A Scala package that produces plots using R ggplot2.
%AddDeps org.ddahl rscala_2.11 2.3.1
import org.ddahl.rscala._
%AddDeps com.climate ggscala2_2.11 0.0.3 --repository file:/Users/ricardo.lemos/scala_jars/
import com.climate.ggscala2.Gigi
Gigi.inline = Some((h: String) => kernel.magics.html(h)) // method that converts html into an inline figure in Toree
Gigi.screenScale = 0.25 // smaller inline plotsMarking org.ddahl:rscala_2.11:2.3.1 for download
Preparing to fetch from:
-> file:/var/folders/zx/tybg3qc92z13w8lp81mlr2rjzqbsdw/T/toree_add_deps1862492987876009503/
-> https://repo1.maven.org/maven2
-> New file at /var/folders/zx/tybg3qc92z13w8lp81mlr2rjzqbsdw/T/toree_add_deps1862492987876009503/https/repo1.maven.org/maven2/org/ddahl/rscala_2.11/2.3.1/rscala_2.11-2.3.1.jar
Marking com.climate:ggscala2_2.11:0.0.3 for download
Preparing to fetch from:
-> file:/var/folders/zx/tybg3qc92z13w8lp81mlr2rjzqbsdw/T/toree_add_deps1862492987876009503/
-> file:/Users/ricardo.lemos/scala_jars/
-> https://repo1.maven.org/maven2
-> New file at /Users/ricardo.lemos/scala_jars/com/climate/ggscala2_2.11/0.0.3/ggscala2_2.11-0.0.3.jar
Use the following code to render an example plot:
Gigi("example = ggplot(data.frame(x = 1, y = 2), aes(x = x, y = y)) + geom_point()")
Gigi.print("example")
Gigi is the name of the package's only object, which you call to plot.
You need the software R (https://www.r-project.org/) installed in your machine. You also need the R packages ggplot2 and grid: in an R terminal, type
install.packages("ggplot2")
install.packages("grid")To confirm that an R interpreter can be instantiated from Scala, try
val R = org.ddahl.rscala.RClient()If that does not work, ggscala2 has a workaround: type
Gigi.path2r = "/path/to/R/here"early in your Scala code, to point rscala in the right direction.
There are several built-in types of plots in this package:
The instruction
Gigi.lineplot(y = Array(1.3, 2.3, 4.5, 1.7))
produces a simple line plot; at the other extreme,
Gigi.lineplot(x = Option(Array(1.0, 2.0, 1.0, 2.0)),
y = Array(1.3, 2.3, 4.5, 1.7),
z = Option(Array("g1", "g1", "g2", "g2")),
xlab = "predictor", ylab = "response", zlab = "group", title = "ggscala2 lineplot",
drawPoints = true)
demonstrates all the features you can include in a lineplot.
Under the hood, ggscala2 employs the same method to build line and scatter plots. End users, however, use a different call
Gigi.scatterplot(x = Array(1.0, 2.0, 1.0, 2.0),
y = Array(1.3, 2.3, 4.5, 1.7),
z = Option(Array("g1", "g1", "g2", "g2")),
xlab = "predictor", ylab = "response", zlab = "group", title = "ggscala2 scatterplot")
Deceptively similar to line plots,
time series plots let you add confidence bands to the
representation of a single variable whose
distribution (mean, variance, etc.) changes over time.
If you also have observations, you can add them using the argument z.
import org.joda.time.DateTime
val x: Array[DateTime] = Array("1980-01-15", "1980-01-16", "1980-01-17", "1980-01-18").map(new DateTime(_))Gigi.timeseriesplot(x = x,
y = Array(1.3, 2.3, 4.5, 1.7),
z = Option(Array(1.4, 1.6, 3.6, 1.6)),
ymin = Option(Array(0.7, 1.7, 3.6, 1.0)),
ymax = Option(Array(1.7, 2.7, 5.6, 2.0)),
xlab = "date", ylab = "response", title = "ggscala2 timeseries plot")
Here you can compare the distribution of several random variables,
for which you have samples: stack them all in a single array, x, and
use z to identify the variables.
Gigi.densityplot(x = Array(1.0, 2.0, 2.1, 2.6, 0.7, 1.2, 1.4, 2.0),
z = Option(Array("v1", "v1", "v1", "v1", "v2", "v2", "v2", "v2")),
xlab = "", zlab = "variable", title = "ggscala2 densityplot")
To draw a simple 2D surface plot, do
Gigi.surfaceplot(x = Array(1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0),
y = Array(1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0),
z = Array(1.2, 2.3, 3.4, 1.4, 2.1, 2.8, 1.1, 2.0, 3.7),
xlab = "longitude", ylab = "latitude", zlab = "height")
The color scheme changes automatically if z includes negative and positive values
Gigi.surfaceplot(x = Array(1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0),
y = Array(1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0),
z = Array(-1.2, 2.3, 3.4, -1.4, 2.1, 2.8, -1.1, 2.0, 3.7),
xlab = "longitude", ylab = "latitude", zlab = "height")
You can add as many layers to a surface plot as you would like. For example, here we overlay a surface with a contour, vectors, points, and text.
val x = Array(1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0)
val y = Array(1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0)
val z = Array(-1.2, 2.3, 3.4, -1.4, 2.1, 2.8, -1.1, 2.0, 3.7)
Gigi.surfaceplot(
layerType = List("surface", "contour", "vectors", "points", "text"),
x = List(x, x, x, x, x),
y = List(y, y, y, y, y),
z = List(z, z, x.map(_ * 0.1) ++ y.map(_ * 0.1), z.map(_ * 0.5), z),
text = Option(z.map(_.toString)),
xlab = "longitude", ylab = "latitude", zlab = "height")
To draw a Brier Score reliability plot (a.k.a. reliability diagram), do
Gigi.reliabilityplot(x = Array(0.1, 0.2, 0.1, 0.5, 0.3, 0.8, 0.9, 0.8, 0.05, 0.0, 0.99, 0.2),
y = Array(false, false, false, false, false, false, true, true, false, false, true, false))
There is a straightforward way of creating multipanel figures
Gigi.split(2,2)
Gigi.spool("lineplot", Option(0), Option(0))
Gigi.spool("timeseriesplot", Option(0), Option(1))
Gigi.spool("scatterplot", Option(1), Option(0))
Gigi.spool("densityplot", Option(1), Option(1))
Gigi.print()
Not pleased with the look of your built-in plot? Ask Gigi to change it.
Gigi("densityplot = densityplot + theme(legend.position='bottom')")
Gigi.print("densityplot")
You can change the color palettes for your plots:
Gigi.paletteZeroExtreme.changeColor(Array("yellow", "dark green"))
Gigi.changePalettes() // to activate change
Gigi.surfaceplot(x = Array(1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0),
y = Array(1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0),
z = Array(1.2, 2.3, 3.4, 1.4, 2.1, 2.8, 1.1, 2.0, 3.7),
xlab = "longitude", ylab = "latitude", zlab = "height")
Similar operations can be applied to paletteLine, paletteFill, and
paletteNegZeroPos.
In the "Getting started" section we saw that it is possible to create a simple
plot by sending all the R commands to Gigi. You can also plot data from your
Scala session, using the set method, and you are not limited to the types of
plots displayed above -- any instruction recognized by ggplot2 is available.
Gigi.set("x", Array(1,1,2,3,3,3,3,4,4,4,4,4,5,5,5,5,5,5,6,6,6,7,7,7,7,8,8,9,9))
Gigi("myplot = ggplot(data.frame(x=x), aes(x=x)) + geom_histogram(binwidth=2)")
Gigi.print("myplot")
- Ricardo Lemos - Initial work - The Climate Corporation
Copyright (C) 2017 The Climate Corporation. Distributed under the Apache License, Version 2.0. You may not use this library except in compliance with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
See the NOTICE file distributed with this work for additional information regarding copyright ownership. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.