I'm having some trouble understanding how to customize graphs using the rPlot function in the rCharts Package. Say I have the following code
#Install rCharts if you do not already have it
#This will require devtools, which can be downloaded from CRAN
require(devtools)
install_github('rCharts', 'ramnathv')
#simulate some random normal data
x <- rnorm(100, 50, 5)
y <- rnorm(100, 30, 2)
#store in a data frame for easy retrieval
demoData <- data.frame(x,y)
#generate the rPlot Object
demoChart <- rPlot(y~x, data = demoData, type = 'point')
#return the object // view the plot
demoChart
This will generate a plot and that is nice, but how would I go about adding horizontal lines along the y-axis? For example, if I wanted to plot a green line which represented the average y-value, and then red lines which represented +/- 3 standard deviations from the average? If anybody knows of some documentation and could point me to it then that would be great. However, the only documentation I could find was on the polychart.js (https://github.com/Polychart/polychart2) and I'm not quite sure how to apply this to the rCharts rPlot function in R.
I have done some digging and I feel like the answer is going to have something to do with adding/modifying the layers parameter within the rPlot object.
#look at the slots in this object
demoChart$params$layers
#doing this will return the following output (which will be different for
#everybody because I didn't set a seed). Also, I removed rows 6:100 of the data.
demoChart$params$layers
[[1]]
[[1]]$x
[1] "x"
[[1]]$y
[1] "y"
[[1]]$data
x y
1 49.66518 32.75435
2 42.59585 30.54304
3 53.40338 31.71185
4 58.01907 28.98096
5 55.67123 29.15870
[[1]]$facet
NULL
[[1]]$type
[1] "point"
If I figure this out I will post a solution, but I would appreciate any help/advice in the meantime! I don't have much experience playing with objects in R. I feel like this is supposed to have some similarity to ggplot2 which I also don't have much experience with.
Thanks for any advice!
You can overlay additional graphs onto your rCharts plot using layers. Add values for any additional layers as columns on to your original data.frame. copy_layer lets you use the values from the data.frame in the extra layers.
# Regression Plots using rCharts
require(rCharts)
mtcars$avg <- mean(mtcars$mpg)
mtcars$sdplus <- mtcars$avg + sd(mtcars$mpg)
mtcars$sdneg <- mtcars$avg - sd(mtcars$mpg)
p1 <- rPlot(mpg~wt, data=mtcars, type='point')
p1$layer(y='avg', copy_layer=T, type='line', color=list(const='red'))
p1$layer(y='sdplus', copy_layer=T, type='line', color=list(const='green'))
p1$layer(y='sdneg', copy_layer=T, type='line', color=list(const='green'))
p1
Here are a couple of examples: one from the main rCharts website and the other showing how to overlay a regression line.
Related
I am using the statspat package because I am working on spatial patterns.
I would like to do in ggplot and with colors instead of numbers (because it is not too readable),
the following graph, produced with the plot.quadratest function: Polygone
The numbers that interest me for the intensity of the colors are those at the bottom of each box.
The test object contains the following data:
Test object
I have looked at the help of the function, as well as the code of the function but I still cannot manage it.
Ideally I would like my final figure to look like this (maybe not with the same colors haha):
Final object
Thanks in advance for your help.
Please provide a reproducible example in the future.
The package reprex may be very helpful.
To use ggplot2 for this my best bet would be to convert
spatstat objects to sf and do the plotting that way,
but it may take some time. If you are willing to use base
graphics and spatstat you could do something like:
library(spatstat)
# Data (using a built-in dataset):
X <- unmark(chorley)
plot(X, main = "")
# Test:
test <- quadrat.test(X, nx = 4)
# Default plot:
plot(test, main = "")
# Extract the the `quadratcount` object (regions with observed counts):
counts <- attr(test, "quadratcount")
# Convert to `tess` (raw regions with no numbers)
regions <- as.tess(counts)
# Add residuals as marks to the tessellation:
marks(regions) <- test$residuals
# Plot regions with marks as colors:
plot(regions, do.col = TRUE, main = "")
I am running a function that returns a custom ggplot from an input data (it is in fact a plot with several layers on it). I run the function over several different input data and obtain a list of ggplots.
I want to create a grid with these plots to compare them but they all have different y axes.
I guess what I have to do is extract the maximum and minimum y axes limits from the ggplot list and apply those to each plot in the list.
How can I do that? I guess its through the use of ggbuild. Something like this:
test = ggplot_build(plot_list[[1]])
> test$layout$panel_scales_x
[[1]]
<ScaleContinuousPosition>
Range:
Limits: 0 -- 1
I am not familiar with the structure of a ggplot_build and maybe this one in particular is not a standard one as it comes from a "custom" ggplot.
For reference, these plots are created whit the gseaplot2 function from the enrichplot package.
I dont know how to "upload" an R object but if that would help, let me know how to do it.
Thanks!
edit after comments (thanks for your suggestions!)
Here is an example of the a gseaplot2 plot. GSEA stands for Gene Set Enrichment Analysis, it is a technique used in genomic studies. The gseaplot2 function calculates a running average and then plots it and another bar plot on the bottom.
and here is the grid I create to compare the plots generated from different data:
I would like to have a common scale for the "Running Enrichment Score" part.
I guess I could try to recreate the gseaplot2 function and input all of the datasets and then create the grid by facet_wrap, but I was wondering if there was an easy way of extracting parameters from a plot list.
As a reproducible example (from the enrichplot package):
library(clusterProfiler)
data(geneList, package="DOSE")
gene <- names(geneList)[abs(geneList) > 2]
wpgmtfile <- system.file("extdata/wikipathways-20180810-gmt-Homo_sapiens.gmt", package="clusterProfiler")
wp2gene <- read.gmt(wpgmtfile)
wp2gene <- wp2gene %>% tidyr::separate(term, c("name","version","wpid","org"), "%")
wpid2gene <- wp2gene %>% dplyr::select(wpid, gene) #TERM2GENE
wpid2name <- wp2gene %>% dplyr::select(wpid, name) #TERM2NAME
ewp2 <- GSEA(geneList, TERM2GENE = wpid2gene, TERM2NAME = wpid2name, verbose=FALSE)
gseaplot2(ewp2, geneSetID=1, subplots=1:2)
And this is how I generate the plot list (probably there is a much more elegant way):
plot_list = list()
for(i in 1:3) {
fig_i = gseaplot2(ewp2,
geneSetID=i,
subplots=1:2)
plot_list[[i]] = fig_i
}
ggarrange(plotlist=plot_list)
I was experimenting with the waffle package in r, and was trying to use a for loop to make multiple plots at once but was not able to get my code to work. I have a dataset with values for each year of renewables,and since it is over 40 years of data, was looking for a simple way to plot these with a for loop rather than manyally year by year. What am I doing wrong?
I have it from 1:16 as an experiment to see if it would work, although in reality I would do it for all the years in my dataset.
for(i in 1:16){
renperc<-islren$Value[i]
parts <- c(`Renewable`=(renperc), `Non-Renewable`=100-renperc)
waffle(parts, rows=10, size=1, colors=c("#00CC00", "#A9A9A9"),
title="Iceland Primary Energy Supply",
xlab=islren$TIME)
}
If I get your question correctly you want to plot all the 16 iterations in a same panel? You can parametrise your plot window to be divided into 16 smaller plots using par(mfrow = c(4,4)) (creating a 4 by 4 matrix and plotting into each cells recursively).
## Setting the graphical parameters
par(mfrow = c(4,4))
## Running the loop normally
for(i in 1:16){
renperc<-islren$Value[i]
parts <- c(`Renewable`=(renperc), `Non-Renewable`=100-renperc)
waffle(parts, rows=10, size=1, colors=c("#00CC00", "#A9A9A9"),
title="Iceland Primary Energy Supply",
xlab=islren$TIME)
}
If you need more plots (e.g. 40) you can increase the numbers in the graphical parameters (e.g. par(mfrow = c(6,7))) but that will create really tiny plots. One solution is to do it in multiple loops (for(i in 1:16); for(i in 17:32); etc.)
UPDATE: The code simply wasn't plotting anything when i tried putting in anything above one value (ex. 1:16) or a letter, both in terms of separate plots or many in one plot window (which I think perhaps waffle does not support in the same way as regular plots). In the end, I managed by making it into a function, although I'm still not sure why my original method wouldn't work if this did. See the code that worked below. I also tweaked it a bit, adding ggsave for example.
#function
waffling <- function(x){
renperc<-islren$Value[x]
parts <- c(`Renewable`=(renperc), `Non-Renewable`=100-renperc)
waffle(parts, rows=10, size=1, colors=c("#00CC00", "#A9A9A9"), title="",
xlab=islren$TIME[x])
ggsave(file=paste0("plot_", x,".png"))}
for(i in 1:57){
waffling(i)
}
There are similar questions on the website, but I could not find an answer to this seemingly very simple problem. I fit a mixture of two gaussians on the Old Faithful Dataset:
if(!require("mixtools")) { install.packages("mixtools"); require("mixtools") }
data_f <- faithful
plot(data_f$waiting, data_f$eruptions)
data_f.k2 = mvnormalmixEM(as.matrix(data_f), k=2, maxit=100, epsilon=0.01)
data_f.k2$mu # estimated mean coordinates for the 2 multivariate Gaussians
data_f.k2$sigma # estimated covariance matrix
I simply want to super-impose two ellipses for the two Gaussian components of the model described by the mean vectors data_f.k2$mu and the covariance matrices data_f.k2$sigma. To get something like:
For those interested, here is the MatLab solution that created the plot above.
If you are interested in the colors as well, you can use the posterior to get the appropriate groups. I did it with ggplot2, but first I show the colored solution using #Julian's code.
# group data for coloring
data_f$group <- factor(apply(data_f.k2$posterior, 1, which.max))
# plotting
plot(data_f$eruptions, data_f$waiting, col = data_f$group)
for (i in 1: length(data_f.k2$mu)) ellipse(data_f.k2$mu[[i]],data_f.k2$sigma[[i]], col=i)
And for my version using ggplot2.
# needs ggplot2 package
require("ggplot2")
# ellipsis data
ell <- cbind(data.frame(group=factor(rep(1:length(data_f.k2$mu), each=250))),
do.call(rbind, mapply(ellipse, data_f.k2$mu, data_f.k2$sigma,
npoints=250, SIMPLIFY=FALSE)))
# plotting command
p <- ggplot(data_f, aes(color=group)) +
geom_point(aes(waiting, eruptions)) +
geom_path(data=ell, aes(x=`2`, y=`1`)) +
theme_bw(base_size=16)
print(p)
You can use the ellipse-function from package mixtools. The initial problem was that this function swaps x and y from your plot. I'll try to figure this out and update the answe. (I'll leave the colors to somebody else...)
plot( data_f$eruptions,data_f$waiting)
for (i in 1: length(data_f.k2$mu)) ellipse(data_f.k2$mu[[i]],data_f.k2$sigma[[i]])
Using mixtools internal plotting function:
plot.mixEM(data_f.k2, whichplots=2)
I am trying to use the animation package to generate an "evolving" plot of points on a map. The map is generated from shapefiles (from the readShapeSpatial/readShapeLines functions).
The problem is when it's plotted in a for loop, the result is additive, whereas the ideal result is to have it evolve.
Are there ways of using par() that I am missing?
My question is: is there a way to clear just the points ploted from the points function
and not clearing the entire figure thus not having to regraph the shapefiles?
in case someone wants to see code:
# plotting underlying map
newyork <- readShapeSpatial('nycpolygon.shp')
routes <- readShapeLines('nyc.shp')
par(bg="grey25")
plot(newyork, lwd=2, col ="lightgray")
plot(routes,add=TRUE,lwd=0.1,col="lightslategrey")
# plotting points and save to GIF
ani.options(interval=.05)
saveGIF({
par(bg="grey25")
# Begin loop
for (i in 13:44){
infile <-paste("Week",i,".csv",sep='')
mydata <-read.csv(file = infile, header = TRUE, sep=",")
plotvar <- Var$Para
nclr <- 4
plotclr <-brewer.pal(nclr,"RdPu")
class<- classIntervals(plotvar,nclr,style = "pretty")
colcode <- findColours(class,plotclr)
points(Var$Lon,Var$Lat,col=colcode)
}
})
If you can accept a residual shadow or halo of ink, you can over-plot with color ="white" or == to your background choices. We cannot access your shape file but you can try it out by adding this line:
points(Var$Lon, Var$Lat, col="grey25")
It may leave gaps in other previously plotted figures or boundaries, because it's definitely not object-oriented. The lattice and ggplot2 graphics models are more object oriented, so if you want to post a reproducible example, that might be an alternate path to "moving" forward. I seem to remember that the rgl package has animation options in its repetoire.