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quick fix to length calc on pubs for sssi table
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@ -550,8 +550,8 @@ pow_sssi_row_pct <- pow_sssi_row/length(pow_pointX)
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dtas_sssi_row_pct <- dtas_sssi_row/length(dtas)
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dtas_sssi_row_pct <- dtas_sssi_row/length(dtas)
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transition_sssi_row_pct <- transition_sssi_row/length(transition)
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transition_sssi_row_pct <- transition_sssi_row/length(transition)
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permaculture_sssi_row_pct <- permaculture_sssi_row/length(permaculture)
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permaculture_sssi_row_pct <- permaculture_sssi_row/length(permaculture)
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grocery_sssi_row_pct <- grocery_sssi_row/length(poi_grocery_sf)
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grocery_sssi_row_pct <- grocery_sssi_row/length(poi_grocery)
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pubs_sssi_row_pct <- pubs_sssi_row/length(poi_pubs_sf)
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pubs_sssi_row_pct <- pubs_sssi_row/length(poi_pubs)
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sssi_counts_pct <- rbind(ecs_sssi_row_pct, pow_sssi_row_pct)
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sssi_counts_pct <- rbind(ecs_sssi_row_pct, pow_sssi_row_pct)
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sssi_counts_pct <- rbind(sssi_counts_pct, dtas_sssi_row_pct)
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sssi_counts_pct <- rbind(sssi_counts_pct, dtas_sssi_row_pct)
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@ -584,7 +584,6 @@ grocery_wildland_row <- c(sum(apply(st_within(poi_grocery_sf, wildland_simplifie
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pubs_wildland_row <- c(sum(apply(st_within(poi_pubs_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, wildland_buf500, sparse=FALSE), 1, any)))
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pubs_wildland_row <- c(sum(apply(st_within(poi_pubs_sf, wildland_simplified, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, wildland_buf50, sparse=FALSE), 1, any)), sum(apply(st_within(poi_pubs_sf, wildland_buf500, sparse=FALSE), 1, any)))
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colnames(wildland_counts) <- c("Within Wildland Areas", "...50m", "...500m")
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colnames(wildland_counts) <- c("Within Wildland Areas", "...50m", "...500m")
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# Generate dataframe from rows based on percentages of totals
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# Generate dataframe from rows based on percentages of totals
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@ -722,56 +721,6 @@ write.csv(scenicareas_counts_merged, "derivedData/scenicareas_counts_merged.csv"
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```
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```
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# Appendix A
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```{r admin_table}
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# Output CSV files for various levels of admin
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write.csv(admin_lev1, "derivedData/admin_lev1.csv", row.names=FALSE)
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write.csv(admin_lev2, "derivedData/admin_lev2.csv", row.names=FALSE)
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write.csv(ecs, "derivedData/ecs.csv", row.names=FALSE)
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write.csv(transition, "derivedData/transition.csv", row.names=FALSE)
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write.csv(permaculture, "derivedData/permaculture.csv", row.names=FALSE)
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write.csv(dtas, "derivedData/dtas.csv", row.names=FALSE)
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write.csv(simd, "derivedData/simd.csv", row.names=FALSE)
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# Output mmd tables using kable
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admin_lev1_df <- as_data_frame(admin_lev1[,c(3,5,7,11,13)])
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admin_lev1_df %>%
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kable(format = "html", col.names = colnames(admin_lev1_df)) %>%
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kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
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kableExtra::scroll_box(width = "100%", height = "800px")
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```
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# Appendix B
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```{r admin_table_withproportions}
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admin_lev1_prop_df <- as_data_frame(admin_lev1[,c(3,5:14,22:25)])
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admin_lev1_prop_df %>%
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kable(format = "html", col.names = colnames(admin_lev1_prop_df)) %>%
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kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
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kableExtra::scroll_box(width = "100%", height = "800px")
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```
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# Appendix C - Data by Urban / Rural Classification
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```{r urbanrural_table}
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# Output CSV files for urbanrural tables
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write.csv(urbanrural, "derivedData/urbanrural.csv", row.names=FALSE)
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# Output mmd tables using kable
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urbanrural_table <- as_data_frame(urbanrural[,c(1,7:16)])
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urbanrural_table %>%
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kable(format = "html", col.names = colnames(urbanrural_table)) %>%
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kable_styling(bootstrap_options = c("striped", "hover", "condensed")) %>%
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kableExtra::scroll_box(width = "100%")
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```
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# Citations
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# Citations
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[^15541312]: This research was jointly funded by the AHRC/ESRC under project numnbers AH/K005456/1 and AH/P005063/1.
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[^15541312]: This research was jointly funded by the AHRC/ESRC under project numnbers AH/K005456/1 and AH/P005063/1.
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