View source: R/summaryfactorlist.R
summary_factorlist  R Documentation 
A function that takes a single dependent variable with a vector of explanatory variable names (continuous or categorical variables) to produce a summary table.
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 )
.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 nonparametric hypothesis test performed (see below). 
cont_nonpara 
Numeric vector of form e.g. 
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 ttest. Note continuous nonparametric test is always Kruskal Wallis (kruskal.test) which in twogroup setting is equivalent to MannWhitney U /Wilcoxon rank sum test. For continous dependent and continuous explanatory, the parametric test pvalue returned is for the Pearson correlation coefficient. The nonparametric equivalent is for the pvalue 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) pvalue, (4) count percentage, (5) weighted count. 
na_include 
Logical: make explanatory variables missing data explicit
( 
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 
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

include_row_totals_percent 
Include row percentage of total. 
include_row_missing_col 
Logical. Include missing data total for each
row. Only used when 
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 
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)). 
This function aims to produce publicationready 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.
Returns a factorlist
dataframe.
fit2df
ff_column_totals
ff_row_totals
ff_label
ff_glimpse
ff_percent_only
. For lots of examples, see https://finalfit.org/
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.