I want to add two sets of bowling scores onto the same distribution in ggplot2, I don't have the same amount of observations in each group but I would like to plot them on top of eachother. Below is the code I have.
m <- ggplot(bowling, aes(x = as.numeric(Kenny)))
n <- ggplot(bowling, aes(x= as.numeric(Group)))
m + n geom_density()
and this is the error.
Error in p + o : non-numeric argument to binary operator
In addition: Warning message:
Incompatible methods ("+.gg", "Ops.data.frame") for "+"
I just want to plot them on top of eachother but I can't figure out what the problem is.
The problem is that you're adding a single geom_density layer to two different plots (m and n) that have different aesthetic mappings.
Here is a potential solution, if I understood your question correctly.
First, creating a small sample dataset
kenny <- rnorm(100, 20, 2)
group <- rnorm(100, 15, 2)
bowling <- data.frame(kenny, group)
Second, plotting first a geom_density layer for kenny as an aesthetic, and then adding a geom_density layer for a different aesthetic, namely group.
ggplot(bowling, aes(x = kenny)) +
geom_density() + geom_density(aes(x=group), colour="red")
Here is what you obtain:
Related
I am trying to find the best way to create barplots in R with standard errors displayed. I have seen other articles but I cannot figure out the code to use with my own data (having not used ggplot before and this seeming to be the most used way and barplot not cooperating with dataframes). I need to use this in two cases for which I have created two example dataframes:
Plot df1 so that the x-axis has sites a-c, with the y-axis displaying the mean value for V1 and the standard errors highlighted, similar to this example with a grey colour. Here, plant biomass should the mean V1 value and treatments should be each of my sites.
Plot df2 in the same way, but so that before and after are located next to each other in a similar way to this, so pre-test and post-test equate to before and after in my example.
x <- factor(LETTERS[1:3])
site <- rep(x, each = 8)
values <- as.data.frame(matrix(sample(0:10, 3*8, replace=TRUE), ncol=1))
df1 <- cbind(site,values)
z <- factor(c("Before","After"))
when <- rep(z, each = 4)
df2 <- data.frame(when,df1)
Apologies for the simplicity for more experienced R users and particuarly those that use ggplot but I cannot apply snippets of code that I have found elsewhere to my data. I cannot even get enough code together to produce a start to a graph so I hope my descriptions are sufficient. Thank you in advance.
Something like this?
library(ggplot2)
get.se <- function(y) {
se <- sd(y)/sqrt(length(y))
mu <- mean(y)
c(ymin=mu-se, ymax=mu+se)
}
ggplot(df1, aes(x=site, y=V1)) +
stat_summary(fun.y=mean, geom="bar", fill="lightgreen", color="grey70")+
stat_summary(fun.data=get.se, geom="errorbar", width=0.1)
ggplot(df2, aes(x=site, y=V1, fill=when)) +
stat_summary(fun.y=mean, geom="bar", position="dodge", color="grey70")+
stat_summary(fun.data=get.se, geom="errorbar", width=0.1, position=position_dodge(width=0.9))
So this takes advantage of the stat_summary(...) function in ggplot to, first, summarize y for given x using mean(...) (for the bars), and then to summarize y for given x using the get.se(...) function for the error-bars. Another option would be to summarize your data prior to using ggplot, and then use geom_bar(...) and geom_errorbar(...).
Also, plotting +/- 1 se is not a great practice (although it's used often enough). You'd be better served plotting legitimate confidence limits, which you could do, for instance, using the built-in mean_cl_normal function instead of the contrived get.se(...). mean_cl_normal returns the 95% confidence limits based on the assumption that the data is normally distributed (or you can set the CL to something else; read the documentation).
I used group_by and summarise_each function for this and std.error function from package plotrix
library(plotrix) # for std error function
library(dplyr) # for group_by and summarise_each function
library(ggplot2) # for creating ggplot
For df1 plot
# Group data by when and site
grouped_df1<-group_by(df1,site)
#summarise grouped data and calculate mean and standard error using function mean and std.error(from plotrix)
summarised_df1<-summarise_each(grouped_df1,funs(mean=mean,std_error=std.error))
# Define the top and bottom of the errorbars
limits <- aes(ymax = mean + std_error, ymin=mean-std_error)
#Begin your ggplot
#Here we are plotting site vs mean and filling by another factor variable when
g<-ggplot(summarised_df1,aes(site,mean))
#Creating bar to show the factor variable position_dodge
#ensures side by side creation of factor bars
g<-g+geom_bar(stat = "identity",position = position_dodge())
#creation of error bar
g<-g+geom_errorbar(limits,width=0.25,position = position_dodge(width = 0.9))
#print graph
g
For df2 plot
# Group data by when and site
grouped_df2<-group_by(df2,when,site)
#summarise grouped data and calculate mean and standard error using function mean and std.error
summarised_df2<-summarise_each(grouped_df2,funs(mean=mean,std_error=std.error))
# Define the top and bottom of the errorbars
limits <- aes(ymax = mean + std_error, ymin=mean-std_error)
#Begin your ggplot
#Here we are plotting site vs mean and filling by another factor variable when
g<-ggplot(summarised_df2,aes(site,mean,fill=when))
#Creating bar to show the factor variable position_dodge
#ensures side by side creation of factor bars
g<-g+geom_bar(stat = "identity",position = position_dodge())
#creation of error bar
g<-g+geom_errorbar(limits,width=0.25,position = position_dodge(width = 0.9))
#print graph
g
i am totally new in R so maybe the answer to the question is trivial but I couldn't find any solution after searching in the net for days.
I am using ggplot2 to create graphs containing the mean of my samples with the confidence interval in a ribbon (I can't post the pic but something like this: S1
I have a data frame (df) with time in the first column and the values of the variable measured in the other columns (each column is a replicate of the measurement).
I do the following:
mdf<-melt(df, id='time', variable_name="samples")
p <- ggplot(data=mdf, aes(x=time, y=value)) +
geom_point(size=1,colour="red")
stat_sum_df <- function(fun, geom="crosbar", ...) {
stat_summary(fun.data=fun, geom=geom, colour="red")
}
p + stat_sum_df("mean_cl_normal", geom = "smooth")
and I get the graph I have shown at the beginning.
My question is: if I have two different data frames, each one with a different variable, measured in the same sample at the same time, how I can plot the 2 graphs in the same plot? Everything I have tried ends in doing the statistics in the both sets of data or just in one of them but not in both. Is it possible just to overlay the plots?
And a second small question: is it possible to change the colour of the ribbon?
Thanks!
something like this:
library(ggplot2)
a <- data.frame(x=rep(c(1,2,3,5,7,10,15,20), 5),
y=rnorm(40, sd=2) + rep(c(4,3.5,3,2.5,2,1.5,1,0.5), 5),
g = rep(c('a', 'b'), each = 20))
ggplot(a, aes(x=x,y=y, group = g, colour = g)) +
geom_point(aes(colour = g)) +
geom_smooth(aes(fill = g))
I'd suggest you reading the basics of ggplot. Check ?ggplot2 for help on ggplot but also available help topics here and particularly how group aesthetic may be manipulated.
You'll find useful the discussion group at Google groups and maybe join it. Also, QuickR have a lot of examples on ggplot graphs and, obviously, here at Stackoverflow.
I am trying to make a QQ-plot in ggplot2, where a select few of the points should have a different shape. But when I map the shape to a variable in the aesthetics, stat_qq includes this variable to split the data (there are 2x3 factors involved).
Here is a reproducible example:
library(ggplot2)
set.seed(331)
df <- do.call(rbind, replicate(10, {expand.grid(method=factor(letters[1:3]), model=factor(LETTERS[1:2]))}, simplify=FALSE ))
df$x <- runif(nrow(df))
df$y <- rnorm(nrow(df), sd=0.2) + 1*as.integer(df$method)
df$top <- FALSE
df <- df[order(df$y, decreasing=TRUE),]
df$top[which(df$method=='a')[1:10]] <- TRUE
So far, I have managed to make a simple QQ-plot:
ggplot(df, aes(sample=y, colour=method)) + stat_qq() + facet_grid(.~model)
This is basically what I want, except for a hand full of the points in method 'a' having a different shape, as indicated by the variable 'top'.
From the code, we know that these corresponds to the top 5 values in method 'a' in each model; i.e. that the five left most of the red dots in each facet should have a different shape.
Here I have attempted to add it as an aesthetics:
ggplot(df, aes(sample=y, colour=method, shape=top)) + stat_qq() + facet_grid(.~model)
Now, it is quite clear, that stat_qq has included the variable 'top' to split the data set, as the top 5 data points are plotted parallel to the the non-top points.
This is not as intended.
How can I instruct stat_qq how to group the data?
I could try the group-aesthetic:
ggplot(df, aes(sample=y, colour=method, shape=top, group=method)) + stat_qq() + facet_grid(.~model)
Warning messages:
1: Removed 10 rows containing missing values (geom_point).
2: Removed 10 rows containing missing values (geom_point).
But for some reason, this entirely removes all data points connected to the model.
Any ideas how to overcome this?
Since you want to violate one of the fundamental concepts of ggplot2 it would be easier to do the calculations outside of ggplot:
library(plyr)
df <- ddply(df, .(model, method),
transform, theo=qqnorm(y, plot.it=FALSE)[["x"]])
ggplot(df, aes(x=theo, y=y, colour=method, shape=top)) +
geom_point() + facet_grid(.~model)
I am trying to write a code that I wrote with a basic graphics package in R to ggplot.
The graph I obtained using the basic graphics package is as follows:
I was wondering whether this type of graph is possible to create in ggplot2. I think we could create this kind of graph by using panels but I was wondering is it possible to use faceting for this kind of plot. The major difficulty I encountered is that maximum and minimum have common lengths whereas the observed data is not continuous data and the interval is quite different.
Any thoughts on arranging the data for this type of plot would be very helpful. Thank you so much.
Jdbaba,
From your comments, you mentioned that you'd like for the geom_point to have just the . in the legend. This is a feature that is yet to be implemented to be used directly in ggplot2 (if I am right). However, there's a fix/work-around that is given by #Aniko in this post. Its a bit tricky but brilliant! And it works great. Here's a version that I tried out. Hope it is what you expected.
# bind both your data.frames
df <- rbind(tempcal, tempobs)
p <- ggplot(data = df, aes(x = time, y = data, colour = group1,
linetype = group1, shape = group1))
p <- p + geom_line() + geom_point()
p <- p + scale_shape_manual("", values=c(NA, NA, 19))
p <- p + scale_linetype_manual("", values=c(1,1,0))
p <- p + scale_colour_manual("", values=c("#F0E442", "#0072B2", "#D55E00"))
p <- p + facet_wrap(~ id, ncol = 1)
p
The idea is to first create a plot with all necessary attributes set in the aesthetics section, plot what you want and then change settings manually later using scale_._manual. You can unset lines by a 0 in scale_linetype_manual for example. Similarly you can unset points for lines using NA in scale_shape_manual. Here, the first two values are for group1=maximum and minimum and the last is for observed. So, we set NA to the first two for maximum and minimum and set 0 to linetype for observed.
And this is the plot:
Solution found:
Thanks to Arun and Andrie
Just in case somebody needs the solution of this sort of problem.
The code I used was as follows:
library(ggplot2)
tempcal <- read.csv("temp data ggplot.csv",header=T, sep=",")
tempobs <- read.csv("temp data observed ggplot.csv",header=T, sep=",")
p <- ggplot(tempcal,aes(x=time,y=data))+geom_line(aes(x=time,y=data,color=group1))+geom_point(data=tempobs,aes(x=time,y=data,colour=group1))+facet_wrap(~id)
p
The dataset used were https://www.dropbox.com/s/95sdo0n3gvk71o7/temp%20data%20observed%20ggplot.csv
https://www.dropbox.com/s/4opftofvvsueh5c/temp%20data%20ggplot.csv
The plot obtained was as follows:
Jdbaba
I'm trying to make a boxplot with ggplot2 using the following code:
p <- ggplot(
data,
aes(d$score, reorder(d$names d$scores, median))
) +
geom_boxplot()
I have factors called names and integers called scores.
My code produces a plot, but the graphic does not depict the boxes (only shows lines) and I get a warning message, "position_dodge requires non-overlapping x intervals." I've tried to adjust the height and width with geom_boxplot(width=5), but this does not seem to fix the problem. Can anyone suggest a possible solution to my problem?
I should point out that my boxplot is rather large and has about 200 name values on the y-axis). Perhaps this is the problem?
The number of groups is not the problem; I can see the same thing even when there are only 2 groups. The issue is that ggplot2 draws boxplots vertically (continuous along y, categorical along x) and you are trying to draw them horizontally (continuous along x, categorical along y).
Also, your example has several syntax errors and isn't reproducible because we don't have data/d.
Start with some mock data
dat <- data.frame(scores=rnorm(1000,sd=500),
names=sample(LETTERS, 1000, replace=TRUE))
Corrected version of your example code:
ggplot(dat, aes(scores, reorder(names, scores, median))) + geom_boxplot()
This is the horizontal lines you saw.
If you instead put the categorical on the x axis and the continuous on the y you get
ggplot(dat, aes(reorder(names, scores, median), scores)) + geom_boxplot()
Finally, if you want to flip the coordinate axes, you can use coord_flip(). There can be some additional problems with this if you are doing even more sophisticated things, but for basic boxplots it works.
ggplot(dat, aes(reorder(names, scores, median), scores)) +
geom_boxplot() + coord_flip()
In case anyone else arrives here wondering why they're seeing
Warning message:
position_dodge requires non-overlapping x intervals
Why this happens
The reason this happens is because some of the boxplot / violin plot (or other plot type) are possibly overlapping. In many cases, you may not care, but in some cases, it matters, hence why it warns you.
How to fix it
You have two options. Either suppress warnings when generating/printing the ggplot
The other option, simply alter the width of the plot so that the plots don't overlap, then the warning goes away. Try altering the width argument to the geom: e.g. geom_boxplot(width = 0.5) (same works for geom_violin())
In addition to #stevec's options, if you're seeing
position_stack requires non-overlapping x intervals
position_fill requires non-overlapping x intervals
position_dodge requires non-overlapping x intervals
position_dodge2 requires non-overlapping x intervals
and if your x variable is supposed to overlap for different aesthetics such as fill, you can try making the x_var into a factor:
geom_bar(aes(x = factor(x_var), fill = type)