Export a statistical table into a 'Microsoft Word' or a R markdown document

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

report.doc(table, ...)

# S3 method for 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 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, ...)

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

Value

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

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
```

Examples




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

library(officer)
library(flextable)
library(reshape2)
library(emmeans)
library(lme4)
library(nlme)
library(ggplot2)
library(car)
#> Le chargement a nécessité le package : carData
#> Registered S3 methods overwritten by 'car':
#>   method                          from
#>   influence.merMod                lme4
#>   cooks.distance.influence.merMod lme4
#>   dfbeta.influence.merMod         lme4
#>   dfbetas.influence.merMod        lme4
library(xtable)
#> 
#> Attachement du package : 'xtable'
#> The following objects are masked from 'package:flextable':
#> 
#>     align, display

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

data(datafake)
head(datafake)
#>    y_numeric y_logistic y_poisson   baseline   VAR GROUP TIMEPOINT SUBJID
#> 1 -0.4203490          1         5 -0.4203490 Cat 1     A        D0 Subj 1
#> 2 -0.1570941          1         5 -0.1570941 Cat 2     A        D0 Subj 1
#> 3         NA          0         3 -2.0853720 Cat 2     A        D0 Subj 1
#> 4 -0.4728527          0         5 -0.4728527 Cat 1     A        D0 Subj 1
#> 5 -0.8651713          1         4 -0.8651713 Cat 1     A        D0 Subj 1
#> 6 -1.5476907          1         3 -1.5476907 Cat 1     A        D0 Subj 1

# 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="")
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
<<<<<<< HEAD
print(doc, target =file)
#> Note: zip::zip() is deprecated, please use zip::zipr() instead
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/RtmpWari3b/file2ac4402d5404.docx"
shell.exec(file)

=======
print(doc, target =file)
#> Note: zip::zip() is deprecated, please use zip::zipr() instead
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/RtmpGUh2VY/file2f20196f354c.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/Rtmp8oexhK/file30687edd322d.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/RtmpwvypGw/filec5422d43528.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/RtmpW2PZ3r/filee201ca0168f.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/RtmpWw7By6/file405874df1694.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/RtmpWwJ4u7/file35f074e555a.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/Rtmpi29jZo/file4b382ae9b76.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/RtmpeqkaF4/file2b7c56114c25.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev
=======
print(doc, target =file)
#> [1] "C:/Users/jfcollin/AppData/Local/Temp/Rtmpa6BHIT/file25b0639c13e3.docx"
shell.exec(file)

>>>>>>> refs/heads/Dev

Export a statistical table into a 'Microsoft Word' or a R markdown document

Arguments

Value

Details

See also

Examples

Contents