have a fully functional copy of R on your computer by now. In the next movie we'll look at another piece of software that I mentioned called RStudio, that will help make life with R a little bit easier.

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This video is part of

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R Statistics Essential Training

70 video lessons · 6901 viewers

Barton Poulson
Author

 
Expand all | Collapse all
  1. 1m 59s
    1. Welcome
      58s
    2. Using the exercise files
      20s
    3. Using the challenges
      41s
  2. 1h 24m
    1. Installing R on your computer
      3m 19s
    2. Using RStudio
      4m 36s
    3. Taking a first look at the interface
      10m 56s
    4. Installing and managing packages
      11m 17s
    5. Using built-in datasets in R
      5m 27s
    6. Entering data manually
      4m 37s
    7. Importing data
      8m 53s
    8. Converting tabular data to row data
      13m 6s
    9. Working with color in R
      10m 18s
    10. Exploring color with Colorbrewer
      6m 36s
    11. Challenge: Creating color palettes in R
      1m 56s
    12. Solution: Creating color palettes in R
      3m 54s
  3. 44m 33s
    1. Creating bar charts for categorical variables
      8m 32s
    2. Creating pie charts for categorical variables
      6m 47s
    3. Creating histograms for quantitative variables
      6m 2s
    4. Creating box plots for quantitative variables
      7m 12s
    5. Overlaying plots
      7m 25s
    6. Saving images
      5m 34s
    7. Challenge: Layering plots
      39s
    8. Solution: Layering plots
      2m 22s
  4. 33m 18s
    1. Calculating frequencies
      3m 33s
    2. Calculating descriptives
      5m 43s
    3. Using a single proportion: Hypothesis test and confidence interval
      4m 2s
    4. Using a single mean: Hypothesis test and confidence interval
      4m 9s
    5. Using a single categorical variable: One sample chi-square test
      5m 9s
    6. Examining robust statistics for univariate analyses
      7m 29s
    7. Challenge: Calculating descriptive statistics
      36s
    8. Solution: Calculating descriptive statistics
      2m 37s
  5. 32m 5s
    1. Examining outliers
      6m 42s
    2. Transforming variables
      9m 26s
    3. Computing composite variables
      6m 18s
    4. Coding missing data
      6m 4s
    5. Challenge: Transforming skewed data to pull in outliers
      42s
    6. Solution: Transforming skewed data to pull in outliers
      2m 53s
  6. 16m 4s
    1. Selecting cases
      5m 30s
    2. Analyzing by subgroup
      3m 14s
    3. Merging files
      5m 16s
    4. Challenge: Analyzing guinea pig data subgroups
      33s
    5. Solution: Analyzing guinea pig data subgroups
      1m 31s
  7. 19m 38s
    1. Creating bar charts of group means
      4m 33s
    2. Creating grouped box plots
      5m 4s
    3. Creating scatter plots
      5m 2s
    4. Challenge: Creating your own grouped box plots
      48s
    5. Solution: Creating your own grouped box plots
      4m 11s
  8. 55m 1s
    1. Calculating correlation
      3m 55s
    2. Computing a bivariate regression
      6m 13s
    3. Comparing means with the t-test
      7m 20s
    4. Comparing paired means: Paired t-test
      6m 35s
    5. Comparing means with a one-factor analysis of variance (ANOVA)
      8m 27s
    6. Comparing proportions
      3m 34s
    7. Creating cross tabs for categorical variables
      5m 10s
    8. Computing robust statistics for bivariate associations
      8m 39s
    9. Challenge: Comparing proportions across several different groups
      1m 1s
    10. Solution: Comparing proportions across several different groups
      4m 7s
  9. 23m 18s
    1. Creating clustered bar charts for means
      3m 43s
    2. Creating scatter plots for grouped data
      2m 49s
    3. Creating scatter plot matrices
      6m 10s
    4. Creating 3D scatter plots
      5m 13s
    5. Challenge: Creating your own scatter plot matrix
      42s
    6. Solution: Creating your own scatter plot matrix
      4m 41s
  10. 45m 14s
    1. Computing a multiple regression
      9m 26s
    2. Comparing means with a two-factor ANOVA
      5m 56s
    3. Conducting a cluster analysis
      14m 14s
    4. Conducting a principal components/factor analysis
      10m 31s
    5. Challenge: Creating a cluster analysis of states in the US
      39s
    6. Solution: Creating a cluster analysis of states in the US
      4m 28s
  11. 3m 40s
    1. Next steps
      3m 40s

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