Environmental Data Analysis and Visualization

Failing Gloriously/Finishing Touches

Visualization critique

NY Times

Visualization critique

NY Times

Some things I heard last night

  • Is it OK if I don’t have a significant relationship?
  • Do I need to revise my project if I can’t answer my question with the data?
  • What if my results don’t show anything exciting/interesting?

Science

Science fails

Science fails all

Science fails all the

Science fails all the time

Science fails all the time.

Science fails all the time.

And that’s OK.

Does this mean I asked a bad question?

Does this mean I asked a bad question?

No.

Does this mean I asked a bad question?

No. Developing research questions takes time and practice. But they should never be asked with the expressed aim of getting a “significant” result.

Perverse incentives

Edwards and Roy 2017 Academic Research in the 21st Century DOI: 10.1089/ees.2016.0223

Fail productively

XKCD

  • A negative result is a result nonetheless.

  • If your assumptions/predictions didn’t work out, ask why not.

  • If your results are inconclusive, ask what data might be needed.

  • How does your result compare with other studies?

Fail productively, fail gloriously

“To fail productively, one has to be aware of the multiple contexts of that fail to find the valuable experience. To fail gloriously is to use the privileges that you have, as you are able, to make it safe for others to fail.” - Shawn Graham, Failing Gloriously

Base plot

library(tidyverse)
library(palmerpenguins)

basePlot<-ggplot(drop_na(penguins),aes(x=body_mass_g,y=bill_depth_mm,color=species)) +
  geom_point() +
  scale_colour_brewer(palette = "Set1") +
  labs(x="body mass (g)",y="bill depth (mm)",title="Penguin Bill Depths")

Base plot

Using complete themes

basePlot + theme_classic()

Using complete themes

basePlot + theme_dark()

Even more complete themes

library(ggthemes)

basePlot + theme_tufte()

Even more complete themes

library(ggthemes)

basePlot + theme_excel()

Customizing appearance with theme

Theme elements are the non-data parts of your plot (e.g., text, These can be accessed using the theme function:

basePlot + theme(element.name = element_function())

Where element.name refers to the element of the graph (e.g., axis.ticks for the tick marks), and the element_function controls what aspect of it is going to be changed (e.g., line width).

Customizing appearance with theme

  • element_text() controls text face, color, size, angle, etc.

  • element_line() controls line color, linewidth, and linetype

  • element_rect() controls rectangular areas fill and border line

  • element_blank() controls whether an element is drawn or not

    • Note: this will remove a drawing rather than make it invisible!

Customizing plot elements

Customizing plot elements

basePlot + theme(
  plot.background = element_rect(fill = "tan")
  )

Customizing plot elements

Try customizing your graph using the following (use ? to get function help).

Element Function Modifies
plot.background element_rect Plot background area
plot.title element_text Plot title

Customizing axis elements

Customizing axis elements

 basePlot + theme(
   axis.line = element_line(colour = "orange", linewidth = 1)
   )

Customizing axis elements

You can modify just one axis by adding .x or .y to your element name.

 basePlot + theme(
   axis.line = element_line(colour = "orange", linewidth = 1),
   axis.text.x = element_text(angle = 45,vjust=1,hjust = 1,color="blue")
   )

Customizing axis elements

You can remove an element by using element_blank()

 basePlot + theme(
   axis.line = element_line(colour = "orange", linewidth = 1),
   axis.text.x = element_text(angle = 45,vjust=1,hjust = 1,color="blue"),
   axis.ticks = element_blank()
   )

Customizing axes

Try customizing your graph using the following (use ? to get function help).

Element Function Modifies
axis.line element_line() Axis line
axis.text element_text() Text at tick marks
axis.title element_text() Axis title
axis.ticks element_line() Tick marks
axis.ticks.length unit() Length of tick marks

Customizing panel elements

Customizing panel elements

Try customizing your graph using the following (use ? to get function help).

Element Function Modifies
panel.background element_rect() Panel background
panel.grid.major element_line() Major gridlines
panel.grid.minor element_line() Minor gridlines

Customizing legend elements

Customizing legend elements

Try customizing your graph using the following (use ? to get function help).

Element Function Modifies
legend.background element_rect() legend background
legend.key element_rect() background of legend keys
legend.text element_text() legend labels
legend.title element_text() legend name

Output to file

Use ggsave to output to a file and control its size

ggsave("baseplot.pdf",basePlot,width=6,height=4,units="in")

Image file formats

Jif / Giphy

Image file formats

There are two main types of image formats

  • Bitmap (also known as raster)

  • Vector

Bitmap vs Vector

Wilke, C. 2019. Fundamentals of Data Visualization

Bitmap vs Vector

Wilke, C. 2019. Fundamentals of Data Visualization

Bitmap vs Vector

Bitmap

  • Sharpness depends on resolution and size

  • Consistent appearance across platforms

  • Best for complex images (photos, large numbers of elements)

Vector

  • Sharpness is scale-independent

  • Can look differently using different programs

  • Best for simple images (line drawings, simple graphs)

Image file formats

File Extension Type Use For
.jpg Bitmap Photos
.png Bitmap Line Drawings
.tif Bitmap Print
.svg Vector Line drawings (online)
.pdf Vector General purpose

Next week

  • Monday: Final final project development session

  • Tuesday: SUBMIT POSTERS BEFORE NOON, present at 3PM