With this data:
df <- data.frame(value =c(20, 50, 90),
group = c(1, 2,3))
I can get a bar chart:
df %>% ggplot(aes(x = group, y = value, fill = value)) +
geom_col() +
coord_flip()+
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
But I would like to have the colors of those bars to vary according to their corresponding values in value.
I have managed to change them using geom_raster:
ggplot() +
geom_raster(aes(x = c(0:20), y = .9, fill = c(0:20)),
interpolate = TRUE) +
geom_raster(aes(x = c(0:50), y = 2, fill = c(0:50)),
interpolate = TRUE) +
geom_raster(aes(x = c(0:90), y = 3.1, fill = c(0:90)),
interpolate = TRUE) +
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
This approach is not efficient when I have many groups in real data. Any suggestions to get it done more efficiently would be appreciated.
I found the accepted answer to a previous similar question, but "These numbers needs to be adjusted depending on the number of x values and range of y". I was looking for an approach that I do not have to adjust numbers based on data. David Gibson's answer fits my purpose.
It does not look like this is supported natively in ggplot. I was able to get something close by adding additional rows, ranging from 0 to value) to the data. Then use geom_tile and separating the tiles by specifying width.
library(tidyverse)
df <- data.frame(value = c(20, 50, 90),
group = c(1, 2, 3))
df_expanded <- df %>%
rowwise() %>%
summarise(group = group,
value = list(0:value)) %>%
unnest(cols = value)
df_expanded %>%
ggplot() +
geom_tile(aes(
x = group,
y = value,
fill = value,
width = 0.9
)) +
coord_flip() +
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
If this is too pixilated you can increase the number of rows generated by replacing list(0:value) with seq(0, value, by = 0.1).
This is a real hack using ggforce. This package has a geom that can take color gradients but it is for a line segment. I've just increased the size to make the line segment look like a bar. I made all the bars the same length to get the correct gradient, then covered a portion of each bar over with the same color as the background color to make them appear to be the correct length. Had to hide the grid lines, however. :-)
df %>%
ggplot() +
geom_link(aes(x = 0, xend = max(value), y = group, yend = group, color = stat(index)), size = 30) +
geom_link(aes(x = value, xend = max(value), y = group, yend = group), color = "grey", size = 31) +
scale_color_viridis_c(option = "C") +
theme(legend.position = "none", panel.background = element_rect(fill = "grey"),
panel.grid = element_blank()) +
ylim(0.5, max(df$group)+0.5 )
Related
Here's my R code
ggplot(dat = Table, aes(x = Group, y = value, fill = Type)) +
geom_boxplot(alpha=0.08)+
geom_jitter()+
scale_fill_brewer(palette="Spectral")+
theme_minimal()
Like you can see the dots are in the middle of the boxplots. What can I add in geom_jitter to have each point in the righ boxplot and not in the middle like this ? I also tried geom_point, it gave the same result !
Thanks to the help now It works, but I wanted to add a line to connect the dots and I got this.. can someone tell how to really connect the dots with lines
I think if you group by interaction(Group, Type) and use position_jitterdodge() you should get what you're looking for.
ggplot(mtcars, aes(as.character(am), mpg, color = as.character(vs),
group = interaction(as.character(vs), as.character(am)))) +
geom_boxplot() +
geom_jitter(position = position_jitterdodge()) # same output with geom_point()
Edit - here's an example with manual jittering applied to data where the each subject appears once in each Group.
I looked for a built-in way to do this, and this answer comes close, but I couldn't get it to work in terms of using position_jitterdodge with position defined by the groups of Group/Type, but line grouping defined by id alone and not by Group/Type. Both aesthetics (position adjustment and series identification) rely on the same group parameter, but they each need a different value for it.
Table = data.frame(id = 1:4,
value = rnorm(8),
Group = rep(c("a","b"), each = 4),
Type = c("1", "2"))
library(dplyr)
Table %>%
mutate(x = as.numeric(as.factor(Group)) +
0.2 * scale(as.numeric(as.factor(Type))) +
rnorm(n(), sd = 0.06)) %>%
ggplot(aes(x = Group, y = value, fill = Type, group = interaction(Group, Type))) +
geom_boxplot(alpha=0.2)+
geom_point(aes(x = x)) +
geom_line(aes(x = x, group = id), alpha = 0.1) +
scale_fill_brewer(palette="Spectral")+
theme_minimal()
Best to use position_dodge instead if you want them to line up:
library(ggplot2)
Table <- tibble::tibble(
Group = rep(c("A", "B"), each = 20),
Type = factor(rep(c(1:2, 1:2), each = 10)),
value = rnorm(40, mean = 10)
)
ggplot(dat = Table, aes(x = Group, y = value, fill = Type)) +
geom_boxplot(alpha=0.08)+
geom_point(position = position_dodge(width = 0.75))+
scale_fill_brewer(palette="Spectral")+
theme_minimal()
To add a line, make sure group = ID goes in both the geom_point and geom_line calls:
library(ggplot2)
Table <- tibble::tibble(
Group = rep(c("A", "B"), each = 20),
Type = factor(rep(c(1:2, 1:2), each = 10)),
ID = factor(rep(1:20, times = 2)),
value = rnorm(40, mean = 10)
)
ggplot(dat = Table, aes(x = Group, y = value, fill = Type)) +
geom_boxplot(alpha = 0.08) +
geom_point(aes(group = ID), position = position_dodge(width = 0.75))+
geom_line(aes(group = ID), position = position_dodge(width = 0.75), colour = "grey")+
scale_fill_brewer(palette = "Spectral") +
theme_minimal()
This is an extension to a previous answer of a question found here
Briefly #Jon Spring uses the following example code to produce a stacked bar plot with lines connecting each bar proportion between the two groups:
library(ggplot2)
set.seed(0)
data_bar <- data.frame(
stringsAsFactors = F,
Sample = rep(c("A", "B"), each = 10),
Percentage = runif(20),
Taxon = rep(1:10, by = 2)
)
library(tidyr)
ggplot() +
geom_bar(data = data_bar,
aes(x = Sample, y =Percentage, fill = Taxon),
colour = 'white', width = 0.3, stat="identity") +
geom_segment(data = tidyr::spread(data_bar, Sample, Percentage),
colour = "white",
aes(x = 1 + 0.3/2,
xend = 2 - 0.3/2,
y = cumsum(A),
yend = cumsum(B))) +
theme(panel.background = element_rect(fill = "black"), # to make connecting points
panel.grid = element_blank())
geom_seg example
While this is an elegant piece of code to address the issue of connecting the bar proportions, I am somehow not able to reproduce it once the bar proportion names are character strings instead on integer as above. Here is my code:
test.matrix<-matrix(c(70,120,65,140,13,68,46,294,52,410),ncol=2,byrow=TRUE)
rownames(test.matrix)<-c("BC.1","BC.2","GC","MO","EB")
colnames(test.matrix)<-c("12m","3m")
test.matrix <- data.frame(test.matrix)
ggplot() +
geom_bar(data = test.matrix,
aes(x = Var2, y =Freq, fill = Var1),
colour = 'black', width = 0.3, stat="identity") +
geom_segment(data = tidyr::spread(test.matrix, Var2, Freq),
colour = "black",
aes(x = 1 + 0.3/2,
xend = 2 - 0.3/2,
y = cumsum(`12m`),
yend = cumsum(`3m`))) +
scale_fill_manual(values=c('BC.1'="gold",'BC.2'="yellowgreen",'GC'="navy",'MO'="royalblue",'EB'="orangered")) +
theme(panel.background = element_rect(fill = "white"), panel.grid = element_blank())
geom_seg char
The result does not match the geom_segment lines to the bar proportions. Maybe it has sth to do with cumsum() using an alphabetic order of the strings, but I cannot figure out how to solve this - or its sth completely different...
So I have two questions:
How can the bar proportions be connected if the proportions order has to be fixed? (a string vector or factor as 'names' for each value group or row)
How can an additional geom_segment at the very bottom of each bar be generated connecting both lower ends of each bar with another?
The issue is that you cumsummed in the wrong "direction" or order, i.e. you start cumsumming at BC.1 while in the bar chart it's on the top. This could simply be fixed by rearranging the dataset before cumulating. Therefore in my opinion it's best to do this outside of the plotting code so that you can easily check the data.
To get another geom_segment at the bottom you can simply add a row to your data.
library(tidyverse)
test.matrix<-matrix(c(70,120,65,140,13,68,46,294,52,410),ncol=2,byrow=TRUE)
rownames(test.matrix)<-c("BC.1","BC.2","GC","MO","EB")
colnames(test.matrix)<-c("12m","3m")
test.matrix <- data.frame(test.matrix)
test.matrix <- test.matrix %>%
setNames(c("12m", "3m")) %>%
rownames_to_column(var = "Var1") %>%
pivot_longer(-Var1, names_to = "Var2", values_to = "Freq")
test.matrix.wide <- tidyr::spread(test.matrix, Var2, Freq) %>%
arrange(desc(Var1)) %>%
mutate(y = cumsum(`12m`),
yend = cumsum(`3m`)) %>%
add_row(y = 0, yend = 0)
ggplot() +
geom_bar(data = test.matrix,
aes(x = Var2, y =Freq, fill = Var1),
colour = 'black', width = 0.3, stat="identity") +
geom_segment(data = test.matrix.wide,
colour = "black",
aes(x = 1 + 0.3/2,
xend = 2 - 0.3/2,
y = y,
yend = yend)) +
scale_fill_manual(values=c('BC.1'="gold",'BC.2'="yellowgreen",'GC'="navy",'MO'="royalblue",'EB'="orangered")) +
theme(panel.background = element_rect(fill = "white"), panel.grid = element_blank())
I am plotting a distribution of two variables on a single histogram. I am interested in highlighting each distribution's mean value on that graph through a doted line or something similar (but hopefully something that matches the color present already in the aes section of the code).
How would I do that?
This is my code so far.
hist_plot <- ggplot(data, aes(x= value, fill= type, color = type)) +
geom_histogram(position="identity", alpha=0.2) +
labs( x = "Value", y = "Count", fill = "Type", title = "Title") +
guides(color = FALSE)
Also, is there any way to show the count of n for each type on this graph?
i've made some reproducible code that might help you with your problem.
library(tidyverse)
# Generate some random data
df <- data.frame(value = c(runif(50, 0.5, 1), runif(50, 1, 1.5)),
type = c(rep("type1", 50), rep("type2", 50)))
# Calculate means from df
stats <- df %>% group_by(type) %>% summarise(mean = mean(value),
n = n())
# Make the ggplot
ggplot(df, aes(x= value, fill= type, color = type)) +
geom_histogram(position="identity", alpha=0.2) +
labs(x = "Value", y = "Count", fill = "Type", title = "Title") +
guides(color = FALSE) +
geom_vline(data = stats, aes(xintercept = mean, color = type), size = 2) +
geom_text(data = stats, aes(x = mean, y = max(df$value), label = n),
size = 10,
color = "black")
If things go as intended, you'll end up something akin to the following plot.
histogram with means
I using ggplot to create a bubble plot. With this code:
ggplot(df, aes(x = order, y = mean, size = n, fill = name)) +
geom_point(shape = 21) +
theme_bw() +
theme() +
scale_size(range = c(1, 50)) +
ylim(0,100)
It is working perfectly apart from 2 things:
For each name (fill) I would like to manually specify the colour used (via a dataframe that maps name to colour) - this is to provide consistency across multiple figures.
I would like to substitute the numbers on the y for text labels (for several reasons I cannot use the text labels from the outset due to ordering issues)
I have tried several methods using scale_color_manual() and scale_y_continuous respectively and I am getting nowhere! Any help would be very gratefully received!
Thanks
Since you have not specified an example df, I created one of my own.
To manually specify the color, you have to use scale_fill_manual with a named vector as the argument of values.
Edit 2
This appears to do what you want. We use scale_y_continuous. The breaks argument specifies the vector of positions, while the labels argument specifies the labels which should appear at those positions. Since we already created the vectors when creating the data frame, we simply pass those vectors as arguments.
ggplot(df, aes(x = order, y = mean, size = n, fill = name)) +
geom_point(shape = 21) +
scale_fill_manual(values = gcolors) +
scale_size(limits = c(min(df$n), max(df$n))) +
scale_y_continuous(breaks = mean, labels = order_label)
Edit 1
From your comment, it appears that you want to label the circles. One option would be to use geom_text. Code below. You may need to experiment with values of nudge_y to get the position correct.
order <- c(1, 2)
mean <- c(0.75, 0.3)
n <- c(180, 200)
name <- c("a", "b")
order_label <- c("New York", "London")
df <- data.frame(order, mean, n, name, order_label, stringsAsFactors = FALSE)
color <- c("blue", "red")
name_color <- data.frame(name, color, stringsAsFactors = FALSE)
gcolors <- name_color[, 2]
names(gcolors) <- name_color[, 1]
ggplot(df, aes(x = order, y = mean, size = n, fill = name)) +
geom_point(shape = 21) +
geom_text(aes(label = order_label), size = 3, hjust = "inward",
nudge_y = 0.03) +
scale_fill_manual(values = gcolors) +
scale_size(limits = c(min(df$n), max(df$n))) +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank()) +
ylab(NULL)
Original Answer
It is not clear what you mean by "substitute the numbers on the y for text labels". In the example below, I have formatted the y-axis as a percentage using the scales::percent_format() function. Is this similar to what you want?
order <- c(1, 2)
mean <- c(0.75, 0.3)
n <- c(180, 200)
name <- c("a", "b")
df <- data.frame(order, mean, n, name, stringsAsFactors = FALSE)
color <- c("blue", "red")
name_color <- data.frame(name, color, stringsAsFactors = FALSE)
gcolors <- name_color[, 2]
names(gcolors) <- name_color[, 1]
ggplot(df, aes(x = order, y = mean, size = n, fill = name)) +
geom_point(shape = 21) +
scale_fill_manual(values = gcolors) +
scale_size(limits = c(min(df$n), max(df$n))) +
scale_y_continuous(labels = scales::percent_format())
Thanks, for all your help, this worked perfectly:
ggplot(df, aes(x = order, y = mean, size = n, fill = name)) +
geom_point(shape = 21) +
scale_fill_manual(values = gcolors) +
scale_size(limits = c(min(df$n), max(df$n))) +
scale_x_continuous(breaks = order, labels = order_label)
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))