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transitioning to sf, working on plots

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Jeremy Kidwell 2018-11-26 12:04:56 +00:00
parent a2e87a98ac
commit cd6793b4f9
1 changed files with 30 additions and 29 deletions
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59
mapping_draft.Rmd
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@ -19,12 +19,13 @@ knitr::opts_chunk$set(fig.path='figures/', warning=FALSE, echo=FALSE, message=FA
```{r load_packages, include=FALSE}
# TODO: need to alter this to use new sf data class as sp is deprecated
# require(sf) # new simplefeature data class, supercedes sp in many ways
require(rgdal) # deprecated by sf()
require(sf) # new simplefeature data class, supercedes sp in many ways
require(rgdal)
require(GISTools) # deprecated by sf()
require(ggplot2)
require(broom) # required for tidying SPDF to data.frame for ggplot2
require(rgeos)
require(sp) # needed for proj4string
require(sp) # needed for proj4string, deprecated by sf()
require(ggmap)
require(maptools)
require(RCurl)
@ -65,10 +66,9 @@ Until recently, environmentalism has been treated by governments and environment
# read in Eco-Congregation Scotland data and-------------------
# ...turn it into a SpatialPointsDataFrame---------------------
# TODO: upload ECS-GIS-Locations_3.0.csv to zenodo repository, i.e.
# ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#")
# TODO: convert from sp() to sf() which will look something like:
# ecs <- st_read(system.file("pow_osm.gpkg", package = "spData"))
ecs <- read.csv(text=getURL("https://zenodo.org/record/1413333/files/ECS-GIS-Locations_2.9_enhanced.csv"))
ecs <- read.csv("data/ECS-GIS-Locations_3.0.csv", comment.char="#")
ecs_sf <- st_as_sf(ecs, coords = c("X", "Y"), crs=27700)
# unnecessary with advent of sf (above)
coordinates(ecs) <- c("X", "Y")
proj4string(ecs) = CRS(bng)
```
@ -98,10 +98,11 @@ Eco-Congregation Scotland began a year before the official launch of Eco-Congreg
```{r calculate_ecs_by_year}
# Tidy up date fields and convert to date data type
ecs$registration <- as.Date(ecs$registration, "%Y-%m-%d")
ecs$award1 <- as.Date(ecs$award1)
ecs$award2 <- as.Date(ecs$award2)
ecs$award3 <- as.Date(ecs$award3)
ecs$award4 <- as.Date(ecs$award4)
# TODO: Fix issues here with R complaining that "character string is not in a std...
# ecs$award1 <- as.Date(ecs$award1)
# ecs$award2 <- as.Date(ecs$award2)
# ecs$award3 <- as.Date(ecs$award3)
# ecs$award4 <- as.Date(ecs$award4)
```
The programme launched officially in 2001 at Dunblane Cathedral in Stirling and by 2005 the project had `length(ecs_complete_cases[ecs_complete_cases$year_begun < 2006, ])` congregations registered to be a part of the programme and 25 which had completed the curriculum successfully and received an Eco-Congregation award. By 2011, the number of registrations had tripled to `length(ecs_complete_cases[ecs_complete_cases$year_begun < 2012, ])` and the number of awarded congregations had quadrupled to `sum(ecs$award1 < "2012-01-01", na.rm=TRUE)`. This process of taking registrations and using a tiered award or recognition scheme is common to many voluntary organisations. The ECS curriculum was developed in part by consulting the Eco-Congregation England and Wales materials which had been released just a year earlier in 1999, though it has been subsequently revised, particularly with a major redesign in 2010. In the USA, a number of similar groups take a similar approach including Earth Ministry (earthministry.org) and Green Faith (greenfaith.org).
@ -112,10 +113,12 @@ In the case of Eco-Congregation Scotland, congregations are invited to begin by
```{r import_admin_data}
# read in polygon for Scottish admin boundaries
# TODO: upload bundle of admin data to new zenodo repository and alter below to use new URLs
# TODO: need to remove readOGR below once st_read is confirmed to be working as sf
download.file("https://borders.ukdataservice.ac.uk/ukborders/easy_download/prebuilt/shape/Scotland_ca_2010.zip",
destfile = "data/Scotland_ca_2010.zip")
unzip("data/Scotland_ca_2010.zip", exdir = "data")
admin_lev1 <- readOGR("./data", "scotland_ca_2010")
admin_lev1_sf <- st_read("data/scotland_ca_2010.shp")
# read in polygon for intermediate admin boundary layers
download.file("http://census.edina.ac.uk/ukborders/easy_download/prebuilt/shape/Scotland_parlcon_2011.zip",
@ -232,25 +235,18 @@ parishes <- rgdal::readOGR(dsn = "parishes", "england_cp_1991")
# Load population statistics for normalising data by population on admin_lev1
# TODO - consider adapting to use ONS mid-year statistics: https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/populationestimatesforukenglandandwalesscotlandandnorthernireland
admin_lev1_pop <- read.csv("./data/scotland_admin_2011pop.csv", stringsAsFactors=FALSE)
# commenting out original
# admin_joined <- merge(x=admin, y=admin_pop, by="CODE")
# TODO, need to tidy up merging process here as well
# TODO, merge is broken - need some help on this!
admin_lev1 <- merge(x=admin_lev1$code, y=admin_lev1_pop, by.x = as.character(admin_lev1$code), by.y = as.character9admin_lev1_pop$CODE)
# convert numbers to proper characters and ditch commas
admin_lev1$X2011_pop <- as.numeric(gsub("[,-]","",admin_lev1$X2011_pop))
# admin_joined$X2011_pop <- as.numeric(gsub("[,-]","",admin_joined$X2011_pop))
admin_lev1$pop_percent<- prop.table(admin_lev1$X2011_pop)
admin_lev1_pop <- read.csv("./data/scotland_admin_2011pop.csv")
admin_lev2_pop <- read.csv("./data/scotland_and_wales_const_scotland_2011pop.csv", stringsAsFactors=FALSE)
admin_joined_lev2 <- merge(x=admin_lev2, y=admin_lev2_pop, by="CODE")
# convert numbers to proper characters and ditch commas
admin_joined_lev2$Postcode.Population <- as.numeric(gsub("[,-]","",admin_joined_lev2$Postcode.Population))
admin_lev1 <- merge(x=admin_lev1, y=admin_lev1_pop, by.x = "code", by.y = "CODE")
admin_lev1$pop_percent<- prop.table(as.numeric(admin_lev1$X2011_pop))
admin_lev3_pop <- read.csv("./data/district_borough_unitary_ward_scotland_2011pop.csv", stringsAsFactors=FALSE)
admin_joined_lev3 <- merge(x=admin_lev3, y=admin_lev3_pop, by="CODE")
admin_joined_lev3$Postcode.Population <- as.numeric(gsub("[,-]","",admin_joined_lev3$Postcode.Population))
admin_lev2_pop <- read.csv("./data/scotland_and_wales_const_scotland_2011pop.csv")
admin_joined_lev2 <- merge(x=admin_lev2, y=admin_lev2_pop, by.x = "code", by.y = "CODE")
# Considering removing admin_lev3 calculations as this seems to be unnecessary level granularity
# admin_lev3_pop <- read.csv("./data/district_borough_unitary_ward_scotland_2011pop.csv", stringsAsFactors=FALSE)
# admin_joined_lev3 <- merge(x=admin_lev3, y=admin_lev3_pop, by="CODE")
# admin_joined_lev3$Postcode.Population <- as.numeric(gsub("[,-]","",admin_joined_lev3$Postcode.Population))
```
Perhaps the first important question to ask of these groups is, where are they? I calculated the spread of eco-congregations and transition groups across each of the 32 council areas in Scotland. Every council area in Scotland has at least one eco-congregation or transition group). The most are located in `as.character(admin_lev1$NAME_2[which.max(admin_lev1$ecs_count)])`, with `max(admin_lev1$ecs_count)`, whereas the mean among all the 32 council areas is `mean(admin_lev1$ecs_count)`, with a median of `median(admin_lev1$ecs_count)`, standard deviation of `sd(admin_lev1$ecs_count)`, and interquartile range of `IQR(admin_lev1$ecs_count)`. The following choropleth maps show the relative concentration of eco-congregations (indicated by yellow to red).
@ -265,7 +261,12 @@ Perhaps the first important question to ask of these groups is, where are they?
# Row 1 plot using polygons from admin_lev1 and row 2 plot using ploygons from admin_lev2
# 3. Need to clip choropleth polygons to buildings shapefile
ggplot()
ggplot() +
geom_sf(data = admin_lev1_sf)
# geom_point(data=as.data.frame(ecs), aes(x=X, y=Y))
pdf(file="figures/admin_choropleth_ecs.pdf", width=4, height=4)