How Do We Construct Frequency Distribution Tables Using R?

Part 2 (Relative Frequeancy Distribution Table, Cumulative Frequency Distribution, and Cumulative Relative Frequency Distribution Table)

We will use States Visited, (StatesV) variable included in first day class survey, Sur1.

1. Upload Sur1.csv data set from your desktop to R

Sur1 <-read.csv("~/Desktop/Sur1.csv")

NOTE: If case you did not save Sur1.csv file correctly to your desktop, execute the following code
Sur1 <-read.csv(read.csv(file.choose(),header = TRUE))

1. Examinne the strucure of Sur1 data set

str(Sur1)

## 'data.frame':    20 obs. of  6 variables:
##  $ SEX      : Factor w/ 2 levels "Female","Male": 1 1 1 1 2 2 2 1 1 1 ...
##  $ POLITICS : Factor w/ 3 levels "Conservative",..: 3 3 3 3 3 3 2 2 1 3 ...
##  $ NSiblings: int  4 4 2 2 2 4 5 4 4 1 ...
##  $ StatesV  : int  5 11 6 3 7 3 4 4 6 3 ...
##  $ ShoeS    : num  6 9 8 7.5 13 10.5 9 8 6 8.5 ...
##  $ UsedExcel: int  2 2 2 1 1 3 3 2 2 1 ...

1. View first six row of the data in spreadsheet format

head(Sur1, 6)

##      SEX POLITICS NSiblings StatesV ShoeS UsedExcel
## 1 Female Moderate         4       5   6.0         2
## 2 Female Moderate         4      11   9.0         2
## 3 Female Moderate         2       6   8.0         2
## 4 Female Moderate         2       3   7.5         1
## 5   Male Moderate         2       7  13.0         1
## 6   Male Moderate         4       3  10.5         3

1. Attach Sur1 to R in order to work with data variables without using $ symbol (remember to detach(Sur1) after completion of analysis)

attach(Sur1)

1. Summarize StatesV variable

summary(StatesV)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    3.00    4.00    6.00    7.15    9.50   18.00

1. Construct a frequency distribution table of States Visited using breaks to list class limits

breaks <-seq(0,18, by=3)

1. Group StatesV variable data into bins

StatesV.cut <-cut(StatesV,breaks)

1. Construct frequency table for StatesV variable in horizontal display

 table(StatesV.cut)

## StatesV.cut
##   (0,3]   (3,6]   (6,9]  (9,12] (12,15] (15,18] 
##       3       8       4       3       1       1

1. Transform this table to 2-column display

transform(table(StatesV.cut))

##   StatesV.cut Freq
## 1       (0,3]    3
## 2       (3,6]    8
## 3       (6,9]    4
## 4      (9,12]    3
## 5     (12,15]    1
## 6     (15,18]    1

1. Convert frequency table to relative frequency table by dividing frequencies by total number of StatesV data, in our case by 20

transform(table(StatesV.cut)/20)

##   StatesV.cut Freq
## 1       (0,3] 0.15
## 2       (3,6] 0.40
## 3       (6,9] 0.20
## 4      (9,12] 0.15
## 5     (12,15] 0.05
## 6     (15,18] 0.05

1. Convert frequency table to cumulative frequency table by summing frequencies of StatesV data cumulatively

transform(table(StatesV.cut),Cum_Freq=cumsum(Freq))

##   StatesV.cut Freq Cum_Freq
## 1       (0,3]    3        3
## 2       (3,6]    8       11
## 3       (6,9]    4       15
## 4      (9,12]    3       18
## 5     (12,15]    1       19
## 6     (15,18]    1       20

1. Convert frequency table to cumulative relative frequency table by dividing cumulative frequencies by total number of StatesV data, in our case by 20

transform(table(StatesV.cut),RelCum_Freq=cumsum(Freq)/20)

##   StatesV.cut Freq RelCum_Freq
## 1       (0,3]    3        0.15
## 2       (3,6]    8        0.55
## 3       (6,9]    4        0.75
## 4      (9,12]    3        0.90
## 5     (12,15]    1        0.95
## 6     (15,18]    1        1.00