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Add legend to ggplot2 line plot

I have a question about legends in ggplot2. I managed to plot three lines in the same graph and want to add a legend with the three colors used. This is the code used

library(ggplot2)    
require(RCurl)

link<-getURL("https://dl.dropbox.com/s/ds5zp9jonznpuwb/dat.txt")
datos<- read.csv(textConnection(link),header=TRUE,sep=";")
datos$fecha <- as.POSIXct(datos[,1], format="%d/%m/%Y")    

temp = ggplot(data=datos,aes(x=fecha, y=TempMax,colour="1")) + 
           geom_line(colour="red") + opts(title="TITULO") +
           ylab("Temperatura (C)") + xlab(" ") + 
           scale_y_continuous(limits = c(-10,40)) + 
           geom_line(aes(x=fecha, y=TempMedia,colour="2"),colour="green") + 
           geom_line(aes(x=fecha, y=TempMin,colour="2"),colour="blue") +
           scale_colour_manual(values=c("red","green","blue"))

temp

and the output

https://i.stack.imgur.com/4LqYa.png

I'd like to add a legend with the three colours used and the name of the variable (TempMax,TempMedia and TempMin). I have tried

scale_colour_manual

but can't find the exact way.

Unfortunately original data were deleted from linked site and could not be recovered. But they came from meteo data files with this format

"date","Tmax","Tmin","Tmed","Precip.diaria","Wmax","Wmed"
2000-07-31 00:00:00,-1.7,-1.7,-1.7,-99.9,20.4,20.4
2000-08-01 00:00:00,22.9,19,21.11,-99.9,6.3,2.83
2000-08-03 00:00:00,24.8,12.3,19.23,-99.9,6.8,3.87
2000-08-04 00:00:00,20.3,9.4,14.4,-99.9,8.3,5.29
2000-08-08 00:00:00,25.7,14.4,19.5,-99.9,7.9,3.22
2000-08-09 00:00:00,29.8,16.2,22.14,-99.9,8.5,3.27
2000-08-10 00:00:00,30,17.8,23.5,-99.9,7.7,3.61
2000-08-11 00:00:00,27.5,17,22.68,-99.9,8.8,3.85
2000-08-12 00:00:00,24,13.3,17.32,-99.9,8.4,3.49
I am still curious wether legends can be tied to seperate elements of the plot (such as different geom_line).
If you only have 3 lines I'd suggest looking at the dirrectlabels package. (LINK)
@TylerRinker I had used it before for other purposes but now the answer from csgillespie works better for me
@EtienneLow-Décarie You can, but in general only if they use different aesthetics. e.g. mapping one set of lines to color and another to linetype. Typically you'd pass in separate data to each geom as well in that case.

C
Community

Since @Etienne asked how to do this without melting the data (which in general is the preferred method, but I recognize there may be some cases where that is not possible), I present the following alternative.

Start with a subset of the original data:

datos <-
structure(list(fecha = structure(c(1317452400, 1317538800, 1317625200, 
1317711600, 1317798000, 1317884400, 1317970800, 1318057200, 1318143600, 
1318230000, 1318316400, 1318402800, 1318489200, 1318575600, 1318662000, 
1318748400, 1318834800, 1318921200, 1319007600, 1319094000), class = c("POSIXct", 
"POSIXt"), tzone = ""), TempMax = c(26.58, 27.78, 27.9, 27.44, 
30.9, 30.44, 27.57, 25.71, 25.98, 26.84, 33.58, 30.7, 31.3, 27.18, 
26.58, 26.18, 25.19, 24.19, 27.65, 23.92), TempMedia = c(22.88, 
22.87, 22.41, 21.63, 22.43, 22.29, 21.89, 20.52, 19.71, 20.73, 
23.51, 23.13, 22.95, 21.95, 21.91, 20.72, 20.45, 19.42, 19.97, 
19.61), TempMin = c(19.34, 19.14, 18.34, 17.49, 16.75, 16.75, 
16.88, 16.82, 14.82, 16.01, 16.88, 17.55, 16.75, 17.22, 19.01, 
16.95, 17.55, 15.21, 14.22, 16.42)), .Names = c("fecha", "TempMax", 
"TempMedia", "TempMin"), row.names = c(NA, 20L), class = "data.frame")

You can get the desired effect by (and this also cleans up the original plotting code):

ggplot(data = datos, aes(x = fecha)) +
  geom_line(aes(y = TempMax, colour = "TempMax")) +
  geom_line(aes(y = TempMedia, colour = "TempMedia")) +
  geom_line(aes(y = TempMin, colour = "TempMin")) +
  scale_colour_manual("", 
                      breaks = c("TempMax", "TempMedia", "TempMin"),
                      values = c("red", "green", "blue")) +
  xlab(" ") +
  scale_y_continuous("Temperatura (C)", limits = c(-10,40)) + 
  labs(title="TITULO")

The idea is that each line is given a color by mapping the colour aesthetic to a constant string. Choosing the string which is what you want to appear in the legend is the easiest. The fact that in this case it is the same as the name of the y variable being plotted is not significant; it could be any set of strings. It is very important that this is inside the aes call; you are creating a mapping to this "variable".

https://i.stack.imgur.com/XU3cA.png

In some cases, the mapping between the levels and colors needs to be made explicit by naming the values in the manual scale (thanks to @DaveRGP for pointing this out):

ggplot(data = datos, aes(x = fecha)) +
  geom_line(aes(y = TempMax, colour = "TempMax")) +
  geom_line(aes(y = TempMedia, colour = "TempMedia")) +
  geom_line(aes(y = TempMin, colour = "TempMin")) +
  scale_colour_manual("", 
                      values = c("TempMedia"="green", "TempMax"="red", 
                                 "TempMin"="blue")) +
  xlab(" ") +
  scale_y_continuous("Temperatura (C)", limits = c(-10,40)) + 
  labs(title="TITULO")

(giving the same figure as before). With named values, the breaks can be used to set the order in the legend and any order can be used in the values.

ggplot(data = datos, aes(x = fecha)) +
  geom_line(aes(y = TempMax, colour = "TempMax")) +
  geom_line(aes(y = TempMedia, colour = "TempMedia")) +
  geom_line(aes(y = TempMin, colour = "TempMin")) +
  scale_colour_manual("", 
                      breaks = c("TempMedia", "TempMax", "TempMin"),
                      values = c("TempMedia"="green", "TempMax"="red", 
                                 "TempMin"="blue")) +
  xlab(" ") +
  scale_y_continuous("Temperatura (C)", limits = c(-10,40)) + 
  labs(title="TITULO")

https://i.stack.imgur.com/eRKNt.png


I love this solution, but I think there may be a limitation. Is there an alphabetic sorting issue between the mapping of the 'breaks' and 'values' variables? TempM{a]x, TempM{e}dia and TempM{i}n sort neatly, though when I adapt this to my variable names, the colours seem to get matched in alphabetical order to the 'breaks', not in the order input. Can the above be clarified/refined to reflect/fix this?
I've managed to find a fix to the issue I bought up earlier re: colour ordering. use the form scale_colour_manual("", values = c("TempMax" = "red", "TempMedia" = "green", "TempMin" = "blue")) where TempMax, TempMedia and TempMin are specified as the colour argument as in the answer above.
@DaveRGP Could it be considered a bug of ggplot?
@StellaBiderman Thank you. It's nice to know that this answer is still useful (nearly) 5 years (!) later.
@BrianDiggs You wouldn't happen to know how to make this show a dot in the scale as opposed to a line would you?
G
Gregor Thomas

I tend to find that if I'm specifying individual colours in multiple geom's, I'm doing it wrong. Here's how I would plot your data:

##Subset the necessary columns
dd_sub = datos[,c(20, 2,3,5)]
##Then rearrange your data frame
library(reshape2)
dd = melt(dd_sub, id=c("fecha"))

All that's left is a simple ggplot command:

ggplot(dd) + geom_line(aes(x=fecha, y=value, colour=variable)) +
  scale_colour_manual(values=c("red","green","blue"))

Example plot

https://i.stack.imgur.com/90n3g.png


I am still curious about how to add legends associated with separate addition of elements such as geom_line, which I though was the original purpose of the question.
Because this answer is referred to a lot, and because reshape2 has been retired, it might be nice to show a tidyr::pivot_longer() approach to reshaping the data too.
J
Justyna

I really like the solution proposed by @Brian Diggs. However, in my case, I create the line plots in a loop rather than giving them explicitly because I do not know apriori how many plots I will have. When I tried to adapt the @Brian's code I faced some problems with handling the colors correctly. Turned out I needed to modify the aesthetic functions. In case someone has the same problem, here is the code that worked for me.

I used the same data frame as @Brian:

data <- structure(list(month = structure(c(1317452400, 1317538800, 1317625200, 1317711600, 
                                       1317798000, 1317884400, 1317970800, 1318057200, 
                                       1318143600, 1318230000, 1318316400, 1318402800, 
                                       1318489200, 1318575600, 1318662000, 1318748400, 
                                       1318834800, 1318921200, 1319007600, 1319094000), 
                                     class = c("POSIXct", "POSIXt"), tzone = ""),
                   TempMax = c(26.58, 27.78, 27.9, 27.44, 30.9, 30.44, 27.57, 25.71, 
                               25.98, 26.84, 33.58, 30.7, 31.3, 27.18, 26.58, 26.18, 
                               25.19, 24.19, 27.65, 23.92), 
                   TempMed = c(22.88, 22.87, 22.41, 21.63, 22.43, 22.29, 21.89, 20.52,
                                 19.71, 20.73, 23.51, 23.13, 22.95, 21.95, 21.91, 20.72, 
                                 20.45, 19.42, 19.97, 19.61), 
                   TempMin = c(19.34, 19.14, 18.34, 17.49, 16.75, 16.75, 16.88, 16.82, 
                               14.82, 16.01, 16.88, 17.55, 16.75, 17.22, 19.01, 16.95, 
                               17.55, 15.21, 14.22, 16.42)), 
              .Names = c("month", "TempMax", "TempMed", "TempMin"), 
              row.names = c(NA, 20L), class = "data.frame")  

In my case, I generate my.cols and my.names dynamically, but I don't want to make things unnecessarily complicated so I give them explicitly here. These three lines make the ordering of the legend and assigning colors easier.

my.cols <- heat.colors(3, alpha=1)
my.names <- c("TempMin", "TempMed", "TempMax")
names(my.cols) <- my.names

And here is the plot:

p <-  ggplot(data, aes(x = month))

for (i in 1:3){
  p <- p + geom_line(aes_(y = as.name(names(data[i+1])), colour = 
colnames(data[i+1])))#as.character(my.names[i])))
}
p + scale_colour_manual("", 
                        breaks = as.character(my.names),
                        values = my.cols)
p

https://i.stack.imgur.com/z47MA.png


At this complexity, it really becomes much easier to just reshape your data into the long form that ggplot expects.
I don't think it really adds complexity compare to the original answer posted by @Brian. Besides, some people might want to do it without reshaping the data.
...and this approach allows different geoms (plot types) by variable