Using Microsoft Excel I’m collecting aggregate data, by state, of various social, political, and economic indicators. I export them into a tab-delimited file called ‘states.txt’ (pretty clever, I know.) I’ve got data on education expenditures, firearm deaths per capita, median household income, etc. I’d like to do some analysis and graphing of these data to see if there are any patterns of interest.
First, I import the data into R with the following command:
states <- read.table("C:/Data/states.txt", header=TRUE, sep = "\t")
This creates a data frame called ‘states’, and reads the text file into it. The command ‘header = TRUE’ tells R that the first row of data contains variable names, and ‘sep = “\t”‘ tells the program that the file is tab-demimited.
Next I ‘attach’ the data frame with the following command:
OK, now that I’ve got my data into R, what can I do with it?
First, I’ll run some correlations and see what’s going on.
cor (read2children, publicedexp)
This tells me that the correlation between public expenditures on education and the percentage of children below the age of five who are read to daily is 0.42. It is unsurprising that there’s a strong relationship between the two. It’s also likely that a third variable, household income, might be related.
cor (hincome, publicedexp)
cor (read2children, hincome)
These relationships are even stronger.
Let’s look at the data a different way, by using scatterplots to see the relationships.
plot is the R command for, well, plotting. It’s very powerful and you can do lots of things with it. First I’ll do a very simple scatterplot of education expenditures and children read to.
plot (publicedexp, read2children)
Figure 1. A very basic scatterplot
Next time we’ll work on making it better looking.
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