R functions to Summarize Data

Reading the .csv file into R

# reading the data
mtcars <- read.csv("mtcars.csv")

Get the number of rows and columns (dimensions) of the imported data

# dimensions of the data frame
dim(mtcars)

[1] 32 12

List the column names in the dataframe

colnames(mtcars)

 [1] "X"    "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"  
[11] "gear" "carb"

Print the data type (data structure) of each data column

str(mtcars)

'data.frame':   32 obs. of  12 variables:
 $ X   : chr  "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : int  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : int  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
 $ qsec: num  16.5 17 18.6 19.4 17 ...
 $ vs  : int  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : int  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: int  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: int  4 4 1 1 2 1 4 2 2 4 ...

Attach the dataframe

attach(mtcars)

Measure the mean, variance and standard deviation of the mpg in the dataframe mtcars

mean(mtcars$mpg)

[1] 20.09062

var(mtcars$mpg)

[1] 36.3241

sd(mtcars$mpg)

[1] 6.026948

Measure the median of the mpg in the dataframe mtcars

median(mtcars$mpg)

[1] 19.2

Measure the minimum value of mpg in the dataframe mtcars

min(mtcars$mpg)

[1] 10.4

Measure the maximum value of mpg in the dataframe mtcars

max(mtcars$mpg)

[1] 33.9

Measure the range of mpg in the dataframe mtcars

range(mtcars$mpg)

[1] 10.4 33.9

Display a summary of the mpg in the dataframe mtcars using summary()

summary(mtcars$mpg)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10.40   15.43   19.20   20.09   22.80   33.90 

Display a summary of the mpg in the dataframe mtcars using describe()

library(psych)
describe(mtcars$mpg)

   vars  n  mean   sd median trimmed  mad  min  max range skew kurtosis   se
X1    1 32 20.09 6.03   19.2    19.7 5.41 10.4 33.9  23.5 0.61    -0.37 1.07