Package: reportRmd 0.1.1

Lisa Avery

reportRmd: Tidy Presentation of Clinical Reporting

Streamlined statistical reporting in 'Rmarkdown' environments. Facilitates the automated reporting of descriptive statistics, multiple univariate models, multivariable models and tables combining these outputs. Plotting functions include customisable survival curves, forest plots from logistic and ordinal regression and bivariate comparison plots.

Authors:Lisa Avery [cre, aut], Ryan Del Bel [aut], Osvaldo Espin-Garcia [aut], Katherine Lajkosz [aut], Clarina Ong [aut], Tyler Pittman [aut], Anna Santiago [aut], Yanning Wang [ctr], Jessica Weiss [aut], Wei Xu [aut]

reportRmd_0.1.1.tar.gz
reportRmd_0.1.1.tar.gz(r-4.5-noble)reportRmd_0.1.1.tar.gz(r-4.4-noble)
reportRmd_0.1.1.tgz(r-4.4-emscripten)reportRmd_0.1.1.tgz(r-4.3-emscripten)
reportRmd.pdf |reportRmd.html✨
reportRmd/json (API)
NEWS

# Install 'reportRmd' in R:

install.packages('reportRmd', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

ctDNA - Tumour size change over time Longitudinal changes in tumour size since baseline for patients by changes in ctDNA status (clearance, decrease or increase) since baseline.
pembrolizumab - Survival data Survival status and ctDNA levels for patients receiving pembrolizumab
uvmodels - Aligning models and parameters

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.18 score 1 packages 234 downloads 28 exports 102 dependencies

Last updated 2 months agofrom:74456d0333. Checks:3 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 26 2025
R-4.5-linux	OK	Mar 26 2025
R-4.4-linux	OK	Mar 26 2025

Exports:clear_labels crrRx excelCol excelColLetters extract_labels forestplot2 forestplotMV forestplotUV forestplotUVMV ggkmcif ggkmcif_paste ggkmcif2 nestTable outTable plotuv replace_plot_labels rm_cifsum rm_compactsum rm_covsum rm_mvsum rm_survdiff rm_survsum rm_survtime rm_uv_mv rm_uvsum scrolling_table set_labels set_var_labels

Dependencies:abind aod backports base64enc boot broom bslib cachem car carData cli cmprsk colorspace corrplot cowplot cpp11 Deriv digest doBy dplyr evaluate fansi farver fastmap fontawesome Formula fs geepack generics ggplot2 ggpubr ggrepel ggsci ggsignif glue gridExtra gtable highr htmltools isoband jquerylib jsonlite kableExtra knitr labeling lattice lifecycle lme4 magrittr MASS Matrix MatrixModels memoise mgcv microbenchmark mime minqa modelr munsell nlme nloptr nnet numDeriv pander pbkrtest pillar pkgconfig plyr polynom purrr quantreg R6 rappdirs rbibutils RColorBrewer Rcpp RcppEigen Rdpack reformulas rlang rmarkdown rstatix rstudioapi sass scales SparseM stringi stringr survival svglite systemfonts tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun xml2 yaml

reportRmd Package

Rendered fromreportRmd.Rmdusingknitr::rmarkdownon Mar 26 2025.

Last update: 2025-01-24
Started: 2022-10-17

Citation

To cite package ‘reportRmd’ in publications use:

Avery L, Del Bel R, Espin-Garcia O, Lajkosz K, Ong C, Pittman T, Santiago A, Weiss J, Xu W (2025). reportRmd: Tidy Presentation of Clinical Reporting. R package version 0.1.1, https://CRAN.R-project.org/package=reportRmd.

Corresponding BibTeX entry:

  @Manual{,
    title = {reportRmd: Tidy Presentation of Clinical Reporting},
    author = {Lisa Avery and Ryan {Del Bel} and Osvaldo Espin-Garcia
      and Katherine Lajkosz and Clarina Ong and Tyler Pittman and Anna
      Santiago and Jessica Weiss and Wei Xu},
    year = {2025},
    note = {R package version 0.1.1},
    url = {https://CRAN.R-project.org/package=reportRmd},
  }

Readme and manuals

reportRmd

The goal of reportRmd is to automate the reporting of clinical data in Rmarkdown environments. Functions include table one-style summary statistics, compilation of multiple univariate models, tidy output of multivariable models and side by side comparisons of univariate and multivariable models. Plotting functions include customisable survival curves, forest plots, and automated bivariate plots.

Installation

Installing from CRAN:

install.packages('reportRmd')

You can install the development version of reportRmd from GitHub with:

# install.packages("devtools")
devtools::install_github("biostatsPMH/reportRmd", ref="development")

New Features

new compact summary table function rm_compactsum
main functions are now pipeable
new function to use variable labels in ggplots replace_plot_labels

Documentation

Online Documentation

Examples

Summary statistics by Sex

library(reportRmd)
data("pembrolizumab")

rm_covsum(data=pembrolizumab, maincov = 'sex',
covs=c('age','pdl1','change_ctdna_group'),
show.tests=TRUE)

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value	StatTest
age				0.30	Wilcoxon Rank Sum
Mean (sd)	57.9 (12.8)	56.9 (12.6)	59.3 (13.1)
Median (Min,Max)	59.1 (21.1, 81.8)	56.6 (34.1, 78.2)	61.2 (21.1, 81.8)
pdl1				0.76	Wilcoxon Rank Sum
Mean (sd)	13.9 (29.2)	15.0 (30.5)	12.1 (27.3)
Median (Min,Max)	0 (0, 100)	0.5 (0.0, 100.0)	0 (0, 100)
Missing	1	0	1
change ctdna group				0.84	Chi Sq
Decrease from baseline	33 (45)	19 (48)	14 (42)
Increase from baseline	40 (55)	21 (52)	19 (58)
Missing	21	18	3

Compact Table

pembrolizumab |> rm_compactsum( grp = 'sex',
xvars=c('age','pdl1','change_ctdna_group'))

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value	Missing
age	59.1 (49.5-68.7)	56.6 (45.8-67.8)	61.2 (52.0-69.4)	0.30	0
pdl1	0.0 (0.0-10.0)	0.5 (0.0-13.8)	0.0 (0.0-4.5)	0.76	1
change ctdna group - Increase from baseline	40 (55%)	21 (52%)	19 (58%)	0.84	21

Using Variable Labels

var_names <- data.frame(var=c("age","pdl1","change_ctdna_group"),
                          label=c('Age at study entry',
                                  'PD L1 percent',
                                  'ctDNA change from baseline to cycle 3'))

pembrolizumab <- set_labels(pembrolizumab,var_names)

rm_covsum(data=pembrolizumab, maincov = 'sex',
covs=c('age','pdl1','change_ctdna_group'))

	Full Sample (n=94)	Female (n=58)	Male (n=36)	p-value
Age at study entry				0.30
Mean (sd)	57.9 (12.8)	56.9 (12.6)	59.3 (13.1)
Median (Min,Max)	59.1 (21.1, 81.8)	56.6 (34.1, 78.2)	61.2 (21.1, 81.8)
PD L1 percent				0.76
Mean (sd)	13.9 (29.2)	15.0 (30.5)	12.1 (27.3)
Median (Min,Max)	0 (0, 100)	0.5 (0.0, 100.0)	0 (0, 100)
Missing	1	0	1
ctDNA change from baseline to cycle 3				0.84
Decrease from baseline	33 (45)	19 (48)	14 (42)
Increase from baseline	40 (55)	21 (52)	19 (58)
Missing	21	18	3

Multiple Univariate Regression Analyses

rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','pdl1','change_ctdna_group'))
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...

	OR(95%CI)	p-value	N	Event
Age at study entry	0.96 (0.91, 1.00)	0.089	94	78
PD L1 percent	0.97 (0.95, 0.98)	\<0.001	93	77
ctDNA change from baseline to cycle 3		0.002	73	58
Decrease from baseline	Reference		33	19
Increase from baseline	28.74 (5.20, 540.18)		40	39

Tidy multivariable analysis

glm_fit <- glm(orr~change_ctdna_group+pdl1+cohort,
               family='binomial',
               data = pembrolizumab)
rm_mvsum(glm_fit,showN=T)

	OR(95%CI)	p-value	N	Event
ctDNA change from baseline to cycle 3		0.009	73	58
Decrease from baseline	Reference		33	19
Increase from baseline	19.99 (2.08, 191.60)		40	39
PD L1 percent	0.97 (0.95, 1.00)	0.066	73	58
cohort			73	58
A	Reference		14	11
B	2.6e+07 (0e+00, Inf)	1.00	11	11
C	4.2e+07 (0e+00, Inf)	1.00	10	10
D	0.07 (4.2e-03, 1.09)	0.057	10	3
E	0.44 (0.04, 5.10)	0.51	28	23

Combining univariate and multivariable models

uvsumTable <- rm_uvsum(data=pembrolizumab, response='orr',
covs=c('age','sex','pdl1','change_ctdna_group'),tableOnly = TRUE)
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...
#> Waiting for profiling to be done...

glm_fit <- glm(orr~change_ctdna_group+pdl1,
               family='binomial',
               data = pembrolizumab)
mvsumTable <- rm_mvsum(glm_fit,tableOnly = TRUE)

rm_uv_mv(uvsumTable,mvsumTable)

	Unadjusted OR(95%CI)	p	Adjusted OR(95%CI)	p (adj)
Age at study entry	0.96 (0.91, 1.00)	0.089
sex		0.11
Female	Reference
Male	0.41 (0.13, 1.22)
PD L1 percent	0.97 (0.95, 0.98)	\<0.001	0.98 (0.96, 1.00)	0.024
ctDNA change from baseline to cycle 3		0.002		0.004
Decrease from baseline	Reference		Reference
Increase from baseline	28.74 (5.20, 540.18)		24.71 (2.87, 212.70)

Simple survival summary table

Shows events, median survival, survival rates at different times and the log rank test. Does not allow for covariates or strata, just simple tests between groups

 rm_survsum(data=pembrolizumab,time='os_time',status='os_status',
 group="cohort",survtimes=c(12,24),
# group="cohort",survtimes=seq(12,36,12),
# survtimesLbls=seq(1,3,1),
 survtimesLbls=c(1,2),
 survtimeunit='yr')

Group	Events/Total	Median (95%CI)	1yr (95% CI)	2yr (95% CI)
A	12/16	8.30 (4.24, Not Estimable)	0.38 (0.20, 0.71)	0.23 (0.09, 0.59)
B	16/18	8.82 (4.67, 20.73)	0.32 (0.16, 0.64)	0.06 (9.6e-03, 0.42)
C	12/18	17.56 (7.95, Not Estimable)	0.61 (0.42, 0.88)	0.44 (0.27, 0.74)
D	4/12	Not Estimable (6.44, Not Estimable)	0.67 (0.45, 0.99)	0.67 (0.45, 0.99)
E	20/30	14.26 (9.69, Not Estimable)	0.63 (0.48, 0.83)	0.34 (0.20, 0.57)
		Log Rank Test	ChiSq	11.3 on 4 df
			p-value	0.023

Summarise Cumulative incidence

library(survival)
data(pbc)
rm_cifsum(data=pbc,time='time',status='status',group=c('trt','sex'),
eventtimes=c(1825,3650),eventtimeunit='day')
#> 106 observations with missing data were removed.

Strata	Event/Total	1825day (95% CI)	3650day (95% CI)
1, f	7/137	0.04 (0.01, 0.08)	0.06 (0.03, 0.12)
1, m	3/21	0.10 (0.02, 0.27)	0.16 (0.03, 0.36)
2, f	9/139	0.05 (0.02, 0.09)	0.09 (0.04, 0.17)
2, m	0/15	0e+00 (NA, NA)	0e+00 (NA, NA)
	Gray’s Test	ChiSq	3.3 on 3 df
		p-value	0.35

Plotting survival curves

ggkmcif2(response = c('os_time','os_status'),
cov='cohort',
data=pembrolizumab)

Plotting odds ratios

require(ggplot2)
#> Loading required package: ggplot2
forestplotMV(glm_fit)
#> Warning in forestplotMV(glm_fit): NAs introduced by coercion

Plotting bivariate relationships

These plots are designed for quick inspection of many variables, not for publication.

require(ggplot2)
plotuv(data=pembrolizumab, response='orr',
covs=c('age','cohort','pdl1','change_ctdna_group'))
#> Boxplots not shown for categories with fewer than 20 observations.
#> Boxplots not shown for categories with fewer than 20 observations.

Replacing variable names with labels in ggplot

data("mtcars")
mtcars <- mtcars |> 
  dplyr::mutate(cyl = as.factor(cyl)) |>
  set_labels(data.frame(var=c("hp","mpg","cyl"),
                        label=c('Horsepower',
                                'Miles per gallon',
                                'Number of cylinders'))) 
p <- mtcars |> 
ggplot(aes(x=hp, y=mpg, color=cyl, shape=cyl)) +
    geom_point()
replace_plot_labels(p)

Help Manual

Help page	Topics
Add spaces to strings in LaTeX	addspace
fit box cox transformed linear model	boxcoxfitRx
Capitalize a string	cap
Clear variable labels from a data frame	clear_labels
Get covariate summary dataframe	covsum
fit crr model	crrRx
Tumour size change over time Longitudinal changes in tumour size since baseline for patients by changes in ctDNA status (clearance, decrease or increase) since baseline.	ctDNA
Retrieve columns number from spreadsheet columns specified as unquoted letters	excelCol
Retrieve spreadsheet column letter-names from columns indices	excelColLetters
Extract variable labels from labelled data frame	extract_labels
Create a forest plot using ggplot2	forestplot2
Create a multivariable forest plot using ggplot2	forestplotMV
Create an univariable forest plot using ggplot2	forestplotUV
Combine an univariable and multivariable forest plot using ggplot2	forestplotUVMV
Specific p-value formatting	formatp
Parameter Estimation for the Box-Cox Transformation	geoR_boxcoxfit
Plot KM and CIF curves with ggplot	ggkmcif
Plot KM and CIF curves with ggplot	ggkmcif_paste
Plot KM and CIF curves with ggplot	ggkmcif2
Bold strings in HTML	hbld
Bold strings in LaTeX	lbld
Formats p-values for LaTeX	lpvalue
Get multivariate summary dataframe	mvsum
Combine two table columns into a single column with levels of one nested within levels of the other.	nestTable
Lean strings for printing	nicename
Round retaining digits	niceNum
Print tables to PDF/Latex HTML or Word	outTable
Survival data Survival status and ctDNA levels for patients receiving pembrolizumab	pembrolizumab
Plot multiple bivariate relationships in a single plot	plotuv
Round and paste with parentheses	psthr
Paste with parentheses	pstprn
Formats p-values	pvalue
Replace variable names with labels in ggplot	replace_plot_labels
Summarize cumulative incidence by group	rm_cifsum
Output a compact summary table	rm_compactsum
Outputs a descriptive covariate table	rm_covsum
Format a regression model nicely for 'Rmarkdown'	rm_mvsum
Display event counts, expected event counts and logrank test of differences	rm_survdiff
Summarise survival data by group	rm_survsum
Display survival rates and events for specified times	rm_survtime
Combine univariate and multivariable regression tables	rm_uv_mv
Output several univariate models nicely in a single table	rm_uvsum
Replace dollar signs with html for proper HTML output	rmds
Sanitizes strings to not break LaTeX	sanitizestr
Output a scrollable table	scrolling_table
Set variable labels	set_labels
Set variable labels	set_var_labels
Aligning models and parameters	uvmodels
Get univariate summary dataframe	uvsum