I am trying to create a histogram for my integer variable which has a very inconsistent values, here is the output of summary function applied to the variable:
Min:347 1st Qu:8786 Median:20886 Mean:69522 3rd Qu:50400 Max:4069360
So as you can see it ranges from 300 to 4,000,000
Here is the code I am using to create the histogram:
ggplot(data=mydata, aes(mydata$variable)) +
geom_histogram(aes(y =..density..),
breaks=seq(300, 2000000, by = 20000),
col="#00AFBB",
fill="#00AFBB",
alpha=.2) +
geom_density(col=2) +
Although I sat the seq argument and using different values, the histogram keep starting from 0 and ending with 4000000 as follows:
What can I do to adjust the histogram so it seems more balanced and plot the values correctly?
You can either place a restriction on the values mapped to the x-axis, effectively filtering them out:
+ scale_x_continouous(limits=c(0, 1000000))
or zoom in on the relevant part of your plot:
+ coord_cartesian(xlim=c(0, 1000000))
Do note that your first line can be reduced to:
ggplot(mydata, aes(variable)) +
as data is the first argument to ggplot, and the variables referenced in aes are always searched for in the data.frame (given to the data argument).
Related
I have used the following code to generate a plot with ggplot:
I want the legend to show the runs 1-8 and only the volumes 12.5 and 25 why doesn't it show it?
And is it possible to show all the points in the plot even though there is an overlap? Because right now the plot only shows 4 of 8 points due to overlap.
OP. You've already been given a part of your answer. Here's a solution given your additional comment and some explanation.
For reference, you were looking to:
Change a continuous variable to a discrete/discontinuous one and have that reflected in the legend.
Show runs 1-8 labeled in the legend
Disconnect lines based on some criteria in your dataset.
First, I'm representing your data here again in a way that is reproducible (and takes away the extra characters so you can follow along directly with all the code):
library(ggplot2)
mydata <- data.frame(
`Run`=c(1:8),
"Time"=c(834, 834, 584, 584, 1184, 1184, 938, 938),
`Area`=c(55.308, 55.308, 79.847, 79.847, 81.236, 81.236, 96.842, 96.842),
`Volume`=c(12.5, 12.5, 12.5, 12.5, 25.0, 25.0, 25.0, 25.0)
)
Changing to a Discrete Variable
If you check the variable type for each column (type str(mydata)), you'll see that mydata$Run is an int and the rest of the columns are num. Each column is understood to be a number, which is treated as if it were a continuous variable. When it comes time to plot the data, ggplot2 understands this to mean that since it is reasonable that values can exist between these (they are continuous), any representation in the form of a legend should be able to show that. For this reason, you get a continuous color scale instead of a discrete one.
To force ggplot2 to give you a discrete scale, you must make your data discrete and indicate it is a factor. You can either set your variable as a factor before plotting (ex: mydata$Run <- as.factor(mydata$Run), or use code inline, referring to aes(size = factor(Run),... instead of just aes(size = Run,....
Using reference to factor(Run) inline in your ggplot calls has the effect of changing the name of the variable to be "factor(Run)" in your legend, so you will have to also add that to the labs() object call. In the end, the plot code looks like this:
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_point(aes(color =as.factor(Volume), size = Run)) +
geom_line() +
labs(
x = "Area", y = "Time",
# This has to be changed now
color='Volume'
) +
theme_bw()
Note in the above code I am also not referring to mydata$Run, but just Run. It is greatly preferable that you refer to just the name of the column when using ggplot2. It works either way, but much better in practice.
Disconnect Lines
The reason your lines are connected throughout the data is because there's no information given to the geom_line() object other than the aesthetics of x= and y=. If you want to have separate lines, much like having separate colors or shapes of points, you need to supply an aesthetic to use as a basis for that. Since the two lines are different based on the variable Volume in your dataset, you want to use that... but keep the same color for both. For this, we use the group= aesthetic. It tells ggplot2 we want to draw a line for each piece of data that is grouped by that aesthetic.
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_point(aes(color =as.factor(Volume), size = Run)) +
geom_line(aes(group=as.factor(Volume))) +
labs(
x = "Area", y = "Time", color='Volume'
) +
theme_bw()
Show Runs 1-8 Labeled in Legend
Here I'm reading a bit into what you exactly wanted to do in terms of "showing runs 1-8" in the legend. This could mean one of two things, and I'll assume you want both and show you how to do both.
Listing and showing sizes 1-8 in the legend.
To set the values you see in the scale (legend) for size, you can refer to the various scale_ functions for all types of aesthetics. In this case, recall that since mydata$Run is an int, it is treated as a continuous scale. ggplot2 doesn't know how to draw a continuous scale for size, so the legend itself shows discrete sizes of points. This means we don't need to change Run to a factor, but what we do need is to indicate specifically we want to show in the legend all breaks in the sequence from 1 to 8. You can do this using scale_size_continuous(breaks=...).
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_point(aes(color =as.factor(Volume), size = Run)) +
geom_line(aes(group=as.factor(Volume))) +
labs(
x = "Area", y = "Time", color='Volume'
) +
scale_size_continuous(breaks=c(1:8)) +
theme_bw()
Showing all of your runs as points.
The note about showing all runs might also mean you want to literally see each run represented as a discrete point in your plot. For this... well, they already are! ggplot2 is plotting each of your points from your data into the chart. Since some points share the same values of x= and y=, you are getting overplotting - the points are drawn over top of one another.
If you want to visually see each point represented here, one option could be to use geom_jitter() instead of geom_point(). It's not really great here, because it will look like your data has different x and y values, but it is an option if this is what you want to do. Note in the code below I'm also changing the shape of the point to be a hollow circle for better clarity, where the color= is the line around each point (here it's black), and the fill= aesthetic is instead used for Volume. You should get the idea though.
set.seed(1234) # using the same randomization seed ensures you have the same jitter
ggplot(data = mydata, aes(x=Area, y=Time)) +
geom_jitter(aes(fill =as.factor(Volume), size = Run), shape=21, color='black') +
geom_line(aes(group=as.factor(Volume))) +
labs(
x = "Area", y = "Time", fill='Volume'
) +
scale_size_continuous(breaks=c(1:8)) +
theme_bw()
I have a set of times that I would like to plot on a histogram.
Toy example:
df <- data.frame(time = c(1,2,2,3,4,5,5,5,6,7,7,7,9,9, ">10"))
The problem is that one value is ">10" and refers to the number of times that more than 10 seconds were observed. The other time points are all numbers referring to the actual time. Now, I would like to create a histogram that treats all numbers as numeric and combines them in bins when appropriate, while plotting the counts of the ">10" at the side of the distribution, but not in a separate plot. I have tried to call geom_histogram twice, once with the continuous data and once with the discrete data in a separate column but that gives me the following error:
Error: Discrete value supplied to continuous scale
Happy to hear suggestions!
Here's a kind of involved solution, but I believe it best answers your question, which is that you are desiring to place next to typical histogram plot a bar representing the ">10" values (or the values which are non-numeric). Critically, you want to ensure that you maintain the "binning" associated with a histogram plot, which means you are not looking to simply make your scale a discrete scale and represent a histogram with a typical barplot.
The Data
Since you want to retain histogram features, I'm going to use an example dataset that is a bit more involved than that you gave us. I'm just going to specify a uniform distribution (n=100) with 20 ">10" values thrown in there.
set.seed(123)
df<- data.frame(time=c(runif(100,0,10), rep(">10",20)))
As prepared, df$time is a character vector, but for a histogram, we need that to be numeric. We're simply going to force it to be numeric and accept that the ">10" values are going to be coerced to be NAs. This is fine, since in the end we're just going to count up those NA values and represent them with a bar. While I'm at it, I'm creating a subset of df that will be used for creating the bar representing our NAs (">10") using the count() function, which returns a dataframe consisting of one row and column: df$n = 20 in this case.
library(dplyr)
df$time <- as.numeric(df$time) #force numeric and get NA for everything else
df_na <- count(subset(df, is.na(time)))
The Plot(s)
For the actual plot, you are asking to create a combination of (1) a histogram, and (2) a barplot. These are not the same plot, but more importantly, they cannot share the same axis, since by definition, the histogram needs a continuous axis and "NA" values or ">10" is not a numeric/continuous value. The solution here is to make two separate plots, then combine them with a bit of magic thanks to cowplot.
The histogram is created quite easily. I'm saving the number of bins for demonstration purposes later. Here's the basic plot:
bin_num <- 12 # using this later
p1 <- ggplot(df, aes(x=time)) + theme_classic() +
geom_histogram(color='gray25', fill='blue', alpha=0.3, bins=bin_num)
Thanks to the subsetting previously, the barplot for the NA values is easy too:
p2 <- ggplot(df_na, aes(x=">10", y=n)) + theme_classic() +
geom_col(color='gray25', fill='red', alpha=0.3)
Yikes! That looks horrible, but have patience.
Stitching them together
You can simply run plot_grid(p1, p2) and you get something workable... but it leaves quite a lot to be desired:
There are problems here. I'll enumerate them, then show you the final code for how I address them:
Need to remove some elements from the NA barplot. Namely, the y axis entirely and the title for x axis (but it can't be NULL or the x axes won't line up properly). These are theme() elements that are easily removed via ggplot.
The NA barplot is taking up WAY too much room. Need to cut the width down. We address this by accessing the rel_widths= argument of plot_grid(). Easy peasy.
How do we know how to set the y scale upper limit? This is a bit more involved, since it will depend on the ..count.. stat for p1 as well as the numer of NA values. You can access the maximum count for a histogram using ggplot_build(), which is a part of ggplot2.
So, the final code requires the creation of the basic p1 and p2 plots, then adds to them in order to fix the limits. I'm also adding an annotation for number of bins to p1 so that we can track how well the upper limit setting works. Here's the code and some example plots where bin_num is set at 12 and 5, respectively:
# basic plots
p1 <- ggplot(df, aes(x=time)) + theme_classic() +
geom_histogram(color='gray25', fill='blue', alpha=0.3, bins=bin_num)
p2 <- ggplot(df_na, aes(x=">10", y=n)) + theme_classic() +
geom_col(color='gray25', fill='red', alpha=0.3) +
labs(x="") + theme(axis.line.y=element_blank(), axis.text.y=element_blank(),
axis.title.y=element_blank(), axis.ticks.y=element_blank()
) +
scale_x_discrete(expand=expansion(add=1))
#set upper y scale limit
max_count <- max(c(max(ggplot_build(p1)$data[[1]]$count), df_na$n))
# fix limits for plots
p1 <- p1 + scale_y_continuous(limits=c(0,max_count), expand=expansion(mult=c(0,0.15))) +
annotate('text', x=0, y=max_count, label=paste('Bins:', bin_num)) # for demo purposes
p2 <- p2 + scale_y_continuous(limits=c(0,max_count), expand=expansion(mult=c(0,0.15)))
plot_grid(p1, p2, rel_widths=c(1,0.2))
So, our upper limit fixing works. You can get really crazy playing around with positioning, etc and the plot_grid() function, but I think it works pretty well this way.
Perhaps, this is what you are looking for:
df1 <- data.frame(x=sample(1:12,50,rep=T))
df2 <- df1 %>% group_by(x) %>%
dplyr::summarise(y=n()) %>% subset(x<11)
df3 <- subset(df1, x>10) %>% dplyr::summarise(y=n()) %>% mutate(x=11)
df <- rbind(df2,df3 )
label <- ifelse((df$x<11),as.character(df$x),">10")
p <- ggplot(df, aes(x=x,y=y,color=x,fill=x)) +
geom_bar(stat="identity", position = "dodge") +
scale_x_continuous(breaks=df$x,labels=label)
p
and you get the following output:
Please note that sometimes you could have some of the bars missing depending on the sample.
I want to make a plot similar to the one attached by Lindfield et al. 2016. I'm familiar with the ggplot command in R with the format:
ggplot(dataframe, aes(x, y)) + geom_bar(stat = 'identity')
However, I don't know how to make a cumulative se error for a stacked barplot; only one that employs a position_dodge command.
I know that there are disadvantages to using stacked bars with se errors, but for my data set, it is more presentable than using the unstacked barplots.
Thanks.
I don't know how you get the cumulative standard errors in an appropriate way (I guess it depends on how your values are generated) but I think you need to do calculate them and store them in a second DF, for example if you have an initial data.frame created like this:
DF <- data.frame( x=c("a","a","b","b"),
sp=c("shark","cod","shark","cod"),
y=c(10,5,15,7),
stringsAsFactors=FALSE )
where y is the value associated with each species at each x point, then you'd create a second DF containing the lower and upper limits of your s.e. for each x value, eg
seDF <- data.frame( x=c('a','b'),
yl=c(12,18),
yu=c(17,24),
stringsAsFactors=FALSE )
Then you can create your plot with:
ggplot() +
geom_bar( data=DF, mapping=aes(x=x,y=y,fill=sp),
position="stack", stat="identity") +
geom_linerange( data=seDF, mapping=aes(x=x, ymin=yl, ymax=yu) )
I used geom_linerange rather then geom_errorbar as it doesn't create crossbars at either end.
With a data frame df I have a large number of discrete values for metric and their counts cnt
I wanted to get a bar plot of the counts for each discrete value of metric.
So I do the following,
df <- read.csv("metric.csv", header=T)
df$metric <- as.factor(df$metric)
ggplot(df, aes(x=metric, y=cnt)) +
geom_bar(stat = 'identity')
With the above I get an empty plot like below with this - why ?
The data I used for the data frame df is here - http://wikisend.com/download/569376/metric.csv
How do I get a bar plot out of this data ?
I'm not immediately aware of any limitations of geom_bar, but it is unsurprising that this doesn't work very well -- I interrupted it on my machine, so I don't even know what it looks like when it finishes rendering.
Are you sure that a bar plot is appropriate for this data? Which is to say, is the "metric" column effectively a factor?
Running a scatter plot completes rapidly with results that might be more useful (here using a log scale because a linear scale is hurt by the outlier)
ggplot(df, aes(x=metric, y=cnt) +
geom_point() +
scale_y_log10()
yields
I am producing heatmaps of measurement using ggplot2. The data contains positive and negative values and I use the rainbow() palette for coloring.
I have different data sets and would like to scale the colora in a way that the minimum, maximum and 0 values of each data set get the same colors assigned. I could only find out to set the minimum and maximum using limits=...
How can I also define a given color for 0?
Here is my minimal example, if I would for example use rainbow(5), I would like the 3rd color to be the zero color.
data <- read.csv("http://protzkeule.de/data.csv")
ggplot(data=data, aes(x=variable, y=meas)) +
geom_tile(aes(fill=value)) +
scale_fill_gradientn(colours=rev(rainbow(255)),limits=c(-.2,.4))
Perhaps a different approach: For my plots I found it easier to cut the values used for the Colors:
ggplot(...) +
stat_bin2d (aes(fill=ifelse(..count..>20,20,..count..)), bins = 10) +
scale_fill_gradientn("Count", colours=c("blue", "yellow", "red")) + ...