Hands-on with data visualization using ggplot, gemo_density, facet_grid, and more with a case study. Thanks, for your time, if you enjoyed this short article there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy

1. Data Visualization - IV
By using case study
Rupak Roy

2. >install.packages(“titanic”)
>library(titanic)
>library(ggplot2)
>str(titanic)
#let’s understand the survival rate
>t<-ggplot(titanic, aes(x= Freq))
>t+geom_density(aes(colour=Survived,fill=Survived),alpha=0.3)
#The survival rate plot therefore infers an estimate of 250 peoples survived.
However lets clarify this with an another graph.
Data Visualization - IV
Rupak Roy

3. #let’s understand the survival rate (2)
t<-ggplot(titanic, aes(x=Freq)
t+geom_density(aes(colour=Freq>300,
fill=Survived),alpha=0.3)
From this plot we can clearly confirm that
more than 300 people
didn’t survived.
Data Visualization - IV
Rupak Roy

4. #understanding which group survived the most
>t<-ggplot(titanic, aes(x= Freq))
>t+geom_density(aes(colour=Survived,
fill=Survived),alpha=0.3)+facet_grid(Class~.)
Clearly we can see the CREW survived the most
followed by the 1st Class
#and what were their ages?
>t+geom_density(aes(colour=Survived,
fill=Survived),alpha=0.3)
+facet_grid(Class~Age)
So most of them were adults.
Data Visualization - IV

5. #Let’s differentiate based on gender
>t+geom_density(aes(colour=Survived,fill=Survived),alpha=0.3)
+facet_grid(Class~Sex)
From these we can defer that most of the
crews survived were Male and Female from
the 1st Class.
One thing we can also observe that
females from all the class except the crew
are given the most priority than the males
might be the reason females survived the most as per to the males.
Data Visualization - IV
Rupak Roy

6. Next:
We learn how to plot our data in a map like Google i.e.
we will perform how to apply geospatial data again by
using ggplot2.
Data Visualization - IV
Rupak Roy