Https download rstudio for mac install#
Https download rstudio for mac update#
Https download rstudio for mac code#
Https download rstudio for mac mac#

As you can see, base R is very much bare-bones software.

Https download rstudio for mac mac#

When you do you should see a screen like the one in Figure 1.6 (this is the Mac version). Once you’ve installed base-R on your computer, try opening it.

Https download rstudio for mac install#

To install Base-R, click on one of the following links and follow the instructions.

Https download rstudio for mac code#

If you don’t, then some of the code and packages in this book might not work.

Https download rstudio for mac update#

If you have a (much) older version of R or RStudio currently installed on your computer, then you should update both R and RStudio to the newest version(s) by installing them again from scratch. At the time that I am writing this sentence (09:48, Thursday, 23 February, 2017), the latest version of Base-R is 3.3.2 “Sincere Pumpkin Patch” (the versions all have funny names) which was released on 31 October, 2016, and the latest version of RStudio is 1.0.136 released on 21 December, 2016. R and RStudio have been around for several years – however, they are constantly being updated with new features and bug-fixes.

18.5 Chapter 8: Matrices and Dataframes.

18.4 Chapter 7: Indexing vectors with.

17.4 Loops over multiple indices with a design matrix.

17.3 Updating a container object with a loop.

17.2 Creating multiple plots with a loop.

17.1.2 Adding the integers from 1 to 100.

16.4.4 Storing and loading your functions to and from a function file with source().

16.4.2 Using stop() to completely stop a function and print an error.

16.3 Using if, then statements in functions.

16.2.3 Including default values for arguments.

16.2 The structure of a custom function.

16.1 Why would you want to write your own function?.

15.5.2 Transforming skewed variables prior to standard regression.

15.5.1 Adding a regression line to a plot.

15.5 Logistic regression with glm(family = 'binomial'.

15.4 Regression on non-Normal data with glm().

15.3 Comparing regression models with anova().

15.2.6 Getting an ANOVA from a regression model with aov().

15.2.5 Center variables before computing interactions!.

15.2.4 Including interactions in models: y ~ x1 * x2.

15.2.3 Using predict() to predict new data from a model.

15.2.2 Getting model fits with fitted.values.

15.2.1 Estimating the value of diamonds with lm().

14.7 Repeated measures ANOVA using the lme4 package.

14.6 Getting additional information from ANOVA objects.

14.5 Type I, Type II, and Type III ANOVAs.

14.1 Full-factorial between-subjects ANOVA.

13.5.1 Getting APA-style conclusions with the apa function.

13.1 A short introduction to hypothesis tests.

12.3.1 Complex plot layouts with layout().

12.3 Arranging plots with par(mfrow) and layout().

11.10 Test your R might! Purdy pictures.

11.8 Saving plots to a file with pdf(), jpeg() and png().

11.7.5 Combining text and numbers with paste().

10.6 Test your R might!: Mmmmm…caffeine.

9.6.3 Reading files directly from a web URL.

9.1.1 Why object and file management is so important.

8.7 Test your R might! Pirates and superheroes.

7.3.1 Ex: Fixing invalid responses to a Happiness survey.

7.2.2 Counts and percentages from logical vectors.

6.2.3 Sample statistics from random samples.

6.2.2 Additional numeric vector functions.

4.4.4 Example: Pirates of The Caribbean.

4.3.1 Commenting code with the # (pound) sign.

4.3 A brief style guide: Commenting and spacing.

4.2.1 Send code from an source to the console.1.5.2 Getting R help and inspiration online.