Gtools Macro Download

A macro is similar to a function definition except for handling of formalarguments. In a function, formal arguments are simply variables thatcontains the result of evaluating the expressions provided to the functioncall. In contrast, macros actually modify the macro body byreplacing each formal argument by the expression (defmacro) orstring (strmacro) provided to the macro call.

For defmacro, the special argument name DOTS will be replacedby ... in the formal argument list of the macro so that ... inthe body of the expression can be used to obtain any additional argumentspassed to the macro. For strmacro you can mimic this behaviorproviding a DOTS="" argument. This is illustrated by the lastexample below.

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Note that because [the defmacro code] works on the parsed expression,not on a text string, defmacro avoids some of the problems of traditionalstring substitution macros such as strmacro and the C preprocessormacros. For example, in

Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords').

The commands here are also faster than the commands provided byftools; further, gtools commands take a mix of string and numericvariables, which is a limitation of ftools. (Note I could not getseveral parts of ftools working on the Linux server where I haveaccess to Stata/MP; hence the IC benchmarks.)

Due to limitations in the Stata Plugin Interface, gtools can only handle as many variables as the largest matsize in the user's Stata version. For MP this is more than 10,000 variables but in IC this is only 800. See this issue.

With the exception of greshape, every gtools command has been written almost entirely from scratch (and even greshape is mostly new code). However, gtools commands typically mimic the functionality of existing Stata commands, including community-contributed programs, meaning many of the ideas and options are based on them (see the respective help files for details). gtools commands based on community-contributed programs include:

Last, when gegen calls a function that is not implemented internallyby gtools, it will hash the by variables and call egen with byset to an id based on the hash. That is, if fcn is not one of thefunctions above,

The reason that the other functions are faster is because they don't deal withall that overhead. By contrast, Stata's gsort is not efficient. To sortdata, you need to make pair-wise comparisons. For real numbers, this is justa > b. However, a generic comparison function can be written as compare(a, b) > 0.This is true if a is greater than b and false otherwise. To invertthe sort order, one need only use compare(b, a) > 0, which is what gtoolsdoes internally.

Hi! I'm Mauricio Caceres; I made gtoolsafter some of my Stata jobs were taking literally days to run because of repeatcalls to egen, collapse, and similar on data with over 100M rows. Feedbackand comments are welcome! I hope you find this package as useful as I do.

G-Tools includes a range of different options to then choose from, for example, to manage media players or shortcuts for programs used for working. The most beneficial alteration for gamers, however, is the Macro Manager. A macro is a pre-recorded sequence of keystrokes and button presses. Imagine you are playing an action adventure and your character can pull out some extremely long attack combinations. By recording the whole command once, you simply press one button to complete the entire combo in the future.

Macro Manager: A macro is a pre-recorded sequence of keystrokes and button presses. You can simply create and save several chains of commands (up to 64 actions per macro) and set the time delay between each input. This lets you execute complex combinations in-game or save time at work.

I am pretty familiar with SAS. I am a beginner in R and am trying to figure out what the R equivalent of macro variables is. Specifically I have 6 datasets with a common variable name, Price. I want to create a loop that changes Price in each data set to DatasetNamePrice. This would be simple in SAS using macro variables for text replacement. So far I have created vector with the names of each data set.

4) gtools::defmacro It would also be possible to use defmacro in gtools to create a macro which alters the names in place. Although not typical of processing in R this does allow one to pass the data frame itself rather than separate name and environment as in (1a).

Take for instance (a slightly modified version of) the first example in the online help for defmacro() in gtools, in which to goal is to write a function to recode as NAs all entries in a data frame column with a certain value. The following will NOT work:

The problem is that within setNA(), df will be a data frame that is only a copy of the one in your call. The recoding to NAs will be made only to the copy. The defmacro() function in gtools would do what you really want:

The code I wrote for my command-history utilities is rather short, but too long to place in a blog post; instead, you can access it here. There is a short sample session at the end of the file, which again I urge you to execute by hand to fully understand it. Take note of the tasks I coded as macros instead of functions, and think about why I needed to do so.

I have used a few languages in the past which had an option called "macro". Say you have 2 non-arrayed variables, var_hello and var_goodbye, upon which I want to run the same code. For one reason or another, they cannot be included in one arrayed variable.

Assigning keyboard shortcuts to simple or complex macros can help you work faster in Excel. This is especially true if you have to perform the same actions repeatedly. In this post we look at two popular ways to create the shortcut keys. These include the Macro Options window and VBA code for the Application.OnKey method. I also explain the pros & cons of each method.

We can also use VBA code to create shortcut keys for macros. The Application.OnKey method allows us to create and delete the shortcuts. It also gives us more options and flexibility with our keyboard shortcuts.

If your macros are stored in your Personal Macro Workbook, you can follow the same procedure as above. Checkout this article on how to create your own Personal Macro Workbook if you don't have yours setup yet.

For both methods, the keyboard shortcut can be used on any file we have open in Excel, as long as the file that contains the macros remains open. Let's look at some of the advantages and disadvantages for each method.

Instead of using one shortcut key per macro (e.g. Ctrl+Shift+1, Ctrl+Shift+2), you can use one shortcut key that displays a menu of macros, selectable with a simple (or few) character(s) (e.g. F12 then 1, F12 then 2).

The Yes button on the message box has focus set to it by default. So all you have to do is press Enter when it appears. This is one way to prevent accidental keyboard shortcut presses that are assigned to macros.

These findings suggest that standardized population and genetic variation analyses will be invaluable for maximizing biological inference, and MetaPop provides a convenient tool package to explore the dual impact of macro- and microdiversity across microbial communities.

MetaPop pipeline overview. MetaPop requires three primary inputs (a genome fasta file, file with the number of reads or bps per library, and unsorted BAM files). The BAM files are sorted and indexed and preprocessed (here showing the default setting for dsDNA viruses). The output of preprocessing goes through the macrodiversity or microdiversity arms of the pipelines. Codon usage bias is calculated as well and can be calculated independent of the whole MetaPop pipeline

Instead, the usual approach is to define a macro that expands to _ _declspec(dllexport) when building your DLL and to _ _declspec(dllimport) otherwise. In Example 1-2, I used the macro GEORGERINGO_DECL for this purpose. On Windows, GEORGERINGO_DECL expands to _ _declspec(dllexport) if the macro GEORGERING_SOURCE is defined and to _ _declspec(dllimport) otherwise. By defining GEORGERING_SOURCE when building the DLL libgeorgeringo.dll but not when compiling code that uses libgeorgeringo.dll, you obtain the desired result. 006ab0faaa