I created a plot with multiple boxplots using this code from the singer data (this is reproducible):
library(tidyverse)
library(plotly)
library(lattice)
plot_ly(singer, y = ~height, color = ~voice.part, type = "box")
It created this beautiful box plot that was broken down by the voice part:
Now, the issue I'm having is that I'm trying to do the same thing but with a quantile plot, but no matter what I do, it ends up being all clumped together still, like this:
Oh, and this is the code I used for that:
fvalfull <- (1:nrow(singer) - 0.5) / nrow(singer)
dffull <- tibble(smpl = singer$height, voice.part = singer$voice.part, fval = fvalfull)
plot2 <- ggplot(dffull, aes(sample = smpl)) +
geom_qq(distribution = qunif)
ggplotly(plot2, color = ~dffull$voice.part)
Is there any way I can get all eight of the quantile plots to show up in the same plot? I know I can just make eight separate plots, but I think it would be more interesting to have all of them in the same plot, similar to the box plots.
Thank you!
I am not sure if this is what you want, but I created a quantile plot which shows all the voice.parts in one plot with different colors. You can use the following code:
library(tidyverse)
library(plotly)
library(lattice)
p <- ggplot(dffull, aes(sample=smpl))+
geom_qq(distribution = qunif, aes(colour=voice.part)) +
xlim(c(0,1))
ggplotly(p)
Output:
Related
I did everything in ggplot, and it was everything working well. Now I need it to show data when I point a datapoint. In this example, the model (to identify point), and the disp and wt ( data in axis).
For this I added the shape (same shape, I do not actually want different shapes) to model data. and asked ggplot not to show shape in legend. Then I convert to plotly. I succeeded in showing the data when I point the circles, but now I am having problems with the legend showing colors and shapes separated with a comma...
I did not wanted to make it again from scrach in plotly as I have no experience in plotly, and this is part of a much larger shiny project, where the chart adjust automatically the axis scales and adds trend lines the the chart among other things (I did not include for simplicity) that I do not know how to do it in plotly.
Many thanks in advance. I have tried a million ways for a couple of days now, and did not succeed.
# choose mtcars data and add rowname as column as I want to link it to shapes in ggplot
data1 <- mtcars
data1$model <- rownames(mtcars)
# I turn cyl data to character as when charting it showed (Error: Continuous value supplied to discrete scale)
data1$cyl <- as.character(data1$cyl)
# linking colors with cylinders and shapes with models
ccolor <- c("#E57373","purple","green")
cylin <- c(6,4,8)
# I actually do not want shapes to be different, only want to show data of model when I point the data point.
models <- data1$model
sshapes <- rep(16,length(models))
# I am going to chart, do not want legend to show shape
graff <- ggplot(data1,aes(x=disp, y=wt,shape=model,col=cyl)) +
geom_point(size = 1) +
ylab ("eje y") + xlab('eje x') +
scale_color_manual(values= ccolor, breaks= cylin)+
scale_shape_manual(values = sshapes, breaks = models)+
guides(shape='none') # do not want shapes to show in legend
graff
chart is fine, but when converting to ggplotly, I am having trouble with the legend
# chart is fine, but when converting to ggplotly, I am having trouble with the legend
graffPP <- ggplotly(graff)
graffPP
legend is not the same as it was in ggplot
I succeeded in showing the model and data from axis when I point a datapoint in the chart... but now I am having problems with the legend....
To the best of my knowledge there is no easy out-of-the box solution to achieve your desired result.
Using pure plotly you could achieve your result by assigning legendgroups which TBMK is not available using ggplotly. However, you could assign the legend groups manually by manipulating the plotly object returned by ggplotly.
Adapting my answer on this post to your case you could achieve your desired result like so:
library(plotly)
p <- ggplot(data1, aes(x = disp, y = wt, shape = model, col = cyl)) +
geom_point(size = 1) +
ylab("eje y") +
xlab("eje x") +
scale_color_manual(values = ccolor, breaks = cylin) +
scale_shape_manual(values = sshapes, breaks = models) +
guides(shape = "none")
gp <- ggplotly(p = p)
# Get the names of the legend entries
df <- data.frame(id = seq_along(gp$x$data), legend_entries = unlist(lapply(gp$x$data, `[[`, "name")))
# Extract the group identifier, i.e. the number of cylinders from the legend entries
df$legend_group <- gsub("^\\((\\d+).*?\\)", "\\1", df$legend_entries)
# Add an indicator for the first entry per group
df$is_first <- !duplicated(df$legend_group)
for (i in df$id) {
# Is the layer the first entry of the group?
is_first <- df$is_first[[i]]
# Assign the group identifier to the name and legendgroup arguments
gp$x$data[[i]]$name <- df$legend_group[[i]]
gp$x$data[[i]]$legendgroup <- gp$x$data[[i]]$name
# Show the legend only for the first layer of the group
if (!is_first) gp$x$data[[i]]$showlegend <- FALSE
}
gp
I have a ggplot with facet_wrap of about 22 different plots. I'm trying to make them interactive using ggplotly, but for some reason certain rows of plots have their header area get larger to the point where I barely see the graph. It looks like all the plots in the same row have the same gray sized area. I'm just trying to generate the plot, but keep the gray title area the same size. Any help would be greatly appreciated. I've tried to look at the panel options, but couldn't find anything that would do what I needed, but I'm not sure if I'm just missing something.
p <- ggplot(data = df, aes(value, fill = FIELD))+
geom_histogram()+
facet_wrap(~variable, scales='free_x')
ggplotly(p)
I was able to replicate the error with...
library(titanic)
library(reshape2)
titanic_long <- melt(titanic_train)
p<-ggplot(data=titanic_long, aes(value))+
geom_histogram(aes(fill=Sex))+
facet_wrap(~variable, scales='free_x')
ggplotly(p)
I'm superimposing two images in R. One image is a boxplot (using boxplot()), the other a scatterplot (using scatterplot()). I noticed a discrepancy in the scale along the x-axis. (A) is the boxplot scale. (B) is for the scatterplot.
What I've been trying to do is re-scale (B) to suit (A). I note there is a condition called xlim in scatterplot. Tried it, didn't work. I've also noted this example came up as I was typing out the question: Change Axis Label - R scatterplot.
Tried it, didn't work.
How can I modify the x-axis to change the scale from 1.0, 1.5, 2.0, 2.5, 3.0 to simply 1,2,3.
In Stata, I'm aware you can specify the x-axis range, and then indicate the step-ups between. For example, the range may be 0-100, and each measurable point would be set to 10. So you'd end up with 10, 20,....,100.
My R code, as it stands, looks something like this:
library(car)
boxplot(a,b,c)
par(new=T)
scatterplot(x, y, smooth=TRUE, boxplots=FALSE)
I've tried modifying scatterplot as such without any success:
scatterplot(x, y, smooth=TRUE, boxplots=FALSE, xlim=c(1,3))
As mentioned in comments use as.factor, then xaxis should align. Here is ggplot solution:
#dummy data
dat1 <- data.frame(group=as.factor(rep(1:3,4)),
var=c(runif(12)))
dat2 <- data.frame(x=as.factor(1:3),y=runif(3))
library(ggplot2)
library(grid)
library(gridExtra)
#plot points on top of boxplot
ggplot(dat1,aes(group,var)) +
geom_boxplot() +
geom_point(aes(x,y),dat2)
Plot as separate plots
gg_boxplot <-
ggplot(dat1,aes(group,var)) +
geom_boxplot()
gg_point <-
ggplot(dat2,aes(x,y)) +
geom_point()
grid.arrange(gg_boxplot,gg_point,
ncol=1,
main="Plotting is easier with ggplot")
EDIT
Using xlim as suggested by #RuthgerRighart
#dummy data - no factors
dat1 <- data.frame(group=rep(1:3,4),
var=c(runif(12)))
dat2 <- data.frame(x=1:3,y=runif(3))
par(mfrow=c(2,1))
boxplot(var~group,dat1,xlim=c(1,3))
plot(dat2$x,dat2$y,xlim=c(1,3))
What I really want to do is plot a histogram, with the y-axis on a log-scale. Obviously this i a problem with the ggplot2 geom_histogram, since the bottom os the bar is at zero, and the log of that gives you trouble.
My workaround is to use the freqpoly geom, and that more-or less does the job. The following code works just fine:
ggplot(zcoorddist) +
geom_freqpoly(aes(x=zcoord,y=..density..),binwidth = 0.001) +
scale_y_continuous(trans = 'log10')
The issue is that at the edges of my data, I get a couple of garish vertical lines that really thro you off visually when combining a bunch of these freqpoly curves in one plot. What I'd like to be able to do is use points at every vertex of the freqpoly curve, and no lines connecting them. Is there a way to to this easily?
The easiest way to get the desired plot is to just recast your data. Then you can use geom_point. Since you don't provide an example, I used the standard example for geom_histogram to show this:
# load packages
require(ggplot2)
require(reshape)
# get data
data(movies)
movies <- movies[, c("title", "rating")]
# here's the equivalent of your plot
ggplot(movies) + geom_freqpoly(aes(x=rating, y=..density..), binwidth=.001) +
scale_y_continuous(trans = 'log10')
# recast the data
df1 <- recast(movies, value~., measure.var="rating")
names(df1) <- c("rating", "number")
# alternative way to recast data
df2 <- as.data.frame(table(movies$rating))
names(df2) <- c("rating", "number")
df2$rating <- as.numeric(as.character(df$rating))
# plot
p <- ggplot(df1, aes(x=rating)) + scale_y_continuous(trans="log10", name="density")
# with lines
p + geom_linerange(aes(ymax=number, ymin=.9))
# only points
p + geom_point(aes(y=number))
I am trying to produce something similar to densityplot() from the lattice package, using ggplot2 after using multiple imputation with the mice package. Here is a reproducible example:
require(mice)
dt <- nhanes
impute <- mice(dt, seed = 23109)
x11()
densityplot(impute)
Which produces:
I would like to have some more control over the output (and I am also using this as a learning exercise for ggplot). So, for the bmi variable, I tried this:
bar <- NULL
for (i in 1:impute$m) {
foo <- complete(impute,i)
foo$imp <- rep(i,nrow(foo))
foo$col <- rep("#000000",nrow(foo))
bar <- rbind(bar,foo)
}
imp <-rep(0,nrow(impute$data))
col <- rep("#D55E00", nrow(impute$data))
bar <- rbind(bar,cbind(impute$data,imp,col))
bar$imp <- as.factor(bar$imp)
x11()
ggplot(bar, aes(x=bmi, group=imp, colour=col)) + geom_density()
+ scale_fill_manual(labels=c("Observed", "Imputed"))
which produces this:
So there are several problems with it:
The colours are wrong. It seems my attempt to control the colours is completely wrong/ignored
There are unwanted horizontal and vertical lines
I would like the legend to show Imputed and Observed but my code gives the error invalid argument to unary operator
Moreover, it seems like quite a lot of work to do what is accomplished in one line with densityplot(impute) - so I wondered if I might be going about this in the wrong way entirely ?
Edit: I should add the fourth problem, as noted by #ROLO:
.4. The range of the plots seems to be incorrect.
The reason it is more complicated using ggplot2 is that you are using densityplot from the mice package (mice::densityplot.mids to be precise - check out its code), not from lattice itself. This function has all the functionality for plotting mids result classes from mice built in. If you would try the same using lattice::densityplot, you would find it to be at least as much work as using ggplot2.
But without further ado, here is how to do it with ggplot2:
require(reshape2)
# Obtain the imputed data, together with the original data
imp <- complete(impute,"long", include=TRUE)
# Melt into long format
imp <- melt(imp, c(".imp",".id","age"))
# Add a variable for the plot legend
imp$Imputed<-ifelse(imp$".imp"==0,"Observed","Imputed")
# Plot. Be sure to use stat_density instead of geom_density in order
# to prevent what you call "unwanted horizontal and vertical lines"
ggplot(imp, aes(x=value, group=.imp, colour=Imputed)) +
stat_density(geom = "path",position = "identity") +
facet_wrap(~variable, ncol=2, scales="free")
But as you can see the ranges of these plots are smaller than those from densityplot. This behaviour should be controlled by parameter trim of stat_density, but this seems not to work. After fixing the code of stat_density I got the following plot:
Still not exactly the same as the densityplot original, but much closer.
Edit: for a true fix we'll need to wait for the next major version of ggplot2, see github.
You can ask Hadley to add a fortify method for this mids class. E.g.
fortify.mids <- function(x){
imps <- do.call(rbind, lapply(seq_len(x$m), function(i){
data.frame(complete(x, i), Imputation = i, Imputed = "Imputed")
}))
orig <- cbind(x$data, Imputation = NA, Imputed = "Observed")
rbind(imps, orig)
}
ggplot 'fortifies' non-data.frame objects prior to plotting
ggplot(fortify.mids(impute), aes(x = bmi, colour = Imputed,
group = Imputation)) +
geom_density() +
scale_colour_manual(values = c(Imputed = "#000000", Observed = "#D55E00"))
note that each ends with a '+'. Otherwise the command is expected to be complete. This is why the legend did not change. And the line starting with a '+' resulted in the error.
You can melt the result of fortify.mids to plot all variables in one graph
library(reshape)
Molten <- melt(fortify.mids(impute), id.vars = c("Imputation", "Imputed"))
ggplot(Molten, aes(x = value, colour = Imputed, group = Imputation)) +
geom_density() +
scale_colour_manual(values = c(Imputed = "#000000", Observed = "#D55E00")) +
facet_wrap(~variable, scales = "free")