The$Grammar$of$Graphics$ - About...

ESSM 689 Quan,ta,ve Methods in Ecology, Evolu,on and Biogeography

Ecosystem Science and Management | Texas A&M University (c) 2015, A. Michelle Lawing

The Grammar of Graphics

A. Michelle Lawing Ecosystem Science and Management Texas A&M University College Sta,on, TX 77843 [email protected]

michellelawing.info/Rcode



ANNOUNCEMENT

•  Ecological Integra,on Symposium – 26 March –  9:15-‐10:00am – Talks Session 1

•  Room 404 – Public Health & Disease •  Room 401 – Hybrid Zones & Selec,on •  Room 402 – Honey Bees

–  11:00am-‐12:00pm – Talks Session 2 •  Room 404 – Changing Landscapes •  Room 401 – From Communi,es to Popula,ons •  Room 402 – Entomology

–  1:00-‐2:15pm – Talks Session 3 •  Room 404 – Ecosystem Ecology •  Room 401 – Phylogene,cs & Evolu,on •  Room 402 – Habitat & Spa,al Ecology



ANNOUNCEMENT

•  Ecological Integra,on Symposium – 27 March –  9:10-‐10:10am – Dr. Paul Gibbs -‐ One health, the socio-‐ecology of disease, and epidemic preven,on

–  10:10-‐11:10am – Dr. Maureen Donnelly -‐ Amphibian conserva,on in the New World Tropics: A tragedy in three acts

–  11:30am-‐12:30pm – Dr. Parviez Hosseini -‐ Understanding the role of land-‐use change and ecological community structure in pathogen emergence

–  2:00-‐3:00pm – Dr. Tyrone Hayes -‐ From Silent Spring to Silent Night: A tale of toads and men

–  3:00-‐4:00pm – Dr. Rosemary Grant -‐ Lessons from a natural ecosystem



Schedule for today

•  My presenta,on

•  Survival Analysis Presenta,on

•  Survival Analysis Demonstra,on

•  BREAK

•  Survival Analysis Tutorial



Visualizing your data



Visualizing your data

•  Most important tool in data analysis

•  To understand your data

•  To share your results

•  DEMONSTRATE



ggvis

•  Hadley Wickham (Rice Uni) •  Grammar of graphics –  A way to think about ANY graph –  A collec,on of parts (think of legos) graph = data + coordinate system + marks + proper,es –  Extends ggplot2 –  Uses R to build html graphs

•  Use analy,c power of R to prepare data •  Visual power of java script to display your results •  Interac,ve data display •  Output in html, so can use in shiny, R markdown, web page…



get ggvis

•  Today we will cover the breadth and depth of ggvis graphing language

•  Learn grammar of graphics •  Learn to customize your ggvis plots > install.packages("ggvis") > library(ggvis)



change iris names for simplicity

> install.packages("ggvis") > library(ggvis) > names(iris) [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"

> names(iris) <-‐ c("SL", "SW", "PL", "PW", "Species")



grammar of graphics

•  Every ggvis graphic starts with a call to ggvis() •  1st argument is the dataset

•  Other arguments describe how to map variables and visualize proper,es

> p <-‐ ggvis(iris, x = ~SL, y = ~SW)



add a layer of points to your scaffold

> p <-‐ ggvis(iris, x = ~SL, y = ~SW)

> layer_points(p) •  Either create a temporary variable, then modify it or use lots of parentheses

> layer_points(ggvis(iris, x = ~SL, y = ~SW))



the plot opens in viewer (Rstudio) or browser (R)




piping in the data


•  pronounced pipe %>% •  allows you to rewrite the above func,on call as…

> iris %>% ggvis(~SL, ~SW) %>% layer_points()

•  no,ce we dropped the argument name x and y



the ,lde > iris %>% ggvis(~SL, ~SW) %>% layer_points()

•  why use a ,lde?

•  the ,lde specifies that you are using a variable WITHIN the given dataset.

•  this is necessary for ggvis to run and MUST be used this way



some op,ons for your points > iris %>% ggvis(~SL, ~SW) %>% layer_points()

•  you can add more variables to the plot –  fill –  stroke –  size –  shape

> iris %>% ggvis(~SL, ~SW, size = ~PL) %>% layer_points()



size gradient > iris %>% ggvis(~SL, ~SW, size = ~PL) %>% layer_points()



no,ce this symbol for export > iris %>% ggvis(~SL, ~SW, size = ~PL) %>% layer_points()

Render: SVG | Canvas



try fill

> iris %>% ggvis(~SL, ~SW, fill = ~Species) %>% layer_points()

> iris %>% ggvis(~SL, ~SW, size = ~PL) %>% layer_points()



try stroke

> iris %>% ggvis(~SL, ~SW, stroke = ~Species) %>% layer_points()

> iris %>% ggvis(~SL, ~SW, fill = ~Species) %>% layer_points()



try shape

> iris %>% ggvis(~SL, ~SW, shape = ~Species) %>% layer_points()

> iris %>% ggvis(~SL, ~SW, stroke = ~Species) %>% layer_points()



constant color

> iris %>% ggvis(~SL, ~SW, fill := “red”) %>% layer_points()

> iris %>% ggvis(~SL, ~SW, shape = ~Species) %>% layer_points()



a special way to specify a constant > iris %>% ggvis(~SL, ~SW, fill := “red”) %>% layer_points()

•  note that := must be used to specify a constant

> iris %>% ggvis(~SL, ~SW, size := 1000, opacity := 0.4) %>% layer_points()



interac,vity



basic interac,ve controls

> iris %>% ggvis(~SL, ~SW, size := 1000, opacity := 0.4) %>% layer_points()

•  replace constant values with a func,on •  input_slider()

> iris %>% ggvis(~SL, ~SW, size := input_slider(100, 1000)) %>% layer_points()



viewing the graphic

> iris %>% ggvis(~SL, ~SW, size := input_slider(100, 1000)) %>% layer_points()

•  from the R, graph opens in web browser •  resize the window to see slider •  press esc to exit interac,vity in Rstudio



input_

> iris %>% ggvis(~SL, ~SW, size := input_slider(100, 1000)) %>% layer_points() •  Other basic interac,ve controls

–  input_checkbox(): a check-‐box –  input_checkboxgroup(): a group of check boxes –  input_numeric(): a spin box –  input_radiobuzons(): pick one from a set op,ons –  input_select(): create a drop-‐down text box –  input_text(): arbitrary text input

•  Note that all interac,ve controls start with input_ so you can use tab comple+on to discover all the op,ons



two interac,ve controls > iris %>% ggvis(~SL, ~SW, size := input_slider(100, 1000)) %>% layer_points() > iris %>% ggvis(~SL) %>%

layer_densi,es(adjust = input_slider(.1, 2, label = "Bandwidth"), kernel = input_select(c("Gaussian" = "gaussian”, "Rectangular" = "rectangular”, "Triangular" = "triangular”), label = "Kernel"))

•  note switching between = and := can be confusing •  read the help pages to know which to use when



arguments

•  specific arguments differ depending on input_ •  all input_ func,ons share three arguments –  label: an informa,ve text label displayed next to the control

–  id: a unique iden,fier for the control –  map: a func,on that enables you to remap the output of the input to something appropriate for your graphic

•  map is extremely useful (e.g., you can make different orders of magnitude visible with a slider)

•  input_slider(-‐5, 5, map = func,on(x) 10 ^ x)



mul,ple outputs

> #first method with pronounced pipe > iris %>% ggvis(~SL, ~SW, size := input_slider(10, 1000)) %>%

layer_points(fill := "red") %>% layer_points(stroke := "black", fill := NA)

> #second method with assignment > slider <-‐ input_slider(10, 1000) > iris %>% ggvis(~SL, ~SW) %>% layer_points(fill := "red", size :=

slider) %>% layer_points(stroke := "black", fill := NA, size := slider)



limita,ons

•  interac,ve inputs can only modify your data, not the underlying plot specifica,ons

•  can’t add or remove layers •  can’t switch between datasets

•  but you can embed in shiny to gain full control •  shiny is an extension of R to create interac,ve web apps

The$Grammar$of$Graphics$ - About...

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