You can get issues ordered by thumbs up votes on the first comment via
ghrecipes! An idea of
Kirill Müller’s
issues <- ghrecipes::get_issues_thumbs(owner = "tidyverse",
repo = "dplyr")You could View(issues) or e.g.
issues <- dplyr::mutate_if(issues, is.character, stringr::str_squish)
knitr::kable(issues)| number | created_at | title | author | thumbs_up_no | body | labels | url | milestone_no | milestone_desc | owner | repo |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2240 | 2016-11-08 08:46:46 | join_by(): Syntax for generic joins | krlmlr | 34 | hadley#557 (comment) and hadley#378 (comment) propose a syntax for generic and rolling joins: left_join( FundMonths, Returns, join_by(FundID == FundID, yearmonth > gmonth + 3, yearmonth <= gmonth + 15) ) left_join( events, days, join_by(collector_id == collector_id, event_timestamp >= la | featuregeneric | tidyverse/dplyr#2240 | NA | NA | tidyverse | dplyr |
| 341 | 2014-03-20 12:57:03 | Preserve zero-length groups | hadley | 9 | http://stackoverflow.com/questions/22523131 Not sure what the interface to this should be - probably should default to drop = FALSE. | data framefeature | tidyverse/dplyr#341 | 10 | tidyverse | dplyr | |
| 2326 | 2016-12-15 23:18:13 | Single table verbs should accept tibbles in conditions | hadley | 8 | Currently mutate() and summarise() only work with vectorised functions: functions that take a vector as input and return a vector (or “scalar”) as output. I don’t see any reason why summarise() and mutate() couldn’t also accept tibbles. The existing restrictions would continue to apply so that in s | data framefeature | tidyverse/dplyr#2326 | NA | NA | tidyverse | dplyr |
| 2047 | 2016-07-30 22:11:31 | FR: before and after arguments to mutate() |
krlmlr | 6 | These arguments would specify the position where the new columns are inserted, before or after some column given as index or name. See also tidyverse/tibble#99. | featuregeneric | tidyverse/dplyr#2047 | NA | NA | tidyverse | dplyr |
| 1185 | 2015-05-30 17:43:23 | Create a group_indices as a new variable | matthieugomez | 5 | Some packages like ggplot2 act on groups defined by one variable only (as opposed to groups defined by several variables). It would be nice to have a function, say group(), that creates a new integer variable from groups defined by multiple variables: Batting %>% mutate(group = group(teamID, yearID) | data framefeature | tidyverse/dplyr#1185 | NA | NA | tidyverse | dplyr |
| 1792 | 2016-04-28 15:09:13 | set_key | hadley | 5 | library(nycflights13) weather <- flights %>% set_key(year, month, day, hour, origin) planes <- planes %>% set_key(tailnum) airlines <- airlines %>% set_key(carrier) airports <- airports %>% set_key(faa) This would check that the combination of variables is a valid key (i.e. no duplicates and no miss | data framefeature | tidyverse/dplyr#1792 | 10 | tidyverse | dplyr | |
| 977 | 2015-02-17 19:26:36 | n_distinct way slower than length(unique) | dpeterson71 | 3 | I would like to move to more uniform implementation of dplyr memes; I really like the syntax. However, I am seeing several instances where dplyr analogues to plyr or base-R functions incur a severe performance hit on my data sets. Here is a simple example ilustrating that dplyr’s n_distinct is a fa | data framefeatureperformance | tidyverse/dplyr#977 | 10 | tidyverse | dplyr | |
| 2132 | 2016-09-21 17:42:51 | Summarising verbs with variable-length outputs | lionel- | 2 | A new dplyr family of verbs for variable-length output may be useful. Like summarise() it would discard all input columns except for the grouping variables. This allows the output to have a different number of rows than the input. Unlike summarise(), it would not require length 1 results and would | featuregeneric | tidyverse/dplyr#2132 | NA | NA | tidyverse | dplyr |
| 2432 | 2017-02-16 20:47:38 | Better support combining for non-base types | hadley | 2 | This is a meta issue related for all bind/grouped-mutate/join/filter issues related to custom S3 + S4 classes chron (#1829) lubridate (#1581, #1708) difftime (#2059) table (#2406) POSIXct (#2322) bit64 (#3210) See also r-lib/vctrs#27 | bugdata frame | tidyverse/dplyr#2432 | 13 | tidyverse | dplyr | |
| 2993 | 2017-07-23 09:06:53 | DBI sources can’t provide sampling implementation | hannesmuehleisen | 2 | When using src_sql, it is possible to attach a custom class to tbl objects. But the documentation for src_sql states that it is deprecated and that src_dbi should be used instead. When using src_dbi, no custom class can be set for tbl objects (at least not as far as I can tell). This is mostly fine, | databasefeaturegeneric | tidyverse/dplyr#2993 | NA | NA | tidyverse | dplyr |
| 2995 | 2017-07-25 01:19:26 | dplyr feature request, between() for character variables. | lhunsicker | 2 | I was referred here by the folks at RStudio. I’m not sure that I’m in the right place, as this is not a bug, but a suggestion for a new feature. If there is a more appropriate place for this, let me know. The between() function in dplyr is very nice and much appreciated. But it presently only w | data framefeature | tidyverse/dplyr#2995 | NA | NA | tidyverse | dplyr |
| 3259 | 2017-12-22 14:53:54 | bind_rows() using tibbles with attributes loses attributes | DavisVaughan | 2 | I assume this is very similar to #2457. Using bind_rows() on two tibbles where either one has extra attributes removes all extra attributes. Perhaps an approach similar to #1692 can be taken where the attributes of the first are kept? Ideally I would like bind_rows() to be generic but I’ve read all | data framefeature | tidyverse/dplyr#3259 | NA | NA | tidyverse | dplyr |
| 3314 | 2018-01-19 14:09:20 | Link to tidyselect from ?select and ?rename | krlmlr | 2 | The documentation of select() and rename() should link to the functions in tidyselect for more details and examples. | docs | tidyverse/dplyr#3314 | NA | NA | tidyverse | dplyr |
| 3357 | 2018-02-13 15:35:15 | order_by() could have an error hint when confused with arrange() | econandrew | 2 | For those of us who sometimes have SQL on the brain… > df <- df %>% order_by(value) Error: call must be a function call, not a symbol > df <- df %>% order_by(-value) Error: Can’t use matrix or array for column indexing > df <- df %>% arrange(-value) e.g. Did you mean to use arrange()? |
data framefeature | tidyverse/dplyr#3357 | NA | NA | tidyverse | dplyr |
| 1092 | 2015-04-21 18:26:54 | Support for integer64 column in data frame? | coloneltriq | 1 | group_by() doesn’t appear to support integer64 columns. I saw an issue from about a year ago that said that support for 64 bit integers wasn’t available, but might be added. Has anything changed in that regard? This is a significant issue for me, as I’m dealing with a database whose index column | data framefeature | tidyverse/dplyr#1092 | 10 | tidyverse | dplyr | |
| 2183 | 2016-10-18 14:57:49 | feature request : add merge indicator after a merge in dplyr |
randomgambit | 1 | Hello there, Congrats for the great work here! I have a suggestion to make. Is there a way to get the equivalent of a _merge indicator variable after a merge in Dplyr? Something similar to Pandas’ indicator = True option that essentially tells you how the merge went (how many matches from each datas | featuregeneric | tidyverse/dplyr#2183 | NA | NA | tidyverse | dplyr |
| 2355 | 2017-01-07 21:53:18 | Use serialization for columns of type “list” | krlmlr | 1 | in join (#2194) and distinct (#2222) operations. We should be able to serialize all elements of a list efficiently by calling R_Serialize() for each. A specialized version of JoinVisitorImpl and VectorVisitor would then operate on the serializations. Perhaps hashing the serialization will be good en | data framefeature | tidyverse/dplyr#2355 | NA | NA | tidyverse | dplyr |
| 2922 | 2017-06-27 20:20:32 | Investigate R_ObjectTables | krlmlr | 1 | instead of bindrcpp. benchmark robustness drop-in replacement? See RProtoBuf for an example implementation: https://github.com/eddelbuettel/rprotobuf/blob/20cc4ab41b36c9582adeed182f1c429e38df6be4/src/lookup.cpp#L222. Existing package: http://www.omegahat.net/RObjectTables/. | data framefeature | tidyverse/dplyr#2922 | NA | NA | tidyverse | dplyr |
| 2984 | 2017-07-19 21:59:06 | Show example of vars() with mutate_at() in select_helpers | eamoncaddigan | 1 | Tidy evaluation has hit CRAN and data analyzers everywhere are probably stumbling over it (I know I did). The deprecation warning that appeared during a call mutate_each() was helpful for me, but perusing the docs wasn’t enough to get my code to work. The documentation for select_helpers would be m | docs | tidyverse/dplyr#2984 | NA | NA | tidyverse | dplyr |
| 3059 | 2017-08-28 14:22:37 | Add warning when two different timezones are joined | danielsjf | 1 | This relates to this issue: #2643 The decision was taken to convert to UTC when two different posixct columns are joined. Indeed, the timezone is often only used for presentation, but some functions such as lubridate::year() work on the current timezone. Therefore such a decision could have an impac | data framefeature | tidyverse/dplyr#3059 | NA | NA | tidyverse | dplyr |
| 3128 | 2017-09-27 15:21:23 | More precise error messages for mutate() et al. | krlmlr | 1 | When propagating an error from the R interpreter, we should mention specifically what caused the error, because the messages may not be very helpful, as in this SO example where the cause is a mistyped column name but error is simply: Error in mutate_impl(.data, dots): cannot coerce type ‘closure’ t | data framefeature | tidyverse/dplyr#3128 | NA | NA | tidyverse | dplyr |
| 3205 | 2017-11-13 14:40:45 | translate_sql, as.character in combination with %in% | jessekps | 1 | dbplyr version 1.1.0 If I use %in% in combination with as.character, translate_sql generates SQL that is not valid for sqlite library(RSQLite) library(dbplyr) db = dbConnect(SQLite(), ‘:memory:’) translate_sql(booklet_id %in% as.character(1:4), con=db ) #> “booklet_id” IN CAST((1, 2, 3, 4) AS | bugdatabase | tidyverse/dplyr#3205 | NA | NA | tidyverse | dplyr |
| 3267 | 2017-12-29 21:28:00 | Feature Request: Preferred Column Values After Merge | billdenney | 1 | I often want to join two data.frames and then select the “best” result from the output columns. This happens when I may have two sources of information with partially overlapping information. One source may be more reliable than the other, so I would prefer to use source 1 if it has a value. If so | featuregeneric | tidyverse/dplyr#3267 | NA | NA | tidyverse | dplyr |
| 3278 | 2018-01-01 21:45:11 | bind_rows should have col_type argument | hadley | 1 | So you could prespecify the types of the columns. For performance and safety. | data framefeature | tidyverse/dplyr#3278 | NA | NA | tidyverse | dplyr |
| 3335 | 2018-02-03 18:24:15 | filtering wide tibble is slow | cnjr2 | 1 | I find that filtering operations can be quite slow with wide tibbles. Here is an example of a 500 x 100,001 table (which is still quite modest), where the first column has a sample_id information. library(dplyr) library(purrr) n_samples <- 500 n_features <- 100000 df <- bind_cols( tibble(sample_ | data frameperformance | tidyverse/dplyr#3335 | NA | NA | tidyverse | dplyr |
| 3347 | 2018-02-08 01:03:51 | Teradata ROW_NUMBER() OVER (PARTITION BY …) issue | jakefrost | 1 | Hi all, thanks for all your work on the Teradata translations for dbplyr. One issue I’ve come across is that ROW_NUMBER() window functions generated by dbplyr produce errors. For example, if I run this code: flights %>% select(record_id, record_create_dt, acct_num, dep_dt, origin) %>% group_by | database | tidyverse/dplyr#3347 | NA | NA | tidyverse | dplyr |
| 3370 | 2018-02-22 05:47:07 | transmute does not work with DBI | danielmcauley | 1 | transmute() exhibits desired behavior with data frames but throws an error when used in the context of a DBI. # this runs library(dplyr) transmute(mtcars, blah = cyl) # this does not library(dbplyr) con <- DBI::dbConnect(RSQLite::SQLite(), “:memory:”) copy_to(con, mtcars) mtcars2 <- tbl(con, “mt | databasereprex | tidyverse/dplyr#3370 | NA | NA | tidyverse | dplyr |
| 3383 | 2018-02-28 21:08:27 | Update performance measurements | krlmlr | 1 | per #2557 (comment), CC @lionel-. Perhaps add tests from tpchr. | data frameperformance | tidyverse/dplyr#3383 | NA | NA | tidyverse | dplyr |
| 3429 | 2018-03-15 14:42:08 | Implement and use reconstruct() internally | krlmlr | 1 | to get rid of redundant tbl_df methods which complicate navigating the code. When sloop is ready, we’ll be ready to switch. | architecturefeaturegeneric | tidyverse/dplyr#3429 | NA | NA | tidyverse | dplyr |