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Generate Rows Analyzing Different Variables Across Columns

Source: R/colby_constructors.R
analyze_colvars.Rd

Generate Rows Analyzing Different Variables Across Columns

Usage

analyze_colvars(
  lyt,
  afun,
  format = NULL,
  nested = TRUE,
  extra_args = list(),
  indent_mod = 0L,
  inclNAs = FALSE
)

Arguments

lyt

layout object pre-data used for tabulation

afun

function or list. Function(s) to be used to calculate the values in each column. The list will be repped out as needed and matched by position with the columns during tabulation. This functions accepts the same parameters as analyze like afun and format. For further information see additional_fun_params.

format

FormatSpec. Format associated with this split. Formats can be declared via strings ("xx.x") or function. In cases such as analyze calls, they can character vectors or lists of functions.

nested

boolean. Should this layout instruction be applied within the existing layout structure if possible (TRUE, the default) or as a new top-level element (`FALSE). Ignored if it would nest a split underneath analyses, which is not allowed.

extra_args

list. Extra arguments to be passed to the tabulation function. Element position in the list corresponds to the children of this split. Named elements in the child-specific lists are ignored if they do not match a formal argument of the tabulation function.

indent_mod

numeric. Modifier for the default indent position for the structure created by this function(subtable, content table, or row) and all of that structure's children. Defaults to 0, which corresponds to the unmodified default behavior.

inclNAs

boolean. Should observations with NA in the var variable(s) be included when performing this analysis. Defaults to FALSE

Value

A PreDataTableLayouts object suitable for passing to further layouting functions, and to build_table.

See also

split_cols_by_multivar()

Author

Gabriel Becker

Examples


library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following object is masked from ‘package:testthat’:
#> 
#>     matches
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
ANL <- DM %>% mutate(value = rnorm(n()), pctdiff = runif(n()))

## toy example where we take the mean of the first variable and the
## count of >.5 for the second.
colfuns <- list(function(x) rcell(mean(x), format = "xx.x"),
                function(x) rcell(sum(x > .5), format = "xx"))

lyt <- basic_table() %>%
    split_cols_by("ARM") %>%
    split_cols_by_multivar(c("value", "pctdiff")) %>%
    split_rows_by("RACE", split_label = "ethnicity",
                  split_fun = drop_split_levels) %>%
    summarize_row_groups() %>%
    analyze_colvars(afun = colfuns)
lyt
#> A Pre-data Table Layout
#> 
#> Column-Split Structure:
#> ARM (lvls) -> value:pctdiff (vars) 
#> 
#> Row-Split Structure:
#> RACE (lvls) -> NA (** col-var analysis **) 
#> 

tbl <- build_table(lyt, ANL)
tbl
#>                                    A: Drug X                B: Placebo              C: Combination     
#>                               value       pctdiff       value       pctdiff       value       pctdiff  
#> ———————————————————————————————————————————————————————————————————————————————————————————————————————
#> ASIAN                       79 (65.3%)   79 (65.3%)   68 (64.2%)   68 (64.2%)   84 (65.1%)   84 (65.1%)
#>                                0.0           41          -0.1          28          -0.1          46    
#> BLACK OR AFRICAN AMERICAN   28 (23.1%)   28 (23.1%)   24 (22.6%)   24 (22.6%)   27 (20.9%)   27 (20.9%)
#>                                0.1           17          -0.1          17          -0.3          16    
#> WHITE                       14 (11.6%)   14 (11.6%)   14 (13.2%)   14 (13.2%)   18 (14.0%)   18 (14.0%)
#>                                0.3           9           0.2           8           0.1           10    

lyt2 <- basic_table() %>%
    split_cols_by("ARM") %>%
    split_cols_by_multivar(c("value", "pctdiff"),
                           varlabels = c("Measurement", "Pct Diff")) %>%
    split_rows_by("RACE", split_label = "ethnicity",
                  split_fun = drop_split_levels) %>%
    summarize_row_groups() %>%
    analyze_colvars(afun = mean, format = "xx.xx")

tbl2 <- build_table(lyt2, ANL)
tbl2
#>                                    A: Drug X                  B: Placebo               C: Combination     
#>                             Measurement    Pct Diff    Measurement    Pct Diff    Measurement    Pct Diff 
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————
#> ASIAN                       79 (65.3%)    79 (65.3%)   68 (64.2%)    68 (64.2%)   84 (65.1%)    84 (65.1%)
#>   mean                         0.01          0.53         -0.15         0.46         -0.13         0.54   
#> BLACK OR AFRICAN AMERICAN   28 (23.1%)    28 (23.1%)   24 (22.6%)    24 (22.6%)   27 (20.9%)    27 (20.9%)
#>   mean                         0.08          0.59         -0.14         0.55         -0.35         0.50   
#> WHITE                       14 (11.6%)    14 (11.6%)   14 (13.2%)    14 (13.2%)   18 (14.0%)    18 (14.0%)
#>   mean                         0.34          0.55         0.16          0.57         0.13          0.48