Problem Set 1
Visualization Process
Pranita Patil
September 1, 2017
Questions
Find the mtcars data in R. This is the dataset that you will use to create your graphics. Use that data to draw by hand graphics for the next 4 questions.
# Data
data("mtcars")
head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1summary(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. :8.0001. Draw a pie chart showing the proportion of cars from the mtcars data set that have different carb values.
# Calculations to draw pie chart
# calculate frequency of different carb values for mtcars dataset
t_cars<-table(mtcars$carb)
# calculate the proporation of cars
total=sum(as.vector(t_cars))
percentage=(as.vector(t_cars)) *100/total
# calculate degrees for pie chart
degrees=(as.vector(t_cars)) *360/total
t_cars_new <- rbind(as.numeric(names(t_cars)), t_cars)
rownames(t_cars_new) <- c("Carb", "Count")
# add proportion and degrees rows in the same table
t_cars_new <- as.data.frame(t_cars_new)
t_cars_new <- rbind(t_cars_new,percentage)
t_cars_new <- rbind(t_cars_new,degrees)
rownames(t_cars_new) <- c("Carb", "Count","Percentage", "Degree")
t_cars_new## 1 2 3 4 6 8
## Carb 1.000 2.00 3.000 4.00 6.000 8.000
## Count 7.000 10.00 3.000 10.00 1.000 1.000
## Percentage 21.875 31.25 9.375 31.25 3.125 3.125
## Degree 78.750 112.50 33.750 112.50 11.250 11.250knitr::include_graphics("E:\\Harrisburg courses\\sem 6-Fall 2017\\Data Visualization\\lecture1\\Question_1-edit.jpg")
2. Draw a bar graph, that shows the number of each gear type in mtcars.
# Calculate the number of each gear type by creating table using one entry gear
g_cars <- table(mtcars$gear)
g_cars_new <- rbind(as.numeric(names(g_cars)), g_cars)
rownames(g_cars_new) <- c("Gear", "Count")
g_cars_new## 3 4 5
## Gear 3 4 5
## Count 15 12 5knitr::include_graphics("E:\\Harrisburg courses\\sem 6-Fall 2017\\Data Visualization\\lecture1\\Question_2-edit.jpg")
3. Next show a stacked bar graph of the number of each gear type and how they are further divded out by cyl.
# Calculate the number of each gear type for each cyl by creating table with two entries gear and cyl
gc_cars <- table(mtcars$gear,mtcars$cyl)
names(dimnames(gc_cars)) <- c("gear", "cyl")
gc_cars## cyl
## gear 4 6 8
## 3 1 2 12
## 4 8 4 0
## 5 2 1 2knitr::include_graphics("E:\\Harrisburg courses\\sem 6-Fall 2017\\Data Visualization\\lecture1\\Question_3-edit.jpg")
4. Draw a scatter plot showing the relationship between wt and mpg.
# Extract only wt and mpg columns from dataset to draw a scatter plot
wm_cars <- subset(mtcars, select=c("wt", "mpg"))
wm_cars## wt mpg
## Mazda RX4 2.620 21.0
## Mazda RX4 Wag 2.875 21.0
## Datsun 710 2.320 22.8
## Hornet 4 Drive 3.215 21.4
## Hornet Sportabout 3.440 18.7
## Valiant 3.460 18.1
## Duster 360 3.570 14.3
## Merc 240D 3.190 24.4
## Merc 230 3.150 22.8
## Merc 280 3.440 19.2
## Merc 280C 3.440 17.8
## Merc 450SE 4.070 16.4
## Merc 450SL 3.730 17.3
## Merc 450SLC 3.780 15.2
## Cadillac Fleetwood 5.250 10.4
## Lincoln Continental 5.424 10.4
## Chrysler Imperial 5.345 14.7
## Fiat 128 2.200 32.4
## Honda Civic 1.615 30.4
## Toyota Corolla 1.835 33.9
## Toyota Corona 2.465 21.5
## Dodge Challenger 3.520 15.5
## AMC Javelin 3.435 15.2
## Camaro Z28 3.840 13.3
## Pontiac Firebird 3.845 19.2
## Fiat X1-9 1.935 27.3
## Porsche 914-2 2.140 26.0
## Lotus Europa 1.513 30.4
## Ford Pantera L 3.170 15.8
## Ferrari Dino 2.770 19.7
## Maserati Bora 3.570 15.0
## Volvo 142E 2.780 21.4knitr::include_graphics("E:\\Harrisburg courses\\sem 6-Fall 2017\\Data Visualization\\lecture1\\Question_4-edit.jpg")
5. Design a visualization of your choice using the data.
# Histogram
# Disp range is from 71 to 472. Plotted histogram to visualize disp. Bin size is selected as 50.
# Divide total data range into bin size of 50.
Range_disp <- cut(mtcars$disp, breaks=seq(0,500, by=50))
# Calculate total count/freqeuncy for each bin/range
as.data.frame(table(Range_disp))## Range_disp Freq
## 1 (0,50] 0
## 2 (50,100] 5
## 3 (100,150] 7
## 4 (150,200] 4
## 5 (200,250] 1
## 6 (250,300] 4
## 7 (300,350] 4
## 8 (350,400] 4
## 9 (400,450] 1
## 10 (450,500] 2knitr::include_graphics("E:\\Harrisburg courses\\sem 6-Fall 2017\\Data Visualization\\lecture1\\Question_5-edit.jpg")