During this project I will be experimenting with the use of R Markdown in order to publish data reports while also developing a greater understanding on how to plot data maximising presentability.

I will be using data from R’s mtcars database:

car

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

Basics of the plot() function:

Plotting Scatter Diagrams in customisable ways:

Basic Scatterplot:

Using GGPlot to create a more presentable Scatterplots:

GGPlot allows you to customise the appearance of points and labels. For example their colour, shape, size and transparency. In addition to this you can add layers to charts such as plotting a regression line using the geom smooth function or increasing the number of variables. The package also comes with built in customisable themes to control the overall appearance of plots.

  # plot scatter diagram
  ggplot(mtcars, aes(x = hp, y = mpg)) +
  # customise appearance of points and labels
  geom_point(shape = 10, color = "darkgreen", size = 3, alpha = 2) + 
  labs(title = "Effect of Horsepower on Miles per Gallon (MPG)",
       x = "Horsepower", y = "MPG") +  
  theme(
    plot.title = element_text(size = 13, face = "bold", hjust = 0.5), 
    axis.title = element_text(size = 11, hjust = 0.5), 
    axis.text = element_text(size = 9, hjust = 0.5)) +
  # add regression line
    geom_smooth(method = "lm", se = F, color ="grey") +
     annotate("text", x = 150, y = 22, 
           label = "Regression Line", color = "Black", size = 3, fontface = "bold") 
## `geom_smooth()` using formula = 'y ~ x'