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The “Statistics” menu provides access to some analysis of variance models via the “Means” sub-menu:

• Multi-way ANOVA – the simplest two-way analysis of variance model that can be applied to a set of data.

The “Models” menu provides access to various diagnostics for analysis of variance models via the “Graphs” sub-menu including:

• Basic diagnostic plots – four commonly used residual diagnostics including fitted values versus residuals and a normal probability plot for the residuals.

Other useful resources are provided on the Supplementary Material page.

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The “Statistics” menu provides access to some analysis of variance models via the “Means” sub-menu:

• One-way ANOVA – the simplest one-way analysis of variance model that can be applied to a set of data.

The “Models” menu provides access to various diagnostics for analysis of variance models via the “Graphs” sub-menu including:

• Basic diagnostic plots – four commonly used residual diagnostics including fitted values versus residuals and a normal probability plot for the residuals.

Other useful resources are provided on the Supplementary Material page.

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The “Statistics” menu provides access to various statistical models via the “Fit models” sub-menu including:

• Generalized Linear model – the most commonly used generalized linear models are avaiable, where we can specify a model formula using the Wilkinson-Rogers notation.

The “Models” menu provides access to various diagnostics for statistical models via the “Graphs” sub-menu including:

• Basic diagnostic plots – four commonly used residual diagnostics including fitted values versus residuals and a normal probability plot for the residuals.

The “Hypothesis Tests” sub-menu can be used to generate comparisons between pairs of models.

Other useful resources are provided on the Supplementary Material page.

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The “Statistics” menu provides access to various statistical models via the “Fit models” sub-menu including:

• Linear regression – the simplest scenario with a single response and one explanatory variable.
• Linear model – various extensions to the simple linear regression model where we can specify a model formula using the Wilkinson-Rogers notation.

The “Models” menu provides access to various diagnostics for statistical models via the “Graphs” sub-menu including:

• Basic diagnostic plots – four commonly used residual diagnostics including fitted values versus residuals and a normal probability plot for the residuals.

Other useful resources are provided on the Supplementary Material page.

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The “Statistics” menu provides access to various hypothesis tests via the “Means” sub-menu including:

• Single sample t-test – for situations where there is one sample of data and we are testing against hypothesised average for the population.
• Independent two sample t-test – where there are two samples and we are comparing the average values for each sample to look for evidence that the averages are different.
• Paired t-test – the situation where each individual in the sample has two directly comparable measurements.

Other useful resources are provided on the Supplementary Material page.

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The R Commander GUI has two menus “Data” and “Statistics” that are used for manipulating data sets and calculating descriptive statistics and various commonly used statistical techniques. In the “Data” menu there is a sub-menu “Manage variables in active data set” that has some useful features. These include:

• Compute new variables – used for transforming variables, e.g. converting to a logarithmic scale.
• Standardise variables – centre data on the mean and scale to the variance of the variable.
• Convert numeric variables to factors – this is useful for categorical data that is recorded as numbers where we would be interested in working with these as factor levels rather than the actual values.
• Bin numeric variable – in some situations converting a continuous measurement to groups can make exploratory analysis easier.

The “Statistics” menu provides access to various descriptive and summary statistics via the “Summaries” sub-menu including:

• Numerical summaries – mean, standard deviation or quantiles for a variable.
• Frequency distributions – used to create tables to summarise the number of times each level of a factor occurs in a variable.
• Table of statistics – mean, standard deviation for a numeric variable for each of the groups within a categorical variable.
• Correlation matrix – the correlation between a set of numeric variables in a data frame.

There are other data manipulation options and summary functions available from these two menus.

Other useful resources are provided on the Supplementary Material page.

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This interface is worth considering for beginners as it provides access to commonly used functions while also producing the R code required for the analysis which will hopefully smooth out the learning curve for new starters. Other bloggers have mentioned this system see here and here.

The GUI is reasonably straightforward as the intention does not appear to be to provide a point and click interface to the wide range of functions available in R. The above video covers the basics and how to import data from text files, such as tab delimited or comma separated variable file formats. It is also possible to import data from Excel spreadsheets assuming that a bit of pre-processing has been undertaken by the user, which is never a bad thing prior to loading data for analysis.

One interesting element of the GUI is a menu dedicated to continuous and discrete distributions that allow calculation of quantiles, probabilites or random numbers in addition to plotting a graph of the distribution for a given set of parameters. This could be a useful tool for understanding more about distributions.

Other useful resources are provided on the Supplementary Material page.