Creating Custom Plots
library(rbcmodel)
#basic plot
Rbc_wheat <- Enzyme("aestivum_Iñiguez_2021",enzyme_name="wheat")
wheat_DH<-DHScale("aestivum_orr_2016_dH",data="abridged")
wheat_DH<-modify_DHScale(wheat_DH,Ko_dH=26.7)
wheat_carbon<-CO2_dependence(Rbc_wheat,wheat_DH)
CO2_seq<-O2_seq<-seq(0,1000,by=10)
temp_seq<-seq(0,40,by=.1)
wheat_grid<-make_4D_grid(wheat_carbon,CO2_seq,O2_seq,temp_seq,var_names=c("CO2","O2","T","wheat"))
s1<-slice_4D_grid(wheat_grid,dim=3,25)
s2<-slice_4D_grid(wheat_grid,dim=2,300)
Overview
This vignette walks through more of the ways to customize the
plot_slice_3D() graphs created by rbcmodel. The basic plot
workflow is discussed in the introductory vignette. Here, we start with
a description of all of the plot_slice_3D() arguments. We
then discuss how to customize labels for the plot, change the color
palette, and add other elements using the base R plot functions.
Finally, we include a description of permute_4D_grid(), a
utility function to change the 4D grid created by
make_4D_grid() in anticipation of slicing and plotting.
1. plot_slice_3D() arguments
For customization, plot_slice_3D() accepts the following
named arguments. When the default is NULL, any default response from the
function is listed in parentheses next to it. Furthermore, additional
arguments whose name does not match the list will be passed to the
image2D() function in the package plot3D used
under the hood.
| grid_slice |
The 3D slice to plot |
(required) |
Any 3D slice created from slice_4D_grid() or
transpose_3D_slice() |
| contours |
The values of the contour lines to be drawn |
NULL (no contours) |
Any vector of values |
| contour_col |
The color used for contours |
"black" |
Any color word or hex code |
| lwd |
Contour line width |
2 |
Any number |
| dims |
Specification of which element of the grid is the horizontal,
vertical, and contour/color variables of the plot |
NULL (auto-detects)* |
Integer vector, length 3. e.g., c(1,2,4) |
| cmin |
The minimum value of the color scale |
NULL (min(z)) |
Any number |
| cmax |
The maximum value of the color scale |
NULL (max(z)) |
Any number |
| colors |
The color scale used |
"default" |
Any list of hex codes |
| NA_col |
The color used for NA values |
"grey" |
Any color word or hex code |
| xlabel |
The label for the x-axis |
"" |
Any string |
| ylabel |
The label for the y-axis |
"" |
Any string |
| clabel |
The label for the color scale |
c("Carbon", "(C/s)") |
Any string |
*The function will attempt to automatically determine which variable
should be the horizontal and the vertical values, and assume that the
last element of the grid is the contour/color variable. If this is not
successful, dims can be specified like so: c(1,3,4).
2. Changing labels
plot_slice_3D() uses the same formatting constraints as
the base R plot() function for labels, so some tricks used
in other plotting packages (such as ggplot2) to get
subscripts, superscripts, Greek letters, etc. may not work in
plot_slice_3D(). We recommend using the Unicode characters
for degree symbols. To make a subscript or superscript, you can use the
base R function expression() to create them. Greek letters
can also be created using expression(). Below, we provide
an example of a CO2 vs. T plot that includes formatted labels
that you can use:
plot_slice_3D(s2,contours=c(2,4,6,8),xlabel=expression(CO[2]~(μM)),ylabel="T (°C)")
#plot_slice_3D(s2,contours=c(2,4,6,8),xlabel=expression(CO[2]~(mu*M)),ylabel="T (°C)") will create mu just as easily.
3. Creating a custom color palette
We have provided an automatic color palette (red for positive values,
white at 0, blue for negative values) for all
plot_slice_3D() graphs. Pure red ("#ff0000")
is used for the maximum value of the color scale, and pure blue
("#0000ff") is used for the minimum value of the color
scale. To adjust the color scale boundaries, you can use the arguments
cmin and cmax. Any values that are present on
the graph that do not fall within that range will end up the color
specified by NA_col, which be default is
"grey".
plot_slice_3D(s1,contours=c(1.5,2,2.5),xlabel=expression(CO[2]~(μM)),ylabel=expression(O[2]~(μM)))
plot_slice_3D(s1,contours=c(1.5,2,2.5),xlabel=expression(CO[2]~(μM)),ylabel=expression(O[2]~(μM)), cmax=5)
Alternatively, you can specify your own color palette from scratch
using colors. This should be a list of many hex code
values, and can be created using functions like ramp.col()
from the plot3D package, a dependency of
rbcmodel.
green_ex<-plot3D::ramp.col(c("white","green"),n=125)
#print color scale for visualization of data structure
green_ex
#> [1] "#FFFFFFFF" "#FCFFFCFF" "#FAFFFAFF" "#F8FFF8FF" "#F6FFF6FF" "#F4FFF4FF"
#> [7] "#F2FFF2FF" "#F0FFF0FF" "#EEFFEEFF" "#ECFFECFF" "#EAFFEAFF" "#E8FFE8FF"
#> [13] "#E6FFE6FF" "#E4FFE4FF" "#E2FFE2FF" "#E0FFE0FF" "#DEFFDEFF" "#DCFFDCFF"
#> [19] "#D9FFD9FF" "#D7FFD7FF" "#D5FFD5FF" "#D3FFD3FF" "#D1FFD1FF" "#CFFFCFFF"
#> [25] "#CDFFCDFF" "#CBFFCBFF" "#C9FFC9FF" "#C7FFC7FF" "#C5FFC5FF" "#C3FFC3FF"
#> [31] "#C1FFC1FF" "#BFFFBFFF" "#BDFFBDFF" "#BBFFBBFF" "#B9FFB9FF" "#B7FFB7FF"
#> [37] "#B4FFB4FF" "#B2FFB2FF" "#B0FFB0FF" "#AEFFAEFF" "#ACFFACFF" "#AAFFAAFF"
#> [43] "#A8FFA8FF" "#A6FFA6FF" "#A4FFA4FF" "#A2FFA2FF" "#A0FFA0FF" "#9EFF9EFF"
#> [49] "#9CFF9CFF" "#9AFF9AFF" "#98FF98FF" "#96FF96FF" "#94FF94FF" "#92FF92FF"
#> [55] "#8FFF8FFF" "#8DFF8DFF" "#8BFF8BFF" "#89FF89FF" "#87FF87FF" "#85FF85FF"
#> [61] "#83FF83FF" "#81FF81FF" "#7FFF7FFF" "#7DFF7DFF" "#7BFF7BFF" "#79FF79FF"
#> [67] "#77FF77FF" "#75FF75FF" "#73FF73FF" "#71FF71FF" "#6FFF6FFF" "#6CFF6CFF"
#> [73] "#6AFF6AFF" "#68FF68FF" "#66FF66FF" "#64FF64FF" "#62FF62FF" "#60FF60FF"
#> [79] "#5EFF5EFF" "#5CFF5CFF" "#5AFF5AFF" "#58FF58FF" "#56FF56FF" "#54FF54FF"
#> [85] "#52FF52FF" "#50FF50FF" "#4EFF4EFF" "#4CFF4CFF" "#4AFF4AFF" "#47FF47FF"
#> [91] "#45FF45FF" "#43FF43FF" "#41FF41FF" "#3FFF3FFF" "#3DFF3DFF" "#3BFF3BFF"
#> [97] "#39FF39FF" "#37FF37FF" "#35FF35FF" "#33FF33FF" "#31FF31FF" "#2FFF2FFF"
#> [103] "#2DFF2DFF" "#2BFF2BFF" "#29FF29FF" "#27FF27FF" "#25FF25FF" "#22FF22FF"
#> [109] "#20FF20FF" "#1EFF1EFF" "#1CFF1CFF" "#1AFF1AFF" "#18FF18FF" "#16FF16FF"
#> [115] "#14FF14FF" "#12FF12FF" "#10FF10FF" "#0EFF0EFF" "#0CFF0CFF" "#0AFF0AFF"
#> [121] "#08FF08FF" "#06FF06FF" "#04FF04FF" "#02FF02FF" "#00FF00FF"
plot_slice_3D(s1,contours=c(1.5,2,2.5),xlabel=expression(CO[2]~(μM)),ylabel=expression(O[2]~(μM)),colors=green_ex)
4. Adding elements to the graph
Because plot_slice_3D() is based on the base R plot
functions, any of the base R plot functions can be combined with the
graph created by plot_slice_3D(). To start,
plot_slice_3D() passes on any unused variables to
plot3D::image2D() and from there to
plot():
#passing an unused variable to image2D()
plot_slice_3D(s1,contours=c(1.5,2,2.5),xlabel=expression(CO[2]~(μM)),ylabel=expression(O[2]~(μM)),rasterImage=TRUE)
Adding a title is as simple as invoking title() after
plot_slice_3D():
plot_slice_3D(s1,contours=c(1.5,2,2.5),xlabel=expression(CO[2]~(μM)),ylabel=expression(O[2]~(μM)))
title("My title")
It is also possible to add points, horizontal/vertical lines, and
other shapes as you would with any base R plot. In this example, we will
add a horizontal line at 300μM O2:
plot_slice_3D(s1,contours=c(1.5,2,2.5),xlabel=expression(CO[2]~(μM)),ylabel=expression(O[2]~(μM)))
abline(h=300)
5. permute_4D_grid()
In the introduction, we described a workflow that starts with
make_4D_grid(), followed by slice_4D_grid()
and possibly transpose_3D_slice(), before visualizing via
plot_slice_3D(). Occasionally, we may want the axes of
multiple plots to be transposed. For example, we may want to produce
plots of CO2 versus temperature and O2 versus
temperature, both of which with temperature on the x-axis. In such case
it may be more convenient to permute the 4D grid so that temperature
becomes the first variable. This can be achieved using the
permute_4D_grid() function:
g2 <- permute_4D_grid(wheat_grid, c(3, 1, 2))
names(g2)
#> [1] "T" "CO2" "O2" "wheat"
Now we can make two slices and plot:
s3 <- slice_4D_grid(g2, dim=2, val=500)
plot_slice_3D(s3, contours=c(2, 4, 6),xlabel=expression("T (°C)"),ylabel=expression(O[2]~(μM)))
s3 <- slice_4D_grid(g2, dim=3, val=500)
plot_slice_3D(s3, contours=c(2, 4, 6), xlabel=expression("T (°C)"), ylabel=expression(CO[2]~(μM)))
Compare this with the workflow described in the introduction, where 2
separate transpose_3D_slice() calls would be needed.