178 Matching Annotations
  1. Last 7 days
  2. Dec 2021
    1. Likewise, the filing cabinet cannot feed itself without user collaboration; indeed, without a user, the filing cabinet cannot even start its combinatory po-tential. Nevertheless, the card index is used as a true ‘communicative partner’ because it has proper autonomy. In a sense, the card index is fully dependent on and fully independent of the user. The inner structure is methodically ar-ranged so that the users, whoever they may be, can in principle use it; entries are linked so that once the combinatory potential begun, combinations repro-duce themselves and increase the available complexity in unexpected ways.34

      There is an interesting analogy here worth pursuing:

      This idea and its structure have lots of similarities to those of growth and evolution in Werner R. Loewenstein's The Touchstone of Life: Molecular Information, Cell Communication, and the Foundations of Life. What if we reframe RNA or mitochondria in the role of the filing cabinet? What emergent properties occur in these processes? What do these processes have in common?

      I need at least some shorthand idea or word for talking about the circular evolving processes of life in Loewenstein's book. Maybe evolution spirals?

      Think inputs and outputs.

  3. Nov 2021
  4. Oct 2021
  5. Sep 2021
    1. This fundamental truth (expressed in economic notation as r > g, or "return on capital is greater than economic growth") means that "meritocracy" is a lie: the richest people in a market economy aren't the people who do the best work, it's the people who started off rich.

      Thomas Piketty's r > g shows that meritocracy is a lie in that the richest people aren't the ones that do the best or most productive work, but simply those who start of rich.

    1. https://www.youtube.com/watch?v=rhgwIhB58PA

      Learning styles have been debunked.

      Learning styles: V.A.R.K. model originated by Neil Flemiing stands for:

      • visual
      • auditory
      • reading/writing
      • kinesthetic

      References:

      Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning styles: Concepts and evidence. Psychological science in the public interest, 9(3), 105-119. — https://ve42.co/Pashler2008

      Willingham, D. T., Hughes, E. M., & Dobolyi, D. G. (2015). The scientific status of learning styles theories. Teaching of Psychology, 42(3), 266-271. — https://ve42.co/Willingham

      Massa, L. J., & Mayer, R. E. (2006). Testing the ATI hypothesis: Should multimedia instruction accommodate verbalizer-visualizer cognitive style?. Learning and Individual Differences, 16(4), 321-335. — https://ve42.co/Massa2006

      Riener, C., & Willingham, D. (2010). The myth of learning styles. Change: The magazine of higher learning, 42(5), 32-35.— https://ve42.co/Riener2010

      Husmann, P. R., & O'Loughlin, V. D. (2019). Another nail in the coffin for learning styles? Disparities among undergraduate anatomy students’ study strategies, class performance, and reported VARK learning styles. Anatomical sciences education, 12(1), 6-19. — https://ve42.co/Husmann2019

      Snider, V. E., & Roehl, R. (2007). Teachers’ beliefs about pedagogy and related issues. Psychology in the Schools, 44, 873–886. doi:10.1002/pits.20272 — https://ve42.co/Snider2007

      Fleming, N., & Baume, D. (2006). Learning Styles Again: VARKing up the right tree!. Educational developments, 7(4), 4. — https://ve42.co/Fleming2006

      Rogowsky, B. A., Calhoun, B. M., & Tallal, P. (2015). Matching learning style to instructional method: Effects on comprehension. Journal of educational psychology, 107(1), 64. — https://ve42.co/Rogowskyetal

      Coffield, Frank; Moseley, David; Hall, Elaine; Ecclestone, Kathryn (2004). — https://ve42.co/Coffield2004

      Furey, W. (2020). THE STUBBORN MYTH OF LEARNING STYLES. Education Next, 20(3), 8-13. — https://ve42.co/Furey2020

      Dunn, R., Beaudry, J. S., & Klavas, A. (2002). Survey of research on learning styles. California Journal of Science Education II (2). — https://ve42.co/Dunn2002

  6. Aug 2021
    1. We think R is a great place to start your data science journey because it is an environment designed from the ground up to support data science. R is not just a programming language, but it is also an interactive environment for doing data science. To support interaction, R is a much more flexible language than many of its peers. This flexibility comes with its downsides, but the big upside is how easy it is to evolve tailored grammars for specific parts of the data science process. These mini languages help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer.
  7. Jul 2021
    1. Why do 87% of data science projects never make it into production?

      It turns out that this phrase doesn't lead to an existing research. If one goes down the rabbit hole, it all ends up with dead links

    1. David Fisman. (2021, July 8). Fascinating new preprint on delta vs older variants in well-investigated outbreaks in China. Viral load for delta is 3 log higher, and latent period is shorter too (estimate is 4 days vs 6 days). This may explain much higher R estimates which may be due to elevated viral load [Tweet]. @DFisman. https://twitter.com/DFisman/status/1413126886570536963

  8. Jun 2021
  9. May 2021
    1. (7) ReconfigBehSci on Twitter: “@ToddHorowitz3 probably- and I think there are many interesting questions around why he is there and whether he should be there. But to answer those properly, looking at the performance of the model seems important and interesting to me- that is all I am saying” / Twitter. (n.d.). Retrieved March 6, 2021, from https://twitter.com/SciBeh/status/1324389147050569734

    1. ReconfigBehSci. (2020, November 5). @ToddHorowitz3 2/2 so I would prefer to treat this as an opportunity for empirical observation and learning. Evaluation should focus on trying to assess actual contribution, not a priori judgments. [Tweet]. @SciBeh. https://twitter.com/SciBeh/status/1324367278352355330

  10. Apr 2021
  11. Mar 2021
  12. Feb 2021
    1. unnest_wider

      unnest_wider( data, col, names_sep = NULL, simplify = TRUE, names_repair = "check_unique", ptype = list(), transform = list() )

    2. unnest_wider

      unnest_wider( data, col, names_sep = NULL, simplify = TRUE, names_repair = "check_unique", ptype = list(), transform = list() )

    3. unnest_wider

      unnest_wider( data, col, names_sep = NULL, simplify = TRUE, names_repair = "check_unique", ptype = list(), transform = list() )

    4. unnest_longer

      unnest_longer( data, col, values_to = NULL, indices_to = NULL, indices_include = NULL, names_repair = "check_unique", simplify = TRUE, ptype = list(), transform = list() )

    5. unnest_longer

      unnest_longer( data, col, values_to = NULL, indices_to = NULL, indices_include = NULL, names_repair = "check_unique", simplify = TRUE, ptype = list(), transform = list() )

    6. unnest_longer

      unnest_longer( data, col, values_to = NULL, indices_to = NULL, indices_include = NULL, names_repair = "check_unique", simplify = TRUE, ptype = list(), transform = list() )

    7. unnest_wider

      unnest_wider( data, col, names_sep = NULL, simplify = TRUE, names_repair = "check_unique", ptype = list(), transform = list() )

    8. unnest_wider

      unnest_wider( data, col, names_sep = NULL, simplify = TRUE, names_repair = "check_unique", ptype = list(), transform = list() )

    9. hoist

      hoist( .data, .col, ..., .remove = TRUE, .simplify = TRUE, .ptype = list(), .transform = list() )

    1. Sass

      Define variables, such as colors (e.g. $primary: #337ab7) in Sass (styles.scss) then compile to css for web.

      R library "bootstraplib" built on foundation of "sass".

      Use "run_with_themer()" to get a live preview GUI for customizing bootstrap theme.

      Also, use "shinyOptions(plot.autocolors=TRUE)" at top of app to get plot outputs that respect Dark Mode.

  13. Jan 2021
  14. Dec 2020
  15. Nov 2020
    1. Let’s fit regression line to our model:

      plot() and lines() seem to plot regression lines

      • Can they be added to a ggplot?
      • Can they be used to print R2 on the plot?
  16. Oct 2020
    1. You need to get out of the habit of thinking using quotes is ugly. Not using them is ugly! Why? Because you've created a function that can only be used interactively - it's very difficult to program with it. – hadley

      Does it seem like Hadley still stands by this statement after tidy evaluation from this article <Do you need tidyeval>

    1. In practice, functional programming is all about hiding for loops, which are abstracted away by the mapper functions that automate the iteration.
    1. All figures were created using R Statistical Computing Software version 3.6.3 (R Core Team, 2020), relying primarily on the dplyr package (Wickham et al., 2015) for data manipulation and the ggplot2 package (Wickham 2016) for plotting. The code used to create each figure can be found at https://github.com/mkc9953/SARS-CoV-2-WW-EPI/tree/master.
  17. Sep 2020
    1. The neighbour‐joining tree was prepared with the R package {Ape} (Paradis, Claude, & Strimmer, 2004) and visualized using the R package {ggtree} (Yu, Smith, Zhu, Guan, & Lam, 2017).
  18. Aug 2020
  19. Jul 2020
  20. Jun 2020
    1. How to prevent the environment from being “invalidated”?Docker containers (Rocker)

      Rocker

    2. SAS, R, Stata, SPSS may return different results even for quantiles, or due to floating number representation! The results should be maximally close to each other, but what about resampling methods (SAS and R gives different random numbers for the same seed)?

      Different results between SAS, R, Stata, SPSS

    3. 99.9% open-source. 0.1% is licensed (free for non-commercial use)

      License of libraries in R

    4. Status of R on the Clinical Research market
      • In general bioscience and academia, S ---> R has built over years its position of one of the industry standards
      • In clinical research, however, SAS reigns par excellence
      • Pharmaceutical companies, CROs and even FDA do use R “internally”.But they resist (or hesitate) to use it in submissions (to FDA)
      • Clinical Programmer or Biostatistician ≝ SAS Programmer. Period
    5. Differences in

      Differences between R and SAS:

      • origin of dates
      • default contrasts
      • used sum of squares
      • calculation of quantiles
      • generation of random numbers
      • implementation of advanced model
      • representation of floating point numbers
    6. Tospeeduptheprocesswithoutsacrificingaccuracy,theteamalsousesRevolutionRanalyticproducts

      Revolution R

    1. In most programming languages, you can only access the values of a function’s arguments. In R, you can also access the code used to compute them. This makes it possible to evaluate code in non-standard ways: to use what is known as non-standard evaluation
  21. May 2020
    1. You can create estimation plots here at estimationstats.com, or with the DABEST packages which are available in R, Python, and Matlab.

      You can create estimation plots with:

  22. Apr 2020
    1. Pharma, which is one of the biggest, richest, most rewarding and promising industries in the world. Especially now, when the pharmaceutical industry, including the FDA, allows R to be used the domain occupied in 110% by SAS.

      Pharma industry is one of the most rewarding industries, especially now

    2. CR is one of the most controlled industries in this world. It's insanely conservative in both used statistical methods and programming. Once a program is written and validated, it may be used for decades. There are SAS macros written in 1980 working still by today without any change. That's because of brilliant backward compatibility of the SAS macro-language. New features DO NOT cause the old mechanisms to be REMOVED. It's here FOREVER+1 day.

      Clinical Research is highly conservative, making SAS macros applicable for decades. Unfortunately, that's not the same case with R

  23. Mar 2020
    1. Thanks to ggforce, you can enhance almost any ggplot by highlighting data groupings, and focusing attention on interesting features of the plot