View source: R/display_table.R
display_table | R Documentation |
Display count (frequency) or mean (standard deviation) table with the test of normality, etc.
display_table( data = NULL, variables = NULL, group = NULL, mean_or_median = "mean", addNA = TRUE, table_margin = 2, discrete_limit = 10, exclude_discrete = TRUE, save_to_file = NULL, normtest = NULL, fill_variable = FALSE ) display_table_group( data = NULL, variables = NULL, group = NULL, super_group = NULL, group_combine = FALSE, mean_or_median = "mean", addNA = TRUE, table_margin = 2, discrete_limit = 10, exclude_discrete = TRUE, normtest = NULL, fill_variable = FALSE )
data |
A data.frame |
variables |
Column indices or names of the variables in the dataset to display, the default columns are all the variables |
group |
Column indices or names of the first subgroup variables. Must provide. |
mean_or_median |
A character to specify mean or median to used for continuous variables, either "mean" or "median". The default is "mean" |
addNA |
Whether to include NA values in the table, see |
table_margin |
Index of generate margin for, see |
discrete_limit |
A numeric defining the minimal of unique value to display the variable as count and frequency, the default is 10 |
exclude_discrete |
Logical, whether to exclude discrete variables with more unique values specified by discrete_limit |
save_to_file |
A character, containing file name or path |
normtest |
A character indicating test of normality, the default method is |
fill_variable |
A logical, whether to fill the variable column in result, the default is FALSE |
super_group |
Column indices or names of the further subgroup variables. |
group_combine |
A logical, subgroup analysis for combination of group variables or for each group variables. The default is FALSE (subgroup analysis for each group variable) |
display_table_group
: Allow more subgroup analysis, see the package vignette for more details
The return table is a data.frame.
- P.value1 is ANOVA P value for continuous variables and chi-square test P value for discrete variables
- P.value2 is Kruskal-Wallis test P value for continuous variables and fisher test P value for discrete variables if expected counts less than 5
- normality is normality test P value for each group
## Not run: data(diabetes) head(diabetes) library(dplyr);library(rlang) result_1<-diabetes %>% group_by(sex) %>% do(display_table(data=.,variables=c("age","smoking"),group="CFHrs2230199")) %>% ungroup() result_2<-display_table_group(data=diabetes,variables=c("age","smoking"), group="CFHrs2230199",super_group = "sex") identical(result,result1) result_3<-display_table_group(data=diabetes,variables=c("age","education"), group=c("smoking"),super_group = c("CFHrs2230199","sex")) result_4<-display_table_group(data=diabetes,variables=c("age","education"), group=c("smoking"),super_group = c("CFHrs2230199","sex"),group_combine=TRUE) ## End(Not run)
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