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
Related
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()
I want to create 3 graphs in ggplot2 as follows:
ggplot(observbest,aes(x=factor(iteration),y=bottles,colour=Team ,group=Team)) + geom_line() + scale_colour_gradientn(colours=rainbow(16))
ggplot(observmedium,aes(x=factor(iteration),y=bottles,colour=Team ,group=Team)) + geom_line() + scale_colour_gradientn(colours=rainbow(16))
ggplot(observweak,aes(x=factor(iteration),y=bottles,colour=Team ,group=Team)) + geom_line() + scale_colour_gradientn(colours=rainbow(16))
That is, three graphs displaying the same thing but for difference dataset each time. I want to compare between them, therefore I want their y axis to be fixed to the same scale with the same margins on all graphs, something the currently doesn't happen automatically.
Any suggestion?
Thanks
It sounds like a facet_wrap on all the observations, combined into a single dataframe, might be what you're looking for. E.g.
library(plyr)
library(ggplot2)
observ <- rbind(
mutate(observbest, category = "best"),
mutate(observmedium, category = "medium"),
mutate(observweak, category = "weak")
)
qplot(iteration, bottles, data = observ, geom = "line") + facet_wrap(~category)
Add + ylim(min_value,max_value) to each graph.
Another option would be to merge the three datasets with an id variable identifying which value is in which dataset, and then plot the three of them together, differentiating them by linetype for instance.
Use scale_y_continuous to define the y axis for each graph and make them all easily comparable.
I am using ggplot2 to produce the attached file.
My question is how can I use R+ggplot2 in order to make this plot less busy and easier to see what is going on in the data? There are about 1000 observations and each observation has between 1 and 15 data points. I connect the observations with >1 datapoint with lines.
Is there maybe something to be done with the color scheme? Or possibly grouping things together?
My code looks something like this:
ggplot(data, aes(variable, value, group=Name, color=Name))+
geom_point(alpha=.2, size=5)+
geom_line()+
geom_text(aes(label=Name),hjust=0, vjust=0, size=2)
Before any advice can be given you need to determine what data you want to group together. Perhaps you can group your data by gender/age. This can be done by changing 'color = Name' into 'color = Gender' if that column exists in your data.
The color scheme can be changed with scale_brewer. However, in every palette there are limited colors available so it will not be possible to have a distinct different color for each individual. http://docs.ggplot2.org/current/scale_brewer.html
If you want, your legend can also be changed in multiple columns with
+ guides(fill=guide_legend(ncol=2))
You can also change your theme options with
axis.text.x = element_text(angle = -330)
To turn your x-axis which will make it readable.
In the data that I am attempting to plot, each sample belongs in one of several groups, that will be plotted on their own grids. I am plotting stacked bar plots for each sample that will be ordered in increasing number of sequences, which is an id attribute of each sample.
Currently, the plot (with some random data) looks like this:
(Since I don't have the required 10 rep for images, I am linking it here)
There are couple things I need to accomplish. And I don't know where to start.
I would like the bars not to be placed at its corresponding nseqs value, rather placed next to each other in ascending nseqs order.
I don't want each grid to have the same scale. Everything needs to fit snugly.
I have tried to set scales and size to for facet_grid to free_x, but this results in an unused argument error. I think this is related to the fact that I have not been able to get the scales library loaded properly (it keeps saying not available).
Code that deals with plotting:
ggfdata <- melt(fdata, id.var=c('group','nseqs','sample'))
p <- ggplot(ggfdata, aes(x=nseqs, y=value, fill = variable)) +
geom_bar(stat='identity') +
facet_grid(~group) +
scale_y_continuous() +
opts(title=paste('Taxonomic Distribution - grouped by',colnames(meta.frame)[i]))
Try this:
update.packages()
## I'm assuming your ggplot2 is out of date because you use opts()
## If the scales library is unavailable, you might need to update R
ggfdata <- melt(fdata, id.var=c('group','nseqs','sample'))
ggfdata$nseqs <- factor(ggfdata$nseqs)
## Making nseqs a factor will stop ggplot from treating it as a numeric,
## which sounds like what you want
p <- ggplot(ggfdata, aes(x=nseqs, y=value, fill = variable)) +
geom_bar(stat='identity') +
facet_wrap(~group, scales="free_x") + ## No need for facet_grid with only one variable
labs(title = paste('Taxonomic Distribution - grouped by',colnames(meta.frame)[i]))
I'm an undergrad researcher and I've been teaching myself R over the past few months. I just started trying ggplot, and have run into some trouble. I've made a series of boxplots looking at the depth of fish at different acoustic receiver stations. I'd like to add a scatterplot that shows the depths of the receiver stations. This is what I have so far:
data <- read.csv(".....MPS.csv", header=TRUE)
df <- data.frame(f1=factor(data$Tagging.location), #$
f2=factor(data$Station),data$Detection.depth)
df2 <- data.frame(f2=factor(data$Station), data$depth)
df$f1f2 <- interaction(df$f1, df$f2) #$
plot1 <- ggplot(aes(y = data$Detection.depth, x = f2, fill = f1), data = df) + #$
geom_boxplot() + stat_summary(fun.data = give.n, geom = "text",
position = position_dodge(height = 0, width = 0.75), size = 3)
plot1+xlab("MPS Station") + ylab("Depth(m)") +
theme(legend.title=element_blank()) + scale_y_reverse() +
coord_cartesian(ylim=c(150, -10))
plot2 <- ggplot(aes(y=data$depth, x=f2), data=df2) + geom_point()
plot2+scale_y_reverse() + coord_cartesian(ylim=c(150,-10)) +
xlab("MPS Station") + ylab("Depth (m)")
Unfortunately, since I'm a new user in this forum, I'm not allowed to upload images of these two plots. My x-axis is "Stations" (which has 12 options) and my y-axis is "Depth" (0-150 m). The boxplots are colour-coded by tagging site (which has 2 options). The depths are coming from two different columns in my spreadsheet, and they cannot be combined into one.
My goal is to to combine those two plots, by adding "plot2" (Station depth scatterplot) to "plot1" boxplots (Detection depths). They are both looking at the same variables (depth and station), and must be the same y-axis scale.
I think I could figure out a messy workaround if I were using the R base program, but I would like to learn ggplot properly, if possible. Any help is greatly appreciated!
Update: I was confused by the language used in the original post, and wrote a slightly more complicated answer than necessary. Here is the cleaned up version.
Step 1: Setting up. Here, we make sure the depth values in both data frames have the same variable name (for readability).
df <- data.frame(f1=factor(data$Tagging.location), f2=factor(data$Station), depth=data$Detection.depth)
df2 <- data.frame(f2=factor(data$Station), depth=data$depth)
Step 2: Now you can plot this with the 'ggplot' function and split the data by using the `col=f1`` argument. We'll plot the detection data separately, since that requires a boxplot, and then we'll plot the depths of the stations with colored points (assuming each station only has one depth). We specify the two different plots by referencing the data from within the 'geom' functions, instead of specifying the data inside the main 'ggplot' function. It should look something like this:
ggplot()+geom_boxplot(data=df, aes(x=f2, y=depth, col=f1)) + geom_point(data=df2, aes(x=f2, y=depth), colour="blue") + scale_y_reverse()
In this plot example, we use boxplots to represent the detection data and color those boxplots by the site label. The stations, however, we plot separately using a specific color of points, so we will be able to see them clearly in relation to the boxplots.
You should be able to adjust the plot from here to suit your needs.
I've created some dummy data and loaded into the chart to show you what it would look like. Keep in mind that this is purely random data and doesn't really make sense.