This is my first Assignment

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
library(ggplot2)

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

ggplot(data=mtcars,
aes(x = factor(1), fill = factor(carb))) +
ylab("Proportions of cars by 'carb' values") + xlab("") +
geom_bar(width = 2) +
coord_polar(theta = "y")

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

ggplot(data=mtcars, aes(x=gear)) + geom_bar(stat="count")

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("Value of cyl") +
ylab("'Gear Count'") +
geom_bar(color="blue")

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("Plot between 'wt' and 'mpg'") +
stat_smooth(method = "loess", formula = y ~ x, size = 1, col = "blue")

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

boxplot(mtcars$mpg ~ mtcars$cyl, main = "Box Plot of Mileage vs Cylinders count", xlab = "Number of Cylinders", ylab = "Mileage by Gallon", 
col = "lightblue")

I preferred box plot to show mileage for cars with various cylinder types. The box plot analysis shows that avg miles per gallon for car is more for least number of cylinders whereas the opposite for high number of cylinders. We also see there is larger interquartile range for group 4 from group 6 and 8 that shows high variability in mileage values of cars with 4 cylinders. Box plot is most useful plot when we are looking in interpreting factor data types with multiple levels. It is best used if we want to highlight the outliers at once. For this kind of analysis, box plot is most efficient as it highlights the interquartile range of the data for each level of independent variable that targets the variance of data by each level.