dplyr-verbs | R Documentation |
These functions adapt dplyr verbs to the factors of a tbl_ord.
The raw verbs are not defined for tbl_ords; instead, each verb
has two analogues, corresponding to the two matrix factors. They each rely
on a common workhorse function, which takes the composition of the
dplyr verb with annotation_*
, applied to the factor, removes any
variables corresponding to coordinates or already annotated, and only then
assigns it as the new "*_annotation"
attribute of .data
(see
annotation). Note that these functions are not generics and so cannot be
extended to other classes.
pull_factor(.data, var = -1, .matrix) pull_rows(.data, var = -1) pull_cols(.data, var = -1) rename_rows(.data, ...) rename_cols(.data, ...) select_rows(.data, ...) select_cols(.data, ...) mutate_rows(.data, ...) mutate_cols(.data, ...) transmute_rows(.data, ...) transmute_cols(.data, ...) cbind_rows(.data, ..., elements = "all") cbind_cols(.data, ..., elements = "all") left_join_rows(.data, ...) left_join_cols(.data, ...)
.data |
An object of class 'tbl_ord'. |
var |
A variable specified as in |
.matrix |
A character string partially matched (lowercase) to several
indicators for one or both matrices in a matrix decomposition used for
ordination. The standard values are |
... |
Comma-separated unquoted expressions as in, e.g.,
|
elements |
Character vector; which elements of each factor for which to
render graphical elements. One of |
A tbl_ord; the wrapped model is unchanged.
# illustrative ordination: LDA of iris data (iris_lda <- ordinate(iris, cols = 1:4, lda_ord, grouping = iris$Species)) # extract a coordinate or annotation head(pull_rows(iris_lda, Species)) pull_cols(iris_lda, LD2) # rename an annotation rename_cols(iris_lda, species = name) # select annotations select_rows(iris_lda, species = name, .element) # create, modify, and delete annotations mutate_cols(iris_lda, vec.length = sqrt(LD1^2 + LD2^2)) transmute_cols(iris_lda, vec.length = sqrt(LD1^2 + LD2^2)) # bind data frames of annotations iris_medians <- stats::aggregate(iris[, 1:4], median, by = iris[, 5, drop = FALSE]) iris_lda %>% # retain '.element' in order to match by `elements` select_rows(.element) %>% cbind_rows(iris_medians, elements = "active")
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