vroom | Launch vim tests - Vroom is for testing vim | Text Editor library
kandi X-RAY | vroom Summary
kandi X-RAY | vroom Summary
Vroom is for testing vim. Let's say you're a vimscript author. You want to test your new plugin. You could find a nice vimscript test suite, but that only lets you test your vimscript functions. What you really want is a way to specify vim commands — actual input keys that that the user hits — and then verify vim's output. The above vroom test opens vim, sends the keys iHello, world! and then verifies that the contents of the current buffer is Hello, world!. Things can get much more complex than this, of course. You need to be able to check the output of multiple buffers. You need to check what messages your functions echo. You need to sandbox vim, capture its system commands, and respond with dummy data. And a few shortcuts would be nice. Never fear! Vroom has it all. Check the examples for details and more documentation. examples/basics.vroom is a good place to start. Run vroom -h to learn about vroom's configuration options. Did you accidentally set off a giant vroom test that's running too fast to halt? Never fear! Pop open another terminal and vroom --murder. Make sure the --servername flag matches with the vroom you're trying to kill. You may need to run reset in the terminal with the murdered vroom. See the Tips and Tricks page page for some strategies for getting the most out of vroom.
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Top functions reviewed by kandi - BETA
- Parse vroom lines
- Find all files in a directory
- Parses a control string
- Parse an action line
- Verify new messages
- Guess new messages from old
- Raise an exception
- List of logs
- Verify the server
- Load a pickled file
- Saves data to file
- Outputs backmatter
- Return the status of the job
- Get current line number
- Log result
- Status of the failed job
- Writes lines to file
- Communicate a command
- Checks the end of the buffer
- Verify that the output matches the given buffer
- Return a list of buffer lines
- Quits the vim
- Write queue to file
- Start the process
- Create a context printer for an error context
- Print the error buffer
- Calls the evaluation
vroom Key Features
vroom Examples and Code Snippets
Community Discussions
Trending Discussions on vroom
QUESTION
I have 2 files. One is car.js
...ANSWER
Answered 2022-Mar-13 at 07:09First you need to export Car from the first file. There are multiple ways to do this, for example you could do
QUESTION
I am currently learning Solidity through a Udemy course and I am currently covering a section that explains Testing with Mocha. I am using the describe function with syntax that is covered in the example and trying to run the test using 'npm run test'.
Below is the code for Inbox.test.js
...ANSWER
Answered 2022-Feb-18 at 18:11describe
shouldn't be inside your Car class.
As it stands, nodejs would expect some class method which would be describe(text, callback)
. Instead of "text" you're supplying a string.
So this is how it should look like:
QUESTION
I have already finished with my RMarkdown and I'm trying to clean up the workspace a little bit. This isn't exactly a necessary thing but more of an organizational practice which I'm not even sure if it's a good practice, so that I can keep the data separate from some scripts and other R and git related files.
I have a bunch of .csv files for data that I used. Previously they were on (for example)
C:/Users/Documents/Project
which is what I set as my working directory. But now I want them in
C:/Users/Document/Project/Data
The problem is that this only breaks the following code because they are not in the wd.
...ANSWER
Answered 2022-Feb-18 at 15:34You can list files including those in all subdirectories (Data
in particular) using list.files(pattern = "*.csv", recursive = TRUE)
Best practices
- Have one directory of raw and only raw data (the stuff you measured)
- Have another directory of external data (e.g. reference data bases). This is something you do can remove afterwards and redownload if required.
- Have another directory for the source code
- Put only the source code directory under version control plus one other file containing check sums of the raw and external data to proof integrity
- Every other thing must be reproducible using raw data and the source code. This can be removed after the project. Maybe you want to keep small result files (e.g. tables) which take long time to reproduce.
QUESTION
I built a function that retrieves data from an Azure table via a REST API. I sourced the function, so I can reuse it in other R scripts.
The function is as below:
...ANSWER
Answered 2022-Feb-09 at 15:09You can ignore the RStudio warnings, they are based on a heuristic, and in this case it’s very imprecise and misleading.
However, there are some errors in your code.
Firstly, you’re only source
ing the code if the function was already defined. Surely you want to do it the other way round: source the code if the function does not yet exist. But furthermore that check is completely redundant: if you haven’t sourced the code which defines the function yet, the function won’t be defined. The existence check is unnecessary and misleading. Remove it:
QUESTION
I often face csv files, which were saved with a German locale and are therefore not properly comma-separated, but rather are separated with a semi-colon. This is of course easily solvable by defining the separator. But vroom
in contrast to for example fread
does not offer the possibility to also define the decimal separator.
Therefore, numerical values with a ,
as decimal separator are imported as characters or wrongly without any decimal separator and thus really large numbers.
Is there a way to directly define the decimal separator similar to the way it works in fread
?
ANSWER
Answered 2022-Feb-02 at 16:04As already mentioned in the comments, the solution is rather straight-forward and the only thing necessary is to include the locale()
option to the vroom
call.
Possible options to the locale
option can be found in its documentation.
QUESTION
I am looking to generate a mean and variance value for every row in a numeric tibble. With my existing code, what I thought to be a very dplyr appropriate solution, it takes a number of hours to complete for 50,000 rows of about 35 columns.
Is there a way to speed up this operation using only dplyr? I know apply and purrr are options, but I am mostly curious if there is something about dplyr I'm overlooking when performing a large series of calculations like this.
Reproducible example:
...ANSWER
Answered 2022-Jan-24 at 23:49It seems like the processing time for the rowwise approach explodes quadratically:
Pivoting longer makes the calculation about 300x faster. For 50k rows, the code below took 1.2 seconds, compared to 372 seconds for the rowwise
approach.
QUESTION
I am trying to install the tidyverse package and not being able to do it. 1-tried via Packages -> Install 2-tried from console-> install.packages("tidyverse")
Gives errors as below and therefore not loading the lirary
...ANSWER
Answered 2021-Sep-27 at 21:44You have a very old version of R. You should update to the current version if you want things to go smoothly.
If you can't do that, here's what you'll have to do:
- Install the version of Rtools suitable for R 3.5.x.
- Install the packages you want. Some of them won't work, because they will need later versions of R, but won't declare that. So when you find one that fails, try installing the previous version of that package. If that also fails, try an even earlier one. Etc. Your R version was current in 2018, so you may need to go that far back in time to find compatible packages.
- Once you finally have everything working, try to update your packages. Maybe some of them could be more recent versions, maybe not. Do them one at a time. Typically
tidyverse
needs about 90 of them, so this will take a while.
So I recommend that you update your R version.
QUESTION
I am making a simple R function to write files on disk consistently in the same way, but in different folders:
...ANSWER
Answered 2021-Nov-02 at 16:33This is prone to opinion and context, to be honest, but some thoughts:
Return the original data. This spirit is conveyed in most of the tidyverse verb-functions and many other packages (and some in base R). If you're using either the
%>%
or|>
pipes, doing this allows processing on the data after your function, which might be very convenient.
QUESTION
I have a csv.gz file that (from what I've been told) before compression was 70GB in size. My machine has 50GB of RAM, so anyway I will never be able to open it as a whole in R.
I can load for example the first 10m rows as follows:
...ANSWER
Answered 2021-Oct-14 at 05:41I haven't been able to figure out vroom
solution for very large more-than-RAM (gzipped) csv files. However, the following approach has worked well for me and I'd be grateful to know about approaches with better querying speed while also saving disk space.
- Use
split
sub-command inxsv
from https://github.com/BurntSushi/xsv to split the large csv file into comfortably-within-RAM chunks of say, 10^5, lines and save them in a folder. - Read all chunks using
data.table::fread
one-by-one (to avoid low-memory error) using afor
loop and save all of them into a folder as compressedparquet
files usingarrow
package which saves space and prepares the large table for fast querying. For even faster operations, it is advisable to re-save theparquet
files partitioned by the fields by which you need to frequently filter. - Now you can use
arrow::open_dataset
and query that multi-file parquet folder usingdplyr
commands. It takes minimum disk space and gives the fastest results in my experience.
I use data.table::fread
with explicit definition of column classes of each field for fastest and most reliable parsing of csv files. readr::read_csv
has also been accurate but slower. However, auto-assignment of column classes by read_csv
as well as the ways in which you can custom-define column classes by read_csv
is actually the best - so less human-time but more machine-time - which means that it may be faster overall depending on scenario. Other csv parsers have thrown errors for the kind of csv files that I work with and waste time.
You may now delete the folder containing chunked csv files to save space, unless you want to experiment loop-reading them with other csv parsers.
Other previously successfully approaches: Loop read all csv chunks as mentioned above and save them into:
- a folder using
disk.frame
package. Then that folder may be queried usingdplyr
ordata.table
commands explained in the documentation. It has facility to save in compressedfst
files which saves space, though not as much asparquet
files. - a table in
DuckDB
database which allows querying withSQL
ordplyr
commands. Using database-tables approach won't save you disk space. ButDuckDB
also allows querying partitioned/un-partitioned parquet files (which saves disk space) withSQL
commands.
EDIT: - Improved Method Below
I experimented a little and found a much better way to do the above operations. Using the code below, the large (compressed) csv file will be chunked automatically within R environment (no need to use any external tool like xsv
) and all chunks will be written in parquet
format in a folder ready for querying.
QUESTION
I have a problem when I use renderPrint()
in combination with a .csv
file uploaded by the user and read by read_csv2()
. I always get a strange kind of indexing bar before the actual output.
When I replace read_csv2()
with base R's read.csv2()
the indexing bar disappears. Therefore, my guess is that the problem is somehow related to the fact that read_csv2()
reads the .csv
file as tibble
and not as data.frame
.
Alternatively, I also tried vroom::vroom()
, but the problem with the progress bar still remains.
In my app I would like to use either read_csv2()
or vroom::vroom()
as they are both noticeably faster than read.csv2()
.
My reprex:
...ANSWER
Answered 2021-Sep-23 at 07:54The progress bar is shown by readr
package by default. You can disable them using options
.
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