Does anyone have an actual approach on how to create UML diagrams directly from R code?
This is pretty much the only resource that I found in that regard. Works, but not really "integrated" in the sense that the required info for the diagrams are automatically detected in the actual code in any sort.
Related to this question.
What do you mean by "required info for the diagrams are automatically detected in the actual code"? PlantUML in its current state (Link) is actually quite nice to easily create simple UML charts in R logic.
Take this example:
library(plantuml)
x <- '
(*) --> "Initialization"
if "Some Test" then
-->[true] "Some Activity"
--> "Another activity"
-right-> (*)
else
->[false] "Something else"
-->[Ending process] (*)
endif
'
x <- plantuml(
x
)
plot(
x = x
# vector = TRUE
)
will plot as
Maybe check it out?
Related
I have been learning Tensorflow and understanding feed_dict has been a challenge. Take for example the following piece of code i am working on
p=0
self.sequence_length=25
with tf.Session() as sess:
init.run()
char_to_ix={ch:ix for ix,ch in enumerate(self.words)}
ix_to_char={ix:ch for ix,ch in enumerate(self.words)}
words_in_input=self.data[p:p+self.sequence_length]
inputs=[char_to_ix[ix] for ix in words_in_input]
words_in_target=self.data[p+1:p+self.sequence_length+1]
targets=[char_to_ix[ix] for ix in words_in_target]
onex=sess.run([selected_next_letter],feed_dict={self.X:inputs,self.y:targets})
p=p+1
This gives the error: Shapes of all inputs must match: values[0].shape = [25] != values[1].shape = []
However, when I edit the code to
with tf.Session() as sess:
init.run()
char_to_ix={ch:ix for ix,ch in enumerate(self.words)}
ix_to_char={ix:ch for ix,ch in enumerate(self.words)}
words_in_input=self.data[p:p+self.sequence_length]
inputs=[char_to_ix[ix] for ix in words_in_input]
words_in_target=self.data[p+1:p+self.sequence_length+1]
targets=[char_to_ix[ix] for ix in words_in_target]
for x,y in zip(inputs,targets):
onex=sess.run([selected_next_letter],feed_dict={self.X:x,self.y:y})
It executes.
My questions is: Is it possible to feed the whole list such as inputs and targets in the feed_dict or must I input it through a loop one by one. I ask this because the tutorials I have been reading, I see a whole list being passed in a feed_dict such as
loss_val = sess.run([train_op, loss_mean], feed_dict={
images_batch:images_batch_val,
labels_batch:labels_batch_val
})
Usually the reason for that error is because your input array(x) isn’t the same size as your labels array(y). As the error states it looks like your labels array is empty. Before doing anything tensorflowy make sure both x and y arrays have values in them and that they are of the same size.
To answer your question, yes you can use lists when training and is the preferred way of using tensorflow.
I wonder if there is a way to display the current time in the R command line, like in MS DOS, we can use
Prompt $T $P$G
to include the time clock in every prompt line.
Something like
options(prompt=paste(format(Sys.time(), "%H:%M:%S"),"> "))
will do it, but then it is fixed at the time it was set. I'm not sure how to make it update automatically.
Chase points the right way as options("prompt"=...) can be used for this. But his solutions adds a constant time expression which is not what we want.
The documentation for the function taskCallbackManager has the rest:
R> h <- taskCallbackManager()
R> h$add(function(expr, value, ok, visible) {
+ options("prompt"=format(Sys.time(), "%H:%M:%S> "));
+ return(TRUE) },
+ name = "simpleHandler")
[1] "simpleHandler"
07:25:42> a <- 2
07:25:48>
We register a callback that gets evaluated after each command completes. That does the trick. More fancy documentation is in this document from the R developer site.
None of the other methods, which are based on callbacks, will update the prompt unless a top-level command is executed. So, pressing return in the console will not create a change. Such is the nature of R's standard callback handling.
If you install the tcltk2 package, you can set up a task scheduler that changes the option() as follows:
library(tcltk2)
tclTaskSchedule(1000, {options(prompt=paste(Sys.time(),"> "))}, id = "ticktock", redo = TRUE)
Voila, something like the MS DOS prompt.
NB: Inspiration came from this answer.
Note 1: The wait time (1000 in this case) refers to the # of milliseconds, not seconds. You might adjust it downward when sub-second resolution is somehow useful.
Here is an alternative callback solution:
updatePrompt <- function(...) {options(prompt=paste(Sys.time(),"> ")); return(TRUE)}
addTaskCallback(updatePrompt)
This works the same as Dirk's method, but the syntax is a bit simpler to me.
You can change the default character that is displayed through the options() command. You may want to try something like this:
options(prompt = paste(Sys.time(), ">"))
Check out the help page for ?options for a full list of things you can set. It is a very useful thing to know about!
Assuming this is something you want to do for every R session, consider moving that to your .Rprofile. Several other good nuggets of programming happiness can be found hither on that topic.
I don't know of a native R function for doing this, but I know R has interfaces with other languages that do have system time commands. Maybe this is an option?
Thierry mentioned system.time() and there is also proc.time() depending on what you need it for, although neither of these give you the current time.
I have a unit test for a function that adds data (untransformed) to the database. The data to insert is given to the create function.
Do I use the input data in my asserts or is it better to specify the data that I’m asserting?
For eample:
$personRequest = [
'name'=>'John',
'age'=>21,
];
$id = savePerson($personRequest);
$personFromDb = getPersonById($id);
$this->assertEquals($personRequest['name'], $personFromDb['name']);
$this->assertEquals($personRequest['age'], $personFromDb['age']);
Or
$id = savePerson([
'name'=>'John',
'age'=>21,
]);
$personFromDb = getPersonById($id);
$this->assertEquals('John', $personFromDb['name']);
$this->assertEquals(21, $personFromDb['age']);
I think 1st option is better. Your input data may change in future and if you go by 2nd option, you will have to change assertion data everytime.
2nd option is useful, when your output is going to be same irrespective of your input data.
I got an answer from Adam Wathan by e-mail. (i took his test driven laravel course and noticed he uses the 'specify' option)
I think it's just personal preference, I like to be able to visually
skim and see "ok this specific string appears here in the output and
here in the input", vs. trying to avoid duplication by storing things
in variables." Nothing wrong with either approach in my opinion!
So i can't choose a correct answer.
I frequently use user defined functions in my code.
RStudio supports the automatic completion of code using the Tab key. I find this amazing because I always can read quickly what is supposed to go in the (...) of functions/calls.
However, my user defined functions just show the parameters, no additional info and obviously, no help page.
This isn't so much pain for me but I would like to share code I think it would be useful to have some information at hand besides the #coments in every line.
Nowadays, when I share, my lines usually look like this
myfun <- function(x1,x2,x3,...){
# This is a function for this and that
# x1 is a factor, x2 is an integer ...
# This line of code is useful for transformation of x2 by x1
some code here
# Now we do this other thing
more code
# This is where the magic happens
return (magic)
}
I think this line by line comment is great but I'd like to improve it and make some things handy just like every other function.
Not really an answer, but if you are interested in exploring this further, you should start at the rcompgen-help page (although that's not a function name) and also examine the code of:
rc.settings
Also, executing this allows you to see what the .CompletionEnv has in it for currently loaded packages:
names(rc.status())
#-----
[1] "attached_packages" "comps" "linebuffer" "start"
[5] "options" "help_topics" "isFirstArg" "fileName"
[9] "end" "token" "fguess" "settings"
And if you just look at:
rc.status()$help_topics
... you see the character items that the tab-completion mechanism uses for matching. On my machine at the moment there are 8881 items in that vector.
I (sort of) already know the answer to this question. But I figured it is one that gets asked so frequently on the R Users list, that there should be one solid good answer. To the best of my knowledge there is no multiline comment functionality in R. So, does anyone have any good workarounds?
While quite a bit of work in R usually involves interactive sessions (which casts doubt on the need for multiline comments), there are times when I've had to send scripts to colleagues and classmates, much of which involves nontrivial blocks of code. And for people coming from other languages it is a fairly natural question.
In the past I've used quotes. Since strings support linebreaks, running an R script with
"
Here's my multiline comment.
"
a <- 10
rocknroll.lm <- lm(blah blah blah)
...
works fine. Does anyone have a better solution?
You can do this easily in RStudio:
select the code and click CTR+SHIFT+C
to comment/uncomment code.
This does come up on the mailing list fairly regularly, see for example this recent thread on r-help. The consensus answer usually is the one shown above: that given that the language has no direct support, you have to either
work with an editor that has region-to-comment commands, and most advanced R editors do
use the if (FALSE) constructs suggested earlier but note that it still requires complete parsing and must hence be syntactically correct
A neat trick for RStudio I've just discovered is to use #' as this creates an self-expanding comment section (when you return to new line from such a line or insert new lines into such a section it is automatically comment).
[Update] Based on comments.
# An empty function for Comments
Comment <- function(`#Comments`) {invisible()}
#### Comments ####
Comment( `
# Put anything in here except back-ticks.
api_idea <- function() {
return TRUE
}
# Just to show api_idea isn't really there...
print( api_idea )
`)
####
#### Code. ####
foo <- function() {
print( "The above did not evaluate!")
}
foo()
[Original Answer]
Here's another way... check out the pic at the bottom. Cut and paste the code block into RStudio.
Multiline comments that make using an IDE more effective are a "Good Thing", most IDEs or simple editors don't have highlighting of text within simple commented -out blocks; though some authors have taken the time to ensure parsing within here-strings. With R we don't have multi-line comments or here-strings either, but using invisible expressions in RStudio gives all that goodness.
As long as there aren't any backticks in the section desired to be used for a multiline comments, here-strings, or non-executed comment blocks then this might be something worth-while.
#### Intro Notes & Comments ####
invisible( expression( `
{ <= put the brace here to reset the auto indenting...
Base <- function()
{ <^~~~~~~~~~~~~~~~ Use the function as a header and nesting marker for the comments
that show up in the jump-menu.
--->8---
}
External <- function()
{
If we used a function similar to:
api_idea <- function() {
some_api_example <- function( nested ) {
stopifnot( some required check here )
}
print("Cut and paste this into RStudio to see the code-chunk quick-jump structure.")
return converted object
}
#### Code. ####
^~~~~~~~~~~~~~~~~~~~~~~~~~ <= Notice that this comment section isnt in the jump menu!
Putting an apostrophe in isn't causes RStudio to parse as text
and needs to be matched prior to nested structure working again.
api_idea2 <- function() {
} # That isn't in the jump-menu, but the one below is...
api_idea3 <- function() {
}
}
# Just to show api_idea isn't really there...
print( api_idea )
}`) )
####
#### Code. ####
foo <- function() {
print( "The above did not evaluate and cause an error!")
}
foo()
## [1] "The above did not evaluate and cause an error!"
And here's the pic...
I can think of two options. The first option is to use an editor that allows to block comment and uncomment (eg. Eclipse). The second option is to use an if statement. But that will only allow you to 'comment' correct R syntax. Hence a good editor is the prefered workaround.
if(FALSE){
#everything in this case is not executed
}
If find it incredible that any language would not cater for this.
This is probably the cleanest workaround:
anything="
first comment line
second comment line
"
Apart from using the overkilled way to comment multi-line codes just by installing RStudio, you can use Notepad++ as it supports the syntax highlighting of R
(Select multi-lines) -> Edit -> Comment/Uncomment -> Toggle Block Comment
Note that you need to save the code as a .R source first (highlighted in red)
I use vim to edit the R script.
Let's say the R script is test.R, containing say "Line 1", "Line 2", and "Line 3" on 3 separate lines.
I open test.R on the command line with Vim by typing "vim test.R".
Then I go to the 1st line I want to comment out, type "Control-V", down arrow to the last line I want to comment out, type a capital I i.e. "I" for insert, type "# ", and then hit the Escape key to add "# " to every line that I selected by arrowing down. Save the file in Vim and then exit Vim by typing ":wq". Changes should show up in Rstudio.
To delete the comments in Vim, start at the first line on top of the character "#" you want to delete, again do "Control-V", and arrow down to the last line you want to delete a "#" from. Then type "dd". The "#" signs should be deleted.
There's seconds-worth of lag time between when changes to test.R in Vim are reflected in Rstudio.
Now there is a workaround, by using package ARTofR or bannerCommenter
Examples here:
In RStudio an easy way to do this is to write your comment and once you have used CTRL + Shift + C to comment your line of code, then use CTRL + SHIFT + / to reflow you comment onto multiple lines for ease of reading.
In RStudio you can use a pound sign and quote like this:
#' This is a comment
Now, every time you hit return you don't need to add the #', RStudio will automatically put that in for you.
Incidentally, for adding parameters and items that are returned, for standardization if you type an # symbol inside those comment strings, RStudio will automatically show you a list of codes associated with those comment parameters:
#' #param tracker_df Dataframe of limit names and limits
#' #param invoice_data Dataframe of invoice data
#' #return return_list List of scores for each limit and rejected invoice rows