report.doc: Export a statistical table into a 'Microsoft Word' or a R...

In ClinReport: Statistical Reporting for Scientific Publications, Epidemiological Studies and Clinical Trials

Description

report.doc This function enables to export the table created with report.quali report.quanti or report.lsmeans to a Microsoft Word or a R markdown document.

It's also possible to use report.doc to have a preview of the table in HTML format if the doc argument is NULL.

For more examples see the website: ClinReport website

Usage

report.doc(table, ...)

## S3 method for class 'desc'
report.doc(table, title = NULL, colspan.value = NULL,
  doc = NULL, init.numbering = F, numbering = T,
  font.name = "Times", page.break = T, font.size = 10, valign = F,
  ...)

## S3 method for class 'anova'
report.doc(table, title = "Anova table",
  type.anova = 3, doc = NULL, numbering = T, init.numbering = F,
  font.name = "Times", font.size = 10, page.break = T,
  pretty.label = FALSE, ...)

## S3 method for class 'anova.lme'
report.doc(table, title = "Anova table",
  type.anova = 3, doc = NULL, numbering = T, init.numbering = F,
  font.name = "Times", font.size = 10, page.break = T,
  pretty.label = FALSE, ...)

Arguments

table	A desc object that report statistics (the results of report.quanti or report.quali)
...	Other arguments
title	Character. The title of the table
colspan.value	Character. Add the label of the x1 variable levels (typically "Treatment Groups")
doc	NULL or a rdocx object
init.numbering	Logical. If TRUE Start numbering of the output at 1, otherwise it increase the output numbering of 1 unit
numbering	Logical. If TRUE Output numbers are added before the title.
font.name	Character. Passed to font function. Set the font of the output in Word
page.break	Logical. If TRUE it adds a page break after the output. Default to TRUE
font.size	Numeric. Passed to fontsize function. Set the font size of the output in Word
valign	Logical. If TRUE it aligns vertically the levels of the merged cells in the first column
type.anova	Passed to Anova function from car package (see its documentation).
pretty.label	Logical. Default to FALSE. If TRUE, use the function make.label with default option on the rownames of the anova table
anova	Logical. Used to specify if the table is an anova table. By default it's not

Details

This function creates a flextable object from a desc object.

For Microsoft Word documents:

The argument doc should be used so the flextable is added to a rdocx object.

Like:

doc=read_docx()

tab=report.quanti(data=data,y="y_numeric",x1="GROUP")

doc=report.doc(tab,doc=doc)

For R markdown documents:

Just don't use the doc argument. Something like:

“'{r, include=TRUE}
tab=report.quanti(data=data,y="y_numeric",x1="GROUP")
doc=report.doc(tab)
doc
“'

Value

A flextable object (if doc=NULL) or a rdocx object (if doc= an rdocx object).

See Also

report.quali report.quanti report.lsmeans desc

Examples

#####################
# Import libraries
#####################

library(officer)
library(flextable)
library(reshape2)
library(emmeans)
library(lme4)
library(nlme)
library(ggplot2)
library(car) 
library(xtable)

#####################
# Load data
#####################

data(datafake) 
head(datafake)

# Removing baseline data for the model

data.mod=droplevels(datafake[datafake$TIMEPOINT!="D0",])

#####################
# Create your stats tables and graphics
#####################

# Quatitative stats (2 explicative variables) ##################################
# since it's a big enough table, we don't want it to overlap 2 pasges
# so we split it in two with split.desc function

tab1=report.quanti(data=datafake,y="y_numeric",
	x1="GROUP",x2="TIMEPOINT",at.row="TIMEPOINT",subjid="SUBJID")


s=split(tab1,variable="TIMEPOINT",at=3)

tab1.1=s$x1
tab1.2=s$x2


gg=plot(tab1,title="Mean response evolution as a function of time by treatment group",
legend.label="Treatment groups",ylab="Y mean")

# Qualitative stats (2 explicative variables) ##################################

tab2=report.quali(data=datafake,y="y_logistic",
	x1="GROUP",x2="TIMEPOINT",at.row="TIMEPOINT",total=TRUE,subjid="SUBJID")

gg2=plot(tab2,title="Response distribution (%) by day and treatment group",
legend.label="Y levels")

# Qualitative stats (no explicative variable)  ###################################

tab3=report.quali(data=datafake,y="VAR",y.label="Whatever")

# Qualitative stats (no explicative variables ; add number of subjects in header)#

tab4=report.quali(data=datafake,y="VAR",y.label="Whatever",
	subjid="SUBJID")

# Qualitative stats (1 explicative variable)#######################################

tab5=report.quali(data=datafake,y="VAR",y.label="Whatever",x1="GROUP",
	subjid="SUBJID")


# Quantitative stats (1 explicative variable)#######################################

tab6=report.quanti(data=datafake,y="y_numeric",y.label="Whatever 2",x1="GROUP",
	subjid="SUBJID")

# Quali-Quanti table

tab5.6=regroup(tab5,tab6)


# Linear model (order 2 interaction): Anova and LS-Means reporting ################

mod1=lm(y_numeric~baseline+GROUP+TIMEPOINT+GROUP*TIMEPOINT,data=data.mod)
test1=emmeans(mod1,~GROUP|TIMEPOINT)

anov1=Anova(mod1)

tab.mod1=report.lsmeans(lsm=test1)

gg.mod1=plot(tab.mod1,title="LS-Means response evolution as a function of time\n
by treatment group (95% CI)",
legend.label="Treatment groups",ylab="Y mean",add.ci=TRUE)

# Linear model (1 group only): Anova and LS-Means and graph reporting ################

mod2=lm(y_numeric~baseline+GROUP,data=data.mod)

anov2=Anova(mod2,type=3)

test2=emmeans(mod2,~GROUP)
tab.mod2=report.lsmeans(lsm=test2)


gg.mod2=plot(tab.mod2,title="LS-Means response\nby treatment group (95% CI)",
	legend.label="Treatment groups",ylab="Y mean",add.ci=TRUE)

# Linear mixed model (order 2 interaction):
# Anova and LS-Means and graph reporting #################

mod3=lme(y_numeric~baseline+GROUP+TIMEPOINT+GROUP*TIMEPOINT,
random=~1|SUBJID,data=data.mod,na.action=na.omit)

anov3=Anova(mod3,3)

test3=emmeans(mod3,~GROUP|TIMEPOINT)

tab.mod3=report.lsmeans(lsm=test3)

gg.mod3=plot(tab.mod3,title="LS-Means response evolution as a function of time\n
by treatment group (95% CI Mixed model)",
	legend.label="Treatment groups",ylab="Y mean",add.ci=TRUE)

# Contrast example

contr=contrast(test3, "trt.vs.ctrl", ref = "A")

tab.mod3.contr=report.lsmeans(lsm=contr)

gg.mod3.contr=plot(tab.mod3.contr,title="LS-Means contrast versus reference A\n
			(95% CI Mixed model)",
	legend.label="Treatment groups",ylab="Y mean",add.ci=TRUE,add.line=FALSE)


############################################################
# Generalized Logistic Linear model (order 2 interaction):
############################################################

# Anova LS-Means and graph reporting ##########

mod4=glm(y_logistic~baseline+GROUP+TIMEPOINT+GROUP*TIMEPOINT,
family=binomial,data=data.mod,na.action=na.omit)

anov4=Anova(mod4,3)

test4=emmeans(mod4,~GROUP|TIMEPOINT)

tab.mod4=report.lsmeans(lsm=test4,at.row="TIMEPOINT")

gg.mod4=plot(tab.mod4,title="LS-Means response evolution as a function of time\n
by treatment group (95% CI Logistic model)",
	legend.label="Treatment groups",ylab="Y mean",add.ci=TRUE)

# Generalized Poisson Linear model (order 2 interaction):
# Anova LS-Means and graph reporting #'


mod5=glm(y_poisson~baseline+GROUP+TIMEPOINT+GROUP*TIMEPOINT,
family=poisson,data=data.mod,na.action=na.omit)

anov5=Anova(mod5,3)


test5=emmeans(mod5,~GROUP|TIMEPOINT)

tab.mod5=report.lsmeans(lsm=test5,at.row="TIMEPOINT")


gg.mod5=plot(tab.mod5,title="LS-Means response evolution as a function of time\n
by treatment group (95% CI Poisson model)",
	legend.label="Treatment groups",ylab="Y mean",add.ci=TRUE)

#####################
# Create your report
#####################


doc=read_docx()
doc=body_add_toc(doc)


doc=body_add_par(doc,"A beautiful reporting using ClinReport", style = "heading 1")

doc=body_add_par(doc,"Descriptive statistics", style = "heading 2")

doc=report.doc(tab1.1,title="Quantitative statistics (2 explicative variables) (Table 1/2)",
	colspan.value="Treatment group",doc=doc,init.numbering=TRUE,
page.break=FALSE)

doc=report.doc(tab1.2,title="Quantitative statistics (2 explicative variables) (Table 2/2)",
	colspan.value="Treatment group",doc=doc)

doc=body_add_par(doc,"Corresponding graphic of outputs 1 & 2", style ="Normal") 

doc=body_add_gg(doc, value = gg, style = "centered" )

doc=body_add_break(doc)

doc=report.doc(tab2,title="Qualitative statistics (2 explicative variables)",
	colspan.value="Treatment group",doc=doc)


doc=report.doc(tab2,title="The same with smaller font size",
	colspan.value="Treatment group",doc=doc,font.size=8)

doc=body_add_par(doc,"Corresponding graphic of output 3", style ="Normal") 

doc=body_add_gg(doc, value = gg2, style = "centered" )

doc=body_add_break(doc)

doc=body_add_par(doc,"Example of mixing qualitative and quantitative
statistics with the function regroup", style ="Normal") 

doc=report.doc(tab5.6,title="Quali-Qanti statistics (1 variable only)",doc=doc)

doc=body_add_par(doc,"Statistical model results", style = "heading 2")

doc=body_add_par(doc,"Model 1", style = "heading 3")

doc=body_add_par(doc,"Anova table example", style = "Normal")

doc=report.doc(anov1,doc=doc)

doc=body_add_par(doc,"LS-Means example", style = "Normal")

doc=report.doc(tab.mod1,title="Linear Model LS-Means results using lm with interactions",
	colspan.value="Treatment group",doc=doc)

doc=body_add_gg(doc, value = gg.mod1, style = "centered" )

doc=body_add_break(doc)


doc=body_add_par(doc,"Model 2", style = "heading 3")


doc=report.doc(anov2,doc=doc)


doc=report.doc(tab.mod2,title="Linear Model LS-Means results using lm without interaction",
	colspan.value="Treatment group",doc=doc)

doc=body_add_gg(doc, value = gg.mod2, style = "centered" )

doc=body_add_break(doc)


doc=body_add_par(doc,"Model 3", style = "heading 3")

doc=report.doc(anov3,doc=doc)


doc=report.doc(tab.mod3,title="Linear Mixed Model LS-Means results using lme",
	colspan.value="Treatment group",doc=doc)

doc=body_add_gg(doc, value = gg.mod3, style = "centered" )

doc=body_add_break(doc)


doc=report.doc(tab.mod3.contr,title="LS-Means Contrast example",
	colspan.value="Timepoints",doc=doc)

doc=body_add_gg(doc, value = gg.mod3.contr, style = "centered" )

doc=body_add_break(doc)




doc=body_add_par(doc,"Model 4", style = "heading 3")

doc=report.doc(anov4,doc=doc)


doc=report.doc(tab.mod4,title="Generalized Linear Mixed Model LS-Means results using glm",
	colspan.value="Treatment group",doc=doc)

doc=body_add_gg(doc, value = gg.mod4, style = "centered" )

doc=body_add_break(doc)


doc=body_add_par(doc,"Model 5", style = "heading 3")

doc=report.doc(anov5,doc=doc)

doc=report.doc(tab.mod5,title="Poisson Model LS-Means results",
	colspan.value="Treatment group",doc=doc)

doc=body_add_gg(doc, value = gg.mod5, style = "centered" )



file=paste(tempfile(),".docx",sep="")
print(doc, target =file)
shell.exec(file)

ClinReport documentation built on Sept. 3, 2019, 5:07 p.m.