Chapter 3 Visualization

Data visualization is integral to understanding both data and models. Computational statistics and data science sometimes focus on models or resulting predictions from the models. But there is no doubt that the structure and format of the data are the key to whether or not a model is appropriate or good. A good data analyst will always spend a lot of time and effort on exploratory data analysis, much of which includes making as many visualizations of the data as possible.

3.1 Deconstructing a graph

3.1.1 The Grammar of Graphics (gg)

Yau (2013) and Wickham (2014) have come up with a taxonomy and a grammar for thinking about the parts of a figure just like we conceptualize the parts of a body or the parts of a sentence.

One great way of thinking of the new process: it is not longer necessary to talk about the name of the graph (e.g., boxplot). Instead we now think in glyphs (geoms), and so we can put whatever we want on the plot. Note also that the transition leads you from a passive consumer (I need to make plot XXX because everyone else does, so I just plug in the data) into an active participant (what do I want my data to say? and how can I put that information onto my graphic?)

The most important questions you can ask with respect to creating figures are:

  1. What do we want R to do? (What is the goal?)
  2. What does R need to know?

3.1.1.1 The grammar of graphics in ggplot2

geom: the geometric “shape” used to display data

  • bar, point, line, ribbon, text, etc.

aesthetic: an attribute controlling how geom is displayed with respect to variables

  • x position, y position, color, fill, shape, size, etc.

scale: adjust information in the aesthetic to map onto the plot

  • particular assignment of colors, shapes, sizes, etc.; making axes continuous or constrained to a particular range of values.

guide: helps user convert visual data back into raw data (legends, axes)

stat: a transformation applied to data before geom gets it

  • example: histograms work on binned data

3.1.2 ggplot2

Important note!

While dplyr chains functions using %>%

ggplot chains functions using +

In ggplot2, an aesthetic refers to a mapping between a variable and the information it conveys on the plot. Further information about plotting and visualizing information is given in chapter 2 (Data visualization) of Baumer, Kaplan, and Horton (2017). Much of the data in the presentation represents all births from 1978 in the US: the date, the day of the year, and the number of births.

Goals

What I will try to do

  • give a tour of ggplot2

  • explain how to think about plots the ggplot2 way

  • prepare/encourage you to learn more later

What I can’t do in one session

  • show every bell and whistle

  • make you an expert at using ggplot2

Getting help

  1. One of the best ways to get started with ggplot is to google what you want to do with the word ggplot. Then look through the images that come up. More often than not, the associated code is there. There are also ggplot galleries of images, one of them is here: https://plot.ly/ggplot2/

  2. ggplot2 cheat sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

  3. Look at the end of the presentation. More help options there.

##         date births wday year month day_of_year day_of_month day_of_week
## 1 1978-01-01   7701  Sun 1978     1           1            1           1
## 2 1978-01-02   7527  Mon 1978     1           2            2           2
## 3 1978-01-03   8825  Tue 1978     1           3            3           3
How can we make the plot?

Two Questions:

  1. What do we want R to do? (What is the goal?)

  2. What does R need to know?

    • data source: Births78

    • aesthetics:

      • date -> x
      • births -> y
      • points (!)

  3. Goal: scatterplot = a plot with points

    • ggplot() + geom_point()

  4. What does R need to know?

    • data source: data = Births78

    • aesthetics: aes(x = date, y = births)

How can we make the plot?

What has changed?

  • new aesthetic: mapping color to day of week
Adding day of week to the data set

The wday() function in the lubridate package computes the day of the week from a date.

How can we make the plot?

Now we have two layers, one with points and one with lines

  • The layers are placed one on top of the other: the points are below and the lines are above.

  • data and aes specified in ggplot() affect all geoms

What does adding the color argument do?

Because there is no variable, we have mapped the color aesthetic to a new variable with only one value (“navy”). So all the dots get set to the same color, but it’s not navy.

Setting vs. Mapping

If we want to set the color to be navy for all of the dots, we do it outside the aesthetic, without a dataset variable:

  • Note that color = "navy" is now outside of the aesthetics list. That’s how ggplot2 distinguishes between mapping and setting.
How can we make the plot?

  • ggplot() establishes the default data and aesthetics for the geoms, but each geom may change the defaults.

  • good practice: put into ggplot() the things that affect all (or most) of the layers; rest in geom_blah()

Setting vs. Mapping (again)

Information gets passed to the plot via:

  1. map the variable information inside the aes (aesthetic) command

  2. set the non-variable information outside the aes (aesthetic) command

Other geoms
 [1] "geom_abline"      "geom_area"        "geom_ash"        
 [4] "geom_bar"         "geom_barh"        "geom_bin2d"      
 [7] "geom_blank"       "geom_boxplot"     "geom_boxploth"   
[10] "geom_col"         "geom_colh"        "geom_contour"    
[13] "geom_count"       "geom_crossbar"    "geom_crossbarh"  
[16] "geom_curve"       "geom_density"     "geom_density_2d" 
[19] "geom_density2d"   "geom_dotplot"     "geom_errorbar"   
[22] "geom_errorbarh"   "geom_errorbarh"   "geom_freqpoly"   
[25] "geom_hex"         "geom_histogram"   "geom_histogramh" 
[28] "geom_hline"       "geom_jitter"      "geom_label"      
[31] "geom_line"        "geom_linerange"   "geom_linerangeh" 
[34] "geom_lm"          "geom_map"         "geom_path"       
[37] "geom_point"       "geom_pointrange"  "geom_pointrangeh"
[40] "geom_polygon"     "geom_qq"          "geom_qq_line"    
[43] "geom_quantile"    "geom_raster"      "geom_rect"       
[46] "geom_ribbon"      "geom_rug"         "geom_segment"    
[49] "geom_sf"          "geom_sf_label"    "geom_sf_text"    
[52] "geom_smooth"      "geom_spline"      "geom_spoke"      
[55] "geom_step"        "geom_text"        "geom_tile"       
[58] "geom_violin"      "geom_violinh"     "geom_vline"

help pages will tell you their aesthetics, default stats, etc.

Let’s try geom_area

Using area does not produce a good plot

  • over plotting is hiding much of the data
  • extending y-axis to 0 may or may not be desirable.
Side note: what makes a plot good?

Most (all?) graphics are intended to help us make comparisons

  • How does something change over time?
  • Do my treatments matter? How much?
  • Do men and women respond the same way?

Key plot metric: Does my plot make the comparisons I am interested in

  • easily, and
  • accurately?
Time for some different data

HELPrct: Health Evaluation and Linkage to Primary care randomized clinical trial

##   age anysubstatus anysub cesd d1 daysanysub dayslink drugrisk e2b female
## 1  37            1    yes   49  3        177      225        0  NA      0
## 2  37            1    yes   30 22          2       NA        0  NA      0
## 3  26            1    yes   39  0          3      365       20  NA      0
## 4  39            1    yes   15  2        189      343        0   1      1
## 5  32            1    yes   39 12          2       57        0   1      0
## 6  47            1    yes    6  1         31      365        0  NA      1
##      sex g1b homeless i1 i2 id indtot linkstatus link       mcs      pcs
## 1   male yes   housed 13 26  1     39          1  yes 25.111990 58.41369
## 2   male yes homeless 56 62  2     43         NA <NA> 26.670307 36.03694
## 3   male  no   housed  0  0  3     41          0   no  6.762923 74.80633
## 4 female  no   housed  5  5  4     28          0   no 43.967880 61.93168
## 5   male  no homeless 10 13  5     38          1  yes 21.675755 37.34558
## 6 female  no   housed  4  4  6     29          0   no 55.508991 46.47521
##   pss_fr racegrp satreat sexrisk substance treat avg_drinks max_drinks
## 1      0   black      no       4   cocaine   yes         13         26
## 2      1   white      no       7   alcohol   yes         56         62
## 3     13   black      no       2    heroin    no          0          0
## 4     11   white     yes       4    heroin    no          5          5
## 5     10   black      no       6   cocaine    no         10         13
## 6      5   black      no       5   cocaine   yes          4          4

Subjects admitted for treatment for addiction to one of three substances.

Who are the people in the study?

  • Hmm. What’s up with y?

    • stat_bin() is being applied to the data before the geom_bar() gets to do its thing. Binning creates the y values.
How old are people in the HELP study?
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Notice the messages

  • stat_bin: Histograms are not mapping the raw data but binned data.
    stat_bin() performs the data transformation.

  • binwidth: a default binwidth has been selected, but we should really choose our own.

Selecting stat and geom manually

Every geom comes with a default stat

  • for simple cases, the stat is stat_identity() which does nothing
  • we can mix and match geoms and stats however we like

Selecting stat and geom manually

Every stat comes with a default geom, every geom with a default stat

  • we can specify stats instead of geom, if we prefer
  • we can mix and match geoms and stats however we like

Boxplots

Boxplots use stat_quantile() which computes a five-number summary (roughly the five quartiles of the data) and uses them to define a “box” and “whiskers”.

The quantitative variable must be y, and there must be an additional x variable.

Horizontal boxplots

Horizontal boxplots are obtained by flipping the coordinate system:

  • coord_flip() may be used with other plots as well to reverse the roles of x and y on the plot.
Give me some space

We’ve triggered a new feature: dodge (for dodging things left/right). We can control how much if we set the dodge manually.

Issues with bigger data
## [1] 10000    76

  • Although we can see a generally positive association (as we would expect), the over plotting may be hiding information.

Want to learn more?

What else can we do?

shiny

plotly

Plotly is an R package for creating interactive web-based graphs via plotly’s JavaScript graphing library, plotly.js. The plotly R library contains the ggplotly function , which will convert ggplot2 figures into a Plotly object. Furthermore, you have the option of manipulating the Plotly object with the style function.

Dynamic documents

  • combination of RMarkdown, ggvis, and shiny

References

Baumer, Kaplan, and Horton. 2017. Modern Data Science with R. CRC Press. https://mdsr-book.github.io/.

Wickham, Hadley. 2014. “Tidy Data.” Journal of Statistical Software 59 (10). http://www.jstatsoft.org/v59/i10/paper.

Yau, Nathan. 2013. Data Points: Visualization That Means Something. Wiley.