I'd like to plot two graphs ontop of each other like in this post.
Experimental data: I have continuous variable displaying the angle of wind on a given day in a list called expt$iso_xs[,8], I then have the wind speed corresponding to that angle in expt$iso_xs[,2].
df<-data.frame(expt$iso.xs)
head(expt$iso.xs)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
736105.4 16.62729 2.183740 7.234774 0.9791632 4.01 4.20 238.62
736105.4 18.96705 2.489668 7.036234 0.9640366 3.82 4.00 243.14
736105.5 20.52089 2.687636 10.355394 1.3698454 4.99 5.14 247.02
736105.5 19.94449 2.611556 10.306912 1.3655301 4.85 5.12 249.57
736105.5 19.43309 2.551787 11.098302 1.4646251 4.83 5.12 243.89
736105.5 20.48259 2.689075 11.928011 1.5710530 4.89 5.09 254.23
Which looks like this:
Simulation data: I have a data.frame z that contains predictions for a subset of the above angles (0-90º).
head(z,15)
Tracer angle treatment bigangle
71.101 0 S 150
71.101 0 S 150
71.105 15 S 165
71.105 15 S 165
71.098 30 S 180
71.098 45 S 195
71.114 60 S 210
71.114 80 S 230
71.110 90 S 240
Plotting it using bigangle as factor and Tracer as :
ggplot() +
geom_boxplot(data=z, aes(y = (3600/Tracer/93.241), x = factor(bigangle)),outlier.shape = NA,outlier.colour = NA)+
coord_cartesian(ylim=c(0, 1))+
labs(x = "Angle", y = "Normalised ACh" )+
scale_x_discrete(labels=seq(0,360,10))+
theme_classic()
looks like this:
I'd like to superimpose the boxplot ontop of the portion of red points (between 150º and 240º) but the following doesn't work:
ggplot() +
geom_boxplot(data=z, aes(y = (3600/Tracer/93.241), x = factor(bigangle)),outlier.shape = NA,outlier.colour = NA)+
geom_point(data=df, aes(y = X2/45, x = X8),color="red")+
coord_cartesian(ylim=c(0, 1))+
labs(x = "Angle", y = "Normalised ACh" )+
scale_x_discrete(labels=seq(0,360,10))+
theme_classic()
Any thoughts would be much appreciated,
Cheers
I think your only problem is trying to specify a discrete x scale for continuous data. That and you need a group for your boxplot geom.
As an illustrative example:
mt = mtcars
mt$wt_bin = cut(mt$wt, breaks = c(1, 3, 4.5, 6))
ggplot(mt, aes(x = wt, y = mpg)) +
geom_point() +
geom_boxplot(aes(group = wt_bin, x = wt), alpha = 0.4)
As the geom_boxplot help says:
You can also use boxplots with continuous x, as long as you supply
a grouping variable. cut_width is particularly useful
The example in the help shows this code:
ggplot(diamonds, aes(carat, price)) +
geom_boxplot(aes(group = cut_width(carat, 0.25)))
You can, of course, add a geom_point layer (though in the diamonds data there are too many points for that to be a nice plot).
For your scale, don't use a discrete scale unless you have factors on the axis. You probably want scale_x_continuous(breaks = seq(0, 360, 10)).
Different data sets can be used in the usual way, with the data argument. Continuing the previous example but using different data for the geom_point layer:
similar_to_mt = data.frame(wt = runif(100, 1, 6), mpg = rnorm(100, 20, 4))
ggplot(mt, aes(x = wt, y = mpg)) +
geom_point(data = similar_to_mt) +
geom_boxplot(data = mt, aes(group = wt_bin, x = wt), alpha = 0.4)
Related
I am using ggplot geom_smooth to plot turnover data of a customer group from previous year against the current year (based on calendar weeks). As the last week is not complete, I would like to use a dashed linetype for the last week. However, I can't figure out how to that. I can either change the linetype for the entire plot or an entire series, but not within a series (depending on the value of x):
To keep it simple, let's just use the following example:
set.seed(42)
frame <- data.frame(series = rep(c('a','b'),50),x = 1:100, y = runif(100))
ggplot(frame,aes(x = x,y = y, group = series, color=series)) +
geom_smooth(size=1.5, se=FALSE)
How would I have to change this to get dashed lines for x >= 75?
The goal would be something like this:
Thx very much for any help!
Edit, 2016-03-05
Of course I fail when trying to use this method on the original plot. The Problem lies with the ribbon, which is calculated using stat_summary and a predefined function. I tried to use use stat_summary on the original data (mdf), and geom_line on the smooth_data. Even when I comment out everything else, I still get "Error: Continuous value supplied to discrete scale". I believe the problem comes from the fact that the original x value (Kalenderwoche) was discrete, whereas the new, smoothed x is continuous. Do I have to somehow transform one into the other? What else could I do?
Here is what I tried (condensed to the essential lines):
quartiles <- function(x) {
x <- na.omit(x) # remove NULL
median <- median(x)
q1 <- quantile(x,0.25)
q3 <- quantile(x,0.75)
data.frame(y = median, ymin = median, ymax = q3)
}
g <- ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable))+
geom_smooth(size=1.5, method="auto", se=FALSE)
# Take out the data for smooth line
smooth_data <- ggplot_build(g)$data[[1]]
ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable))+
stat_summary(fun.data = quartiles,geom="ribbon", colour="NA", alpha=0.25)+
geom_line(data=smooth_data, aes(x=x, y=y, group=group, colour=group, fill=group))
mdf looks like this:
str(mdf)
'data.frame': 280086 obs. of 5 variables:
$ konto_id : int 1 1 1 1 1 1 1 1 1 1 ...
$ Kalenderwoche: Factor w/ 14 levels "2015-48","2015-49",..: 4 12 1 3 7 13 10 6 5 9 ...
$ variable : Factor w/ 2 levels "Umsatz","Umsatz Vorjahr": 1 1 1 1 1 1 1 1 1 1 ...
$ value : num 0 428.3 97.8 76 793.1 ...
There are many accounts (konto_id), and for each account and calendar week (Kalenderwoche), there is a current turnover value (Umsatz) and a turnover value from last year (Umsatz Vorjahr). I can provide a smaller version of the data.frame and the entire code, if required.
Thx very much for any help!
P.S. I am a total novice in R, so my code probably looks rather stupid to pros, sorry for that :(
Edit, 2016-03-06
I have uploaded a subset of the data (mdf):
mdf
The full code of the original graph is the following (looking somewhat weird with so little data, but that's not the point ;)
library(dtw)
library(reshape2)
library(ggplot2)
library(RODBC)
library(Cairo)
# custom breaks for X axis
breaks.custom <- unique(mdf$Kalenderwoche)[c(TRUE,rep(FALSE,0))]
# function called by stat_summary
quartiles <- function(x) {
x <- na.omit(x)
median <- median(x)
q1 <- quantile(x,0.25)
q3 <- quantile(x,0.75)
data.frame(y = median, ymin = median, ymax = q3)
}
# Positions for guidelines and labels
horizontal.center <- (length(unique(mdf$Kalenderwoche))+1)/2
kw.horizontal.center <- as.vector(sort(unique(mdf$Kalenderwoche))[c(horizontal.center-0.5,horizontal.center+0.5)])
vpos.P75.label <- max(quantile(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[1]],0.75)
,quantile(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[2]],0.75))+10
# use the higher P75 value of the two weeks around the center
vpos.mean.label <- min(mean(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[1]])
,mean(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[2]]))-10
vpos.median.label <- min(median(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[1]])
,median(mdf$value[mdf$Kalenderwoche==kw.horizontal.center[2]]))-10
hpos.vline <- which(as.vector(sort(unique(mdf$Kalenderwoche))=="2016-03"))
# custom colour palette (2 colors)
cbPaletteLine <- c("#DA2626", "#2626DA")
cbPaletteFill <- c("#F0A8A8", "#7C7CE9")
# ggplot
ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable))+
geom_smooth(size=1.5, method="auto", se=FALSE)+
# SE=FALSE to suppress drawing of the SE of the fit.SE of the data shall be used instead:
stat_summary(fun.data = quartiles,geom="ribbon", colour="NA", alpha=0.25)+
scale_x_discrete(breaks=breaks.custom)+
scale_colour_manual(values=cbPaletteLine)+
scale_fill_manual(values=cbPaletteFill)+
#coord_cartesian(ylim = c(0, 250)) +
theme(legend.title = element_blank(), title = element_text(face="bold", size=12))+
#scale_color_brewer(palette="Dark2")+
labs(title = "Tranche 1", x = "Kalenderwoche", y = "Konto-Umsatz [CHF]")+
geom_vline(xintercept = hpos.vline, linetype=2)+
annotate("text", x=horizontal.center, y=vpos.median.label, label = "Median", size=4)+
annotate("text", x=horizontal.center, y=vpos.mean.label, label= "Mean", size=4)+
annotate("text", x=horizontal.center, y=vpos.P75.label, label = "P75%", size=4)+
theme(axis.text.x=element_text(angle = 90, hjust = 0.5, vjust = 0.5))
Edit, 2016-03-06
The final plot now looks like this (thx, Jason!!)
I am not so sure how to smooth all data and use different line types for subsets by geom_smooth function. My idea is to pull out the data which ggplot used to construct the plot and use geom_line to reproduce it. This was the way I did it:
set.seed(42)
frame <- data.frame(series=rep(c('a','b'), 50),
x = 1:100, y = runif(100))
library(ggplot2)
g <- ggplot(frame, aes(x=x, y=y, color=series)) + geom_smooth(se=FALSE)
# Take out the data for smooth line
smooth_data <- ggplot_build(g)$data[[1]]
ggplot(smooth_data[smooth_data$x <= 76, ], aes(x=x, y=y, color=as.factor(group), group=group)) +
geom_line(size=1.5) +
geom_line(data=smooth_data[smooth_data$x >= 74, ], linetype="dashed", size=1.5) +
scale_color_discrete("Series", breaks=c("1", "2"), labels=c("a", "b"))
You're right. The problem is that you add a continuous x to a discrete x in the original layer. One way to deal with it is to create a lookup table which in this case, it is easy because x is a sequence from 1 to 14. We can transform discrete x by indexing. In your code, it should work if you add:
level <- levels(mdf$Kalenderwoche)
ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable))+
stat_summary(fun.data = quartiles,geom="ribbon", colour="NA", alpha=0.25) +
geom_line(data=smooth_data, aes(x=level[x], y=y, group=group, colour=as.factor(group), fill=NA))
Here is my attempt for the question:
g <- ggplot(mdf, aes(x=Kalenderwoche, y=value, group=variable, colour=variable,fill=variable)) +
geom_smooth(size=1.5, method="auto", se=FALSE) +
# SE=FALSE to suppress drawing of the SE of the fit.SE of the data shall be used instead:
stat_summary(fun.data = quartiles,geom="ribbon", colour="NA", alpha=0.25)
smooth_data <- ggplot_build(g)$data[[1]]
ribbon_data <- ggplot_build(g)$data[[2]]
# Use them as lookup table
level <- levels(mdf$Kalenderwoche)
clevel <- levels(mdf$variable)
ggplot(smooth_data[smooth_data$x <= 13, ], aes(x=level[x], y=y, group=group, color=as.factor(clevel[group]))) +
geom_line(size=1.5) +
geom_line(data=smooth_data[smooth_data$x >= 13, ], linetype="dashed", size=1.5) +
geom_ribbon(data=ribbon_data,
aes(x=x, ymin=ymin, ymax=ymax, fill=as.factor(clevel[group]), color=NA), alpha=0.25) +
scale_x_discrete(breaks=breaks.custom) +
scale_colour_manual(values=cbPaletteLine) +
scale_fill_manual(values=cbPaletteFill) +
#coord_cartesian(ylim = c(0, 250)) +
theme(legend.title = element_blank(), title = element_text(face="bold", size=12))+
#scale_color_brewer(palette="Dark2")+
labs(title = "Tranche 1", x = "Kalenderwoche", y = "Konto-Umsatz [CHF]")+
geom_vline(xintercept = hpos.vline, linetype=2)+
annotate("text", x=horizontal.center, y=vpos.median.label, label = "Median", size=4)+
annotate("text", x=horizontal.center, y=vpos.mean.label, label= "Mean", size=4)+
annotate("text", x=horizontal.center, y=vpos.P75.label, label = "P75%", size=4)+
theme(axis.text.x=element_text(angle = 90, hjust = 0.5, vjust = 0.5))
Note that the legend has borderline.
this must be a FAQ, but I can't find an exactly similar example in the other answers (feel free to close this if you can point a similar Q&A). I'm still a newbie with ggplot2 and can't seem to wrap my head around it quite so easily.
I have 2 data.frames (that come from separate mixed models) and I'm trying to plot them both into the same graph. The data.frames are:
newdat
id Type pred SE
1 1 15.11285 0.6966029
2 1 13.68750 0.9756909
3 1 13.87565 0.6140860
4 1 14.61304 0.6187750
5 1 16.33315 0.6140860
6 1 16.19740 0.6140860
1 2 14.88805 0.6966029
2 2 13.46270 0.9756909
3 2 13.65085 0.6140860
4 2 14.38824 0.6187750
5 2 16.10835 0.6140860
6 2 15.97260 0.6140860
and
newdat2
id pred SE
1 14.98300 0.6960460
2 13.25893 0.9872502
3 13.67650 0.6150701
4 14.39590 0.6178266
5 16.37662 0.6171588
6 16.08426 0.6152017
As you can see, the second data.frame doesn't have Type, whereas the first does, and therefore has 2 values for each id.
What I can do with ggplot, is plot either one, like this:
fig1
fig2
As you can see, in fig 1 ids are stacked by Type on the x-axis to form two groups of 6 ids. However, in fig 2 there is no Type, but instead just the 6 ids.
What I would like to accomplish is to plot fig2 to the left/right of fig1 with similar grouping. So the resulting plot would look like fig 1 but with 3 groups of 6 ids.
The problem is also, that I need to label and organize the resulting figure so that for newdat the x-axis would include a label for "model1" and for newdat2 a label for "model2", or some similar indicator that they are from different models. And to make things even worse, I need some labels for Type in newdat.
My (hopefully) reproducible (but obviously very bad) code for fig 1:
library(ggplot2)
pd <- position_dodge(width=0.6)
ggplot(newdat,aes(x=Type,y=newdat$pred,colour=id))+
geom_point(position=pd, size=5)
geom_linerange(aes(ymin=newdat$pred-1.96*SE,ymax=newdat$pred+1.96*SE), position=pd, size=1.5, linetype=1) +
theme_bw() +
scale_colour_grey(start = 0, end = .8, name="id") +
coord_cartesian(ylim=c(11, 18)) +
scale_y_continuous(breaks=seq(10, 20, 1)) +
scale_x_discrete(name="Type", limits=c("1","2"))
Code for fig 2 is identical, but without the limits in the last line and with id defined for x-axis in ggplot(aes())
As I understand it, defining stuff at ggplot() makes that stuff "standard" along the whole graph, and I've tried to remove the common stuff and separately define geom_point and geom_linerange for both newdat and newdat2, but no luck so far... Any help is much appreciated, as I'm completely stuck.
How about adding first adding some new variables to each dataset and then combining them:
newdat$model <- "model1"
newdat2$model <- "model2"
newdat2$Type <- 3
df <- rbind(newdat, newdat2)
# head(df)
Then we can plot with:
library(ggplot2)
ggplot(df, aes(x = interaction(model, factor(Type)), y = pred, color = factor(id))) +
geom_point(position = position_dodge(width = 0.6), size = 5) +
geom_linerange(aes(ymin = pred - 1.96 * SE, ymax = pred + 1.96 * SE),
position = position_dodge(width = 0.6),
size = 1.5, linetype = 1)
Alternatively, you pass an additional aesthetic to geom_linerange to further delineate the model type:
ggplot(df, aes(x = interaction(model, factor(Type)), y = pred, color = factor(id))) +
geom_point(position = position_dodge(width = 0.6), size = 5) +
geom_linerange(aes(ymin = pred - 1.96 * SE, ymax = pred + 1.96 * SE, linetype = model),
position = position_dodge(width = 0.6),
size = 1.5)
Finally, you may want to considered facets:
ggplot(df, aes(x = interaction(model, factor(Type)), y = pred, color = factor(id))) +
geom_point(position = position_dodge(width = 0.6), size = 5) +
geom_linerange(aes(ymin = pred - 1.96 * SE, ymax = pred + 1.96 * SE),
position = position_dodge(width = 0.6),
size = 1.5) +
facet_wrap(~ id)
I want to create a custom heatmap, whereby a matrix defines the intensity (alpha) of a series of squares with uniquely specified colors. Additionally, the axis defining each square will be unique (see example below).
Which packages may help do this? The geom_rect function from ggplot2 (used in this different question seems promising but too tightly coupled to a given plot?
e.g
Data:
[,1] [,2]
[1,] 30 5
[2,] 3 50
Axis:
x_bounds <- t(matrix(c(
0,10,
10,30
), 2))
y_bounds <- t(matrix(c(
0,-50,
-50,1000
), 2))
Result mock-up:
Does this give you what you want?
library(ggplot2)
x_bounds <- c(0,10,30)
y_bounds <- c(0,-50,1000)
df <- data.frame(x = c(0,1,0,1),
y = c(0,0,1,1),
fill = c("red","green","blue","yellow"),
alpha = c(0.6,0.6,0.5,0.8))
ggplot(data = df) +
geom_rect(aes(xmin = x, xmax = x+1, ymin = y, ymax = y+1,
fill = fill, alpha = alpha)) +
scale_x_continuous(breaks = min(df$x):(max(df$x)+1),
labels = x_bounds) +
scale_y_continuous(breaks = min(df$y):(max(df$y)+1),
labels = y_bounds) +
scale_fill_identity() +
theme(panel.background=element_blank(),
panel.border=element_blank(),
panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),
plot.background=element_blank())
I've seen some examples when constructing a heatmap of having the fill variable set to ..level...
Such as in this example:
library(MASS)
ggplot(geyser, aes(x = duration, y = waiting)) +
geom_point() +
geom_density2d() +
stat_density2d(aes(fill = ..level..), geom = "polygon")
I suspect that the ..level.. means that the fill is set to the relative amount of layers present? Also could someone link me a good example of how to interpret these 2D-density plots, what does each contour represent etc.? I have searched online but couldn't find any suitable guide.
the stat_ functions compute new values and create new data frames. this one creates a data frame with a level variable. you can see it if you use ggplot_build vs plotting the graph:
library(ggplot2)
library(MASS)
gg <- ggplot(geyser, aes(x = duration, y = waiting)) +
geom_point() +
geom_density2d() +
stat_density2d(aes(fill = ..level..), geom = "polygon")
gb <- ggplot_build(gg)
head(gb$data[[3]])
## fill level x y piece group PANEL
## 1 #132B43 0.002 3.876502 43.00000 1 1-001 1
## 2 #132B43 0.002 3.864478 43.09492 1 1-001 1
## 3 #132B43 0.002 3.817845 43.50833 1 1-001 1
## 4 #132B43 0.002 3.802885 43.65657 1 1-001 1
## 5 #132B43 0.002 3.771212 43.97583 1 1-001 1
## 6 #132B43 0.002 3.741335 44.31313 1 1-001 1
The ..level.. tells ggplot to reference that column in the newly build data frame.
Under the hood, ggplot is doing something similar to (this is not a replication of it 100% as it uses different plot limits, etc):
n <- 100
h <- c(bandwidth.nrd(geyser$duration), bandwidth.nrd(geyser$waiting))
dens <- kde2d(geyser$duration, geyser$waiting, n=n, h=h)
df <- data.frame(expand.grid(x = dens$x, y = dens$y), z = as.vector(dens$z))
head(df)
## x y z
## 1 0.8333333 43 9.068691e-13
## 2 0.8799663 43 1.287684e-12
## 3 0.9265993 43 1.802768e-12
## 4 0.9732323 43 2.488479e-12
## 5 1.0198653 43 3.386816e-12
## 6 1.0664983 43 4.544811e-12
And also calling contourLines to get the polygons.
This is a decent introduction to the topic. Also look at ?kde2d in R help.
Expanding on the answer provided by #hrbrmstr -- first, the call to geom_density2d() is redundant. That is, you can achieve the same results with:
library(ggplot2)
library(MASS)
gg <- ggplot(geyser, aes(x = duration, y = waiting)) +
geom_point() +
stat_density2d(aes(fill = ..level..), geom = "polygon")
Let's consider some other ways to visualize this density estimate that may help clarify what is going on:
base_plot <- ggplot(geyser, aes(x = duration, y = waiting)) +
geom_point()
base_plot +
stat_density2d(aes(color = ..level..))
base_plot +
stat_density2d(aes(fill = ..density..), geom = "raster", contour = FALSE)
base_plot +
stat_density2d(aes(alpha = ..density..), geom = "tile", contour = FALSE)
Notice, however, we can no longer see the points generated from geom_point().
Finally, note that you can control the bandwidth of the density estimate. To do this, we pass x and y bandwidth arguments to h (see ?kde2d):
base_plot +
stat_density2d(aes(fill = ..density..), geom = "raster", contour = FALSE,
h = c(2, 5))
Again, the points from geom_point() are hidden as they are behind the call to stat_density2d().
I am trying to produce a bar graph that has multiple groupings of factors. An example from excel of what I am attempting to create, subgrouped by Variety and Irrigation treatment:
I know I could produce multiple graphs using facet_wrap(), but I would like to produce multiple graphs for this same type of data for multiple years of similar data. An example of the data I used in this example:
Year Trt Variety geno yield SE
2010-2011 Irr Variety.2 1 6807 647
2010-2011 Irr Variety.2 2 5901 761
2010-2011 Irr Variety.1 1 6330 731
2010-2011 Irr Variety.1 2 5090 421
2010-2011 Dry Variety.2 1 3953 643
2010-2011 Dry Variety.2 2 3438 683
2010-2011 Dry Variety.1 1 3815 605
2010-2011 Dry Variety.1 2 3326 584
Is there a way to create multiple groupings in ggplot2? I have searched for quite some time and have yet to see an example of something like the example graph above.
Thanks for any help you may have!
This may be a start.
dodge <- position_dodge(width = 0.9)
ggplot(df, aes(x = interaction(Variety, Trt), y = yield, fill = factor(geno))) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymax = yield + SE, ymin = yield - SE), position = dodge, width = 0.2)
Update: labelling of x axis
I have added:
coord_cartesian, to set limits of y axis, mainly the lower limit to avoid the default expansion of the axis.
annotate, to add the desired labels. I have hard-coded the x positions, which I find OK in this fairly simple example.
theme_classic, to remove the gray background and the grid.
theme, increase lower plot margin to have room for the two-row label, remove default labels.
Last set of code: Because the text is added below the x-axis, it 'disappears' outside the plot area, and we need to remove the 'clipping'. That's it!
library(grid)
g1 <- ggplot(data = df, aes(x = interaction(Variety, Trt), y = yield, fill = factor(geno))) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymax = yield + SE, ymin = yield - SE), position = dodge, width = 0.2) +
coord_cartesian(ylim = c(0, 7500)) +
annotate("text", x = 1:4, y = - 400,
label = rep(c("Variety 1", "Variety 2"), 2)) +
annotate("text", c(1.5, 3.5), y = - 800, label = c("Irrigated", "Dry")) +
theme_classic() +
theme(plot.margin = unit(c(1, 1, 4, 1), "lines"),
axis.title.x = element_blank(),
axis.text.x = element_blank())
# remove clipping of x axis labels
g2 <- ggplot_gtable(ggplot_build(g1))
g2$layout$clip[g2$layout$name == "panel"] <- "off"
grid.draw(g2)