Retrieve cell values by row and column path
Usage
cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE)
value_at(tt, rowpath = NULL, colpath = NULL)
# S4 method for VTableTree
value_at(tt, rowpath = NULL, colpath = NULL)Arguments
- tt
TableTree(or related class). ATableTreeobject representing a populated table.- rowpath
character. Path in row-split space to the desired row(s). Can include
"@content".- colpath
character. Path in column-split space to the desired column(s). Can include
"*".- omit_labrows
logical(1). Should label rows underneath
rowpathbe omitted (TRUE, the default), or return empty lists of cell "values" (FALSE).
Value
for cell_values, a list (regardless of the type of
value the cells hold). if rowpath defines a path to a single row,
cell_values returns the list of cell values for that row, otherwise
a list of such lists, one for each row captured underneath rowpath.
This occurs after subsetting to colpath has occurred.
For value_at the "unwrapped" value of a single cell, or an error, if
the combination of rowpath and colpath do not define the
location of a single cell in tt.
Note
cell_values will return a single cell's value wrapped in a list.
Use value_at to receive the "bare" cell value.
Examples
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
split_cols_by("SEX") %>%
split_rows_by("RACE") %>%
summarize_row_groups() %>%
split_rows_by("STRATA1") %>%
analyze("AGE")
library(dplyr) ## for mutate
tbl <- build_table(lyt, DM %>%
mutate(SEX = droplevels(SEX), RACE = droplevels(RACE)))
row_paths_summary(tbl)
#> rowname node_class path
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> ASIAN ContentRow RACE, ASIAN, @content, ASIAN
#> A LabelRow RACE, ASIAN, STRATA1, A
#> Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean
#> B LabelRow RACE, ASIAN, STRATA1, B
#> Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean
#> C LabelRow RACE, ASIAN, STRATA1, C
#> Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean
#> BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN
#> A LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A
#> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean
#> B LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B
#> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean
#> C LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C
#> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean
#> WHITE ContentRow RACE, WHITE, @content, WHITE
#> A LabelRow RACE, WHITE, STRATA1, A
#> Mean DataRow RACE, WHITE, STRATA1, A, AGE, Mean
#> B LabelRow RACE, WHITE, STRATA1, B
#> Mean DataRow RACE, WHITE, STRATA1, B, AGE, Mean
#> C LabelRow RACE, WHITE, STRATA1, C
#> Mean DataRow RACE, WHITE, STRATA1, C, AGE, Mean
col_paths_summary(tbl)
#> label path
#> —————————————————————————————————————————————
#> A: Drug X ARM, A: Drug X
#> F ARM, A: Drug X, SEX, F
#> M ARM, A: Drug X, SEX, M
#> B: Placebo ARM, B: Placebo
#> F ARM, B: Placebo, SEX, F
#> M ARM, B: Placebo, SEX, M
#> C: Combination ARM, C: Combination
#> F ARM, C: Combination, SEX, F
#> M ARM, C: Combination, SEX, M
cell_values(
tbl, c("RACE", "ASIAN", "STRATA1", "B"),
c("ARM", "A: Drug X", "SEX", "F")
)
#> $`A: Drug X.F`
#> [1] 33.75
#>
# it's also possible to access multiple values by being less specific
cell_values(
tbl, c("RACE", "ASIAN", "STRATA1"),
c("ARM", "A: Drug X", "SEX", "F")
)
#> $A.AGE.Mean
#> $A.AGE.Mean$`A: Drug X.F`
#> [1] 30.4
#>
#>
#> $B.AGE.Mean
#> $B.AGE.Mean$`A: Drug X.F`
#> [1] 33.75
#>
#>
#> $C.AGE.Mean
#> $C.AGE.Mean$`A: Drug X.F`
#> [1] 36.92308
#>
#>
cell_values(tbl, c("RACE", "ASIAN"), c("ARM", "A: Drug X", "SEX", "M"))
#> $ASIAN
#> $ASIAN$`A: Drug X.M`
#> [1] 35.0000000 0.6862745
#>
#>
#> $STRATA1.A.AGE.Mean
#> $STRATA1.A.AGE.Mean$`A: Drug X.M`
#> [1] 34.41667
#>
#>
#> $STRATA1.B.AGE.Mean
#> $STRATA1.B.AGE.Mean$`A: Drug X.M`
#> [1] 34.875
#>
#>
#> $STRATA1.C.AGE.Mean
#> $STRATA1.C.AGE.Mean$`A: Drug X.M`
#> [1] 35.6
#>
#>
## any arm, male columns from the ASIAN content (i.e. summary) row
cell_values(
tbl, c("RACE", "ASIAN", "@content"),
c("ARM", "B: Placebo", "SEX", "M")
)
#> $`B: Placebo.M`
#> [1] 31.00 0.62
#>
cell_values(
tbl, c("RACE", "ASIAN", "@content"),
c("ARM", "*", "SEX", "M")
)
#> $`A: Drug X.M`
#> [1] 35.0000000 0.6862745
#>
#> $`B: Placebo.M`
#> [1] 31.00 0.62
#>
#> $`C: Combination.M`
#> [1] 44.0000000 0.6470588
#>
## all columns
cell_values(tbl, c("RACE", "ASIAN", "STRATA1", "B"))
#> $`A: Drug X.F`
#> [1] 33.75
#>
#> $`A: Drug X.M`
#> [1] 34.875
#>
#> $`B: Placebo.F`
#> [1] 32.46154
#>
#> $`B: Placebo.M`
#> [1] 30.9375
#>
#> $`C: Combination.F`
#> [1] 33.3
#>
#> $`C: Combination.M`
#> [1] 35.91667
#>
## all columns for the Combination arm
cell_values(
tbl, c("RACE", "ASIAN", "STRATA1", "B"),
c("ARM", "C: Combination")
)
#> $`C: Combination.F`
#> [1] 33.3
#>
#> $`C: Combination.M`
#> [1] 35.91667
#>
cvlist <- cell_values(
tbl, c("RACE", "ASIAN", "STRATA1", "B", "AGE", "Mean"),
c("ARM", "B: Placebo", "SEX", "M")
)
cvnolist <- value_at(
tbl, c("RACE", "ASIAN", "STRATA1", "B", "AGE", "Mean"),
c("ARM", "B: Placebo", "SEX", "M")
)
stopifnot(identical(cvlist[[1]], cvnolist))