Problem Set 1
Visualization Process by Pallavi Saitu
Questions
Find the mtcars data in R. This is the dataset that you will use to create your graphics.
- Create a pie chart showing the proportion of cars from the
mtcarsdata set that have differentcarbvalues.
Dataset=mtcars
Dataset## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2carb1 <- length(which(Dataset$carb == 1))
carb2 <- length(which(Dataset$carb == 2))
carb3 <- length(which(Dataset$carb == 3))
carb4 <- length(which(Dataset$carb == 4))
carb6 <- length(which(Dataset$carb == 6))
carb8 <- length(which(Dataset$carb == 8))
carbs <- c(carb1, carb2, carb3, carb4, carb6, carb8)
pcnt <- sprintf("%1.1f%%",100*(carbs/sum(carbs)))
lbl <- c("Carbvalue_1", "Carbvalue_2", "Carbvalue_3", "Carbvalue_4", "Carbvalue_6", "Carbvalue_8")
lbl <- paste(lbl, pcnt, sep="\n")
pie(carbs, labels=lbl, col=rainbow(6), radius=1, main="Car Proportions\n (by carb values)")
Summary: This pie chart shows us the Car proportions by their carb values.Carbvalue_2 and Carbvalue_4 have the largest share of 31.2% followed by Carbvalue_1 21.9%.
- Create a bar graph, that shows the number of each “gear” type in “mtcars”
CarGear=table(Dataset$gear)
barplot(CarGear, xlab="Number of Gears", ylab="Number of Cars", col=rainbow(3))
axis(2,at=seq(0, 15, 1))
Summary:The dataset has most cars with 15 cars having 3 number of gears. 4 and 5Number of gears are to be found in 12 and 5 cars respectively.
- Next show a stacked bar graph of the number of each “gear” type and how they are further divided out by “cyl”.
Cylinder <- table(mtcars$cyl, mtcars$gear)
barplot(Cylinder, main="Car Proportion by Gear and Cylinder",
xlab="Number of Gears",
names.arg=c("3 Gears", "4 Gears", "5 Gears"),
cex.names=1,
ylab="Number of Cars",
col=rainbow(3),
legend=rownames(Cylinder), args.legend=list(title="# of Cylinder"))
axis(2,at=seq(0, 15, 1))
Summary: This chart shows us a step deeper into the car proportions by gear as we can also see the number of cylinders. This is a stacked bar chart which is showing us a 3 dimension visual. It is clearly seen that cars with 3 gears are more likely to have 8 cylinders. Cars with 4 gears have 4 and 6 cylinders. Whereas 5 gear cars are almost evenly distributed in terms of number of cylinders.
- Draw a scatter plot showing the relationship between “wt” and “mpg”.
plot(mtcars$wt, mtcars$mpg, xlab= "Weight", ylab= "Miles per Gallon")
title("Relationship between Weight (wt) and Miles per Gallon (mpg)", line=1)
Summary: The scatter plot shows us the relationship of Weight and Miles per Gallon. Here the relationship shows light weight cars would have greater miles per gallon to consume whereas if the weight increase the mile per gallon reduces respectively.
- Design a visualization of your choice using the data and write a brief summary about why you chose that visualization.
Pie Chart 1:
Transmission <- table(mtcars$am)
lbl2 <- c("Automatic", "Manual")
pcnt2 <- sprintf("%1.2f%%",100*(Transmission/sum(Transmission[1]+Transmission[2])))
lbl2 <- paste(lbl2, pcnt2)
pie(Transmission, lbl2, main="Proportion of Auto and Manual Cars")
Summary: The car’s transmission type was chosen here to tell the proportions of cars (0 = automatic, 1 = manual). Here I’ve made a Pie chart which tells about 59.38% cars in our sample are Automatic where as rest of the 40.62% cars are Manual.
Pie Chart 2:
Engine <- table(mtcars$vs)
lbl3 <- c("V-shaped", "Straight")
pcnt3 <- sprintf("%1.2f%%",100*(Engine/sum(Engine[1]+Engine[2])))
lbl3 <- paste(lbl3, pcnt3)
pie(Engine, lbl3, main="Proportion of V-shaped Vs Straight Engine Types")
Summary: I have tried the same pie chart to look at the Engine type (0 = V-shaped, 1 = straight) The data shows 56.25% cars in the sample have V-shaped engine and 43.75% have Straight engine type.