summary_factorlist: Summarise a set of factors (or continuous variables) by a...

In finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

Summarise a set of factors (or continuous variables) by a dependent variable

Description

A function that takes a single dependent variable with a vector of explanatory variable names (continuous or categorical variables) to produce a summary table.

Usage

summary_factorlist(
  .data,
  dependent = NULL,
  explanatory = NULL,
  formula = NULL,
  cont = "mean",
  cont_nonpara = NULL,
  cont_cut = 5,
  cont_range = TRUE,
  p = FALSE,
  p_cont_para = "aov",
  p_cat = "chisq",
  column = TRUE,
  total_col = FALSE,
  orderbytotal = FALSE,
  digits = c(1, 1, 3, 1, 0),
  na_include = FALSE,
  na_include_dependent = FALSE,
  na_complete_cases = FALSE,
  na_to_p = FALSE,
  na_to_prop = TRUE,
  fit_id = FALSE,
  add_dependent_label = FALSE,
  dependent_label_prefix = "Dependent: ",
  dependent_label_suffix = "",
  add_col_totals = FALSE,
  include_col_totals_percent = TRUE,
  col_totals_rowname = NULL,
  col_totals_prefix = "",
  add_row_totals = FALSE,
  include_row_totals_percent = TRUE,
  include_row_missing_col = TRUE,
  row_totals_colname = "Total N",
  row_missing_colname = "Missing N",
  catTest = NULL,
  weights = NULL
)

Arguments

.data	Dataframe.
dependent	Character vector of length 1: name of dependent variable (2 to 5 factor levels).
explanatory	Character vector of any length: name(s) of explanatory variables.
formula	an object of class "formula" (or one that can be coerced to that class). Optional instead of standard dependent/explanatory format. Do not include if using dependent/explanatory.
cont	Summary for continuous explanatory variables: "mean" (standard deviation) or "median" (interquartile range). If "median" then non-parametric hypothesis test performed (see below).
cont_nonpara	Numeric vector of form e.g. c(1,2). Specify which variables to perform non-parametric hypothesis tests on and summarise with "median".
cont_cut	Numeric: number of unique values in continuous variable at which to consider it a factor.
cont_range	Logical. Median is show with 1st and 3rd quartiles.
p	Logical: Include null hypothesis statistical test.
p_cont_para	Character. Continuous variable parametric test. One of either "aov" (analysis of variance) or "t.test" for Welch two sample t-test. Note continuous non-parametric test is always Kruskal Wallis (kruskal.test) which in two-group setting is equivalent to Mann-Whitney U /Wilcoxon rank sum test. For continous dependent and continuous explanatory, the parametric test p-value returned is for the Pearson correlation coefficient. The non-parametric equivalent is for the p-value for the Spearman correlation coefficient.
p_cat	Character. Categorical variable test. One of either "chisq" or "fisher".
column	Logical: Compute margins by column rather than row.
total_col	Logical: include a total column summing across factor levels.
orderbytotal	Logical: order final table by total column high to low.
digits	Number of digits to round to (1) mean/median, (2) standard deviation / interquartile range, (3) p-value, (4) count percentage, (5) weighted count.
na_include	Logical: make explanatory variables missing data explicit (NA).
na_include_dependent	Logical: make dependent variable missing data explicit.
na_complete_cases	Logical: include only rows with complete data.
na_to_p	Logical: include missing as group in statistical test.
na_to_prop	Logical: include missing in calculation of column proportions.
fit_id	Logical: allows merging via finalfit_merge.
add_dependent_label	Add the name of the dependent label to the top left of table.
dependent_label_prefix	Add text before dependent label.
dependent_label_suffix	Add text after dependent label.
add_col_totals	Logical. Include column total n.
include_col_totals_percent	Include column percentage of total.
col_totals_rowname	Logical. Row name for column totals.
col_totals_prefix	Character. Prefix to column totals, e.g. "N=".
add_row_totals	Logical. Include row totals. Note this differs from total_col above particularly for continuous explanatory variables.
include_row_totals_percent	Include row percentage of total.
include_row_missing_col	Logical. Include missing data total for each row. Only used when add_row_totals is TRUE.
row_totals_colname	Character. Column name for row totals.
row_missing_colname	Character. Column name for missing data totals for each row.
catTest	Deprecated. See p_cat above.
weights	Character vector of length 1: name of column to use for weights. Explanatory continuous variables are multiplied by weights. Explanatory categorical variables are counted with a frequency weight (sum(weights)).

Details

This function aims to produce publication-ready summary tables for categorical or continuous dependent variables. It usually takes a categorical dependent variable to produce a cross table of counts and proportions expressed as percentages or summarised continuous explanatory variables. However, it will take a continuous dependent variable to produce mean (standard deviation) or median (interquartile range) for use with linear regression models.

Value

Returns a factorlist dataframe.

See Also

fit2df ff_column_totals ff_row_totals ff_label ff_glimpse ff_percent_only. For lots of examples, see https://finalfit.org/

Examples

library(finalfit)
library(dplyr)
# Load example dataset, modified version of survival::colon
data(colon_s)

# Table 1 - Patient demographics ----
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
  summary_factorlist(dependent, explanatory, p=TRUE)

# summary.factorlist() is also commonly used to summarise any number of
# variables by an outcome variable (say dead yes/no).

# Table 2 - 5 yr mortality ----
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
  summary_factorlist(dependent, explanatory)

finalfit documentation built on Sept. 11, 2024, 9:01 p.m.