Tag: programming

calmcode.io > video tutorials for open source tools

calmcode.io > video tutorials for open source tools

calmcode.io is an e-learning platform that I really really really recommend to programmers and data scientists:

It is free.

It involves open source tools.

It uses bite-sized tutorial videos.

It explains tools clearly.

It explains everything calmly.

There’s tons of content about computer programming, data science, and personal productivity.

On top of this all, it’s by Vincent Warmerdam, and everything he touches seems to turn to gold.

Check it out here: https://calmcode.io/

You can subscribe to their newsletter here, to receive new content fresh from the presses, straight in your inbox!

There are just so many tutorials on so many different topics! Here are some quick glances at some topics and tools:

Computerphile on Cyber Security

Computerphile on Cyber Security

Computerphile is a Youtube sister channel of Numberphile. Where Numberphile’s videos are about the magic behind match and numbers, Computerphile’s videos are all about computers and computer stuff. I recommend both channels in general, and have watched many of their videos already.

Yet, over the past weeks I specifically enjoyed what seems to be several series of videos on Cyber Security related topics.

What makes a good password?

One series is all about passwords.

What are strong passwords, which are bad? How can hackers crack yours? And how do websites secure user passwords?

The videos below are in somewhat of the right order and they make for an interesting insight in the world of password management. They give you advice on how to pick you password, and even a nice tool to check whether your password has ever been leaked.

Probably, you will want to change your password afterwards!

Hacking and attacking

If you are up to no good, please do not watch this second series, which revolves all around hacks and computer attacks.

How do people get access to a websites database? How can we prevent it? How can we recognize security dangers?

You might know of SQL injections, but do you know what a slow loris attack is? Or how ransomware works? Or what exploitX is?

These videos nicely continue the line of a previous post on Try Hack Me’s Cyber Security Challenges, where you can learn how computers work and where there vulnerabilities lie.

JavaScript for R — ebook

JavaScript for R — ebook

The R programming language has seen the integration of many languages; C, C++, Python, to name a few, can be seamlessly embedded into R so one can conveniently call code written in other languages from the R console. Little known to many, R works just as well with JavaScript—this book delves into the various ways both languages can work together.

https://book.javascript-for-r.com/

John Coene is an well-known R and JavaScript developer. He recently wrote a book on JavaScript for R users, of which he published an online version free to access here.

The book is definitely worth your while if you want to better learn how to develop front-end applications (in JavaScript) on top of your statistical R programs. Think of better understanding, and building, yourself Shiny modules or advanced data visualizations integrated right into webpages.

A nice step on your development path towards becoming a full stack developer by combining R and JavaScript!

Yet most R developers are not familiar with one of web browsers’ core technology: JavaScript. This book aims to remedy that by revealing how much JavaScript can greatly enhance various stages of data science pipelines from the analysis to the communication of results.

https://book.javascript-for-r.com/

Want to learn more about JavaScript in general, then I recommend this book:

Create a publication-ready correlation matrix, with significance levels, in R

Create a publication-ready correlation matrix, with significance levels, in R

In most (observational) research papers you read, you will probably run into a correlation matrix. Often it looks something like this:

FACTOR ANALYSIS

In Social Sciences, like Psychology, researchers like to denote the statistical significance levels of the correlation coefficients, often using asterisks (i.e., *). Then the table will look more like this:

Table 4 from Family moderators of relation between community ...

Regardless of my personal preferences and opinions, I had to make many of these tables for the scientific (non-)publications of my Ph.D..

I remember that, when I first started using R, I found it quite difficult to generate these correlation matrices automatically.

Yes, there is the cor function, but it does not include significance levels.

Then there the (in)famous Hmisc package, with its rcorr function. But this tool provides a whole new range of issues.

What’s this storage.mode, and what are we trying to coerce again?

Soon you figure out that Hmisc::rcorr only takes in matrices (thus with only numeric values). Hurray, now you can run a correlation analysis on your dataframe, you think…

Yet, the output is all but publication-ready!

You wanted one correlation matrix, but now you have two… Double the trouble?

To spare future scholars the struggle of the early day R programming, I would like to share my custom function correlation_matrix.

My correlation_matrix takes in a dataframe, selects only the numeric (and boolean/logical) columns, calculates the correlation coefficients and p-values, and outputs a fully formatted publication-ready correlation matrix!

You can specify many formatting options in correlation_matrix.

For instance, you can use only 2 decimals. You can focus on the lower triangle (as the lower and upper triangle values are identical). And you can drop the diagonal values:

Or maybe you are interested in a different type of correlation coefficients, and not so much in significance levels:

For other formatting options, do have a look at the source code below.

Now, to make matters even more easy, I wrote a second function (save_correlation_matrix) to directly save any created correlation matrices:

Once you open your new correlation matrix file in Excel, it is immediately ready to be copy-pasted into Word!

If you are looking for ways to visualize your correlations do have a look at the packages corrr and corrplot.

I hope my functions are of help to you!

Do reach out if you get to use them in any of your research papers!

I would be super interested and feel honored.

correlation_matrix

#' correlation_matrix
#' Creates a publication-ready / formatted correlation matrix, using `Hmisc::rcorr` in the backend.
#'
#' @param df dataframe; containing numeric and/or logical columns to calculate correlations for
#' @param type character; specifies the type of correlations to compute; gets passed to `Hmisc::rcorr`; options are `"pearson"` or `"spearman"`; defaults to `"pearson"`
#' @param digits integer/double; number of decimals to show in the correlation matrix; gets passed to `formatC`; defaults to `3`
#' @param decimal.mark character; which decimal.mark to use; gets passed to `formatC`; defaults to `.`
#' @param use character; which part of the correlation matrix to display; options are `"all"`, `"upper"`, `"lower"`; defaults to `"all"`
#' @param show_significance boolean; whether to add `*` to represent the significance levels for the correlations; defaults to `TRUE`
#' @param replace_diagonal boolean; whether to replace the correlations on the diagonal; defaults to `FALSE`
#' @param replacement character; what to replace the diagonal and/or upper/lower triangles with; defaults to `""` (empty string)
#'
#' @return a correlation matrix
#' @export
#'
#' @examples
#' `correlation_matrix(iris)`
#' `correlation_matrix(mtcars)`
correlation_matrix <- function(df, 
                               type = "pearson",
                               digits = 3, 
                               decimal.mark = ".",
                               use = "all", 
                               show_significance = TRUE, 
                               replace_diagonal = FALSE, 
                               replacement = ""){
  
  # check arguments
  stopifnot({
    is.numeric(digits)
    digits >= 0
    use %in% c("all", "upper", "lower")
    is.logical(replace_diagonal)
    is.logical(show_significance)
    is.character(replacement)
  })
  # we need the Hmisc package for this
  require(Hmisc)
  
  # retain only numeric and boolean columns
  isNumericOrBoolean = vapply(df, function(x) is.numeric(x) | is.logical(x), logical(1))
  if (sum(!isNumericOrBoolean) > 0) {
    cat('Dropping non-numeric/-boolean column(s):', paste(names(isNumericOrBoolean)[!isNumericOrBoolean], collapse = ', '), '\n\n')
  }
  df = df[isNumericOrBoolean]
  
  # transform input data frame to matrix
  x <- as.matrix(df)
  
  # run correlation analysis using Hmisc package
  correlation_matrix <- Hmisc::rcorr(x, type = type)
  R <- correlation_matrix$r # Matrix of correlation coeficients
  p <- correlation_matrix$P # Matrix of p-value 
  
  # transform correlations to specific character format
  Rformatted = formatC(R, format = 'f', digits = digits, decimal.mark = decimal.mark)
  
  # if there are any negative numbers, we want to put a space before the positives to align all
  if (sum(!is.na(R) & R < 0) > 0) {
    Rformatted = ifelse(!is.na(R) & R > 0, paste0(" ", Rformatted), Rformatted)
  }

  # add significance levels if desired
  if (show_significance) {
    # define notions for significance levels; spacing is important.
    stars <- ifelse(is.na(p), "", ifelse(p < .001, "***", ifelse(p < .01, "**", ifelse(p < .05, "*", ""))))
    Rformatted = paste0(Rformatted, stars)
  }
  
  # make all character strings equally long
  max_length = max(nchar(Rformatted))
  Rformatted = vapply(Rformatted, function(x) {
    current_length = nchar(x)
    difference = max_length - current_length
    return(paste0(x, paste(rep(" ", difference), collapse = ''), sep = ''))
  }, FUN.VALUE = character(1))
  
  # build a new matrix that includes the formatted correlations and their significance stars
  Rnew <- matrix(Rformatted, ncol = ncol(x))
  rownames(Rnew) <- colnames(Rnew) <- colnames(x)
  
  # replace undesired values
  if (use == 'upper') {
    Rnew[lower.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (use == 'lower') {
    Rnew[upper.tri(Rnew, diag = replace_diagonal)] <- replacement
  } else if (replace_diagonal) {
    diag(Rnew) <- replacement
  }
  
  return(Rnew)
}

save_correlation_matrix

#' save_correlation_matrix
#' Creates and save to file a fully formatted correlation matrix, using `correlation_matrix` and `Hmisc::rcorr` in the backend
#' @param df dataframe; passed to `correlation_matrix`
#' @param filename either a character string naming a file or a connection open for writing. "" indicates output to the console; passed to `write.csv`
#' @param ... any other arguments passed to `correlation_matrix`
#'
#' @return NULL
#'
#' @examples
#' `save_correlation_matrix(df = iris, filename = 'iris-correlation-matrix.csv')`
#' `save_correlation_matrix(df = mtcars, filename = 'mtcars-correlation-matrix.csv', digits = 3, use = 'lower')`
save_correlation_matrix = function(df, filename, ...) {
  return(write.csv2(correlation_matrix(df, ...), file = filename))
}

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Best Tech & Programming Talks Ever

Best Tech & Programming Talks Ever

Every now and then, Twitter will offer these golden resources.

Ashley Willis recently asked people to name the best tech talk they’ve ever seen and the results are a resource I don’t want to lose.

Hundreds of people responded, sharing their contenders for the title.

Below, I selected some of the top-rated talks and clustered them accordingly. Click a category to jump to the section.


Big Idea & Programming Meta-Talks

The Future of Programming

Growing a Language

The Mess We’re In

Making Users Awesome

Ethical Dilemmas in Software Engineering


Testing code

Adding Eyes to Your Test Automation Framework

TATFT – Test All The F*cking Time


Language-Specific talks

Concurrency (Python)

How we program multicores (erlang)

Y Not- Adventures in Functional Programming (Ruby)

JavaScript: The Good Parts


Code Design

Core Design Principles for Software Developers

Design Patterns vs Anti pattern in APL


Containers & Kubernetes

The Container Operator’s Manual

Write a Container in Go From Scratch

Container Hacks and Fun Images

Kubernetes and the Path to Serverless

Let’s Build Kubernetes, With a Spreadsheet and Volunteers

Cover image via: https://toggl.com/blog/best-tech-websites

David Robinson’s R Programming Screencasts

David Robinson’s R Programming Screencasts

David Robinson (aka drob) is one of the best known R programmers.

Since a couple of years David has been sharing his knowledge through streaming screencasts of him programming. It’s basically part of R’s #tidytuesday movement.

Alex Cookson decided to do us all a favor and annotate all these screencasts into a nice overview.

https://docs.google.com/spreadsheets/d/1pjj_G9ncJZPGTYPkR1BYwzA6bhJoeTfY2fJeGKSbOKM/edit#gid=444382177

Here you can search for video material of David using a specific function or method. There are already over a thousand linked fragments!

Very useful if you want to learn how to visualize data using ggplot2 or plotly, how to work with factors in forcats, or how to tidy data using tidyr and dplyr.

For instance, you could search for specific R functions and packages you want to learn about:

Thanks David for sharing your knowledge, and thanks Alex for maintaining this overview!