| NEWS | R Documentation |
Minor fixes for 1.2.0 issues that prevented correct installation on Mac OS X and a vignette rebuilding error on solaris.
fmode.grouped_df with groups and weights now saves the sum of the weights instead of the max (this makes more sense as the max only applies if all elements are unique).
grouped_df methods for fast statistical functions now always attach the grouping variables to the output in aggregations, unless argument keep.group_vars = FALSE. (formerly grouping variables were only attached if also present in the data. Code hinged on this feature should be adjusted.)
qF ordered argument default was changed to ordered = FALSE, and the NA level is now only added if na.exclude = FALSE. Thus qF now behaves exactly like as.factor.
Recode is depreciated in favor of recode_num and recode_char, it will be removed soon. Similarly replace_non_finite was renamed to replace_Inf.
In mrtl and mctl the argument ret was renamed return and now takes descriptive character arguments (the previous version was a direct C++ export and unsafe, code written with these functions should be adjusted).
GRP argument order is depreciated in favor of argument decreasing. order can still be used but will be removed at some point.
Fixed a bug in flag where unused factor levels caused a group size error.
Faster grouping with GRP and faster factor generation with added radix method + automatic dispatch between hash and radix method. qF is now ~ 5x faster than as.factor on character and around 30x faster on numeric data. Also qG was enhanced.
Further slight speed tweaks here and there.
collap now provides more control for weighted aggregations with additional arguments w, keep.w and wFUN to aggregate the weights as well. The defaults are keep.w = TRUE and wFUN = fsum. A specialty of collap remains that keep.by = TRUE and keep.w = TRUE also work for external objects passed, so code of the form collap(data, by, FUN, catFUN, w = data$weights) will now have an aggregated weights vector in the first column.
qsu now also allows weights to be passed in formula i.e. qsu(data, by = ~ group, pid = ~ panelid, w = ~ weights).
fdiff now supports quasi-differences i.e. x_t - ρ x_{t-1} and quasi-log differences i.e. log(x_t) - ρ log(x_{t-1}). an arbitrary ρ can be supplied.
Added a Dlog operator for faster access to log-differences.
fgrowth now has a scale argument, the default is scale = 100 which provides growth rates in percentage terms (as before), but this may now be changed.
All statistical and transformation functions now have a hidden list method, so they can be applied to unclassed list-objects as well. An error is however provided in grouped operations with unequal-length columns.
Added a suite of functions for fast data manipulation:
fselect selects variables from a data frame and is equivalent but much faster than dplyr::select.
fsubset is a much faster version of base::subset to subset vectors, matrices and data.frames. The function ss was also added as a faster alternative to [.data.frame.
ftransform is a much faster update of base::transform, to transform data frames by adding, modifying or deleting columns. The function settransform does all of that by reference.
fcompute is equivalent to ftransform but returns a new data frame containing only the columns computed from an existing one.
na_omit is a much faster and enhanced version of base::na.omit.
replace_NA efficiently replaces missing values in multi-type data.
Added function fgroup_by as a much faster version of dplyr::group_by based on collapse grouping. It attaches a 'GRP' object to a data frame, but only works with collapse's fast functions. This allows dplyr like manipulations that are fully collapse based and thus significantly faster, i.e. data %>% fgroup_by(g1,g2) %>% fselect(cola,colb) %>% fmean. Note that data %>% dplyr::group_by(g1,g2) %>% dplyr::select(cola,colb) %>% fmean still works, in which case the dplyr 'group' object is converted to 'GRP' as before. However data %>% fgroup_by(g1,g2) %>% dplyr::summarize(...) does not work.
Added function varying to efficiently check the variation of multi-type data over a dimension or within groups.
Added function radixorder, same as base::order(..., method = "radix") but more accessible and with built-in grouping features.
Added functions seqid and groupid for generalized run-length type id variable generation from grouping and time variables. seqid in particular strongly facilitates lagging / differencing irregularly spaced panels using flag, fdiff etc.
Fixed remaining gcc10, LTO and valgrind issues in C/C++ code, and added some more tests (there are now ~ 5300 tests ensuring that collapse statistical functions perform as expected).
Fixed the issue that supplying an unnamed list to GRP(), i.e. GRP(list(v1, v2)) would give an error. Unnamed lists are now automatically named 'Group.1', 'Group.2', etc...
Fixed an issue where aggregating by a single id in collap() (i.e. collap(data, ~ id1)), the id would be coded as factor in the aggregated data.frame. All variables including id's now retain their class and attributes in the aggregated data.
Added weights (w) argument to fsum and fprod. Note: fmedian will also support weights as soon as I am able to implement a sufficiently fast (i.e. linear time) algorithm. I also hope to introduce (weighted) quantiles. I am happy for any help with these features.
Added an argument mean = 0 to fwithin / W. This allows simple and grouped centering on an arbitrary mean, 0 being the default. For grouped centering mean = "overall.mean" can be specified, which will center data on the overall mean of the data. The logical argument add.global.mean = TRUE used to toggle this in collapse 1.0.0 is therefore depreciated.
Added arguments mean = 0 (the default) and sd = 1 (the default) to fscale / STD. These arguments now allow to (group) scale and center data to an arbitrary mean and standard deviation. Setting mean = FALSE just scales data while preserving the mean(s). Special options for grouped scaling are mean = "overall.mean", and sd = "within.sd". The latter will scale the data such that the standard deviation of each group is equal to the within- standard deviation (= the standard deviation computed on the group-centered data). The fast but numerically unstable algorithm for variance calculation toggled with stable.algo = FALSE was removed from fscale. It is still available for fvar and fsd.
Added the modulus (%%) and subtract modulus (-%%) operations to TRA().
Added the function finteraction, for fast interactions, and as.character_factor to coerce a factor, or all factors in a list, to character (analogous to as.numeric_factor). Also exported the function ckmatch, for matching with an error message showing non-matched elements.