I have troubles adding linear regression lines to my ggplots.
This is how it should look like:
This is how it currently looks like:
This is my code:
p <- ggplot(data = wage, aes(x = educ, y = lwage, colour = black,
cex = IQ, pch = married, alpha = 0.7)) + geom_jitter()
p1 <- p + facet_grid(urban~experclass) + geom_smooth(se=F,method="lm")
p1 + labs(x = "Education (year)", y = "Log Wage", shape = "Marital status",
colour = "Ethnicity") + guides(alpha = FALSE)
Is the position of my geom_smooth wrong? What I want is only one black regression line for each element of the plot - and not one by layer.
Furthermore what happens when I add a regression line is that the legend symbols change. Especially the IQ legend looks pretty weird. Is there something I did not consider here?
How it should look:
How it looks:
I can try to answer at least one part of your question - which is the part about plotting one regression line instead of two per panel. I don't have your data so I can't fully replicate your problem, but I think this will work.
The aesthetics in your original ggplot() call will be inherited by all the subsequent layers, including the geom_smooth.
What you seem to want is the color aesthetic (which happens to be a grouping identifier) to apply only to the jittered points and not to the line. So you can write your code like this:
p <- ggplot(data = wage, aes(x = educ, y = lwage,
cex = IQ, pch = married, alpha = 0.7)) +
geom_jitter()
p1 <- p + facet_grid(urban~experclass) +
geom_smooth(se=F,method="lm",
aes(colour = black))
or, alternatively, as one single ggplot call in a modified style:
p3 <- ggplot(data = wage,
aes(x = educ, y = lwage,
size = IQ, shape = married, alpha = 0.7)) +
geom_jitter() +
geom_smooth(se=F,method="lm",
aes(colour = black))+
facet_grid(urban~experclass)
p3
Related
I have a data.frame with observed success/failure outcomes per two groups along with expected probabilities:
library(dplyr)
observed.probability.df <- data.frame(group = c("A","B"), p = c(0.4,0.6))
expected.probability.df <- data.frame(group = c("A","B"), p = qlogis(c(0.45,0.55)))
observed.data.df <- do.call(rbind,lapply(c("A","B"), function(g)
data.frame(group = g, value = c(rep(0,1000*dplyr::filter(observed.probability.df, group != g)$p),rep(1,1000*dplyr::filter(observed.probability.df, group == g)$p)))
)) %>% dplyr::left_join(expected.probability.df)
observed.probability.df$group <- factor(observed.probability.df$group, levels = c("A","B"))
observed.data.df$group <- factor(observed.data.df$group, levels = c("A","B"))
I'm fitting a logistic regression (binomial glm with a logit link function) to these data with the offset term:
fit <- glm(value ~ group + offset(p), data = observed.data.df, family = binomial(link = 'logit'))
Now, I'd like to plot these data as a bar graph using ggplot2's geom_bar, color-coded by group, and to add to that the trend line and shaded standard error area estimated in fit.
I'd use stat_smooth for that but I don't think it can handle the offset term in it's formula, so looks like I need to resort to assembling this figure in an alternative way.
To get the bars and the trend line I used:
slope.est <- function(x, ests) plogis(ests[1] + ests[2] * x)
library(ggplot2)
ggplot(observed.probability.df, aes(x = group, y = p, fill = group)) +
geom_bar(stat = 'identity') +
stat_function(fun = slope.est,args=list(ests=coef(fit)),size=2,color="black") +
scale_x_discrete(name = NULL,labels = levels(observed.probability.df$group), breaks = sort(unique(observed.probability.df$group))) +
theme_minimal() + theme(legend.title = element_blank()) + ylab("Fraction of cells")
So the question is how to add to that the shaded standard error around the trend line?
Using stat_function I am able to shade the entire area from the upper bound of the standard error all the way down to the X-axis:
ggplot(observed.probability.df, aes(x = group, y = p, fill = group)) +
geom_bar(stat = 'identity') +
stat_function(fun = slope.est,args=list(ests=coef(fit)),size=2,color="black") +
stat_function(fun = slope.est,args=list(ests=summary(fit)$coefficients[,1]+summary(fit)$coefficients[,2]),geom='area',fill="gray",alpha=0.25) +
scale_x_discrete(name = NULL,labels = levels(observed.probability.df$group), breaks = sort(unique(observed.probability.df$group))) +
theme_minimal() + theme(legend.title = element_blank()) + ylab("Fraction of cells")
Which is close but not quite there.
Any idea how to subtract from the shaded area above the area that's below the lower bound of the standard error? Perhaps geom_ribbon is the way to go here, but I don't know how to combine it with the slope.est function
I really struggle to set the correct legend for a geom_point plot with loess regression, while there is 2 data set used
I got a data set, who is summarizing activity over a day, and then I plot on the same graph, all the activity per hours and per days recorded, plus a regression curve smoothed with a loess function, plus the mean of each hours for all the days.
To be more precise, here is an example of the first code, and the graph returned, without legend, which is exactly what I expected:
# first graph, which is given what I expected but with no legend
p <- ggplot(dat1, aes(x = Hour, y = value)) +
geom_point(color = "darkgray", size = 1) +
geom_point(data = dat2, mapping = aes(x = Hour, y = mean),
color = 20, size = 3) +
geom_smooth(method = "loess", span = 0.2, color = "red", fill = "blue")
and the graph (in grey there is all the data, per hours, per days. the red curve is the loess regression. The blue dots are the means for each hours):
When I tried to set the legend I failed to plot one with the explanation for both kind of dots (data in grey, mean in blue), and the loess curve (in red). See below some example of what I tried.
# second graph, which is given what I expected + the legend for the loess that
# I wanted but with not the dot legend
p <- ggplot(dat1, aes(x = Hour, y = value)) +
geom_point(color = "darkgray", size = 1) +
geom_point(data = dat2, mapping = aes(x = Hour, y = mean),
color = "blue", size = 3) +
geom_smooth(method = "loess", span = 0.2, aes(color = "red"), fill = "blue") +
scale_color_identity(name = "legend model", guide = "legend",
labels = "loess regression \n with confidence interval")
I obtained the good legend for the curve only
and another trial :
# I tried to combine both date set into a single one as following but it did not
# work at all and I really do not understand how the legends works in ggplot2
# compared to the normal plots
A <- rbind(dat1, dat2)
p <- ggplot(A, aes(x = Heure, y = value, color = variable)) +
geom_point(data = subset(A, variable == "data"), size = 1) +
geom_point(data = subset(A, variable == "Moy"), size = 3) +
geom_smooth(method = "loess", span = 0.2, aes(color = "red"), fill = "blue") +
scale_color_manual(name = "légende",
labels = c("Data", "Moy", "loess regression \n with confidence interval"),
values = c("darkgray", "royalblue", "red"))
It appears that all the legend settings are mixed together in a "weird" way, the is a grey dot covering by a grey line, and then the same in blue and in red (for the 3 labels). all got a background filled in blue:
If you need to label the mean, might need to be a bit creative, because it's not so easy to add legend manually in ggplot.
I simulate something that looks like your data below.
dat1 = data.frame(
Hour = rep(1:24,each=10),
value = c(rnorm(60,0,1),rnorm(60,2,1),rnorm(60,1,1),rnorm(60,-1,1))
)
# classify this as raw data
dat1$Data = "Raw"
# calculate mean like you did
dat2 <- dat1 %>% group_by(Hour) %>% summarise(value=mean(value))
# classify this as mean
dat2$Data = "Mean"
# combine the data frames
plotdat <- rbind(dat1,dat2)
# add a dummy variable, we'll use it later
plotdat$line = "Loess-Smooth"
We make the basic dot plot first:
ggplot(plotdat, aes(x = Hour, y = value,col=Data,size=Data)) +
geom_point() +
scale_color_manual(values=c("blue","darkgray"))+
scale_size_manual(values=c(3,1),guide=FALSE)
Note with the size, we set guide to FALSE so it will not appear. Now we add the loess smooth, one way to introduce the legend is to introduce a linetype, and since there's only one group, you will have just one variable:
ggplot(plotdat, aes(x = Hour, y = value,col=Data,size=Data)) +
geom_point() +
scale_color_manual(values=c("blue","darkgray"))+
scale_size_manual(values=c(3,1),guide=FALSE)+
geom_smooth(data=subset(plotdat,Data="Raw"),
aes(linetype=line),size=1,alpha=0.3,
method = "loess", span = 0.2, color = "red", fill = "blue")
There are similar posts to this, namely here and here, but they address instances where both point color and size are continuous. Is it possible to:
Combine discrete colors and continuous point size within a single legend?
Within that same legend, add a description to each point in place of the numerical break label?
Toy data
xval = as.numeric(c("2.2", "3.7","1.3"))
yval = as.numeric(c("0.3", "0.3", "0.2"))
color.group = c("blue", "red", "blue")
point.size = as.numeric(c("200", "11", "100"))
description = c("descript1", "descript2", "descript3")
df = data.frame(xval, yval, color.group, point.size, description)
ggplot(df, aes(x=xval, y=yval, size=point.size)) +
geom_point(color = df$color.group) +
scale_size_continuous(limits=c(0, 200), breaks=seq(0, 200, by=50))
Doing what you originally asked - continuous + discrete in a single legend - in general doesn't seem to be possible even conceptually. The only sensible thing would be to have two legends for size, with a different color for each legend.
Now let's consider having a single legend. Given your "In my case, each unique combination of point size + color is associated with a description.", it sounds like there are very few possible point sizes. In that case, you could use both scales as discrete. But I believe even that is not enough as you use different variables for size and color scales. A solution then would be to create a single factor variable with all possible combinations of color.group and point.size. In particular,
df <- data.frame(xval, yval, f = interaction(color.group, point.size), description)
ggplot(df, aes(x = xval, y = yval, size = f, color = f)) +
geom_point() + scale_color_discrete(labels = 1:3) +
scale_size_discrete(labels = 1:3)
Here 1:3 are those descriptions that you want, and you may also set the colors the way you like. For instance,
ggplot(df, aes(x = xval, y = yval, size = f, color = f)) +
geom_point() + scale_size_discrete(labels = 1:3) +
scale_color_manual(labels = 1:3, values = c("red", "blue", "green"))
However, we may also exploit color.group by using
ggplot(df, aes(x = xval, y = yval, size = f, color = f)) +
geom_point() + scale_size_discrete(labels = 1:3) +
scale_color_manual(labels = 1:3, values = gsub("(.*)\\..*", "\\1", sort(df$f)))
I have discreet data that looks like this:
height <- c(1,2,3,4,5,6,7,8)
weight <- c(100,200,300,400,500,600,700,800)
person <- c("Jack","Jim","Jill","Tess","Jack","Jim","Jill","Tess")
set <- c(1,1,1,1,2,2,2,2)
dat <- data.frame(set,person,height,weight)
I'm trying to plot a graph with same x-axis(person), and 2 different y-axis (weight and height). All the examples, I find is trying to plot the secondary axis (sec_axis), or discreet data using base plots.
Is there an easy way to use sec_axis for discreet data on ggplot2?
Edit: Someone in the comments suggested I try the suggested reply. However, I run into this error now
Here is my current code:
p1 <- ggplot(data = dat, aes(x = person, y = weight)) +
geom_point(color = "red") + facet_wrap(~set, scales="free")
p2 <- p1 + scale_y_continuous("height",sec_axis(~.*1.2, name="height"))
p2
I get the error: Error in x < range[1] :
comparison (3) is possible only for atomic and list types
Alternately, now I have modified the example to match this example posted.
p <- ggplot(dat, aes(x = person))
p <- p + geom_line(aes(y = height, colour = "Height"))
# adding the relative weight data, transformed to match roughly the range of the height
p <- p + geom_line(aes(y = weight/100, colour = "Weight"))
# now adding the secondary axis, following the example in the help file ?scale_y_continuous
# and, very important, reverting the above transformation
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*100, name = "Relative weight [%]"))
# modifying colours and theme options
p <- p + scale_colour_manual(values = c("blue", "red"))
p <- p + labs(y = "Height [inches]",
x = "Person",
colour = "Parameter")
p <- p + theme(legend.position = c(0.8, 0.9))+ facet_wrap(~set, scales="free")
p
I get an error that says
"geom_path: Each group consists of only one observation. Do you need to
adjust the group aesthetic?"
I get the template, but no points get plotted
R function arguments are fed in by position if argument names are not specified explicitly. As mentioned by #Z.Lin in the comments, you need sec.axis= before your sec_axis function to indicate that you are feeding this function into the sec.axis argument of scale_y_continuous. If you don't do that, it will be fed into the second argument of scale_y_continuous, which by default, is breaks=. The error message is thus related to you not feeding in an acceptable data type for the breaks argument:
p1 <- ggplot(data = dat, aes(x = person, y = weight)) +
geom_point(color = "red") + facet_wrap(~set, scales="free")
p2 <- p1 + scale_y_continuous("weight", sec.axis = sec_axis(~.*1.2, name="height"))
p2
The first argument (name=) of scale_y_continuous is for the first y scale, where as the sec.axis= argument is for the second y scale. I changed your first y scale name to correct that.
Let's say I have the following data frame:
library(ggplot2)
set.seed(101)
n=10
df<- data.frame(delta=rep(rep(c(0.1,0.2,0.3),each=3),n), metric=rep(rep(c('P','R','C'),3),n),value=rnorm(9*n, 0.0, 1.0))
My goal is to do a boxplot by multiple factors:
p<- ggplot(data = df, aes(x = factor(delta), y = value)) +
geom_boxplot(aes(fill=factor(metric)))
The output is:
So far so good, but if I do:
p+ geom_point(aes(color = factor(metric)))
I get:
I do not know what it is doing. My goal is to color the outliers as it is done here. Note that this solution changes the inside color of the boxes to white and set the border to different colors. I want to keep the same color of the boxes while having the outliers inherit those colors. I want to know how to make the outliers get the same colors from their respective boxplots.
Do you want just to change the outliers' colour ? If so, you can do it easily by drawing boxplot twice.
p <- ggplot(data = df, aes(x = factor(delta), y = value)) +
geom_boxplot(aes(colour=factor(metric))) +
geom_boxplot(aes(fill=factor(metric)), outlier.colour = NA)
# outlier.shape = 21 # if you want a boarder
[EDITED]
colss <- c(P="firebrick3",R="skyblue", C="mediumseagreen")
p + scale_colour_manual(values = colss) + # outliers colours
scale_fill_manual(values = colss) # boxes colours
# the development version (2.1.0.9001)'s geom_boxplot() has an argument outlier.fill,
# so I guess under code would return the similar output in the near future.
p2 <- ggplot(data = df, aes(x = factor(delta), y = value)) +
geom_boxplot(aes(fill=factor(metric)), outlier.shape = 21, outlier.colour = NA)
Maybe this:
ggplot(data = df, aes(x = as.factor(delta), y = value,fill=as.factor(metric))) +
geom_boxplot(outlier.size = 1)+ geom_point(pch = 21,position=position_jitterdodge(jitter.width=0))