I have a dataframe with Wikipedia edits, with information about the number of edit for the user (1st edit, 2nd edit and so on), the timestamp when the edit was made, and how many words were added.
In the actual dataset, I have up to 20.000 edits per user and in some edits, they add up to 30.000 words.
However, here is a downloadable small example dataset to exemplify my problem. The header looks like this:
I am trying to plot the distribution of added words across the Edit Progression and across time. If I use the regular R barplot, i works just like expected:
barplot(UserFrame3$NoOfAdds,UserFrame3$EditNo)
But I want to do it in ggplot for nicer graphics and more customizing options.
If I plot this as a scatterplot, I get the same result:
ggplot(data = UserFrame3, aes(x = UserFrame3$EditNo, y = UserFrame3$NoOfAdds)) + geom_point(size = 0.1)
Same for a linegraph:
ggplot(data = UserFrame3, aes(x = UserFrame3$EditNo, y = UserFrame3$NoOfAdds)) +geom_line(size = 0.1)
But when I try to plot it as a bargraph in ggplot, I get this result:
ggplot(data = UserFrame3, aes(x = UserFrame3$EditNo, y = UserFrame3$NoOfAdds)) + geom_bar(stat = "identity", position = "dodge")
There appear to be a lot more holes on the X-axis and the maximum is nowhere close to where it should be (y = 317).
I suspect that ggplot somehow groups the bars and uses means instead of the actual values despite the "dodge" parameter? How can I avoid this? and how would I go about plotting the time progression as a bargraph aswell without ggplot averaging over multiple edits?
You should expect more x-axis "holes" using bars as compared with lines. Lines connect the zero values together, bars do not.
I used geom_col with your data download, it looks as expected:
UserFrame3 %>%
ggplot(aes(EditNo, NoOfAdds)) + geom_col()
Related
Background
I have a dataframe, df, of athlete injuries:
df <- data.frame(number_of_injuries = c(1,2,3,4,5,6),
number_of_people = c(73,52,43,12,7,2),
stringsAsFactors=FALSE)
The Problem
I'd like to use ggplot2 to make a bar chart or histogram of this simple data using geom_bar or geom_histogram. Important point: I'm pretty novice with ggplot2.
I'd like something where the x-axis shows bins of the number of injuries (number_of_injuries), and the y-axis shows the counts in number_of_people. Like this (from Excel):
What I've tried
I know this is the most trivial dang ggplot issue, but I keep getting errors or weird results, like so:
ggplot(df, aes(number_of_injuries)) +
geom_bar(stat = "count")
Which yields:
I've been in the tidyverse reference website for an hour at this and I can't crack the code.
It can cause confusion from time to time. If you already have "count" statistics, then do not count data using geom_bar(stats = "count") again, otherwise you simply get 1 in all categories. You want to plot those values as they are with geom_col:
ggplot(df, aes(x = number_of_injuries, y = number_of_people)) + geom_col()
I would like to make a graph in R, which I managed to make in excel. It is a bargraph with species on the x-axis and the log number of observations on the y-axis. My current data structure in R is not suitable (I think) to make this graph, but I do not know how to change this (in a smart way).
I have (amongst others) a column 'camera_site' (site 1, site2..), 'species' (agouti, paca..), 'count'(1, 2..), with about 50.000 observations.
I tried making a dataframe with a column 'species" (with 18 species) and a column with 'log(total observation)' for each species (see dataframe) But then I can only make a point graph.
this is how I would like the graph to look:
desired graph made in excel
Your data seems to be in the correct format from what I can tell from your screenshot.
The minimum amount of code you would need to get a plot like that would be the following, assuming your data.frame is called df:
ggplot(df, aes(VRM_species, log_obs_count_vrm)) +
geom_col()
Many people intuitively try geom_bar(), but geom_col() is equivalent to geom_bar(stat = "identity"), which you would use if you've pre-computed observations and don't need ggplot to do the counting for you.
But you could probably decorate the plot a bit better with some additions:
ggplot(df, aes(VRM_species, log_obs_count_vrm)) +
geom_col() +
scale_x_discrete(name = "Species") +
scale_y_continuous(name = expression("Log"[10]*" Observations"),
expand = c(0,0,0.1,0)) +
theme(axis.text.x = element_text(angle = 90))
Of course, you could customize the theme anyway you would like.
Groetjes
I have a dataframe that I want to reorder to make a ggplot so I can easily see which items have the highest and lowest values in them. In my case, I've grouped the data into two groups, and it'd be nice to have a visual representation of which group tends to score higher. Based on this question I came up with:
library(ggplot2)
cor.data<- read.csv("https://dl.dropbox.com/s/p4uy6uf1vhe8yzs/cor.data.csv?dl=0",stringsAsFactors = F)
cor.data.sorted = cor.data[with(cor.data,order(r.val,pic)),] #<-- line that doesn't seem to be working
ggplot(cor.data.sorted,aes(x=pic,y=r.val,size=df.val,color=exp)) + geom_point()
which produces this:
I've tried quite a few variants to reorder the data, and I feel like this should be pretty simple to achieve. To clarify, if I had succesfully reorganised the data then the y-values would go up as the plot moves along the x-value. So maybe i'm focussing on the wrong part of the code to achieve this in a ggplot figure?
You could do something like this?
library(tidyverse);
cor.data %>%
mutate(pic = factor(pic, levels = as.character(pic)[order(r.val)])) %>%
ggplot(aes(x = pic, y = r.val, size = df.val, color = exp)) + geom_point()
This obviously still needs some polishing to deal with the x axis label clutter etc.
Rather than try to order the data before creating the plot, I can reorder the data at the time of writing the plot:
cor.data<- read.csv("https://dl.dropbox.com/s/p4uy6uf1vhe8yzs/cor.data.csv?dl=0",stringsAsFactors = F)
cor.data.sorted = cor.data[with(cor.data,order(r.val,pic)),] #<-- This line controls order points drawn created to make (slightly) more readible plot
gplot(cor.data.sorted,aes(x=reorder(pic,r.val),y=r.val,size=df.val,color=exp)) + geom_point()
to create
I want to compare the distribution of several variables (here X1 and X2) with a single value (here bm). The issue is that these variables are too many (about a dozen) to use a single boxplot.
Additionaly the levels are too different to use one plot. I need to use facets to make things more organised:
However with this plot my benchmark category (bm), which is a single value in X1 and X2, does not appear in X1 and seems to have several values in X2. I want it to be only this green line, which it is in the first plot. Any ideas why it changes? Is there any good workaround? I tried the options of facet_wrap/facet_grid, but nothing there delivered the right result.
I also tried combining a bar plot with bm and three empty categories with the boxplot. But firstly it looked terrible and secondly it got similarly screwed up in the facetting. Basically any work around would help.
Below the code to create the minimal example displayed here:
# Creating some sample data & loading libraries
library(ggplot2)
library(RColorBrewer)
set.seed(10111)
x=matrix(rnorm(40),20,2)
y=rep(c(-1,1),c(10,10))
x[y==1,]=x[y==1,]+1
x[,2]=x[,2]+20
df=data.frame(x,y)
# creating a benchmark point
benchmark=data.frame(y=rep("bm",2),key=c("X1","X2"),value=c(-0.216936,20.526312))
# melting the data frame, rbinding it with the benchmark
test_dat=rbind(tidyr::gather(df,key,value,-y),benchmark)
# Creating a plot
p_box <- ggplot(data = test_dat, aes(x=key, y=value,color=as.factor(test_dat$y))) +
geom_boxplot() + scale_color_manual(name="Cluster",values=brewer.pal(8,"Set1"))
# The first line delivers the first plot, the second line the second plot
p_box
p_box + facet_wrap(~key,scales = "free",drop = FALSE) + theme(legend.position = "bottom")
The problem only lies int the use of test_dat$y inside the color aes. Never use $ in aes, ggplot will mess up.
Anyway, I think you plot would improve if you use a geom_hline for the benchmark, instead of hacking in a single value boxplot:
library(ggplot2)
library(RColorBrewer)
ggplot(tidyr::gather(df,key,value,-y)) +
geom_boxplot(aes(x=key, y=value, color=as.factor(y))) +
geom_hline(data = benchmark, aes(yintercept = value), color = '#4DAF4A', size = 1) +
scale_color_manual(name="Cluster",values=brewer.pal(8,"Set1")) +
facet_wrap(~key,scales = "free",drop = FALSE) +
theme(legend.position = "bottom")
I have a time series dataset in which the x-axis is a list of events in reverse chronological order such that an observation will have an x value that looks like "n-1" or "n-2" all the way down to 1.
I'd like to make a line graph using ggplot that creates a smooth, continuous line that connects all of the points, but it seems when I try to input my data, the x-axis is extremely wonky.
The code I am currently using is
library(ggplot2)
theoretical = data.frame(PA = c("n-1", "n-2", "n-3"),
predictive_value = c(100, 99, 98));
p = ggplot(data=theoretical, aes(x=PA, y=predictive_value)) + geom_line();
p = p + scale_x_discrete(labels=paste("n-", 1:3, sep=""));
The fitted line and grid partitions that would normally appear using ggplot are replaced by no line and wayyy too many partitions.
When you use geom_line() with a factor on at least one axis, you need to specify a group aesthetic, in this case a constant.
p = ggplot(data=theoretical, aes(x=PA, y=predictive_value, group = 1)) + geom_line()
p = p + scale_x_discrete(labels=paste("n-", 1:3, sep=""))
p
If you want to get rid of the minor grid lines you can add
theme(panel.grid.minor = element_blank())
to your graph.
Note that it can be a little risky, scale-wise, to use factors on one axis like this. It may work better to use a typical continuous scale, and just relabel the points 1, 2, and 3 with "n-1", "n-2", and "n-3".