I made a trial where I compare 2 different conditions for multiple treatments. However, on the plot, some of the value have two points where they should only be one.
here is the code I used for my plot
ggplot(Bites, aes(x=Treatment, y=Biting, colour=Condition, fill=Condition))+
geom_point(position=position_jitterdodge(dodge.width=0.7), size=2)+
geom_boxplot(alpha=0.5, position= position_dodge(width=0.8), fatten=NULL)+
stat_summary(fun.y= mean, geom="errorbar", aes(ymax=..y.., ymin=..y..),
width=0.65, size= 1.5, linetype= "solid", position = position_dodge(width=0.7))+
xlab("Treatment") +
ylab("Bites")+
labs(Color ="Condition")+
theme_classic()
Here are part of the data I used.
Biting Treatment Condition
1 0 A X
2 0 A X
3 0 A X
4 0 A X
5 0 A X
6 0 A X
7 0 A X
8 0 A X
9 1 A X
10 1 A X
11 1 A X
12 2 A X
13 4 A X
14 7 A X
15 9 A X
I should only get one point in the graph for 4 bites, 7 bites and 9 bites; yet I get 2 dots (one red and one paler).
How can I get rid of the paler dots ?
This duplicated dots are the outliers from geom_boxplot() (notice, how they are always centered on boxplot)
Simply add outlier.shape = NA inside geom_boxplot() (as it was suggested under this question) and then they won't appear.
Data:
Bites <- data.frame(Biting = sample(c(rep(0, 7), rep(1, 3), 2, 4, 7, 9)),
Treatment = c(rep("A", 7), rep("B", 7)),
Condition = rep(c("X", "Y"), 7))
The lighter points are the outliers from the geom_boxplot call. Look up the option to suppress outliers on that call and you should be sweet.
ps - use dput() to share data frames in a stack overflow question to make it easy for others to assist.
Related
I'm pretty new to R and I have a problem with plotting a barplot out of my data which looks like this:
condition answer
2 H
1 H
8 H
5 W
4 M
7 H
9 H
10 H
6 H
3 W
The data consists of 100 rows with the conditions 1 to 10, each randomly generated 10 times (10 times condition 1, 10 times condition 8,...). Each of the conditions also has a answer which could be H for Hit, M for Miss or W for wrong.
I want to plot the number of Hits for each condition in a barplot (for example 8 Hits out of 10 for condition 1,...) for that I tried to do the following in ggplot2
ggplot(data=test, aes(x=test$condition, fill=answer=="H"))+
geom_bar()+labs(x="Conditions", y="Hitrate")+
coord_cartesian(xlim = c(1:10), ylim = c(0:10))+
scale_x_continuous(breaks=seq(1,10,1))
And it looked like this:
This actually exactly what I need except for the red color which covers everything. You can see that conditions 3 to 5 have no blue bar, because there are no hits for these conditions.
Is there any way to get rid of this red color and to maybe count the amount of hits for the different conditions? -> I tried the count function of dplyr but it only showed me the amount of H when there where some for this particular condition. 3-5 where just "ignored" by count, there wasn't even a 0 in the output.-> but I'd still need those numbers for the plot
I'm sorry for this particular long post but I'm really at the end of knowledge considering this. I'd be open for suggestions or alternatives! Thanks in advance!
This is a situation where a little preprocessing goes a long way. I made sample data that would recreate the issue, i.e. has cases where there won't be any "H"s.
Instead of relying on ggplot to aggregate data in the way you want it, use proper tools. Since you mention dplyr::count, I use dplyr functions.
The preprocessing task is to count observations with answer "H", including cases where the count is 0. To make sure all combinations are retained, convert condition to a factor and set .drop = F in count, which is in turn passed to group_by.
library(dplyr)
library(ggplot2)
set.seed(529)
test <- data.frame(condition = rep(1:10, times = 10),
answer = c(sample(c("H", "M", "W"), 50, replace = T),
sample(c("M", "W"), 50, replace = T)))
hit_counts <- test %>%
mutate(condition = as.factor(condition)) %>%
filter(answer == "H") %>%
count(condition, .drop = F)
hit_counts
#> # A tibble: 10 x 2
#> condition n
#> <fct> <int>
#> 1 1 0
#> 2 2 1
#> 3 3 4
#> 4 4 2
#> 5 5 3
#> 6 6 0
#> 7 7 3
#> 8 8 2
#> 9 9 1
#> 10 10 1
Then just plot that. geom_col is the version of geom_bar for where you have your y-values already, instead of having ggplot tally them up for you.
ggplot(hit_counts, aes(x = condition, y = n)) +
geom_col()
One option is to just filter out anything but where answer == "H" from your dataset, and then plot.
An alternative is to use a grouped bar plot, made by setting position = "dodge":
test <- data.frame(condition = rep(1:10, each = 10),
answer = sample(c('H', 'M', 'W'), 100, replace = T))
ggplot(data=test) +
geom_bar(aes(x = condition, fill = answer), position = "dodge") +
labs(x="Conditions", y="Hitrate") +
coord_cartesian(xlim = c(1:10), ylim = c(0:10)) +
scale_x_continuous(breaks=seq(1,10,1))
Also note that if the condition is actually a categorical variable, it may be better to make it a factor:
test$condition <- as.factor(test$condition)
This means that you don't need the scale_x_continuous call, and that the grid lines will be cleaner.
Another option is to pick your fill colors explicitly and make FALSE transparent by using scale_fill_manual. Since FALSE comes alphabetically first, the first value to specify is FALSE, the second TRUE.
ggplot(data=test, aes(x=condition, fill=answer=="H"))+
geom_bar()+labs(x="Conditions", y="Hitrate")+
coord_cartesian(xlim = c(1:10), ylim = c(0:10))+
scale_x_continuous(breaks=seq(1,10,1)) +
scale_fill_manual(values = c(alpha("red", 0), "cadetblue")) +
guides(fill = F)
I am using the ..count.. transformation in geom_bar and get the warning
position_stack requires non-overlapping x intervals when some of my categories have few counts.
This is best explained using some mock data (my data involves direction and windspeed and I retain names relating to that)
#make data
set.seed(12345)
FF=rweibull(100,1.7,1)*20 #mock speeds
FF[FF>60]=59
dir=sample.int(10,size=100,replace=TRUE) # mock directions
#group into speed classes
FFcut=cut(FF,breaks=seq(0,60,by=20),ordered_result=TRUE,right=FALSE,drop=FALSE)
# stuff into data frame & plot
df=data.frame(dir=dir,grp=FFcut)
ggplot(data=df,aes(x=dir,y=(..count..)/sum(..count..),fill=grp)) + geom_bar()
This works fine, and the resulting plot shows the frequency of directions grouped according to speed. It is of relevance that the velocity class with the fewest counts (here "[40,60)") will have 5 counts.
However more velocity classes leads to a warning. For instance, with
FFcut=cut(FF,breaks=seq(0,60,by=15),ordered_result=TRUE,right=FALSE,drop=FALSE)
the velocity class with the fewest counts (now "[45,60)") will have only 3 counts and ggplot2 will warn that
position_stack requires non-overlapping x intervals
and the plot will show data in this category spread out along the x axis.
It seems that 5 is the minimum size for a group to have for this to work correctly.
I would appreciate knowing if this is a feature or a bug in stat_bin (which geom_bar is using) or if I am simply abusing geom_bar.
Also, any suggestions how to get around this would be appreciated.
Sincerely
This occurs because df$dir is numeric, so the ggplot object assumes a continuous x-axis, and aesthetic parameter group is based on the only known discrete variable (fill = grp).
As a result, when there simply aren't that many dir values in grp = [45,60), ggplot gets confused over how wide each bar should be. This becomes more visually obvious if we split the plot into different facets:
ggplot(data=df,
aes(x=dir,y=(..count..)/sum(..count..),
fill = grp)) +
geom_bar() +
facet_wrap(~ grp)
> for(l in levels(df$grp)) print(sort(unique(df$dir[df$grp == l])))
[1] 1 2 3 4 6 7 8 9 10
[1] 1 2 3 4 5 6 7 8 9 10
[1] 2 3 4 5 7 9 10
[1] 2 4 7
We can also check manually that the minimum difference between sorted df$dir values is 1 for the first three grp values, but 2 for the last one. The default bar width is thus wider.
The following solutions should all achieve the same result:
1. Explicitly specify the same bar width for all groups in geom_bar():
ggplot(data=df,
aes(x=dir,y=(..count..)/sum(..count..),
fill = grp)) +
geom_bar(width = 0.9)
2. Convert dir to a categorical variable before passing it to aes(x = ...):
ggplot(data=df,
aes(x=factor(dir), y=(..count..)/sum(..count..),
fill = grp)) +
geom_bar()
3. Specify that the group parameter should be based on both df$dir & df$grp:
ggplot(data=df,
aes(x=dir,
y=(..count..)/sum(..count..),
group = interaction(dir, grp),
fill = grp)) +
geom_bar()
This doesn't directly solve the issue, because I also don't get what's going on with the overlapping values, but it's a dplyr-powered workaround, and might turn out to be more flexible anyway.
Instead of relying on geom_bar to take the cut factor and give you shares via ..count../sum(..count..), you can easily enough just calculate those shares yourself up front, and then plot your bars. I personally like having this type of control over my data and exactly what I'm plotting.
First, I put dir and FF into a data frame/tbl_df, and cut FF. Then count lets me group the data by dir and grp and count up the number of observations for each combination of those two variables, then calculate the share of each n over the sum of n. I'm using geom_col, which is like geom_bar but when you have a y value in your aes.
library(tidyverse)
set.seed(12345)
FF <- rweibull(100,1.7,1) * 20 #mock speeds
FF[FF > 60] <- 59
dir <- sample.int(10, size = 100, replace = TRUE) # mock directions
shares <- tibble(dir = dir, FF = FF) %>%
mutate(grp = cut(FF, breaks = seq(0, 60, by = 15), ordered_result = T, right = F, drop = F)) %>%
count(dir, grp) %>%
mutate(share = n / sum(n))
shares
#> # A tibble: 29 x 4
#> dir grp n share
#> <int> <ord> <int> <dbl>
#> 1 1 [0,15) 3 0.03
#> 2 1 [15,30) 2 0.02
#> 3 2 [0,15) 4 0.04
#> 4 2 [15,30) 3 0.03
#> 5 2 [30,45) 1 0.01
#> 6 2 [45,60) 1 0.01
#> 7 3 [0,15) 6 0.06
#> 8 3 [15,30) 1 0.01
#> 9 3 [30,45) 2 0.02
#> 10 4 [0,15) 6 0.06
#> # ... with 19 more rows
ggplot(shares, aes(x = dir, y = share, fill = grp)) +
geom_col()
This question already has answers here:
Add legend to ggplot2 line plot
(4 answers)
Closed 2 years ago.
I was attempting (unsuccessfully) to show a legend in my R ggplot2 graph which involves multiple plots. My data frame df and code is as follows:
Individuals Mod.2 Mod.1 Mod.3
1 2 -0.013473145 0.010859793 -0.08914021
2 3 -0.011109863 0.009503278 -0.09049672
3 4 -0.006465788 0.011304668 -0.08869533
4 5 0.010536718 0.009110458 -0.09088954
5 6 0.015501212 0.005929766 -0.09407023
6 7 0.014565584 0.005530390 -0.09446961
7 8 -0.009712516 0.012234843 -0.08776516
8 9 -0.011282278 0.006569570 -0.09343043
9 10 -0.011330579 0.003505439 -0.09649456
str(df)
'data.frame': 9 obs. of 4 variables:
$ Individuals : num 2 3 4 5 6 7 8 9 10
$ Mod.2 : num -0.01347 -0.01111 -0.00647 0.01054 0.0155 ...
$ Mod.1 : num 0.01086 0.0095 0.0113 0.00911 0.00593 ...
$ Mod.3 : num -0.0891 -0.0905 -0.0887 -0.0909 -0.0941 ...
ggplot(df, aes(df$Individuals)) +
geom_point(aes(y=df[,2]), colour="red") + geom_line(aes(y=df[,2]), colour="red") +
geom_point(aes(y=df[,3]), colour="lightgreen") + geom_line(aes(y=df[,3]), colour="lightgreen") +
geom_point(aes(y=df[,4]), colour="darkgreen") + geom_line(aes(y=df[,4]), colour="darkgreen") +
labs(title = "Modules", x = "Number of individuals", y = "Mode")
I looked up the following stackflow threads, as well as Google searches:
Merging ggplot2 legend
ggplot2 legend not showing
`ggplot2` legend not showing label for added series
ggplot2 legend for geom_area/geom_ribbon not showing
ggplot and R: Two variables over time
ggplot legend not showing up in lift chart
Why ggplot2 legend not show in the graph
ggplot legend not showing up in lift chart.
This one was created 4 days ago
This made me realize that making legends appear is a recurring issue, despite the fact that legends usually appear automatically.
My first question is what are the causes of a legend to not appear when using ggplot? The second is how to solve these causes. One of the causes appears to be related to multiple plots and the use of aes(), but I suspect there are other reasons.
colour= XYZ should be inside the aes(),not outside:
geom_point(aes(data, colour=XYZ)) #------>legend
geom_point(aes(data),colour=XYZ) #------>no legend
Hope it helps, it took me a hell long way to figure out.
You are going about the setting of colour in completely the wrong way. You have set colour to a constant character value in multiple layers, rather than mapping it to the value of a variable in a single layer.
This is largely because your data is not "tidy" (see the following)
head(df)
x a b c
1 1 -0.71149883 2.0886033 0.3468103
2 2 -0.71122304 -2.0777620 -1.0694651
3 3 -0.27155800 0.7772972 0.6080115
4 4 -0.82038851 -1.9212633 -0.8742432
5 5 -0.71397683 1.5796136 -0.1019847
6 6 -0.02283531 -1.2957267 -0.7817367
Instead, you should reshape your data first:
df <- data.frame(x=1:10, a=rnorm(10), b=rnorm(10), c=rnorm(10))
mdf <- reshape2::melt(df, id.var = "x")
This produces a more suitable format:
head(mdf)
x variable value
1 1 a -0.71149883
2 2 a -0.71122304
3 3 a -0.27155800
4 4 a -0.82038851
5 5 a -0.71397683
6 6 a -0.02283531
This will make it much easier to use with ggplot2 in the intended way, where colour is mapped to the value of a variable:
ggplot(mdf, aes(x = x, y = value, colour = variable)) +
geom_point() +
geom_line()
ind = 1:10
my.df <- data.frame(ind, sample(-5:5,10,replace = T) ,
sample(-5:5,10,replace = T) , sample(-5:5,10,replace = T))
df <- data.frame(rep(ind,3) ,c(my.df[,2],my.df[,3],my.df[,4]),
c(rep("mod.1",10),rep("mod.2",10),rep("mod.3",10)))
colnames(df) <- c("ind","value","mod")
Your data frame should look something likes this
ind value mod
1 5 mod.1
2 -5 mod.1
3 3 mod.1
4 2 mod.1
5 -2 mod.1
6 5 mod.1
Then all you have to do is :
ggplot(df, aes(x = ind, y = value, shape = mod, color = mod)) +
geom_line() + geom_point()
I had a similar problem with the tittle, nevertheless, I found a way to show the title: you can add a layer using
ggtitle ("Name of the title that you want to show")
example:
ggplot(data=mtcars,
mapping = aes(x=hp,
fill = factor(vs)))+
geom_histogram(bins = 9,
position = 'identity',
alpha = 0.8, show.legend = T)+
labs(title = 'Horse power',
fill = 'Vs Motor',
x = 'HP',
y = 'conteo',
subtitle = 'A',
caption = 'B')+
ggtitle("Horse power")
I have the following problem:
I need to create a mosaic plot but want to display the number of cases for each mosaic, as total numbers per country differ. The plot is based on the following data:
1 - not agree 2 3 4 5 - fully agree
DE 6 2 0 0 1
ES 5 3 1 1 0
FR 6 3 1 2 0
SE 4 3 0 0 0
I used the following code:
> mosaicplot(Q1, col=c("red", "orange", "yellow", "green", "green4"),
+ las = 1,
+ main = "There is no need to do anything about it.",
+ ylab = "",
+ xlab = "Country")
Giving me this graph:
Now I would like to divide the first red bar into six bars of the same colour, as there were 6 votes in Germany a.s.o. Any ideas on how to accomplish that?
I applied the procedure explained here:
https://learnr.wordpress.com/2009/03/29/ggplot2_marimekko_mosaic_chart/
Only I had to use two data frames, one for the percentages and one for the absolute values.
Both data frames went through the same calculations. Whilst dfm1 created the chart, dfm21 was used for the labels:
p2 <- p1 + geom_text(aes(x = xtext, y = ytext,
label = ifelse(dfm21$value == "0", paste(" "), paste(dfm21$value))), size = 3.5)
I'm trying to graph the quality of a production process.
For this question lets say the data.frame is described as:
df2 <- data.frame(size = c("XS", "S", "M", "L", "XL"),
ok = c(1, 3, 4, 2, 1),
notok = c(0, 1, 1, 2, 0))
size ok notok
XS 1 0
S 3 1
M 4 1
L 2 2
XL 1 0
Now I want to show hoy many of the total products produced were ok. So I try:
ggplot(df2, aes(x=size, y=(ok+notok), fill=ok)) +
geom_bar(stat="identity")
However, the result I got is a a graph showing the ok variable as if "ok" was a scale.
I also tried using fill=factor(ok) but the result wasn't what I was expecting.
How can I get a graph that shows how many of the total were ok in each bin?
Something like this
ggplot tends to work best if data are in long, rather than wide form. To convert between the two, you can make use of the excellent reshape package. Try this:
require(reshape)
df3 <- melt(df2, id.vars = "size")
Your data are now in long format as shown below, and can be more easily plotted:
size variable value
1 XS ok 1
2 S ok 3
3 M ok 4
4 L ok 2
5 XL ok 1
6 XS notok 0
7 S notok 1
8 M notok 1
9 L notok 2
10 XL notok 0
ggplot(df3, aes(x = size, y = value)) +
geom_bar(stat = "identity", aes(fill = variable))
...Which produces the following graph: