Eine Wohlfühlpraxis sucht ZA/ZÄ in TZ

we're not called to like everybody but we are called to love everybody i i have a a practice um and it involves taking the image of two people one whom i love deeply and who i like deeply and i take my son for instance that i love him that i feel one with him goes without saying but i also like him very much i then take a second image of someone who i dislike intensely vladimir putin

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what I'm asking people to do is to start considering what that means to your life what I'm asking 00:38:53 governments to do by if like I'm screaming is don't wait until the first patient you know start doing something about we're about to see Mass job losses we're about to see you know Replacements 00:39:07 of of categories of jobs at large

如何理解和熟练运用 JS 中的 call 及 apply？

The console needs to be readable in development and to provide the best DX I have to design my libraries in ways that prevent these warnings. This results in design decisions that are detrimental to functionality and/or code readability/simplicity.

I see a 'virtual attribute' as something we're forced to implement when using frameworks, ORMs and the like. Something that lets us inject our code into the path of whatever metaprogramming has been put in place for us. In a simple PORO like this, I don't see how it has meaning; it's just a method. :)

certainly I wouldn't want it to start telling me that I'm not catching these!

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R Grouping functions: sapply vs. lapply vs. apply. vs. tapply vs. by vs. aggregate var ados = ados || {}; ados.run = ados.run || []; ados.run.push(function () { ados_add_placement(22,8277,"adzerk794974851",4).setZone(43); }); up vote 463 down vote favorite 606 Whenever I want to do something "map"py in R, I usually try to use a function in the apply family. (Side question: I still haven't learned plyr or reshape -- would plyr or reshape replace all of these entirely?) However, I've never quite understood the differences between them [how {sapply, lapply, etc.} apply the function to the input/grouped input, what the output will look like, or even what the input can be], so I often just go through them all until I get what I want. Can someone explain how to use which one when? [My current (probably incorrect/incomplete) understanding is... sapply(vec, f): input is a vector. output is a vector/matrix, where element i is f(vec[i]) [giving you a matrix if f has a multi-element output] lapply(vec, f): same as sapply, but output is a list? apply(matrix, 1/2, f): input is a matrix. output is a vector, where element i is f(row/col i of the matrix) tapply(vector, grouping, f): output is a matrix/array, where an element in the matrix/array is the value of f at a grouping g of the vector, and g gets pushed to the row/col names by(dataframe, grouping, f): let g be a grouping. apply f to each column of the group/dataframe. pretty print the grouping and the value of f at each column. aggregate(matrix, grouping, f): similar to by, but instead of pretty printing the output, aggregate sticks everything into a dataframe.] r sapply tapply r-faq