David Lillis

In Part 14, let’s see how to create pie charts in R. Let’s create a simple pie chart using the pie() command. As always, we set up a vector of numbers and then we plot them.

B <- c(2, 4, 5, 7, 12, 14, 16) (more…)

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In Part 13, let’s see how to create box plots in R. Let’s create a simple box plot using the boxplot() command, which is easy to use. First, we set up a vector of numbers and then we plot them.

Box plots can be created for individual variables or for variables by group (more…)

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I’m sure you’ve heard that R creates beautiful graphics.

It’s true, and it doesn’t have to be hard to do so.  Let’s start with a simple histogram using the hist() command, which is easy to use, but actually quite sophisticated.

First, we set up a vector of numbers and then we create a histogram.

B <- c(2, 4, 5, 7, 12, 14, 16)
hist(B)

That was easy, but you need more from your histogram. (more…)

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Let’s create a simple bar chart in R using the barplot() command, which is easy to use.

First, we set up a vector of numbers. Then we count them using the table() command, and then we plot them.

The table() command creates a simple table of counts of the elements in a data set.

H <- c(2,3,3,3,4,5,5,5,5,6)

Now we count the elements using the table() command, as follows: (more…)

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In Part 10, let’s look at the aggregate command for creating summary tables using R.

You may have a complex data set that includes categorical variables of several levels, and you may wish to create summary tables for each level of the categorical variable.

For example, your data set may include the variable Gender, a two-level categorical variable with levels Male and Female. Your data set may include other categorical variables such as Ethnicity, Hair Colour, the Treatments received by patients in a medical study, or the number of cylinders in motor vehicles.

In any case, you may wish to produce summary statistics for each level of the categorical variable. This is where the aggregate command is so helpful. (more…)

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In Part 9, let’s look at sub-setting in R. I want to show you two approaches.

Let’s provide summary tables on the following data set of tourists from different nations, their gender and numbers of children. Copy and paste the following array into R. (more…)

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