R/FullTable1.R
FullTable1.RdThe FullTable1 function can be used to create a Table1 for
scientific publication. This is intended to summarize demographic
and other variables (vars) split by a grouping variable (strata)
from an input dataset (data).
Continuous variables will be summarized as mean (SD)
and tested across groups using t-test or ANOVA (for 3+ level strata).
Categorical variables will be summarized as N (%)
and tested across groups as chi-squared.
Effect sizes for group differences will be calculated as Cohen's d,
partial eta-squared, Odds Ratio, Cramer's V depending on the test.
Requires tidyverse and stats libraries.
FullTable1( data, strata = NULL, vars = NULL, var_names = vars, factor_vars = NULL, round_n = 2, es_col = c(TRUE, FALSE), p_col = c(TRUE, FALSE), stars = c("col", "name", "stat", "none"), html = c(FALSE, TRUE) )
| data | The input dataset (will be converted to tibble). |
|---|---|
| strata | The grouping variable of interest (converted to factor), if NULL will make one column table. |
| vars | A list of variables to summarize, e.g. c("Age","sex","WASI"). |
| var_names | An optional list to rename the variable colnames in the
output table, e.g. c("Age (years)","Sex","IQ"). Must match |
| factor_vars | An optional list of variables from |
| round_n | The number of decimal places to round output to (default=2). |
| es_col | Include a column for effect size of group difference? (default=T). |
| p_col | Include a column for p-value of group difference? (default=TRUE). |
| stars | Where to include stars indicating significance of group differences. Options: "col"=separate column (default), "name"= append to variable name, "stat"= append to group difference statistic, "none" for no stars. |
| html | Format as html in viewer or not (default=FALSE, print in console), needs library(htmlTable) installed. |
Output Table 1
#> Warning: N=4 missing/NA in grouping variable: Sex#> $table #> Variable F (N=2364) M (N=2632) Stat p sig es #> 1 Age 120.65 (7.5) 121.05 (7.66) 1.86 .06 0.05 #> 2 Height 57.34 (3.48) 57.27 (3.28) -0.83 .41 -0.02 #> 3 depressT 55.1 (5.27) 55.88 (6.01) 4.87 <.001 *** 0.14 #> #> $caption #> [1] "Note. N=4 excluded for missing group variable. N=5 missing Height. N=4 missing depressT. * p<.05, ** p<.01, *** p<.001" #>#> Warning: N=4 missing/NA in grouping variable: Sex#> $table #> Variable F (N=2364) M (N=2632) Stat p sig es #> 1 Age 120.65 (7.5) 121.05 (7.66) 1.86 .06 0.05 #> 2 Height 57.34 (3.48) 57.27 (3.28) -0.83 .41 -0.02 #> 3 depressT 55.1 (5.27) 55.88 (6.01) 4.87 <.001 *** 0.14 #> #> $caption #> [1] "Note. N=4 excluded for missing group variable. N=5 missing Height. N=4 missing depressT. * p<.05, ** p<.01, *** p<.001" #>FullTable1( data = psydat, vars = c("Age", "Sex", "Height", "depressT"), var_names = c("Age (months)", "Sex", "Height (inches)", "Depression T"), strata = "Income", stars = "name", p_col = FALSE )#> Warning: N=404 missing/NA in grouping variable: Income#> $table #> Variable [<50K] (N=1331) [>=100K] (N=1957) [>=50K&<100K] (N=1308) #> 1 Age (months) * 120.61 (7.53) 121.23 (7.6) 120.55 (7.56) #> 2 Sex (M) 690 (51.88%) 1034 (52.86%) 700 (53.52%) #> 3 Height (inches) 57.42 (3.49) 57.29 (3.25) 57.23 (3.25) #> 4 Depression T *** 56.4 (6.56) 54.83 (4.9) 55.63 (5.72) #> Stat es #> 1 F=4.17 η2=0.00 #> 2 χ2=0.72 V=0.01 #> 3 F=1.15 η2=0.00 #> 4 F=31.18 η2=0.01 #> #> $caption #> [1] "Note. N=404 excluded for missing group variable. N=2 missing Sex. N=5 missing Height (inches). N=4 missing Depression T. * p<.05, ** p<.01, *** p<.001" #>#> Warning: N=4 missing/NA in grouping variable: Sextmp$caption <- "Write your own caption" #print(htmlTable(x$table, useViewer=T, rnames=F,caption=x$caption, pos.caption="bottom"))