Iterate Quickly

In a typical R Markdown document, you must re-knit the document to see your changes, which can take some time if it contains non-trivial computations. R Notebooks, however, let you run code and see the results in the document immediately. They can include just about any kind of content R produces, including console output, plots, data frames, and interactive HTML widgets.

You can see the progress of the code as it runs:

You can preview the results of individual inline expressions, too:

Even your LaTeX equations render in real-time as you type:

This focused mode of interaction doesn’t require you to keep the console, viewer, or output panes open. Everything you need is at your fingertips in the editor, reducing distractions and helping you concentrate on your analysis. When you’re done, you’ll have a formatted, reproducible record of what you’ve accomplished, with plenty of context, perfect for your own records or sharing with others.

"How can I make my code faster?"

If you write R code, then you’ve probably asked yourself this question. A profiler is an important tool for doing this: it records how the computer spends its time, and once you know that, you can focus on the slow parts to make them faster.

RStudio now includes integrated support for profiling R code and for visualizing profiling data. R itself has long had a built-in profiler, and now it’s easier than ever to use the profiler and interpret the results.

To profile code with RStudio, select it in the editor, and then click on Profile -> Profile Selected Line(s). R will run that code with the profiler turned on, and then open up an interactive visualization.

In the visualization, there are two main parts: on top, there is the code with information about the amount of time spent executing each line, and on the bottom there is a flame graph, which shows what R was doing over time. In the flame graph, the horizontal direction represents time, moving from left to right, and the vertical direction represents the call stack, which are the functions that are currently being called. (Each time a function calls another function, it goes on top of the stack, and when a function exits, it is removed from the stack.)

The Data tab contains a call tree, showing which function calls are most expensive:

Armed with this information, you’ll know what parts of your code to focus on to speed things up!

Data Import

RStudio now integrates with the readr, readxl, and haven packages to provide comprehensive tools for importing data from many text file formats, Excel worksheets, as well as SAS, Stata, and SPSS data files. The tools are focused on interactively refining an import then providing the code required to reproduce the import on new datasets.

For example, here’s the workflow we would use to import the Excel worksheet at http://www.fns.usda.gov/sites/default/files/pd/slsummar.xls.

First provide the dataset URL and review the import in preview mode (notice that this file contains two tables and as a result requires the first few rows to be removed):

We can clean this up by skipping 6 rows from this file and unchecking the "First Row as Names" checkbox:

The file is looking better but some columns are being displayed as strings when they are clearly numerical data. We can fix this by selecting "numeric" from the column drop-down:

The final step is to click "Import" to run the code displayed under "Code Preview" and import the data into R. The code is executed within the console and imported dataset is displayed automatically: