Tag Archives: formatting

Conditional Formatting of a Table in R

Background

There are a few ways to approach the problem of a conditionally formatted table in R. You can use the ReporteRs package's FlexTable() function, the formattable package, or the condformat package. These allow you to produce a conditionally formatted tables in HTML. You can also use xtable package and essentially program what you want in LaTeX via the xtable() function.

In my desire for something simple-ish, I am going do this graphically using the image() function as suggested here. The benefit is that I can then push the table into an RMarkdown generated PDF document easily.

The Problem

Suppose that you want to prepare a summary of how resident and medical student orders are placed on various wards. You obtain data that is formatted in the following manner.

There are 4 wards: medicine, surgery, ER and orthopedics. Orders can come in as computerized physician order entry (CPOE), verbal or written. The orders have to be cosigned by staff and this is recorded as TRUE/FALSE because staff are not always compliant in logging on to the EMR to cosign the trainee orders.

Preparing Proportions Table

Let's start with the assumption that we want to apply the same conditional formatting to all data in the table. That is, we want to color code all results with the same algorithm. We can used the image() function to get this done. Let's display the rates at which different order types (CPOE, verbal,or written) from the four wards. We can generate the proportions table in percent very easily with the prop.table() and table() functions operating on the first two columns of our orders data:

A DIY Approach with the Image Function

The image() function produces a tile plot based on matrix of z values, where z = f(x,y) using colours we can define and thresholds for switching from one colour to the next based on a breaks parameter. In our case, we will say that if the result is less than equal to 25%, we will colour the tile blue, if it is greater than 25% but less than or equal to 50%, we will colour it red, and if it greater than 50%, it will be yellow.

You will note that we have to transpose the data with the t() function because the image function plots the rows on the x axis on the columns on the y axis. You will also notice that we need to plot y descending on the y-axis to account for the fact that our tabular data has increasing index going down but the tile plot will default to have increasing y going up. We can also need to suppress the axes and their labels. The reader can comment out the lines xaxt = 'n' and yaxt = 'n' to see what is going on in terms of x and y values.

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Now we can write our values over top with the text() function.

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And then we can write the variable names (which we yank from the attributes of the table) into the figure margin and draw some lines to make it look pretty. It was necessary to use the adj and padj parameters to make it look a little cleaner.

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Conditionally Coloured Text

Now, if you want to make the text colour match the background colour, we will need a little function.

and then apply it over the values of the matrix:

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Different Conditions for Different Columns

Now suppose you wanted different conditional formatting for each column. This is kind of a pain because you will need to provide the image() function a matrix to generate an appropriate fill-colour and a different matrix for the data to be written in each cell. Let's imagine for example that we want to include the compliance rate for co-signing in a fourth column and this is the only column we want coloured. To this column we want a colour scheme applied wherein if compliance is less than or equal to 20%, the colour is red, between 20% and 80%, it is yellow, and above 80% it is green.

We can calculate a proportions table based on columns 1 and 3 of the orders dataframe and then we can define a matrix fill.data that has NA on all the rates we calculated above.

Now the proportions matrix is as follows:

and the fill data is:

Now we can apply the image() function to the fill.data matrix. When it comes to writing the data in the cells, we will use the original my.data matrix and we will adjust out color.picker() function.

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So, it looks like this could become super–awkward if we had elaborate conditions to apply. This is where a packages like condformat and formattable come in handy. If you use the condformat package, you can include the table in an RMarkdown generated PDF or HTML document. However, the formattable() function, though capable of much prettier output, does not work with PDFs generated using RMarkdown.

First, here is a condformat example. Suppose we wanted to colourized CPOE in shades of green because CPOE is more operationally desirable and verbal/written orders in shades of red because they are less operationally desirable. We also want the red/yellow/green formatting in the Cosigned column. Using condformat we could do the following:

CPOE Verbal Written Cosigned
1 49.3 8.7 42.0 75.3
2 30.0 4.0 66.0 52.0
3 89.5 7.0 3.5 88.5
4 8.0 23.0 69.0 13.0

You can see that the rownames are suppressed with condformat(). You could circumvent this by putting the rownames into their own column. This package is pretty easy to use and with PDF rendering (shown below) it produces something more LaTeX-ish than what is shown above which was generated straight to HTML.

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For something more attractive looking, here is an example of something similar using the formattable package (borrowing heavily from the code author's examples ):

CPOE Verbal Written Cosigned
Med 49.3 8.7 42.0 75.30 (rank: 02)
Surg 30.0 4.0 66.0 52.00 (rank: 03)
ER 89.5 7.0 3.5 88.50 (rank: 01)
Orth 8.0 23.0 69.0 13.00 (rank: 04)

I hope that this points you in the right direction.





And as for conditions:

“If you declare with your mouth, “Jesus is Lord,” and believe in your heart that God raised him from the dead, you will be saved.”

Romans 10:9

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