I would like to group a series of lines by 2 factors using group = interaction in ggplot. Here is some sample code:
set.seed(123)
N <- 18
means <- rnorm(N,0,1)
ses <- rexp(N,2)
upper<- means+qnorm(0.975)*ses
lower<- means+qnorm(0.025)*ses
fruit <- rep(c("Apples","Bananas","Pears"), each=6)
size <- rep(rep(c("Small","Medium","Big"), each=2),3)
GMO <- rep(c("Yes","No"), 9)
d<- data.frame(means,upper,lower,fruit,size,GMO)
ggplot(data=d,
aes(x = fruit,y = means, ymin = lower, ymax = upper, col=size,linetype=GMO,group=interaction(GMO, size)))+
geom_hline(aes(fill=size),yintercept =1, linetype=2)+
xlab('labels')+ ylab("Parameter estimates (95% Confidence Interval)")+
geom_pointrange(position=position_dodge(width = 0.6)) +
scale_x_discrete(name="Fruits")+
coord_flip()-> fplot
dev.new()
fplot
Here's a link to the resulting graph: https://i.stack.imgur.com/5YF4F.png
I would like to bring the same coloured lines for each of the three groups closer together. In other words I would like the lines to cluster not only by the 'Fruit' variable but also the 'Size' variable for each of the fruits. poisition_dodge seems to only work for one of the interacting groups.
Thanks for your advice.
As far as I know that is not possible with position_dodge, i.e. it dodges according to the categories of the group aes. And it does not matter whether you map one variable on the group aes or an interaction of two or more. The groups are simply placed equidistant from one another.
One option to achieve your desired result would be to use the "facets that don't look like facets" trick which means faceting by fruit, mapping size on x and afterwards using theme options to get rid of the facet look plus some tweaking of the x scale:
set.seed(123)
N <- 18
means <- rnorm(N, 0, 1)
ses <- rexp(N, 2)
upper <- means + qnorm(0.975) * ses
lower <- means + qnorm(0.025) * ses
fruit <- rep(c("Apples", "Bananas", "Pears"), each = 6)
size <- rep(rep(c("Small", "Medium", "Big"), each = 2), 3)
GMO <- rep(c("Yes", "No"), 9)
d <- data.frame(means, upper, lower, fruit, size, GMO)
library(ggplot2)
ggplot(data = d, aes(x = size, y = means, ymin = lower, ymax = upper, col = size, linetype = GMO, group = GMO)) +
geom_hline(yintercept = 1, linetype = 2) +
xlab("labels") +
ylab("Parameter estimates (95% Confidence Interval)") +
geom_pointrange(position = position_dodge(width = 0.6)) +
scale_x_discrete(name = "Fruits", breaks = "Medium", labels = NULL, expand = c(0, 1)) +
coord_flip() +
facet_grid(fruit ~ ., switch = "y") +
theme(strip.placement = "outside",
strip.background.y = element_blank(),
strip.text.y.left = element_text(angle = 0),
panel.spacing.y = unit(0, "pt"))
Maybe you want to facet_wrap your size variable:
set.seed(123)
N <- 18
means <- rnorm(N,0,1)
ses <- rexp(N,2)
upper<- means+qnorm(0.975)*ses
lower<- means+qnorm(0.025)*ses
fruit <- rep(c("Apples","Bananas","Pears"), each=6)
size <- rep(rep(c("Small","Medium","Big"), each=2),3)
GMO <- rep(c("Yes","No"), 9)
d<- data.frame(means,upper,lower,fruit,size,GMO)
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.1.2
ggplot(data=d,
aes(x = fruit,y = means, ymin = lower, ymax = upper, col=size,linetype=GMO,group=interaction(GMO, size)))+
geom_hline(aes(fill=size),yintercept =1, linetype=2)+
xlab('labels')+ ylab("Parameter estimates (95% Confidence Interval)")+
geom_pointrange(position=position_dodge(width = 0.6)) +
scale_x_discrete(name="Fruits")+
coord_flip() +
facet_wrap(~size)-> fplot
#> Warning: geom_hline(): Ignoring `mapping` because `yintercept` was provided.
fplot
Created on 2022-07-13 by the reprex package (v2.0.1)
Related
I'm studying the returns to college admission for marginal student and i'm trying to make a ggplot2 of the following data which is, average salaries of students who finished or didn't finish their masters in medicin and the average 'GPA' (foreign equivalent) distance to the 'acceptance score':
SalaryAfter <- c(287.780,305.181,323.468,339.082,344.738,370.475,373.257,
372.682,388.939,386.994)
DistanceGrades <- c("<=-1.0","[-0.9,-0.5]","[-0.4,-0.3]","-0,2","-0.1",
"0.0","0.1","[0.2,0.3]","[0.4,0.5]",">=0.5")
I have to do a Regression Discontinuity Design (RDD), so to do the regression - as far as i understand it - i have to rewrite the DistanceGrades to numeric so i just created a variable z
z <- -5:4
where 0 is the cutoff (ie. 0 is equal to "0.0" in DistanceGrades).
I then make a dataframe
df <- data.frame(z,SalaryAfter)
Now my attempt to create the plot gets a bit messy (i use the package 'fpp3', but i suppose that it is just the ggplot2 and maybe dyplr packages)
df %>%
select(z, SalaryAfter) %>%
mutate(D = as.factor(ifelse(z >= -0.1, 1, 0))) %>%
ggplot(aes(x = z, y = SalaryAfter, color = D)) +
geom_point(stat = "identity") +
geom_smooth(method = "lm") +
geom_vline(xintercept = 0) +
theme(panel.grid = element_line(color = "white",
size = 0.75,
linetype = 1)) +
xlim(-6,5) +
xlab("Distance to acceptance score") +
labs(title = "Figur 1.1", subtitle = "Salary for every distance to the acceptance score")
Which plots:
What i'm trying to do is firstly, split the data with a dummy variable D=1 if z>0 and D=0 if z<0. Then i plot it with a linear regression and a vertical line at z=0. Lastly i write the title and subtilte. Now i have two problems:
The x axis is displaying -5, -2.5, ... but i would like for it to show all the integers, the rational numbers have no relation to the z variable which is discrete. I have tried to fix this with several different methods, but none of them have worked, i can't remember all the ways i have tried (theme(panel.grid...),scale_x_discrete and many more), but the outcome has all been pretty similar. They all cause the x-axis to be completely removed such that there is no numbers and sometimes it even removes the axis title.
i would like for the regression channel for the first part of the data to extend to z=0
When i try to solve both of these problems i again get similar results, most of the things i try is not producing an error message when i run the code, but they either do nothing to my plot or they remove some of the existing elements which leaves me made of questions. I suppose that the error is caused by some of the elements not working together but i have no idea.
Try this:
library(tidyverse)
SalaryAfter <- c(287.780,305.181,323.468,339.082,344.738,370.475,373.257,
372.682,388.939,386.994)
DistanceGrades <- c("<=-1.0","[-0.9,-0.5]","[-0.4,-0.3]","-0,2","-0.1",
"0.0","0.1","[0.2,0.3]","[0.4,0.5]",">=0.5")
z <- -5:4
df <- data.frame(z,SalaryAfter) %>%
select(z, SalaryAfter) %>%
mutate(D = as.factor(ifelse(z >= -0.1, 1, 0)))
# Fit a lm model for the left part of the panel
fit_data <- lm(SalaryAfter~z, data = filter(df, z <= -0.1)) %>%
predict(., newdata = data.frame(z = seq(-5, 0, 0.1)), interval = "confidence") %>%
as.data.frame() %>%
mutate(z = seq(-5, 0, 0.1), D = factor(0, levels = c(0, 1)))
# Plot
ggplot(mapping = aes(color = D)) +
geom_ribbon(data = filter(fit_data, z <= 0 & -1 <= z),
aes(x = z, ymin = lwr, ymax = upr),
fill = "grey70", color = "transparent", alpha = 0.5) +
geom_line(data = fit_data, aes(x = z, y = fit), size = 1) +
geom_point(data = df, aes(x = z, y = SalaryAfter), stat = "identity") +
geom_smooth(data = df, aes(x = z, y = SalaryAfter), method = "lm") +
geom_vline(xintercept = 0) +
theme(panel.grid = element_line(color = "white",
size = 0.75,
linetype = 1)) +
scale_x_continuous(limits = c(-6, 5), breaks = -6:5) +
xlab("Distance to acceptance score") +
labs(title = "Figure 1.1", subtitle = "Salary for every distance to the acceptance score")
I'm trying to write my own Central Limit Theorem demonstration using ggplot2 and am unable to get my stat_function to display a changing normal distribution.
below is my code, I want the normal distribution in stat_function to transition through different states; specifically, I'm hoping for it to change the standard deviation to correspond with each value in dataset. Any help would be greatly appreciated.
#library defs
library(gganimate)
library(ggplot2)
library(transformr)
#initialization for distribution, rolls, and vectors
k = 2
meanr = 1/k
sdr = 1/k
br = sdr/10
rolls <- 200
avg <- 1
dataset <- 1
s <- 1
#loop through to create vectors of sample statistics from 200 samples of size i
#avg is sample average, s is standard deviations of sample means, and dataset is the indexes to run the transition states
for (i in c(1:40)){
for (j in 1:rolls){
avg <- c(avg,mean(rexp(i,k)))
}
dataset <- c(dataset, rep(i,rolls))
s <- c(s,rep(sdr/sqrt(i),rolls))
}
#remove initialized vector information as it was only created to start loops
avg <- avg[-1]
rn <- rn[-1]
dataset <- dataset[-1]
s <- s[-1]
#dataframe
a <- data.frame(avgf=avg, rnf = rn,datasetf = dataset,sf = s)
#plot histogram, density function, and normal distribution
ggplot(a,aes(x=avg,y=s))+
geom_histogram(aes(y = ..density..), binwidth = br,fill='beige',col='black')+
geom_line(aes(y = ..density..,colour = 'Empirical'),lwd=2, stat = 'density') +
stat_function(fun = dnorm, aes(colour = 'Normal', y = s),lwd=2,args=list(mean=meanr,sd = mean(s)))+
scale_y_continuous(labels = scales::percent_format()) +
scale_color_discrete(name = "Densities", labels = c("Empirical", "Normal"))+
labs(x = 'Sample Average',title = 'Sample Size: {closest_state}')+
transition_states(dataset,4,4)+ view_follow(fixed_x = TRUE)
I think it's difficult to use stat_function here because the dnorm function that you are passing includes a grouped variable (mean(s)). There is no way to indicate that you wish to group s by the dataset column, and the transition_states function doesn't filter the whole data frame. You could use transition_filter to filter the whole data frame, but this would be laborious.
It's not much work to just add a dnorm to your input data frame and plot it as a line, particularly since the rest of your code can be simplified substantially. Here's a fully reproducible example:
library(gganimate)
library(ggplot2)
library(transformr)
k <- 2
meanr <- sdr <- 1/k
br <- sdr/10
rolls <- 200
a <- do.call(rbind, lapply(1:40, function(i){
data.frame(avg = replicate(rolls, mean(rexp(i, k))),
dataset = rep(i, rolls),
x = seq(0, 2, length.out = rolls),
s = dnorm(seq(0, 2, length.out = rolls),
meanr, sdr/sqrt(i))) }))
ggplot(a, aes(x = avg, group = dataset)) +
geom_histogram(aes(y = ..density..), fill = 'beige',
colour = "black", binwidth = br) +
geom_line(aes(y = ..density.., colour = 'Empirical'),
lwd = 2, stat = 'density', alpha = 0.5) +
geom_line(aes(x = x, y = s, colour = "Normal"), size = 2, alpha = 0.5) +
scale_y_continuous(labels = scales::percent_format()) +
coord_cartesian(xlim = c(0, 2)) +
scale_color_discrete(name = "Densities", labels = c("Empirical", "Normal")) +
labs(x = 'Sample Average', title = 'Sample Size: {closest_state}') +
transition_states(dataset, 4, 4) +
view_follow(fixed_x = TRUE, fixed_y = TRUE)
I'm trying to re-create a plot like this in ggplot:.
This graph takes the residuals from a regression output, and plots them in order (with the X-axis being a rank of residuals).
My best attempt at this was something like the following:
library(ggplot2)
library(modelr)
d <- d %>% add_residuals(mod1, var = "resid")
d$resid_rank <- rank(d$resid)
ggplot(data = d, aes(x = resid_rank, y = resid)) +
geom_bar(stat="identity") +
theme_bw()
However, this yields a completely blank graph. I tried something like this:
ggplot(data = d, aes(x = resid_rank, y = resid)) +
geom_segment(yend = 0, aes(xend=resid)) +
theme_bw()
But this yields the segments that go in the wrong direction. What is the right way to do this, and to color those lines by a third factor?
FAKE DATASET:
library(estimatr)
library(fabricatr)
#simulation
dat <- fabricate(
N = 10000,
y = runif(N, 0, 10),
x = runif(N, 0, 100)
)
#add an outlier
dat <- rbind(dat, c(300, 5))
dat <- rbind(dat, c(500, 3))
dat$y_log <- log(dat$y)
dat$x_log <- log(dat$x)
dat$y_log_s <- scale(log(dat$y))
dat$x_log_s <- scale(log(dat$x))
mod1 <- lm(y_log ~ x_log, data = dat))
I used the build in dataset from the help page on lm() to create this example. I also just directly used resid() to get the residuals. It's unclear where / why the colored bars would be different, but basically you'd need to add a column to your data.frame that specificies why they are red or blue, then pass that to fill.
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 3.4.4
#example from lm
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm.D9 <- lm(weight ~ group)
resids <- data.frame(resid = resid(lm.D9))
#why are some bars red and some blue? No clue - so I'll pick randomly
resids$group <- sample(c("group 1", "group 2"), nrow(resids), replace = TRUE)
#rank
resids$rank <- rank(-1 * resids$resid)
ggplot(resids, aes(rank, resid, fill = group)) +
geom_bar(stat = "identity", width = 1) +
geom_hline(yintercept = c(-1,1), colour = "darkgray", linetype = 2) +
geom_hline(yintercept = c(-2,2), colour = "lightgray", linetype = 1) +
theme_bw() +
theme(panel.grid = element_blank()) +
scale_fill_manual(values = c("group 1" = "red", "group 2" = "blue"))
Created on 2019-01-24 by the reprex package (v0.2.1)
I have recently came across a problem with ggplot2::geom_density that I am not able to solve. I am trying to visualise a density of some variable and compare it to a constant. To plot the density, I am using the ggplot2::geom_density. The variable for which I am plotting the density, however, happens to be a constant (this time):
df <- data.frame(matrix(1,ncol = 1, nrow = 100))
colnames(df) <- "dummy"
dfV <- data.frame(matrix(5,ncol = 1, nrow = 1))
colnames(dfV) <- "latent"
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.2, position = "identity") +
geom_vline(data = dfV, aes(xintercept = latent, color = 'ls'), size = 2)
This is OK and something I would expect. But, when I shift this distribution to the far right, I get a plot like this:
df <- data.frame(matrix(71,ncol = 1, nrow = 100))
colnames(df) <- "dummy"
dfV <- data.frame(matrix(75,ncol = 1, nrow = 1))
colnames(dfV) <- "latent"
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.2, position = "identity") +
geom_vline(data = dfV, aes(xintercept = latent, color = 'ls'), size = 2)
which probably means that the kernel estimation is still taking 0 as the centre of the distribution (right?).
Is there any way to circumvent this? I would like to see a plot like the one above, only the centre of the kerner density would be in 71 and the vline in 75.
Thanks
Well I am not sure what the code does, but I suspect the geom_density primitive was not designed for a case where the values are all the same, and it is making some assumptions about the distribution that are not what you expect. Here is some code and a plot that sheds some light:
# Generate 10 data sets with 100 constant values from 0 to 90
# and then merge them into a single dataframe
dfs <- list()
for (i in 1:10){
v <- 10*(i-1)
dfs[[i]] <- data.frame(dummy=rep(v,100),facet=v)
}
df <- do.call(rbind,dfs)
# facet plot them
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.5, position = "identity") +
facet_wrap( ~ facet,ncol=5 )
Yielding:
So it is not doing what you thought it was, but it is also probably not doing what you want. You could of course make it "translation-invariant" (almost) by adding some noise like this for example:
set.seed(1234)
noise <- +rnorm(100,0,1e-3)
dfs <- list()
for (i in 1:10){
v <- 10*(i-1)
dfs[[i]] <- data.frame(dummy=rep(v,100)+noise,facet=v)
}
df <- do.call(rbind,dfs)
ggplot() +
geom_density(data = df, aes(x = dummy, colour = 's'),
fill = '#FF6666', alpha = 0.5, position = "identity") +
facet_wrap( ~ facet,ncol=5 )
Yielding:
Note that there is apparently a random component to the geom_density function, and I can't see how to set the seed before each instance, so the estimated density is a bit different each time.
this is my first stack overflow post and I am a relatively new R user, so please go gently!
I have a data frame with three columns, a participant identifier, a condition (factor with 2 levels either Placebo or Experimental), and an outcome score.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
I would like to construct a bar plot with two bars with the mean outcome score for each condition and the standard deviation as an error bar. I would like to then overlay lines connecting points for each participant's score in each condition. So the plot displays the individual response as well as the group mean.If it is also possible I would like to include an axis break.
I don't seem to be able to find any advice in other threads, apologies if I am repeating a question.
Many Thanks.
p.s. I realise that presenting data in this way will not be to everyones tastes. It is for a specific requirement!
This ought to work:
library(ggplot2)
library(dplyr)
dat.summ <- dat %>% group_by(Condition) %>%
summarize(mean.outcome = mean(Outcome),
sd.outcome = sd(Outcome))
ggplot(dat.summ, aes(x = Condition, y = mean.outcome)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin = mean.outcome - sd.outcome,
ymax = mean.outcome + sd.outcome),
color = "dodgerblue", width = 0.3) +
geom_point(data = dat, aes(x = Condition, y = Outcome),
color = "firebrick", size = 1.2) +
geom_line(data = dat, aes(x = Condition, y = Outcome, group = ID),
color = "firebrick", size = 1.2, alpha = 0.5) +
scale_y_continuous(limits = c(0, max(dat$Outcome)))
Some people are better with ggplot's stat functions and arguments than I am and might do it differently. I prefer to just transform my data first.
set.seed(1)
dat <- data.frame(Condition = c(rep("Placebo",10),rep("Experimental",10)),
Outcome = rnorm(20,15,2),
ID = factor(rep(1:10,2)))
dat.w <- reshape(dat, direction = 'wide', idvar = 'ID', timevar = 'Condition')
means <- colMeans(dat.w[, 2:3])
sds <- apply(dat.w[, 2:3], 2, sd)
ci.l <- means - sds
ci.u <- means + sds
ci.width <- .25
bp <- barplot(means, ylim = c(0,20))
segments(bp, ci.l, bp, ci.u)
segments(bp - ci.width, ci.u, bp + ci.width, ci.u)
segments(bp - ci.width, ci.l, bp + ci.width, ci.l)
segments(x0 = bp[1], x1 = bp[2], y0 = dat.w[, 2], y1 = dat.w[, 3], col = 1:10)
points(c(rep(bp[1], 10), rep(bp[2], 10)), dat$Outcome, col = 1:10, pch = 19)
Here is a method using the transfomations inside ggplot2
ggplot(dat) +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.y="mean", geom="bar") +
stat_summary(aes(x=Condition, y=Outcome, group=Condition), fun.data="mean_se", geom="errorbar", col="green", width=.8, size=2) +
geom_line(aes(x=Condition, y=Outcome, group=ID), col="red")