Is there any way to add points to a ggplot graph like with the points() function in base graphics? I don't often use ggplot and always prefer base graphics, but this time I must to deal with it. With + geom_point(x = c(1,2,3), y = c(1,2,3)) there is an error:
Error: Aesthetics must be either length 1 or the same as the data (33049): x, y
I'm not quite sure what you're looking for, but you can use the data= argument to geom_point() to override the default behaviour (which is to inherit data from the original ggplot call); as #dc37 points out, x and y need to be specified within a data frame, but you can do this on the fly. You might also need to specify the mapping, if the original x and y variables aren't called x and y ...
+ geom_point(data= data.frame(x = c(1,2,3), y = c(1,2,3)),
mapping = aes(x=x, y=y))
Alternatively (and maybe better):
+ annotate( geom="point", x = 1:3, y = 1:3)
From ?annotate:
This function adds geoms to a plot, but unlike [a typical] geom
function, the properties of the geoms are not mapped from
variables of a data frame, but are instead passed in as vectors.
This is useful for adding small annotations (such as text labels)
or if you have your data in vectors, and for some reason don't
want to put them in a data frame.
Related
I use the following library for mosaic plots. In the documentation it provides the following example:
ggplot(data = fly) +
geom_mosaic(aes(x = product(DoYouRecline, RudeToRecline), fill=DoYouRecline)
So the DoYouRecline and RudeToRecline are x and y variables in a plot, but for some reason they are combined with product function which returns a list of names. How does geom_mosaic internally use product(DoYouRecline, RudeToRecline) in order to figure out that the two parameters need to be used for x and y axis?
The main problem that I have is that I need to have one of those attribute names as a variable, for instance something like this:
attr_name = 'foo'
ggplot(data = fly) +
geom_mosaic(aes(x = product(DoYouRecline, attr_name), fill=DoYouRecline)
Thanks!
I am trying to loop a ggplot2 plot with a linear regression line over it. It works when I type the y column name manually, but the loop method I am trying does not work. It is definitely not a dataset issue.
I've tried many solutions from various websites on how to loop a ggplot and the one I've attempted is the simplest I could find that almost does the job.
The code that works is the following:
plots <- ggplot(Everything.any, mapping = aes(x = stock_VWRETD, y = stock_10065)) +
geom_point() +
labs(x = 'Market Returns', y = 'Stock Returns', title ='Stock vs Market Returns') +
geom_smooth(method='lm',formula=y~x)
But I do not want to do this another 40 times (and then 5 times more for other reasons). The code that I've found on-line and have tried to modify it for my means is the following:
plotRegression <- function(z,na.rm=TRUE,...){
nm <- colnames(z)
for (i in seq_along(nm)){
plots <- ggplot(z, mapping = aes(x = stock_VWRETD, y = nm[i])) +
geom_point() +
labs(x = 'Market Returns', y = 'Stock Returns', title ='Stock vs Market Returns') +
geom_smooth(method='lm',formula=y~x)
ggsave(plots,filename=paste("regression1",nm[i],".png",sep=" "))
}
}
plotRegression(Everything.any)
I expect it to be the nice graph that I'd expect to get, a Stock returns vs Market returns graph, but instead on the y-axis, I get one value which is the name of the respective column, and the Market value plotted as normally, but as if on a straight number-line across the one y-axis value. Please let me know what I am doing wrong.
Desired Plot:
Actual Plot:
Sample Data is available on Google Drive here:
https://drive.google.com/open?id=1Xa1RQQaDm0pGSf3Y-h5ZR0uTWE-NqHtt
The problem is that when you assign variables to aesthetics in aes, you mix bare names and strings. In this example, both X and Y are supposed to be variables in z:
aes(x = stock_VWRETD, y = nm[i])
You refer to stock_VWRETD using a bare name (as required with aes), however for y=, you provide the name as a character vector produced by colnames. See what happens when we replicate this with the iris dataset:
ggplot(iris, aes(Petal.Length, 'Sepal.Length')) + geom_point()
Since aes expects variable names to be given as bare names, it doesn't interpret 'Sepal.Length' as a variable in iris but as a separate vector (consisting of a single character value) which holds the y-values for each point.
What can you do? Here are 2 options that both give the proper plot
1) Use aes_string and change both variable names to character:
ggplot(iris, aes_string('Petal.Length', 'Sepal.Length')) + geom_point()
2) Use square bracket subsetting to manually extract the appropriate variable:
ggplot(iris, aes(Petal.Length, .data[['Sepal.Length']])) + geom_point()
you need to use aes_string instead of aes, and double-quotes around your x variable, and then you can directly use your i variable. You can also simplify your for loop call. Here is an example using iris.
library(ggplot2)
plotRegression <- function(z,na.rm=TRUE,...){
nm <- colnames(z)
for (i in nm){
plots <- ggplot(z, mapping = aes_string(x = "Sepal.Length", y = i)) +
geom_point()+
geom_smooth(method='lm',formula=y~x)
ggsave(plots,filename=paste("regression1_",i,".png",sep=""))
}
}
myiris<-iris
plotRegression(myiris)
I am looking for a way to summarize data within a ggplot call, not before. I could pre-aggregate the data and then plot it, but I know there is a way to do it within a ggplot call. I'm just unsure how.
In this example, I want to get a mean for each (x,y) combo, and map it onto the colour aes
library(tidyverse)
df <- tibble(x = rep(c(1,2,4,1,5),10),
y = rep(c(1,2,3,1,5),10),
col = sample(c(1:100), 50))
df_summar <- df %>%
group_by(x,y) %>%
summarise(col_mean = mean(col))
ggplot(df_summar, aes(x=x, y=y, col=col_mean)) +
geom_point(size = 5)
I think there must be a better way to avoid the pre-ggplot step (yes, I could also have piped dplyr transformations into the ggplot, but the mechanics would be the same).
For instance, geom_count() counts the instances and plots them onto size aes:
ggplot(df, aes(x=x, y=y)) + geom_count()
I want the same, but mean instead of count, and col instead of size
I'm guessing I need stat_summary() or a stat() call (a replacement for ..xxx.. notation), but I can't get it to give me what I need.
You'll need stat_summary_2d:
ggplot(df, aes(x, y, z = col)) +
stat_summary_2d(aes(col = ..value..), fun = 'mean', geom = 'point', size = 5)
(Or calc(value), if you use the ggplot dev version, or read this in the future.)
You can pass any arbitrary function to fun.
While stat_summary seems like it would be useful, it is not in this case. It is specialized in the common transformation for plotting, summarizing a range of y values, grouped by x, into a set of summary statistics that are plotted as y(, ymin and ymax). You want to group by both x and y, so 2d it is.
Note that this uses binning however, so to get the points to accurately line up, you need to increase bin size (e.g. to 1e3). Unfortunately, there is no non-binning 2d summary stat.
I'm plotting a dense scatter plot in ggplot2 where each point might be labeled by a different color:
df <- data.frame(x=rnorm(500))
df$y = rnorm(500)*0.1 + df$x
df$label <- c("a")
df$label[50] <- "point"
df$size <- 2
ggplot(df) + geom_point(aes(x=x, y=y, color=label, size=size))
When I do this, the scatter point labeled "point" (green) is plotted on top of the red points which have the label "a". What controls this z ordering in ggplot, i.e. what controls which point is on top of which?
For example, what if I wanted all the "a" points to be on top of all the points labeled "point" (meaning they would sometimes partially or fully hide that point)? Does this depend on alphanumerical ordering of labels?
I'd like to find a solution that can be translated easily to rpy2.
2016 Update:
The order aesthetic has been deprecated, so at this point the easiest approach is to sort the data.frame so that the green point is at the bottom, and is plotted last. If you don't want to alter the original data.frame, you can sort it during the ggplot call - here's an example that uses %>% and arrange from the dplyr package to do the on-the-fly sorting:
library(dplyr)
ggplot(df %>%
arrange(label),
aes(x = x, y = y, color = label, size = size)) +
geom_point()
Original 2015 answer for ggplot2 versions < 2.0.0
In ggplot2, you can use the order aesthetic to specify the order in which points are plotted. The last ones plotted will appear on top. To apply this, you can create a variable holding the order in which you'd like points to be drawn.
To put the green dot on top by plotting it after the others:
df$order <- ifelse(df$label=="a", 1, 2)
ggplot(df) + geom_point(aes(x=x, y=y, color=label, size=size, order=order))
Or to plot the green dot first and bury it, plot the points in the opposite order:
ggplot(df) + geom_point(aes(x=x, y=y, color=label, size=size, order=-order))
For this simple example, you can skip creating a new sorting variable and just coerce the label variable to a factor and then a numeric:
ggplot(df) +
geom_point(aes(x=x, y=y, color=label, size=size, order=as.numeric(factor(df$label))))
ggplot2 will create plots layer-by-layer and within each layer, the plotting order is defined by the geom type. The default is to plot in the order that they appear in the data.
Where this is different, it is noted. For example
geom_line
Connect observations, ordered by x value.
and
geom_path
Connect observations in data order
There are also known issues regarding the ordering of factors, and it is interesting to note the response of the package author Hadley
The display of a plot should be invariant to the order of the data frame - anything else is a bug.
This quote in mind, a layer is drawn in the specified order, so overplotting can be an issue, especially when creating dense scatter plots. So if you want a consistent plot (and not one that relies on the order in the data frame) you need to think a bit more.
Create a second layer
If you want certain values to appear above other values, you can use the subset argument to create a second layer to definitely be drawn afterwards. You will need to explicitly load the plyr package so .() will work.
set.seed(1234)
df <- data.frame(x=rnorm(500))
df$y = rnorm(500)*0.1 + df$x
df$label <- c("a")
df$label[50] <- "point"
df$size <- 2
library(plyr)
ggplot(df) + geom_point(aes(x = x, y = y, color = label, size = size)) +
geom_point(aes(x = x, y = y, color = label, size = size),
subset = .(label == 'point'))
Update
In ggplot2_2.0.0, the subset argument is deprecated. Use e.g. base::subset to select relevant data specified in the data argument. And no need to load plyr:
ggplot(df) +
geom_point(aes(x = x, y = y, color = label, size = size)) +
geom_point(data = subset(df, label == 'point'),
aes(x = x, y = y, color = label, size = size))
Or use alpha
Another approach to avoid the problem of overplotting would be to set the alpha (transparancy) of the points. This will not be as effective as the explicit second layer approach above, however, with judicious use of scale_alpha_manual you should be able to get something to work.
eg
# set alpha = 1 (no transparency) for your point(s) of interest
# and a low value otherwise
ggplot(df) + geom_point(aes(x=x, y=y, color=label, size=size,alpha = label)) +
scale_alpha_manual(guide='none', values = list(a = 0.2, point = 1))
The fundamental question here can be rephrased like this:
How do I control the layers of my plot?
In the 'ggplot2' package, you can do this quickly by splitting each different layer into a different command. Thinking in terms of layers takes a little bit of practice, but it essentially comes down to what you want plotted on top of other things. You build from the background upwards.
Prep: Prepare the sample data. This step is only necessary for this example, because we don't have real data to work with.
# Establish random seed to make data reproducible.
set.seed(1)
# Generate sample data.
df <- data.frame(x=rnorm(500))
df$y = rnorm(500)*0.1 + df$x
# Initialize 'label' and 'size' default values.
df$label <- "a"
df$size <- 2
# Label and size our "special" point.
df$label[50] <- "point"
df$size[50] <- 4
You may notice that I've added a different size to the example just to make the layer difference clearer.
Step 1: Separate your data into layers. Always do this BEFORE you use the 'ggplot' function. Too many people get stuck by trying to do data manipulation from with the 'ggplot' functions. Here, we want to create two layers: one with the "a" labels and one with the "point" labels.
df_layer_1 <- df[df$label=="a",]
df_layer_2 <- df[df$label=="point",]
You could do this with other functions, but I'm just quickly using the data frame matching logic to pull the data.
Step 2: Plot the data as layers. We want to plot all of the "a" data first and then plot all the "point" data.
ggplot() +
geom_point(
data=df_layer_1,
aes(x=x, y=y),
colour="orange",
size=df_layer_1$size) +
geom_point(
data=df_layer_2,
aes(x=x, y=y),
colour="blue",
size=df_layer_2$size)
Notice that the base plot layer ggplot() has no data assigned. This is important, because we are going to override the data for each layer. Then, we have two separate point geometry layers geom_point(...) that use their own specifications. The x and y axis will be shared, but we will use different data, colors, and sizes.
It is important to move the colour and size specifications outside of the aes(...) function, so we can specify these values literally. Otherwise, the 'ggplot' function will usually assign colors and sizes according to the levels found in the data. For instance, if you have size values of 2 and 5 in the data, it will assign a default size to any occurrences of the value 2 and will assign some larger size to any occurrences of the value 5. An 'aes' function specification will not use the values 2 and 5 for the sizes. The same goes for colors. I have exact sizes and colors that I want to use, so I move those arguments into the 'geom_plot' function itself. Also, any specifications in the 'aes' function will be put into the legend, which can be really useless.
Final note: In this example, you could achieve the wanted result in many ways, but it is important to understand how 'ggplot2' layers work in order to get the most out of your 'ggplot' charts. As long as you separate your data into different layers before you call the 'ggplot' functions, you have a lot of control over how things will be graphed on the screen.
It's plotted in order of the rows in the data.frame. Try this:
df2 <- rbind(df[-50,],df[50,])
ggplot(df2) + geom_point(aes(x=x, y=y, color=label, size=size))
As you see the green point is drawn last, since it represents the last row of the data.frame.
Here is a way to order the data.frame to have the green point drawn first:
df2 <- df[order(-as.numeric(factor(df$label))),]
This question follows from these two topics:
How to use stat_bin2d() to compute counts labels in ggplot2?
How to show the numeric cell values in heat map cells in r
In the first topic, a user wants to use stat_bin2d to generate a heatmap, and then wants the count of each bin written on top of the heat map. The method the user initially wants to use doesn't work, the best answer stating that stat_bin2d is designed to work with geom = "rect" rather than "text". No satisfactory response is given.
The second question is almost identical to the first, with one crucial difference, that the variables in the second question question are text, not numeric. The answer produces the desired result, placing the count value for a bin over the bin in a stat_2d heat map.
To compare the two methods i've prepared the following code:
library(ggplot2)
data <- data.frame(x = rnorm(1000), y = rnorm(1000))
ggplot(data, aes(x = x, y = y))
geom_bin2d() +
stat_bin2d(geom="text", aes(label=..count..))
We know this first gives you the error:
"Error: geom_text requires the following missing aesthetics: x, y".
Same issue as in the first question. Interestingly, changing from stat_bin2d to stat_binhex works fine:
library(ggplot2)
data <- data.frame(x = rnorm(1000), y = rnorm(1000))
ggplot(data, aes(x = x, y = y))
geom_binhex() +
stat_binhex(geom="text", aes(label=..count..))
Which is great and all, but generally, I don't think hex binning is very clear, and for my purposes wont work for the data i'm trying to desribe. I really want to use stat_2d.
To get this to work, i've prepared the following work around based on the second answer:
library(ggplot2)
data <- data.frame(x = rnorm(1000), y = rnorm(1000))
x_t<-as.character(round(data$x,.1))
y_t<-as.character(round(data$y,.1))
x_x<-as.character(seq(-3,3),1)
y_y<-as.character(seq(-3,3),1)
data<-cbind(data,x_t,y_t)
ggplot(data, aes(x = x_t, y = y_t)) +
geom_bin2d() +
stat_bin2d(geom="text", aes(label=..count..))+
scale_x_discrete(limits =x_x) +
scale_y_discrete(limits=y_y)
This works around allows one to bin numerical data, but to do so, you have to determine bin width (I did it via rounding) before bringing it into ggplot. I actually figured it out while writing this question, so I may as well finish.
This is the result: (turns out I can't post images)
So my real question here, is does any one have a better way to do this? I'm happy I at least got it to work, but so far I haven't seen an answer for putting labels on stat_2d bins when using a numerical variable.
Does any one have a method for passing on x and y arguments to geom_text from stat_2dbin without having to use a work around? Can any one explain why it works with text variables but not with numbers?
Another work around (but perhaps less work). Similar to the ..count.. method you can extract the counts from the plot object in two steps.
library(ggplot2)
set.seed(1)
dat <- data.frame(x = rnorm(1000), y = rnorm(1000))
# plot
p <- ggplot(dat, aes(x = x, y = y)) + geom_bin2d()
# Get data - this includes counts and x,y coordinates
newdat <- ggplot_build(p)$data[[1]]
# add in text labels
p + geom_text(data=newdat, aes((xmin + xmax)/2, (ymin + ymax)/2,
label=count), col="white")