This question already has answers here:
Plotting two variables as lines using ggplot2 on the same graph
(5 answers)
Add legend to ggplot2 line plot
(4 answers)
Closed 2 years ago.
I am having trouble adding a legend to my plot. I want the plot to have points and lines, that is why I am using both geom_line() and geom(points). Here is my code with some made up numbers. When I move "color" into "aes", somehow I get an error and I cannot plot it.
meanted=rnorm(13)
meantotal=rnorm(13)
meantedneg=rnorm(13)
meantedpos=rnorm(13)
totaldf=data.frame(x=c(0:12),meanted,meantotal,meantedneg,meantedpos)
pic=ggplot()+
geom_point(data=totaldf,aes(x=-x,y=meantedneg), color = "red")+
geom_point(data=totaldf,aes(x=-x,y=meantedpos), color = "blue")+
geom_point(data=totaldf,aes(x=-x,y=meanted), color = "green")+
geom_point(data=totaldf,aes(x=-x,y=meantotal),color = "black")+
geom_line(data=totaldf,aes(x=-x,y=meantedneg), color = "red")+
geom_line(data=totaldf,aes(x=-x,y=meantedpos), color = "blue")+
geom_line(data=totaldf,aes(x=-x,y=meanted), color = "green")+
geom_line(data=totaldf,aes(x=-x,y=meantotal),color = "black")
print(pic)
As markus said, ggplot2 will do this for you if you pivot/reshape the data so that each of your desired legend objects are defined in a single column.
Pivoting/reshaping means going from a "wide" format to a "long" format. I'll use tidyr::pivot_longer, though it can be done with reshape (not my preference) or data.table::melt:
tidyr::pivot_longer(totaldf, -x)
# # A tibble: 52 x 3
# x name value
# <int> <chr> <dbl>
# 1 0 meanted 1.37
# 2 0 meantotal -0.279
# 3 0 meantedneg -0.257
# 4 0 meantedpos 0.0361
# 5 1 meanted -0.565
# 6 1 meantotal -0.133
# 7 1 meantedneg -1.76
# 8 1 meantedpos 0.206
# 9 2 meanted 0.363
# 10 2 meantotal 0.636
# # ... with 42 more rows
From here,
library(ggplot2)
ggplot(tidyr::pivot_longer(totaldf, -x), aes(x, value, color = name, group = name)) +
geom_path() +
geom_point() +
scale_color_manual(values = c(meantedneg="red", meantedpos="blue", meanted="green", meantotal="black"))
(FYI, I pre-seeded the randomness with set.seed(42) to get this random data.)
Related
I have some experience with base R but am trying to learn tidyverse and ggplot. I have a dataframe with 4 columns of data. I want a simple x-y plot, where the first column of data is on the x-axis, and the data in the other 3 columns is plotted on the y-axis, resulting in 3 lines on one plot. The first 15 lines of my data look like this (sorry about the image - I don't know how to insert a sample of my data):
screen shot - first 15 rows of data
I tried to plot the second and third columns of data as follows: ,
ggplot(data=SWRC_SL, aes(x=SWRC_SL$pressure_head, y=SWRC_SL$UNSODA_theta)) +
geom_line(colour="red") + scale_x_log10() +
ggplot(data=SWRC_SL, aes(x=SWRC_SL$pressure_head, y=SWRC_SL$Vrugt_theta)) +
geom_line(colour="blue") + scale_x_log10()
I get this error:
Error: Don't know how to add ggplot(data = SWRC_SL, aes(x = SWRC_SL$pressure_head, y = SWRC_SL$Vrugt_theta)) to a plot
I believe I should be using something like "group=" to indicate which columns should be plotted, but I haven't been able to find an example that shows how you can use gglot to plot data across multiple columns. What am I missing ?
ggplot() is only ever called once when you create a chart. Try with the following:
ggplot() +
geom_line(data=SWRC_SL, aes(x=pressure_head, y=UNSODA_theta), colour="red") +
geom_line(data=SWRC_SL, aes(x=pressure_head, y=Vrugt_theta), colour="blue") +
scale_x_log10()
A better method would be to turn your data to long, where the UNSODA_theta and Vrugt_theta data are in the same column (say thetas), and have another column (say type_theta) indicating whether the data is for UNSODA_theta or Vrugt_theta. Then you could do the following:
ggplot(data=SWRC_SL, aes(x=pressure_head, y=thetas, colour=type_theta)) +
geom_line() +
scale_x_log10()
This is more desirable because ggplot2 will include a legend indicating what type of theta the colours are applied to.
As suggested by #Marius, the most efficient way to plot your data is to convert them into a long format.
Using tidyverse, you can have the use of pivot_longer function (from tidyr package) and write the following code:
library(tidyverse)
SWRC_SL %>% pivot_longer(.,-pressure_head, names_to = "variable", values_to = "value") %>%
ggplot(aes(x = pressure_head, y = value, color = variable))+
geom_line()+
scale_x_log10()
EDIT: Illustrating example
Using this dummy dataset:
pressure UNSODA_theta Vrugt_theta Cassel_theta
1 0 -1.4672500 1.4119747 -2.0553118
2 1 0.5210227 0.6189239 1.4817574
3 2 -0.1587546 1.4094018 2.2796175
4 3 1.4645873 2.6888733 -0.4631109
5 4 -0.7660820 2.5865884 -1.8799346
6 5 -0.4302118 0.6690922 0.9633620
First, you pivot your data into a long format:
df %>% pivot_longer(.,-pressure, names_to = "variable", values_to = "value")
# A tibble: 45 x 3
pressure variable value
<int> <chr> <dbl>
1 0 UNSODA_theta -1.47
2 0 Vrugt_theta 1.41
3 0 Cassel_theta -2.06
4 1 UNSODA_theta 0.521
5 1 Vrugt_theta 0.619
6 1 Cassel_theta 1.48
7 2 UNSODA_theta -0.159
8 2 Vrugt_theta 1.41
9 2 Cassel_theta 2.28
10 3 UNSODA_theta 1.46
# … with 35 more rows
Now, your data are suitable for the plotting with ggplot2, you can directly add ggplot command to the previous command by adding a "pipe" (%>%) between them:
library(tidyverse)
df %>% pivot_longer(.,-pressure, names_to = "variable", values_to = "value") %>%
ggplot(aes(x = pressure, y = value, color = variable))+
geom_line()+
scale_x_log10()
And you get the following plot with legend included:
Data example
structure(list(pressure = 0:14, UNSODA_theta = c(-1.46725002909224,
0.521022742648139, -0.158754604716016, 1.4645873119698, -0.766081999604665,
-0.430211753928547, -0.926109497377437, -0.17710396143654, 0.402011779486338,
-0.731748173119606, 0.830373167981674, -1.20808278630446, -1.04798441280774,
1.44115770684428, -1.01584746530465), Vrugt_theta = c(1.41197471231751,
0.61892394889108, 1.40940183965093, 2.68887328620405, 2.58658843344197,
0.669092199317234, -1.28523553529247, 3.49766158983416, 1.66706616676549,
1.5413273359637, 0.986600476854091, 1.51010842295293, 0.835624168230333,
1.42069464325451, 0.599753256022356), Cassel_theta = c(-2.05531181632119,
1.48175740118232, 2.27961753824932, -0.46311085383842, -1.87993463341154,
0.963361958516736, -0.0670637053409687, -2.59982761023726, 0.00319778952040447,
-0.945450500892219, -0.511452869790608, -1.73485854395378, 2.7047128618762,
-0.496698054586832, -2.40827011837962)), class = "data.frame", row.names = c(NA,
-15L))
I need some help. Here is my data which i want to plot. I want to keep $path.ID on y axis and numerics of all other columns added stepwise. this is a subset of very large dataset so i want to pathID labels attached to each line. and also the values of the other columns with each point if possible.
head(table)
Path.ID sc st rc rt
<chr> <dbl> <dbl> <dbl> <dbl>
1 map00230 1 12 5 52
2 map00940 1 20 10 43
3 map01130 NA 15 8 34
4 map00983 NA 14 5 28
5 map00730 NA 5 3 26
6 map00982 NA 16 2 24
somewhat like this
Thank you
Here is the pseudo code.
library(tidyr)
library(dplyr)
library(ggplot2)
# convert your table into a long format - sorry I am more used to this type of data
table_long <- table %>% gather(x_axis, value, sc:rt)
# Plot with ggplot2
ggplot() +
# draw line
geom_line(data=table_long, aes(x=x_axis, y=value, group=Path.ID, color=Path.ID)) +
# draw label at the last x_axis in this case is **rt**
geom_label(data=table_long %>% filter(x_axis=="rt"),
aes(x=x_axis, y=value, label=Path.ID, fill=Path.ID),
color="#FFFFFF")
Note that with this code if a Path.ID doesn't have the rt value then it will not have any label
p<-ggplot() +
# draw line
geom_line(data=table_long, aes(x=x_axis, y=value, group=Path.ID, color=Path.ID)) +
geom_text(data=table_long %>% filter(x_axis=="rt"),
aes(x=x_axis, y=value, label=Path.ID),
color= "#050505", size = 3, check_overlap = TRUE)
p +labs(title= "title",x = "x-lable", y="y-label")
I had to use geom_text as i had large dataset and it gave me somewhat more clear graph
thank you #sinh it it helped a lot.
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 would like to create a multivariate boxplot time series with ggplot2 and I need to have an x axis that positions the boxplots based on their associated dates.
I found two posts about this question: one is Time series plot with groups using ggplot2 but the x axis is not a scale_x_axis so graph is biased in my case. The other one is ggplot2 : multiple factors boxplot with scale_x_date axis in R but the person uses an interaction function which i don't use in my case.
Here is an example file and my code:
dtm <- read.table(text="date ruche mortes trmt
03.10.2013 1 8 P+
04.10.2013 1 7 P+
07.10.2013 1 34 P+
03.10.2013 7 16 P+
04.10.2013 7 68 P+
07.10.2013 7 170 P+
03.10.2013 2 7 P-
04.10.2013 2 7 P-
07.10.2013 2 21 P-
03.10.2013 5 8 P-
04.10.2013 5 27 P-
07.10.2013 5 24 P-
03.10.2013 3 15 T
04.10.2013 3 6 T
07.10.2013 3 13 T
03.10.2013 4 6 T
04.10.2013 4 18 T
07.10.2013 4 19 T ", h=T)
require(ggplot2)
require(visreg)
require(MASS)
require(reshape2)
library(scales)
dtm$asDate = as.Date(dtm[,1], "%d.%m.%Y")
## Plot 1: Nearly what I want but is biased by the x-axis format where date should not be a factor##
p2<-ggplot(data = dtm, aes(x = factor(asDate), y = mortes))
p2 + geom_boxplot(aes(fill = factor(dtm$trmt)))
## Plot 2: Doesn't show me what I need, ggplot apparently needs a factor as x##
p<-ggplot(data = dtm, aes(x = asDate, y = mortes))
p + geom_boxplot(aes( group = asDate, fill=trmt) ) `
Can anyone help me with this issue, please?
Is this what you want?
Code:
p <- ggplot(data = dtm, aes(x = asDate, y = mortes, group=interaction(date, trmt)))
p + geom_boxplot(aes(fill = factor(dtm$trmt)))
The key is to group by interaction(date, trmt) so that you get all of the boxes, and not cast asDate to a factor, so that ggplot treats it as a date. If you want to add anything more to the x axis, be sure to do it with + scale_x_date().