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  1. Having gotten my head fully around R pipes, I feel I need to write an article about it before I forget the fiddly details:

    |>, %>%, with(), lambdas, %$%, ...

    there are a lot of clever tricks for edge cases! (The with() hack I should have figured out for myself though!)

    Also, %$% -- the exposition pipe -- why didn't any of you tell me about this one!?!? You're slacking!

    #rlang #datascience #stats #statistics #rprogramming #tidyverse #tidydata #Rpipes #pipes!

  2. Having gotten my head fully around R pipes, I feel I need to write an article about it before I forget the fiddly details:

    |>, %>%, with(), lambdas, %$%, ...

    there are a lot of clever tricks for edge cases! (The with() hack I should have figured out for myself though!)

    Also, %$% -- the exposition pipe -- why didn't any of you tell me about this one!?!? You're slacking!

    #rlang #datascience #stats #statistics #rprogramming #tidyverse #tidydata #Rpipes #pipes!

  3. Having gotten my head fully around R pipes, I feel I need to write an article about it before I forget the fiddly details:

    |>, %>%, with(), lambdas, %$%, ...

    there are a lot of clever tricks for edge cases! (The with() hack I should have figured out for myself though!)

    Also, %$% -- the exposition pipe -- why didn't any of you tell me about this one!?!? You're slacking!

    #rlang #datascience #stats #statistics #rprogramming #tidyverse #tidydata #Rpipes #pipes!

  4. Having gotten my head fully around R pipes, I feel I need to write an article about it before I forget the fiddly details:

    |>, %>%, with(), lambdas, %$%, ...

    there are a lot of clever tricks for edge cases! (The with() hack I should have figured out for myself though!)

    Also, %$% -- the exposition pipe -- why didn't any of you tell me about this one!?!? You're slacking!

    #rlang #datascience #stats #statistics #rprogramming #tidyverse #tidydata #Rpipes #pipes!

  5. Having gotten my head fully around R pipes, I feel I need to write an article about it before I forget the fiddly details:

    |>, %>%, with(), lambdas, %$%, ...

    there are a lot of clever tricks for edge cases! (The with() hack I should have figured out for myself though!)

    Also, %$% -- the exposition pipe -- why didn't any of you tell me about this one!?!? You're slacking!

    #rlang #datascience #stats #statistics #rprogramming #tidyverse #tidydata #Rpipes #pipes!

  6. Want to visualize a 2D Random Walk of the Wilcox distribution in #R then the RandomWalker package developed by myself and Antti Rask can do that. #R #RStats #RandomWalker #RandomWalk #Visualization #ggplot2 #TidyData

  7. Want to visualize a 2D Random Walk of the Wilcox distribution in #R then the RandomWalker package developed by myself and Antti Rask can do that. #R #RStats #RandomWalker #RandomWalk #Visualization #ggplot2 #TidyData

  8. Want to visualize a 2D Random Walk of the Wilcox distribution in #R then the RandomWalker package developed by myself and Antti Rask can do that. #R #RStats #RandomWalker #RandomWalk #Visualization #ggplot2 #TidyData

  9. @lwpembleton I actually feel the exact opposite! Merged excel cells are my bane and the opposite of #tidydata

    Saying this as a 11 year OneNote ELN user

    Markdown and code syntax are fair :)

  10. @lwpembleton I actually feel the exact opposite! Merged excel cells are my bane and the opposite of #tidydata

    Saying this as a 11 year OneNote ELN user

    Markdown and code syntax are fair :)

  11. @lwpembleton I actually feel the exact opposite! Merged excel cells are my bane and the opposite of #tidydata

    Saying this as a 11 year OneNote ELN user

    Markdown and code syntax are fair :)

  12. @lwpembleton I actually feel the exact opposite! Merged excel cells are my bane and the opposite of #tidydata

    Saying this as a 11 year OneNote ELN user

    Markdown and code syntax are fair :)

  13. @lwpembleton I actually feel the exact opposite! Merged excel cells are my bane and the opposite of #tidydata

    Saying this as a 11 year OneNote ELN user

    Markdown and code syntax are fair :)

  14. 📋🧹 Ordnung ist das A und O in der Datenanalyse! Erfahre hier, wie du deine Daten in einem "tidy" Datensatz organisierst. 💡💻

    #data4goodmonday #data4good #dataforgood #correlaid #data #datascience #statistics #ehrenamt #volunteering #tidydata #tidy

  15. 📋🧹 Ordnung ist das A und O in der Datenanalyse! Erfahre hier, wie du deine Daten in einem "tidy" Datensatz organisierst. 💡💻

    #data4goodmonday #data4good #dataforgood #correlaid #data #datascience #statistics #ehrenamt #volunteering #tidydata #tidy

  16. 📋🧹 Ordnung ist das A und O in der Datenanalyse! Erfahre hier, wie du deine Daten in einem "tidy" Datensatz organisierst. 💡💻

    #data4goodmonday #data4good #dataforgood #correlaid #data #datascience #statistics #ehrenamt #volunteering #tidydata #tidy

  17. 📋🧹 Ordnung ist das A und O in der Datenanalyse! Erfahre hier, wie du deine Daten in einem "tidy" Datensatz organisierst. 💡💻

    #data4goodmonday #data4good #dataforgood #correlaid #data #datascience #statistics #ehrenamt #volunteering #tidydata #tidy

  18. 📋🧹 Ordnung ist das A und O in der Datenanalyse! Erfahre hier, wie du deine Daten in einem "tidy" Datensatz organisierst. 💡💻

    #data4goodmonday #data4good #dataforgood #correlaid #data #datascience #statistics #ehrenamt #volunteering #tidydata #tidy

  19. With R's Infer library, one can test point hypotheses, such as "the work week has 40 hours".

    I think this is a great improvement on testing point null hypotheses of no difference, which we know a priori to be false.

    infer.netlify.app/

    #rstats #statistics part of #tidymodels and #tidydata

  20. With R's Infer library, one can test point hypotheses, such as "the work week has 40 hours".

    I think this is a great improvement on testing point null hypotheses of no difference, which we know a priori to be false.

    infer.netlify.app/

    #rstats #statistics part of #tidymodels and #tidydata

  21. With R's Infer library, one can test point hypotheses, such as "the work week has 40 hours".

    I think this is a great improvement on testing point null hypotheses of no difference, which we know a priori to be false.

    infer.netlify.app/

    #rstats #statistics part of #tidymodels and #tidydata

  22. With R's Infer library, one can test point hypotheses, such as "the work week has 40 hours".

    I think this is a great improvement on testing point null hypotheses of no difference, which we know a priori to be false.

    infer.netlify.app/

    #rstats #statistics part of #tidymodels and #tidydata

  23. gcplyr emerged out of my own meh experiences w/ growth curves in #RStats, where I wrote many lines of custom code every time to import data and metadata, combine it with notes of what was in ea well, and reshape it to be #tidydata for #ggplot visualization and #tidyverse analyses
    2/12

  24. gcplyr emerged out of my own meh experiences w/ growth curves in #RStats, where I wrote many lines of custom code every time to import data and metadata, combine it with notes of what was in ea well, and reshape it to be #tidydata for #ggplot visualization and #tidyverse analyses
    2/12

  25. gcplyr emerged out of my own meh experiences w/ growth curves in #RStats, where I wrote many lines of custom code every time to import data and metadata, combine it with notes of what was in ea well, and reshape it to be #tidydata for #ggplot visualization and #tidyverse analyses
    2/12

  26. gcplyr emerged out of my own meh experiences w/ growth curves in #RStats, where I wrote many lines of custom code every time to import data and metadata, combine it with notes of what was in ea well, and reshape it to be #tidydata for #ggplot visualization and #tidyverse analyses
    2/12

  27. gcplyr emerged out of my own meh experiences w/ growth curves in #RStats, where I wrote many lines of custom code every time to import data and metadata, combine it with notes of what was in ea well, and reshape it to be #tidydata for #ggplot visualization and #tidyverse analyses
    2/12

  28. The last hard thing is trying to generalize the way we reshape the FERC data, which typically comes in a wide format (like... 500 columns sometimes) into #TidyData that's more relational.

    We do have a nice way to concatenate the old DBF and new XBRL data, which also aligns all of the old data, whose row numbers changed meaning from year to year as new fields were added, split, or removed.

  29. The last hard thing is trying to generalize the way we reshape the FERC data, which typically comes in a wide format (like... 500 columns sometimes) into #TidyData that's more relational.

    We do have a nice way to concatenate the old DBF and new XBRL data, which also aligns all of the old data, whose row numbers changed meaning from year to year as new fields were added, split, or removed.

  30. The last hard thing is trying to generalize the way we reshape the FERC data, which typically comes in a wide format (like... 500 columns sometimes) into #TidyData that's more relational.

    We do have a nice way to concatenate the old DBF and new XBRL data, which also aligns all of the old data, whose row numbers changed meaning from year to year as new fields were added, split, or removed.

  31. The last hard thing is trying to generalize the way we reshape the FERC data, which typically comes in a wide format (like... 500 columns sometimes) into #TidyData that's more relational.

    We do have a nice way to concatenate the old DBF and new XBRL data, which also aligns all of the old data, whose row numbers changed meaning from year to year as new fields were added, split, or removed.