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Regression Wasm

This repository serves a simple web assembly (wasm) application to perform a regression, using data from a table in the browser, which can be loaded as a delimited file by the user. We use a simple regression library to do the work. See the demo here or continue reading.


  • Run a multiple or single regression using Web Assembly
  • Two variables (one predictor, one regression) will generate a line plot showing X vs. Y and predictions
  • More than two variables (one predictor, multiple regressors) performs multiple regression to generate a residual histogram
  • Upload your own data file, change the delimiter, the file name to be saved, or the predictor column


When you load the page, you are presented with a loaded data frame. The data is a bit dark, but it's a nice dataset to show how this works. The first column is the number of murders (per million habitants) for some city, and each of the remaining columns are variables that might be used to predict it (inhabitants, percent with incomes below $5000, and percent unemployed). This is what you see:



The formula for our regression model is shown below the plot, in human friendly terms.

Predicted = -36.7649 + Inhabitants*0.0000 + Percent with incomes below $5000*1.1922 + Percent unemployed*4.7198

Residual Plot

Given that we have more than one regressor variable, we need to run a multiple regression, and so the plot in the upper right is a histogram of the residuals.

the residuals are the difference between the actual values (number of murders per million habitants) and the values predicted by our model.


If you remove any single value from a row, it invalidates it, and it won't be included in the plot. If you remove a column heading, it's akin to removing the entire column.

Line Plot

But what if we want to plot the relationship between one of the variables X, and our Y? This is where the tool gets interesting! By removing a column header, we essentially remove the column from the dataset. Let's first try removing just one, Inhabitants:


We still see a residual plot because it would require more than two dimensions to plot. Let's remove another one, the percent unemployed:


Now we see a line plot, along with the plotting of the predictions! By simply removing each column one at a time (and leaving only one Y, and one X) we are actually running a single regression, and we can do this for each variable:

Inhabitants to predict murders


Unemployment to predict murders


Low Income Percentage to predict murders


As we can see, the number of inhabitants (on its own) is fairly useless. The variables that are strongest here are unemployment and income.

Download Data

This of course is a very superficial overview, you would want to download the full model data to get more detail: The "Download Results" will appear after you generate any kind of plot, and it downloads a text file with the model output. Here is an example:

Dinosaur Regression Wasm
Predicted = -36.7649 + Inhabitants*0.0000 + Percent with incomes below $5000*1.1922 + Percent unemployed*4.7198
Murders per annum per one million inhabitants|	Inhabitants|	Percent with incomes below $5000|	Percent unemployed
11.20|	587000.00|	16.50|	6.20
13.40|	643000.00|	20.50|	6.40
40.70|	635000.00|	26.30|	9.30
5.30|	692000.00|	16.50|	5.30
24.80|	1248000.00|	19.20|	7.30
12.70|	643000.00|	16.50|	5.90
20.90|	1964000.00|	20.20|	6.40
35.70|	1531000.00|	21.30|	7.60
8.70|	713000.00|	17.20|	4.90
9.60|	749000.00|	14.30|	6.40
14.50|	7895000.00|	18.10|	6.00
26.90|	762000.00|	23.10|	7.40
15.70|	2793000.00|	19.10|	5.80
36.20|	741000.00|	24.70|	8.60
18.10|	625000.00|	18.60|	6.50
28.90|	854000.00|	24.90|	8.30
14.90|	716000.00|	17.90|	6.70
25.80|	921000.00|	22.40|	8.60
21.70|	595000.00|	20.20|	8.40
25.70|	3353000.00|	16.90|	6.70

N = 20
Variance observed = 92.76010000000001
Variance Predicted = 75.90724706481737
R2 = 0.8183178658153383



Web assembly can allow us to interact with compiled code directly in the browser, doing away with any need for a server. While I don't do a large amount of data analysis for my role proper, I realize that many researchers do, and so with this in mind, I wanted to create a starting point for developers to interact with data in the browser. The minimum conditions for success meant:

  1. being able to load a delimited file into the browser
  2. having the file render as a table
  3. having the data be processed by a compiled wasm
  4. updating a plot based on output from 3.

Thus, the application performs a simple regression based on loading data in the table, and then plotting the result. To make it fun, I added a cute gopher logo and used an xkcd plotting library for the result.


The basics are here for a developer to create (some GoLang based) functions to perform data analysis on an input file, and render back to the screen as a plot. If you need any help, or want to request a custom tool, please don't hesitate to open up an issue.



If you are comfortable with GoLang, and have installed emscripten, you can clone the repository into your $GOPATH under the github folder:

$ mkdir -p $GOPATH/
$ cd $GOPATH/
$ git clone

And then build the wasm.

$ cd regression-wasm
$ make

Add your own Go version specific wasm_exec.js file :

$ cp "$(go env GOROOT)/misc/wasm/wasm_exec.js" ./docs

And cd into the "docs" folder and start a server to see the result.

$ cd docs
$ python -m http.server 9999

Open the browser to http://localhost:9999


If you don't want to install dependencies, just clone the repository, and build the Docker image:

$ docker build -t vanessa/regression-wasm .

It will install emscripten, add the source code to the repository, and compile to wasm. You can then run the container and expose port 80 to see the compiled interface:

$ docker run -it --rm -p 80:80 vanessa/regression-wasm

Then you can proceed to use the interface.