Module # 6 Visual Differences & Deviation Analysis via R

 

Visual Differences and Deviation Analysis via R

1) This week I make a basic graphic in R. I have done a lot of graphics in R in the past. Tons of them. The only difference is that this time, I know exactly what to look for when constructing them to create the most effective visual for the data that I am presenting. This understanding of mine has been enhanced by this weeks lesson. 

2) What I am going to be focusing on with this graphic is that it is clear, has a lie factor of 1, no chart junk, and I used the best graphic for the information I want to convey. To do this I am going to use something very simple and readily available to me, aka something that is free; the mtcars data base in RStudio. 

3) I am going to make bar chart displaying the number of cars that have different types of cylinders in their engines in the mtcars dataset. Although this graphic won't be very in-depth, it sheds vital light on a database that comes with RStudio, and a dataset that most students use often when understanding how to filter, manipulate and pull data. 

> #Creating a Perfect graphic in R > #See what we are working with > 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 1 > mtcars 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 2 > > #making the graph > ggplot(mtcars, aes(x = factor(cyl))) + + geom_bar(fill = "steelblue", color = "black") + + labs(title = "Number of Cars by Cylinder Count (mtcars dataset)", + x = "Number of Cylinders", + y = "Number of Cars") + + scale_y_continuous(breaks = seq(0, 15, by = 1), + labels = function(x) paste0(x, " cars")) + + theme_minimal() + + theme(plot.title = element_text(hjust = 0.5)) >

4) Above is the console and code used to create the following chart. You are free to use this code as well when practicing with mtcars in your free time. 

5) The graphic above has a clear and detailed title, a lie factor that is equal to 1, no chart junk and I believe is the best graph that can be used to display this data. We get a clear picture comparing the number of cars that have which cylinders in this dataset and can see that they are not even, eight cylinders have the most and six cylinders have the least. Despite most of the cars on the road having only 4 cylinders and very few having 8, we can see that there was some deviation from the norm in the creation of the mtcars dataset. 

6) If you would like to view this code on my GitHub you may do so here.

7) The last thing we have to do is to think about how this fits into Few's and Yau's discussions on how to conduct basic visualization based on simple descriptive analysis. Looking at the graph, it aligns well with Stephen Few’s and Nathan Yau’s visualization principles. Using a clear bar chart, appropriate for showing car counts by cylinder, and the labels and title provide necessary context, fitting Few’s focus on clarity and simplicity. The minimal design and scant use of color reflect on Yau’s sense of aesthetics and function.