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Basic data operations in R

In this article we present basic statistical operations over data matrices, like computing frequencies, means and variances.
Reading and storing the data are always inevitable steps in data analysis.
© PRACE and University of Ljubljana


In this article we provide a presentation that describes how to obtain a fast overview of the (normal size) data. Note that all these examples assume that you have
  • started RStudio (by executing rstudio & in the terminal) and
  • you have opened a new R script file.
If you have not, then with ctrl+shift+n you start a new script file that you have to save first to a local folder. Once you type (copy) the R code into the script file, you run it by, e.g., selecting the part of the code you want to run and typing ctrl+enter.
Suppose we have a data frame containing 3 numerical ratio variables and 1 categorical variable

M1 <- matrix(rnorm(150,0,1), ncol=3)

M2 <- matrix(round(runif(50,1,4),0),ncol=1)
M3 <- mapvalues(M2, from = 1:4, to = group)


Descriptive statistics

If you want to see the distribution (frequencies) of the different category values for the variable group (we address it as M$group) you should use table or summary.
In both cases we obtain
Group_A Group_B Group_D Group_E 
10 14 20 6
We might also be interested in the mean values of the first three columns in M (the centroid) and the group centroids for these columns, where the groups are defined by group. Here is the code and the results.
centr=colMeans(M[,1:3]) # CENTROID

X1 X2 X3
-0.15863016 0.19138859 0.06853306

aggregate(M[,1:3],by=list(M$group),FUN=mean) # GROUP CENTROIDS
group X1 X2 X3
1 Group_A -0.1159164 0.187124494 0.51985248
2 Group_B -0.1770462 0.532357620 -0.16423459
3 Group_D -0.1651467 0.013811050 0.06511391
4 Group_E -0.1651271 -0.005173836 -0.12914429
© PRACE and University of Ljubljana
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