How to add a legend for two geom layers in one ggplot2 plot?
Solution 1
In ggplot, legends are automatically created for mapped aesthetics. You can add such mappings as follows:
ggplot(data = df,
mapping = aes(x = x)) +
# specify fill for bar / color for line inside aes(); you can use
# whatever label you wish to appear in the legend
geom_col(aes(y = y.bar, fill = "bar.label")) +
geom_line(aes(y = y.line, color = "line.label")) +
xlab("Month of year") +
scale_y_continuous(name = "Daily classifications per Spotter") +
# the labels must match what you specified above
scale_fill_manual(name = "", values = c("bar.label" = "grey")) +
scale_color_manual(name = "", values = c("line.label" = "black")) +
theme_bw()
In the above example, I've also moved the data & common aesthetic mapping (x) to ggplot()
.
Sample dataset:
set.seed(7)
df <- data.frame(
x = 1:20,
y.bar = rpois(20, lambda = 5),
y.line = rpois(20, lambda = 10)
)
Solution 2
This solution is aimed at plotting 2 curves from 1 data frame. One column data is plotted as line graph against the x-axis (Observation Date) and the other is plotted as Area Graph against the same x-axis(Observation Date). Area Curve will be on secondary y-axis. The Legend plotting and Formatting are also shown in the images attached. Hope you enjoy the solution.
Data Frame structure will look like as follows/ This is a sample of the whole dataset, I will pick 2 columns, obsdate, revenue, and value:
I am using Shiny library to host it on a server; if you don't know about shiny you may ignore, this is just the layout~HTML.
library(xlsx) # Library for Excel import
library (ggplot2) # Library for plot
library(openxlsx) #FOR Running the xlsx file
library(shiny) #SERVER UI LIBRARY IN R
library(lubridate) # For converting the imported dates to Date Format
file <- "C:/Users/Nikhil Asati/Desktop/Office/Bicurve_data.xlsx"
data1<- read.xlsx( file ,1)
data1$obsdate <- dmy(data1$obsdate)
ui <- fluidPage(
sidebarLayout(
sidebarPanel(),
mainPanel(
plotOutput("Plot")
)
)
)
server <- function(input, output) {
output$Plot <- renderPlot({
#plot value
p <- ggplot(data1, aes(x=obsdate))
p <- p + geom_line(aes(y=revenue, colour ="revenue"))
#plot revenue
p <- p + geom_ribbon(aes(ymin = 0, ymax=value/16, fill = 'value'),
linetype=1,
#solid, dashed or other line types
colour="grey70",
#border line color
size=1
# ,show.legend= TRUE
)
p <- p + scale_y_continuous(sec.axis = sec_axis(~.*16, name = "value"))
p<- p + scale_colour_manual("", values=c("revenue" = "Grey" ))
p<- p +scale_fill_manual(name = "", values = c("value" = "green"))
p<- p +theme(legend.position = "top")
p
})
}
shinyApp(ui = ui, server = server)
Output will be:
hpy
Updated on June 07, 2022Comments
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hpy almost 2 years
I've got a data frame that looks like this:
glimpse(spottingIntensityByMonth) # Observations: 27 # Variables: 3 # $ yearMonth <dttm> 2015-05-01, 2015-06-01, 2015-07-01, 2015-08-01, 2015-09-01, 2015-10-01, 2... # $ nClassificationsPerDayPerSpotter <dbl> 3.322581, 13.212500, 13.621701, 6.194700, 18.127778, 12.539589, 8.659722, ... # $ nSpotters <int> 8, 8, 22, 28, 24, 22, 24, 27, 25, 29, 32, 32, 21, 14, 18, 13, 20, 19, 15, ...
I am trying to plot it with ggplot2 like so:
ggplot() + geom_col(data = spottingIntensityByMonth, mapping = aes(x = yearMonth, y = nClassificationsPerDayPerSpotter) ) + xlab("Month of year") + scale_y_continuous(name = "Daily classifications per Spotter") + geom_line(data = spottingIntensityByMonth, mapping = aes(x = yearMonth, y = nSpotters) ) + theme_bw()
This produces a plot like so:
Now I want to add a legend that says what the line and columns mean. How do I do this? Thanks!
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hpy over 6 yearsIt worked just as you described, THANK YOU very much for your detailed answer! The only thing is that I don't understand the logic behind the scale_*_manual() function calls, how do I understand it? Thanks anyway!! :)
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Z.Lin over 6 years@hpy Glad it helped :) The
scale_*_manual()
functions allow you to specify your own palette in the form of a named vector e.g.c("a" = "red", "b" = "green")
, where "a" & "b" are values in the relevant variables. In this case, since I don't want fill / colour to vary by values withiny.bar
/y.line
, I let each take on a single value as far as fill / colour are concerned. This is somewhat unorthodox, but I find it works quite well in practice. -
hpy over 6 yearsGot it, thanks for your explanation! This is super helpful to know.