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Matt Upson

Yo no soy marinero

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I went to my first LondonR meeting tonight hosted by Mango solutions. Some really great talks - especially presentatiosn by Matt Sundquist of plotly.

Mango solutions also presented a good introduction to ggvis and some of the interactive elements. I’ve included my notes from the event below. Note that the visualisations from ggvis will not render properly here. You will need to reproduce the document in RStudio to see them.

Note that much of the code used for ggvis had already become deprecated!

library(dplyr)
library(ggplot2)

tubeData <- read.table(
"tubeData.csv",
sep = ",",
header = T
)

str(tubeData)
## 'data.frame':	1050 obs. of  9 variables:
## $ Line : Factor w/ 10 levels "Bakerloo","Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Month : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Scheduled: num 29.4 29.4 29.3 29.3 29.3 ...
## $ Excess : num 6.04 6.54 4.77 5.4 5.23 5.03 5.14 5.73 4.8 5.95 ...
## $ TOTAL : num 35.5 36 34.1 34.7 34.5 ...
## $ Opened : int 1906 1906 1906 1906 1906 1906 1906 1906 1906 1906 ...
## $ Length : num 23.2 23.2 23.2 23.2 23.2 23.2 23.2 23.2 23.2 23.2 ...
## $ Type : Factor w/ 2 levels "DT","SS": 1 1 1 1 1 1 1 1 1 1 ...
## $ Stations : int 25 25 25 25 25 25 25 25 25 25 ...

Outline

  • ggplot2
  • ggvis
  • %>%
  • Aesthetics
  • Layers
  • Interactivity

The Data

ggplot2 recap

  • qplot or ggplot
  • Add layers with +
  • Change aesthetics by variable with aes
  • Control plot type with geom
  • Panel using facet_
head(tubeData)
##       Line Month Scheduled Excess TOTAL Opened Length Type Stations
## 1 Bakerloo 1 29.42 6.04 35.46 1906 23.2 DT 25
## 2 Bakerloo 2 29.42 6.54 35.96 1906 23.2 DT 25
## 3 Bakerloo 3 29.30 4.77 34.08 1906 23.2 DT 25
## 4 Bakerloo 4 29.30 5.40 34.70 1906 23.2 DT 25
## 5 Bakerloo 5 29.30 5.23 34.53 1906 23.2 DT 25
## 6 Bakerloo 6 29.30 5.03 34.33 1906 23.2 DT 25
qplot(
data = tubeData,
x = Month,
y = Excess
)

plot of chunk ggplot2 recap

qplot(
data = tubeData,
x = Month,
y = Excess,
col = Line
)

plot of chunk ggplot2 recap

qplot(
data = tubeData,
x = Month,
y = Excess,
col = Line
) +
facet_wrap(
~Line
)

plot of chunk ggplot2 recap

qplot(
data = tubeData,
x = Month,
y = Excess,
col = Line
) +
facet_wrap(
~Line
) +
geom_smooth(
col = "red",
size = 1
)

plot of chunk ggplot2 recap

The ‘geoms’

grep(
"geom",
objects("package:ggplot2"),
value = TRUE
)
##  [1] "geom_abline"          "geom_area"            "geom_bar"            
## [4] "geom_bin2d" "geom_blank" "geom_boxplot"
## [7] "geom_contour" "geom_crossbar" "geom_density"
## [10] "geom_density2d" "geom_dotplot" "geom_errorbar"
## [13] "geom_errorbarh" "geom_freqpoly" "geom_hex"
## [16] "geom_histogram" "geom_hline" "geom_jitter"
## [19] "geom_line" "geom_linerange" "geom_map"
## [22] "geom_path" "geom_point" "geom_pointrange"
## [25] "geom_polygon" "geom_quantile" "geom_raster"
## [28] "geom_rect" "geom_ribbon" "geom_rug"
## [31] "geom_segment" "geom_smooth" "geom_step"
## [34] "geom_text" "geom_tile" "geom_violin"
## [37] "geom_vline" "update_geom_defaults"

Facetting

  • Panels using facet_wrap and facet_grid.

Scales and themes

  • axes and styles
  • themes e.g. theme_bw etc
qplot(
data = tubeData,
x = Month,
y = Excess,
col = Line
) +
facet_wrap(
~Line
) +
geom_smooth(
col = "red",
size = 1
) +
theme_bw()

plot of chunk themes

Getting started with ggvis

  • Plot with ggvis function
  • Only a single function unlike ggplot1
  • Use ~ when referring to variables in a dataset, e.g. ~Ozone
  • This refers to variables as formulas
  • First variable always data.
require(ggvis)

myPlot <- ggvis(
tubeData,
~Month,
~Excess
)


# Creates a ggvis object:

class(myPlot)
## [1] "ggvis"
# Graphic is produced in the Viewer pane, not the Plots pane. Works via java vega a .d3 package

myPlot
# Note settings cog in the top right which allows you to change the rendering of teh plot.

# Can view in web browser and then be saved as an html file.
# Because it is not written to standard plotting device, you need to render the graphoc before you can save it out - i.e. no png or pdf command
# No equivalent script to save out of ggvis - must be saved from a browser

layer_points(myPlot)
# Can also be used in the pupe

myPlot %>% layer_points

The %>% operator

  • ggvis uses %>% from magrittr like dplyr
mean(airquality$Ozone,na.rm=TRUE)
## [1] 42.12931
# Now with the pipe

airquality$Ozone %>% mean(na.rm = TRUE)
## [1] 42.12931
# dplyr example

require(dplyr)

tubeData %>%
dplyr::group_by(Line) %>%
dplyr::summarise(mean = mean(Excess)) %>%
qplot(Line, mean, data = ., geom="bar", stat = "identity", fill = Line)

plot of chunk the_pipe

%>% in ggvis

  • We pass ggvis objects mostly.
  • All functions accept a ggvis object first, except the command ggvis
  • Initial ggvis object is created with the ggvis command.
  • e.g.:
tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points

Changing properties

  • Properties in ggvis are the same as aesthetics in ggplot2
  • Number of aesthetics that can be set:
  • stroke – refers to lines
  • fill
  • size
  • opacity – instead of alpha

Changing based on variables

  • Mapping and setting as with aes
  • Map a variable to a property with =
  • Remember to use ~ with all variable names
  • fill = ~Line would set the fill based on the Line variable
tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points(
fill = ~Line
)
tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points(
fill = ~Line,
shape = ~Line
)
tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points(
size = ~Stations
)
# can be set for all layers:

tubeData %>%
ggvis(
~Month,
~Excess,
fill = ~Line
) %>%
layer_points

Setting property values

  • Instead of col = I("red") in ggplot2 is not required. This prevents ggplot2 picking red up as a fcator.
  • fill := "red" will work in ggvis
tubeData %>%
ggvis(
~Month,
~Excess,
fill = "red",
opacity := 0.5
) %>%
layer_points
tubeData %>%
ggvis(
~Month,
~Excess,
fill := "red",
opacity := 0.5
) %>%
layer_points
  • Shaping has changed in ggvis as it is dependent on .d3
  • At the moment a limited subset only is available
tubeData %>%
ggvis(
~Month,
~Excess,
fill := "red",
opacity := 0.5,
shape := "square"
) %>%
layer_points

Exercise

  • Create a plot of mpg against wt using mtcars data
  • Use colour for the cyl variable, and make it a factor
  • Update the plotting symbol to be triangles
mtcars %>%
ggvis(
~mpg,
~wt
) %>%
layer_points(
fill = ~factor(cyl),
# Why doesn't this work!?
shape := "triangle-up"
)

Adding layers

  • In ggvis we use layer_ instead of geom_
  • Major limitation of ggvis at present, as not all of the geoms_ are vailable as layer_ in ggvis.
  • Check package manual:
tubeData %>%
ggvis(
~Line,
~Excess
) %>%
layer_boxplots()
# Adding some extra layers

mtcars %>%
ggvis(
~mpg,
~wt
) %>%
layer_points(
fill = ~factor(cyl),
# Why doesn't this work!?
shape := "triangle"
) %>%
layer_smooths() %>%
layer_model_predictions(
model = "lm"
)
# Note that formula can be specified with formula = ...

mtcars %>%
ggvis(
~mpg,
~wt
) %>%
layer_points(
fill = ~factor(cyl),
# Why doesn't this work!?
shape := "triangle"
) %>%
layer_smooths(
stroke := "blue",
se = TRUE
) %>%
layer_model_predictions(
model = "lm",
stroke := "red",
se = TRUE
)

Making plots interactive

Basic interactivity

  • Most basic level is ‘hover over’ just like in javascript.
  • Properties of the properties are changed to achive this.
  • property.hover argument: fill.hover := "red", or size.hover, opacity.hover, etc.
tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points(
fill = ~Line,
fill.hover := "red",
size.hover := 1500 # sizes are very different to R graphics!
)
# This behaviour is saved into the html or svg file!

Tooltips

  • add_tooltip adds other behaviour on hover..
  • We can provide a function that provide information as we hover.
tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points(
fill = ~Line,
fill.hover := "red",
size.hover := 1500 # sizes are very different to R graphics!
) %>%
add_tooltip(
function(data) data$Excess
)
# Locks off R console - cannot be used in markdown
pkData$id <- seq_along(pkData$Subject)

all_values <- function(x) {

}

pkData %>% ggvis(
~Time,
~Conc,
key = ~id # ggvis defined
) %>%
layer_points() %>%
add_tooltip(
all_values,
"hover"
)

Interactive input

  • We can set outputs to be taken from interactive inputs

opacity := input_slider(0,1, label = "Opacity")

  • We use the ":=" for this input
  • We can optionally set labels next to the control - unlink shiny where it is not optional
  • Currently you are limited to changing the properties of the data, not the data itself.
tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points(
fill = ~Line,
size := input_slider(10,1000, label = "Size of points")
)

Interactive input functions

tubeData %>%
ggvis(
~Month,
~Excess
) %>%
layer_points(
size := input_numeric(30, label = "Size"),
opacity := input_slider(0,1,value = 0.7, label = "Opacity"),
fill := input_select(c("red","blue","orange"), label = "Colour")
)

Common plot functions

Controlling axes and legends

  • We can control the axes using the add_axis function
  • This controls acis labels, tick marks and even grid lines
  • Title workaround is to use add_axis

add_axis("x", title = "Month")

  • add_axis controls colour of gridlines, etc
  • The add_legend and hide_legend functions allow use to control if we see a legend and wheere it appears

add_legend("fill") add_legend(c("fill","shape"))

Scales

  • ggvis had fewer scale functions than in ggplot2 but control much more.
  • just seven functions at present
grep(
"^scale",
objects("package:ggvis"),
value = TRUE
)
## [1] "scale_datetime" "scaled_value"   "scale_logical"  "scale_nominal" 
## [5] "scale_numeric" "scale_ordinal" "scale_singular"

ggvis vs ggplot2

  • we can layer graphics in a simlar fashion
  • aesthetics can be set baswed on by variables in the data
  • We cancontrol the type of plot

How are they different?

  • Only one main function
  • Layering with %>%
  • Fewer scale functions
  • Much functionality not available… but coming…

Which should I use

  • Static graphics: ggplot2
  • Interactive graphics ggvis

Documentation