How to Customize a Table with Multiple Statistics Using CreateCustomTable in R – Displayr Help

This article describes how to create a CreateCustomTable R table with multiple statistics so that you can take advantage of all the customizations in the CreateCustomTable function.

The article explains how to go from a table with multiple statistics, like this:

To a table that enables you to add in more font, color, and formatting customizations (examples under See Also below):

Requirements

A Displayr table showing multiple statistics, or a combined table created with R (see: How to Combine Tables with Multiple Statistics Using R).

Method

The example below reformats a multiple-statistic table in R for the CreateCustomTable function. The code then automatically creates a list of spans to span both the Row % and Count rows of each brand. To modify for your own document, please read through the comments (denoted with #) and edit where appropriate.

The code below is on the more complex side, so familiarity with How to Work with Data in R, How to Perform Mathematical Calculations Using R, and How to Work with Conditional R Formulas is very useful.

You can see other options for further customizing the final table at CreateCustomTable. Click here for a similar example (and more) in your Displayr account.

From the toolbar, click Calculation > Custom Code and draw a box for your table on the page.

Paste in the following R code in the Code panel and then click Calculate:

###Identify table to use; change this to reference a table in your document.
mytable = your.table.reference.name

###Convert the table (which is an array because there are multiple stats) 
###to a 2 dimensional table
#load the reshape2 functions needed
library(reshape2)
#use melt() to make the table "long" with a row for each stat/row name pair
mytable=melt(mytable)
#use dcast() to make the table "wide" keeping two columns for brand (Var1) and statistic (Var3) and creating columns for each rating categories
mytable=dcast(mytable, Var1 + Var3 ~ Var2)

###Add in custom formatting for row% and counts
#round the data (in columns 3-5) to whole numbers
mytable[3:5]=apply(mytable[,3:5],2,round,0)
#add % to the row % rows (in columns 3-5)
mytable[mytable$Var3 == "Row %",3:5]=apply(mytable[mytable$Var3 == "Row %",3:5],2,paste0,"%")

###automatically create list of spans and format for final table
###(each brand span will span over the Row % and Count rows)
#create the list of lists automatically that create spans for final table using Var1
spans=lapply(unique(mytable$Var1),function(x) list(height=NROW(mytable)/length(unique(mytable$Var1)), label=x))
#create placeholder rownames which will be hidden later
rownames(mytable)=paste(mytable$Var1, mytable$Var3)

###Create the custom table
#row.header.labels are overridden as blank because we want to use spans to label
#col.widths sets the second column (the blank row labels) to 0 to hide it
flipFormat::CreateCustomTable(mytable[,!colnames(mytable) %in% c("Var1","Var3")],
         row.header.labels=rep(" ",NROW(mytable)),
         col.widths=c("25%","0%","25%","25%","25%"),
         row.span.align.horizontal="right",
         row.spans=spans,
         row.span.pad=5,
         border.width=1,
         border.color="black")

Given the data manipulation above, it can be helpful to see a preview of what the table looks like after melting and dcasting.

After melting:

After dcasting using the formula Var1 + Var3 ~ Var2:

Articles in this section

Requirements

Method

See Also

Related articles