Big Data Analytics - Charts & Graphs

Univariate Graphical Methods

Univariate is a statistical term. In practice, it means we want to analyze a variable independently from the rest of the data. The plots that allow to do this efficiently are −

Box-Plots

Box-Plots are normally used to compare distributions. It is a great way to visually inspect if there are differences between distributions. We can see if there are differences between the price of diamonds for different cut.

# We will be using the ggplot2 library for plotting
library(ggplot2)  
data("diamonds")  

# We will be using the diamonds dataset to analyze distributions of numeric variables 
head(diamonds) 

#    carat   cut       color  clarity  depth  table   price    x     y     z 
# 1  0.23    Ideal       E      SI2    61.5    55     326     3.95  3.98  2.43 
# 2  0.21    Premium     E      SI1    59.8    61     326     3.89  3.84  2.31 
# 3  0.23    Good        E      VS1    56.9    65     327     4.05  4.07  2.31 
# 4  0.29    Premium     I      VS2    62.4    58     334     4.20  4.23  2.63 
# 5  0.31    Good        J      SI2    63.3    58     335     4.34  4.35  2.75 
# 6  0.24    Very Good   J      VVS2   62.8    57     336     3.94  3.96  2.48 

### Box-Plots
p = ggplot(diamonds, aes(x = cut, y = price, fill = cut)) + 
   geom_box-plot() + 
   theme_bw() 
print(p)

We can see in the plot there are differences in the distribution of diamonds price in different types of cut.

Histograms

source('01_box_plots.R')

# We can plot histograms for each level of the cut factor variable using 
facet_grid 
p = ggplot(diamonds, aes(x = price, fill = cut)) + 
   geom_histogram() + 
   facet_grid(cut ~ .) + 
   theme_bw() 

p  
# the previous plot doesn’t allow to visuallize correctly the data because of 
the differences in scale 
# we can turn this off using the scales argument of facet_grid  

p = ggplot(diamonds, aes(x = price, fill = cut)) + 
   geom_histogram() + 
   facet_grid(cut ~ ., scales = 'free') + 
   theme_bw() 
p  

png('02_histogram_diamonds_cut.png') 
print(p) 
dev.off()

The output of the above code will be as follows −

Multivariate Graphical Methods

Multivariate graphical methods in exploratory data analysis have the objective of finding relationships among different variables. There are two ways to accomplish this that are commonly used: plotting a correlation matrix of numeric variables or simply plotting the raw data as a matrix of scatter plots.

In order to demonstrate this, we will use the diamonds dataset. To follow the code, open the script bda/part2/charts/03_multivariate_analysis.R.

library(ggplot2)
data(diamonds) 

# Correlation matrix plots  
keep_vars = c('carat', 'depth', 'price', 'table') 
df = diamonds[, keep_vars]  
# compute the correlation matrix 
M_cor = cor(df) 

#          carat       depth      price      table 
# carat 1.00000000  0.02822431  0.9215913  0.1816175 
# depth 0.02822431  1.00000000 -0.0106474 -0.2957785 
# price 0.92159130 -0.01064740  1.0000000  0.1271339 
# table 0.18161755 -0.29577852  0.1271339  1.0000000  

# plots 
heat-map(M_cor)

The code will produce the following output −

This is a summary, it tells us that there is a strong correlation between price and caret, and not much among the other variables.

A correlation matrix can be useful when we have a large number of variables in which case plotting the raw data would not be practical. As mentioned, it is possible to show the raw data also −

library(GGally)
ggpairs(df)

We can see in the plot that the results displayed in the heat-map are confirmed, there is a 0.922 correlation between the price and carat variables.

It is possible to visualize this relationship in the price-carat scatterplot located in the (3, 1) index of the scatterplot matrix.

Previous Page Print Page