Pieces of the Graph

Order Matters

Cues Together

Advice for Plotting

Advice for Plotting

Advice for Plotting

Advice for Plotting

Advice for Plotting

Advice for Plotting

Advice for Plotting

What are the visual cues on this plot?

What are the visual cues on this plot?

What are the visual cues on this plot?

The grammar of graphics ggplot

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.

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

Set up

library(mosaic)
data(Births78)

head(Births78) 

How do we make this plot?

ggplot(data = Births78, 
       aes(x = date, y = births)) + 
  geom_point() +
  labs(title = "US Births in 1978")

ggplot() +
  geom_point(data = Births78, 
             aes(x = date, y = births)) +
  labs(title = "US Births in 1978")

Layers

Layer 1

ggplot(data = Births78, 
       aes(x = date, y = births)) 

Layers

Layer 2

ggplot(data = Births78, 
       aes(x = date, y = births)) + 
  geom_point()

Layers

Layer 3

ggplot(data = Births78, 
       aes(x = date, y = births)) + 
  geom_point() +
  labs(title = "US Births in 1978")

How do we make this plot?

How do we make this plot?

How do we make this plot?

ggplot(data = Births78,
       aes(x = date,
           y = births, 
           color = wday)) +
  geom_point() +
  labs(title = "US Births in 1978")

How do we make this plot?

How do we make this plot?

ggplot(data = Births78,
         aes(x = date, 
             y = births,
             color = wday)) +
  geom_line() +
  labs(title = "US Births in 1978")

How do we make this plot?

How do we make this plot?

ggplot(data = Births78,
       aes(x = date,
           y = births,
           color = wday)) + 
  geom_point() +  
  geom_line() +
  labs(title = "US Births in 1978")

What does this code do?

ggplot(data = Births78,
       aes(x = date, y = births, color = "navy")) + 
  geom_point() +
  labs(title = "US Births in 1978") 

What does this code do?

ggplot(data = Births78,
       aes(x = date, y = births, color = "navy")) + 
  geom_point()  +
  labs(title = "US Births in 1978")

This is mapping 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 aes() designation:

ggplot(data = Births78,
       aes(x = date, y = births)) +   # map variables 
  geom_point(color = "navy")      +   # set attributes
  labs(title = "US Births in 1978")

• Note that color = "navy" is now outside of the aesthetics list. aes() is how ggplot2 distinguishes between mapping and setting.

How do we make this plot?

How do we make this plot?

ggplot(data = Births78,
       aes(x = date,
           y = births)) + 
  geom_line(aes(color = wday)) +      
  geom_point(color = "navy")  +         
  labs(title = "US Births in 1978")

Other geoms

apropos("^geom_")

 [1] "geom_abline"            "geom_area"              "geom_ash"              
 [4] "geom_bar"               "geom_bin_2d"            "geom_bin2d"            
 [7] "geom_blank"             "geom_boxplot"           "geom_col"              
[10] "geom_contour"           "geom_contour_filled"    "geom_count"            
[13] "geom_crossbar"          "geom_curve"             "geom_density"          
[16] "geom_density_2d"        "geom_density_2d_filled" "geom_density2d"        
[19] "geom_density2d_filled"  "geom_dotplot"           "geom_errorbar"         
[22] "geom_errorbarh"         "geom_freqpoly"          "geom_function"         
[25] "geom_hex"               "geom_histogram"         "geom_hline"            
[28] "geom_jitter"            "geom_label"             "geom_line"             
[31] "geom_linerange"         "geom_lm"                "geom_map"              
[34] "geom_path"              "geom_point"             "geom_pointrange"       
[37] "geom_polygon"           "geom_qq"                "geom_qq_line"          
[40] "geom_quantile"          "geom_rangeframe"        "geom_raster"           
[43] "geom_rect"              "geom_ribbon"            "geom_rug"              
[46] "geom_segment"           "geom_sf"                "geom_sf_label"         
[49] "geom_sf_text"           "geom_smooth"            "geom_spline"           
[52] "geom_spoke"             "geom_step"              "geom_text"             
[55] "geom_tile"              "geom_tufteboxplot"      "geom_violin"           
[58] "geom_vline"            

Other geoms

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

?geom_area             # for example

Let’s try geom_area

ggplot(data = Births78,
       aes(x = date,
           y = births, 
           fill = wday)) + 
  geom_area() +
  labs(title = "US Births in 1978")

Let’s try geom_area

ggplot(data = Births78,
       aes(x = date, y = births, fill = wday)) + 
  geom_area() +
  labs(title = "US Births in 1978")

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 treatment and control respond the same way?

Time for some different data

HELPrct: Health Evaluation and Linkage to Primary care randomized clinical trial. Subjects admitted for treatment for addiction to one of three substances.

head(HELPrct)

Who are the people in the study?

ggplot(data = HELP_data,
       aes(x = substance)) + 
  geom_bar() +
  labs(title = "HELP trial")

Who are the people in the study?

ggplot(data = HELP_data,
       aes(x = substance,
           fill = children)) + 
  geom_bar() +
  labs(title = "HELP trial")

Who are the people in the study?

ggplot(HELP_data,
       aes(x = substance,
           fill = children)) + 
  geom_bar(position = "fill") +
  labs(title = "HELP trial",
       y = "actually, percent")

How old are people in the HELP study?

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_histogram() +
  labs(title = "HELP trial")

Setting the binwidth manually

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_histogram(binwidth=2) +
  labs(title = "HELP trial")

How old are people in the HELP study? – Other geoms

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_freqpoly(binwidth=2) +
  labs(title = "HELP clinical trial at detoxification unit")

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_density() +
  labs(title = "HELP clinical trial at detoxification unit")

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

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_line(stat = "density") +
  labs(title = "HELP clinical trial at detoxification unit")

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

ggplot(data = HELP_data,
       aes(x = age)) + 
  stat_density(geom = "line") +
  labs(title = "HELP clinical trial at detoxification unit")

More combinations

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_point(stat = "bin", binwidth=3) + 
  geom_line(stat = "bin", binwidth=3)  +
  labs(title = "HELP clinical trial at detoxification unit")

More combinations

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_area(stat = "bin", binwidth=3)  +
  labs(title = "HELP clinical trial at detoxification unit")

More combinations

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_point(stat = "bin", 
             binwidth=3, 
             aes(size = ..count..)) +
  geom_line(stat = "bin", binwidth=3) +
  labs(title = "HELP clinical trial at detoxification unit")

How much drinking? (i1)

ggplot(data = HELP_data,
       aes(x = i1)) + geom_histogram() +
  labs(title = "HELP clinical trial at detoxification unit")

How much drinking? (i1)

ggplot(data = HELP_data,
       aes(x = i1)) + 
  geom_density() +
  labs(title = "HELP clinical trial at detoxification unit")

How much drinking? (i1)

ggplot(data = HELP_data, 
       aes(x = i1)) + 
  geom_area(stat = "density") +
  labs(title = "HELP clinical trial at detoxification unit")

Covariates: Adding in more variables

Using color and linetype:

ggplot(data = HELP_data,
       aes(x = i1,
           color = substance,
           linetype = children)) + 
  geom_line(stat = "density") +
  labs(title = "HELP clinical trial at detoxification unit")

Using color and facets

ggplot(data = HELP_data,
       aes(x = i1, color = substance)) + 
  geom_line(stat = "density") + 
  facet_grid( . ~ children ) +
  labs(title = "HELP clinical trial at detoxification unit")

ggplot(data = HELP_data,
       aes(x = i1, color = substance)) + 
  geom_line(stat = "density") + 
  facet_grid( children ~ . ) +
  labs(title = "HELP clinical trial at detoxification unit")

Boxplots

Boxplots use stat_quantile() (five number summary).

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

ggplot(data = HELP_data,
       aes(x = substance, y = age, color = children)) + 
  geom_boxplot() +
  labs(title = "HELP clinical trial at detoxification unit")

Horizontal boxplots

• coord_flip() may be used with other plots as well to reverse the roles f x and y on the plot.

ggplot(data = HELP_data,
       aes(x = substance, 
           y = age, 
           color = children)) + 
  geom_boxplot() +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

Axes scaling with boxplots

We can scale the continuous axis

ggplot(data = HELP_data,
       aes(x = substance, 
           y = age, 
           color = children)) + 
  geom_boxplot() +
  coord_trans(y = "exp") +
  labs(title = "HELP clinical trial at detoxification unit")

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.

ggplot(data = HELP_data,
       aes(x = substance, 
           y = age, 
           color = children)) + 
  geom_boxplot(position = position_dodge(width=1)) +
  labs(title = "HELP clinical trial at detoxification unit")

Issues with bigger data

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

library(NHANES)
dim(NHANES)

[1] 10000    76

head(NHANES)

Overplotting

ggplot(data = NHANES,
       aes(x = Height, y = Weight)) +
  geom_point() + 
  facet_grid( Gender ~ PregnantNow )

Using alpha (opacity)

One way to deal with overplotting is to set the opacity low.

ggplot(data = NHANES,
       aes(x = Height, y = Weight)) +
  geom_point(alpha = 0.01) + 
  facet_grid( Gender ~ PregnantNow )

geom_density2d

Alternatively (or simultaneously) we might prefer a different geom altogether.

ggplot(data = NHANES,
       aes(x = Height, y = Weight)) +
  geom_density2d() + 
  facet_grid( Gender ~ PregnantNow )

Multiple layers

ggplot(data = HELP_data, 
       aes(x = children, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_point(alpha = .6) +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

ggplot(data = HELP_data,
       aes(x = children, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_jitter(alpha = .6, width=0.1) +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

Multiple layers

ggplot(data = HELP_data,
       aes(x = children, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_point(alpha = .6, 
             position = position_jitter(width = .1, height = 0)) +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

Things I haven’t mentioned (much)

• coords (coord_flip() is good to know about)

• themes (for customizing appearance)

• position (position_dodge(), position_jitterdodge() (for use with points on top of dodged boxplots), position_stack(), etc.)

• transforming axes

themes

library(ggthemes)
ggplot(Births78, aes(x = date, y = births)) + 
  geom_point() + 
  theme_wsj()

jitterdodge()

ggplot(data = HELP_data, 
       aes(x = substance, y = age, color = children)) +
  geom_boxplot(position = position_dodge(width = 1)) +
  geom_point(aes(color = children, 
                 fill = children), 
             position = position_jitterdodge(dodge.width = 1, jitter.width = 0.1), 
             size = 0.5) +
  labs(title = "HELP clinical trial at detoxification unit")

A little bit of everything

ggplot(data = HELP_data, aes(x = substance, y = age, color = children)) +
  geom_boxplot(position = position_dodge(width=1)) +
  geom_point(aes(fill = children), 
             alpha = .5, 
             position = position_jitterdodge(dodge.width = 1, jitter.width = 0.2)) + 
  facet_wrap(~homeless) +
  labs(title = "HELP clinical trial at detoxification unit")

Want to learn more?

• docs.ggplot2.org/

• R for Data Science by Hadley Wickham and Garrett Grolemund

What’s around the corner?

shiny

• interactive graphics / modeling

• https://shiny.rstudio.com/

plotly

Plotly is an R package for creating interactive web-based graphs via plotly’s JavaScript graphing library, plotly.js. The plotly R libary 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.

• https://plot.ly/ggplot2/getting-started/

gganimate

• gganimate tutorial

Cholera - a picture tells 1000 words

How many aspects of this graph can you point out which are relevant to figuring out that cholera infection was coming from a single pump? Are there any distracting aspects?

Cholera - difficult to interpret

Why would the outbreak already have begun to decline before the pump handle was removed?

Challenger - Problematic

One of the graphics which was particularly unconvincing in trying to explain that O-rings fail in the cold.

Challenger - Better????

A different graph of the Challenger information, now sorted by temperature

Challenger - Improved

The graphic the engineers should have led with in trying to persuade the administrators not to launch. It is evident that the number of O-ring failures is quite highly associated with the ambient temperature. Note the vital information on the x-axis associated with the large number of launches at warm temperatures that had zero O-ring failures.

Note that the “improved” Challenger graphic was made by Tufte, not by the engineers working on the problem at the time.

Fonts matter

Advice on plotting, specific

• Avoid having other graph elements interfere with data
• Use visually prominent symbols
• Avoid over-plotting (One way to avoid over plotting: jitter the values)
• Different values of data may obscure each other
• Include all or nearly all of the data
• Fill data region

Advice on plotting, general

• Eliminate superfluous material
• Facilitate comparisons
• Choose the best scale
• Make the plot data / information rich
• Use good captions, alt text, conclusions

Simplify

image credit: https://www.darkhorseanalytics.com/portfolio-data-looks-better-naked

Simplified

image credit: https://www.darkhorseanalytics.com/portfolio-data-looks-better-naked

NYT 9/7/21

Worth a Mention

In 1900 Du Bois contributed approximately 60 data visualizations to an exhibit at the Exposition Universelle in Paris, an exhibit designed to illustrate the progress made by African Americans since the end of slavery (only 37 years prior, in 1863).

Beautiful & Informative Graphics

https://drawingmatter.org/w-e-b-du-bois-visionary-infographics/