swapping out ggplot for tmap

mapping_draft.Rmd

@@ -31,6 +31,8 @@ output:
     fig_caption: true
     citation_package: natbib
     latex_engine: xelatex
+  always_allow_html: yes
+  
 ---
 
 ```{r setup, include=FALSE}
@@ -198,9 +200,8 @@ proj4string(transition_wgs) <- CRS(wgs84)
 transition <- spTransform(transition_wgs, bng)
 transition_sf <- st_as_sf(transition, coords = c("X", "Y"), crs=27700)
 
-# read in all_churches data and turn it into a SpatialPointsDataFrame
+# read in all_churches data (data set generated by Jeremy Kidwell to replace PointX data used from Ordnance Survey)
-# TODO: need to remove all data points which are outside BNG area to 
+# TODO: need to remove all data points which are outside BNG area to resolve error
-# resolve error
 # also need to make symmetrical with ECS denominations, add Methodist 
 # churches, remove nazarene and salvation army
  
@@ -217,7 +218,6 @@ coordinates(pow_pointX) <- c("feature_easting", "feature_northing")
 proj4string(pow_pointX) <- proj4string(admin_lev1)
 pow_pointX_sf <- st_as_sf(pow_pointX, coords = c("X", "Y"), crs=27700)
 
-
 # read in Scottish Community Dev. trust data and turn it into a SpatialPointsDataFrame
 dtas <- read.csv("data/community-dev-trusts-2.6.csv")
 coordinates(dtas) <- c("X", "Y")
@@ -229,7 +229,6 @@ permaculture <- read.csv("data/permaculture_scot-0.8.csv")
 coordinates(permaculture) <- c("X", "Y")
 proj4string(permaculture) <- proj4string(admin_lev1)
 permaculture_sf <- st_as_sf(permaculture, coords = c("X", "Y"), crs=27700)
-
 ```
 
 ```{r process_admin_data}
@@ -298,10 +297,8 @@ admin_lev1$ecs_count_pownorm <- admin_lev1$ecs_count * admin_lev1$pow_percent
 
 # Preserve scale
 admin_lev1$ecs_count_popnorm_scaled <- admin_lev1$ecs_count_popnorm*(sum(admin_lev1$ecs_count)/sum(admin_lev1$ecs_count_popnorm))
-
 admin_lev1$ecs_count_pownorm_scaled <- admin_lev1$ecs_count_pownorm*(sum(admin_lev1$ecs_count)/sum(admin_lev1$ecs_count_pownorm))
 
-
 # Load population statistics for normalising data by population on admin_lev2
 admin_lev2_pop <- read.csv("./data/scotland_and_wales_const_scotland_2011pop.csv")
 admin_lev2 <- merge(x=admin_lev2, y=admin_lev2_pop, by.x = "code", by.y = "CODE")
@@ -326,14 +323,6 @@ Perhaps the first important question to ask of these groups is, where are they?
 # Note: for more information on EU administrative levels, see here: https://ec.europa.eu/eurostat/web/nuts/national-structures-eu
 # TODO: Need to clip choropleth polygons to buildings shapefile
 
-
-# Prepare admin_lev1 for tidyr and reinsert dropped columns
-names(admin_lev1)[names(admin_lev1) == "newcode"] <- "id"
-admin_lev1@data$id <- as.integer(rownames(admin_lev1@data))
-admin_lev1@data$id <- admin_lev1@data$id-1
-admin_lev1_fortified <- tidy(admin_lev1)
-admin_lev1_fortified <- join(admin_lev1_fortified,admin_lev1@data, by="id")
-
 # Draw initial choropleth map of ECS concentration (using sp, rather than sf data)
 # Note: some ideas taken from here: https://unconj.ca/blog/choropleth-maps-with-r-and-ggplot2.html
 # See also here: https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/
@@ -342,29 +331,23 @@ admin_lev1_fortified <- join(admin_lev1_fortified,admin_lev1@data, by="id")
 # Reference here: https://ggplot2.tidyverse.org/reference/cut_interval.html
 # Reference re: size and scale for plots: http://sape.inf.usi.ch/quick-reference/ggplot2/size
 
-ggplot() + 
+# Switching to tmap from ggplot
-  geom_polygon(aes(x = long, y = lat, group = group, 
+
-                   fill = cut_interval(admin_lev1_fortified$ecs_count, 5)), 
+tm_shape(admin_lev1) + 
-               data = admin_lev1_fortified, 
+  tm_fill(col = "ecs_count", palette = "Oranges") +
-               colour = 'black', 
+#  tm_shape(ecs_sf) +
-               alpha = .7, 
+#  tm_dots("red", size = .05, alpha = .4) +
-               size = .005) + 
+#  tm_scale_bar(position = c("left", "bottom")) +
-  scale_fill_brewer(palette = "Oranges") + 
+  tm_style("gray", title = "Figure 1a") +
-  labs(x = NULL, y = NULL, fill = "Groups",
+  tm_credits("Data: UK Data Service (OGL)
-       title = "Figure 1a", 
+              & Jeremy H. Kidwell,
-       subtitle="Concentration of ECS groups",
+              Graphic is CC-by-SA 4.0",
-       caption = paste("Jeremy H. Kidwell :: jeremykidwell.info",
+    position = c("right", "bottom")) +
-                       "Data: UK Data Service (OGL) & Jeremy H. Kidwell",
+  tm_layout(title = "Concentration of ECS groups", 
-                       "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.",
+            frame = FALSE, 
-                       sep = "\n")) + 
+            title.size = .7, 
-  theme_void() + 
+            inner.margins = c(0.1, 0.1, 0.05, 0.05)
-  theme(text = element_text(family = "Arial Narrow", size = 9),
+            )
-          plot.title = element_text(size = 12, face = "bold"),
-          plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"),
-          panel.border = element_rect(fill = NA, colour = "#cccccc"),
-          legend.text = element_text(size = 9),
-          legend.position = c(0.25, 0.85))
-# TODO: consider a shift to use of grobs: https://cran.r-project.org/web/packages/egg/vignettes/Ecosystem.html
 
 ```
 
@@ -372,35 +355,21 @@ ggplot() +
 
 ```{r plot_admin_ecs_admin2_choropleth, fig.width=4, fig.show="hold", fig.cap="Figure 3"}
 
-# Prepare admin_lev2 for tidyr and reinsert dropped columns
+tm_shape(admin_lev2) + 
-names(admin_lev2)[names(admin_lev2) == "newcode"] <- "id"
+  tm_fill(col = "ecs_count", palette = "Oranges") +
-admin_lev2@data$id <- as.integer(rownames(admin_lev2@data))
+#  tm_shape(ecs_sf) +
-admin_lev2@data$id <- admin_lev2@data$id-1
+#  tm_dots("red", size = .05, alpha = .4) +
-admin_lev2_fortified <- tidy(admin_lev2)
+#  tm_scale_bar(position = c("left", "bottom")) +
-admin_lev2_fortified <- join(admin_lev2_fortified,admin_lev2@data, by="id")
+  tm_style("gray", title = "Figure 3") +
-
+  tm_credits("Data: UK Data Service (OGL)
-ggplot() + 
+              & Jeremy H. Kidwell,
-  geom_polygon(aes(x = long, y = lat, group = group, 
+              Graphic is CC-by-SA 4.0",
-                   fill = cut_interval(admin_lev2_fortified$ecs_count, 5)), 
+    position = c("right", "bottom")) +
-               data = admin_lev2_fortified, 
+  tm_layout(title = "Concentration of ECS groups", 
-               colour = 'black', 
+            frame = FALSE, 
-               alpha = .7, 
+            title.size = .7, 
-               size = .005) + 
+            inner.margins = c(0.1, 0.1, 0.05, 0.05)
-  scale_fill_brewer(palette = "Oranges") + 
+            )
-  labs(x = NULL, y = NULL, fill = "Groups",
-       title = "Figure 1b", 
-       subtitle="Concentration of ECS groups (LAU)",
-       caption = paste("Jeremy H. Kidwell :: jeremykidwell.info",
-                       "Data: UK Data Service (OGL) & Jeremy H. Kidwell",
-                       "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.",
-                       sep = "\n")) + 
-  theme_void() + 
-  theme(text = element_text(family = "Arial Narrow", size = 9),
-          plot.title = element_text(size = 12, face = "bold"),
-          plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"),
-          panel.border = element_rect(fill = NA, colour = "#cccccc"),
-          legend.text = element_text(size = 9),
-          legend.position = c(0.25, 0.85))
 ```
 
 
@@ -408,52 +377,34 @@ ggplot() +
 ```{r plot_admin_ecs_normed_choropleth, fig.width=4, fig.show="hold", fig.cap="Figure 2"}
 
 # Plot out first figure with normalised data: 
-ggplot() + 
+
-  geom_polygon(aes(x = long, y = lat, group = group, 
+tm_shape(admin_lev1) + 
-                   fill = cut_interval(admin_lev1_fortified$ecs_count_pownorm_scaled, 5)), 
+  tm_fill(col = "ecs_count_pownorm_scaled", palette = "Oranges") +
-               data = admin_lev1_fortified, 
+  tm_style("gray", title = "Figure 4") +
-               colour = 'black', 
+  tm_credits("Data: UK Data Service (OGL)
-               alpha = .7, 
+              & Jeremy H. Kidwell,
-               size = .005) + 
+              Graphic is CC-by-SA 4.0",
-  scale_fill_brewer(palette = "Oranges") + 
+    position = c("right", "bottom")) +
-  labs(x = NULL, y = NULL, fill = "Groups",
+  tm_layout(title = "Concentration of ECS groups, data normalised by places of worship", 
-       title = "Figure 2", 
+            frame = FALSE, 
-       subtitle="Concentration of ECS groups, data normalised by places of worship",
+            title.size = .7, 
-       caption = paste("Jeremy H. Kidwell :: jeremykidwell.info",
+            inner.margins = c(0.1, 0.1, 0.05, 0.05)
-                       "Data: UK Data Service (OGL) & Jeremy H. Kidwell",
+            )
-                       "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.",
-                       sep = "\n")) + 
-  theme_void() + 
-  theme(text = element_text(family = "Arial Narrow", size = 8),
-          plot.title = element_text(size = 12, face = "bold"),
-          plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"),
-          panel.border = element_rect(fill = NA, colour = "#cccccc"),
-          legend.text = element_text(size = 8),
-          legend.position = c(0.25, 0.85))
 
 # Plot out second figure with normalised data:  
-ggplot() + 
+
-  geom_polygon(aes(x = long, y = lat, group = group, 
+tm_shape(admin_lev1) + 
-                   fill = cut_interval(admin_lev1_fortified$ecs_count_popnorm_scaled, 5)), 
+  tm_fill(col = "ecs_count_popnorm_scaled", palette = "Oranges") +
-               data = admin_lev1_fortified, 
+  tm_style("gray", title = "Figure 5") +
-               colour = 'black', 
+  tm_credits("Data: UK Data Service (OGL)
-               alpha = .7, 
+              & Jeremy H. Kidwell,
-               size = .005) + 
+              Graphic is CC-by-SA 4.0",
-  scale_fill_brewer(palette = "Oranges") + 
+    position = c("right", "bottom")) +
-  labs(x = NULL, y = NULL, fill = "Groups",
+  tm_layout(title = "Concentration of ECS groups, data normalised by places of worship", 
-       title = "Figure 3", 
+            frame = FALSE, 
-       subtitle="Concentration of ECS groups, data normalised by population",
+            title.size = .7, 
-       caption = paste("Jeremy H. Kidwell :: jeremykidwell.info",
+            inner.margins = c(0.1, 0.1, 0.05, 0.05)
-                       "Data: UK Data Service (OGL) & Jeremy H. Kidwell",
+            )
-                       "You may redistribute this graphic under the terms of the CC-by-SA 4.0 license.",
-                       sep = "\n")) + 
-  theme_void() + 
-  theme(text = element_text(family = "Arial Narrow", size = 8),
-          plot.title = element_text(size = 12, face = "bold"),
-          plot.margin = unit(c(0, 0.25, 0.0, 0.25), "in"),
-          panel.border = element_rect(fill = NA, colour = "#cccccc"),
-          legend.text = element_text(size = 8),
-          legend.position = c(0.25, 0.85))
 
 # TODO: Force bins to be consistently at count of 5?
 

mapping_draft.md

@@ -1,13 +1,13 @@
 ---
 title:  "Mapping Environmental Action in Scotland"
 abstract:    
-# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: February 14, 2019
+# thanks: "Replication files are available on the author's Github account (https://github.com/kidwellj/mapping_environmental_action). **Current version**: February 15, 2019
 style:  jeremy1
 author: "[Jeremy H. Kidwell](http://jeremykidwell.info)"
 affiliation: University of Birmingham
 institute: University of Birmingham
 e-mail: "[j.kidwell@bham.ac.uk](mailto:j.kidwell@bham.ac.uk)"
-date: "2019-02-14"
+date: "2019-02-15"
 bibliography: biblio.bib
 linkcolor: black
 geometry: margin=1in
@@ -61,7 +61,7 @@ For the sake of comparison, we also measured the geographical footprint of two o
 
 # Technical Background
 
-Analysis was conducted using QGIS 2.8 and R 3.5.1, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614]
+Analysis was conducted using QGIS 2.8 and R 3.5.2, and data-sets were generated in CSV format.[^15541313] To begin with, I assembled a data set consisting of x and y coordinates for each congregation in Scotland and collated this against a variety of other specific data. Coordinates were checked by matching UK postcodes of individual congregations against geo-referencing data in the Office for National Statistics postcode database. In certain instances a single "congregation" is actually a series of sites which have joined together under one administrative unit. In these cases, each site was treated as a separate data point if worship was held at that site at least once a month, but all joined sites shared a single unique identifier. As noted above, two other datasets were generated for the sake of comparative analysis.[^177171536] These included one similar Environmental Non-Governmental Organisation (ENGO) in Scotland (1) Transition Scotland (which includes Scotland Communities Climate Action Network);[^15541342] and another community-based NGO, Scottish Community Development Trusts.[^158261232] As this report will detail, these three overlap in certain instances both literally and in terms of their aims, but each also has a separate identity and footprint in Scotland. Finally, in order to normalise data, we utilised the PointX POI dataset which maintains a complete database of Places of Worship in Scotland.[^15541614]
 
 # Background and History of Eco-Congregation Scotland
 
@@ -207,13 +207,13 @@ While Roman Catholic churches make up just over 10% of the church buildings in S
 
 ```
 ## OGR data source with driver: ESRI Shapefile 
-## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "SG_UrbanRural_2016"
+## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "SG_UrbanRural_2016"
 ## with 8 features
 ## It has 6 fields
 ```
 
 ```
-## Reading layer `SG_UrbanRural_2016' from data source `/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data/SG_UrbanRural_2016.shp' using driver `ESRI Shapefile'
+## Reading layer `SG_UrbanRural_2016' from data source `/Users/jeremy/gits/mapping_environmental_action/data/SG_UrbanRural_2016.shp' using driver `ESRI Shapefile'
 ## Simple feature collection with 8 features and 6 fields
 ## geometry type:  MULTIPOLYGON
 ## dimension:      XY
@@ -248,7 +248,7 @@ Of all the groups surveyed in this study, Eco-Congregation Scotland is the most
 
 ```
 ## OGR data source with driver: ESRI Shapefile 
-## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "sc_dz_11"
+## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "sc_dz_11"
 ## with 6976 features
 ## It has 9 fields
 ```
@@ -278,7 +278,7 @@ We can find divergence between transition communities and eco-congregation when
 
 
 ```
-## Reading layer `SSSI_SCOTLAND' from data source `/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data/SSSI_SCOTLAND.shp' using driver `ESRI Shapefile'
+## Reading layer `SSSI_SCOTLAND' from data source `/Users/jeremy/gits/mapping_environmental_action/data/SSSI_SCOTLAND.shp' using driver `ESRI Shapefile'
 ## Simple feature collection with 15872 features and 7 fields
 ## geometry type:  POLYGON
 ## dimension:      XY
@@ -289,14 +289,14 @@ We can find divergence between transition communities and eco-congregation when
 
 ```
 ## OGR data source with driver: ESRI Shapefile 
-## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "SSSI_SCOTLAND"
+## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "SSSI_SCOTLAND"
 ## with 15872 features
 ## It has 7 fields
 ## Integer64 fields read as strings:  PA_CODE
 ```
 
 ```
-## Reading layer `WILDLAND_SCOTLAND' from data source `/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data/WILDLAND_SCOTLAND.shp' using driver `ESRI Shapefile'
+## Reading layer `WILDLAND_SCOTLAND' from data source `/Users/jeremy/gits/mapping_environmental_action/data/WILDLAND_SCOTLAND.shp' using driver `ESRI Shapefile'
 ## Simple feature collection with 42 features and 3 fields
 ## geometry type:  MULTIPOLYGON
 ## dimension:      XY
@@ -307,13 +307,13 @@ We can find divergence between transition communities and eco-congregation when
 
 ```
 ## OGR data source with driver: ESRI Shapefile 
-## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "WILDLAND_SCOTLAND"
+## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "WILDLAND_SCOTLAND"
 ## with 42 features
 ## It has 3 fields
 ```
 
 ```
-## Reading layer `National_Forest_Inventory_Woodland_Scotland_2017' from data source `/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data/National_Forest_Inventory_Woodland_Scotland_2017.shp' using driver `ESRI Shapefile'
+## Reading layer `National_Forest_Inventory_Woodland_Scotland_2017' from data source `/Users/jeremy/gits/mapping_environmental_action/data/National_Forest_Inventory_Woodland_Scotland_2017.shp' using driver `ESRI Shapefile'
 ## Simple feature collection with 199698 features and 7 fields
 ## geometry type:  POLYGON
 ## dimension:      XY
@@ -324,7 +324,7 @@ We can find divergence between transition communities and eco-congregation when
 
 ```
 ## OGR data source with driver: ESRI Shapefile 
-## Source: "/Users/kidwellj/OneDrive - bham.ac.uk/writing/201708_mapping_environmental_action/data", layer: "National_Forest_Inventory_Woodland_Scotland_2017"
+## Source: "/Users/jeremy/gits/mapping_environmental_action/data", layer: "National_Forest_Inventory_Woodland_Scotland_2017"
 ## with 199698 features
 ## It has 7 fields
 ## Integer64 fields read as strings:  OBJECTID