Analysis: Tables From R Into Word » G Forgeanirban

Category: Finance

As a seasoned financial writer, I've always been fascinated by the power of tables in conveying complex information. In recent years, I've noticed a significant shift towards converting research output into Word format, particularly with the help of tools like R Markdown and LibreOffice Writer. In this analysis, we'll delve into the world of tables from R to Word, exploring how they can be used to enhance our understanding of financial concepts.

Introduction

Tables are an essential tool for presenting data in a clear and concise manner. However, converting research output into Word can be a challenging task, especially when it comes to formatting and styling. In this tutorial, we'll use the G-Forge package to create tables from R data into Word format. We'll cover three main steps: loading the necessary packages, creating labels and units for the dataset, and combining the tables using htmlTable.

Step 1: Loading the Packages

To start, we need to load the necessary packages in our R environment. We'll use the Gmisc package for HTML manipulation and the knitr package for combining R Markdown with Word output.

 library(Gmisc) library(knitr) 

Step 2: Creating Labels and Units

Next, we need to create labels and units for our dataset. We'll use the Hmisc package for this purpose.

 data(mtcars) label(mtcars$mpg) <- "Gas" units(mtcars$mpg) <- "Miles/gal" 

We'll also create labels for the other variables in our dataset.

 label(mtcars$wt) <- "Weight" units(mtcars$wt) <- "10^3 lb" label(mtcars$am) <- "Transmission" units(mtcars$am) <- "" label(mtcars$gear) <- "Gears" units(mtcars$gear) <- "" 

Step 3: Creating the Tables

Now that we have our labels and units, it's time to create the tables. We'll use the htmlTable function from the Gmisc package.

 htmlTable(x = rbind(geardata, coldata, mpgdata, wtdata), caption = paste("My table 1. All continuous values are reported with mean and standard deviation, x̄ (± SD), while categories are reported in percentages, no (%)."), label = "Table1", rowlabel = "Variables", rgroup = c(label(geardata), label(coldata), label(mpgdata), label(wtdata)), n.rgroup = c(NROW(geardata), NROW(coldata), NROW(mpgdata), NROW(wtdata)), ctable = TRUE) 

The htmlTable function takes four main arguments: x, caption, label, and rowlabel. We'll use the rbind function to combine our data into a single table.

Step 4: Combining Tables

We can also have an internal reference to the table using the link attribute.

 htmlTable(x = rbind(geardata, coldata, mpgdata, wtdata), caption = paste("My table 1. All continuous values are reported with mean and standard deviation, x̄ (± SD), while categories are reported in percentages, no (%)."), label = "Table1", rowlabel = "Variables", rgroup = c(label(geardata), label(coldata), label(mpgdata), label(wtdata)), n.rgroup = c(NROW(geardata), NROW(coldata), NROW(mpgdata), NROW(wtdata)), ctable = TRUE) 

Conclusion

In this tutorial, we've explored the world of tables from R to Word. By using the G-Forge package and combining R Markdown with Word output, we can create informative and engaging tables that enhance our understanding of financial concepts.

As a seasoned financial writer, I encourage you to experiment with different table formatting options and styles. Remember to keep your paragraphs short, use blank lines between them, and include concrete examples and actionable insights.