One of the coolest packages for R is knitr. Essentially, it allows you to combine explanatory writing, such as a paper or blog post, directly with your analysis code in a Markdown document.
Zillow is a well-known website widely used by those searching for a home or curious to find out the value of their current home. What you may not know is that Zillow has a dedicated research page.
I frequently find myself needing to concatenate data sets but also wanting to be able to distinguish which row came from which data set originally. Introductory SAS courses tend to teach the in keyword, for a workflow similar to this:
Sometimes we have to deal with manually entered data, which means there is a good chance that the data needs to be cleaned for consistency due to the inevitable errors that creep in when typing in data, not to speak of any inconsistencies between individuals entering data.
I find myself needing to import CSV files with a relatively large number of columns. In many cases, proc import works surprisingly well in giving me what I want. But sometimes, I need to do some work while reading in the file and it would be nice to just use a data step to do so, but I don’t want to type it in by hand.
I am currently working with a database provided by the North Carolina Department of Public Safety that consists of several fixed-width files. Each of these has an associated codebook that gives the internal variable name, a label of the variable, its data type, as well as the start column and the length of the fields for each column.
I have been using both SAS and Python extensively for a while now. With each having great features, it was very useful to combine my skills in both languages by seamlessly moving between SAS and Python in a single notebook.
The Census Bureau has updated its population estimates for 2020 with county level data. This means any projects that have had to rely on the 2019 estimates can now switch to the 2020 estimates.