I have been working in flexdashboards using rbokeh to draw some dynamic graphs. Because this is a bar chart mapped to categorical data in the variable Classification, I am unable to manually create a legend, which is fine because rbokeh does it automatically.
However, I am having some issues with the legend and labeling:
It is placed horribly, I would like to tell r to place in the upper left hand corner to get it away from bars with content.
I would like to drop the red ab_line into the legend and label it (using standard legend = will not work because of the mapped variables in the base chart
This graph needs to be 508 compliant for translation, the flexdashboard is, as well as the bokeh tools on the left hand margin and the keys in the pop ups, but the values and labels remain in English. Does anyone have a way of making the charts responsive to google translate? I am fine if that involves editing the extruded page....I will just need more guidance in doing it there.
figure(title=" Confirmed & Probable Cases by Year",width= 1400, height
=350)%>%
ly_bar(x=Year, y= count, position='stack', data=probConf,
width=.9,hover=TRUE, legend=TRUE, color=Classification) %>%
x_axis(label ='Year')%>%
y_axis(label ='Cases')%>%
ly_abline(v=17.5, legend=NULL, color = "red", width =1, alpha=.5)%>%
[![enter image description here][1]][1]set_palette(discrete_color = pal_color(c("#ee9f00", "#ffcc66")))
To answer number 1, you can use legend_location to specify the placement of the legend (see example below and note I replaced the probConf data with the lattice barley dataset so that it is reproducible for others).
figure(title = " Confirmed & Probable Cases by Year", width = 1400,
height = 350, legend_location = "top_left") %>%
ly_bar(x = variety, y = yield, position = "stack", data = lattice::barley,
width = 0.9, hover = TRUE, legend = TRUE, color = year) %>%
x_axis(label = "Year") %>%
y_axis(label = "Cases") %>%
ly_abline(v = "Svansota:1", color = "red", width = 1, alpha = 0.5) %>%
set_palette(discrete_color = pal_color(c("#ee9f00", "#ffcc66")))
#2 is currently difficult to achieve in rbokeh but it should be more robust to situations like this (mixing mapped and user-defined legend entries) in the future. If ly_abline() were to accept data as an argument, you could use use a mapped variable for color to resolve the issue.
One solution that isn't very pretty is to manually draw the bar chart polygons with ly_rect (one call to ly_rect for each classification) and use custom legend entries for those and then add a custom legend entry for ly_abline.
I do not know the answer to #3.
Related
I am using the Treemap package in R to highlight the number of COVID outbreaks in different settings. I am making a number of different reports using R Markdown. Each one describes a different type of settings and I would like to highlight that setting in the treemap for each report, showing what proportion of total outbreaks occur in the setting in question. For example you I am currently working on the K-12 school report and would like to highlight the box representing that category in the figure.
I was previously using an exploded donut pie chart however there were two many subcategories and the graph became hard to read.
I am picturing a way to change the label or border on one specific box, ie. put a yellow border around the box or make the label yellow. I found a way to do both these things for all the boxes but not just one specific box. I made this image using the snipping tool to further illustrate what the desired outcome might look like. The code to generate the treemap can be found in the link below. It looks like this:
# library
library(treemap)
# Build Dataset
group <- c(rep("group-1",4),rep("group-2",2),rep("group-3",3))
subgroup <- paste("subgroup" , c(1,2,3,4,1,2,1,2,3), sep="-")
value <- c(13,5,22,12,11,7,3,1,23)
data <- data.frame(group,subgroup,value)
# treemap
treemap(data,
index=c("group","subgroup"),
vSize="value",
type="index"
)
This is the most straightforward information I can find about the package, this is where I took the sample image and code from: https://www.r-graph-gallery.com/236-custom-your-treemap.html
It looks like the treemap package doesn't have a built-in way to do this. But we can hack it by using the data frame returned by treemap() and adding a rectangle to the appropriate viewport.
# Plot the treemap and save the data used for plotting.
t = treemap(data,
index = c("group", "subgroup"),
vSize = "value",
type = "index"
)
# Add a rectangle around subgroup-2.
library(grid)
library(dplyr)
with(
# t$tm is a data frame with one row per rectangle. Filter to the group we
# want to highlight.
t$tm %>%
filter(group == "group-1",
subgroup == "subgroup-2"),
{
# Use grid.rect to add a rectangle on top of the treemap.
grid.rect(x = x0 + (w / 2),
y = y0 + (h / 2),
width = w,
height = h,
gp = gpar(col = "yellow", fill = NA, lwd = 4),
vp = "data")
}
)
I am plotting a SpatialPoint dataframe in R using spplot, and I would like to use a colorbar rather than a legend, to portray color values. (It's more efficient, and I want the map to "match" previous, raster data maps.) I'm sure this is possible, but can find no examples of it online. Could anyone give me a hint?
My current code is:
my.palette <- brewer.pal(n = 9, name = "Spectral")
my.palette<- rev(my.palette)
pols1 <- list("sp.lines", as(ugborder, 'SpatialLines'), col = gray(0.4), lwd = 1)
pols2 <- list("sp.polygons", as(water_ug, 'SpatialPolygons'), fill = 'skyblue1',col="transparent", first = FALSE)
spplot(ughouseszn,zcol="lzn_sg_clng",cex = .75,
key.space="right", digits=1,
par.settings = list(axis.line = list(col = 'transparent')),
xlim = bbox(ugborder)[1, ],ylim = bbox(ugborder)[2, ],
col.regions = my.palette, cuts=8,
sp.layout=list(pols1, pols2))
Where ugborder and water_ug give Uganda's borders and water, ughouseszn is a SpatialPointsDataframe, and the resulting map is here:
(As a side note, I'm hoping that adding a colorbar will lead to a more efficient use of space -- right now there's a lot of extra space at the top and bottom of Uganda's border, which is useless, and also does NOT appear when I map raster data using spplot, with the same pols1 and pols2.)
If "lzn_sg_clng" is converted to a factor, does this give you the map you desire? (rendering by the category rather than a graduated scale).
I'm plotting results from various British elections in Leaflet and ran into a bit of a problem with legends.
For the various results in the general election I'm using the same colour function with different domain for the different data (the yellow-> purple scale in the picture)
This is created with (for the first two as examples):
labvotescols <- colorNumeric(
c("Yellow", "Purple"),
domain = Westminster$LabourVotes,
ukipvotescols <- colorNumeric(
c("Yellow", "Purple"),
domain = Westminster$UKIPVotes,
and so on...
Currently I have the legend
map = map %>% addLegend("bottomright", pal = ukipvotescols, values = Westminster$UKIPVotes,
title = "(e.g.) % voting UKIP at GE2015",
opacity = 1)
as one example of this, but really I'd like to get rid of all the values on the legend and just have "less" at the yellow end and "more" at the purple end. Is this possible?
I tried playing around and then googling but to no avail.
You could change it from a yellow -> purple scale and make your own scale:
map %>%
addLegend("bottomright",
colors = c("#FFC125", "#FFC125", "#8A4117", "#7D0552", "#571B7E"),
labels = c("less", "", "", "", "more"),
title = "(e.g.) % voting UKIP at GE2015",
opacity = 1)
If you get the correct colors then it should look similar. Not the answer you were looking for, but it's a good workaround. Your output would look like this:
Spend more time looking for better color transition and you could get a legend that looks similar to the yellow-purple color pallet you have up top.
I'm still relatively inexperienced manipulating plots in R, and am in need of assistance. I ran a redundancy analysis in R using the rda() function, but now I need to simplify the figure to exclude unnecessary information. The code I'm currently using is:
abio1516<-read.csv("1516 descriptors.csv")
attach(abio1516)
bio1516<-read.csv("1516habund.csv")
attach(bio1516)
rda1516<-rda(bio1516[,2:18],abio1516[,2:6])
anova(rda1516)
RsquareAdj(rda1516)
summary(rda1516)
varpart(bio1516[,2:18],~Distance_to_source,~Depth, ~Veg._cover, ~Surface_area,data=abio1516)
plot(rda1516,bty="n",xaxt="n",yaxt="n",main="1516; P=, R^2=",
ylab="Driven by , Var explained=",xlab="Driven by , Var explained=")
The produced plot looks like this:
Please help me modify my code to: exclude the sites (sit#), all axes, and the internal dashed lines.
I'd also like to either expand the size of the field, or move the vector labels to all fit in the plotting field.
updated as per responses, working code below this point
plot(rda,bty="n",xaxt="n",yaxt="n",type="n",main="xxx",ylab="xxx",xlab="xxx
Overall best:xxx")
abline(h=0,v=0,col="white",lwd=3)
points(rda,display="species",col="blue")
points(rda,display="cn",col="black")
text(rda,display="cn",col="black")
Start by plotting the rda with type = "n" which generates an empty plot to which you can add the things you want. The dotted lines are hard coded into the plot.cca function, so you need either make your own version, or use abline to hide them (then use box to cover up the holes in the axes).
require(vegan)
data(dune, dune.env)
rda1516 <- rda(dune~., data = dune.env)
plot(rda1516, type = "n")
abline(h = 0, v = 0, col = "white", lwd = 3)
box()
points(rda1516, display = "species")
points(rda1516, display = "cn", col = "blue")
text(rda1516, display = "cn", col = "blue")
If the text labels are not in the correct position, you can use the argument pos to move them (make a vector as long as the number of arrows you have with the integers 1 - 4 to move the label down, left, up, or right. (there might be better solutions to this)
I have a grid and I want to produce a map out of this grid with some map elements (scale, north arrow, etc). I have no problem drawing the grid and the coloring I need, but the additional map elements won't show on the map. I tried putting first=TRUE to the sp.layout argument according to the sp manual, but still no success.
I reproduced the issue with the integrated meuse dataset, so you may just copy&paste that code. I use those package versions: lattice_0.20-33 and sp_1.2-0
library(sp)
library(lattice) # required for trellis.par.set():
trellis.par.set(sp.theme()) # sets color ramp to bpy.colors()
alphaChannelSupported = function() {
!is.na(match(names(dev.cur()), c("pdf")))
}
data(meuse)
coordinates(meuse)=~x+y
data(meuse.riv)
library(gstat, pos = match(paste("package", "sp", sep=":"), search()) + 1)
data(meuse.grid)
coordinates(meuse.grid) = ~x+y
gridded(meuse.grid) = TRUE
v.uk = variogram(log(zinc)~sqrt(dist), meuse)
uk.model = fit.variogram(v.uk, vgm(1, "Exp", 300, 1))
meuse[["ff"]] = factor(meuse[["ffreq"]])
meuse.grid[["ff"]] = factor(meuse.grid[["ffreq"]])
zn.uk = krige(log(zinc)~sqrt(dist), meuse, meuse.grid, model = uk.model)
zn.uk[["se"]] = sqrt(zn.uk[["var1.var"]])
meuse.sr = SpatialPolygons(list(Polygons(list(Polygon(meuse.riv)),"meuse.riv")))
rv = list("sp.polygons", meuse.sr, fill = "lightblue")
sampling = list("sp.points", meuse.riv, color = "black")
scale = list("SpatialPolygonsRescale", layout.scale.bar(),
offset = c(180500,329800), scale = 500, fill=c("transparent","black"), which = 4)
text1 = list("sp.text", c(180500,329900), "0", cex = .5, which = 4)
text2 = list("sp.text", c(181000,329900), "500 m", cex = .5, which = 4)
arrow = list("SpatialPolygonsRescale", layout.north.arrow(),
offset = c(181300,329800),
scale = 400, which = 4)
library(RColorBrewer)
library(lattice)
trellis.par.set(sp.theme())
precip.pal <- colorRampPalette(brewer.pal(7, name="Blues"))
spplot(zn.uk, "var1.pred",
sp.layout = list(rv, sampling, scale, text1, text2),
main = "log(zinc); universal kriging standard errors",
col.regions=precip.pal,
contour=TRUE,
col='black',
pretty=TRUE,
scales=list(draw = TRUE),
labels=TRUE)
And that's how it looks...all naked:
So my questions:
Where is the scale bar, north arrow, etc hiding? Did I miss something? Every example I could find on the internet looks similar to that. On my own dataset I can see the scale bar and north arrow being drawn at first, but as soon as the grid is rendered, it superimposes the additional map elements (except for the scale text, that is shown on the map - not the bar and north arrow for some reason I don't seem to comprehend).
The error message appearing on the map just shows when I try to add the sampling locations sampling = list("sp.points", meuse.riv, color = "black"). Without this entry, the map shows without error, but also without additional map elements. How can I show the sampling points on the map (e.g. in circles whose size depends on the absolute value of this sampling point)?
This bothered me for many, many hours by now and I can't find any solution to this. In Bivand et al's textbook (2013) "Applied Spatial Data Analysis with R" I could read the following entry:
The order of items in the sp.layout argument matters; in principle objects
are drawn in the order they appear. By default, when the object of spplot has
points or lines, sp.layout items are drawn before the points to allow grids
and polygons drawn as a background. For grids and polygons, sp.layout
items are drawn afterwards (so the item will not be overdrawn by the grid
and/or polygon). For grids, adding a list element first = TRUE ensures that
the item is drawn before the grid is drawn (e.g. when filled polygons are added). Transparency may help when combining layers; it is available for the
PDF device and several other devices.
Function sp.theme returns a lattice theme that can be useful for plots
made by spplot; use trellis.par.set(sp.theme()) after a device is opened
or changed to make this effective.
However, also with this additional information I wasn't able to solve this problem. Glad for any hint!
The elements you miss are being drawn in panel four, which does not exist, so are not being drawn. Try removing the which = 4.
meuse.riv in your example is a matrix, which causes the error message, but should be a SpatialPoints object, so create sampling by:
sampling = list("sp.points", SpatialPoints(meuse.riv), color = "black")
When working from examples, my advice is to choose examples as close as possible to what you need, and only change one thing at a time.