ggplot

extended and beyond…

1 GGPLOT Extended

1.0.1 Load data and libraries

We will load the following packages

• Tidyverse (we will use tidyr, dplyr, and ggplot)

• here (to pull the data from its absolute path)

We will continue using our “data_to_explore.csv” file. This is the one that we wrangled from three different data frames; log files, grade data and survey data originally form Macfadyen, L. P., & Dawson, S. (2010). “Mining LMS data to develop an early warning system for educators: A proof of concept.”

# A tibble: 943 × 34
   student_id subject semester section total_po…¹ total…² propo…³ gender enrol…⁴
   <chr>      <chr>   <chr>    <chr>        <dbl>   <dbl>   <dbl> <chr>  <chr>  
 1 43146      FrScA   S216     02            1217   1150   0.945  F      Course…
 2 44638      OcnA    S116     01            1676   1384.  0.826  M      Course…
 3 47448      FrScA   S216     01            1232   1116   0.906  F      Other  
 4 47979      OcnA    S216     01            1833   1493.  0.814  M      Course…
 5 48797      PhysA   S116     01            2225   1995.  0.897  M      Learni…
 6 51943      FrScA   S216     03            1222     70   0.0573 M      Learni…
 7 52326      AnPhA   S216     01            1775   1519.  0.855  F      Other  
 8 52446      PhysA   S116     01            2225   2198   0.988  F      Learni…
 9 53447      FrScA   S116     01            1212   1173   0.968  F      Course…
10 53475      FrScA   S116     02            1212      0   0      F      Course…
# … with 933 more rows, 25 more variables: enrollment_status <chr>,
#   time_spent <dbl>, time_spent_hours <dbl>, course_id <chr>, int <dbl>,
#   val <dbl>, percomp <dbl>, tv <dbl>, q1 <dbl>, q2 <dbl>, q3 <dbl>, q4 <dbl>,
#   q5 <dbl>, q6 <dbl>, q7 <dbl>, q8 <dbl>, q9 <dbl>, q10 <dbl>, date_x <dttm>,
#   post_int <dbl>, post_uv <dbl>, post_tv <dbl>, post_percomp <dbl>,
#   date_y <dttm>, date <dttm>, and abbreviated variable names
#   ¹​total_points_possible, ²​total_points_earned, ³​proportion_earned, …

2 Wrangle the data frame

I dropped NA’s in proportion earned and time spent in hours. I also rename the subject names to their full course name. Finally, I created a whole grade point from the proportion earned by multiplying the percentage points by 100.

3 Visualizations

3.1 Grouping features(variables)

To group together features you must have a categorical feature - factor or character class. If it is not, then you will need to convert using ‘as. character()’

3.1.1 Group by subset and change graph scale

Here we are going to subset right in ggplot to only look at 3 subjects trend lines compared to the trend line of all of the subjects, specifically the points earned for amount of time spend in the online LMS.

3.1.2 Add a shape

Below we are grouping by gender to show the gender we will use filled triangles and filled circles. Shape numbers & symbols that are shown in the R Graphics book.

Shape numbers from R Graphics book

3.1.3 Specify shape with scale_shape_manual

Here we are adding a new function so that we can choose the shape

4 Use Plotly

We can make our graphs interactive with the Plotly package. Use your mouse to scroll over the data points. We see the the hours spent by the grade earned.

4.1 Add Color to the Plotly graph

Just like with ggplot we can color our data points by variable. We can see that the subjects are denoted by color.

4.2 Use Shape

Again, like in ggplot you can parse by a shape style.

4.3 Create interactive with ggplot object

We will use our originally saved gg_viz data and change to a plotly interactive object.

5 Annotate

We will now learn to annotate on the graph. Below you will see that we can use the annotate() to write out information. By including you can go to the next line.

5.1 Draw an arrow

You can draw an arrow calling the x and y and always including xend and yend.

6 Annotations with {ggforce} - the next session was adopted RLadies of Frieborn

This package offers some more annotation options: - geom_mark_rect() encloses the data in the smallest enclosing rectangle - geom_mark_circle() encloses the data in the smallest enclosing circle - geom_mark_ellipse() encloses the data in the smallest enclosing ellipse - geom_mark_hull() encloses the data with a concave or convex hull - the {concaveman} package is required for this geom

6.1 filter by Anatomy by gender

For this part, we’ll look at data about almost 1800 films and whether they pass the Bechdel test (i.e.: are there two named female characters in the film who talk to each other about a topic that’s not a man):

6.2 Labelling single data points

If we want to label specific data points, we can filter them within geom_mark_circle(). The label will be the film’s title, but we can also add a description, for which we’ll pick the plot summary:

6.3 One more

See the blue point that is higher than all the rest, between year 2000 and 2010? Find the title of that film using a filter command (hint, use the mov df and the columns budget and clean_test), then add a circle to the plot that identifies it by title. If you have time, also find the highest yellow dot between 1975 and 1980, and circle it too!

# A tibble: 2 × 34
   year imdb     title test  clean…¹ binary budget domgr…² intgr…³ code  budge…⁴
  <dbl> <chr>    <chr> <chr> <fct>   <chr>   <dbl> <chr>     <dbl> <chr>   <dbl>
1  2009 tt04995… Avat… men-… the wo… FAIL      425 760507…  2.78e9 2009…  4.61e8
2  2007 tt04490… Pira… ok    pass!   PASS      300 309420…  9.61e8 2007…  3.37e8
# … with 23 more variables: domgross_2013 <chr>, intgross_2013 <chr>,
#   period_code <dbl>, decade_code <dbl>, imdb_id <chr>, plot <chr>,
#   rated <chr>, response <lgl>, language <chr>, country <chr>, writer <chr>,
#   metascore <dbl>, imdb_rating <dbl>, director <chr>, released <chr>,
#   actors <chr>, genre <chr>, awards <chr>, runtime <chr>, type <chr>,
#   poster <chr>, imdb_votes <dbl>, error <lgl>, and abbreviated variable names
#   ¹​clean_test, ²​domgross, ³​intgross, ⁴​budget_2013

# A tibble: 2 × 34
   year imdb     title test  clean…¹ binary budget domgr…² intgr…³ code  budge…⁴
  <dbl> <chr>    <chr> <chr> <fct>   <chr>   <dbl> <chr>     <dbl> <chr>   <dbl>
1  1978 tt00783… Supe… nota… the wo… FAIL     55   134218…  3.00e8 1978…  1.96e8
2  1971 tt00677… Shaft nota… the wo… FAIL     53.0 703278…  1.07e8 1971…  3.05e8
# … with 23 more variables: domgross_2013 <chr>, intgross_2013 <chr>,
#   period_code <dbl>, decade_code <dbl>, imdb_id <chr>, plot <chr>,
#   rated <chr>, response <lgl>, language <chr>, country <chr>, writer <chr>,
#   metascore <dbl>, imdb_rating <dbl>, director <chr>, released <chr>,
#   actors <chr>, genre <chr>, awards <chr>, runtime <chr>, type <chr>,
#   poster <chr>, imdb_votes <dbl>, error <lgl>, and abbreviated variable names
#   ¹​clean_test, ²​domgross, ³​intgross, ⁴​budget_2013

7 Blurring data points with {ggfx}

This package offers a variety of filters - here’s an overview: https://www.r-bloggers.com/2021/03/say-goodbye-to-good-taste/

We’ll use the with_blur() command here. To draw attention to a specific group within the data, we can draw circles as we did earlier, but alternatively, we can blur the data we don’t want to highlight. To achieve that, we need to wrap the geom_jitter() command in with_blur():

We can also blur other plot elements in theme:

Code

#| echo: false
#| warning: false
#| message: false


bechdel +
  theme(plot.caption = with_blur(element_text(), sigma = 2))

Code

bechdel +
  theme(legend.text = with_blur(element_text(), sigma = 2))

8 Zooming in

Finally, we might want to zoom in on a range or a group of data points. The facet_zoom() command achieves just that: