Updating scripts, added license, contributor guidelines, shifting TODO lines to github issues

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kidwellj 2020-03-08 12:18:21 +00:00
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# Contributor Code of Conduct
As contributors and maintainers of this project, we pledge to respect all people who
contribute through reporting issues, posting feature requests, updating documentation,
submitting pull requests or patches, and other activities.
We are committed to making participation in this project a harassment-free experience for
everyone, regardless of level of experience, gender, gender identity and expression,
sexual orientation, disability, personal appearance, body size, race, ethnicity, age, or religion.
Examples of unacceptable behavior by participants include the use of sexual language or
imagery, derogatory comments or personal attacks, trolling, public or private harassment,
insults, or other unprofessional conduct.
Project maintainers have the right and responsibility to remove, edit, or reject comments,
commits, code, wiki edits, issues, and other contributions that are not aligned to this
Code of Conduct. Project maintainers who do not follow the Code of Conduct may be removed
from the project team.
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by
opening an issue or contacting one or more of the project maintainers.
This Code of Conduct is adapted from the Contributor Covenant
(https://www.contributor-covenant.org), version 1.0.0, available at
https://contributor-covenant.org/version/1/0/0/.

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GNU AFFERO GENERAL PUBLIC LICENSE
Version 3, 19 November 2007
Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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Preamble
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The licenses for most software and other practical works are designed
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interface could display a "Source" link that leads users to an archive
of the code. There are many ways you could offer source, and different
solutions will be better for different programs; see section 13 for the
specific requirements.
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU AGPL, see
<https://www.gnu.org/licenses/>.

8
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@ -0,0 +1,8 @@
html:
Rscript -e 'library(rmarkdown); rmarkdown::render("mapping_draft.Rmd", "html_document")'
hpc:
Rscript -e 'library(rmarkdown); rmarkdown::render("mapping_draft-hpc_optimised.Rmd", "html_document")'
pdf:
Rscript -e 'library(rmarkdown); rmarkdown::render("./mapping_draft.Rmd", "pdf_document")'

View file

@ -1,10 +1,12 @@
# Mapping Environmental Action
This code is associated with a scholarly paper "Mapping Environmental Action" (currently unpublished). In the paper, I draw on original data gathered from my research with Eco-groups in Scotland (2013-2017) in order to do comparative geospatial analysis of the coincidence of these groups with a number of standard demographics. You can read (an unpublished version of) the paper at (http://mapenvcom.jeremykidwell.info/mapping_draft.html).
## Why Reproducible Research? ##
If you're new to github and reproducible research, welcome! It's nice to have you here. Github is ordinarily a place where software developers working on open source software projects deposit their code as they write software collaboratively. However, in recent years a number of scholarly researchers, especially people working on research which involves a digital component (including me!) have begun to deposit their papers in these same software repositories. The idea here is that you can download all of the source-code and data used in this paper alongside the actual text, run it yourself and ["reproduce" the results](http://kbroman.org/steps2rr/). This can serve as a useful safeguard, a layer of research transparency, and a cool teaching tool for other persons interested in doing similar work.
If you're new to github and reproducible research, welcome! It's nice to have you here. Github is ordinarily a place where software developers working on open source software projects deposit their code as they write software collaboratively. However, in recent years a number of scholarly researchers, especially people working on research that involves a digital component (including me!) have begun to deposit their papers in these same software repositories. The idea is that you can download all of the source-code and data used in this paper alongside the actual text, run it yourself and ["reproduce" the results](http://kbroman.org/steps2rr/). This can serve as a useful safeguard, a layer of research transparency, and a cool teaching tool for other persons interested in doing similar work. Particularly when, as is the case in subject areas that are only just starting to get involved in the digital humanities, like religious studies, there is a dearth of work of this nature, it can be helpful to have examples of practice which can be reused, or at least used as an example.
Eschewing proprietary, expensive and unreliable software like Microsoft Word, I write in a combination of two languages: (1) [Markdown](https://en.wikipedia.org/wiki/Markdown) which is intended to be as close as possible to plain text while still allowing for things like boldfaced type, headings and footnotes; and (2) a programming language called [R](https://en.wikipedia.org/wiki/R_(programming_language)) to do all the data analysis. R is an object oriented language which was specifically designed for statistical analysis. It's also great fun to tinker with. As you look through this paper, you'll see that R code is integrated into the text of the document. This is indicated by a series of three backticks (```). There is a formal specification now at a mature stage of development, which is RMarkdown. You can read semi-official specification [for this here](https://bookdown.org/yihui/rmarkdown/pdf-document.html).
Eschewing proprietary, expensive and unreliable software like Microsoft Word, I write in a combination of two languages: (1) [Markdown](https://en.wikipedia.org/wiki/Markdown) which is intended to be as close as possible to plain text while still allowing for things like boldfaced type, headings and footnotes; and (2) a programming language called [R](https://en.wikipedia.org/wiki/R_(programming_language)) to do all the data analysis. R is an object oriented language that was specifically designed for statistical analysis. It's also great fun to tinker with. As you look through this paper, you'll see that R code is integrated into the text of the document. This is indicated by a series of three backticks (```). There is a formal specification now at a mature stage of development, which is RMarkdown. You can read semi-official specification [for this here](https://bookdown.org/yihui/rmarkdown/pdf-document.html).
To read a bit more on these things and start on your own path towards plain text reproducible research, I highly recommend:
- Karl Broman's guide, "[Initial Steps Toward Reproducible Research](http://kbroman.org/steps2rr/)"
@ -12,25 +14,54 @@ To read a bit more on these things and start on your own path towards plain text
The other advantage of putting this paper here is that readers and reviewers can suggest changes and point out errors in the document. To do this, I recommend that you create a github issue by clicking on the green "New issue" button [here](https://github.com/kidwellj/mapping_environmental_action/issues). If you must, you can also send me emails. More stuff about me [can be found here](http://jeremykidwell.info).
To skip ahead and start reading the actual paper, click on [`mapping_draft.rmd`](https://github.com/kidwellj/mapping_environmental_action/blob/master/mapping_draft.Rmd) above.
To skip ahead and start reading the actual paper in raw format, click on [`mapping_draft.rmd`](https://github.com/kidwellj/mapping_environmental_action/blob/master/mapping_draft.Rmd) above. If you were looking for the article (without code) you can also find a working draft here: (http://mapenvcom.jeremykidwell.info/mapping_draft.html).
Now for...
## The quick technical version ##
This repository contains the code and writing towards a (working draft of a) scholarly paper which presents my analysis of the geospatial footprint of eco-groups in the UK. This is based on research I have been conducting since 2013 and which is ongoing. The paper is written in R Markdown and for the most part, I'm using the conventions outlined by Kieran Healy [here](https://kieranhealy.org/blog/archives/2014/01/23/plain-text/) and is best viewed (I think) in [R Studio](https://www.rstudio.com) though it will be reasonably comprehensible to anyone using a Markdown editor. If I'm not working in RStudio, I'm probably in Sublime text, FYI. Co-authors and collaborators take note, generally, I use [Hadley Wickham's venerable R Style Guide](http://adv-r.had.co.nz/Style.html).
This repository contains the code and writing towards a (working draft of a) scholarly paper that presents my analysis of the geospatial footprint of eco-groups in the UK. This is based on research I have been conducting since 2013 and that is ongoing. The paper is written in R Markdown and for the most part, I'm using the conventions outlined by Kieran Healy [here](https://kieranhealy.org/blog/archives/2014/01/23/plain-text/) and is best viewed (I think) in [R Studio](https://www.rstudio.com) though it will be reasonably comprehensible to anyone using a Markdown editor. If I'm not working in RStudio, I'm probably in Sublime text, FYI. Co-authors and collaborators take note, generally, I use [Hadley Wickham's venerable R Style Guide](http://adv-r.had.co.nz/Style.html).
I'd be extremely happy if someone found errors, or imagined a more efficient means of analysis and either reported them as an issue on this github repository or sent me an email.
I'd be extremely happy if someone found errors, or imagined a more efficient means of analysis and either reported them as an issue on this github repository or sent me an email.
The actual article is in `mapping_draft.Rmd` and can be compiled using knitr (assuming you have R installed as well as required packages) using the script provided `knit_it_html.sh`
The actual article is in `mapping_draft.Rmd` and can be compiled using knitr (assuming you have R installed as well as required packages) using the `Makefile` provided.
Note: actual execution may take over an hour, as calls to `st_buffer` and `st_within` under `wilderness_data_prep` are computationally intensive. To compile more briskly, I recommend you comment out this final section and knit the markdown/html files.
Note: actual execution may take over an hour, as calls to `st_buffer` and `st_within` under `wilderness_data_prep` are computationally intensive. To compile more briskly, I recommend you comment out this final section and knit the markdown/html files. I have been relying on the University of Birmingham supercomputing cluster for execution, which has resulted in a parallel version of this script `mapping_draft-hpc_optimised.Rmd`. The latter will only run on the BlueBEAR cluster at UOB, though other scholars may want to consult this script to get a sense of how geospatial operations can be parallelised for more efficient execution.
Paths in this folder are used mostly for R processing. Towards this end folders have the following significance:
Paths in this folder are used mostly for R processing. I'm using a "project" oriented workflow, on which you can read more [in a blog by Jenny Bryan here](https://www.tidyverse.org/blog/2017/12/workflow-vs-script/). This uses the R package [here](https://cran.r-project.org/web/packages/here/index.html).
Towards this end folders have the following significance:
- `data` contains datasets used for analysis.
- `derived_data` contains files which represent modified forms of files in the above path.
- `figures` contains images and visualisations (graphic files) which are generated by R for the final form of the document.
- `cache` isn't included in github but is usually used for working files
Note: none of the contents of the above are included in the github repository unless they are unavailable from an external repository.
Note: none of the contents of the above are included in the github repository unless they are unavailable from an external repository.
## And, a few notes for the data scientists
Over the course of this research project (since 2013, really), the state of geospatial tools for datascience in R (and python) has shifted and the increased attention and resources that have been brought to bear on geospatial has resulted in a dramatic improvement in the quality and precision of tools available, particularly the development of SimpleFeatures and the [sf](https://github.com/r-spatial/sf) and [tmap](https://github.com/mtennekes/tmap) packages for R. Ggplot2 is awesome, but starts to creak quickly when you push it in more creative geospatial directions. There are also inefficiencies with data handling in some of the older packages (such as sp) that aren't apparent until you start working with large datasets. Underlying data formats have been shifting quite a lot as well, from csvt and [very problematic and proprietary ESRI shapefiles](http://switchfromshapefile.org/) to geojson/topojson and [Geopackage](http://switchfromshapefile.org/#geopackage) formats. The result of this has a need to completely rewrite this script mid-way through the research process. I've left some of the messy bits in with as comprehensive comments as possible to give a sense of things, but there remain some bits which are accidentally messy.
There are a few aspects of this code which are novel or were difficult that I'm proud of, which I hope may be useful and on ehich I'd especially value
- The use of sf() and tmap()
- The creation of vignettes for visualisations
- The level of reproducibility
- Optimisation of intensive geospatial operations for htpc and parallel computing
# Prerequisites
I've tried to follow best practices in setting up this script for reproducibility, but given some of the choices I've had to make computationally (e.g. running some operations in PostGIS) some setup is required before execution will be successful.
These steps are:
1. Acquire a working installation of R. I have produced a Docker container which replicates the environment I have used to execute this script which is probably the easiest way to complete this task.
2. Set up a working Postgres database with PostGIS extensions installed. The script will download necessary data and load it into your database if it is not already in place.
# Contributing
Please note that this project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.
# License
The content of this research paper are licensed under the [Creative Commons Attribution-ShareAlike 4.0 International Public License](https://creativecommons.org/licenses/by-sa/4.0/legalcode), and the underlying source code used to generate the paper is licensed under the [GNU AGPLv3](https://www.gnu.org/licenses/agpl-3.0.en.html) license. Underlying datasets designed as part of this research have their own licenses that are specified in their respective repositories.

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This script is a work in progress! In a spirit of open-ended collaboration and a continuous development cycle, I've collected aspirations towards improvement here in this file. There is a list of urgent tasks which need to be completed before the research is complete, and a batch of subsequent work which, while not necessary, can improve and extend the work here.
# Pre-pub changes todo:
Optimising for command-line use of knitr:
- [ ] fix issues preventing simultaneous output towards PDF/md, see [here](https://stackoverflow.com/questions/23621012/display-and-save-the-plot-simultaneously-in-r-rstudio) Related to problems with colliding knitr preferences for each document type
Complete transition away from sp() to sf()
- [ ] remove use of sp(), rgdal(), GISTools(), rgeos() and commands: readOGR, spTransform, poly.counts, prop.table
- [ ] remove use of ggplot2 (in favour of tmap)
Code changes:
- [ ] Shift intensive geospatial analysis using sf() within R to operations within PostGIS per https://www.r-bloggers.com/interact-with-postgis-from-r/ and https://rviews.rstudio.com/2019/03/21/how-to-avoid-publishing-credentials-in-your-code/
- [ ] Shift to "projects" as per (https://www.tidyverse.org/blog/2017/12/workflow-vs-script/)
- [ ] Set up conventions (per https://annakrystalli.me/talks/r-in-repro-research-dc.html#58) and
- [ ] Shift appendices to compendium
Streamline code:
- [ ] Merge htpc and wilderness versions back into main draft streamline drafts
note: htpc version created 25 Mar 2019, commit 9a8934935a57c4e9790b7c420eef7454d3fb7326; wilderness mods include pub data line 373 and lines 480ff
- [ ] remove "cuts" versions
Process oriented tasks:
- [ ] Install and use [ReDoc](https://github.com/noamross/redoc/blob/master/README.md) for reversible conversion to docx
- [ ] Consider implementing [knitcitations](https://github.com/cboettig/knitcitations)
- [ ] Install and use [here](https://here.r-lib.org/)
- [ ] Install and use [renv](https://rstudio.github.io/renv/)
Underlying data work:
- [ ] Convert shapefiles and csv to geopackages or geojson

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# Future changes (post-publication) to consider:
Spin off replicable operations into functions or packages:
- [ ] Ingest OrdnanceSurvey open data
- [ ] ProcessPubs, ProcessPlacesofWorship
- [ ] Ingest geolytics grocery store data

0
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Can't render this file because it is too large.

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@ -36,48 +36,67 @@ output:
---
```{r setup, include=FALSE}
require(knitr)
require(kableExtra)
# Note, this script has been written largely with RStudio on MacOS, but is compiled on
# an hpc cluster which runs Linux, so some tweaks below ensure smooth execution
# in both environments. Also, as above, the script is meant to output to both PDF and
# html_document knitr formats.
require(knitr) # used to knit RMarkdown format script into working documents in various formats
require(kableExtra) # used for markdown table formatting compatible with knitr
# note: some features of the below line are specific to html/pdf format and will need to be adapted pre-compile until dual outputs are working (see https://github.com/kidwellj/mapping_environmental_action/issues/2)
knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FALSE, dpi=300, fig.width=7)
# TODO: consider implementing knitcitations - https://github.com/cboettig/knitcitations
# TODO: fix simultaneous output towards PDF, see here: https://stackoverflow.com/questions/23621012/display-and-save-the-plot-simultaneously-in-r-rstudio
```
```{r load_packages, message=FALSE, warning=FALSE, include=FALSE}
## Default repo
# setwd("/Users/jeremy/gits/mapping_environmental_action")
# setwd("/Users/kidwellj/OneDrive\ -\ bham.ac.uk/writing/201708_mapping_environmental_action")
# Set up working machine independent working directory and environment
require(here) # used to keep working directory organised and portable
require(renv) # used to set up environment
require(usethis) # integrates git support for rStudio
# Set repository to be new standard, e.g. cloud server.
# This will avoid a dialogue box if packages are to be installed for below on first run.
# For smooth execution on command-line knitr - this will avoid a dialogue box
# if packages are to be installed for below on first run
local({r <- getOption("repos")
r["CRAN"] <- "https://cloud.r-project.org"
options(repos=r)
})
# TODO: remove sp etc. once sf is fully implemented
# TODO: automatically test for packages below on given execution environment and run install.packages() as needed.
require(RCurl) # used for fetching reproducible datasets
require(sf) # new simplefeature data class, supercedes sp in many ways
require(sp) # needed for proj4string, deprecated by sf()
require(rgdal) # deprecated by sf()
require(GISTools) # deprecated by sf()
require(rgeos) # deprecated by sf()
require(maptools)
# See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp()
# require(sp) # needed for proj4string, deprecated by sf()
# require(rgdal) # deprecated by sf()
# require(GISTools) # deprecated by sf()
# require(rgeos) # deprecated by sf()
# require(maptools)
require(ggplot2)
require(tmap) # using as an alternative to base r graphics and ggplot for geospatial plots
require(tmaptools) # for get_asp_ratio below
require(grid) # using for inset maps on tmap
require(broom) # required for tidying SPDF to data.frame for ggplot2
require(tidyr) # using for grouped bar plot
require(plyr)
require(dplyr)
# require(plyr) # already a dependency of knitr, remove?
# require(dplyr) # already a dependency of knitr, remove?
require(reshape2) # using for grouped bar plot
require(scales)
# require(sqldf) # using sqldf to filter before loading very large data sets
## Packages required for PostGIS database access
# Many thanks to Sébastien Rochette for documentation here: https://www.r-bloggers.com/interact-with-postgis-from-r/
library(DBI)
library(RPostgres)
library(sqlpetr) # useful for visual DB panels in RStudio, see https://smithjd.github.io/sqlpetr/
# library(rpostgis)
library(dbplyr)
## Packages required for knitr output
## Packages used for features or issues relating to html_document knitr output
require(plotly) # allows for export of plots to dynamic web pages
require(gtable) # more powerful package for multi-plot layouts, not necessary for knitr
require(showtext) # for loading in fonts
require(extrafont) # font support
## Packages used for features or issues relating to pdf_document knitr format
# Note: implementation of fonts (currently commented out) is specific to pdf_document output
# require(showtext) # for loading in fonts
# require(extrafont) # font support
# Set up local workspace:
if (dir.exists("data") == FALSE) {
@ -96,12 +115,15 @@ if (dir.exists("derivedData") == FALSE) {
# data-sets and papers.
# Working with EPSG codes for spatialfeature CRS given the usage of this approach with sf()
# for discussion related to this fix, see https://gis.stackexchange.com/q/313761/41474
# TODO: remove below as part of overall migration to sf()
# See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp()
bng <- CRS("+init=epsg:27700")
wgs84 <- CRS("+init=epsg:4326")
# Configure fonts for plots below
## Configure fonts for plots below, commented out currently because of incompatibilities
## Loading Google fonts (http://www.google.com/fonts)
# Note: implementation of fonts (currently commented out) is specific to pdf_document output
# font_add_google("Merriweather", "merriweather")
# The following will load in system fonts (uncomment and run as needed on first execution)
# font_import(pattern="[A/a]rial", prompt=FALSE)
@ -116,12 +138,25 @@ Until recently, environmentalism has been treated by governments and environment
```{r load_ecs_data, message=FALSE, warning=FALSE}
# read in Eco-Congregation Scotland data and-------------------
# ...turn it into a SpatialPointsDataFrame---------------------
# TODO: upload ECS-GIS-Locations_3.0.csv to zenodo repository, i.e.
# TODO: update below to match new dataset once it has been uploaded to zenodo
# if (file.exists("data/ECS-GIS-Locations_3.0.csv") == FALSE) {
# download.file("https://____.zip",
# destfile = "data/____.zip")
# unzip("data/____.zip", exdir = "data")
# }
# TODO: remove below as part of overall migration to sf()
# See issue https://github.com/kidwellj/mapping_environmental_action/issues/3 for progress re: migration from sp()
ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#")
# unnecessary with advent of sf (above)
coordinates(ecs) <- c("X", "Y")
# Modified to use EPSG code directly 27 Feb 2019
proj4string(ecs) <- bng
# Note, use of paste0 here relates to fix noted above.
# for discussion related to this approach, see https://gis.stackexchange.com/q/313761/41474
# read in Eco-Congregation Scotland data and-------------------
# ...turn it into a SpatialPointsDataFrame---------------------
ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=paste0("+init=epsg:",27700))
```
@ -357,7 +392,7 @@ Though there are too few eco-congregations and transition groups for a numerical
```{r 01_admin_ecs_choropleth, fig.width=4, fig.cap="Figure 1"}
# Note: for more information on EU administrative levels, see here: https://ec.europa.eu/eurostat/web/nuts/national-structures-eu
# TODO: clip choropleth polygons to buildings shapefile (possble superceded by pverlay on lev2)
# TODO: clip choropleth polygons to buildings shapefile (possibly superceded by pverlay on lev2)
# Draw initial choropleth map of ECS concentration (using tmap and sf below by default)
# Revising re: CRS inset maps complete to here

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@ -35,7 +35,7 @@ output:
---
```{r setup, include=FALSE}
```{R setup, include=FALSE}
require(knitr)
require(kableExtra)
knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FALSE, dpi=300, fig.width=7)
@ -43,7 +43,7 @@ knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FA
# TODO: fix simultaneous output towards PDF, see here: https://stackoverflow.com/questions/23621012/display-and-save-the-plot-simultaneously-in-r-rstudio
```
```{r load_packages, message=FALSE, warning=FALSE, include=FALSE}
```{R load_packages, message=FALSE, warning=FALSE, include=FALSE}
## Default repo
# setwd("/Users/jeremy/gits/mapping_environmental_action")
# setwd("/Users/kidwellj/OneDrive\ -\ bham.ac.uk/writing/201708_mapping_environmental_action")