Brief tutorial to perform descriptive statistics using R with two examples.

January 3, 2017
By

(This article was first published on Data R Value, and kindly contributed to R-bloggers)

Using a pair of databases we will do a brief and basic analysis of descriptive statistics using R.
At the end of the article you can find the corresponding links to get both the script and the databases so that you can perform the exercise.

Install and load the packages we are going to use

install.packages(“sm”)
install.packages(“plotrix”)
library(sm)
library(plotrix)

library(grDevices)
 

It is Necessary to define the working directory. In this directory are your databases. To define it is through the function:

setwd(“my_working_directory”)

However, if we are working in RStudio, it is easy to define our working directory: at the top of our program we choose Tools -> Set Working Directory -> Choose Directory …
Then select the folder where the data is located.
 
Once we have defined the working directory, we read my data:
 

First Example

OAS <- read.csv(“OAS.csv”, header = T)
 

In this case, the data is stored in a csv (coma separate value)
format, a very efficient way for fairly large databases.
The database is defined as the name of the variable and their respective values; This is why I write header = T. It is also possible to refer to the variables by their name, this by means of:

attach(OAS)

Now we plot a first graph: graph of vertical bars.

barplot(Population, names.arg = StatesMembers, las = 2, cex.axis = 0.7, cex.names = 0.6, col = terrain.colors(length(Poblacion)), main = “Population of states belonging to the OAS”, horiz = F)

Now graph of horizontal bars.
 

barplot(Population, names.arg = StatesMembers, las = 1, cex.axis = 0.7, cex.names = 0.6, col = terrain.colors(length(Poblacion)), main = “Population of states belonging to the OAS”, horiz = T)

Now sector graph.

pie3D(Population[1:5], labels = StatesMembers[1:5], explode = 0,main=”Population of states belonging to the OAS”,col = terrain.colors(length(StatesMembers[1:5])))

Second Example

We now consider the heights of 100 students.
First read the data:
 

est <- read.csv(“height.csv”)

We calculate the frequency table:
We will need the “sm” library and define a function:
Where it receives the data and the number of class intervals:

freq.tab <- function(data, n.int){
  raw.tab <- binning(data, nbins = n.int)
  tab <- list()
  tab$intleft <- raw.tab$breaks[1:n.int]
  tab$intright <- raw.tab$breaks[2:(n.int+1)]
  tab$mc <- raw.tab$x
  tab$freq <- raw.tab$table.freq
  tab$freqrel <- raw.tab$table.freq / length(data)
  tab$freqacum <- cumsum(raw.tab$table.freq / length(data))
  return(tab)
}

For example, if you have 6 class intervals, write:

freq.tab(est$Height, 6)

And automatically gives us a list of objects.
First the class intervals appear, the Class, frequencies, relative frequencies and cumulative frequencies.
We can vary the number of class intervals, for example:

freq.tab(est$Height, 9)

Now we graph the histogram of the data “est”:
 

hist(est$Height, xlab = “Sample”, ylab = “”, main = “Frequency Histogram”, col = terrain.colors(13), border = “white”, cex.lab = 0.7, cex.main = 0.9, cex.axis = 0.7)


In this case, R considers 8 class intervals.

You can find this material in:
https://github.com/pakinja/Data-R-Value/tree/master/Frequency_Distribution

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