Introduction to chartbookR

An example chart

Let’s look at an example chart to demonstrate chartbookR’s functionality.

Fig. 1 shows that manufacturing employment has fallen by 25% since the turn of the century. Despite this decline, manufacturing output increased by almost 15% since the year 2000. This was possible because of a 60% increase in productivity, mainly in the years before the global financial crisis.

These trends were further accelerated by the global financial crisis in 2007-08 and partially explain the ongoing trade war that the US is having with China. You can read more about these trends here.

This chart was created by downloading the data from either Bloomberg or FRED, rebasing, and then plotting it, using just three lines of short code:

zoo <- getFRED(tickers=c("PRS30006013", "OUTMS", "OPHMFG"), names=c("Employment", "Output", "Productivity"))
zoo <- Transform(data=zoo, rebase=TRUE)
LineChart(zoo, d1=4:6, no=1, title="The Decline of US Manufacturing Employment (indexed, 1987=100)", y1_def=c(50,250,25), grid=TRUE, rec=TRUE)

In the following sections, we will re-create this chart from scratch, step-by-step, explaining the features of chartbookR.

As you will see, the efficiency with which chartbookR does the data wrangling for you makes it possible to create large chartbooks in very short time.

chartbookR installation

Before we can start, we need to install chartbookR from github. If you haven’t installed devtools yet, now is a good time:

install.packages("devtools")

Load devtools and install chartbookR:

library(devtools)
install_github("kilianreber/chartbookR")

If you are behind a corporate proxy server, try the following and adjust with your own parameters:

install.packages("httr")
library(httr)

set_config(use_proxy(url="your.proxyserver.com", port=8080, username="proxy_username", password="proxy_password")) 
devtools::install_github("kilianreber/chartbookR")

Data download

For the data download there are two functions to choose from: getBBG and getFRED, which return the downloaded time series as zoo object.

zoo <- getBBG(tickers=c("USMMMANU", "PRODMANO", "PRODMANH"), names=c("Employment", "Output", "Productivity"), freq="QUARTERLY", time="31Y")

zoo <- getFRED(tickers=c("PRS30006013", "OUTMS", "OPHMFG"), names=c("Employment", "Output", "Productivity"))

Data transformation

Transforming the data is made easy with the Transform function, which can do rebasing, compute periodical changes, as well as other transformations. For this chart, we want to rebase all three time series, by doing:

zoo <- Transform(data=zoo, rebase=TRUE)

The Transform function computes the rebased indices and appends them to the exising zoo object.

We are now ready to visualize the rebased data.

Data visualization

To create the above line plot we can use the LineChart function. It is based on R’s plot function but simplifies a lot of the chart fine-tuning, such as secondary y-axes, adding a grid, shading recessions, adding ablines, etc.

To plot the rebased indices in columns 4 to 6, use:

LineChart(zoo, d1=4:6)

This creates a basic line plot:

We can easily produce a more sophisticated chart instead by including a title, grid lines, recession shading, and a horizontal line at 100. You can add a title by specifying title="The Decline of US Manufacturing Employment (indexed, 1987=100)". Grid lines are added by specifying grid=TRUE, recession shading is added with rec=TRUE.

Finally, add a figure number by adding no=1 and a more granular y-axis by specifying y1_def=c(50,250,25):

LineChart(zoo, d1=4:6, no=1, title="The Decline of US Manufacturing Employment (indexed, 1987=100)", y1_def=c(50,250,25), grid=TRUE, rec=TRUE)

This will produce:

LineChart uses its own default color palette, but you can change this anytime with the following code in the console – just plug in your preferred color scheme, for example:

palette(c("#4663ac", "#97a4cc", "#CEBC9A", "#595959", "#BF7057", "#ADAFB2", "#E7C667"))

Saving to Powerpoint

You can easily save this chart to Powerpoint, which is particularly useful once you have a whole chartbook that you’d like to update automatically. Firstly, install and load the package officer:

install.packages("officer")
library(officer)

Then, load an existing Powerpoint (*.pptx) file as template, which includes a correctly formatted title slide:

doc <- read_pptx("D:/Your/path/PPT_Template.pptx")

Then, copy the current plot to a new slide within that document, by providing an optional header for the slide, as well as an optional source footnote, for example:

makePPT(header="US GDP Nowcasts", source="FRED (Fed St. Louis)")

Finally, save the Powerpoint file by doing:

print(doc, target="D:/Your/target/path/Slideset.pptx")

This is it! Your first fully automated chartbook. Rinse and repeat above steps for a comprehensive chartbook that can be automatically updated from within R.

For more information on chartbookR revisit this site regularly and check out the