1989 search results for "RStudio"

R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

June 9, 2016
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R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

Today we’ll be moving from linear regression to logistic regression. This lesson also introduces a lot of new dplyr verbs for data cleaning and summarizing that we haven’t used before. Once again, I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go Lesson 3: Logistic...

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Last Call for EARL Conference Early Bird Tickets

June 7, 2016
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Last Call for EARL Conference Early Bird Tickets

EARL 2016 – Early Bird Tickets Deadline EARLY BIRD Conference Tickets are only available until midnight on the 10th June. For all Early Bird options please check our website. Speakers Announced EARL 2016 is an exciting cross-sector Conference dedicated to the … Continue reading →

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Rexer Data Science Survey: Satisfaction Results

by Bob Muenchen I previously reported on the initial results of Rexer Analytics’ 2015 survey of data science tools here. More results are now available, and the comprehensive report should be released soon.  One of the more interesting questions on the survey … Continue reading →

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Curated list of R tutorials for Data Science

June 3, 2016
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Curated list of R tutorials for Data Science

Here is topic wise list of R tutorials for Data Science, Time Series Analysis, Natural Language Processing and Machine Learning. This list also serves as a reference guide for several common data analysis tasks. The R Language Awesome-R Repository on GitHub R Reference Card: Cheatsheet R bloggers: blog aggregator R Resources on GitHub Awesome R

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Using geom_step

June 3, 2016
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Using geom_step

geom_step is an interesting geom supplied by the R package ggplot2. It is an appropriate rendering option for financial market data and we will show how and why to use it in this article. Let’s take a simple example of plotting market data. In this case we are plotting the "ask price" (the publicly published … Continue reading...

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R for Publication by Page Piccinini: Lesson 2 – Linear Regression

June 2, 2016
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R for Publication by Page Piccinini: Lesson 2 – Linear Regression

This is our first lesson where we actually learn and use a new statistic in R. For today’s lesson we’ll be focusing on linear regression. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go back and do it. By the Lesson 2: Linear...

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Covcalc: Shiny App for Calculating Coverage Depth or Read Counts for Sequencing Experiments

June 1, 2016
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Covcalc: Shiny App for Calculating Coverage Depth or Read Counts for Sequencing Experiments

How many reads do I need? What's my sequencing depth? These are common questions I get all the time. Calculating how much sequence data you need to hit a target depth of coverage, or the inverse, what's the coverage depth given a set amount of sequenci...

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Recent presentations

June 1, 2016
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Recent presentations

The last month or so has been a whirlwind of awesomeness with a veritable bevvy of user group and conference talks on my part! I thought I would share the materials with you and provide some brief thoughts on how each presentation went. Sessions SQL Saturday Exeter : Stats 101 London Business Analytics (LBAG) : The post

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heatmaply: interactive heat maps (with R)

May 31, 2016
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I am pleased to announce heatmaply, my new R package for generating interactive heat maps, based on the plotly R package. tl;dr By running the following 3 lines of code: install.packages("heatmaply") library(heatmaply) heatmaply(mtcars, k_col = 2, k_row = 3) %>% layout(margin = list(l = 130, b = 40)) You will get this output in your browser … Continue reading...

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heatmaply: interactive heat maps (with R)

May 31, 2016
By
heatmaply: interactive heat maps (with R)

I am pleased to announce heatmaply, my new R package for generating interactive heat maps, based on the plotly R package. tl;dr By running the following 3 lines of code: install.packages("heatmaply") library(heatmaply) heatmaply(mtcars, k_col = 2, k_row = 3) %>% layout(margin = list(l = 130, b = 40)) You will get this output in your browser … Continue...

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