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Sep 26, 2014 Blogo 2.0: WordPress editor returns from the dead. By Kirk McElhearn. Contributor Macworld Sep 25, 2014 9:30 pm PDT. Blogo is a version 2 app that feels like a 1.0.
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Game 2.0
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Photo by Anastasia Zhenina
 2021/07/20 Sep 24, 2018 Azure Functions 2.0 is production ready and capable of handling your most demanding workloads, backed by our 99.95 percent SLA. We strongly encourage to start any new development on 2.0 and recommend that customers upgrade their existing 1.0 apps to the 2.0 version, to experience all the benefits of this new release.
Feb 23, 2018 Version: 2.0 Wow, thanks for bringing Blogo back to life - my prefered blogging tool on the mac. I also own MarsEdit but I like to concentrate on writing only and don't want to get confused by too many options.
 Jim Hester 
We're thrilled to announce the release of readr 2.0.0!
The readr package makes it easy to get rectangular data out of comma separated (csv), tab separated (tsv) or fixed width files (fwf) and into R. It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.
The easiest way to install the latest version from CRAN is to install the whole tidyverse.

Alternatively, install just readr from CRAN:
This blog post will show off the most important changes to the package. Wifi explorer 2 1 1 download free. These include built-in support for reading multiple files at once, lazy reading and automatic guessing of delimiters among other changes.
You can see a full list of changes in the readr release notes and vroom release notes.
readr 2nd edition readr 2.0.0 is a major release of readr and introduces a new 2nd edition parsing and writing engine implemented via the vroom package. This engine takes advantage of lazy reading, multi-threading and performance characteristics of modern SSD drives to significantly improve the performance of reading and writing compared to the 1st edition engine.
We have done our best to ensure that the two editions parse csv files as similarly as possible, but in case there are differences that affect your code, you can use the with_edition() or local_edition() functions to temporarily change the edition of readr for a section of code:
with_edition(1, read_csv('my_file.csv')) will read my_file.csv with the 1st edition of readr.
readr::local_edition(1) placed at the top of your function or script will use the 1st edition for the rest of the function or script.
We will continue to support the 1st edition for a number of releases, but our goal is to ensure that the 2nd edition is uniformly better than the 1st edition so we plan to eventually deprecate and then remove the 1st edition code.
Reading multiple files at once The 2nd edition has built-in support for reading sets of files with the same columns into one output table in a single command. Just pass the filenames to be read in the same vector to the reading function.
First we generate some files to read by splitting the nycflights dataset by airline.
Then we can efficiently read them into one tibble by passing the filenames directly to readr.
If the filenames contain data, such as the date when the sample was collected, use id argument to include the paths as a column in the data. You will likely have to post-process the paths to keep only the relevant portion for your use case.
Lazy reading Like vroom, the 2nd edition uses lazy reading by default. This means when you first call a read_*() function the delimiters and newlines throughout the entire file are found, but the data is not actually read until it is used in your program. This can provide substantial speed improvements for reading character data. It is particularly useful during interactive exploration of only a subset of a full dataset.
However this also means that problematic values are not necessarily seen immediately, only when they are actually read. Because of this a warning will be issued the first time a problem is encountered, which may happen after initial reading.
Run problems() on your dataset to read the entire dataset and return all of the problems found. Run problems(lazy = TRUE) if you only want to retrieve the problems found so far.
Deleting files after reading is also impacted by laziness. On Windows open files cannot be deleted as long as a process has the file open. Because readr keeps a file open when reading lazily this means you cannot read, then immediately delete the file. readr will in most cases close the file once it has been completely read. However, if you know you want to be able to delete the file after reading it is best to pass lazy = FALSE when reading the file.
Delimiter guessing The 2nd edition supports automatic guessing of delimiters. This feature is inspired by the automatic guessing in data.table::fread(), though the precise method used to perform the guessing differs. Because of this you can now use read_delim() without specifying a delim argument in many cases.
New column specification output On February 11, 2021 we conducted a survey on twitter asking for the community's opinion on the column specification output in readr. We received over 750 😲 responses to the survey and it revealed a lot of useful information
3/4 of respondents found printing the column specifications helpful. 👍
2/3 of respondents preferred the 2nd edition output vs 1st edition output. 💅
Only 1/5 of respondents correctly knew how to suppress printing of the column specifications. 🤯
Based on these results we have added two new ways to more easily suppress the column specification printing.
Use read_csv(show_col_types = FALSE) to disable printing for a single function call.
Use options(readr.show_types = FALSE) to disable printing for the entire session.
We will also continue to print the column specifications and use the new style output.
Note you can still obtain the old output style by printing the column specification object directly.
Or show the new style by calling summary() on the specification object.
Column selection The 2nd edition introduces a new argument, col_select, which makes selecting columns to keep (or omit) more straightforward than before. col_select uses the same interface as dplyr::select(), so you can perform very flexible selection operations.
Select with the column names directly.
Or by numeric column.
Drop columns by name by prefixing them with -.
Use the selection helpers such as ends_with().
Or even rename columns by using a named list.
Name repair Often the names of columns in the original dataset are not ideal to work with. The 2nd edition uses the same name_repair argument as in the tibble package, so you can use one of the default name repair strategies or provide a custom function. One useful approach is to use the janitor::make_clean_names() function.
UTF-16 and UTF-32 support The 2nd edition now has much better support for UTF-16 and UTF-32 multi-byte unicode encodings. When files with these encodings are read they are automatically converted to UTF-8 internally in an efficient streaming fashion.

Alternatively, install just readr from CRAN:
This blog post will show off the most important changes to the package. Wifi explorer 2 1 1 download free. These include built-in support for reading multiple files at once, lazy reading and automatic guessing of delimiters among other changes.
You can see a full list of changes in the readr release notes and vroom release notes.
readr 2nd edition readr 2.0.0 is a major release of readr and introduces a new 2nd edition parsing and writing engine implemented via the vroom package. This engine takes advantage of lazy reading, multi-threading and performance characteristics of modern SSD drives to significantly improve the performance of reading and writing compared to the 1st edition engine.
We have done our best to ensure that the two editions parse csv files as similarly as possible, but in case there are differences that affect your code, you can use the with_edition() or local_edition() functions to temporarily change the edition of readr for a section of code:
with_edition(1, read_csv('my_file.csv')) will read my_file.csv with the 1st edition of readr.
readr::local_edition(1) placed at the top of your function or script will use the 1st edition for the rest of the function or script.
We will continue to support the 1st edition for a number of releases, but our goal is to ensure that the 2nd edition is uniformly better than the 1st edition so we plan to eventually deprecate and then remove the 1st edition code.
Reading multiple files at once The 2nd edition has built-in support for reading sets of files with the same columns into one output table in a single command. Just pass the filenames to be read in the same vector to the reading function.
First we generate some files to read by splitting the nycflights dataset by airline.
Then we can efficiently read them into one tibble by passing the filenames directly to readr.
If the filenames contain data, such as the date when the sample was collected, use id argument to include the paths as a column in the data. You will likely have to post-process the paths to keep only the relevant portion for your use case.
Lazy reading Like vroom, the 2nd edition uses lazy reading by default. This means when you first call a read_*() function the delimiters and newlines throughout the entire file are found, but the data is not actually read until it is used in your program. This can provide substantial speed improvements for reading character data. It is particularly useful during interactive exploration of only a subset of a full dataset.
However this also means that problematic values are not necessarily seen immediately, only when they are actually read. Because of this a warning will be issued the first time a problem is encountered, which may happen after initial reading.
Run problems() on your dataset to read the entire dataset and return all of the problems found. Run problems(lazy = TRUE) if you only want to retrieve the problems found so far.
Deleting files after reading is also impacted by laziness. On Windows open files cannot be deleted as long as a process has the file open. Because readr keeps a file open when reading lazily this means you cannot read, then immediately delete the file. readr will in most cases close the file once it has been completely read. However, if you know you want to be able to delete the file after reading it is best to pass lazy = FALSE when reading the file.
Delimiter guessing The 2nd edition supports automatic guessing of delimiters. This feature is inspired by the automatic guessing in data.table::fread(), though the precise method used to perform the guessing differs. Because of this you can now use read_delim() without specifying a delim argument in many cases.
New column specification output On February 11, 2021 we conducted a survey on twitter asking for the community's opinion on the column specification output in readr. We received over 750 😲 responses to the survey and it revealed a lot of useful information
3/4 of respondents found printing the column specifications helpful. 👍
2/3 of respondents preferred the 2nd edition output vs 1st edition output. 💅
Only 1/5 of respondents correctly knew how to suppress printing of the column specifications. 🤯
Based on these results we have added two new ways to more easily suppress the column specification printing.
Use read_csv(show_col_types = FALSE) to disable printing for a single function call.
Use options(readr.show_types = FALSE) to disable printing for the entire session.
We will also continue to print the column specifications and use the new style output.
Note you can still obtain the old output style by printing the column specification object directly.
Or show the new style by calling summary() on the specification object.
Column selection The 2nd edition introduces a new argument, col_select, which makes selecting columns to keep (or omit) more straightforward than before. col_select uses the same interface as dplyr::select(), so you can perform very flexible selection operations.
Select with the column names directly.
Or by numeric column.
Drop columns by name by prefixing them with -.
Use the selection helpers such as ends_with().
Or even rename columns by using a named list.
Name repair Often the names of columns in the original dataset are not ideal to work with. The 2nd edition uses the same name_repair argument as in the tibble package, so you can use one of the default name repair strategies or provide a custom function. One useful approach is to use the janitor::make_clean_names() function.
UTF-16 and UTF-32 support The 2nd edition now has much better support for UTF-16 and UTF-32 multi-byte unicode encodings. When files with these encodings are read they are automatically converted to UTF-8 internally in an efficient streaming fashion.
Control over quoting and escaping when writing You can now explicitly control how fields are quoted and escaped when writing with the quote and escape arguments to write_*() functions.
quote has three options.
‘needed' - which will quote fields only when needed.
‘all' - which will always quote all fields.
‘none' - which will never quote any fields.
escape also has three options, to control how quote characters are escaped.
‘double' - which will use double quotes to escape quotes.
‘backslash' - which will use a backslash to escape quotes.
‘none' - which will not do anything to escape quotes.
2 Divided By 0We hope these options will give people the flexibility they need when writing files using readr.
Literal data In the 1st edition the reading functions treated any input with a newline in it or vectors of length > 1 as literal data. In the 2nd edition two vectors of length > 1 are now assumed to correspond to multiple files. Because of this we now have a more explicit way to represent literal data, by putting I() around the input.
Lighter installation requirements readr now should be much easier to install. Previous versions of readr used the Boost C++ library to do some of the numeric parsing. While these are well written, robust libraries, the BH package which contains them has a large number of files (1500+) which can take a long time to install. In addition the code within these headers is complicated and can take a large amount of memory (2+ Gb) to compile, which made it challenging to compile readr from source in some cases.
readr no longer depends on Boost or the BH package, so should compile more quickly in most cases.
Blogo 2 0 20Deprecated and superseded functions and features melt_csv(), melt_delim(), melt_tsv() and melt_fwf() have been superseded by functions in the same name in the meltr package. The versions in readr have been deprecated. These functions rely on the 1st edition parsing code and would be challenging to update to the new parser. When the 1st edition parsing code is eventually removed from readr they will be removed.
read_table2() has been renamed to read_table() and read_table2() has been deprecated. Most users seem to expect read_table() to work like utils::read.table(), so the different names caused confusion. If you want the previous strict behavior of read_table() you can use read_fwf() with fwf_empty() directly (#717).
Normalizing newlines in files with just carriage returns r is no longer supported. The last major OS to use only CR as the newline was ‘classic' Mac OS, which had its final release in 2001.
License changes We are systematically re-licensing tidyverse and r-lib packages to use the MIT license, to make our package licenses as clear and permissive as possible.
Game 2.0To this end the readr and vroom packages are now released under the MIT license.
Blogo 2 0 2 Bike Hitch RackAcknowledgements A big thanks to everyone who helped make this release possible by testing the development versions, asking questions, providing reprexes, writing code and more! @Aariq, @adamroyjones, @antoine-sachet, @basille, @batpigandme, @benjaminhlina, @bigey, @billdenney, @binkleym, @BrianOB, @cboettig, @CTMCBP, @Dana996, @DarwinAwardWinner, @deeenes, @dernst, @dicorynia, @estroger34, @FixTestRepeat, @GegznaV, @giocomai, @GiuliaPais, @hadley, @HedvigS, @HenrikBengtsson, @hidekoji, @hongooi73, @hsbadr, @idshklein, @jasyael, @JeremyPasco, @jimhester, @jonasfoe, @jzadra, @KasperThystrup, @keesdeschepper, @kingcrimsontianyu, @KnutEBakke, @krlmlr, @larnsce, @ldecicco-USGS, @M3IT, @maelle, @martinmodrak, @meowcat, @messersc, @mewu3, @mgperry, @michaelquinn32, @MikeJohnPage, @mine-cetinkaya-rundel, @msberends, @nbenn, @niheaven, @peranti, @petrbouchal, @pfh, @pgramme, @Raesu, @rmcd1024, @rmvpaeme, @sebneus, @seth127, @Shians, @sonicdoe, @svraka, @timothy-barry, @tmalsburg, @vankesteren, @xuqingyu, and @yutannihilation.