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 variable Gender, a two-level categorical …
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 less than the set value …
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 = structure(c(3L, 3L, 3L, 3L, 1L, …
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 me to explain each command, …
The following video tutorial gives a brief overview of the Sample Size Calculator application. The Sample Size Calculator is an interactive Shiny application which allows you to calculate sample size when estimating population mean value or population proportion.
In Part 7, let’s look at further plotting in R. Try entering the following three commands to create three variables. X <- c(3, 4, 6, 6, 7, 8, 9, 12) B1 <- c(4, 5, 6, 7, 17, 18, 19, 22) B2 <- c(3, 5, 8, 10, 19, 21, 22, 26) Graph B1 using a y …
In Part 6, let’s look at basic plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line). x <- seq(-4, 4, 0.2) ; y <- 2*x^2 + 4*x – 7 plot(x, y) This is a very basic plot, but we can do much …
In Parts 3 and 4 we used the lm() command to perform least squares regressions. We saw how to check for non-linearity in our data by fitting polynomial models and checking whether they fit the data better than a linear model. Now let’s see how to fit an exponential model in R. As before, we …
In Part 3 we used the lm() command to perform least squares regressions. In Part 4 we will look at more advanced aspects of regression models and see what R has to offer. One way of checking for non-linearity in your data is to fit a polynomial model and check whether the polynomial model fits …
In Part 2 we created two variables and used the lm() command to perform a least squares regression on them, treating one of them as the dependent variable and the other as the independent variable. Here they are again. height = c(186, 165, 149, 206, 143, 187, 191, 179, 162, 185) weight = c(89, 56, …