A Fine Slice Of SQL Server
Evgeni Chasnovski has a new CRAN package:
I am glad to announce that my latest, long written R package ‘pdqr’ is accepted to CRAN. It provides tools for creating, transforming and summarizing custom random variables with distribution functions (as base R ‘p*()’, ‘d*()’, ‘q*()’, and ‘r*()’ functions). You can read a brief overview in one of my previous posts.
Click through for a description of the package.
Comments closedSebastian Sauer takes us through two of the most important assumptions of linear models:
Additivity and linearity as the second most important assumptions in linear models
We assume that \(y\) is a linear function of the predictors. If y is not a linear function of the predictors, we cannot expect the model to deliver correct insights (predictions, causal coefficients). Let’s check an example.
Read on to understand what this means, as well as the most important assumption.
Comments closedHong Ooi announces changes to several AzureR packages:
AzureVM 2.1.0
You can now create VM scalesets with attached data disks. In addition, you can specify the disk type (Standard_LRS, StandardSSD_LRS, or Premium_LRS) for the OS disk and, for a Linux Data Science Virtual Machine, the supplied data disk. This enables using VM sizes that don’t support Premium storage.
Click through for the full set of updates.
Comments closedAnisa Dhana shows off several tidyverse methods for combining data sets together:
semi_join
Thesemi_joinfunction is different than the previous examples of joins. A semi join creates a new dataset in which there are all rows from thedata1where there is a corresponding matching value indata2. Still, instead of the final dataset merging both the first (data1) and second (data2) datasets, it only contains the variables from the first one (data1).
Most of this looks like standard SQL joins, but read through to the end for a bonus which doesn’t typically appear in relational database products.
Comments closedThe R-hub blog has an interesting post on creating mocks in R for unit testing:
In some of these cases, the programming concept you’re after is mocking, i.e. making a function act as if something were a certain way! In this blog post we shall offer a round-up of resources around mocking, or not mocking, when unit testing an R package.
It’s interesting watching data scientists work through the same sorts of problems which traditional developers have hit, whether that be testing, deployment, or source control management. H/T R-bloggers
Comments closedSebastian Sauer shows a few techniques for visualizing linear models with two predictors:
Linear models are a standard way of predicting or explaining some data. Visualizing data is not only of didactical value but provides heuristical value too, as demonstrated by Anscombe’s Quartet.
Visualizing linear models in 2D is straightforward, but visualizing linear models with more than one predictor is much less so. The aim of this post is to demonstrate some ways do visualize linear models with more than one predictor, using popular R packages. We will focus on 3D examples, that is, two predictors.
I have a strong bias against 3D visuals because they tend to be so difficult to see clearly. There are times when they’re necessary, though.
Comments closedJohn Mount has a new introduction to rquery:
rqueryis a data wrangling system designed to express complex data manipulation as a series of simple data transforms. This is in the spirit ofR’sbase::transform(), ordplyr’sdplyr::mutate()and uses a pipe in the style popularized inRwithmagrittr. The operators themselves follow the selections in Codd’s relational algebra, with the addition of the traditionalSQL“window functions.” More on the background and context ofrquerycan be found here.The
R/rqueryversion of this introduction is here, and thePython/data_algebraversion of this introduction is here.
Check it out.
Comments closedMatthias Nistler walks through troubleshooting rBokeh missing argument errors:
This approach is my go-to solution to change a rBokeh plot for which there is an argument missing in rBokeh that is available in python.
– Create the plot.
– Inspect the structure (str(plot)) of the rBokeh object.
– Search for the python’s argument name.
– Overwrite the value with the desired option as derived from python’s bokeh.
Given how nice the bokeh package looks, I really want rBokeh to work well. Hopefully this experience improves over time.
Comments closedThomas Roh takes us through an oddity in R’s DBI library:
I ran into an issue the other day where I was tring to write a new table to a SQL Server Database with a non-default schema. I did end up spending a bit of time debugging and researching so I wanted to share for anyone else that runs into the issue. Using the
DBI::Idfunction, allows you to specify the schema when you are trying to write a table to a SQL Server database.
Click through for the end result. I will say that the more I work with DBI, the more I’m tempted to keep using rodbc, at least when working with SQL Server. H/T R-Bloggers.
Comments closedBrad Lindblad takes us through building a custom package in R:
Don’t repeat yourself (DRY) is a well-known maxim in software development, and most R programmers follow this rule and build functions to avoid duplicating code. But how often do you:
– Reference the same dataset in different analyses
– Create the same ODBC connection to a database
– Tinker with the same colors and themes in ggplot
– Produce markdown docs from the same templateand so on? Notice a pattern? The word “same” is sprinkled in each bullet point. I smell an opportunity to apply DRY!
This is a good point: packages don’t have to go out to the broader world. They’re useful even if they just help you (or your team) out. H/T R-bloggers
Comments closed