Monthly Archives: March 2018

building historical socio-demographic profiles

March 28, 2018
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building historical socio-demographic profiles

Some preliminaries Socio-economic profiles Age distribution profiles Summary This post demonstrates a simple workflow for building census-based, historical socio-demographic profiles using the R package tidycensus. The goal is to outline a reproducible method for quick visual exploration of trend data made available via the American Community Survey (ACS). We focus mostly on socio-economic summary data included in ACS data profile tables; however, we also...

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Automated Data Collection with R and mlbgameday

March 28, 2018
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Automated Data Collection with R and mlbgameday

Opening day is on the way Time to set up a persistent database to collect every pitch thrown in this year’s baseball season. The mlbgameday package is designed to facilitate extract, transform and load for MLBAM “Gameday” data. The package is optimized for parallel processing of data that may be larger than memory. Learn more about the project here. Install from...

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Compute Signal Detection Theory Indices with R

March 28, 2018
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Compute Signal Detection Theory Indices with R

Signal Detection Theory Indices (dprime, beta…) Signal detection theory (SDT) is used when psychologists want to measure the way we make decisions under conditions of uncertainty. SDT assumes that the decision maker is not a passive receiver of information, but an active decision-maker who makes difficult perceptual judgments under conditions of uncertainty. To apply signal detection theory to a data...

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Standardize (Z-score) a dataframe

March 28, 2018
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Standardize (Z-score) a dataframe

Standardize / Normalize / Z-score / Scale The standardize() function allows you to easily scale and center all numeric variables of a dataframe. It is similar to the base function scale(), but presents some advantages: it is tidyverse-friendly, data-type friendly (i.e., does not transform it into a matrix) and can handle dataframes with categorical data. library(psycho) library(tidyverse) z_iris % psycho::standardize() summary(z_iris) ...

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Automated Data Collection with R and mlbgameday

March 28, 2018
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Automated Data Collection with R and mlbgameday

Opening day is on the way Time to set up a persistent database to collect every pitch thrown in this year’s baseball season. The mlbgameday package is designed to facilitate extract, transform and load for MLBAM “Gameday” data. The package is op...

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Teaching an advanced R course

March 28, 2018
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Teaching an advanced R course

In this post, I come back to my first experience teaching an advanced R course over the past month. Content This course was programmed for 10 sessions (3 hours each) and I initially wanted to talk about the following subjects: R programming and g...

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Feb 2018: “Top 40” New Package Picks

March 28, 2018
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Feb 2018: “Top 40” New Package Picks

Here are my picks for the “Top 40” packages of the 171 new packages that made it to CRAN (and stuck) in February, organized into the following categories: Computational Methods, Data, Finance, Science, Statistics, Time Series, and Utilities. Computational Methods adnuts v1.0.0: Provides an implementation of the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) for ADMB and TMB models. The...

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Learn to R blog series – R and RStudio

March 28, 2018
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Hello everyone, welcome back! This post marks the beginning, hopefully, of your foray into the wonderful world of R and RStudio…and my delve into the odd vlog to go with the blog! I’ve brushed my hair for you, and I don’t do that for just anyone, so you’d better watch it at least once! So without further ado. R R...

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DT 0.4: Editing Tables, Smart Filtering, and More

March 28, 2018
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DT 0.4: Editing Tables, Smart Filtering, and More

It has been more than two years since we announced the initial version of the DT package. Today we want to highlight a few significant changes and new features in the recent releases v0.3 and v0.4. The full changes can be found in the release notes. Editable tables Now you can make a table editable through the new argument datatable(..., editable...

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Tap Water Sentiment Analysis using Tidytext

March 28, 2018
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Tap Water Sentiment Analysis using Tidytext

In developed countries, tap water is safe to drink and available for a meagre price. Despite the fact that high-quality drinking water is almost freely available, the consumption of bottled water is increasing every year. Bottled water companies use sophisticated … Continue reading → The post Tap Water Sentiment Analysis using Tidytext appeared first on The Devil is in the...

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