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…)
Let’s look at some basic commands in R.
Set up the following vector 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, but I will explain a few. (more…)
In Part 7, let’s look at further plotting in R. Try entering the following three commands together (the semi-colon allows you to place several commands on the same line).
Let’s take an example with two variables and enhance it.
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)
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) (more…)
In Part 3 and Part 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 will use a data set of counts (atomic disintegration events that take place within a radiation source), taken with a Geiger counter at a nuclear plant.
The counts were registered over a 30 second period for a short-lived, man-made radioactive compound. We read in the data and subtract the background count of 623.4 counts per second in order to obtain
We were recently fortunate to host a free The Craft of Statistical Analysis Webinar with guest presenter David Lillis. As usual, we had hundreds of attendees and didn’t get through all the questions. So David has graciously agreed to answer questions here.
If you missed the live webinar, you can download the recording here: Ten Data Analysis Tips in R.
Q: Is the M=structure(.list(.., class = “data.frame) the same as M=data.frame(..)? Is there some reason to prefer to use structure(list, … ,) as opposed to M=data.frame?
A: They are not the same. The structure( .. .) syntax is a short-hand way of storing a data set. If you have a data set called M, then the command dput(M) provides a shorthand way of storing the dataset. You can then reconstitute it later as follows: M <- structure( . . . .). Try it for yourselves on a rectangular dataset. For example, start off with (more…)