I have a dataframe which looks like this:
> df
Year mpft value type index
1 1996 2 0.033827219 solid 2.1
2 1997 2 0.133278701 solid 2.1
3 1998 2 0.261428650 solid 2.1
4 1999 2 0.394702438 solid 2.1
5 1996 3 0.019079686 solid 3.1
6 1997 3 0.074332942 solid 3.1
7 1998 3 0.149042964 solid 3.1
8 1999 3 0.227812452 solid 3.1
9 1996 4 0.009909126 solid 4.1
10 1997 4 0.026231721 solid 4.1
11 1998 4 0.052912805 solid 4.1
12 1999 4 0.086256016 solid 4.1
13 1996 17 0.017256492 solid 17.1
14 1997 17 0.079446280 solid 17.1
15 1998 17 0.166014538 solid 17.1
16 1999 17 0.316175339 solid 17.1
17 1996 18 0.080072523 solid 18.1
18 1997 18 0.313289644 solid 18.1
19 1998 18 0.629398957 solid 18.1
20 1999 18 1.024946245 solid 18.1
110 1996 2 0.031634282 dashed 2.2
21 1997 2 0.139244701 dashed 2.2
31 1998 2 0.273270126 dashed 2.2
41 1999 2 0.412409808 dashed 2.2
51 1996 3 0.019430502 dashed 3.2
61 1997 3 0.079252516 dashed 3.2
71 1998 3 0.161607337 dashed 3.2
81 1999 3 0.252595611 dashed 3.2
91 1996 4 0.009976637 dashed 4.2
101 1997 4 0.027057403 dashed 4.2
111 1998 4 0.055755671 dashed 4.2
121 1999 4 0.093064641 dashed 4.2
171 1996 18 0.061041422 dashed 18.2
181 1997 18 0.245554619 dashed 18.2
191 1998 18 0.490633135 dashed 18.2
201 1999 18 0.758070060 dashed 18.2
I am trying to plot the data and have the right legend, so far I have initially tried with
ggplot(df,aes(x=Year,y=value, colour = factor(mpft),linetype=type)) +
geom_line(aes(group = index), size = 1.4) +
#scale_linetype_manual(name= "Run Type", values = unique(df$type), labels = run.type) +
scale_color_manual(name = "PFT",
values = setNames(mycol[unique(df$mpft)], unique(df$mpft)),
labels = setNames(mynam[unique(df$mpft)], unique(df$mpft)))
Which gives me
I have tried adding a scale_linetype_manual with
ggplot(df,aes(x=Year,y=value, colour = factor(mpft),linetype=type)) +
geom_line(aes(group = index), size = 1.4) +
scale_linetype_manual(name= "Run Type", values = unique(df$type), labels = run.type) +
scale_color_manual(name = "PFT",
values = setNames(mycol[unique(df$mpft)], unique(df$mpft)),
labels = setNames(mynam[unique(df$mpft)], unique(df$mpft)))
with
> run.type
[1] "current" "origED3"
But I get
which has the right names for the legend but has a different linetype. What am I missing?
EDIT
The dput
of my dataframe is
> dput(df)
structure(list(Year = c(1996, 1997, 1998, 1999, 1996, 1997, 1998,
1999, 1996, 1997, 1998, 1999, 1996, 1997, 1998, 1999, 1996, 1997,
1998, 1999, 1996, 1997, 1998, 1999, 1996, 1997, 1998, 1999, 1996,
1997, 1998, 1999, 1996, 1997, 1998, 1999), mpft = c(2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 17L, 17L, 17L, 17L, 18L,
18L, 18L, 18L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
18L, 18L, 18L, 18L), value = c(0.0338272191848643, 0.133278701149992,
0.261428650232716, 0.394702437670559, 0.0190796862689925, 0.0743329421068756,
0.149042964352043, 0.227812451937011, 0.00990912614900737, 0.0262317206863519,
0.0529128049802722, 0.0862560162908444, 0.017256491619149, 0.0794462797803606,
0.166014537897384, 0.31617533869767, 0.0800725232220131, 0.31328964372358,
0.629398957462415, 1.02494624459608, 0.0316342818911836, 0.139244700529005,
0.273270126484303, 0.412409807917143, 0.0194305022713642, 0.0792525159706922,
0.161607337403947, 0.252595610607411, 0.00997663742883768, 0.0270574028188436,
0.0557556714277292, 0.0930646413413941, 0.0610414215913856, 0.245554619318541,
0.490633135315979, 0.758070059865948), type = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L), .Label = c("solid", "dashed"), class = "factor"),
index = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L,
7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L), .Label = c("2.1",
"3.1", "4.1", "17.1", "18.1", "2.2", "3.2", "4.2", "18.2"
), class = "factor")), .Names = c("Year", "mpft", "value",
"type", "index"), row.names = c("1", "2", "3", "4", "5", "6",
"7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20", "110", "21", "31", "41", "51", "61", "71",
"81", "91", "101", "111", "121", "171", "181", "191", "201"), class = "data.frame")
EDIT
Not a very elegant solution but substituting "dashed"
with "22"
and then using:
ggplot(df,aes(x=Year,y=value, colour = factor(mpft),linetype=type)) +
geom_line(aes(group = index), size = 1.4) +
scale_linetype_manual(name= "Run Type", values = unique(as.character(df$type)), labels = run.type) +
scale_color_manual(name = "PFT",
values = setNames(mycol[unique(df$mpft)], unique(df$mpft)),
labels = setNames(mynam[unique(df$mpft)], unique(df$mpft)))
I am able to get it right
Interesting question, and one I haven't come across on SO before.
Short answer
The difference is because the linetype in your first version isn't "dashed"
at all.
Long answer
In your first version, without specifying anything for the linetype
aesthetic, ggplot defaults to scale_linetype_discrete()
, and the current code for that is:
scale_linetype <- function(..., na.value = "blank") {
discrete_scale("linetype", "linetype_d", linetype_pal(),
na.value = na.value, ...)
}
Hence, scale_linetype_discrete()
gets its linetype values from the linetype_pal
function, courtesy of the scales
package (at least, that's the only place I found it):
> scales::linetype_pal()(2)
[1] "solid" "22"
When you specified the linetype
aesthetic mapping in the second version, using scale_linetype_manual()
, the corresponding current code is:
scale_linetype_manual <- function(..., values) {
manual_scale("linetype", values, ...)
}
Thus when you explicitly ask for c("solid", "dashed")
as the two linetype values in your plot, ggplot uses them. When you don't, the default values are c("solid", "22")
, and "22"
corresponds to a different, more tightly spaced pattern than "dashed"
's pattern.
Demonstration below, using built-in data:
df.sample <- diamonds %>%
filter(cut %in% c("Fair", "Good")) %>%
group_by(cut, clarity) %>%
summarise(price = mean(price / carat)) %>%
ungroup()
p <- ggplot(df.sample,
aes(x = clarity, y = price, group = cut,
linetype = cut)) +
geom_line(size = 1) +
guides(linetype = guide_legend(keywidth = 3, keyheight = 1)) +
theme(legend.position = c(1, 0), legend.justification = c(1, 0))
library(gridExtra)
grid.arrange(p +
labs(title = "Default scale",
subtitle = c("values = linetype_pal()(2)")),
p + scale_linetype_manual(values = c("solid", "dashed")) +
labs(title = "Manual scale",
subtitle = "values = c('solid', 'dashed')"),
p + scale_linetype_manual(values = c("solid", "22")) +
labs(title = "Manual scale",
subtitle = "values = c('solid', '22')"),
nrow = 1)
The third plot mimics the behaviour of the default scale.
you're just not able to see the linetype difference - if you make the legend wider it's visible:
ggplot(df,aes(x=Year,y=value, colour = factor(mpft),linetype=type)) +
geom_line(aes(group = index), size = 1.4) +
scale_linetype_manual(name= "Run Type", values = unique(df$type), labels = run.type) +
guides(linetype = guide_legend(keywidth = 3, keyheight = 1))
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