Here is my problem:
I have a big dataset that in R that represent an object of ~500MB that I plot with ggplot2.
There is 20 millions num values to plot along an int axis that are associated with a 5 level factor for color aesthetics.
I would like to set up a webapps where users could visualize this dataset, using different filter that rely on the factor to display all the data are once or for example a subset corresponding to 1 level of the factor.
The problem is that when I write the plot it takes a couple of minute (~10 minutes)
Solution 1 : The best one for the user would be to use Shiny UI. But is there a way to have the plot already somehow prewritten thanks to ggplot2 or shiny tricks so it can be quickly displayed?
Solution 2 : Without shiny, I would have done different plots of the dataset already and I will have to rebuild a UI to let user visualizes the different pictures. If I do that I will have to restrict the possible use cases of displaying the data.
Looking forward for advices and discussions
Ideally, you shouldn't need to plot anything this big really. If you're getting the data from a database then just write a sequence of queries that will aggregate the data on the DB side and drag very little data to output in shiny. Seems to be a bad design on your part.
That being said, the author of highcharter package did work on implementing boost.js module to help with plotting millions of points. https://rpubs.com/jbkunst/highcharter-boost.
Also have a look at the bigvis package, which allows 'Exploratory data analysis for large datasets (10-100 million observations)' and has been built by #Hadley Wickham https://github.com/hadley/bigvis. There is a nice presentation about the package at this meetup
Think about following procedure:
With ggplot2 you can produce an R object.
plot_2_save <- ggplot()
an object can be saved by
saveRDS(object, "file.rds")
and in the shiny server.R you can load this data
plot_from_data <- readRDS("path/.../file.rds")
I used this setup for some kind of text classification with a really (really) huge svm model implemented as an application on shiny-server.
Related
I am asking a question to a similar post posted up 2 years ago, with no full answer to it (subset of prcomp object in R). P.S. sorry for commenting on it for an answer..
Basically, my question is the same. I have generated a PCA table using prcomp that has 10000+ genes, and 1700+ cells, made up of 7 timepoints. Plotting all of them in a single file makes it difficult to see.
I would like to plot each timepoint separately, using the same PCA results table (ie without re-running prcomp).
Thanks Dean for giving me tips on posting. To think of a way to describe my dataset without actually loading it here, will take me a week I believe. I also tried the
dput(droplevels(head(object,2)))
option, but it was just too much info since I have such a large dataset. In short, it is a large matrix of single-cell dataset where people can commonly see on packages such as Seurat (https://satijalab.org/seurat/pbmc3k_tutorial_1_4.html). EDIT: I have posted a screenshot of a subset of my matrix here ().
Sorry I don't know how to re-create this or even export a text format.. But this is what I can provide:
My TPM matrix has 16541 rows (defining genes), and 1798 columns (defining cells).
In it, I have "re-labelled" my columns based on timepoints, using codes such as:
D0<-c(colnames(TPM[,grep("20180419-24837-1-*", colnames(TPM))])) #D0: 286 cells
D7<-c(colnames(TPM[,grep("20180419-24837-2-*", colnames(TPM))])) #D7: 237 cells
D10<-c(colnames(TPM[,grep("20180419-24947-5-*", colnames(TPM))])) #D10: 304 cells
...... and I continued to label each timepoint.
Each timepoint was also given a specific colour.
rc<-rep("white", ncol(TPM))
rc<-[,grep("20180419-24837-1-*", colnames(TPM))]= "magenta"
...... and I continued to give colour to each timepoint.
I performed a PCA using this code:
pcaRes<-prcomp(t(log(TPM+1)), center= TRUE, scale. = TRUE)
Then I proceeded to plot a PCA plot using:
plot(pcaRes$x[,1], pcaRes$x[,2], xlab="PC1", ylab="PC2",
cex=1.0, col= rc, pch=16, main="")
Then I when I wanted to plot a PCA plot only with D0, using the same PCA output (pcaRes).. This is where I am stuck.
P.S. If anyone else has an easier way of advising how to input an example data here from my large matrix, I welcome any help. Thanks so much! Sorry I am very new in bioinformatics.
Stack Exchange for
Bioinformatics is where you you will need to go to ask question(s) or learn about the package(s) and function(s) you need to deal with you area of specialty. Stack Exchange for Bioinformatics is linked with Stackoverflow so you will just need to join, you'll have the same login.
Classes S3, S4 and Base.
This Very basic over view of Classes in R. Think of a Class as the parent you inherit all of their skills or abilities from and as a result you are able to achieve certain tasks better than others and some cases, you will not be able to do the task at all.
In R and all programming, to save re-inventing the wheel, parent classes are created so that the average person does not have to repeatedly write a function to do something simple like plot() a graph. This stuff is hidden, to access it, you inherit from the parent. The child reads the traits off the parent(s), and then it either performs the task or gives you a cryptic error message.
Base and S3 classes work well together, they are like the working class people of the R world. S4 is a specialized class made for specific fields of study to be able to provide specific functionality needed in their industry. This mean you can only use certain Base and S3 functions with Class S4 functions, most are just not compatible. So it's nothing you've done wrong, plot() and ggplot() just have the wrong parent(s) to work with your dataset.
Typical Base and S3 Class dataframe: Box like structure. Along the left hand side is all the column names, nice and neatly stacked on top of each other.
Seurat S4 Class dataframe: Tree like structure, formatted to be read by a specific function(s).
Well hope that helps and I wish you well in your career. Cheers Conrad
Ps if this helps, then click the arrow up. :)
thanks #ConradThiele for your suggestion, I will check out that site.
I had a chat with other bioinformatics around the institute. My query has little to do with the object being an S4 class, since I am performing prcomp outside of the package. I have extracted my matrix out of the object and then ran prcomp on it.
Solution is simple: run prcomp with full dataset, transform the prcomp output into a dataframe, input additional columns to input additional details like "timepoint", create new dataframe(s) only with the "timepoint"/ "variable" of interest from the prcomp result, make multiple sub-dataframe and then plotting these using "plot" or whatever function you use.
This was not my solution but from a bioinformatition I went for help to in my institute. Hope this helps others! Thanks again for your time.
P.S. If I have the time, I will post a copy of the code I suggested soon.
I am working with weighted survey data of the class survey.design2 and survey.design. With the package survey, and the function call svytable, I can create contingency tables for survey data. With these contingency tables, I can then create normal bar-charts using lattice. The standard way for doing this (e.g. barchart(cars ~ mpg | factor(cyl), data=mtcars,...)) doesn't work for this data type.
I am used to working with ggplot2, and would like to create either stacked or grouped bar-charts, if possible even with facet-wraps. Unfortunately, ggplot2 does not know how to deal with data of the type survey.design2 either. As far as I am concerned, there also does not exist some sort of add-on, which would allow ggplot2 to deal with this kind of data.
So far I have:
sub-set my data set
converted it into class survey.design2 with the function call svydesign(),
plotted multiple bar-charts in one window using grid.arrange(). This sort of provides for a work around for facetting, but still doesn't allow me to create stacked or grouped bar-charts.
I'd be grateful for any suggestions.
Thank you
Good morning MatthewR
I have a data set with 62732 observations and 691 variables.
Original Data Set
So any example based on a random number generator should work as well, I guess. I am really just interested in a work around to this issue, not necessarily the final code.
I then convert the data frame into survey.design format using:
df_Survey <- svydesign(id=~1, weights=~IXPXHJ, data=df). IXPXHJ is the variable by which the original sample data set will be weighted so as to get the entire population. head(df$IXPXHJ) looks something like this:
87.70876
78.51809
91.95209
94.38899
105.32005
56.30210
str(df_Survey) looks something like this.
Survey Data Structure
At work when I want to understand a dataset (I work with portfolio data in life insurance), I would normally use pivot tables in Excel to look at e.g. the development of variables over time or dependencies between variables.
I remembered from university the nice R-function where you can plot every column of a dataframe against every other column like in:
For the dependency between issue.age and duration this plot is actually interesting because you can clearly see that high issue ages come with shorter policy durations (because there is a maximum age for each policy). However the plots involving the issue year iss.year are much less "visual". In fact you cant see anything from them. I would like to see with once glance if the distribution of issue ages has changed over the different issue.years, something like
where you could see immediately that the average age of newly issue policies has been increasing from 2014 to 2016.
I don't want to write code that needs to be customized for every dataset that I put in because then I can also do it faster manually in Excel.
So my question is, is there an easy way to plot each column of a matrix against every other column with more flexible chart types than with the standard plot(data.frame)?
The ggpairs() function from the GGally library. It has a lot of capability for visualizing columns of all different types, and provides a lot of control over what to visualize.
For example, here is a snippet from the vignette linked to above:
data(tips, package = "reshape")
ggpairs(tips)
I have a dataset with > 1000K rows and 5 columns. (material & prices been the relevant columns)
I have written a 'reactive' Shiny app which uses ggplot2 to create a boxplot of the price of the various materials.
e.g the user selects 4-5 materials from a list and then Shiny creates a boxplot of the price of each material :
Price spread of: Made of Cotton, Made of Paper, Made of Wood
It also creates a material combination data plot of the pricing spread of the combination of all the materials
e.g Boxplot of
Price spread of: Made of Cotton & Paper & Wood
It is working relatively quickly for the sample dataset (~5000 rows) but I am worried about scaling it effectively.
The dataset is static so I look at the following solutions:
Calculate the quartile ranges of the various materials (data <-
summary(data)) and then use googleViz to create a candle stick,
however I run into problems when trying to calculate the material combination plot as there are over 100 materials, so calculating
all the possible combinations offline is not feasible.
Calculate the quartile ranges of the various materials (data <- summary(data)) and then create a matrix which stores the row numberof the summary data (min,median,max,1st&3rd quartile) for each material. I can then use some rough calculations to establish the summary() data for the material combination plot,
and then plot using GoogleVIZ however I have little experience with this type of calculation using Shiny.
Can anyone suggest the most robust and scalable way to calculate & boxplot reactive subsets using Shiny?
I understand this a question related to method, rather than code, but I am new to the capabilities of R and am still digesting the different class capabilities, and don't want to 'miss a trick' so to speak.
As always thanks!
Please see below for methods reviewed.
Quartile Clustering: A quartile based technique for Generating Meaningful Clusters
http://arxiv.org/ftp/arxiv/papers/1203/1203.4157.pdf
Conditionally subsetting and calculating a new variable in dataframe in shiny
If you really have a dataset that has more than 1000K, which is 1M. It is probably in a flat file or in a database. You can always do some precalculations and store the result in a database table and use shiny app to call that table instead of loading everything into R every time people open up your shiny app.
I have built several shiny apps for internal use and the lesson I have learned is that: before you build your app, you need to carefully think about, how can I minimize the calculations for R and at the same time deliver the info to app user. Some of our data is 10billion+ and use Hive query will take more than 1 hour. Then I ended up precalculate result and put it on the crontab to update the result table every midnight.
I prefer, maybe your method2? or store the precalculation in a mysql database. (Maybe a Python script update the table once a day if you need some real-time feature later).
I have 334 records, with two columns:
Column (1): Resolution
Column (2): Number of images with a specific resolution
How can I make a scatter plot in R with this data? Is there a way to import the records, since it will be time consuming to enter 334 records?
If you do not know how to get data into R nor create a scatterplot, it sounds like you are very new to R.
You might want to use a program that lends a hand.
rstudio has a Workspace - import dataset menu; I'd recommend Rstudio, particularly if you are very new to R.
Rcmdr also has GUI options for getting data into R
As always Quick-R provides a helpful starting point:
importing data
scatterplots
More generally, it sounds like you need to spend some time with some introductory instructional material on R. Here are my suggested startings points.