Data Visualization with R Ch 5/6

 Abinezer Abebe

STEAM 

Data Preparation CH 5/6

In Chapter 5 I worked on Multivariate Graphs. They display graphs between multiple variable. The two most known ways to accommodate variables is grouping and faceting. The first example we looked at is grouping and we mapped them to the x and y axes. Grouping allows us to plot multiple groups of data  in a single graph. We looked at the relationship between yrs.since.phd and salary. Next we added rank and gender to the graph. Another method of graphing the years since Ph.D and Salary using the size of the point is referred to a bubble plot. In the bubble plot we have two legends yrs.service and rank. Inferring from the graph we can see that more years in service the more likely to become a Prof and not a AssocProf or AssProf. In the final example we take a look at the life expectancy of counties in North and South America. The life expectancy is increasing in each country expect Haiti they lag behind in the graph.

In Chapter 6 we learned about Dot Density maps. Dot Density maps use points on a map to show relationship. The first example we took a look at was the Houston crime dataset. It contains the date time and address of six type of crimes reported in-between January and August.  First we got a map of downtown Houston and plotted all the rape crimes represented by a red dot. Nest we added a incident location with titles. Another way to look at data on a map is on Choropleth maps. They use shaded color to show the areas. In this example we created a world map to show the life expectancy using a Choropleth map. Reading the shaded region North America and Europe have the highest life expectancy while Sub Sharen Africa and Southwest Asia have the lowest. Next we took a look at the Mexican American population in America. Inferring from the map South Western states have the highest concentration of Mexican Americans. Lastly we looked at the Violent crimes committed in Connecticut accordioning to FBI stats. The highest concentration happens to be in New Haven and the lowest in Waterbury and Middletown.