In this vignette we will see the different graphics that you can produce with ClinReport.
Start by loading all usual libraries.
library(ClinReport)
library(officer)
library(flextable)
library(dplyr)
library(reshape2)
library(nlme)
library(emmeans)
library(car)
Load your data.
# We will use fake data
data(datafake)
print(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
First you need to produce a quantitative table using report.quanti() function
then you use the plot() function on the result:
If there is no explicative variable, the corresponding graphic of the call to report.quanti()
is a barplot of the mean of the response value.
# Only one numerical response
tab1=report.quanti(data=datafake,y="y_numeric")
plot(tab1,title="Mean (+/-SD) of y",add.sd=T,ylab="Mean Response value",xlab="")
If there is one explicative variable, the corresponding graphic will be a barplot per treatment group.
You can personnalize the outputs using, the usual, title xlab ylab ylim etc.. options.
# one numerical response ; one explicative variable
tab2=report.quanti(data=datafake,y="y_numeric",x1="GROUP")
plot(tab2,title="Mean (+/-SD) of y per treatment group",add.sd=T,ylab="Mean of y",xlab="",
ylim=c(-1,8))
If there is also a second explicative variable. The corresponding plot will be a lineplot:
The means will be connected according to this second variable to capture the evolution across its different levels.
# one numerical response ; two explicative variable
tab3=report.quanti(data=datafake,y="y_numeric",x1="GROUP",x2="TIMEPOINT")
plot(tab3,title="Mean (+/-SD) of y per treatment group",add.sd=T,ylab="Mean of y",xlab="",
ylim=c(-1,8))
#> Warning: Removed 1 rows containing missing values (geom_point).
#> Warning: Removed 1 rows containing missing values (geom_path).
#> Warning: Removed 3 rows containing missing values (geom_errorbar).
For qualitative statistics, the corresponding graphics will be barplots.
If only the argument y is supplied, the corresponding graphic will looks like:
# Only one categorical response
tab1=report.quali(data=datafake,y="y_logistic")
plot(tab1,title="Distribution of y (%)",ylab="Percentages",xlab="",ylim=c(0,100))
If there is one explicative variable, it will be split by color and split on the x-axis:
# one categorical response ; one categorical explicative variable
tab1=report.quali(data=datafake,y="y_logistic",x1="GROUP")
plot(tab1,title="Distribution of y per treatment group (%)",
ylab="Percentages",xlab="",ylim=c(0,100),legend.label="Levels of y")
If there is two explicative variables, it will be split by color, split on the x-axis and split by panels:
# one categorical response ; two categorical explicative variables
tab1=report.quali(data=datafake,y="y_logistic",x1="GROUP",x2="VAR")
plot(tab1,title="Distribution of y per treatment group\nand by level of VAR (%)",
ylab="Percentages",xlab="",ylim=c(0,100),legend.label="Levels of y")
For LS-Means graphics, it will be pretty much the same graphics as the graphics of quantitative statistics.
Instead of displaying the standard deviations, it's the 95% confidence intervals that are displayed.
For one explicative variable:
# Only one categorical response
mod=lme(y_numeric~GROUP,
random=~1|SUBJID,data=datafake,na.action=na.omit)
test=emmeans(mod,~GROUP)
tab.mod=report.lsmeans(lsm=test)
plot(tab.mod,title="LS-means of y per treatment group",
ylab="LS-Means",xlab="",add.ci=TRUE)
With two explicative variables:
# Only one categorical response
mod=lme(y_numeric~baseline+GROUP+TIMEPOINT+GROUP*TIMEPOINT,
random=~1|SUBJID,data=datafake,na.action=na.omit)
test=emmeans(mod,~GROUP|TIMEPOINT)
tab.mod=report.lsmeans(lsm=test)
plot(tab.mod,title="LS-means evolution of y per treatment group\nas a function of time",
ylab="LS-Means",xlab="",add.ci=TRUE)
