Open R shiny App from a new window here! Play with the App here: In this project I set out to build an interactive app to

Introduction In this post my package ‘cricketr’ takes a swing at One Day Internationals(ODIs). Like test batsman who adapt to ODIs with some innovative strokes, the cricketr package has some additional functions and some modified functions to handle the high strike and economy rates in ODIs. As before I have chosen my top 4 ODI

R allows you to create different plot types, ranging from the basic graph types like density plots, dot plots, bar charts, line charts, pie charts, boxplots and scatter plots, to the more statistically complex types of graphs such as probability plots, mosaic plots and correlograms. In addition, R is pretty known for its data visualization The post

This afternoon, we’ve seen in the training on data science that it was possible to use AIC criteria for model selection. > library(splines) > AIC(glm(dist ~ speed, data=train_cars, family=poisson(link="log"))) 438.6314 > AIC(glm(dist ~ speed, data=train_cars, family=poisson(link="identity"))) 436.3997 > AIC(glm(dist ~ bs(speed), data=train_cars, family=poisson(link="log"))) 425.6434 > AIC(glm(dist ~ bs(speed), data=train_cars, family=poisson(link="identity"))) 428.7195 And I’ve been asked...

I don’t understand why any researcher would choose not to use panel/multilevel methods on panel/hierarchical data. Let’s take the following linear regression as an example: , where is a random effect for the i-th group. A pooled OLS regression model for the above is unbiased and consistent. However, it will be inefficient, unless for all

Introduction In some circles the Ashes is considered the ‘mother of all cricketing battles’. But, being a staunch supporter of all things Indian, cricket or otherwise, I have to say that the Ashes pales in comparison against a India-Pakistan match. After all, what are a few frowns and raised eyebrows at the Ashes in comparison

We were asked a question on how to (in R) aggregate quarterly data from what I believe was a daily time series. This is a pretty common task and there are many ways to do this in R, but we’ll focus on one method using the zoo and dplyr packages. Let’t get those imports out of the way: library(dplyr) library(zoo) library(ggplot2) Now, we need...

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