Publications by Aura Frizzati
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tidyverts tutorial Aura Frizzati Content Intro: tidyverts packages Data retrieval from StatsWales using statwalesr package Data cleaning Conversion of dataframe into a tsibble object Time series plotting 5.1 Interactive time lines with dygraphs 5.2 Seasonal plots Time series decomposition Forecasting 1. Intro: Tidyverts packages Based on the...
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tidyverts-tutorial
tidyverts tutorial Aura Frizzati Content Intro: tidyverts packages Data retrieval from StatsWales using statwalesr package Data cleaning Conversion of dataframe into a tsibble object Time series plotting 5.1 Interactive time lines with dygraphs 5.2 Seasonal plots Time series decomposition Forecasting 1. Intro: Tidyverts packages The tidyvert...
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R-tidyverts-tutorial 1. Intro: Tidyverts packages Based on the tidyverse collection of packages They have been created specifically to handle time series data...
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Sequence Analysis - R Tutorial Content Introduction to State Sequence Analysis R Libraries & example dataset Create a STate Sequence (STS) object Sequences’ visualisation: representation plots Index plots Frequency plots and tables Sequences’ visualisation: summarisation plots Sequence distribution plots (chronograms) Entropy Transition ...
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Survival Analysis: Tutorial in R
library(dplyr) library(survival) library(ggsurvfit) Tutorial from online YouTube video collection: https://www.youtube.com/watch?v=vX3l36ptrTU&list=PLqzoL9-eJTNDdnKvep_YHIwk2AMqHhuJ0&index=1 For an online version of this rmarkdown file, please check: https://rpubs.com/auraf285/SurvAnalysisR For the RMarkdown file, see https://github.com/AuraFrizz...
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Survival Analysis: Tutorial in R
library(dplyr) library(survival) library(ggsurvfit) Tutorial from online YouTube video collection: https://www.youtube.com/watch?v=vX3l36ptrTU&list=PLqzoL9-eJTNDdnKvep_YHIwk2AMqHhuJ0&index=1 1 What is censoring? Survival analysis models time-to-event as an outcome (e.g. time to death, how long does one wait on a waiting list for surgery, etc). ...
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An effective initial step for characterizing the nature of a time series and for detecting potential problems is to use data visualization. By visualizing the series we can detect initial patterns, identify its components and spot potential problems such as extreme values, unequal spacing, and missing values. The most basic and informative plot for...
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Kaggle challenge Get started on this competition through Kaggle Scripts Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a diff...
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TS basics
library(fable) library(ggplot2) White Noise err<-rnorm(n=200,mean=0,sd=1) wn<-data.frame(t = 0:199,y = err) %>% as_tsibble(index=t) wn %>% autoplot() + ggtitle("White Noise TS") TS with a stationary trend x <- 0:199 err<-rnorm(n=200,mean=0,sd=1) y<-10+0.03*x+err trend.var<-data.frame(t=x,y) %>% as_tsibble(index=t) trend.var %>% autoplot() ...
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