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.
Usage
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, ...)Arguments
- .data
An object of class 'tbl_ord'.
- var
A variable specified as in
dplyr::pull().- .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
"rows","cols", and"dims"(for both).- ...
Comma-separated unquoted expressions as in, e.g.,
dplyr::select().- elements
Character vector; which elements of each factor for which to render graphical elements. One of
"active"(the default) 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'), via partial matching.
Examples
# illustrative ordination: LDA of iris data
(iris_lda <- ordinate(iris, cols = 1:4, lda_ord, grouping = iris$Species))
#> # A tbl_ord of class 'lda_ord': (153 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 6 ]
#> LD1 LD2 | name prior counts grouping .element Species
#> [2366] [20.98] | <chr> <dbl> <int> <chr> <chr> <fct>
#> 1 7.61 -0.215 | setosa 0.333 50 setosa active NA
#> 2 -1.83 0.728 | versicolor 0.333 50 versicol… active NA
#> 3 -5.78 -0.513 | virginica 0.333 50 virginica active NA
#> 4 8.06 -0.300 | NA NA NA setosa score setosa
#> 5 7.13 0.787 | NA NA NA setosa score setosa
#> ⋮ ⋮
#> # Columns (standard, 0%): [ 4 × 2 | 2 ]
#> LD1 LD2 | name .element
#> [1] [1] | <chr> <chr>
#> 1 0.829 -0.0241 | Sepal.Length active
#> 2 1.53 -2.16 | Sepal.Width active
#> 3 -2.20 0.932 | Petal.Length active
#> 4 -2.81 -2.84 | 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 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 6 ]
#> LD1 LD2 | name prior counts grouping .element Species
#> [2366] [20.98] | <chr> <dbl> <int> <chr> <chr> <fct>
#> 1 7.61 -0.215 | setosa 0.333 50 setosa active NA
#> 2 -1.83 0.728 | versicolor 0.333 50 versicol… active NA
#> 3 -5.78 -0.513 | virginica 0.333 50 virginica active NA
#> 4 8.06 -0.300 | NA NA NA setosa score setosa
#> 5 7.13 0.787 | NA NA NA setosa score setosa
#> ⋮ ⋮
#> # Columns (standard, 0%): [ 4 × 2 | 2 ]
#> LD1 LD2 | species .element
#> [1] [1] | <chr> <chr>
#> 1 0.829 -0.0241 | Sepal.Length active
#> 2 1.53 -2.16 | Sepal.Width active
#> 3 -2.20 0.932 | Petal.Length active
#> 4 -2.81 -2.84 | Petal.Width active
# select annotations
select_rows(iris_lda, species = name, .element)
#> # A tbl_ord of class 'lda_ord': (153 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 2 ]
#> LD1 LD2 | species .element
#> [2366] [20.98] | <chr> <chr>
#> 1 7.61 -0.215 | setosa active
#> 2 -1.83 0.728 | versicolor active
#> 3 -5.78 -0.513 | virginica active
#> 4 8.06 -0.300 | NA score
#> 5 7.13 0.787 | NA score
#> ⋮ ⋮
#> # Columns (standard, 0%): [ 4 × 2 | 2 ]
#> LD1 LD2 | name .element
#> [1] [1] | <chr> <chr>
#> 1 0.829 -0.0241 | Sepal.Length active
#> 2 1.53 -2.16 | Sepal.Width active
#> 3 -2.20 0.932 | Petal.Length active
#> 4 -2.81 -2.84 | 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 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 6 ]
#> LD1 LD2 | name prior counts grouping .element Species
#> [2366] [20.98] | <chr> <dbl> <int> <chr> <chr> <fct>
#> 1 7.61 -0.215 | setosa 0.333 50 setosa active NA
#> 2 -1.83 0.728 | versicolor 0.333 50 versicol… active NA
#> 3 -5.78 -0.513 | virginica 0.333 50 virginica active NA
#> 4 8.06 -0.300 | NA NA NA setosa score setosa
#> 5 7.13 0.787 | NA NA NA setosa score setosa
#> ⋮ ⋮
#> # Columns (standard, 0%): [ 4 × 2 | 3 ]
#> LD1 LD2 | name .element vec.length
#> [1] [1] | <chr> <chr> <dbl>
#> 1 0.829 -0.0241 | Sepal.Length active 0.830
#> 2 1.53 -2.16 | Sepal.Width active 2.65
#> 3 -2.20 0.932 | Petal.Length active 2.39
#> 4 -2.81 -2.84 | Petal.Width active 3.99
transmute_cols(iris_lda, vec.length = sqrt(LD1^2 + LD2^2))
#> # A tbl_ord of class 'lda_ord': (153 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 6 ]
#> LD1 LD2 | name prior counts grouping .element Species
#> [2366] [20.98] | <chr> <dbl> <int> <chr> <chr> <fct>
#> 1 7.61 -0.215 | setosa 0.333 50 setosa active NA
#> 2 -1.83 0.728 | versicolor 0.333 50 versicol… active NA
#> 3 -5.78 -0.513 | virginica 0.333 50 virginica active NA
#> 4 8.06 -0.300 | NA NA NA setosa score setosa
#> 5 7.13 0.787 | NA NA NA setosa score setosa
#> ⋮ ⋮
#> # Columns (standard, 0%): [ 4 × 2 | 1 ]
#> LD1 LD2 | vec.length
#> [1] [1] | <dbl>
#> 1 0.829 -0.0241 | 0.830
#> 2 1.53 -2.16 | 2.65
#> 3 -2.20 0.932 | 2.39
#> 4 -2.81 -2.84 | 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 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 6 ]
#> LD1 LD2 | .element Species Sepal.Length Sepal.Width
#> [2366] [20.98] | <chr> <fct> <dbl> <dbl>
#> 1 7.61 -0.215 | active setosa 5 3.4
#> 2 -1.83 0.728 | active versicolor 5.9 2.8
#> 3 -5.78 -0.513 | active virginica 6.5 3
#> 4 8.06 -0.300 | score NA NA NA
#> 5 7.13 0.787 | score NA NA NA
#> ⋮ ⋮
#> # ℹ 2 more variables: Petal.Length <dbl>,
#> # Petal.Width <dbl>
#> # Columns (standard, 0%): [ 4 × 2 | 2 ]
#> LD1 LD2 | name .element
#> [1] [1] | <chr> <chr>
#> 1 0.829 -0.0241 | Sepal.Length active
#> 2 1.53 -2.16 | Sepal.Width active
#> 3 -2.20 0.932 | Petal.Length active
#> 4 -2.81 -2.84 | 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 × 2) · (4 × 2)´
#> # 2 coordinates: LD1 and LD2
#> # Rows (principal, 100%): [ 153 × 2 | 6 ]
#> LD1 LD2 | name Species Sepal.Length Sepal.Width Petal.Length
#> [2366] [20.98] | <chr> <fct> <dbl> <dbl> <dbl>
#> 1 7.61 -0.215 | seto… NA 5 3.4 1.5
#> 2 -1.83 0.728 | vers… NA 5.9 2.8 4.35
#> 3 -5.78 -0.513 | virg… NA 6.5 3 5.55
#> 4 8.06 -0.300 | NA setosa NA NA NA
#> 5 7.13 0.787 | NA setosa NA NA NA
#> ⋮ ⋮
#> # ℹ 1 more variable: Petal.Width <dbl>
#> # Columns (standard, 0%): [ 4 × 2 | 2 ]
#> LD1 LD2 | name .element
#> [1] [1] | <chr> <chr>
#> 1 0.829 -0.0241 | Sepal.Length active
#> 2 1.53 -2.16 | Sepal.Width active
#> 3 -2.20 0.932 | Petal.Length active
#> 4 -2.81 -2.84 | Petal.Width active