Ggplot2 Error "No Layers in Plot"

ggplot2 error no layers in plot

The error message is due to fact that you didn't save d+geom_line() as an object.

#Save ggplot() as object
d <- ggplot(data=data3, aes(x=MonthNB, y=Ptot, colour=StationNAME))

#Add to d geom_line() - this makes the plot to appear on the screen but not saved.
d + geom_line()

To save layer to object

d<-d+geom_line()
#No error message
d

No layers in plot (R)

Just a simple example to see how to use the commands:

library(ggplot2)

dt = data.frame(Chr = c("c1","c1","c1","c2","c2","c2","c3","c3","c3"),
                x = c(1,2,3,4,5,6,7,8,9),
                y = c(2,4,5,2,3,4,6,6,7))

ggplot(dt, aes(x,y, col=Chr)) +
  geom_point(size = 3) +
  geom_line() +
  facet_grid(. ~ Chr) # remove this to have all lines in same plot

Error: No layers in plot when using ggplot

Since you have presummarised data, you need to specify stat = "identity" in geom_bar.

library(ggplot2)
ggplot(AD0, aes(group, mean)) + geom_bar(stat = "identity")

Furthermore, I suppose you want to use group for the x-axis and mean for the y-axis. I switched the order of both names.

R ggplot2 facetting - Error: No Layers in Plot

You had everything there, just an error in your assignment to m. Either of these should do the trick:

m <- ggplot(mydata, aes(x=T_MEAN))
m + geom_histogram(aes(y = ..density..)) + geom_density() + facet_grid(~ POSCAT) 

or

m <- ggplot(mydata, aes(x=T_MEAN))
m <- m + geom_histogram(aes(y = ..density..)) + geom_density() 
m + facet_grid(~ POSCAT) 

ggplot overlay plot error, no layers in plot

You have to tell ggplot() which points to connect by lines. This is done by adding group=variable inside the aes().

ggplot(data = dat2,
       aes(x = Locs, y = value, colour = variable,group=variable)) +
  geom_line()

Check for NULL ggplot object before adding layers in dplyr pipe

Option 1: Blank plot

This doesn't directly answer your question, but how about a different approach? When the dataframe has zero rows, use geom_blank() to create an empty plot. Adding a title later doesn't return an error.

plot_fun <- function(df) {
  if (nrow(df) > 0) {
    df %>%
      ggplot(., aes(Sepal.Length, Sepal.Width)) +
      geom_point()
  } else {
    ggplot(data = data.frame()) +
      geom_blank()
  }
}

iris %>% 
  filter(Sepal.Length > 1000) %>% 
  plot_fun() +
  ggtitle("Plot me")

Option 2: Pass layers into a function

You can pass layers as function arguments. Here's a function that checks whether the plot exists, and then adds the layers only if it does.

append_layers_maybe <- function(p, l) {
  if(!is.null(p)) {
    p + l
  }
}

iris %>%
  plot_fun() %>%
  append_layers_maybe(facet_wrap(~ Species)) %>%
  append_layers_maybe(ggtitle("Foo"))

iris %>%
  filter(Sepal.Length > 1000) %>%
  plot_fun() %>%
  append_layers_maybe(facet_wrap(~ Species)) %>%
  append_layers_maybe(ggtitle("Plot me"))

ggplot object not found error when adding layer with different data

Aesthetic mapping defined in the initial ggplot call will be inherited by all layers. Since you initialized your plot with color = GROUP, ggplot will look for a GROUP column in subsequent layers and throw an error if it's not present. There are 3 good options to straighten this out:

Option 1: Set inherit.aes = F in the layer the you do not want to inherit aesthetics. Most of the time this is the best choice.

ggplot(dummy,aes(x = X, y = Y, color = GROUP)) + 
  geom_point() +
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2),
               data = df,
               inherit.aes = FALSE) 

Option 2: Only specify aesthetics that you want to be inherited (or that you will overwrite) in the top call - set other aesthetics at the layer level:

ggplot(dummy,aes(x = X, y = Y)) + 
  geom_point(aes(color = GROUP)) +
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2),
               data = df) 

Option 3: Specifically NULL aesthetics on layers when they don't apply.

ggplot(dummy,aes(x = X, y = Y, color = GROUP)) + 
  geom_point() +
  geom_segment(aes(x = x1, y = y1, xend = x2, yend = y2, color = NULL),
               data = df) 

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Which to use?

Most of the time option 1 is just fine. It can be annoying, however, if you want some aesthetics to be inherited by a layer and you only want to modify one or two. Maybe you are adding some errorbars to a plot and using the same x and color column names in your main data and your errorbar data, but your errorbar data doesn't have a y column. This is a good time to use Option 2 or Option 3 to avoid repeating the x and color mappings.)

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