Karen Grace-Martin

I received a question recently about R Commander, a free R package.

R Commander overlays a menu-based interface to R, so just like SPSS or JMP, you can run analyses using menus.  Nice, huh?

The question was whether R Commander does everything R does, or just a small subset.

Unfortunately, R Commander can’t do everything R does. Not even close.

But it does a lot. More than just the basics.

So I thought I would show you some of the things R Commander can do entirely through menus–no programming required, just so you can see just how unbelievably useful it is.

Since R commander is a free R package, it can be installed easily through R! Just type install.packages("Rcmdr") in the command line the first time you use it, then type library("Rcmdr") each time you want to launch the menus.

Data Sets and Variables

Import data sets from other software:

• SPSS
• Stata
• Excel
• Minitab
• Text
• SAS Xport
  

Define Numerical Variables as categorical and label the values

Open the data sets that come with R packages

Merge Data Sets

Edit and show the data in a data spreadsheet

Personally, I think that if this was all R Commander did, it would be incredibly useful. These are the types of things I just cannot remember all the commands for, since I just don’t use R often enough.

Data Analysis

Yes, R Commander does many of the simple statistical tests you’d expect:

• Chi-square tests
• Paired and Independent Samples t-tests
• Tests of Proportions
• Common nonparametrics, like Friedman, Wilcoxon, and Kruskal-Wallis tests
• One-way ANOVA and simple linear regression

What is surprising though, is how many higher-level statistics and models it runs:

• Hierarchical and K-Means Cluster analysis (with 7 linkage methods and 4 options of distance measures)
• Principal Components and Factor Analysis
• Linear Regression (with model selection, influence statistics, and multicollinearity diagnostic options, among others)
• Logistic regression for binary, ordinal, and multinomial responses
• Generalized linear models, including Gamma and Poisson models

In other words–you can use R Commander to run in R most of the analyses that most researchers need.

Graphs

A sample of the types of graphs R Commander creates in R without you having to write any code:

• QQ Plots
• Scatter plots
• Histograms
• Box Plots
• Bar Charts

The nice part is that it does not only do simple versions of these plots.  You can, for example, add regression lines to a scatter plot or run histograms by a grouping factor.

If you’re ready to get started practicing, click here to learn about making scatterplots in R commander, or click here to learn how to use R commander to sample from a uniform distribution.

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Latent Class Analysis is a method for finding and measuring unobserved latent subgroups in a population based on responses to a set of observed categorical variables.

This webinar will present an overview and an example of how latent class analysis works to find subgroups, how to interpret the output, the steps involved in running it.  We will discuss extensions and uses of the latent classes in other analyses and similarities and differences with related techniques.

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This free, one-hour webinar is part of our regular Craft of Statistical Analysis series. In it, we will introduce and demonstrate two of the core concepts of mixed modeling—the random intercept and the random slope.

Most scientific fields now recognize the extraordinary usefulness of mixed models, but they’re a tough nut to crack for someone who didn’t receive training in their methodology.

But it turns out that mixed models are actually an extension of linear models. If you have a good foundation in linear models, the extension to mixed models is more of a step than a leap. (Okay, a large step, but still).

You’ll learn what random intercepts and slopes mean, what they do, and how to decide if one or both are needed. It’s the first step in understanding mixed modeling.

Date: Friday, August 21, 2015
Time: 12pm EDT (New York time)
Cost: Free

***Note: This webinar has already taken place. Sign up below to get access to the video recording of the webinar.

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Why is it we can model non-linear effects in linear regression?

What the heck does it mean for a model to be “linear in the parameters?” (more…)

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Lately, I’ve gotten a lot of questions about learning how to run models for repeated measures data that isn’t continuous.

Mostly categorical. But once in a while discrete counts.

A typical study is in linguistics or psychology where (more…)

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