A Complete Guide to the Boston Dataset in R

The Boston dataset from the MASS package in R contains information about various attributes for suburbs in Boston, Massachusetts.

This tutorial explains how to explore, summarize, and visualize the Boston dataset in R.

Load the Boston Dataset

Before we can view the Boston dataset, we must first load the MASS package:

library(MASS)

We can then use the head() function to view the first six rows of the dataset:

#view first six rows of Boston dataset
head(Boston)

     crim zn indus chas   nox    rm  age    dis rad tax ptratio  black lstat
1 0.00632 18  2.31    0 0.538 6.575 65.2 4.0900   1 296    15.3 396.90  4.98
2 0.02731  0  7.07    0 0.469 6.421 78.9 4.9671   2 242    17.8 396.90  9.14
3 0.02729  0  7.07    0 0.469 7.185 61.1 4.9671   2 242    17.8 392.83  4.03
4 0.03237  0  2.18    0 0.458 6.998 45.8 6.0622   3 222    18.7 394.63  2.94
5 0.06905  0  2.18    0 0.458 7.147 54.2 6.0622   3 222    18.7 396.90  5.33
6 0.02985  0  2.18    0 0.458 6.430 58.7 6.0622   3 222    18.7 394.12  5.21
  medv
1 24.0
2 21.6
3 34.7
4 33.4
5 36.2
6 28.7

To view a description of each variable in the dataset, we can type the following:

#view description of each variable in dataset
?Boston

     This data frame contains the following columns:

     'crim' per capita crime rate by town.

     'zn' proportion of residential land zoned for lots over 25,000
          sq.ft.

     'indus' proportion of non-retail business acres per town.

     'chas' Charles River dummy variable (= 1 if tract bounds river; 0
          otherwise).

     'nox' nitrogen oxides concentration (parts per 10 million).

     'rm' average number of rooms per dwelling.

     'age' proportion of owner-occupied units built prior to 1940.

     'dis' weighted mean of distances to five Boston employment
          centres.

     'rad' index of accessibility to radial highways.

     'tax' full-value property-tax rate per \$10,000.

     'ptratio' pupil-teacher ratio by town.

     'black' 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by
          town.

     'lstat' lower status of the population (percent).

     'medv' median value of owner-occupied homes in \$1000s.

Summarize the Boston Dataset

We can use the summary() function to quickly summarize each variable in the dataset:

#summarize Boston dataset
summary(Boston)

      crim                zn             indus            chas        
 Min.   : 0.00632   Min.   :  0.00   Min.   : 0.46   Min.   :0.00000  
 1st Qu.: 0.08205   1st Qu.:  0.00   1st Qu.: 5.19   1st Qu.:0.00000  
 Median : 0.25651   Median :  0.00   Median : 9.69   Median :0.00000  
 Mean   : 3.61352   Mean   : 11.36   Mean   :11.14   Mean   :0.06917  
 3rd Qu.: 3.67708   3rd Qu.: 12.50   3rd Qu.:18.10   3rd Qu.:0.00000  
 Max.   :88.97620   Max.   :100.00   Max.   :27.74   Max.   :1.00000  
      nox               rm             age              dis        
 Min.   :0.3850   Min.   :3.561   Min.   :  2.90   Min.   : 1.130  
 1st Qu.:0.4490   1st Qu.:5.886   1st Qu.: 45.02   1st Qu.: 2.100  
 Median :0.5380   Median :6.208   Median : 77.50   Median : 3.207  
 Mean   :0.5547   Mean   :6.285   Mean   : 68.57   Mean   : 3.795  
 3rd Qu.:0.6240   3rd Qu.:6.623   3rd Qu.: 94.08   3rd Qu.: 5.188  
 Max.   :0.8710   Max.   :8.780   Max.   :100.00   Max.   :12.127  
      rad              tax           ptratio          black       
 Min.   : 1.000   Min.   :187.0   Min.   :12.60   Min.   :  0.32  
 1st Qu.: 4.000   1st Qu.:279.0   1st Qu.:17.40   1st Qu.:375.38  
 Median : 5.000   Median :330.0   Median :19.05   Median :391.44  
 Mean   : 9.549   Mean   :408.2   Mean   :18.46   Mean   :356.67  
 3rd Qu.:24.000   3rd Qu.:666.0   3rd Qu.:20.20   3rd Qu.:396.23  
 Max.   :24.000   Max.   :711.0   Max.   :22.00   Max.   :396.90  
     lstat            medv      
 Min.   : 1.73   Min.   : 5.00  
 1st Qu.: 6.95   1st Qu.:17.02  
 Median :11.36   Median :21.20  
 Mean   :12.65   Mean   :22.53  
 3rd Qu.:16.95   3rd Qu.:25.00  
 Max.   :37.97   Max.   :50.00

For each of the numeric variables we can see the following information:

Min: The minimum value.
1st Qu: The value of the first quartile (25th percentile).
Median: The median value.
Mean: The mean value.
3rd Qu: The value of the third quartile (75th percentile).
Max: The maximum value.

We can use the dim() function to get the dimensions of the dataset in terms of number of rows and number of columns:

#display rows and columns
dim(Boston)

[1] 506  14

We can see that the dataset has 506 rows and 14 columns.

Visualize the Boston Dataset

We can also create some plots to visualize the values in the dataset.

For example, we can use the hist() function to create a histogram of the values for a certain variable:

#create histogram of values for 'rm' column
hist(Boston$rm,
     col='steelblue',
     main='Histogram of Rooms per Dwelling',
     xlab='Rooms',
     ylab='Frequency')

We can also use the plot() function to create a scatterplot of any pairwise combination of variables:

#create scatterplot of median home value vs crime rate
plot(Boston$medv, Boston$crime,
     col='steelblue',
     main='Median Home Value vs. Crime Rate',
     xlab='Median Home Value',
     ylab='Crime Rate',
     pch=19)