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I recently did some pro bono work for Gun Control NZ reviewing the analysis by a market research firm of the survey that led to this media release: “Most New Zealanders back stronger gun laws”. The analysis all checked out ok. The task at that time... [Read more...]

The fifth release of the binb package just arrived on CRAN. binb regroups four rather nice themes for writing LaTeX Beamer presentations much more easily in (R)Markdown. As a teaser, a quick demo combining all four themes follows; documentation and e... [Read more...]

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Introduction The State of Connecticut is required to test all of its students over the course of their academic careers. The data by year and school is disclosed as it becomes available at the State Department of Education’s Edsight website. In keeping with its explorations of open CT data, ... [Read more...]

How could we apply certain functions conditionally without leaving the pipeflow? This way: df %__% { if(apply_filter == TRUE) filter(., condition) else . } %__% ... [Read more...]

Introduction Imputing missing values is quite an important task, but in my experience, very often, it is performed using very simplistic approaches. The basic approach is to impute missing values for numerical features using the average of each feature, or using the mode for categorical features. There are better ways ... [Read more...]

This post is to announce that the latest update to AzureRMR is now available on CRAN. Version 2.3.0 brings several changes to make life easier when managing resources in Azure. New in this version is a facility for parallelising connections to Azure, using a pool of background processes. Some operations, such ... [Read more...]

Recently, our team at Draper and Dash have been busy creating an NHS operational command centre. This command centre is different, as it uses a collection and ensemble of cutting edge predictive and machine learning techniques. To read the blog you can access this below: We have really enjoyed the ... [Read more...]

Let’s say we have a simple data frame as below and we want to select the female rows only. df % filter(., gender == "female")

## id gender
## 1 2 female
## 2 4 female
## 3 5 female

The filter() function in dplyr (and other similar functions from the package) use something called non-standard evaluation (NSE). In NSE, names are treated as string ... [Read more...]

Let’s say we have a simple data frame as below and we want to select the female rows only. df % filter(., gender == "female")

## id gender
## 1 2 female
## 2 4 female
## 3 5 female

The filter() function in dplyr (and other similar functions from the package) use something called non-standard evaluation (NSE). In NSE, names are treated as string ... [Read more...]

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Longitudinal changes in a population of interest are often heterogeneous and may be influenced by a combination of baseline factors. The longitudinal tree (that is, regression tree with longitudinal data) can be very helpful to identify and characterize the sub-groups with distinct longitudinal profile in a heterogenous population. This blog ... [Read more...]