Q:

ggplot2: object 'y' not found with stat="bin"

I'm sorry to ask a question that has been asked before on SO, but I'm trying to plot some simple data  in ggplot2 and am having trouble binning the data along the x-axis. My data consists of visual elements in old books (diagrams, engravings, etc.), and I can plot the frequency of each type of visual element in each year:
#this works
df <- read.table("cleaned_estc_visuals.txt",
                 header = F,
                 sep = "\t")

ggplot(data=df, aes(x=V1, y=V3)) + 
  geom_bar(aes(fill=V2),stat="identity") +
  labs(title = "Visuals in Early Modern Books",fill="") +
  xlab("Year") + 
  ylab("Titles") 

This yields:

To make the data more legible, I want to bin the values along the x-axis by decade, but can't quite seem to get the call right:
#this doesn't
ggplot(data=df, aes(x=V1, y=V3)) + 
  geom_bar(aes(fill=V2),binwidth=10,stat="bin")

Running the latter code, I get:
Mapping a variable to y and also using stat="bin".
  With stat="bin", it will attempt to set the y value to the count of cases in each group.
  This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
  If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
  If you want y to represent values in the data, use stat="identity".
  See ?geom_bar for examples. (Deprecated; last used in version 0.9.2)
Error in pmin(y, 0) : object 'y' not found

Does anyone know how I can bin by decade along the x-axis? I would be grateful for any advice others can offer.

A:

In your situation, I find it easier to do some data manipulation before calling ggplot(). I personally prefer these packages: dplyr for data management and scales for working with graphics, but you could do this using base functions as well.
library(dplyr)
library(scales)

df2 <- df %>%
  mutate(decade = floor(V1 / 10) * 10) %>% 
  group_by(decade, V2) %>%
  summarise(V3 = sum(V3)) %>%
  filter(decade != 1800)

ggplot(df2, aes(x = decade, y = V3)) +
  geom_bar(aes(fill = V2), stat = "identity") +
  labs(x = "Decade", y = "Titles", title = "Visuals in Early Modern Books") +
  scale_x_continuous(breaks = pretty_breaks(20)) # using scales::pretty_breaks()

