### R Tutorial 12.0

In our last blog, we have covered "

**How to get Basic Statistics in R**", let's now go ahead and learn process of frequency analysis in R and also the Chi-Square test.

The tutorial is quite useful, especially, when you perform a Logistic Regression.

For the purpose of this analysis we would be using an inbuilt data of R "

**mpg**".

Let's first make a data :

Data_1 = mpg[c("cyl","year","class")]

# Let's attach data and then see year wise distribution of Cyl

attach(Data_1)

table_1 = table(cyl,year)

print(table_1)

and you get the result like this >>>>>>>>>>>>>>>>>>.

So, it takes the first argument in the "table" function as "Row Labels" and second one as "Column Label" and then gives frequency.

You can try various types of such tables :

table_2 = table(cyl,class)

print(table_2)

or

table_2 = table(year,class)

print(table_2)

### margin.table for row/column wise total

Now, if you want to further analyze these resultant tables e.g. You want

**year wise total**or

**cyl wise total**, use margin statement:

margin.table(table_2, 1)

margin.table(table_2, 2)

### prop.table for %distribution Analysis

prop.table(table_2, 1) # gives the proportion row wiseprop.table(table_2, 2) # gives the proportion column wise

prop.table(table_2) # gives the proportion in matrix

*You can notice that in first result, the summation o*

*f proportion across rows are 1 and in the second one summation of proportions across columns is 1. In third one, the summation of all the proportions in the matrix is 1.*

The frequency analysis is not limited for 2 variables at a time, you can go n-way.

Let's try a 3 way frequency analysis :

table_3 = table(cyl, year, class)

ftable(table_3) # fttable is used to have a compact view of n-way frequency table

There is one more function "xtabs", that can be used to doing same.

Try this one:

table_4 = xtabs(~cyl+year+class, data = Data_1)

ftable(table_4)

### Chi-Square Test in R

Last thing in the Frequency Analysis, that we need to learn is "How to perform a Chi-Square Test ?".

use "summary" function on derived table using table function :

**Test Case 1:**

print(table_1)

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summary(table_1)

we can see that p value is less than generally accepted cut-off 0.05 and hence we can reject the Null Hypothesis of independence of variables year and cyl.

**Test Case 2:**

table_2 = table(year,class)

print(table_2)

summary(table_2)

Here we fail to reject the Null Hypothesis and can say that variables are independent.

Enjoy reading our other articles and stay tuned with us.

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