Visualization Process

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.3.3
library(RColorBrewer)

Questions

Find the mtcars data in R. This is the dataset that you will use to create your graphics.

data("mtcars")
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

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

carbtable<- table(mtcars$carb)

labs<- paste("      (",names(carbtable)," carbs in the car)", "\n", carbtable, sep="")

pie(carbtable, labels = labs, col = brewer.pal(6, "Spectral"),
main="PIE CHART OF (CARB NUMBERS)\n with sample sizes")

There are 6 different Carbs with corresponding sample size 1 carb - 7 2 carbs - 10 3 carbs - 3 4 carbs - 10 6 carbs - 1 8 carbs - 1

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

labels <- paste("(",table(mtcars$gear)," gears in the car )", "\n", carbtable, sep="")

barplot(table(mtcars$gear),main="BAR PLOT OF GEAR TYPES\n with sample sizes", xlab="Gear Types", ylab = "Count of each gear type")

There are more 3-geared MTcars than the 4-geared and 5-geared. The count of 5-geared MTcars is the least.

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

ggplot(mtcars, aes(x = factor(cyl), fill = factor(gear))) + 
xlab("Values of 'cyl'") + 
ylab("Values of 'count of gear'") + 
geom_bar(color="black") +
ggtitle("\t\t                Number of each gear type divided out by Cyl") +
guides(fill=guide_legend(title="Different\n Gears"))

For the 4-Cylinder MTCars, 4-Gears seem to be a more common occurence while 3-gears is the least. For the 6-Cylinder MTCars, again 4-Gears is a more common occurence while 5-gears are on the lower end. For the 8-Cylinder MTCars, 3-Gears is the most common(almost 70%) while the remaining percentage of 8-Cylindered MTCars have 5-Gears.

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

ggplot(mtcars, aes(x = wt, y = mpg)) +
xlab("wt") + 
ylab("mpg") +
geom_point() +
geom_line() +
ggtitle("Relationship between 'wt' and 'mpg'") +
stat_smooth(method = "loess", formula = y ~ x, size = 1, col = "red")

This above figure gives us a good view of the negative sloping relationship between mpg and wt.Since the above graph doesn’t show a scatter plot, the following figure has been plotted.

plot(mtcars$mpg~mtcars$wt)

abline(lm(mtcars$mpg~mtcars$wt))

The abline tries to fit most of the points on the graph, however it isn’t fully successful. There is a downward sloping(negative) relation between the MTCars’ MilesPerGallon and the Cars’ weight.

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

ggplot(mtcars, aes(x = factor(gear), fill = factor(carb))) + 
xlab("Values of 'Gear'") + 
ylab("Values of 'count of Carborators for each Gear value'") + 
geom_bar(color="black") +
guides(fill=guide_legend(title="Carborators"))

I chose this Visualization to just give an idea of Different Carborators used for differernt Gears. Also it gives a count of number of MTCars present with a specific Gear Value. This is an extesion of the Graph on Question 2. We learn, from this graph, there are more 3-geared MTcars than the 4-geared and 5-geared. The count of 5-geared MTcars is the least. In addition to this, among the 3-geared MTcars there are equal proportion of 1,2,3,4 Carbonated MTCars. While among the 4-geared MTCars, there are equal proportion of 1,2,3-Carbonated MTCars. Similarly in the 5-geared MTCars,there are 2,4,6,8-Carbonated MTCars but not in equal porportion.

Thank you!