I'm currently analysing some data I've retrieved from a survey and I want to create a histogram with it.
The problem is that the data is in pairs of range-absolute frequency, something like with different ranges:
Since the intervals are not the same, how can I generate the histogram in R?
Thank you in advance.
I think you want a bar chart instead of a histogram. Here's an article that explains the difference nicely.
For a barchart with the data you provided in the format you've indicated you could do something like this:
my_data <- data.frame(range = c('[0-2]','[2-5]','[5-9]'),
abs_frequency = c(2,10,5))
library(ggplot2)
plot <- ggplot(data = my_data, aes(x = range, y = abs_frequency))
plot +
geom_bar(stat="identity")
Related
I have a dataset with a few organisms, which I would like to plot on my y-axis, against date, which I would like to plot on the x-axis. However, I want the fluctuation of the curve to represent the abundance of the organisms. I.e I would like to plot a time series with the relative abundance separated by the organism to show similar patterns with time.
However, of course, plotting just date against an organism does not yield any information on the abundance. So, my question is, is there a way to make the curve represent abundance using ggridges?
Here is my code for an example dataset:
set.seed(1)
Data <- data.frame(
Abundance = sample(1:100),
Organism = sample(c("organism1", "organism2"), 100, replace = TRUE)
)
Date = rep(seq(from = as.Date("2016-01-01"), to = as.Date("2016-10-01"), by =
'month'),times=10)
Data <- cbind(Date, Data)
ggplot(Data, aes(x = Abundance, y = Organism)) +
geom_density_ridges(scale=1.15, alpha=0.6, color="grey90")
This produces a plot with the two organisms, however, I want the date on the x-axis and not abundance. However, this doesn't work. I have read that you need to specify group=Date or change date into julian day, however, this doesn't change the fact that I do not get to incorporate abundance into the plot.
Does anyone have an example of a plot with date vs. a categorical variable (i.e. organism) plotted against a continuous variable in ggridges?
I really like to output from ggridges and would like to be able to use it for these visualizations. Thank you in advance for your help!
Cheers,
Anni
To use geom_density_ridges, it'll help to reshape the data to show observations in separate rows, vs. as summarized by Abundance.
library(ggplot2); library(ggridges); library(dplyr)
# Uncount copies the row "Abundance" number of times
Data_sum <- Data %>%
tidyr::uncount(Abundance)
ggplot(Data_sum, aes(x = Date, y = Organism)) +
ggridges::geom_density_ridges(scale=1, alpha=0.6, color="grey90")
So I have 10.000 values in a vector from a Monte Carlo simulation. I want to plot this data as a histogram and a density plot. Doing this with the hist() function is easy, and it will calculate the frequency of the of the different values automatically. My ambition is however doing this in ggplot.
My biggest problem right now is how to transform the data so ggplot can handle it. I would like my x-axis to show the "price" while the x-axis shows the frequency or density. My data has a lot decimals as shown in the example data below.
myData <- c(266.8997, 271.5137, 225.4786, 223.3533, 258.1245, 199.5601, 234.2341, 231.7850, 260.2091, 184.5102, 272.8287, 203.7482, 212.5140, 220.9094, 221.2627, 236.3224)
My current code using the hist()-function, and the plot is shown below.
hist(myData,
xlab ="Price",
prob=TRUE)
lines(density(myData))
Histogram for the data vector containing 10000 values
How would you sort the data, and how would you do this with ggplot? I am thinking if I should round the numbers as well?
Hard to say exactly without seeing a sample of your data, but have you tried:
ggplot(myData, aes(Price)) + geom_histogram()
or:
ggplot(myData, aes(Price)) + geom_density()
Just try this:
ggplot() +
geom_bar(aes(myData)) +
geom_density(aes(myData))
I started learning R for data analysis and, most importantly, for data visualisation.
Since I am still in the switching process, I am trying to reproduce the activities I was doing with Graphpad Prism or Origin Pro in R. In most of the cases everything was smooth, but I could not find a smart solution for plotting multiple y columns in a single graph.
What I usually get from the softwares I use for data visualisations look like this:
Each single black trace is a measurement, and I would like to obtain the same plot in R. In Prism or Origin, this will take a single copy-paste in a XY graph.
I exported the matrix of data (one X, which indicates the time, and multiple Y values, which are the traces you see in the image).
I imported my data in R with the following commands:
library(ggplot2) #loaded ggplot2
Data <- read.csv("Directory/File.txt", header=F, sep="") #imported data
DF <- data.frame(Data) #transformed data into data frame
If I plot my data now, I obtain a series of columns, where the first one (called V1) is the X axis and all the others (V2 to V140) are the traces I want to put on the same graph.
To plot the data, I tried different solutions:
ggplot(data=DF, aes(x=DF$V1, y=DF[V2:V140]))+geom_line()+theme_bw() #did not work
plot(DF, xy.coords(x=DF$V1, y=DF$V2:V140)) #gives me an error
plot(DF, xy.coords(x=V1, y=c(V2:V10))) #gives me an error
I tried the matplot, without success, following the EZH guide:
The code I used is the following: matplot(x=DF$V1, type="l", lty = 2:100)
The only solution I found would be to individually plot a command for each single column, but it is a crazy solution. The number of columns varies among my data, and manually enter commands for 140 columns is insane.
What would you suggest?
Thank you in advance.
Here there are also some data attached.Data: single X, multiple Y
I tried using the matplot(). I used a very sample data which has no trend at all. so th eoutput from my code shall look terrible, but my main focus is on the code. Since you have already tried matplot() ,just recheck with below solution if you had done it right!
set.seed(100)
df = matrix(sample(1:685765,50000,replace = T),ncol = 100)
colnames(df)=c("x",paste0("y", 1:99))
dt=as.data.frame(df)
matplot(dt[["x"]], y = dt[,c(paste0("y",1:99))], type = "l")
If you want to plot in base R, you have to make a plot and add lines one at a time, however that isn't hard to do.
we start by making some sample data. Since the data in the link seemed to all be on the same scale, I will assume your data frame only has y values and the x value is stored separately.
plotData <- as.data.frame(matrix(sort(rnorm(500)),ncol = 5))
xval <- sort(sample(200, 100))
Now we can initialize a plot with the first column.
plot(xval, plotData[[1]], type = "l",
ylim = c(min(plotData), max(plotData)))
type = "l" makes a line plot instead of a scatter plot
ylim = c(min(plotData), max(plotData)) makes sure the y-axis will fit all the data.
Now we can add the rest of the values.
apply(plotData[-1], 2, lines, x = xval)
plotData[-1] removes the column we already plotted,
apply function with 2 as the second parameter means we want to execute a function on every column,
lines defines the function we are applying to the columns. lines adds a new line to the current plot.
x = xval passes an extra parameter (x) to the lines function.
if you wat to plot the data using ggplot2, the data should be transformed to long format;
library(ggplot2)
library(reshape2)
dat <- read.delim('AP.txt', header = F)
# plotting only first 9 traces
# my rstudio will crach if I plot the full data;
df <- melt(dat[1:10], id.vars = 'V1')
ggplot(df, aes(x = V1, y = value, color = variable)) + geom_line()
# if you want all traces to be in same colour, you can use
ggplot(df, aes(x = V1, y = value, group = variable)) + geom_line()
I create a dummy timeseries xts object with missing data on date 2-09-2015 as:
library(xts)
library(ggplot2)
library(scales)
set.seed(123)
seq <- seq(as.POSIXct("2015-09-01"),as.POSIXct("2015-09-02"), by = "1 hour")
ob1 <- xts(rnorm(length(seq),150,5),seq)
seq2 <- seq(as.POSIXct("2015-09-03"),as.POSIXct("2015-09-05"), by = "1 hour")
ob2 <- xts(rnorm(length(seq2),170,5),seq2)
final_ob <- rbind(ob1,ob2)
plot(final_ob)
# with ggplot
df <- data.frame(time = index(final_ob), val = coredata(final_ob) )
ggplot(df, aes(time, val)) + geom_line()+ scale_x_datetime(labels = date_format("%Y-%m-%d"))
After plotting my data looks like this:
The red coloured rectangular portion represents the date on which data is missing. How should I show that data was missing on this day in the main plot?
I think I should show this missing data with a different colour. But, I don't know how should I process data to reflect the missing data behaviour in the main plot.
Thanks for the great reproducible example.
I think you are best off to omit that line in your "missing" portion. If you have a straight line (even in a different colour) it suggests that data was gathered in that interval, that happened to fall on that straight line. If you omit the line in that interval then it is clear that there is no data there.
The problem is that you want the hourly data to be connected by lines, and then no lines in the "missing data section" - so you need some way to detect that missing data section.
You have not given a criteria for this in your question, so based on your example I will say that each line on the plot should consist of data at hourly intervals; if there's a break of more than an hour then there should be a new line. You will have to adjust this criteria to your specific problem. All we're doing is splitting up your dataframe into bits that get plotted by the same line.
So first create a variable that says which "group" (ie line) each data is in:
df$grp <- factor(c(0, cumsum(diff(df$time) > 1)))
Then you can use the group= aesthetic which geom_line uses to split up lines:
ggplot(df, aes(time, val)) + geom_line(aes(group=grp)) + # <-- only change
scale_x_datetime(labels = date_format("%Y-%m-%d"))
The whole dataset describes a module (or cluster if you prefer).
In order to reproduce the example, the dataset is available at:
https://www.dropbox.com/s/y1905suwnlib510/example_dataset.txt?dl=0
(54kb file)
You can read as:
test_example <- read.table(file='example_dataset.txt')
What I would like to have in my plot is this
On the plot, the x-axis is my Timepoints column, and the y-axis are the columns on the dataset, except for the last 3 columns. Then I used facet_wrap() to group by the ConditionID column.
This is exactly what I want, but the way I achieved this was with the following code:
plot <- ggplot(dataset, aes(x=Timepoints))
plot <- plot + geom_line(aes(y=dataset[,1],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,2],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,3],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,4],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,5],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,6],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,7],colour = dataset$InModule))
plot <- plot + geom_line(aes(y=dataset[,8],colour = dataset$InModule))
...
As you can see it is not very automated. I thought about putting in a loop, like
columns <- dim(dataset)[2] - 3
for (i in seq(1:columns))
{
plot <- plot + geom_line(aes(y=dataset[,i],colour = dataset$InModule))
}
(plot <- plot + facet_wrap( ~ ConditionID, ncol=6) )
That doesn't work.
I found this topic
Use for loop to plot multiple lines in single plot with ggplot2 which corresponds to my problem.
I tried the solution given with the melt() function.
The problem is that when I use melt on my dataset, I lose information of the Timepoints column to plot as my x-axis. This is how I did:
data_melted <- dataset
as.character(data_melted$Timepoints)
dataset_melted <- melt(data_melted)
I tried using aggregate
aggdata <-aggregate(dataset, by=list(dataset$ConditionID), FUN=length)
Now with aggdata at least I have the information on how many Timepoints for each ConditionID I have, but I don't know how to proceed from here and combine this on ggplot.
Can anyone suggest me an approach.
I know I could use the ugly solution of creating new datasets on a loop with rbind(also given in that link), but I don't wanna do that, as it sounds really inefficient. I want to learn the right way.
Thanks
You have to specify id.vars in your call to melt.data.frame to keep all information you need. In the call to ggplot you then need to specify the correct grouping variable to get the same result as before. Here's a possible solution:
melted <- melt(dataset, id.vars=c("Timepoints", "InModule", "ConditionID"))
p <- ggplot(melted, aes(Timepoints, value, color = InModule)) +
geom_line(aes(group=paste0(variable, InModule)))
p