I would like to 'annotate' a text on the top right hand corner of ggplot2 bar chart that has character for x axis and numeric for y axis. All the documentation I see is that, to annotate a text, both x and y coordinates have to be given numeric value.
Here is an example chart:-
Here is the data frame
df1 <- data.frame( p=c("a","b","c","a","b","c"),
v=c(10,9,8,6,5,2),
u=c("aa","bb","cc","aa","bb","cc")
)
summarized data frame
df2 <- df1 %>% select(p, v) %>% group_by(p) %>% summarise_each(funs(sum))
bar plot
p <- ggplot(data = df2, aes(p, v, label = v)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(position = position_dodge(.9), vjust = -1, fontface = "bold", size = 5)
p
You should be able to do it just putting the location inside of aes(). This worked for me (unless I am misunderstanding your intent):
ggplot(data = df2, aes(p, v, label = v)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(position = position_dodge(.9), vjust = -1, fontface = "bold", size = 5) +
geom_text(aes(x = "c", y = 15, label = "Here I am"))
Related
Alright, after a long silent read along, here's my first question. I am trying to add corresponding labels of unhighlighted items for a grouped barplot. When I insert gghighlight in front of the geom_text I get the following plot:
library(tidyverse)
library(gghighlight)
df <- data.frame (group = c("A", "A", "B", "B", "C", "C"),
value = c("value_1", "value_2","value_1", "value_2","value_1", "value_2"),
mean = c(1.331, 1.931, 3.231, 3.331, 4.631, 3.331)
)
ggplot(data = df, aes(x = group, y = mean, fill = value)) +
geom_bar(stat = "identity", position = "dodge") +
gghighlight(group != "B",
label_key = group
) +
geom_text(aes(label = round(mean, digits = 2)),
stat= "identity",
vjust = -.5,
position = position_dodge(width = .9)
)
If I move gghightlight behind the geom_text I get the following plot:
ggplot(data = df, aes(x = group, y = mean, fill = value)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(aes(label = round(mean, digits = 2)),
stat= "identity",
vjust = -.5,
position = position_dodge(width = .9)
) +
gghighlight(group != "B",
label_key = group)
Is there a way to label the unhighligthed bars like the highlighted ones?
Thanks in advance.
############## EDIT ###########
Besides graying out certain columns (see #TarJae's answer), there is also the possibility to make them transparent (essential parts are from this post: ggplot transparency on individual bar):
subset_df <- df %>%
mutate(alpha.adj = as.factor(ifelse(group != "B", 1, 0.6)))
ggplot(data = subset_df, aes(x = group, y = mean, fill = value, alpha=factor(alpha.adj))) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(aes(label = round(mean, digits = 2)),
stat= "identity",
vjust = -.5,
position = position_dodge(width = .9)
) +
scale_alpha_manual(values = c("0.6"=0.6, "1"=1), guide='none')
[]
Are you looking for this?
This is a solution without using gghighlight package:
library(tidyverse)
subset_df <- df %>%
mutate(highlight = if_else(group != "B", mean, NA_real_))
ggplot(data = subset_df, aes(x = group, y = mean, group=value)) +
geom_col(fill = 'grey', alpha = 0.6, position = 'dodge') +
geom_col(aes(y = highlight, fill = value), position = 'dodge') +
geom_text(aes(group, label = round(mean, digits = 2)),
position = position_dodge(width = 1))
This is a solution with the gghighlight package and some limited hacky code.
When reading the vignette, I noticed that the author of the package "filters out" the data that are not highlighted. You can see that if you save your highlighted plot in p_h and then look at p_h$data, the values for group B have disappeared.
library(tidyverse)
library(gghighlight)
p_h <- ggplot(data = df, aes(x = group, y = mean, fill = value)) +
geom_bar(stat = "identity", position = "dodge") +
gghighlight(group != "B",
label_key = group) +
geom_text(aes(label = round(mean, digits = 2)),
stat= "identity",
vjust = -.5,
position = position_dodge(width = .9))
> p_h$data
group value mean
1 A value_1 1.331
2 A value_2 1.931
5 C value_1 4.631
6 C value_2 3.331
If we re-insert the data (after the call to gghighlight() has removed them), then geom_text() will be able to find the means for group B again.
One can "recover" the data and re-insert them with the following code:
### create a ggplot object with the original complete data
### you could check that with p_to_copy_data$data
p_to_copy_data <- ggplot(data = df)
### copy the complete data to your highlighted plot data section
p_h$data <- p_to_copy_data$data
p_h
This yields the following graph:
I would like to draw plot with the same width of the bars. Here's my minimal example code:
data <- data.frame(A = letters[1:17],
B = sample(1:500, 17),
C = c(rep(1, 5), rep(2, 6), rep(c(3,4,5), each = 2)))
ggplot(data,
aes(x = C, y = B, label = A,
fill = A)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(position = position_dodge(width = 0.9), angle = 90)
The result is shown in the picture above:
The width of the bars is dependent on numbers of observation in group given in variable C. I want to have each bar to have the same width.
The facet_grid(~C) works (bars are the same width) it's not what I mean:
ggplot(data,
aes(x = C, y = B, label = A,
fill = A)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(position = position_dodge(width = 0.9), angle = 90) +
facet_grid(~C)
What I want is to have plot like in the first picture but with bars's width independent on number of observation in each level from column C. How can I do it?
[EDIT] geom_bar(width) changes width of the bars'group but still bars in fifth group are wider than in the first group, so it's not the answer to my question.
Update
Since ggplot2_3.0.0 version you are now be able to use position_dodge2 with preserve = c("total", "single")
ggplot(data,aes(x = C, y = B, label = A, fill = A)) +
geom_col(position = position_dodge2(width = 0.9, preserve = "single")) +
geom_text(position = position_dodge2(width = 0.9, preserve = "single"), angle = 90, vjust=0.25)
Original answer
As already commented you can do it like in this answer:
Transform A and C to factors and add unseen variables using tidyr's complete. Since the recent ggplot2 version it is recommended to use geom_col instead of geom_bar in cases of stat = "identity":
data %>%
as.tibble() %>%
mutate_at(c("A", "C"), as.factor) %>%
complete(A,C) %>%
ggplot(aes(x = C, y = B, fill = A)) +
geom_col(position = "dodge")
Or work with an interaction term:
data %>%
ggplot(aes(x = interaction(C, A), y = B, fill = A)) +
geom_col(position = "dodge")
And by finally transforming the interaction to numeric you can setup the x-axis according to your desired output. By grouping (group_by) you can calculate the matching breaks. The fancy stuff with the {} around the ggplot argument is neseccary to directly use the vaiables Breaks and C within the pipe.
data %>%
mutate(gr=as.numeric(interaction(C, A))) %>%
group_by(C) %>%
mutate(Breaks=mean(gr)) %>%
{ggplot(data=.,aes(x = gr, y = B, fill = A, label = A)) +
geom_col(position = "dodge") +
geom_text(position = position_dodge(width = 0.9), angle = 90 ) +
scale_x_continuous(breaks = unique(.$Breaks),
labels = unique(.$C))}
Edit:
Another approach would be to use facets. Using space = "free_x" allows to set the width proportional to the length of the x scale.
library(tidyverse)
data %>%
ggplot(aes(x = A, y = B, fill = A)) +
geom_col(position = "dodge") +
facet_grid(~C, scales = "free_x", space = "free_x")
You can also plot the facet labels on the bottom using switch and remove x axis labels
data %>%
ggplot(aes(x = A, y = B, fill = A)) +
geom_col(position = "dodge") +
facet_grid(~C, scales = "free_x", space = "free_x", switch = "x") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.background = element_blank())
I would like to draw plot with the same width of the bars. Here's my minimal example code:
data <- data.frame(A = letters[1:17],
B = sample(1:500, 17),
C = c(rep(1, 5), rep(2, 6), rep(c(3,4,5), each = 2)))
ggplot(data,
aes(x = C, y = B, label = A,
fill = A)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(position = position_dodge(width = 0.9), angle = 90)
The result is shown in the picture above:
The width of the bars is dependent on numbers of observation in group given in variable C. I want to have each bar to have the same width.
The facet_grid(~C) works (bars are the same width) it's not what I mean:
ggplot(data,
aes(x = C, y = B, label = A,
fill = A)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(position = position_dodge(width = 0.9), angle = 90) +
facet_grid(~C)
What I want is to have plot like in the first picture but with bars's width independent on number of observation in each level from column C. How can I do it?
[EDIT] geom_bar(width) changes width of the bars'group but still bars in fifth group are wider than in the first group, so it's not the answer to my question.
Update
Since ggplot2_3.0.0 version you are now be able to use position_dodge2 with preserve = c("total", "single")
ggplot(data,aes(x = C, y = B, label = A, fill = A)) +
geom_col(position = position_dodge2(width = 0.9, preserve = "single")) +
geom_text(position = position_dodge2(width = 0.9, preserve = "single"), angle = 90, vjust=0.25)
Original answer
As already commented you can do it like in this answer:
Transform A and C to factors and add unseen variables using tidyr's complete. Since the recent ggplot2 version it is recommended to use geom_col instead of geom_bar in cases of stat = "identity":
data %>%
as.tibble() %>%
mutate_at(c("A", "C"), as.factor) %>%
complete(A,C) %>%
ggplot(aes(x = C, y = B, fill = A)) +
geom_col(position = "dodge")
Or work with an interaction term:
data %>%
ggplot(aes(x = interaction(C, A), y = B, fill = A)) +
geom_col(position = "dodge")
And by finally transforming the interaction to numeric you can setup the x-axis according to your desired output. By grouping (group_by) you can calculate the matching breaks. The fancy stuff with the {} around the ggplot argument is neseccary to directly use the vaiables Breaks and C within the pipe.
data %>%
mutate(gr=as.numeric(interaction(C, A))) %>%
group_by(C) %>%
mutate(Breaks=mean(gr)) %>%
{ggplot(data=.,aes(x = gr, y = B, fill = A, label = A)) +
geom_col(position = "dodge") +
geom_text(position = position_dodge(width = 0.9), angle = 90 ) +
scale_x_continuous(breaks = unique(.$Breaks),
labels = unique(.$C))}
Edit:
Another approach would be to use facets. Using space = "free_x" allows to set the width proportional to the length of the x scale.
library(tidyverse)
data %>%
ggplot(aes(x = A, y = B, fill = A)) +
geom_col(position = "dodge") +
facet_grid(~C, scales = "free_x", space = "free_x")
You can also plot the facet labels on the bottom using switch and remove x axis labels
data %>%
ggplot(aes(x = A, y = B, fill = A)) +
geom_col(position = "dodge") +
facet_grid(~C, scales = "free_x", space = "free_x", switch = "x") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.background = element_blank())
Consider this sample data.
df <- data.frame(
x = factor(c(1, 1, 2, 2)),
y = c(.1, .3, .2, .1),
grp = c("a", "b", "a", "b")
)
Now I create the graph using ggplot, and annotate it using geom_text()
ggplot(data = df, aes(x, y, fill = grp, label = y)) +
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(limits=c(0,1)) +
geom_text(position = position_dodge(0.9))
How do I specify that all the text values align perfectly horizontal at the top of the graph window?
You can specify the aes(y=...) in geom_text. So, for the numbers at the top of the graph window you'll have
ggplot(data = df, aes(x, y, fill = grp, label = y)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(aes(y=Inf), position = position_dodge(0.9))
And you may want to chuck in a + ylim(0, 4) to expand the plot area.
To match the edited question:
ggplot(data = df, aes(x, y, fill = grp, label = y)) +
geom_bar(stat = "identity", position = "dodge") +
scale_y_continuous(limits=c(0,1)) +
geom_text(aes(y=0.9), position = position_dodge(0.9)) ## can specify any y=.. value
I would like to create a simple bar chart with ggplot2 and my problem is that my x variable contains long strings so the labels are overlaid.
Here are fake datas and the plot :
library(dplyr)
library(tidyr)
library(ggplot2)
set.seed(42)
datas <- data.frame(label = sprintf("aLongLabel%d", 1:8),
ok = sample(seq(0, 1, by = 0.1), 8, rep = TRUE)) %>%
mutate(err = abs(ok - 1)) %>%
gather(type, freq, ok, err)
datas %>%
ggplot(aes(x = label, y = freq)) +
geom_bar(aes(fill = type), stat = "identity")
I would like to replace the labels by shorter ones and create a legend to show the matches.
What I've tried :
I use the shape aes parameter in geo_point which will create a legend with shapes (and plots shapes that I hide with alpha = 0). Then I change the shapes with scale_shape_manual and replace the x labels with scale_x_discrete. With guides I override the alpha parameter of my shapes so they wont be invisible in the legend.
leg.txt <- levels(datas$label)
x.labels <- structure(LETTERS[seq_along(leg.txt)],
.Names = leg.txt)
datas %>%
ggplot(aes(x = label, y = freq)) +
geom_bar(aes(fill = type), stat = "identity") +
geom_point(aes(shape = label), alpha = 0) +
scale_shape_manual(name = "Labels", values = x.labels) +
guides(shape = guide_legend(override.aes = list(size = 5, alpha = 1))) +
scale_x_discrete(name = "Label", labels = x.labels)
It gives me the expected output but I feel like this is very hacky.
Does ggplot2 provides a way to do this more directly ? Thanks.
Rotation solution suggested by Pascal
Rotate the labels and align them to the edge :
datas %>%
ggplot(aes(x = label, y = freq)) +
geom_bar(aes(fill = type), stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))