Summarizing and Processing Data

In R, you can easily obtain summary statistics of the data. Below are some of the examples.

data$score             # Note that 'score' variable is a continuous variable

##  [1] 35 23 14 17 23 35 27 33 32 31 34 27 51 36 39 45 31 40 25 32

mean(data$score)       # returns the mean of 'score' variable

## [1] 31.5

median(data$score)     # returns the median of 'score' variable

## [1] 32

var(data$score)        # returns the variance of 'score' variable

## [1] 78.36842

sd(data$score)         # returns the standard deviation of 'score' variable

## [1] 8.852594

range(data$score)      # returns the range of 'score' variable

## [1] 14 51

summary(data$score)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   14.00   26.50   32.00   31.50   35.25   51.00

data$method            # Note that 'method' variable is a categorical variable

##  [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2

table(data$method)     # counts the number of observations for each 'method'

## 
##  1  2 
## 10 10

summary(data$method)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     1.0     1.0     1.5     1.5     2.0     2.0

Summary statistics of ‘score’ variable for each category of ‘method’ can be also produced by using tapply() function. tapply() requires you to type in a vector, an index (i.e., factor(s)), and a function to apply. Note that the vector and factor must have the same length.

# tapply(vector object, index, function)
tapply(data$score, as.factor(data$method), mean)  # returns the mean of 'score' for each 'method'

##  1  2 
## 27 36

To write the codes more efficiently, you can use with() function. For example:

# with(data, expression)
with(data, mean(score))   # returns the mean of 'score' in 'data'

## [1] 31.5

with(data, tapply(score, method, mean))   # returns the mean of 'score' for each 'method' in 'data'

##  1  2 
## 27 36