| title | author | date | output | ||||
|---|---|---|---|---|---|---|---|
Visualising weekly mortality data using polar graphs. |
Jason Hilton |
8 March 2019 |
|
Code for the Paper "Visualising Weekly Mortality Data using Polar Graphs
The paper itself is available here.
This repo provides the code needed for producing the paper.
The repository is designed so that the results can be reproduced by running a series of R scripts.
The following code chunk gives all the R packages used in this analysis.
install.packages(curl)
install.packages(dplyr)
install.packages(tidyr)
install.packages(ggplot2)
install.packages(magrittr)
install.packages(readxl)
install.packages(purrr)
install.packages(ggfan)
install.packages(httr)
install.packages(HMDHFDplus)
install.packages(lubridate)
install.packages(timeDate)
install.packages(rstan)
install.packages(bookdown)
install.packages(splines)
install.packages(viridis)
install.packages(broom)
install.packages(rmarkdown)
install.packages(knitr)
install.packages(xts)
install.packages(dygraphs)
install.packages(shiny)To get the data used in the analysis, you need to run an R script that downloads various files from the UK Office of National Statistics website and from the Human Mortality Database (HMD). To obtain the Human Mortality Database files you need to have a HMD username and password. If you want to run be able to run the relevant script from the command line, you can add the below lines to your .Renviron file to create environment variables containing your HMD username and password. You may need to create this in your home directory. I am not an expert on security, but I understand that using environment variables may not be completely secure, so do not use a password you use for anything else in this step.
HMD_user=your_user_name
HMD_pass=your_user_nameThen, you can run:
cd /path/to/viz_weekly
Rscript R/data_script.Rfrom the command line to obtain the files you need, ensuring the path to this repository is correct. Alternatively, you can run the code in the same script interactively, and enter your HMD username and password when prompted.
Once these files have been obtained, you can two more Rscripts which create rds
files containing the exposure and the death data needed to run the models described in the paper.
Rscript R/generate_exposures.R
Rscript R/death_data.RNext, we need to run the models themselves. This involves the below command
Rscript R/model_fitting.RFinally, the final pdf and html papers need to be produced.
Thanks to bookdown, this can be done pretty easily be execuating the command below.
Rscript paper/process_md.R
library(curl)
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(magrittr)##
## Attaching package: 'magrittr'
## The following object is masked from 'package:tidyr':
##
## extract
library(readxl)
library(purrr)##
## Attaching package: 'purrr'
## The following object is masked from 'package:magrittr':
##
## set_names
library(ggfan)
library(bookdown)
library(httr)##
## Attaching package: 'httr'
## The following object is masked from 'package:curl':
##
## handle_reset
library(HMDHFDplus)
library(lubridate)##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
library(timeDate)
library(rstan)## Loading required package: StanHeaders
## rstan (Version 2.18.2, GitRev: 2e1f913d3ca3)
## For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores()).
## To avoid recompilation of unchanged Stan programs, we recommend calling
## rstan_options(auto_write = TRUE)
##
## Attaching package: 'rstan'
## The following object is masked from 'package:magrittr':
##
## extract
## The following object is masked from 'package:tidyr':
##
## extract
library(splines)
library(viridis)## Loading required package: viridisLite
library(broom)
library(rmarkdown)
library(knitr)
library(xts)## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric
##
## Attaching package: 'xts'
## The following objects are masked from 'package:dplyr':
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## first, last
library(dygraphs)
library(shiny)
devtools::session_info()## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.5.2 (2018-12-20)
## os Ubuntu 18.04.2 LTS
## system x86_64, linux-gnu
## ui X11
## language en_GB:en
## collate en_GB.UTF-8
## ctype en_GB.UTF-8
## tz Europe/London
## date 2019-03-11
##
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