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Do cars with big engines use more fuel than cars with small engines?

Hypothesis: Cars with bigger engines use more fuel, i.e. the fuel efficiency declines as the engine size gets bigger. If miles per gallon was on the y-axis and engine size on the x-axis we would see a decreasing trend.

# A tibble: 234 x 11
   manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    class
   <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> <chr>
 1 audi         a4         1.8  1999     4 auto(l~ f        18    29 p     comp~
 2 audi         a4         1.8  1999     4 manual~ f        21    29 p     comp~
 3 audi         a4         2    2008     4 manual~ f        20    31 p     comp~
 4 audi         a4         2    2008     4 auto(a~ f        21    30 p     comp~
 5 audi         a4         2.8  1999     6 auto(l~ f        16    26 p     comp~
 6 audi         a4         2.8  1999     6 manual~ f        18    26 p     comp~
 7 audi         a4         3.1  2008     6 auto(a~ f        18    27 p     comp~
 8 audi         a4 quat~   1.8  1999     4 manual~ 4        18    26 p     comp~
 9 audi         a4 quat~   1.8  1999     4 auto(l~ 4        16    25 p     comp~
10 audi         a4 quat~   2    2008     4 manual~ 4        20    28 p     comp~
# ... with 224 more rows
# create coordinate system
ggplot(data = mpg, aes(x = displ,
                       y = hwy))

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ,
                           y = hwy))

My hypothesis has been confirmed.

num_rows <- nrow(mtcars)
num_cols <- ncol(mtcars)
ex4_plot <- ggplot(data = mpg,
                   aes(x = hwy,
                       y = cyl)) +
  1. Run ggplot(data = mpg). What do you see?
    Ans: An empty canvas of a plot. If you add the aes(x = xx, y = yy) you will see an empty canvas with the axes drawn.
  2. How many rows are there in mtcars? Columns?
    Ans: Number of rows is 32, cols is 11.
  3. What does the drv variable describe? Ans: ‘The type of drive train, where f = front-wheel drive, r = rear wheel drive, 4 = 4wd’
  4. Make a scatterplot of hwy versus cyl.

  1. What happens if you make a scatterplot of class versus drv? Why is this plot not useful?
ggplot(data = mpg, aes(x = class, 
                       y = drv)) +

These are 2 categorical variables here so this isn’t very useful.

You can give extra information about your dataset by mapping data to aesthetics like size, colour, shape.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy,
                           colour = class))

When you put a feature against an aesthetic ggplot will assign a unique level (here colour to each class of the feature) -> this process is called scaling.

ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy,
                 size = class))

Mapping a unordered variable like class to an ordered aesthetic like size is not a good idea, and we get a warning here.

ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy,
                 alpha = class))

alpha is another aesthetic that we can use.

ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy,
                 shape = class))

Warning: The shape palette can deal with a maximum of 6 discrete values because
more than 6 becomes difficult to discriminate; you have 7. Consider
specifying shapes manually if you must have them.

Warning: Removed 62 rows containing missing values (geom_point).

ggplot only uses 6 shapes at a time, so we’re missing suv! When mapping to aesthetics think carefully about which aesthetic makes sense.

If you look at the colour = class plot vs the shape = class plot you may well think - it looks like previously labelled suv is now being considered to be pickup!? There are many points and the points get plotted on top of each other - if you look near displ = 5 and hwy = 12 notice that there is an pickup point there … previously there was an suv point there. Let’s jitter the data so that the points lie a little away from each other to show that there’s not any issue with ggplot, instead the data lying on top of each other shows the last class.

ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy,
                 colour = class))

ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy,
                 shape = class))

ggplot(data = mpg) +
  geom_jitter(aes(x = displ, y = hwy,
                 colour = class))

ggplot(data = mpg) +
  geom_jitter(aes(x = displ, y = hwy,
                 shape = class))

Occassionally you may want to change all the points sizes uniformly (irrespective of any other data feature) - in this case you may put the aesthetic on the outside of the aes().

ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy),
             colour = "blue")

ggplot(data = mpg) +
  geom_point(aes(x = displ,
                 y = hwy),
             size = 4)

  1. What’s gone wrong with this code? Why are the points not blue?
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ,
                           y = hwy, color = "blue"))

The color attribute maps to a static colour and yet appears within aes().

  1. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

manufacturer, model, cyl, trans, drv, fl and class are categories; displ, year, cty, hwy are continuous. You can convert mpg to a tibble (if it is not already) and check the types using glimpse().

Rows: 234
Columns: 11
$ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi"...
$ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro"...
$ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0,...
$ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, ...
$ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, ...
$ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "a...
$ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4",...
$ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17...
$ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25...
$ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p",...
$ class        <chr> "compact", "compact", "compact", "compact", "compact",...
  1. Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?
ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy, 
                 colour = cyl))

ggplot(data = mpg) +
  geom_point(aes(x = displ, y = hwy, 
                 colour = cty))

The colours are kinda scaled from the lowest to the highest.

R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)

Matrix products: default

[1] LC_COLLATE=English_South Africa.1252  LC_CTYPE=English_South Africa.1252   
[3] LC_MONETARY=English_South Africa.1252 LC_NUMERIC=C                         
[5] LC_TIME=English_South Africa.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] flair_0.0.2     forcats_0.5.0   stringr_1.4.0   dplyr_1.0.0    
 [5] purrr_0.3.4     readr_1.3.1     tidyr_1.1.0     tibble_3.0.3   
 [9] ggplot2_3.3.0   tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.0 xfun_0.13        haven_2.2.0      lattice_0.20-38 
 [5] colorspace_1.4-1 vctrs_0.3.2      generics_0.0.2   htmltools_0.4.0 
 [9] yaml_2.2.1       utf8_1.1.4       rlang_0.4.7      later_1.0.0     
[13] pillar_1.4.6     withr_2.2.0      glue_1.4.1       DBI_1.1.0       
[17] dbplyr_1.4.3     modelr_0.1.6     readxl_1.3.1     lifecycle_0.2.0 
[21] munsell_0.5.0    gtable_0.3.0     cellranger_1.1.0 rvest_0.3.5     
[25] evaluate_0.14    labeling_0.3     knitr_1.28       httpuv_1.5.2    
[29] fansi_0.4.1      broom_0.5.6      Rcpp_1.0.4.6     promises_1.1.0  
[33] backports_1.1.6  scales_1.1.0     jsonlite_1.7.0   farver_2.0.3    
[37] fs_1.4.1         hms_0.5.3        digest_0.6.25    stringi_1.4.6   
[41] grid_3.6.3       rprojroot_1.3-2  cli_2.0.2        tools_3.6.3     
[45] magrittr_1.5     crayon_1.3.4     whisker_0.4      pkgconfig_2.0.3 
[49] ellipsis_0.3.1   xml2_1.3.2       reprex_0.3.0     lubridate_1.7.8 
[53] rstudioapi_0.11  assertthat_0.2.1 rmarkdown_2.1    httr_1.4.2      
[57] R6_2.4.1         nlme_3.1-144     git2r_0.26.1     compiler_3.6.3