strcode – structure your code better
I am pleased to announce my package strcode, a package that should make structuring code easier. You can install it from GitHub, a CRAN submission is planned at a later stage.
February 2017
Data Science for Doctors – Part 4 : Inferential Statistics (1/5)
Data science enhances people’s decision making. Doctors and researchers are making critical decisions every day. Therefore, it is absolutely necessary for those people to have some basic knowledge of data science. This series aims to help people that are around medical field to enhance their data science skills. We ... [Read more...]
Yes, you can run R in the cloud securely
Once thought of as the ‘little programming language that could’, R has fundamentally transformed the way data scientists and organisations use their data. It gives businesses the power to leverage big data and develop predictive models that enable action, not … Continue reading → [Read more...]
PCA – hierarchical tree – partition: Why do we need to choose for visualizing data?
Principal component methods such as PCA (principal component analysis) or MCA (multiple correspondence analysis) can be used as a pre-processing step before clustering. But principal component methods give also a framework to visualize data. Thus, the clustering methods can be represented onto the map provided by the principal component method. ...
More tidyverse: using dplyr functions
This week, we return to our “Getting Started With R” series. Today we are going to look at some tools from the “dplyr” package. Hadley Wickham, the creator of dplyr, calls it “A Grammar of Data Manipulation.” filter() Use filter() for […] [Read more...]
Is my time series additive or multiplicative?
Time series data is an important area of analysis, especially if you do a lot of web analytics. To be able to analyse time series effectively, it helps to understand the interaction between general seasonality in activity and the underlying
The post Is my time series additive or multiplicative? appeared ... [Read more...]
future: Reproducible RNGs, future_lapply() and more
future 1.3.0 is available on CRAN. With futures, it is easy to write R code once, which the user can choose to evaluate in parallel using whatever resources s/he has available, e.g. a local machine, a set of local machines, a set of remote machines, a... [Read more...]
strcode – structure your code better
I am pleased to announce my package strcode, a package that should make structuring code easier. You can install it from GitHub, a CRAN submission is planned at a later stage.
devtools::install_github("lorenzwalthert/strcode")
A concept for code structuring
The main feature of the package is its function to insert code breaks. ...
strcode – structure your code better
I am pleased to announce my package strcode, a package that should make structuring code easier. You can install it from GitHub, a CRAN submission is planned at a later stage.
devtools::install_github("lorenzwalthert/strcode")
A concept for code structuring
The main feature of the package is its function to insert code breaks. ...RcppMLPACK2 and the MLPACK Machine Learning Library
mlpack
mlpack is, to quote, a scalable machine learning library, written in C++,
that aims to provide fast, extensible implementations of cutting-edge machine learning
algorithms. It has been written by Ryan Curtin and others, and is
described in two ... [Read more...]
Animated Spirals
Ed Hawkins’ Global Temperature Spiral is my new favourite visualization.
It’s powerful, compelling, and super tangible.
I wanted to apply the spiral to my own data, so I got janky with ggplot2 and figured out how to do it.
Here’s my own spiral... [Read more...]
SatRday and visual inference of vine copulas
SatRday
From the 16th to the 18th of February, satRday was held in the City of Cape Town in South Africa. The programme kicked off with two days of workshops and then the conference on Saturday. The workshops were divided up into three large sections:... [Read more...]
Building Shiny App exercises part 7
Connect widgets & plots In the seventh part of our journey we are ready to connect more of the widgets we created before with our k-means plot in order to totally control its output. Of cousre we will also reform the plot itself properly in order to make it a real ... [Read more...]
Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization
factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including:
Principal Component Analysis (PCA), which is used to summarize the information contained in a continuous (i.e, quantitative) multivariate data by reducing the dimensionality of the data without loosing important information.
... [Read more...]
Television Trends as a Social Indicator
Contributed by Emil Parikh. He is currently in the NYC Data Science Academy 12-week, full-time Data Science Bootcamp program taking place between January 9th to March 31st, 2017. This post […]
The post Television Trends as a Social Indicator appeared first on NYC Data Science Academy Blog.
future 1.3.0: Reproducible RNGs, future_lapply() and More
future 1.3.0 is available on CRAN. With futures, it is easy to write R code once, which the user can choose to evaluate in parallel using whatever resources s/he has available, e.g. a local machine, a set of local machines, a set of remote machines, a high-end compute cluster (... [Read more...]
Bar bar plots but not Babar plots
You might have heard of the “bar bar plots” movement whose goal is to prevent using (let’s use ggplot2 language shall we) geom_bar when you could have used e.g. geom_boxplot or geom_histogram because the bar plot hides the variability of the dist... [Read more...]
padr::pad does now do group padding
A few weeks ago padr was introduced on CRAN, allowing you to quickly get datetime data ready for analysis. If you have missed this, see the introduction blog or vignette("padr") for a general introduction. In v0.2.0 the pad function is extended with a group argument, which makes your life ... [Read more...]
Predicting food preferences with sparklyr (machine learning)
This week I want to show how to run machine learning applications on a Spark cluster. I am using the sparklyr package, which provides a handy interface to access Apache Spark functionalities via R.
The question I want to address with machine learning ...
Bar bar plots but not Babar plots
You might have heard of the “bar bar plots” movement whose goal is to prevent using (let’s use ggplot2 language shall we) geom_bar when you could have used e.g. geom_boxplot or geom_histogram because the bar plot hides the variability of the dist... [Read more...]
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