knitr::opts_chunk$set(echo = TRUE)

1. Create a pie chart showing the proportion of cars from the mtcars data set that have different carb values.

Summary

Simple R funtion pie describes how card is proportionally divided, highest proportion being 31.

summary(mtcars)
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
##       drat             wt             qsec             vs        
##  Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
##  1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
##  Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
##  Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
##  3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
##  Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
##        am              gear            carb      
##  Min.   :0.0000   Min.   :3.000   Min.   :1.000  
##  1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
##  Median :0.0000   Median :4.000   Median :2.000  
##  Mean   :0.4062   Mean   :3.688   Mean   :2.812  
##  3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
##  Max.   :1.0000   Max.   :5.000   Max.   :8.000
pie(mtcars$carb)

2. Create a bar graph, that shows the number of each gear type in mtcars.

Summary

Following plot gives frequency distribtion for gears from mtchars dataset.The dataset has 3,4,5 gears.

barplot(mtcars$gear, main = "Frequency of Gear", xlab ="Gear",col=c("green") )

3. Next show a stacked bar graph of the number of each gear type and how they are further divided out by cyl.

Summary

Following plot describs the frequency of numbers of cylinders within gear categories.For example gear 4 has 8, 4 cylinder engines and 12, 6 cylinders engines.

Frq <-table(mtcars$cyl,mtcars$gear)
barplot(Frq,main = "Distribution of cyl & gear",
        xlab = "Number of gears", col = c("blue","darkblue","lightblue"),
        legend=rownames(Frq))

4.Draw a scatter plot showing the relationship between wt and mpg.

Summary

Following plot shows if there is any relationship between mpg and wt of the car.There is negative linear relationship between mpg and wt. As weight of the car increases the miles per gallon decreases, which confirms our common knowledge.

plot(mtcars$wt, mtcars$mpg, main="Scatterplot Example", 
   xlab="Car Weight ", ylab="Miles Per Gallon ", pch=19, col=c("green"))

5.Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.

Summary

I am using correlation plot to visualize the data. It quickly gives information about which factors are highly correlted.Positive correlations are displayed in blue and negative correlations in red color. Color intensity and the size of the circle are proportional to the correlation coefficients.mpg and wt is shown with dark bigger red circle which means it strong negative correlation which is also confirmed from previous scatter plot .

library(corrplot)
## Warning: package 'corrplot' was built under R version 3.4.4
## corrplot 0.84 loaded
corrplot(cor(mtcars))