knitr::opts_chunk$set(echo = TRUE)
#install.packages("ggcorrplot")
#install.packages("ggplot2")
#install.packages("ggthemes")
library(ggplot2)
library(ggcorrplot)
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 3.4.4
library(DT)
## Warning: package 'DT' was built under R version 3.4.3

Data

We’ll use ‘Big Mart data’ set from a publicly available source to understand visualizations in R

giturl = "https://raw.githubusercontent.com/ahussan/DATA_607_CUNY_SPS/master/Visualization/Big%20Mart.csv"
marketdata = read.table(giturl, sep = ",", stringsAsFactors = FALSE, header = TRUE)
cat("Number of rows in the dataset: ", nrow(marketdata),"\n")
## Number of rows in the dataset:  8523
cat("Number of columns in the dataset: ", ncol(marketdata), "\n")
## Number of columns in the dataset:  12
datatable(marketdata)

Scatter Plot

Scatter Plot is used to see the relationship between two continuous variables.

In our dataset, if we want to visualize the items as per their cost data, then we can use scatter plot chart using two continuous variables, namely Item_Visibility & Item_MRP as shown below.

ggplot(marketdata, aes(Item_Visibility, Item_MRP)) + geom_point() + scale_x_continuous("Item Visibility", breaks = seq(0,0.35,0.05))+ scale_y_continuous("Item MRP", breaks = seq(0,270,by = 30))+ theme_bw()

#aes = how variables in the data are mapped to visual properties

We can even make it more visually clear by creating separate scatter plots for each separate Item_Type:

ggplot(marketdata, aes(Item_Visibility, Item_MRP)) +
  geom_point(aes(color = Item_Type)) + 
  scale_x_continuous("Item Visibility", breaks = seq(0,0.35,0.05))+
  scale_y_continuous("Item MRP", breaks = seq(0,270,by = 30))+ 
  labs(title="Scatterplot") + facet_wrap( ~ Item_Type)

Histogram

Histogram is used to plot continuous variable. It breaks the data into different buckets and shows frequency distribution of these buckets.

In this example I used MPG data set, we are going to find the count of cars by manufacturer

theme_set(theme_classic())

# Histogram on a Categorical variable
g <- ggplot(mpg, aes(manufacturer))
g + geom_bar(aes(fill=class), width = 0.5) + 
  theme(axis.text.x = element_text(angle=65, vjust=0.6)) + 
  labs(title="Histogram on Categorical Variable", 
       subtitle="Manufacturer across Vehicle Classes")

Bar charts

Bar charts are recommended when we want to plot a categorical variable or a combination of continuous and categorical variable.

Here, I used our mart data set. if we want to know number of marts established in particular year, then bar chart would be most suitable option, use variable Establishment Year as shown below.

ggplot(marketdata, aes(Outlet_Establishment_Year)) + geom_bar(fill = "red")+theme_bw()+
  scale_x_continuous("Establishment Year", breaks = seq(1985,2010, 2)) + 
  scale_y_continuous("Count", breaks = seq(0,1500,150)) +
  labs(title = "Bar Chart")

Box Plot

Box Plots are used to plot a combination of categorical and continuous variables. This plot is useful for visualizing the spread of the data and detect outliers. It shows statistically significant numbers:

The dark line inside the box represents the median

The top of box is 75%ile

bottom of box is 25%ile

From any dataset, if we want to identify important data points including minimum, maximum & median numbers, box plot can be helpful.

theme_set(theme_classic())

# Plot
g <- ggplot(mpg, aes(class, cty))
g + geom_boxplot(varwidth=T, fill="plum") + 
    labs(title="Box plot", 
         subtitle="City Mileage grouped by Class of vehicle",
         caption="Source: mpg",
         x="Class of Vehicle",
         y="City Mileage")

Correlogram

Correlogram is used to test the level of co-relation among the variable available in the data set. The cells of the matrix can be shaded or colored to show the co-relation value.

Darker the color, higher the co-relation between variables. Positive co-relations are displayed in blue and negative correlations in red color. Color intensity is proportional to the co-relation value.

From our Mart dataset, let’s check co-relation between Item cost, weight, visibility along with Outlet establishment year and Outlet sales from below plot.

In our example, we can see that Item cost & Outlet sales are positively correlated while Item weight & its visibility are negatively correlated. ,data visualizations R

Here is the R code for simple correlogram using function corrgram().

# Correlation matrix
data(mtcars)
corr <- round(cor(mtcars), 1)

# Plot
ggcorrplot(corr, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)

Population Pyramid

Population pyramids offer a unique way of visualizing how much population or what percentage of population fall under a certain category. The below pyramid is an excellent example of how many users are retained at each stage of a email marketing campaign funnel.

options(scipen = 999)  # turn off scientific notations i.e. 1e+40

# Read data
email_campaign_funnel <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv")

# X Axis Breaks and Labels 
brks <- seq(-15000000, 15000000, 5000000)
lbls = paste0(as.character(c(seq(15, 0, -5), seq(5, 15, 5))), "m")

# Plot
ggplot(email_campaign_funnel, aes(x = Stage, y = Users, fill = Gender)) +   # Fill column
                              geom_bar(stat = "identity", width = .6) +   # draw the bars
                              scale_y_continuous(breaks = brks,   # Breaks
                                                 labels = lbls) + # Labels
                              coord_flip() +  # Flip axes
                              labs(title="Email Campaign Funnel") +
                              theme(plot.title = element_text(hjust = .5), 
                                    axis.ticks = element_blank()) +   # Centre plot title
                              scale_fill_brewer(palette = "Dark2")  # Color palette

Sources: Data camp R for Data Science https://www.r-bloggers.com https://www.analyticsvidhya.com