ggplot: aes vs aes_string, or how to programmatically specify column names?

Question

Let\'s assume we have the following data frame

data <- data.frame(time=1:10, y1=runif(10), y2=runif(10), y3=runif(10))

and we want to create

Answer 1

This is old now but in case anyone else comes across it, I had a very similar problem that was driving me crazy. The solution I found was to pass aes_q() to geom_line() using the as.name() option. You can find details on aes_q() here. Below is the way I would solve this problem, though the same principle should work in a loop. Note that I add multiple variables with geom_line() as a list here, which generalizes better (including to one variable).

varnames <- c("y1", "y2", "y3")
add_lines <- lapply(varnames, function(i) geom_line(aes_q(y = as.name(i), colour = i)))

p <- ggplot(data, aes(x = time))
p <- p + add_lines
plot(p)

Hope that helps!

Answer 2

You could melt (thanks for reminding me of this function, rawr) all of your data into a few columns. For example, it could look like this:

library(reshape2)    
data2 <- melt(data, id = "time")
head(data2)
#    time variable       value
# 1     1       y1 0.353088575
# 2     2       y1 0.621565368
# 3     3       y1 0.696031085
# 4     4       y1 0.507112969
# 5     5       y1 0.009560710
# 6     6       y1 0.158993988
ggplot(data2, aes(x = time, y = value, color = variable)) + geom_line()

Answer 3

NOTE: This is not really an answer, just a very partial explanation of what is going on behind the scenes that might set on you on the right track. I have to admit my understanding of NSE is still very basic.

I have struggled and am still struggling with this particular issue. I have narrowed down the issue to NSE. I am not familiar with R's native substitute/quote/eval stuff, so I am going to demonstrate using the lazyeval package.

library(lazyeval)

a <- lapply(c(1:9,13), function(i) lazy(i))

head(a)
# [[1]]
# <lazy>
#   expr: c(1, 2, 3, 4, 5, 6, 7, 8, 9, 13)[[10L]]
#   env:  <environment: 0x25889a00>
# 
# [[2]]
# <lazy>
#   expr: c(1, 2, 3, 4, 5, 6, 7, 8, 9, 13)[[10L]]
#   env:  <environment: 0x25889a00>
#
# ...........

lazy_eval(a[[1]])
# [1] 13

lazy_eval(a[[2]])
# [1] 13

I think this happens because lazy(i) binds to the promise of i. By the time we get to evaluating any of these i evaluations, i is whatever was LAST assigned to it -- in this case, 13. Perhaps this is due to the environment in which i is evaluated being shared over all iterations of the lapply function?

I have had to resort to the same workarounds as you through aes_string and aes_q. I found them quite unsatisfactory as they neither (1) fully consistent with aes behavior and (2) particularly clean. Oh, the joys of learning NSE ;)

You can find the source code of the + and aes operators here:

ggplot2:::`+.gg`
ggplot2:::aes
ggplot2:::aes_q
ggplot2:::aes_string