4 ms·

Show HN: Simple Binary Data Visualization in R

63 points by tincholio 9y ago

lottin 9y ago
> as.tibble(cbind(x,y,z)) %>% na.omit() Why on earth would anyone write that instead of na.omit(as.tibble(cbind(x,y,z)))? Baffles me.
- stared 9y ago
  ...reading from left to right for starter? It's why I adore %>% as code gets reads as a sequence of step-by-step instructions, of one forcing a reader to constantly keep switching between left-to-right and right-to-left.
  - lottin 9y ago
    f(x,y) reads "f of x and y". How do you say x %>% f(y) in words?
    - jamessb 9y ago
      Since "%>%" is the pipe operator I would guess "x pipe f of y", or "x pipe to f of y"
      - Tarq0n 9y ago
        Hadley Wickham suggests using "then" for pipes. SO we take object x, then perform f(y) with it.
- tincholio 9y ago
  Hi, author here. I just find it much more intuitive, like unix pipes, or Clojure's threading macros. Nested calls are more confusing to me. Though it should have probably been better as cbind(...) %>% as.tibble() %>% na.omit...
  - tincholio 9y ago
    And it's fixed now
- confounded 9y ago
  Alternatively, x %>% bind_cols(y, z) %>% as.tibble() %>% na.omit() Makes the order of execution a little clearer (and uses more dplyr conventions). I still nest functions when working interactively with a REPL, but for code I'll have to reread in the future, the pipes save me much head scratching. Death to the pyramid of doom.
  - tincholio 9y ago
    That's indeed a nicer style
  - minimaxir 9y ago
    For emphasis, bind_cols is a dplyr function (meaning it gets the speed boost from using Rcpp, albeit not much difference when adding a few columns), while cbind is a base function which does not. dplyr nowadays obsoletes most of the basic building blocks of data frame construction, although they are hidden in the docs.
- minimaxir 9y ago
  See previous discussion on the perks of dplyr/margittr piping: https://news.ycombinator.com/item?id=15119957 https://news.ycombinator.com/item?id=15119957
fractal618 9y ago
thank you for sharing this! i'd like to try and recreate this in R.
- tincholio 9y ago
  Hi, author here. You can use the code in there as a starting point, it's rather simple!
rcthompson 9y ago
For the observation toward the end that compressed files seem highly uniform, this makes sense intuitively. If there were any visible patterns, that would imply that the data is further compressible, which would mean the compressor didn't do it's job very well.

63 points by tincholio 9y ago

> as.tibble(cbind(x,y,z)) %>% na.omit() Why on earth would anyone write that instead of na.omit(as.tibble(cbind(x,y,z)))? Baffles me.

stared 9y ago
...reading from left to right for starter? It's why I adore %>% as code gets reads as a sequence of step-by-step instructions, of one forcing a reader to constantly keep switching between left-to-right and right-to-left.
- lottin 9y ago
  f(x,y) reads "f of x and y". How do you say x %>% f(y) in words?
  - jamessb 9y ago
    Since "%>%" is the pipe operator I would guess "x pipe f of y", or "x pipe to f of y"
    - Tarq0n 9y ago
      Hadley Wickham suggests using "then" for pipes. SO we take object x, then perform f(y) with it.
tincholio 9y ago
Hi, author here. I just find it much more intuitive, like unix pipes, or Clojure's threading macros. Nested calls are more confusing to me. Though it should have probably been better as cbind(...) %>% as.tibble() %>% na.omit...
- tincholio 9y ago
  And it's fixed now
confounded 9y ago
Alternatively, x %>% bind_cols(y, z) %>% as.tibble() %>% na.omit() Makes the order of execution a little clearer (and uses more dplyr conventions). I still nest functions when working interactively with a REPL, but for code I'll have to reread in the future, the pipes save me much head scratching. Death to the pyramid of doom.
- tincholio 9y ago
  That's indeed a nicer style
- minimaxir 9y ago
  For emphasis, bind_cols is a dplyr function (meaning it gets the speed boost from using Rcpp, albeit not much difference when adding a few columns), while cbind is a base function which does not. dplyr nowadays obsoletes most of the basic building blocks of data frame construction, although they are hidden in the docs.
minimaxir 9y ago
See previous discussion on the perks of dplyr/margittr piping: https://news.ycombinator.com/item?id=15119957 https://news.ycombinator.com/item?id=15119957

fractal618 9y ago

thank you for sharing this! i'd like to try and recreate this in R.

tincholio 9y ago
Hi, author here. You can use the code in there as a starting point, it's rather simple!

rcthompson 9y ago

For the observation toward the end that compressed files seem highly uniform, this makes sense intuitively. If there were any visible patterns, that would imply that the data is further compressible, which would mean the compressor didn't do it's job very well.