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, ...)An object of class 'tbl_ord'.
A variable specified as in dplyr::pull().
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 "rows", "cols", and "dims" (for
both).
Comma-separated unquoted expressions as in, e.g.,
dplyr::select().
Character vector; which elements of each factor for which to
render graphical elements. One of "all" (the default), "active", or any
supplementary element type defined by the specific class methods (e.g.
"score" for 'factanal', 'lda_ord', and 'cancord_ord' and "intraset" and
"interset" for 'cancor_ord').
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))
#> Warning: Could not locate data used to fit 'x'.
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 5 ]
#> LD1 LD2 | name prior counts .element
#> | <chr> <dbl> <int> <chr>
#> 1 7.61 0.215 | 1 setosa 0.333 50 active
#> 2 -1.83 -0.728 | 2 versicolor 0.333 50 active
#> 3 -5.78 0.513 | 3 virginica 0.333 50 active
#> 4 NA NA | 4 NA NA NA score
#> 5 NA NA | 5 NA NA NA score
#> # ℹ 148 more rows
#> # ℹ 1 more variable: Species <fct>
#> #
#> # Columns (standard): [ 4 x 2 | 2 ]
#> LD1 LD2 | name .element
#> | <chr> <chr>
#> 1 0.829 0.0241 | 1 Sepal.Length active
#> 2 1.53 2.16 | 2 Sepal.Width active
#> 3 -2.20 -0.932 | 3 Petal.Length active
#> 4 -2.81 2.84 | 4 Petal.Width active
# extract a coordinate or annotation
head(pull_rows(iris_lda, Species))
#> [1] <NA> <NA> <NA> setosa setosa setosa
#> Levels: setosa versicolor virginica
pull_cols(iris_lda, LD2)
#> [1] 0.02410215 2.16452123 -0.93192121 2.83918785
# rename an annotation
rename_cols(iris_lda, species = name)
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 5 ]
#> LD1 LD2 | name prior counts .element
#> | <chr> <dbl> <int> <chr>
#> 1 7.61 0.215 | 1 setosa 0.333 50 active
#> 2 -1.83 -0.728 | 2 versicolor 0.333 50 active
#> 3 -5.78 0.513 | 3 virginica 0.333 50 active
#> 4 NA NA | 4 NA NA NA score
#> 5 NA NA | 5 NA NA NA score
#> # ℹ 148 more rows
#> # ℹ 1 more variable: Species <fct>
#> #
#> # Columns (standard): [ 4 x 2 | 2 ]
#> LD1 LD2 | species .element
#> | <chr> <chr>
#> 1 0.829 0.0241 | 1 Sepal.Length active
#> 2 1.53 2.16 | 2 Sepal.Width active
#> 3 -2.20 -0.932 | 3 Petal.Length active
#> 4 -2.81 2.84 | 4 Petal.Width active
# select annotations
select_rows(iris_lda, species = name, .element)
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 2 ]
#> LD1 LD2 | species .element
#> | <chr> <chr>
#> 1 7.61 0.215 | 1 setosa active
#> 2 -1.83 -0.728 | 2 versicolor active
#> 3 -5.78 0.513 | 3 virginica active
#> 4 NA NA | 4 NA score
#> 5 NA NA | 5 NA score
#> # ℹ 148 more rows
#> #
#> # Columns (standard): [ 4 x 2 | 2 ]
#> LD1 LD2 | name .element
#> | <chr> <chr>
#> 1 0.829 0.0241 | 1 Sepal.Length active
#> 2 1.53 2.16 | 2 Sepal.Width active
#> 3 -2.20 -0.932 | 3 Petal.Length active
#> 4 -2.81 2.84 | 4 Petal.Width active
# create, modify, and delete annotations
mutate_cols(iris_lda, vec.length = sqrt(LD1^2 + LD2^2))
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 5 ]
#> LD1 LD2 | name prior counts .element
#> | <chr> <dbl> <int> <chr>
#> 1 7.61 0.215 | 1 setosa 0.333 50 active
#> 2 -1.83 -0.728 | 2 versicolor 0.333 50 active
#> 3 -5.78 0.513 | 3 virginica 0.333 50 active
#> 4 NA NA | 4 NA NA NA score
#> 5 NA NA | 5 NA NA NA score
#> # ℹ 148 more rows
#> # ℹ 1 more variable: Species <fct>
#> #
#> # Columns (standard): [ 4 x 2 | 3 ]
#> LD1 LD2 | name .element vec.length
#> | <chr> <chr> <dbl>
#> 1 0.829 0.0241 | 1 Sepal.Length active 0.830
#> 2 1.53 2.16 | 2 Sepal.Width active 2.65
#> 3 -2.20 -0.932 | 3 Petal.Length active 2.39
#> 4 -2.81 2.84 | 4 Petal.Width active 3.99
transmute_cols(iris_lda, vec.length = sqrt(LD1^2 + LD2^2))
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 5 ]
#> LD1 LD2 | name prior counts .element
#> | <chr> <dbl> <int> <chr>
#> 1 7.61 0.215 | 1 setosa 0.333 50 active
#> 2 -1.83 -0.728 | 2 versicolor 0.333 50 active
#> 3 -5.78 0.513 | 3 virginica 0.333 50 active
#> 4 NA NA | 4 NA NA NA score
#> 5 NA NA | 5 NA NA NA score
#> # ℹ 148 more rows
#> # ℹ 1 more variable: Species <fct>
#> #
#> # Columns (standard): [ 4 x 2 | 1 ]
#> LD1 LD2 | vec.length
#> | <dbl>
#> 1 0.829 0.0241 | 1 0.830
#> 2 1.53 2.16 | 2 2.65
#> 3 -2.20 -0.932 | 3 2.39
#> 4 -2.81 2.84 | 4 3.99
# bind data frames of annotations
iris_medians <-
stats::aggregate(iris[, 1:4], median, by = iris[, 5, drop = FALSE])
# TODO: Requirement of `.elements` for matching is fragile.
iris_lda %>%
# retain '.element' in order to match by `elements`
select_rows(.element) %>%
cbind_rows(iris_medians, elements = "active")
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 6 ]
#> LD1 LD2 | .element Species Sepal.Length
#> | <chr> <fct> <dbl>
#> 1 7.61 0.215 | 1 active setosa 5
#> 2 -1.83 -0.728 | 2 active versicolor 5.9
#> 3 -5.78 0.513 | 3 active virginica 6.5
#> 4 NA NA | 4 score NA NA
#> 5 NA NA | 5 score NA NA
#> # ℹ 148 more rows
#> # ℹ 3 more variables:
#> # Sepal.Width <dbl>,
#> # Petal.Length <dbl>,
#> # Petal.Width <dbl>
#> #
#> # Columns (standard): [ 4 x 2 | 2 ]
#> LD1 LD2 | name .element
#> | <chr> <chr>
#> 1 0.829 0.0241 | 1 Sepal.Length active
#> 2 1.53 2.16 | 2 Sepal.Width active
#> 3 -2.20 -0.932 | 3 Petal.Length active
#> 4 -2.81 2.84 | 4 Petal.Width active
iris_lda %>%
select_rows(name, Species) %>%
left_join_rows(iris_medians, by = c("name" = "Species"))
#> # A tbl_ord of class 'lda_ord': (153 x 2) x (4 x 2)'
#> # 2 coordinates: LD1 and LD2
#> #
#> # Rows (principal): [ 153 x 2 | 6 ]
#> LD1 LD2 | name Species Sepal.Length
#> | <chr> <fct> <dbl>
#> 1 7.61 0.215 | 1 setosa NA 5
#> 2 -1.83 -0.728 | 2 versicolor NA 5.9
#> 3 -5.78 0.513 | 3 virginica NA 6.5
#> 4 NA NA | 4 NA setosa NA
#> 5 NA NA | 5 NA setosa NA
#> # ℹ 148 more rows
#> # ℹ 3 more variables:
#> # Sepal.Width <dbl>,
#> # Petal.Length <dbl>,
#> # Petal.Width <dbl>
#> #
#> # Columns (standard): [ 4 x 2 | 2 ]
#> LD1 LD2 | name .element
#> | <chr> <chr>
#> 1 0.829 0.0241 | 1 Sepal.Length active
#> 2 1.53 2.16 | 2 Sepal.Width active
#> 3 -2.20 -0.932 | 3 Petal.Length active
#> 4 -2.81 2.84 | 4 Petal.Width active