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#' Remove regression reference level row from table
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#'
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#' This looks for a column with a name including "Coefficient", "OR", or "HR"
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#' (\code{\link{finalfit}} defaults) and removes any rows with "-" (the default
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#' for the reference level). Can also be combined to produce an
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#' \code{\link{or_plot}}, see below.
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#'
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#' @param .data Output from \code{\link{finalfit}} or similar.
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#' @param only_binary Logical. Remove reference level only for two-level
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10
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#' factors. When set to false, reference level for all factors removed.
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#'
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#' @return Data frame.
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#' @export
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#'
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15
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#' @examples
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#' # Table example
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#' explanatory = c("age.factor", "age", "sex.factor", "nodes", "obstruct.factor", "perfor.factor")
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#' dependent = 'mort_5yr'
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#' colon_s %>%
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#' finalfit(dependent, explanatory, add_dependent_label = FALSE) %>%
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#' ff_remove_ref() %>%
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#' dependent_label(colon_s, dependent)
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#'
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#' # Plot example
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#' explanatory = c("age.factor", "age", "sex.factor", "nodes", "obstruct.factor", "perfor.factor")
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#' dependent = 'mort_5yr'
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#' colon_s %>%
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#' summary_factorlist(dependent, explanatory, total_col = TRUE, fit_id=TRUE) %>%
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#' ff_merge(
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#' glmuni(colon_s, dependent, explanatory) %>%
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#' fit2df()) %>%
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#' ff_remove_ref() %>%
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#' dplyr::select(-`OR`) -> factorlist_plot
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#'
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#' colon_s %>%
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#' or_plot(dependent, explanatory, factorlist = factorlist_plot)
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ff_remove_ref <- function(.data, only_binary = TRUE){
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38
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1
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if(!any(names(.data) == "label")) stop("finalfit function must include: add_dependent_label = FALSE")
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# estimate_col = grep("Coefficient*|OR*|HR*", names(.data), value=TRUE)[1]
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40
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1
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df.out = .data %>%
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41
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1
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dplyr::mutate(label = ifelse(label == "", NA, label)) %>%
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1
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tidyr::fill(label) %>%
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1
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dplyr::group_by(label)
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1
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if(only_binary){
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45
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1
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df.out = df.out %>%
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46
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1
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dplyr::filter(levels %in% c("Mean (SD)", "Median (IQR)") |
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47
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1
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dplyr::row_number() != 1 |
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48
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1
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dplyr::n() > 2)
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49
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} else {
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50
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0
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df.out = df.out %>%
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51
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0
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dplyr::filter(levels %in% c("Mean (SD)", "Median (IQR)") |
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52
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0
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dplyr::row_number() != 1)
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53
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}
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54
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1
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df.out %>%
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55
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1
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as.data.frame() %>%
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56
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1
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rm_duplicate_labels()
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57
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}
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58
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59
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#' @rdname ff_remove_ref
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60
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#' @export
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61
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finalfit_remove_ref = ff_remove_ref
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62
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63
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64
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#' Remove p-value from output
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65
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#'
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66
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#' This will work with \code{\link{finalfit}} and any \code{\link{fit2df}}
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67
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#' output.
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68
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#'
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69
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#' @param .data Output from \code{\link{finalfit}} or similar.
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70
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#'
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71
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#' @return Data frame.
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72
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#' @export
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73
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#'
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74
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#' @examples
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75
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#' explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
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76
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#' dependent = 'mort_5yr'
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77
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#' colon_s %>%
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78
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#' finalfit(dependent, explanatory) %>%
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79
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#' ff_remove_p()
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80
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ff_remove_p <- function(.data){
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81
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0
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.data %>%
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82
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0
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dplyr::mutate_all(~ gsub(", p[=<][0123456789.]*", "", .))
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83
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}
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84
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85
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#' @rdname ff_remove_p
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86
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#' @export
|
|
87
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finalfit_remove_p = ff_remove_p
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88
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89
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90
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91
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#' Include only percentages for factors in \code{\link{summary_factorlist}} output
|
|
92
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|
#'
|
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93
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|
#' @param .data Output from \code{\link{finalfit}} or similar.
|
|
94
|
|
#'
|
|
95
|
|
#' @return Data frame.
|
|
96
|
|
#' @export
|
|
97
|
|
#'
|
|
98
|
|
#' @examples
|
|
99
|
|
#' explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
|
|
100
|
|
#' dependent = 'mort_5yr'
|
|
101
|
|
#' colon_s %>%
|
|
102
|
|
#' summary_factorlist(dependent, explanatory) %>%
|
|
103
|
|
#' ff_percent_only()
|
|
104
|
|
ff_percent_only <- function(.data){
|
|
105
|
0
|
if(!any(names(.data) == "label")) stop("summary_factorlist() must include: add_dependent_label = FALSE")
|
|
106
|
0
|
.data %>%
|
|
107
|
0
|
dplyr::mutate_at(vars(-dplyr::one_of("label", "levels", "p")), ~ dplyr::case_when(
|
|
108
|
0
|
!levels %in% c("Mean (SD)", "Median (IQR)") ~ stringr::str_extract(., "(?<=\\().+?(?=\\))"),
|
|
109
|
0
|
TRUE ~ .))
|
|
110
|
|
}
|
|
111
|
|
|
|
112
|
|
#' @rdname ff_percent_only
|
|
113
|
|
#' @export
|
|
114
|
|
finalfit_percent_only = ff_percent_only
|
Nelson-Gon