Related
I'm trying to replicate the image below in R (Original post). I have seen similar posts (Post 1 and Post 2) but none similar to this plot. I'm just wondering if anyone knows how to do something similar in R. There's a couple of observations:
Bubbles do not overlap
Smaller bubbles tend to be closer to the axis (but not always!)
Bubbles are in two categories
I'm sure that data from Post 1 would be helpful!
https://docs.google.com/spreadsheets/d/11nq5AK3Y1uXQJ8wTm14w9MkZOwAxHHTyPyEvUxCeGVc/edit?usp=sharing
Thank you so much,
Ok so this is just a starting point that people could use to formulate a better answer to the question. It uses the packcircles package to (surprisingly) pack circles. It doesn't qualify all of your criteria, but can serve as a useful starting point. We're just going to pretend that the eruptions column from the faithful dataset is your time variable.
library(packcircles)
#> Warning: package 'packcircles' was built under R version 4.0.2
library(ggplot2)
library(scales)
library(ggrepel)
# Setup some data, suppose we'd like to label 5 samples
set.seed(0)
faith2 <- faithful
faith2$label <- ""
faith2$label[sample(nrow(faith2), 5)] <- LETTERS[1:5]
# Initialise circle pack data
init <- data.frame(
x = faith2$eruptions,
y = runif(nrow(faith2)),
areas = rescale(faith2$waiting, to = c(0.01, 0.1))
)
# Use the repelling layout
res <- circleRepelLayout(
init,
xlim = range(init$x) + c(-1, 1),
ylim = c(0, Inf),
xysizecols = c(1, NA, 3),
sizetype = "radius",
weights = 0.1
)
# Prepare for ggplot2
df <- circleLayoutVertices(res$layout)
df <- cbind(df, faith2[df$id,])
This is showing that the circles are reasonably placed with respect to our fake time variable.
# Plot
ggplot(df, aes(x, y, group = id)) +
geom_polygon(aes(fill = eruptions,
colour = I(ifelse(nzchar(label), "black", NA)))) +
scale_fill_viridis_c() +
coord_equal()
And this is showing that the circle size is reasonably corresponding to a different variable.
ggplot(df, aes(x, y, group = id)) +
geom_polygon(aes(fill = waiting,
colour = I(ifelse(nzchar(label), "black", NA)))) +
scale_fill_viridis_c() +
coord_equal()
Created on 2020-07-11 by the reprex package (v0.3.0)
There are few flaws in this, notably it doesn't satisfy the 2nd criterion (circles aren't hugging the axis). Also, for reasons beyond my understanding, the packcircles layout couldn't place about 12% of datapoints, which are assigned NaN in df. Anyway, hopefully somebody smarter than me will do a better job at this.
I want to make a bar plot where one of the values is much bigger than all other values. Is there a way of having a discontinuous y-axis? My data is as follows:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
p <- ggplot(data = df, aes(x = b, y = a)) + geom_bar()
p <- p + opts(axis.text.x=theme_text(angle= 90, hjust=1)) + coord_flip()
p
Is there a way that I can make my axis run from 1- 10, then 490 - 500? I can't think of any other way of plotting the data (aside from transforming it, which I don't want to do)
[Edit 2019-05-06]:
8 years later, above code needs to be amended to work with version 3.1.1 of ggplot2 in order to create the same chart:
library(ggplot2)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
As noted elsewhere, this isn't something that ggplot2 will handle well, since broken axes are generally considered questionable.
Other strategies are often considered better solutions to this problem. Brian mentioned a few (faceting, two plots focusing on different sets of values). One other option that people too often overlook, particularly for barcharts, is to make a table:
Looking at the actual values, the 500 doesn't obscure the differences in the other values! For some reason tables don't get enough respect as data a visualization technique. You might object that your data has many, many categories which becomes unwieldy in a table. If so, it's likely that your bar chart will have too many bars to be sensible as well.
And I'm not arguing for tables all the time. But they are definitely something to consider if you are making barcharts with relatively few bars. And if you're making barcharts with tons of bars, you might need to rethink that anyway.
Finally, there is also the axis.break function in the plotrix package which implements broken axes. However, from what I gather you'll have to specify the axis labels and positions yourself, by hand.
Eight years later, the ggforce package offers a facet_zoom() extension which is an implementation of Hadley Wickham's suggestion to show two plots (as referenced in Brian Diggs' answer).
Zoom facet
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10))
Unfortunately, the current version 0.2.2 of ggforce throws an error with coord_flip() so only vertical bars can be shown.
The zoomed facet shows the variations of the small values but still contains the large - now cropped - a4 bar. The zoom.data parameter controls which values appear in the zoomed facet:
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10), zoom.data = ifelse(a <= 10, NA, FALSE))
Two plots
Hadley Wickham suggested
I think it's much more appropriate to show two plots - one of all the
data, and one of just the small values.
This code creates two plots
library(ggplot2)
g1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
g2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip() +
ylim(NA, 10)
which can be combined into one plot by
cowplot::plot_grid(g1, g2) # or ggpubr::ggarrange(g1, g2)
or
gridExtra::grid.arrange(g1, g2) # or egg::ggarrange(g1, g2)
Two facets
This was suggested in a comment by Chase and also by Brian Diggs in his answer who interpreted Hadley's suggestion to use
faceted plots, one with all the data, one zoomed in a particular region
but no code was supplied for this approach, so far.
As there is no simple way to scale facets separately (see related question, e.g.) the data needs to be manipulated:
library(dplyr)
library(ggplot2)
ggplot() +
aes(x = b, y = a) +
geom_col(data = df %>% mutate(subset = "all")) +
geom_col(data = df %>% filter(a <= 10) %>% mutate(subset = "small")) +
coord_flip() +
facet_wrap(~ subset, scales = "free_x")
No, not using ggplot. See the discussion in the thread at http://groups.google.com/group/ggplot2/browse_thread/thread/8d2acbfc59d2f247 where Hadley explains why it is not possible but gives a suggested alternative (faceted plots, one with all the data, one zoomed in a particular region).
Not with ggplot, but with plotrix you can easily do that:
library(plotrix)
gap.barplot(df$a, gap=c(5,495),horiz=T)
No, unfortunately not
The fear is that allowing discontinuous axes will lead to deceit of the audience. However, there are cases where not having a discontinuous axis leads to distortion.
For example, if the axis is truncated, but usually lies within some interval (say [0,1]), the audience may not notice the truncation and make distorted conclusions about the data. In this case, an explicit discontinuous axis would be more appropriate and transparent.
Compare:
An option could be using the ggbreak package using the scale_y_cut() or scale_x_cut() function. This function makes it possible to cut the ggplot object into parts with the possibility to specify which part is zoom in or zoom out. Here is a reproducible example with left plot normal and right plot with the function used:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
library(ggplot2)
library(ggbreak)
library(patchwork)
p1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col()
p2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
scale_y_cut(breaks=c(4, 30), which=c(1, 3), scales=c(0.5, 3))
p1 + p2
Created on 2022-08-22 with reprex v2.0.2
As you can see from the example, some parts are zoomed in and zoomed out. This can be changed by using different arguments.
Arguments used:
breaks:
a numeric or numeric vector, the points to be divided
which:
integer, the position of subplots to scales, started from left to
right or top to bottom.
scales:
numeric, relative width or height of subplots.
To change the space between the subplots, you can use the argument space.
For some extra information and examples check this tutorial.
A clever ggplot solution is provided by Jörg Steinkamp, using facet_grid. Simplified, it is something like this:
library("tidyverse")
df <- data.frame(myLetter=LETTERS[1:4], myValue=runif(12) + rep(c(4,0,0),2)) # cluster a few values well above 1
df$myFacet <- df$myValue > 3
(ggplot(df, aes(y=myLetter, x=myValue))
+ geom_point()
+ facet_grid(. ~ myFacet, scales="free", space="free")
+ scale_x_continuous(breaks = seq(0, 5, .25)) # this gives both facets equal interval spacing.
+ theme(strip.text.x = element_blank()) # get rid of the facet labels
)
As of 2022-06-01, we have the elegant-looking ggbreak package, which appears to answer the OP's question. Although I haven't tried it on my own data, it looks to be compatible with many or all other ggplot2 functionality. Offers differential scaling too, perhaps useful to OP's and similar uses.
library(ggplot2)
library(ggbreak)
set.seed(2019-01-19)
d <- data.frame(x = 1:20,
y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22))
p1 <- ggplot(d, aes(y, x)) + geom_col(orientation="y") +
theme_minimal()
p1 + scale_x_break(c(7, 17), scales = 1.5) + scale_x_break(c(18, 21), scales=2)
I doubt there's anything off the shelf in R, but you could show the data as a series of 3D partial cubes. 500 is only 5*10*10, so it would scale well. The exact value could be a label.
This probably should only be used if you must have a graphic representation for some reason.
One strategy is to change the axis to plot Log Scale. This way you get to reduce exponentially higher value by a factor of 10
I want to make a bar plot where one of the values is much bigger than all other values. Is there a way of having a discontinuous y-axis? My data is as follows:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
p <- ggplot(data = df, aes(x = b, y = a)) + geom_bar()
p <- p + opts(axis.text.x=theme_text(angle= 90, hjust=1)) + coord_flip()
p
Is there a way that I can make my axis run from 1- 10, then 490 - 500? I can't think of any other way of plotting the data (aside from transforming it, which I don't want to do)
[Edit 2019-05-06]:
8 years later, above code needs to be amended to work with version 3.1.1 of ggplot2 in order to create the same chart:
library(ggplot2)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
As noted elsewhere, this isn't something that ggplot2 will handle well, since broken axes are generally considered questionable.
Other strategies are often considered better solutions to this problem. Brian mentioned a few (faceting, two plots focusing on different sets of values). One other option that people too often overlook, particularly for barcharts, is to make a table:
Looking at the actual values, the 500 doesn't obscure the differences in the other values! For some reason tables don't get enough respect as data a visualization technique. You might object that your data has many, many categories which becomes unwieldy in a table. If so, it's likely that your bar chart will have too many bars to be sensible as well.
And I'm not arguing for tables all the time. But they are definitely something to consider if you are making barcharts with relatively few bars. And if you're making barcharts with tons of bars, you might need to rethink that anyway.
Finally, there is also the axis.break function in the plotrix package which implements broken axes. However, from what I gather you'll have to specify the axis labels and positions yourself, by hand.
Eight years later, the ggforce package offers a facet_zoom() extension which is an implementation of Hadley Wickham's suggestion to show two plots (as referenced in Brian Diggs' answer).
Zoom facet
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10))
Unfortunately, the current version 0.2.2 of ggforce throws an error with coord_flip() so only vertical bars can be shown.
The zoomed facet shows the variations of the small values but still contains the large - now cropped - a4 bar. The zoom.data parameter controls which values appear in the zoomed facet:
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10), zoom.data = ifelse(a <= 10, NA, FALSE))
Two plots
Hadley Wickham suggested
I think it's much more appropriate to show two plots - one of all the
data, and one of just the small values.
This code creates two plots
library(ggplot2)
g1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
g2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip() +
ylim(NA, 10)
which can be combined into one plot by
cowplot::plot_grid(g1, g2) # or ggpubr::ggarrange(g1, g2)
or
gridExtra::grid.arrange(g1, g2) # or egg::ggarrange(g1, g2)
Two facets
This was suggested in a comment by Chase and also by Brian Diggs in his answer who interpreted Hadley's suggestion to use
faceted plots, one with all the data, one zoomed in a particular region
but no code was supplied for this approach, so far.
As there is no simple way to scale facets separately (see related question, e.g.) the data needs to be manipulated:
library(dplyr)
library(ggplot2)
ggplot() +
aes(x = b, y = a) +
geom_col(data = df %>% mutate(subset = "all")) +
geom_col(data = df %>% filter(a <= 10) %>% mutate(subset = "small")) +
coord_flip() +
facet_wrap(~ subset, scales = "free_x")
No, not using ggplot. See the discussion in the thread at http://groups.google.com/group/ggplot2/browse_thread/thread/8d2acbfc59d2f247 where Hadley explains why it is not possible but gives a suggested alternative (faceted plots, one with all the data, one zoomed in a particular region).
Not with ggplot, but with plotrix you can easily do that:
library(plotrix)
gap.barplot(df$a, gap=c(5,495),horiz=T)
No, unfortunately not
The fear is that allowing discontinuous axes will lead to deceit of the audience. However, there are cases where not having a discontinuous axis leads to distortion.
For example, if the axis is truncated, but usually lies within some interval (say [0,1]), the audience may not notice the truncation and make distorted conclusions about the data. In this case, an explicit discontinuous axis would be more appropriate and transparent.
Compare:
An option could be using the ggbreak package using the scale_y_cut() or scale_x_cut() function. This function makes it possible to cut the ggplot object into parts with the possibility to specify which part is zoom in or zoom out. Here is a reproducible example with left plot normal and right plot with the function used:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
library(ggplot2)
library(ggbreak)
library(patchwork)
p1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col()
p2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
scale_y_cut(breaks=c(4, 30), which=c(1, 3), scales=c(0.5, 3))
p1 + p2
Created on 2022-08-22 with reprex v2.0.2
As you can see from the example, some parts are zoomed in and zoomed out. This can be changed by using different arguments.
Arguments used:
breaks:
a numeric or numeric vector, the points to be divided
which:
integer, the position of subplots to scales, started from left to
right or top to bottom.
scales:
numeric, relative width or height of subplots.
To change the space between the subplots, you can use the argument space.
For some extra information and examples check this tutorial.
A clever ggplot solution is provided by Jörg Steinkamp, using facet_grid. Simplified, it is something like this:
library("tidyverse")
df <- data.frame(myLetter=LETTERS[1:4], myValue=runif(12) + rep(c(4,0,0),2)) # cluster a few values well above 1
df$myFacet <- df$myValue > 3
(ggplot(df, aes(y=myLetter, x=myValue))
+ geom_point()
+ facet_grid(. ~ myFacet, scales="free", space="free")
+ scale_x_continuous(breaks = seq(0, 5, .25)) # this gives both facets equal interval spacing.
+ theme(strip.text.x = element_blank()) # get rid of the facet labels
)
As of 2022-06-01, we have the elegant-looking ggbreak package, which appears to answer the OP's question. Although I haven't tried it on my own data, it looks to be compatible with many or all other ggplot2 functionality. Offers differential scaling too, perhaps useful to OP's and similar uses.
library(ggplot2)
library(ggbreak)
set.seed(2019-01-19)
d <- data.frame(x = 1:20,
y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22))
p1 <- ggplot(d, aes(y, x)) + geom_col(orientation="y") +
theme_minimal()
p1 + scale_x_break(c(7, 17), scales = 1.5) + scale_x_break(c(18, 21), scales=2)
I doubt there's anything off the shelf in R, but you could show the data as a series of 3D partial cubes. 500 is only 5*10*10, so it would scale well. The exact value could be a label.
This probably should only be used if you must have a graphic representation for some reason.
One strategy is to change the axis to plot Log Scale. This way you get to reduce exponentially higher value by a factor of 10
I want to make a bar plot where one of the values is much bigger than all other values. Is there a way of having a discontinuous y-axis? My data is as follows:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
p <- ggplot(data = df, aes(x = b, y = a)) + geom_bar()
p <- p + opts(axis.text.x=theme_text(angle= 90, hjust=1)) + coord_flip()
p
Is there a way that I can make my axis run from 1- 10, then 490 - 500? I can't think of any other way of plotting the data (aside from transforming it, which I don't want to do)
[Edit 2019-05-06]:
8 years later, above code needs to be amended to work with version 3.1.1 of ggplot2 in order to create the same chart:
library(ggplot2)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
As noted elsewhere, this isn't something that ggplot2 will handle well, since broken axes are generally considered questionable.
Other strategies are often considered better solutions to this problem. Brian mentioned a few (faceting, two plots focusing on different sets of values). One other option that people too often overlook, particularly for barcharts, is to make a table:
Looking at the actual values, the 500 doesn't obscure the differences in the other values! For some reason tables don't get enough respect as data a visualization technique. You might object that your data has many, many categories which becomes unwieldy in a table. If so, it's likely that your bar chart will have too many bars to be sensible as well.
And I'm not arguing for tables all the time. But they are definitely something to consider if you are making barcharts with relatively few bars. And if you're making barcharts with tons of bars, you might need to rethink that anyway.
Finally, there is also the axis.break function in the plotrix package which implements broken axes. However, from what I gather you'll have to specify the axis labels and positions yourself, by hand.
Eight years later, the ggforce package offers a facet_zoom() extension which is an implementation of Hadley Wickham's suggestion to show two plots (as referenced in Brian Diggs' answer).
Zoom facet
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10))
Unfortunately, the current version 0.2.2 of ggforce throws an error with coord_flip() so only vertical bars can be shown.
The zoomed facet shows the variations of the small values but still contains the large - now cropped - a4 bar. The zoom.data parameter controls which values appear in the zoomed facet:
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10), zoom.data = ifelse(a <= 10, NA, FALSE))
Two plots
Hadley Wickham suggested
I think it's much more appropriate to show two plots - one of all the
data, and one of just the small values.
This code creates two plots
library(ggplot2)
g1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
g2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip() +
ylim(NA, 10)
which can be combined into one plot by
cowplot::plot_grid(g1, g2) # or ggpubr::ggarrange(g1, g2)
or
gridExtra::grid.arrange(g1, g2) # or egg::ggarrange(g1, g2)
Two facets
This was suggested in a comment by Chase and also by Brian Diggs in his answer who interpreted Hadley's suggestion to use
faceted plots, one with all the data, one zoomed in a particular region
but no code was supplied for this approach, so far.
As there is no simple way to scale facets separately (see related question, e.g.) the data needs to be manipulated:
library(dplyr)
library(ggplot2)
ggplot() +
aes(x = b, y = a) +
geom_col(data = df %>% mutate(subset = "all")) +
geom_col(data = df %>% filter(a <= 10) %>% mutate(subset = "small")) +
coord_flip() +
facet_wrap(~ subset, scales = "free_x")
No, not using ggplot. See the discussion in the thread at http://groups.google.com/group/ggplot2/browse_thread/thread/8d2acbfc59d2f247 where Hadley explains why it is not possible but gives a suggested alternative (faceted plots, one with all the data, one zoomed in a particular region).
Not with ggplot, but with plotrix you can easily do that:
library(plotrix)
gap.barplot(df$a, gap=c(5,495),horiz=T)
No, unfortunately not
The fear is that allowing discontinuous axes will lead to deceit of the audience. However, there are cases where not having a discontinuous axis leads to distortion.
For example, if the axis is truncated, but usually lies within some interval (say [0,1]), the audience may not notice the truncation and make distorted conclusions about the data. In this case, an explicit discontinuous axis would be more appropriate and transparent.
Compare:
An option could be using the ggbreak package using the scale_y_cut() or scale_x_cut() function. This function makes it possible to cut the ggplot object into parts with the possibility to specify which part is zoom in or zoom out. Here is a reproducible example with left plot normal and right plot with the function used:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
library(ggplot2)
library(ggbreak)
library(patchwork)
p1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col()
p2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
scale_y_cut(breaks=c(4, 30), which=c(1, 3), scales=c(0.5, 3))
p1 + p2
Created on 2022-08-22 with reprex v2.0.2
As you can see from the example, some parts are zoomed in and zoomed out. This can be changed by using different arguments.
Arguments used:
breaks:
a numeric or numeric vector, the points to be divided
which:
integer, the position of subplots to scales, started from left to
right or top to bottom.
scales:
numeric, relative width or height of subplots.
To change the space between the subplots, you can use the argument space.
For some extra information and examples check this tutorial.
A clever ggplot solution is provided by Jörg Steinkamp, using facet_grid. Simplified, it is something like this:
library("tidyverse")
df <- data.frame(myLetter=LETTERS[1:4], myValue=runif(12) + rep(c(4,0,0),2)) # cluster a few values well above 1
df$myFacet <- df$myValue > 3
(ggplot(df, aes(y=myLetter, x=myValue))
+ geom_point()
+ facet_grid(. ~ myFacet, scales="free", space="free")
+ scale_x_continuous(breaks = seq(0, 5, .25)) # this gives both facets equal interval spacing.
+ theme(strip.text.x = element_blank()) # get rid of the facet labels
)
As of 2022-06-01, we have the elegant-looking ggbreak package, which appears to answer the OP's question. Although I haven't tried it on my own data, it looks to be compatible with many or all other ggplot2 functionality. Offers differential scaling too, perhaps useful to OP's and similar uses.
library(ggplot2)
library(ggbreak)
set.seed(2019-01-19)
d <- data.frame(x = 1:20,
y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22))
p1 <- ggplot(d, aes(y, x)) + geom_col(orientation="y") +
theme_minimal()
p1 + scale_x_break(c(7, 17), scales = 1.5) + scale_x_break(c(18, 21), scales=2)
I doubt there's anything off the shelf in R, but you could show the data as a series of 3D partial cubes. 500 is only 5*10*10, so it would scale well. The exact value could be a label.
This probably should only be used if you must have a graphic representation for some reason.
One strategy is to change the axis to plot Log Scale. This way you get to reduce exponentially higher value by a factor of 10
I want to make a bar plot where one of the values is much bigger than all other values. Is there a way of having a discontinuous y-axis? My data is as follows:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
p <- ggplot(data = df, aes(x = b, y = a)) + geom_bar()
p <- p + opts(axis.text.x=theme_text(angle= 90, hjust=1)) + coord_flip()
p
Is there a way that I can make my axis run from 1- 10, then 490 - 500? I can't think of any other way of plotting the data (aside from transforming it, which I don't want to do)
[Edit 2019-05-06]:
8 years later, above code needs to be amended to work with version 3.1.1 of ggplot2 in order to create the same chart:
library(ggplot2)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
As noted elsewhere, this isn't something that ggplot2 will handle well, since broken axes are generally considered questionable.
Other strategies are often considered better solutions to this problem. Brian mentioned a few (faceting, two plots focusing on different sets of values). One other option that people too often overlook, particularly for barcharts, is to make a table:
Looking at the actual values, the 500 doesn't obscure the differences in the other values! For some reason tables don't get enough respect as data a visualization technique. You might object that your data has many, many categories which becomes unwieldy in a table. If so, it's likely that your bar chart will have too many bars to be sensible as well.
And I'm not arguing for tables all the time. But they are definitely something to consider if you are making barcharts with relatively few bars. And if you're making barcharts with tons of bars, you might need to rethink that anyway.
Finally, there is also the axis.break function in the plotrix package which implements broken axes. However, from what I gather you'll have to specify the axis labels and positions yourself, by hand.
Eight years later, the ggforce package offers a facet_zoom() extension which is an implementation of Hadley Wickham's suggestion to show two plots (as referenced in Brian Diggs' answer).
Zoom facet
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10))
Unfortunately, the current version 0.2.2 of ggforce throws an error with coord_flip() so only vertical bars can be shown.
The zoomed facet shows the variations of the small values but still contains the large - now cropped - a4 bar. The zoom.data parameter controls which values appear in the zoomed facet:
library(ggforce)
ggplot(df) +
aes(x = b, y = a) +
geom_col() +
facet_zoom(ylim = c(0, 10), zoom.data = ifelse(a <= 10, NA, FALSE))
Two plots
Hadley Wickham suggested
I think it's much more appropriate to show two plots - one of all the
data, and one of just the small values.
This code creates two plots
library(ggplot2)
g1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip()
g2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
coord_flip() +
ylim(NA, 10)
which can be combined into one plot by
cowplot::plot_grid(g1, g2) # or ggpubr::ggarrange(g1, g2)
or
gridExtra::grid.arrange(g1, g2) # or egg::ggarrange(g1, g2)
Two facets
This was suggested in a comment by Chase and also by Brian Diggs in his answer who interpreted Hadley's suggestion to use
faceted plots, one with all the data, one zoomed in a particular region
but no code was supplied for this approach, so far.
As there is no simple way to scale facets separately (see related question, e.g.) the data needs to be manipulated:
library(dplyr)
library(ggplot2)
ggplot() +
aes(x = b, y = a) +
geom_col(data = df %>% mutate(subset = "all")) +
geom_col(data = df %>% filter(a <= 10) %>% mutate(subset = "small")) +
coord_flip() +
facet_wrap(~ subset, scales = "free_x")
No, not using ggplot. See the discussion in the thread at http://groups.google.com/group/ggplot2/browse_thread/thread/8d2acbfc59d2f247 where Hadley explains why it is not possible but gives a suggested alternative (faceted plots, one with all the data, one zoomed in a particular region).
Not with ggplot, but with plotrix you can easily do that:
library(plotrix)
gap.barplot(df$a, gap=c(5,495),horiz=T)
No, unfortunately not
The fear is that allowing discontinuous axes will lead to deceit of the audience. However, there are cases where not having a discontinuous axis leads to distortion.
For example, if the axis is truncated, but usually lies within some interval (say [0,1]), the audience may not notice the truncation and make distorted conclusions about the data. In this case, an explicit discontinuous axis would be more appropriate and transparent.
Compare:
An option could be using the ggbreak package using the scale_y_cut() or scale_x_cut() function. This function makes it possible to cut the ggplot object into parts with the possibility to specify which part is zoom in or zoom out. Here is a reproducible example with left plot normal and right plot with the function used:
df <- data.frame(a = c(1,2,3,500), b = c('a1', 'a2','a3', 'a4'))
library(ggplot2)
library(ggbreak)
library(patchwork)
p1 <- ggplot(df) +
aes(x = b, y = a) +
geom_col()
p2 <- ggplot(df) +
aes(x = b, y = a) +
geom_col() +
scale_y_cut(breaks=c(4, 30), which=c(1, 3), scales=c(0.5, 3))
p1 + p2
Created on 2022-08-22 with reprex v2.0.2
As you can see from the example, some parts are zoomed in and zoomed out. This can be changed by using different arguments.
Arguments used:
breaks:
a numeric or numeric vector, the points to be divided
which:
integer, the position of subplots to scales, started from left to
right or top to bottom.
scales:
numeric, relative width or height of subplots.
To change the space between the subplots, you can use the argument space.
For some extra information and examples check this tutorial.
A clever ggplot solution is provided by Jörg Steinkamp, using facet_grid. Simplified, it is something like this:
library("tidyverse")
df <- data.frame(myLetter=LETTERS[1:4], myValue=runif(12) + rep(c(4,0,0),2)) # cluster a few values well above 1
df$myFacet <- df$myValue > 3
(ggplot(df, aes(y=myLetter, x=myValue))
+ geom_point()
+ facet_grid(. ~ myFacet, scales="free", space="free")
+ scale_x_continuous(breaks = seq(0, 5, .25)) # this gives both facets equal interval spacing.
+ theme(strip.text.x = element_blank()) # get rid of the facet labels
)
As of 2022-06-01, we have the elegant-looking ggbreak package, which appears to answer the OP's question. Although I haven't tried it on my own data, it looks to be compatible with many or all other ggplot2 functionality. Offers differential scaling too, perhaps useful to OP's and similar uses.
library(ggplot2)
library(ggbreak)
set.seed(2019-01-19)
d <- data.frame(x = 1:20,
y = c(rnorm(5) + 4, rnorm(5) + 20, rnorm(5) + 5, rnorm(5) + 22))
p1 <- ggplot(d, aes(y, x)) + geom_col(orientation="y") +
theme_minimal()
p1 + scale_x_break(c(7, 17), scales = 1.5) + scale_x_break(c(18, 21), scales=2)
I doubt there's anything off the shelf in R, but you could show the data as a series of 3D partial cubes. 500 is only 5*10*10, so it would scale well. The exact value could be a label.
This probably should only be used if you must have a graphic representation for some reason.
One strategy is to change the axis to plot Log Scale. This way you get to reduce exponentially higher value by a factor of 10