scale_color_manual: specified breaks do not correspond in order to specified values

[jwhendy]

I noticed unexpected behavior and wanted to bring it here for comment/input.

Toy example, default behavior:

tmp = pd.DataFrame({'x': [1, 2, 3],
                    'y': [4, 5, 6],
                    'col': [True, True, False]})

p = ggplot(tmp, aes(x='x', y='y', color='col')) + geom_point()
p

Changing the order of the breaks (colors stay the same):

p = ggplot(tmp, aes(x='x', y='y', color='col')) + geom_point()
p = p + scale_color_discrete(name='col', breaks=[True, False])
p

In adding custom labels, the order follows the breaks:

p = ggplot(tmp, aes(x='x', y='y', color='col')) + geom_point()
p = p + scale_color_discrete(name='col', breaks=[True, False], labels=['true is blue', 'false is red'])
p

If you want to specify custom colors, however, values appears to follow original (presumably 'factor()` order):

p = ggplot(tmp, aes(x='x', y='y', color='col')) + geom_point()
p = p + scale_color_manual(name='col', breaks=[True, False], labels=['true is blue', 'false is red'], values=['red', 'black'])
p

Using limits instead of breaks fixes this:

p = ggplot(tmp, aes(x='x', y='y', color='col')) + geom_point()
p = p + scale_color_manual(name='col',
                           limits=[True, False],
                           labels=['true is blue', 'false is red'],
                           values=['blue', 'red'])
p

This is not the ggplot2 behavior, however.

library(ggplot2)
tmp <- data.frame(x=c(1, 2, 3), y=c(4, 5, 6), col=c(T, T, F))
ggplot(tmp, aes(x=x, y=y, color=col)) + geom_point() + scale_color_manual("col", breaks=c(T, F), labels=c("true is blue", "false is red"), values=c("blue", "red"))

I tend to use limits to, say, exclude something from a range, using breaks/labels to change labels, with the assumption that values should correspond to break order, as is specified by ggplot2 docs:

The values will be matched in order (usually alphabetical) with the limits of the scale, or with breaks if provided.

If this is the intended behavior, maybe a note in the docs would help understand what's ordered by breaks vs. what's ordered by the underlying, say, pd.Categorical order. Many thanks for taking a look.

[jwhendy]

You are a machine! Thanks so much.

Btw, I just made a submission to r/dataisbeautiful on an analysis of 3.1 million reddit comments. plotnine generated the scatter plots used, which I think it turned out really nice :) Thanks for continuing to provide and maintain the best plotting library in python. I tell everyone at work about it any chance I get.

[has2k1]

Btw, I just made a submission to r/dataisbeautiful on an analysis of 3.1 million reddit comments. plotnine generated the scatter plots used, which I think it turned out really nice :)

Nice plots there. It would be interesting to see how the vary over time!