I'm having trouble creating a figure with ggplot2. I am using geom_dotplot with center stacking to display my data which are discrete values for 4 categories.
For aesthetic reasons I want to customize the positions of the dots so that
reduce the empty space between dots along the y axis, (ie the dots are 1 value large)
The distributions fit and don't overlap
I've adjusted the bin and dotsize to achieve aesthetic goal 1, but that requires me to fiddle with the ylim() parameter to make sure that the groups fit in the plot. This results in a plot with more whitw space and few numbers on the y axis.
Question: Can anyone explain a way to resize the empty space on this plot?
My code is below:.
plot <- ggplot(figdata, aes(y=Counts, x=category, col=strain)) +
geom_dotplot(aes(fill=strain), dotsize=1, binwidth=.7,
binaxis= "y",stackdir ="centerwhole", stackratio=.7) +
ylim(18,59)
plot + scale_color_manual(values=c("#E69F00", "#56B4E9")) +
geom_errorbar(stat="hline", yintercept="mean",
aes( ymax=..y..,ymin=..y.., group = category, width = 0.5),
color="black")
Which produces:
EDIT: Incorporating jitter will allow the all the data to fit, but I don't want to add noise to this data and would prefer to show it as discreet data.
adjusting the binwidth and dotsize to 0.3 as suggested below also fits all the data, however it leaves too much white space.
I think that I might have to transform my data so that the values are steps smaller than 1, in order to get everything to fit horizontally and dot sizes to big large enough to reduce white space.
I think the easiest way is using coord_cartesian:
plot + scale_color_manual(values=c("#E69F00", "#56B4E9")) +
geom_errorbar(stat="hline", yintercept="mean",
aes( ymax=..y..,ymin=..y.., group = category, width = 0.5),
color="black") +
coord_cartesian(ylim=c(17,40))
Which gives me this plot (with fake data that are not as neatly distributed as yours):
Related
I've been trying to standardise multiple bar plots so that the bars are all identical in width regardless of the number of bars. Note that this is over multiple distinct plots - faceting is not an option. It's easy enough to scale the plot area so that, for instance, a plot with 6 bars is 1.5* the width of a plot with 4 bars. This would work perfectly, except that each plot has an expanded x axis by default, which I would like to keep.
"The defaults are to expand the scale by 5% on each side for continuous variables, and by 0.6 units on each side for discrete variables."
https://ggplot2.tidyverse.org/reference/scale_discrete.html
My problem is that I can't for the life of me work out what '0.6 units' actually means. I've manually measured the distance between the bars and the y axis in various design tools and gotten inconsistent answers, so I can't factor '0.6 units' into my calculations when working out what size the panel windows should be. Additionally I can't find any answers on how many 'units' long a discrete x axis is - I assumed at first it would be 1 unit per category but that doesn't fit with the visuals at all. I've included an image that hopefully shows what I mean - the two graphs
In this image, the top graph has a plot area exactly 1.5* that of the bottom graph. Seeing as it has 6 bars compared with 4, that would mean each bar is the same width, except that that extra space between the axis and the first bar messes this up. Setting expand = expansion(add = c(0, 0)) clears this up but results in not-so-pretty graphs. What I'd like is for the bars to be identical in width between the two plots, accounting for this extra space. I'm specifically looking for a general solution that I can use for future plots, not for the individual solution for this sample. As such, what I'd really like to know is how many 'units' long are these two x axes? Many thanks for any and all help!
Instead of using expansion for the axis, I would probably use the fact that categorical variables are actually plotted on the positive integers on Cartesian co-ordinates. This means that, provided you know the maximum number of columns you are going to use in your plots, you can set this as the range in coord_cartesian. There is a little arithmetic involved to keep the bars centred, but it should give consistent results.
We start with some reproducible data:
library(ggplot2)
set.seed(1)
df <- data.frame(group = letters[1:6], value = 100 * runif(6))
Now we set the value for the maximum number of bars we will need:
MAX_BARS <- 6
And the only thing "funny" about the plot code is the calculation of the x axis limits in coord_cartesian:
ggplot(df, aes(group, value)) +
geom_col() +
coord_cartesian(xlim = c(1 -(MAX_BARS - length(unique(df$group)))/2,
MAX_BARS - (MAX_BARS - length(unique(df$group)))/2))
Now let us remove one factor level and run the exact same plot code:
df <- df[-1,]
ggplot(df, aes(group, value)) +
geom_col() +
coord_cartesian(xlim = c(1 -(MAX_BARS - length(unique(df$group)))/2,
MAX_BARS - (MAX_BARS - length(unique(df$group)))/2))
And again:
df <- df[-1,]
ggplot(df, aes(group, value)) +
geom_col() +
coord_cartesian(xlim = c(1 -(MAX_BARS - length(unique(df$group)))/2,
MAX_BARS - (MAX_BARS - length(unique(df$group)))/2))
And again:
df <- df[-1,]
ggplot(df, aes(group, value)) +
geom_col() +
coord_cartesian(xlim = c(1 -(MAX_BARS - length(unique(df$group)))/2,
MAX_BARS - (MAX_BARS - length(unique(df$group)))/2))
You will see the bars remain constant width and centralized, yet the panel size remains fixed.
Created on 2021-11-06 by the reprex package (v2.0.0)
I have an ethogram-like ggplot where I plot the value of a factor quadrant (1 to 4), which is plotted for each frame of a movie (frameID). The color is given by 3 animals that are being tracked.
I am fairly satisfied with the graph but the amount of points makes it difficult to see, even with alpha. I was wondering how to add position_dodge in a way that doesn't destroy the plot.
ggplot(dataframe) ,
aes(frameID, quadrant, color=animal)) +
geom_jitter(alpha=0.5) +
scale_color_manual(values = c("#1334C1","#84F619", "#F43900")) +
theme_classic()+
theme(legend.position = 'none')
This link has useful info about dodging using geom_point.
R: How to spread (jitter) points with respect to the x axis?
I can change to geom_point with height, which works but it produces something awful.
+ geom_point(position = position_jitter(w = 0, h = 2))
Update
Data lives in GitHub
Lowering the alpha or changing size helps, adds trouble when rescaling the image.
https://github.com/matiasandina/MLA2_Tracking/blob/master/demo_data/sample_data.csv
Update 2022
It's been a while since I posted this initially, my original thoughts changed and are better reflected here, but I am still looking for a ggplot2 version of doing this!
I have a plot in which I am trying to highlight different points along the line. My plot looks like this:
library(ggplot2)
X<-c(seq(1:20))
Y<-c(2,4,6,3,5,8,6,5,4,3,2,4,6,6,9,8,9,5,4,3)
Col<-as.character(c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5))
value<-c(NA,NA,NA,NA,10,NA,NA,NA,20,NA,NA,20,20,10,NA,NA,NA,10,NA,NA)
DF<-data.frame(X,Y,Col,value)
p<-ggplot(DF, aes(x=X, y=Y)) +geom_line(size=.02) +geom_point(col='black', size=.5)+ geom_jitter(aes(color = Col, size = value),position = position_jitter(height = .2, width = .2)) + scale_colour_manual(values=c("red", "violetred","orange",'blue','steelblue'))
p+guides(size = "none")
On top of the basic line plot, I have 5 different colored dot possibilities defined by the "Col" column. I also have 2 different size classes I'd like to represent using the "value" column. My issue is that the default sizes chosen to represent the "value" column is scaled so that the larger size appears too big in the plot in comparison to the smaller size.
Is there a way to manually set the scale so I can control the drawn size of the added points (hopefully make them closer is size to each other)? The final plot should be similar to the one here, only the value=10 points would be only slightly smaller than than the value=20 points.
I've tried using some of the "manual" options, but when I do it always plots the other "Y" column points, which for now are suppressed because of the NAs
In the same way you manually define the colours for your points using a scale_colour_ variant, you can define the sizes using scale_size.
In this case you can define the range of sizes to use. Adding + scale_size(range = c(4,6)) seems to give results that fit your description. You can tweak the size of, and the difference in size between the points by changing the numbers.
Take a look at the following plotting code:
library(ggplot2)
library(cowplot)
a <- data.frame(a1=1:10, a2=1:10)
b <- data.frame(b1=1:5, b2=2*(1:5))
aplot <- ggplot(a, aes(x=a1, ymin=0, ymax=12)) +
geom_line(aes(y=a2))
bplot <- ggplot(b, aes(x=b1, ymin=0, ymax=12)) +
geom_line(aes(y=b2))
plot_grid(aplot,bplot, ncol=2)
It yields two side-by-side plots of identical dimensions showing similar lines. But the x-axis scales are rather different. In fact, the second line has twice the slope of the first.
I am looking for a way to plot this figure so that the width of a plot is scaled by the limits of its x-axis, so that the slopes can be compared visually. The real plots I am interested in visualizing are five in number and will lack y-axis labels except for the leftmost. I can use grid.arrange() to plot them all in a row with whatever widths I want, but the problem is that I don't know what width to assign to each panel to make sure they come out right (the panel width has to be large enough to accommodate the plot margins, the y-axis tick marks, and the y-axis text). I can set the margins myself and account for them in my panel widths, but I cannot find a good way to figure out how wide (e.g. in cm) the y-axis text is.
You can use the rel_widths option in plot_grid to achieve this. You need to calculate the relative size you want each plot to be using the ratio of the ranges xmax-xmin of each panel. But, there is an extra catch. rel_widths sets the relative width of the whole panel, including the margins. So we also need to account for the margins in calculating the relative size. In the following code, adding an offset value of 2 to the numerator and denominator of relative.size works for this. But note that this offset value may change if you alter the size of the margins.
aplot <- ggplot(a, aes(x=a1, ymin=0, ymax=12, xmin=0, xmax=max(a$a1))) +
geom_line(aes(y=a2))
bplot <- ggplot(b, aes(x=b1, ymin=0, ymax=12, xmin=0, xmax=max(b$b1))) +
geom_line(aes(y=b2))
relative.size <- (2+max(b$b1)) / (2+max(a$a1)) # the addition of 2 here is to account for the plot margins
plot_grid(aplot,bplot, ncol=2, rel_widths=c(1,relative.size), align = "h")
gives
I want to draw vertical boxplots of counts, and show the counts as points, overlaid over the boxplots. Because they are discrete values, there are going to be multiple points with the same value. To show the data in ggplot2, I could use geom_jitter() to spread the data and get a slightly better impression, but jitter screws up the values (the vertical component), and the randomness of the horizontal spread means that if the jitter height is set to 0, there is still a high chance of overlapping points.
Is there a way to spread all points which have the same value, evenly and horizontally? Something along the lines of this:
Here is some example data:
wine_votes <- melt(list(a=c(7,7,7,8,8,7,7,8,4,7,7,6,8,6),
b=c(5,8,6,4,3,4,4,9,5,8,4,5,4),
c=c(7.5,8,5,8,6,8,5,6,6.5,7,5,5,6),
d=c(4,4,5,5,6,8,5,8,5,6,3,6,5),
e=c(7,4,6,7,4,6,7,5,6.5,8.5,8,5)
))
names(wine_votes) <- c('vote', 'option')
# Example plot with jitter:
ggplot(wine_votes, aes(x=blend, y=vote)) +
geom_boxplot() + geom_jitter(position=position_jitter(height=0, width=0.2)) +
scale_y_continuous(breaks=seq(0,10,2))
While this isn't exactly what the image looks like it can be adjusted to get there. geom_dotplot (added in version 0.9.0-ish I think) does this:
ggplot(wine_votes, aes(x=option, y=vote)) +
geom_boxplot() +
scale_y_continuous(breaks=seq(0,10,2)) +
geom_dotplot(binaxis = "y", stackdir = "center", aes(fill=option))