In this vignette we will see how to modify your outputs. Basically you can:
regroup() functionreport.quanti() functionreport.quali() functionadd.stat() functionsplit() functionStart 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(data)
print(head(data))
#> 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 1regroup() functionYou can mix qualitative and quantitative outputs. It’s possible only if there is only 1 explicative variable, and it should be the same variable for both response:
tab1=report.quanti(data=data,y="y_numeric",
x1="GROUP",subjid="SUBJID",y.label="Y numeric")
tab2=report.quali(data=data,y="y_logistic",
x1="GROUP",subjid="SUBJID",y.label="Y logistic")
tab3=regroup(tab1,tab2,rbind.label="The label of your choice")
report.doc(tab3,title="Mixed Qualitative and Quantitative output",
colspan.value="Treatment group")Output 1: Mixed Qualitative and Quantitative output | |||||
Treatment group | |||||
The label of your choice | Levels | Statistics | A (N=30) | B (N=21) | C (N=17) |
Y numeric | N | 180 | 120 | 96 | |
Mean (SD) | 1.46(1.50) | 3.15(2.00) | 3.87(2.52) | ||
Median | 1.59 | 3.75 | 4.73 | ||
[Q1;Q3] | [0.45;2.50] | [2.46;4.44] | [3.44;5.30] | ||
[Min;Max] | [-2.34;4.36] | [-2.44;6.19] | [-2.99;7.96] | ||
Missing | 4 | 4 | 2 | ||
Y logistic | 0 | n (column %) | 79(43.89%) | 60(50.00%) | 47(48.96%) |
1 | n (column %) | 97(53.89%) | 59(49.17%) | 44(45.83%) | |
Missing n(%) | 4(2.22%) | 1(0.83%) | 5(5.21%) | ||
report.quanti() functionYou will have to specify the function corresponding to the statistics and use the func.stat argument
es=function(x) mean(x,na.rm=T)/sd(x,na.rm=T)
tab=report.quanti(data=data,y="y_numeric",x1="GROUP",
total=TRUE,subjid="SUBJID",
func.stat=es,
func.stat.name="Effect size")
report.doc(tab,title="Example of a specific statistic reporting",
colspan.value="Treatment group")Output 2: Example of a specific statistic reporting | ||||
Treatment group | ||||
Statistics | A (N=30) | B (N=21) | C (N=17) | Total (N=68) |
Effect size | 0.98 | 1.57 | 1.53 | 1.16 |
report.quali() functionIf you want to display the row percentages instead of column percentages in report.quali() function, you just have to set the argument percent.col to FALSE:
tab=report.quali(data=data,y="y_logistic",x1="GROUP",
total=TRUE,subjid="SUBJID",percent.col=FALSE)
report.doc(tab,title="Example of row percentage reporting",
colspan.value="Treatment group")Output 3: Example of row percentage reporting | |||||
Treatment group | |||||
Levels | Statistics | A (N=30) | B (N=21) | C (N=17) | Total (N=68) |
0 | n (row %) | 79(42.47%) | 60(32.26%) | 47(25.27%) | 186(46.97%) |
1 | n (row %) | 97(48.50%) | 59(29.50%) | 44(22.00%) | 200(50.51%) |
Missing n(%) | 4(40.00%) | 1(10.00%) | 5(50.00%) | 10(2.53%) | |
add.stat() functionYou can also add a specific (or several) statistics to an existing table.
Let’s say you want the default statistics (mean, median, sd etc..) plus some specific statistics like the mode or the coefficient of variation.
In this case, you can use the add.stat() function with the pos argument to choose where you want to add this statistic:
# The default statistics are given here:
tab1=report.quanti(data=data,y="y_numeric",x1="GROUP",total=TRUE,subjid="SUBJID")
# Define the function corresponding to the coefficient of variation for example
cv=function(y) sd(y,na.rm=TRUE)/mean(y,na.rm=TRUE)
# We use the add.stat function to add CV at the second row:
tab1.cv=add.stat(tab1,data,func.stat=cv,func.stat.name="Coef. Var",
pos=2)
report.doc(tab1.cv,title="Example of adding a coefficient of variation")Output 4: Example of adding a coefficient of variation | ||||
Statistics | A (N=30) | B (N=21) | C (N=17) | Total (N=68) |
N | 180 | 120 | 96 | 396 |
Coef. Var | 1.03 | 0.64 | 0.65 | 0.86 |
Mean (SD) | 1.46(1.50) | 3.15(2.00) | 3.87(2.52) | 2.56(2.20) |
Median | 1.59 | 3.75 | 4.73 | 2.71 |
[Q1;Q3] | [0.45;2.50] | [2.46;4.44] | [3.44;5.30] | [1.04;4.33] |
[Min;Max] | [-2.34;4.36] | [-2.44;6.19] | [-2.99;7.96] | [-2.99;7.96] |
Missing | 4 | 4 | 2 | 10 |
# Same with 2 explicative variables
tab=report.quanti(data=data,y="y_numeric",x1="GROUP",
x2="TIMEPOINT",total=TRUE,subjid="SUBJID",
at.row="TIMEPOINT")
tab=add.stat(tab,data,func.stat=cv,func.stat.name="Coef. Var",
pos=2)
# And on position 5, we can add for example the mode
mode=function(x)
{
x=na.omit(x)
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
tab=add.stat(tab,data,func.stat=mode,func.stat.name="Mode",
pos=5)
report.doc(tab,title="Example of adding 2 more statistics in an existing table",
colspan.value="Treatment Group")Output 5: Example of adding 2 more statistics in an existing table | |||||
Treatment Group | |||||
TIMEPOINT | Statistics | A (N=30) | B (N=21) | C (N=17) | Total (N=68) |
D0 | N | 30 | 20 | 16 | 66 |
Coef. Var | -0.92 | -1.62 | -0.78 | -1.04 | |
Mean (SD) | -0.93(0.86) | -0.67(1.09) | -1.19(0.92) | -0.92(0.95) | |
Median | -0.82 | -0.69 | -1.26 | -0.86 | |
Mode | -0.42 | -0.36 | -0.75 | -0.42 | |
[Q1;Q3] | [-1.59;-0.16] | [-1.39;-0.06] | [-1.62;-0.83] | [-1.55;-0.16] | |
[Min;Max] | [-2.34;0.36] | [-2.44;2.10] | [-2.99;0.66] | [-2.99;2.10] | |
Missing | 1 | 1 | 0 | 2 | |
D1 | N | 30 | 20 | 16 | 66 |
Coef. Var | 0.57 | 0.31 | 0.14 | 0.52 | |
Mean (SD) | 1.83(1.04) | 4.17(1.28) | 4.98(0.69) | 3.33(1.73) | |
Median | 1.78 | 4.19 | 5.08 | 3.57 | |
Mode | 3.88 | 5.71 | 5.02 | 3.88 | |
[Q1;Q3] | [ 0.94; 2.54] | [ 3.23; 4.92] | [ 4.58; 5.46] | [ 1.78; 4.91] | |
[Min;Max] | [ 0.11;3.88] | [ 1.48;6.19] | [ 3.80;6.23] | [ 0.11;6.23] | |
Missing | 1 | 0 | 0 | 1 | |
D2 | N | 30 | 20 | 16 | 66 |
Coef. Var | 0.60 | 0.22 | 0.28 | 0.51 | |
Mean (SD) | 1.97(1.17) | 4.04(0.89) | 4.90(1.36) | 3.32(1.70) | |
Median | 1.66 | 4.19 | 5.06 | 3.57 | |
Mode | 1.35 | 4.35 | 6.58 | 1.35 | |
[Q1;Q3] | [ 1.23; 2.86] | [ 3.62; 4.36] | [ 4.34; 5.20] | [ 1.89; 4.44] | |
[Min;Max] | [-0.18;4.36] | [ 2.03;5.63] | [ 2.39;7.96] | [-0.18;7.96] | |
Missing | 1 | 1 | 0 | 2 | |
D3 | N | 30 | 20 | 16 | 66 |
Coef. Var | 0.66 | 0.25 | 0.22 | 0.56 | |
Mean (SD) | 1.78(1.17) | 3.81(0.94) | 5.07(1.12) | 3.15(1.75) | |
Median | 1.78 | 3.63 | 5.22 | 3.15 | |
Mode | 2.54 | 4.44 | 5.66 | 2.54 | |
[Q1;Q3] | [ 0.93; 2.42] | [ 3.13; 4.44] | [ 4.11; 5.66] | [ 1.80; 4.39] | |
[Min;Max] | [-0.16;3.90] | [ 2.46;6.01] | [ 3.16;7.37] | [-0.16;7.37] | |
Missing | 0 | 1 | 1 | 2 | |
D4 | N | 30 | 20 | 16 | 66 |
Coef. Var | 0.46 | 0.25 | 0.20 | 0.52 | |
Mean (SD) | 1.83(0.85) | 3.80(0.95) | 5.17(1.03) | 3.22(1.66) | |
Median | 1.67 | 3.83 | 4.88 | 3.16 | |
Mode | 2.32 | 3.50 | 4.88 | 2.32 | |
[Q1;Q3] | [ 1.26; 2.32] | [ 3.12; 4.42] | [ 4.69; 5.50] | [ 1.69; 4.48] | |
[Min;Max] | [ 0.38;3.97] | [ 2.31;5.41] | [ 3.24;6.96] | [ 0.38;6.96] | |
Missing | 1 | 1 | 1 | 3 | |
D5 | N | 30 | 20 | 16 | 66 |
Coef. Var | 0.53 | 0.33 | 0.22 | 0.45 | |
Mean (SD) | 2.27(1.20) | 3.64(1.19) | 4.43(0.98) | 3.21(1.45) | |
Median | 2.50 | 3.86 | 4.57 | 3.28 | |
Mode | 1.87 | 3.89 | 2.95 | 1.87 | |
[Q1;Q3] | [ 1.77; 3.21] | [ 2.59; 4.60] | [ 3.44; 4.97] | [ 2.42; 4.44] | |
[Min;Max] | [-1.19;4.31] | [ 0.91;5.12] | [ 2.95;6.54] | [-1.19;6.54] | |
Missing | 0 | 0 | 0 | 0 | |