A Fine Slice Of SQL Server
Specifically, let us assume that we wish to analyze traffic density for buses and coaches. The main thing we are interested in is the frequency of traffic across a particular route.
Let’s take an example. If buses cover 100 miles on a route that is 5 miles long within a certain timeframe, then the frequency will be greater than 100 miles covered on a route that is 10 miles long over the same time period.
Read on for an interesting example.
Comments closedNotice only grouping columns and columns passed through an aggregating calculation (such as
max()) are passed through (the columnzis not in the result). Now becauseyis a function ofxno substantial aggregation is going on, we call this situation a “pseudo aggregation” and we have taught this before. This is also why we made the seemingly strange choice of keeping the variable namey(instead of picking a new name such asmax_y), we expect theyvalues coming out to be the same as the one coming in- just with changes of length. Pseudo aggregation (using the projectiony[[1]]) was also used in the solutions of the column indexing problem.Our
wraprpackage now supplies a special case pseudo-aggregator (or in a mathematical sense: projection):psagg(). It works as follows.
In this post, John calls the act of grouping functional dependencies (where we can determine the value of y based on the value of x, for any number of columns in y or x) pseudo-aggregation.
Comments closedSo, you’ve been asked to make a reprex and you want to include a bit of data that you have in a spreadsheet. Meet {datapasta}, a package by Miles McBain that can make your life a whole lot easier. Once you’ve installed datapasta, you simply copy a selected number of rows from your spreadsheet (remember, this is a minimal reproducible example), and click the
Paste as tribbleoption from the DATAPASTA section of the Addins dropdown
Click through for a demo.
Comments closedBrad Lewellyn shows us how to create custom R visuals within Power BI:
Over the last few posts, we’ve shown how to use custom R visuals built by others. Today, we’re going to build our own using the Custom R Visual available in Power BI Desktop. If you haven’t read the second post in this series, Getting Started with R Scripts, it is highly recommended you do so now, as it provides necessary context for how to link Power BI to your local R ISE.
In the previous post, we created a bunch of log-transformed measures to find good predictors for Revenue. We’re going to use these same measures today to create a basic linear regression model to predict Revenue. If you want to follow along, the dataset can be found here. Here’s the custom DAX we used to create the necessary measures.
Click through for the example.
Comments closedJohn Mount explains, using Conway’s Game of Life, the importance of using vectors in R over scalars:
R is an interpreted programming language with vectorized data structures. This means a single R command can ask for very many arithmetic operations to be performed. This also means R computation can be fast. We will show an example of this using Conway’s Game of Life.
Conway’s Game of Life is one of the most interesting examples of cellular automata. It is traditionally simulated on a rectangular grid (like a chessboard) and each cell is considered either live or dead. The rules of evolution are simple: the next life grid is computed as follows:
To compute the state of a cell on the next grid sum the number of live cells in the eight neighboring cells on the current grid.
If this sum is 3 or if the current cell is live and the sum is 2 or 3, then the cell in the next grid will be live.
Not only is the R code faster, but it’s also terser.
Comments closedKristian Larsen demonstrates the corrplot package in R:
First we need to read the packages into the R library. For descriptive statistics of the dataset we use the
skimrpackage and for visualization of correlation matrix we use thecorrplotpackage. We will work with windspeed dataset from thebReezepackage:# Read packages into R library library(bReeze) library(corrplot) library(skimr)
Click through for the demo.
Comments closedIn R, there is a handy function called
available.packages()that returns a matrix of details corresponding to packages currently available at one or more repositories. Unfortunately, the format isn’t initially amenable to manipulation. For example, consider the readr packagereadr_desc = available.packages() %>% as_tibble() %>% filter(Package == "readr")I immediately converted the data to a tibble, as that
changed the rownames to a proper column
changed the matrix to a data frame/tibble, which made selecting easier
There’s a good use of R functionality to delve into package requirements, as well as a script to try it out yourself.
Comments closedMaelle Salmon shows us how to test our R packages within R:
If you’re brand-new to unit testing your R package, I’d recommend reading this chapter from Hadley Wickham’s book about R packages.
There’s an R package called
RUnitfor unit testing, but in the whole post we’ll mention resources around thetestthatpackage since it’s the one we use in our packages, and arguably the most popular one.testthatis great! Don’t hesitate to reads its docs again if you started using it a while ago, since the latest major release added thesetup()andteardown()functions to run code before and after all tests, very handy.To setup testing in an existing package i.e. creating the test folder and adding
testthatas a dependency, runusethis::use_testthat(). In our WIPpRojectspackage, we set up the tests directory for you so you don’t forget. Then, in any case, add new tests for a function usingusethis::use_test().The
testthispackage might help make your testing workflow even smoother. In particular,test_this()“reloads the package and runs tests associated with the currently open R script file.”, and there’s also a function for opening the test file associated with the current R script.
This is an area where I know I need to get better, and Maelle gives us a plethora of tooling for tests.
Comments closedAnisa Dhana shows off some of the data reshaping functionality available in the tidyr package:
As it is shown above, the variable
agegphas 6 groups (i.e., 25-34, 35-44) which has different alcohol intake and smoking use combinations. I think it would be interesting to transform this dataset from long to wide and to create a column for each age group and show the respective cases. Let see how the dataset will look like.dt %>% spread(agegp, ncases) %>% slice(1:5)
Click through for a few additional transformations.
Comments closedNina Zumel shows how to use the cdata package to create faceted ggplot2 plots:
First, load the packages and data:
library("ggplot2") library("cdata") iris <- data.frame(iris)Now define the data-shaping transform, or control table. The control table is basically a picture that sketches out the final data shape that I want. I want to specify the
xandycolumns of the plot (call these the value columns of the data frame) and the column that I am faceting by (call this the key column of the data frame). And I also need to specify how the key and value columns relate to the existing columns of the original data frame.
Read on to see how you can use cdata to tie together different faceted plots.