A Fine Slice Of SQL Server
Steph Locke notes that there was a bug in the R 3.4.0 for Windows installation executable:
If you’re getting the following error when you’ve installed R 3.4.0 on Windows, you’re not alone.
Error in if (file.exists(dest) && file.mtime(dest) > file.mtime(lib) && :
missing value where TRUE/FALSE needed
Read on for the solution.
Comments closedSteph Locke shows how to create a package in R:
Then we need to add github repository to our project. I use the git command line for this:
git remote add origin git@github.com:stephlocke/datasauRus.git git push --set-upstream origin masterWith just these things, I have a package that contains the unit test framework, documentation stubs, continuous integration and test coverage, and source control.
That is all you need to do to get things going!
This is great timing for me, as I’m starting to look at packaging internal code. Also, it’s great timing because it includes dinosaurs.
Comments closedIn shiny, you can use the
fileInputwith the parametermultiple = TRUEto enable you to upload multiple files at once. But how do you process those multiple files in shiny and consolidate into a single dataset?The bit we need from shiny is the
input$param$fileinputpathvalue.
Read the whole thing.
Comments closedI ran into an error in SQL Server R Services:
I recently worked through a strange error (with help from a couple sharp cookies at Microsoft) and wanted to throw together a quick blog post in case anybody else sees it.
I have SQL Server R Services set up, and in the process of running a fairly complex stored procedure, got the following error message:
Msg 39004, Level 16, State 22, Line 0
A ‘R’ script error occurred during execution of ‘sp_execute_external_script’ with HRESULT 0x80004005.
Check those output variable and result set definitions.
Comments closedJoseph Rickert explains what survival analysis is and shows an example with R:
Looking at the Task View on a small screen is a bit like standing too close to a brick wall – left-right, up-down, bricks all around. It is a fantastic edifice that gives some idea of the significant contributions R developers have made both to the theory and practice of Survival Analysis. As well-organized as it is, however, I imagine that even survival analysis experts need some time to find their way around this task view. (I would be remiss not to mention that we all owe a great deal of gratitude to Arthur Allignol and Aurielien Latouche, the task view maintainers.) Newcomers, people either new to R or new to survival analysis or both, must find it overwhelming. So, it is with newcomers in mind that I offer the following slim trajectory through the task view that relies on just a few packages: survival, KMsurv, Oisurv and ranger
The survival package, which began life as an S package in the late ’90s, is the cornerstone of the entire R Survival Analysis edifice. Not only is the package itself rich in features, but the object created by the
Surv()function, which contains failure time and censoring information, is the basic survival analysis data structure in R.
Survival analysis is an interesting field of study. In engineering fields, the most common use is calculating mean time to failure, but that’s certainly not the only place you’re liable to see it.
Comments closedEdwin Thoen has an interesting solution to a classic problem with histograms:
Two strategies that make the above into something more interpretable are taking the logarithm of the variable, or omitting the outliers. Both do not show the original distribution, however. Another way to go, is to create one bin for all the outlier values. This way we would see the original distribution where the density is the highest, while at the same time getting a feel for the number of outliers. A quick and dirty implementation of this would be
hist_data %>%
mutate(x_new = ifelse(x > 10, 10, x)) %>%
ggplot(aes(x_new)) +
geom_histogram(binwidth = .1, col = "black", fill = "cornflowerblue")
Edwin then shows a nicer solution, so read the whole thing.
Comments closedMala Mahadevan explains the Birthday problem and demonstrates it with SQL and R:
Given a room of 23 random people, what are chances that two or more of them have the same birthday?
This problem is a little different from the earlier ones, where we actually knew what the probability in each situation was.
What are chances that two people do NOT share the same birthday? Let us exclude leap years for now..chances that two people do not share the same birthday is 364/365, since one person’s birthday is already a given. In a group of 23 people, there are 253 possible pairs (23*22)/2. So the chances of no two people sharing a birthday is 364/365 multiplied 253 times. The chances of two people sharing a birthday, then, per basics of probability, is 1 – this.
The funny thing for me is that I’ve had the Birthday problem explained three separate times using as a demo the 20-30 people in the classroom. In none of those three cases was there a match, so although I understand that it is correct and how it is correct, the 100% failure to replicate led a little nagging voice in the back of my mind to discount it.
Comments closedA new version of R is now available:
Accumulating vectors in a loop is faster – Assigning to an element of a vector beyond the current length now over-allocates by a small fraction. The new vector is marked internally as growable, and the true length of the new vector is stored in the truelength field. This makes building up a vector result by assigning to the next element beyond the current length more efficient, though pre-allocating is still preferred. The implementation is subject to change and not intended to be used in packages at this time.
There’s a big list of changes, so check it out and think about upgrading.
Comments closedSteph Locke has shared how she put together a training lab for her R workshop:
This starts with the tidyverse & Rstudio then:
adds the requisite programs for dependencies in my package and
whoisformkpasswdto be able to work
installs packages from github, notably the one designed to facilitate the day of text analysis
get the shell script and the csv from the gist
make the shell script executable and then run it
I loved the business card touch. It’s easy enough to print out little strips of paper with the username and password, but this has a bit more staying power.
Comments closedSteph Locke has a presentation on performing logistic regression using R:
Logistic regressions are a great tool for predicting outcomes that are categorical. They use a transformation function based on probability to perform a linear regression. This makes them easy to interpret and implement in other systems.
Logistic regressions can be used to perform a classification for things like determining whether someone needs to go for a biopsy. They can also be used for a more nuanced view by using the probabilities of an outcome for thinks like prioritising interventions based on likelihood to default on a loan.
It’s a good introduction to an important statistical method.
Comments closed