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New function idea: add_row() #1021
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@kevinushey @jennybc any thoughts on what this should look like? |
I've always found the way I guess I'd expect library(dplyr)
mtcars2 <- mtcars %>%
add_rownames()
new_row <- data_frame(rowname = "novel", cyl = 4, disp = 7)
mtcars2 %>%
full_join(new_row) %>%
tail(3) %>%
select(-rowname)
#> Joining by: c("rowname", "cyl", "disp")
#> Source: local data frame [3 x 11]
#>
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
#> 1 15.0 8 301 335 3.54 3.57 14.6 0 1 5 8
#> 2 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
#> 3 NA 4 7 NA NA NA NA NA NA NA NA I wonder if you would ever want the ability to insert a row, i.e. specify a row number? Or to make the new row the first row instead of the last? |
To me, silently expanding the levels of a factor just seems like the wrong thing to do - the levels of a factor represent a predefined set of values that you already known. |
Your factors are not like my factors. Mine have ... one level per gene or other genomic feature, so lots of levels. During exploration the set that survive various steps of analysis is very fluid. But OK, it seems like a row-inserting user should be content to get the usual |
@jennybc so why do you use factors instead of strings? |
Some is probably habit? All the pieces of your But also: I tend to be fitting models with fixed gene effects and I need to control the reference level. Also I make lots of facetted plots where facets/genes need to be |
@jennybc ok, that makes sense. I think we're missing a variable type that's a string + custom ordering. |
I didn't appreciate that But this |
- Initial CRAN release - Extracted from `dplyr` 0.4.3 - Exported functions: - `tbl_df()` - `as_data_frame()` - `data_frame()`, `data_frame_()` - `frame_data()`, `tibble()` - `glimpse()` - `trunc_mat()`, `knit_print.trunc_mat()` - `type_sum()` - New `lst()` and `lst_()` create lists in the same way that `data_frame()` and `data_frame_()` create data frames (tidyverse/dplyr#1290). `lst(NULL)` doesn't raise an error (#17, @jennybc), but always uses deparsed expression as name (even for `NULL`). - New `add_row()` makes it easy to add a new row to data frame (tidyverse/dplyr#1021). - New `rownames_to_column()` and `column_to_rownames()` (#11, @zhilongjia). - New `has_rownames()` and `remove_rownames()` (#44). - New `repair_names()` fixes missing and duplicate names (#10, #15, @r2evans). - New `is_vector_s3()`. - Features - New `as_data_frame.table()` with argument `n` to control name of count column (#22, #23). - Use `tibble` prefix for options (#13, #36). - `glimpse()` now (invisibly) returns its argument (tidyverse/dplyr#1570). It is now a generic, the default method dispatches to `str()` (tidyverse/dplyr#1325). The default width is obtained from the `tibble.width` option (#35, #56). - `as_data_frame()` is now an S3 generic with methods for lists (the old `as_data_frame()`), data frames (trivial), matrices (with efficient C++ implementation) (tidyverse/dplyr#876), and `NULL` (returns a 0-row 0-column data frame) (#17, @jennybc). - Non-scalar input to `frame_data()` and `tibble()` (including lists) creates list-valued columns (#7). These functions return 0-row but n-col data frame if no data. - Bug fixes - `frame_data()` properly constructs rectangular tables (tidyverse/dplyr#1377, @kevinushey). - Minor modifications - Uses `setOldClass(c("tbl_df", "tbl", "data.frame"))` to help with S4 (tidyverse/dplyr#969). - `tbl_df()` automatically generates column names (tidyverse/dplyr#1606). - `tbl_df`s gain `$` and `[[` methods that are ~5x faster than the defaults, never do partial matching (tidyverse/dplyr#1504), and throw an error if the variable does not exist. `[[.tbl_df()` falls back to regular subsetting when used with anything other than a single string (#29). `base::getElement()` now works with tibbles (#9). - `all_equal()` allows to compare data frames ignoring row and column order, and optionally ignoring minor differences in type (e.g. int vs. double) (tidyverse/dplyr#821). Used by `all.equal()` for tibbles. (This package contains a pure R implementation of `all_equal()`, the `dplyr` code has identical behavior but is written in C++ and thus faster.) - The internals of `data_frame()` and `as_data_frame()` have been aligned, so `as_data_frame()` will now automatically recycle length-1 vectors. Both functions give more informative error messages if you are attempting to create an invalid data frame. You can no longer create a data frame with duplicated names (tidyverse/dplyr#820). Both functions now check that you don't have any `POSIXlt` columns, and tell you to use `POSIXct` if you do (tidyverse/dplyr#813). `data_frame(NULL)` raises error "must be a 1d atomic vector or list". - `trunc_mat()` and `print.tbl_df()` are considerably faster if you have very wide data frames. They will now also only list the first 100 additional variables not already on screen - control this with the new `n_extra` parameter to `print()` (tidyverse/dplyr#1161). The type of list columns is printed correctly (tidyverse/dplyr#1379). The `width` argument is used also for 0-row or 0-column data frames (#18). - When used in list-columns, S4 objects only print the class name rather than the full class hierarchy (#33). - Add test that `[.tbl_df()` does not change class (#41, @jennybc). Improve `[.tbl_df()` error message. - Documentation - Update README, with edits (#52, @bhive01) and enhancements (#54, @jennybc). - `vignette("tibble")` describes the difference between tbl_dfs and regular data frames (tidyverse/dplyr#1468). - Code quality - Test using new-style Travis-CI and AppVeyor. Full test coverage (#24, #53). Regression tests load known output from file (#49). - Renamed `obj_type()` to `obj_sum()`, improvements, better integration with `type_sum()`. - Internal cleanup.
From an exchange on Twitter: An idea for an add_row() function that would make it easy to add a row to a data frame when the columns are of a different class. As you then suggested:
you could make it like add_row(mtcars, cyl = 4, disp = 7) and it filled in the other values with missings
I took a first stab at writing such a function for myself here:
https://github.com/smach/rmiscutils/blob/master/R/add_row.R
Code that's a bit more elegant and efficient would be a nice addition to dplyr!
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