In Part 15, we saw how to create a simple Generalised Linear Model on binary data using the glm() command. We continue with the same glm (modelling the vs variable on the weight and engine displacement) on the same data set – the mtcars dataset – which is built into…
Quick start with R: Generalised Linear Models (Part 15)
In Part 15, let’s see how to create simple Generalised Linear Models in R. Ordinary Least Squares regression provides linear models of continuous variables. However, much data of interest to statisticians and researchers are not continuous and so other methods must be used to create useful predictive models. The glm()…
Quick start with R: Pie charts (Part 14)
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) Create a simple pie chart….
Quick start with R: Box plots (Part 13)
In Part 13, let’s see how to create boxplots in R. Let’s create a simple boxplot using the boxplot() command, which is easy to use. First, we set up a vector of numbers and then we plot them. Boxplots can be created for individual variables or for variables by group….
Quick start with R: Histograms (Part 12)
In Part 12, let’s see how to create histograms in R. Let’s create 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,…
Quick start with R: Bar charts (Part 11)
In Part 11, let’s see how to create bar charts in R. Let’s create a simple bar chart 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()…
Quick start with R: aggregate() command (Part 10)
In Part 10, let’s look at the aggregate() command for creating summary tables using R. You may have a complex dataset 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 dataset may include the…
Using a recursive function to conditionally aggregate a contingency table
In this post, we will show on a simple example the application of a recursive function. The problem that we want to solve is the following. We have a matrix of dimensions N x M. This can be, for example, a two-dimensional contingency table. Columns that have a mean value…
Quick start with R: Sub-setting (Part 9)
In Part 9, let’s look at sub-setting in R. Let’s provide summary tables on the following data set of tourists from different countries, the numbers of their children, and the amount of money they spent while on vacation. Copy and paste the following array into R. A <- structure(list(NATION =…
Quick start with R: Basic commands (Part 8)
In Part 8, let’s look at some basic commands in R. Set up the following vectors by cutting and pasting from this document: a <- c(3,-7,-3,-9,3,-1,2,-12, -14) b <- c(3,7,-5, 1, 5,-6,-9,16, -8) d <- c(1,2,3,4,5,6,7,8,9) Now figure out what each of the following commands do. You should not need…