I have a somewhat "weird" two-dimensional distribution (not normal with some uniform values, but it kinda looks like this.. this is just a minimal reproducible example), and want to log-transform the values and plot them.
library("ggplot2")
library("scales")
df <- data.frame(x = c(rep(0,200),rnorm(800, 4.8)), y = c(rnorm(800, 3.2),rep(0,200)))
Without the log transformation, the scatterplot (incl. rug plot which I need) works (quite) well, apart from a marginally narrower rug plot on the x axis:
p <- ggplot(df, aes(x, y)) + geom_point() + geom_rug(alpha = I(0.5)) + theme_minimal()
p
When plotting the same with a log10-transform though, the points at the margin (at x = 0 and y = 0, respectively) are plotted outside the rug plot or just on the axis (with other data, and only one half side of a point is visible).
p + scale_x_log10() + scale_y_log10()
How can I "rescale" the axes so that all the points are contained fully within the grid and the rug plots are unaffected, as in the first example?
Maybe you want
p + scale_x_log10(oob=squish_infinite) + scale_y_log10(oob=squish_infinite)
I don't really know what you expect to happen for those values that can be negative or infinite, but one general advice when transformations don't do what you want is to perform them outside of ggplot2. Something like this might be useful,
library(plyr)
df2 <- colwise(log10)(df) # log transform columns
df2 <- colwise(squish_infinite)(df2) # do something with infinites
p %+% df2 # plot the transformed data
Related
I was creating histograms with ggplot2 in R whose bins are separated with colors and noticed one thing. When the bins of a histogram are separated by colors with fill option, the density value of the histogram turns funny.
Here is the data.
set.seed(42)
x <- rnorm(10000,0,1)
df <- data.frame(x=x, b=x>1)
This is a histogram without fill.
ggplot(df, aes(x = x)) +
geom_histogram(aes(y=..density..))
This is a histogram with fill.
ggplot(df, aes(x = x, fill=b)) +
geom_histogram(aes(y=..density..))
You can see the latter is pretty crazy. The left side of the bins is sticking out. The density values of the bins of each color are obviously wrong.
I thought over this issue for a while. The data can't be wrong for the first histogram was normal. It should be something in ggplot2 or geom_histogram function. I googled "geom_histogram density fill" and couldn't find much help.
I want the end product to look like:
Separated by colors as you see in the second histogram
Size and shape identical to the first histogram
The vertical axis being density
How would you deal with issue?
I think what you may want is this:
ggplot(df, aes(x = x, fill=b)) +
geom_histogram()
Rather than the density. As mentioned above the density is asking for extra calcuations.
One thing that is important (in my opinion) is that histograms are graphs of one variable. As soon as you start adding data from other variables you start to change them more into bar charts or something else like that.
You will want work on setting the axis manually if you want it to range from 0 to .4.
The solution is to hand-compute density like this (instead of using the built-in ggplot2 version):
library(ggplot2)
# Generate test data
set.seed(42)
x <- rnorm(10000,0,1)
df <- data.frame(x=x, b=x>1)
ggplot(df, aes(x = x, fill=b)) +
geom_histogram(mapping = aes(y = ..count.. / (sum(..count..) * ..width..)))
when you provide a column name for the fill parameter in ggplot it groups varaiables and plots them according to each group with a unique color.
if you want a single color for the plot just specify the color you want:
FIXED
ggplot(df, aes(x = x)) +
geom_histogram(aes(y=..density..),fill="Blue")
What's the ggplot2 equivalent of "dotplot" histograms? With stacked points instead of bars? Similar to this solution in R:
Plot Histogram with Points Instead of Bars
Is it possible to do this in ggplot2? Ideally with the points shown as stacks and a faint line showing the smoothed line "fit" to these points (which would make a histogram shape.)
ggplot2 does dotplots Link to the manual.
Here is an example:
library(ggplot2)
set.seed(789); x <- data.frame(y = sample(1:20, 100, replace = TRUE))
ggplot(x, aes(y)) + geom_dotplot()
In order to make it behave like a simple dotplot, we should do this:
ggplot(x, aes(y)) + geom_dotplot(binwidth=1, method='histodot')
You should get this:
To address the density issue, you'll have to add another term, ylim(), so that your plot call will have the form ggplot() + geom_dotplot() + ylim()
More specifically, you'll write ylim(0, A), where A will be the number of stacked dots necessary to count 1.00 density. In the example above, the best you can do is see that 7.5 dots reach the 0.50 density mark. From there, you can infer that 15 dots will reach 1.00.
So your new call looks like this:
ggplot(x, aes(y)) + geom_dotplot(binwidth=1, method='histodot') + ylim(0, 15)
Which will give you this:
Usually, this kind of eyeball estimate will work for dotplots, but of course you can try other values to fine-tune your scale.
Notice how changing the ylim values doesn't affect how the data is displayed, it just changes the labels in the y-axis.
As #joran pointed out, we can use geom_dotplot
require(ggplot2)
ggplot(mtcars, aes(x = mpg)) + geom_dotplot()
Edit: (moved useful comments into the post):
The label "count" it's misleading because this is actually a density estimate may be you could suggest we changed this label to "density" by default. The ggplot implementation of dotplot follow the original one of Leland Wilkinson, so if you want to understand clearly how it works take a look at this paper.
An easy transformation to make the y axis actually be counts, i.e. "number of observations". From the help page it is written that:
When binning along the x axis and stacking along the y axis, the numbers on y axis are not meaningful, due to technical limitations of ggplot2. You can hide the y axis, as in one of the examples, or manually scale it to match the number of dots.
So you can use this code to hide y axis:
ggplot(mtcars, aes(x = mpg)) +
geom_dotplot(binwidth = 1.5) +
scale_y_continuous(name = "", breaks = NULL)
I introduce an exact approach using #Waldir Leoncio's latter method.
library(ggplot2); library(grid)
set.seed(789)
x <- data.frame(y = sample(1:20, 100, replace = TRUE))
g <- ggplot(x, aes(y)) + geom_dotplot(binwidth=0.8)
g # output to read parameter
### calculation of width and height of panel
grid.ls(view=TRUE, grob=FALSE)
real_width <- convertWidth(unit(1,'npc'), 'inch', TRUE)
real_height <- convertHeight(unit(1,'npc'), 'inch', TRUE)
### calculation of other values
width_coordinate_range <- diff(ggplot_build(g)$panel$ranges[[1]]$x.range)
real_binwidth <- real_width / width_coordinate_range * 0.8 # 0.8 is the argument binwidth
num_balls <- real_height / 1.1 / real_binwidth # the number of stacked balls. 1.1 is expanding value.
# num_balls is the value of A
g + ylim(0, num_balls)
Apologies : I don't have enough reputation to 'comment'.
I like cuttlefish44's "exact approach", but to make it work (with ggplot2 [2.2.1]) I had to change the following line from :
### calculation of other values
width_coordinate_range <- diff(ggplot_build(g)$panel$ranges[[1]]$x.range)
to
### calculation of other values
width_coordinate_range <- diff(ggplot_build(g)$layout$panel_ranges[[1]]$x.range)
I an working with ggplot. I want to desine a graphic with ggplot. This graphics is with two continuous variables but I would like to get a graphic like this:
Where x and y are the continuous variables. My problem is I can't get it to show circles in the line of the plot. I would like the plot to have circles for each pair of observations from the continuous variables. For example in the attached graphic, it has a circle for pairs (1,1), (2,2) and (3,3). It is possible to get it? (The colour of the line doesn't matter.)
# dummy data
dat <- data.frame(x = 1:5, y = 1:5)
ggplot(dat, aes(x,y,color=x)) +
geom_line(size=3) +
geom_point(size=10) +
scale_colour_continuous(low="blue",high="red")
Playing with low/high will change the colours.
In general, to remove the legend, use + theme(legend.position="none")
In the following example, how do I set separate ylims for each of my facets?
qplot(x, value, data=df, geom=c("smooth")) + facet_grid(variable ~ ., scale="free_y")
In each of the facets, the y-axis takes a different range of values and I would like to different ylims for each of the facets.
The defaults ylims are too long for the trend that I want to see.
This was brought up on the ggplot2 mailing list a short while ago. What you are asking for is currently not possible but I think it is in progress.
As far as I know this has not been implemented in ggplot2, yet. However a workaround - that will give you ylims that exceed what ggplot provides automatically - is to add "artificial data". To reduce the ylims simply remove the data you don't want plot (see at the and for an example).
Here is an example:
Let's just set up some dummy data that you want to plot
df <- data.frame(x=rep(seq(1,2,.1),4),f1=factor(rep(c("a","b"),each=22)),f2=factor(rep(c("x","y"),22)))
df <- within(df,y <- x^2)
Which we could plot using line graphs
p <- ggplot(df,aes(x,y))+geom_line()+facet_grid(f1~f2,scales="free_y")
print(p)
Assume we want to let y start at -10 in first row and 0 in the second row, so we add a point at (0,-10) to the upper left plot and at (0,0) ot the lower left plot:
ylim <- data.frame(x=rep(0,2),y=c(-10,0),f1=factor(c("a","b")),f2=factor(c("x","y")))
dfy <- rbind(df,ylim)
Now by limiting the x-scale between 1 and 2 those added points are not plotted (a warning is given):
p <- ggplot(dfy,aes(x,y))+geom_line()+facet_grid(f1~f2,scales="free_y")+xlim(c(1,2))
print(p)
Same would work for extending the margin above by adding points with higher y values at x values that lie outside the range of xlim.
This will not work if you want to reduce the ylim, in which case subsetting your data would be a solution, for example to limit the upper row between -10 and 1.5 you could use:
p <- ggplot(dfy,aes(x,y))+geom_line(subset=.(y < 1.5 | f1 != "a"))+facet_grid(f1~f2,scales="free_y")+xlim(c(1,2))
print(p)
There are actually two packages that solve that problem now:
https://github.com/zeehio/facetscales, and https://cran.r-project.org/package=ggh4x.
I would recommend using ggh4x because it has very useful tools, such as facet grid multiple layers (having 2 variables defining the rows or columns), scaling the x and y-axis as you wish in each facet, and also having multiple fill and colour scales.
For your problems the solution would be like this:
library(ggh4x)
scales <- list(
# Here you have to specify all the scales, one for each facet row in your case
scale_y_continuous(limits = c(2,10),
scale_y_continuous(breaks = c(3, 4))
)
qplot(x, value, data=df, geom=c("smooth")) +
facet_grid(variable ~ ., scale="free_y") +
facetted_pos_scales(y = scales)
I have one example of function facet_wrap
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
facet_wrap(vars(class), scales = "free",
nrow=2,ncol=4)
Above code generates plot as:
my level too low to upload an image, click here to see plot
The following code works well:
p <- ggplot(df,aes(x=x,y=y))
p <- p + geom_tile(aes(fill=z))
p
It plots a nice heatmap. Here df contains x and y, created using expand.grid(), and z which contains the value at each (x,y) co-ordinate.
This code, on the other hand
p <- ggplot(df,aes(x=x,y=y))
p <- p + coord_map(project="lagrange")
p <- p + geom_tile(aes(fill=z))
p
doesn't plot anything much at all (and doesn't plot anything with all the coordinate transforms I've tried). My understanding is that the coord_map works on the x and y data, and the fill should be drawn on top of the transformed co-ordinates. However, this must be wrong as nothing's being plotted once the co-ordinates have been mapped to a new frame.
So my question is: how should I go about this so that it works properly? Could it be something to do with my data.frame df?
I think the problem is in how the tile geom calculates the area it should cover interacting with the new coordinate system. I tried recreating your problem with this code.
vol.m <- melt(volcano)
#points & cartesian
p <- ggplot(vol.m, aes(X1, X2, color = value)) + geom_point()
#points and map
p + coord_map()
#hex & cartesian
p <- ggplot(vol.m, aes(X1, X2, fill = value)) + geom_hex(stat = "identity")
#hex & map
p + coord_map
I sat there for a while trying the same thing with geom_tile(), but it wouldn't plot, so I'll take your word for it that it doesn't work. Notice how the hexagons get all screwed up on the map coordinates. I guess when trying to plot tiles which have been calculated to fill the area, it can't tolerate plotting them on a different coordinate system.
Edit:
That is, changing the coordinate system merely changes the "graph paper" the plot is drawn on. So, if you computed a linear regression statistic, then changed the coordinates to be log-transformed, the plotted regression line would be bent. I'm guessing that ggplot2 can't "bend" a tile to fit into a non-cartesian coordinate system.