mirror of
https://github.com/kidwellj/mapping_environmental_action.git
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fixed simd, penultimate draft
This commit is contained in:
Jeremy Kidwell
parent
ee1def48c8
commit
c54aa75925
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@ -320,7 +320,7 @@ admin_lev1_fortified <- join(admin_lev1_fortified,admin_lev1@data, by="id")
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# Draw initial choropleth map of ECS concentration (using sp, rather than sf data)
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# Note: some ideas taken from here: https://unconj.ca/blog/choropleth-maps-with-r-and-ggplot2.html
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# See also here: https://timogrossenbacher.ch/2016/12/beautiful-thematic-maps-with-ggplot2-only/
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# TODO: fix issues with cut_interval / cut_number below. Current error: "'breaks' are not unique calls"
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# See here: https://stackoverflow.com/questions/16184947/cut-error-breaks-are-not-unique
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# Reference here: https://ggplot2.tidyverse.org/reference/cut_interval.html
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@ -646,220 +646,84 @@ unzip("data/simd2016_withgeog.zip", exdir = "data", junkpaths = TRUE)
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}
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simd_shapes <- readOGR("./data", "sc_dz_11")
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simd_indicators <- read.csv("./data/simd2016_withinds.csv")
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simd_wgs <- merge(x=simd_shapes, y=simd_indicators, by.x = "DataZone", by.y = "Data_Zone")
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simd_indicators_min <- simd_indicators[c(1,6:17)]
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simd_wgs <- merge(x=simd_shapes, y=simd_indicators_min, by.x = "DataZone", by.y = "Data_Zone")
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simd <- spTransform(simd_wgs, bng)
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simd_df <- data.frame(simd)
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simd_min <- simd[,-(26:55)]
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simd_min <- simd[,-(3:13)]
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simd@data[c(1:2,14:25)]
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simd@data[-c(3:13,26:55)]
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simd[, -(3:13)]
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# SIMD_2016_Quintile
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# SIMD_2016_Decile
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#
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# Income_Domain_2016_Rank
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# Employment_Domain_2016_Rank
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# Health_Domain_2016_Rank
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# Education_Domain_2016_Rank
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# Geographic_Access_Domain_2016_Rank
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# Crime_Domain_2016_Rank
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# Housing_Domain_2016_Rank
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# STAGE 1, augment each dataset with relevant (geolocated) columns from SIMD
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# examine which ecs fall within each SIMD classification
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cbind(ecs@data, over(ecs, simd))
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# Augment each dataset with relevant (geolocated) columns from SIMD
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# assign combined table with SIMD columns to attribute table slot of ecs table
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ecs@data=cbind(ecs@data,over(ecs,simd))
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# examine where pointX falls within each SIMD classification
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cbind(pow_pointX@data, over(pow_pointX, simd))
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# assign combined table with SIMD columns to attribute table slot of ecs table
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pow_pointX@data=cbind(pow_pointX@data,over(pow_pointX,simd))
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# examine which transition fall within each SIMD classifications
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cbind(transition@data, over(transition, simd))
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# assign combined table with SIMD columns to attribute table slot of transition table
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transition@data=cbind(transition@data,over(transition,simd))
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# examine which permaculture fall within each SIMD classifications
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cbind(permaculture@data, over(permaculture, simd))
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# assign combined table with SIMD columns to attribute table slot of permaculture table
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permaculture@data=cbind(permaculture@data,over(permaculture,simd))
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# examine which dtas fall within each SIMD classifications
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cbind(dtas@data, over(dtas, simd))
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# assign combined table with SIMD columns to attribute table slot of dtas table
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dtas@data=cbind(dtas@data,over(dtas,simd))
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# STAGE 2, extract NULL cells from each data set to prevent errors in stage 3
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# convert back to data frame for null cell extraction
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ecs_df<-data.frame(ecs)
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# split out null and normal cells
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ecs_clean<-ecs_df[complete.cases(ecs_df),]
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ecs_null<-ecs_df[!complete.cases(ecs_df),]
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# convert back to data frame for null cell extraction
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transition_df<-data.frame(transition)
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# split out null and normal cells
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transition_clean<-transition_df[complete.cases(transition_df),]
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transition_null<-transition_df[!complete.cases(transition_df),]
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# convert back to data frame for null cell extraction
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permaculture_df<-data.frame(permaculture)
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# split out null and normal cells
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permaculture_clean<-permaculture_df[complete.cases(permaculture_df),]
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permaculture_null<-permaculture_df[!complete.cases(permaculture_df),]
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# convert back to data frame for null cell extraction
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dtas_df<-data.frame(dtas)
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# split out null and normal cells
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dtas_clean<-dtas_df[complete.cases(dtas_df),]
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dtas_null<-dtas_df[!complete.cases(dtas_df),]
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# TODO: change names to match simd2016 conventions
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# Overal_SIMD16_Rank
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# SIMD_2016_Quintile
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# SIMD_2016_Decile
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#
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# Income_Domain_2016_Rank
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# Employment_Domain_2016_Rank
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# Health_Domain_2016_Rank
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# Education_Domain_2016_Rank
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# Geographic_Access_Domain_2016_Rank
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# Crime_Domain_2016_Rank
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# Housing_Domain_2016_Rank
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# Augment simd with group counts
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# Augment SIMD with group counts
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simd$ecs_count <- poly.counts(ecs,simd)
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simd$transition_count <- poly.counts(transition,simd)
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simd$dtas_count <- poly.counts(dtas,simd)
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simd$permaculture_count <- poly.counts(permaculture,simd)
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simd$pointx_count <- poly.counts(pointx,simd)
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simd$pow_count <- poly.counts(pow_pointX,simd)
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# Run plots
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# Generate simplified dataframes with counts for each group
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ecs_simd <- data.frame(ecs[c(1,16,25,30:36)])
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colnames(ecs_simd)[1] <- "group_name"
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ecs_simd$group_type <- "ecs"
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transition_simd <- data.frame(transition[c(2,5,14,19:25)])
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colnames(transition_simd)[1] <- "group_name"
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transition_simd$group_type <- "transition"
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dtas_simd <- data.frame(dtas[c(1,5,14,19:25)])
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colnames(dtas_simd)[1] <- "group_name"
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dtas_simd$group_type <- "dtas"
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permaculture_simd <- data.frame(permaculture[c(1,11,20,25:31)])
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colnames(permaculture_simd)[1] <- "group_name"
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permaculture_simd$group_type <- "permaculture"
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# Simplify dataframes to: name, simd categories (above) then add row and fill with group_type
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# bind tables together
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# flatten simd columns into two with value and simd_domain as resulting columns for faceting
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# Bind into single long data frame
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allgroups_simd <- bind_rows(ecs_simd, transition_simd)
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allgroups_simd <- bind_rows(allgroups_simd, dtas_simd)
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allgroups_simd <- bind_rows(allgroups_simd, permaculture_simd)
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y bars by group_type, fill = quantiles via cut_interval() or mutate ntile()
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x value
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facet: simd_domain
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ecs_simd_min <- ecs[1:4, ]
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two <- mtcars[11:14, ]
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# You can supply data frames as arguments:
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bind_rows(ecs_simd_min, two)
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# Faceted stacked bar plot
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ggplot(data, aes(y=value, x=Overal_SIMD16_Rank, color=specie, fill=cut_interval(Overal_SIMD16_Rank, 5))) +
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geom_bar( stat="group_type") +
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facet_wrap(~condition)
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cut_interval(x, n = NULL, length = NULL, ...)
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# Boxplot
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ggplot(dat, aes(x=cond, y=rating, fill=cond)) + geom_boxplot()
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# STAGE 3a, calculate sums based on SIMD12R columns and generate new integer sets with quintile count data
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simd_rownames = c("Quintile 1","Quintile 2","Quintile 3","Quintile 4","Quintile 5")
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simdr12_ecs = c((sum(ecs_clean$SIMDR12<1301)), (sum(ecs_clean$SIMDR12 > 1300 & ecs_clean$SIMDR12 < 2602)), (sum(ecs_clean$SIMDR12 > 2601 & ecs_clean$SIMDR12 < 3903)), (sum(ecs_clean$SIMDR12 > 3902 & ecs_clean$SIMDR12 < 5204)), (sum(ecs_clean$SIMDR12 > 5203 & ecs_clean$SIMDR12 < 6505)))
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# names(simdr12_ecs) <- simd_rownames
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simdr12_transition = c((sum(transition_clean$SIMDR12<1301)), (sum(transition_clean$SIMDR12 > 1300 & transition_clean$SIMDR12 < 2602)), (sum(transition_clean$SIMDR12 > 2601 & transition_clean$SIMDR12 < 3903)), (sum(transition_clean$SIMDR12 > 3902 & transition_clean$SIMDR12 < 5204)), (sum(transition_clean$SIMDR12 > 5203 & transition_clean$SIMDR12 < 6505)))
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# names(simdr12_transition) <- simd_rownames
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simdr12_permaculture = c((sum(permaculture_clean$SIMDR12<1301)), (sum(permaculture_clean$SIMDR12 > 1300 & permaculture_clean$SIMDR12 < 2602)), (sum(permaculture_clean$SIMDR12 > 2601 & permaculture_clean$SIMDR12 < 3903)), (sum(permaculture_clean$SIMDR12 > 3902 & permaculture_clean$SIMDR12 < 5204)), (sum(permaculture_clean$SIMDR12 > 5203 & permaculture_clean$SIMDR12 < 6505)))
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# names(simdr12_permaculture) <- simd_rownames
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simdr12_dtas = c((sum(dtas_clean$SIMDR12<1301)), (sum(dtas_clean$SIMDR12 > 1300 & dtas_clean$SIMDR12 < 2602)), (sum(dtas_clean$SIMDR12 > 2601 & dtas_clean$SIMDR12 < 3903)), (sum(dtas_clean$SIMDR12 > 3902 & dtas_clean$SIMDR12 < 5204)), (sum(dtas_clean$SIMDR12 > 5203 & dtas_clean$SIMDR12 < 6505)))
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# names(simdr12_dtas) <- simd_rownames
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# STAGE 3b, calculate sums based on INCR12 columns and generate new integer sets with quintile count data
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incr12_ecs = c((sum(ecs_clean$INCR12<1301)), (sum(ecs_clean$INCR12 > 1300 & ecs_clean$INCR12 < 2602)), (sum(ecs_clean$INCR12 > 2601 & ecs_clean$INCR12 < 3903)), (sum(ecs_clean$INCR12 > 3902 & ecs_clean$INCR12 < 5204)), (sum(ecs_clean$INCR12 > 5203 & ecs_clean$INCR12 < 6505)))
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incr12_transition = c((sum(transition_clean$INCR12<1301)), (sum(transition_clean$INCR12 > 1300 & transition_clean$INCR12 < 2602)), (sum(transition_clean$INCR12 > 2601 & transition_clean$INCR12 < 3903)), (sum(transition_clean$INCR12 > 3902 & transition_clean$INCR12 < 5204)), (sum(transition_clean$INCR12 > 5203 & transition_clean$INCR12 < 6505)))
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incr12_permaculture = c((sum(permaculture_clean$INCR12<1301)), (sum(permaculture_clean$INCR12 > 1300 & permaculture_clean$INCR12 < 2602)), (sum(permaculture_clean$INCR12 > 2601 & permaculture_clean$INCR12 < 3903)), (sum(permaculture_clean$INCR12 > 3902 & permaculture_clean$INCR12 < 5204)), (sum(permaculture_clean$INCR12 > 5203 & permaculture_clean$INCR12 < 6505)))
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incr12_dtas = c((sum(dtas_clean$INCR12<1301)), (sum(dtas_clean$INCR12 > 1300 & dtas_clean$INCR12 < 2602)), (sum(dtas_clean$INCR12 > 2601 & dtas_clean$INCR12 < 3903)), (sum(dtas_clean$INCR12 > 3902 & dtas_clean$INCR12 < 5204)), (sum(dtas_clean$INCR12 > 5203 & dtas_clean$INCR12 < 6505)))
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# STAGE 3c, calculate sums based on EMPR12 columns and generate new integer sets with quintile count data
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empr12_ecs = c((sum(ecs_clean$EMPR12<1301)), (sum(ecs_clean$EMPR12 > 1300 & ecs_clean$EMPR12 < 2602)), (sum(ecs_clean$EMPR12 > 2601 & ecs_clean$EMPR12 < 3903)), (sum(ecs_clean$EMPR12 > 3902 & ecs_clean$EMPR12 < 5204)), (sum(ecs_clean$EMPR12 > 5203 & ecs_clean$EMPR12 < 6505)))
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empr12_transition = c((sum(transition_clean$EMPR12<1301)), (sum(transition_clean$EMPR12 > 1300 & transition_clean$EMPR12 < 2602)), (sum(transition_clean$EMPR12 > 2601 & transition_clean$EMPR12 < 3903)), (sum(transition_clean$EMPR12 > 3902 & transition_clean$EMPR12 < 5204)), (sum(transition_clean$EMPR12 > 5203 & transition_clean$EMPR12 < 6505)))
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empr12_permaculture = c((sum(permaculture_clean$EMPR12<1301)), (sum(permaculture_clean$EMPR12 > 1300 & permaculture_clean$EMPR12 < 2602)), (sum(permaculture_clean$EMPR12 > 2601 & permaculture_clean$EMPR12 < 3903)), (sum(permaculture_clean$EMPR12 > 3902 & permaculture_clean$EMPR12 < 5204)), (sum(permaculture_clean$EMPR12 > 5203 & permaculture_clean$EMPR12 < 6505)))
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empr12_dtas = c((sum(dtas_clean$EMPR12<1301)), (sum(dtas_clean$EMPR12 > 1300 & dtas_clean$EMPR12 < 2602)), (sum(dtas_clean$EMPR12 > 2601 & dtas_clean$EMPR12 < 3903)), (sum(dtas_clean$EMPR12 > 3902 & dtas_clean$EMPR12 < 5204)), (sum(dtas_clean$EMPR12 > 5203 & dtas_clean$EMPR12 < 6505)))
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# STAGE 3d, calculate sums based on HER12 columns and generate new integer sets with quintile count data
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her12_ecs = c((sum(ecs_clean$HER12<1301)), (sum(ecs_clean$HER12 > 1300 & ecs_clean$HER12 < 2602)), (sum(ecs_clean$HER12 > 2601 & ecs_clean$HER12 < 3903)), (sum(ecs_clean$HER12 > 3902 & ecs_clean$HER12 < 5204)), (sum(ecs_clean$HER12 > 5203 & ecs_clean$HER12 < 6505)))
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her12_transition = c((sum(transition_clean$HER12<1301)), (sum(transition_clean$HER12 > 1300 & transition_clean$HER12 < 2602)), (sum(transition_clean$HER12 > 2601 & transition_clean$HER12 < 3903)), (sum(transition_clean$HER12 > 3902 & transition_clean$HER12 < 5204)), (sum(transition_clean$HER12 > 5203 & transition_clean$HER12 < 6505)))
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her12_permaculture = c((sum(permaculture_clean$HER12<1301)), (sum(permaculture_clean$HER12 > 1300 & permaculture_clean$HER12 < 2602)), (sum(permaculture_clean$HER12 > 2601 & permaculture_clean$HER12 < 3903)), (sum(permaculture_clean$HER12 > 3902 & permaculture_clean$HER12 < 5204)), (sum(permaculture_clean$HER12 > 5203 & permaculture_clean$HER12 < 6505)))
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her12_dtas = c((sum(dtas_clean$HER12<1301)), (sum(dtas_clean$HER12 > 1300 & dtas_clean$HER12 < 2602)), (sum(dtas_clean$HER12 > 2601 & dtas_clean$HER12 < 3903)), (sum(dtas_clean$HER12 > 3902 & dtas_clean$HER12 < 5204)), (sum(dtas_clean$HER12 > 5203 & dtas_clean$HER12 < 6505)))
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# STAGE 3e, calculate sums based on ESTR12 columns and generate new integer sets with quintile count data
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estr12_ecs = c((sum(ecs_clean$ESTR12<1301)), (sum(ecs_clean$ESTR12 > 1300 & ecs_clean$ESTR12 < 2602)), (sum(ecs_clean$ESTR12 > 2601 & ecs_clean$ESTR12 < 3903)), (sum(ecs_clean$ESTR12 > 3902 & ecs_clean$ESTR12 < 5204)), (sum(ecs_clean$ESTR12 > 5203 & ecs_clean$ESTR12 < 6505)))
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estr12_transition = c((sum(transition_clean$ESTR12<1301)), (sum(transition_clean$ESTR12 > 1300 & transition_clean$ESTR12 < 2602)), (sum(transition_clean$ESTR12 > 2601 & transition_clean$ESTR12 < 3903)), (sum(transition_clean$ESTR12 > 3902 & transition_clean$ESTR12 < 5204)), (sum(transition_clean$ESTR12 > 5203 & transition_clean$ESTR12 < 6505)))
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estr12_permaculture = c((sum(permaculture_clean$ESTR12<1301)), (sum(permaculture_clean$ESTR12 > 1300 & permaculture_clean$ESTR12 < 2602)), (sum(permaculture_clean$ESTR12 > 2601 & permaculture_clean$ESTR12 < 3903)), (sum(permaculture_clean$ESTR12 > 3902 & permaculture_clean$ESTR12 < 5204)), (sum(permaculture_clean$ESTR12 > 5203 & permaculture_clean$ESTR12 < 6505)))
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estr12_dtas = c((sum(dtas_clean$ESTR12<1301)), (sum(dtas_clean$ESTR12 > 1300 & dtas_clean$ESTR12 < 2602)), (sum(dtas_clean$ESTR12 > 2601 & dtas_clean$ESTR12 < 3903)), (sum(dtas_clean$ESTR12 > 3902 & dtas_clean$ESTR12 < 5204)), (sum(dtas_clean$ESTR12 > 5203 & dtas_clean$ESTR12 < 6505)))
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# STAGE 4a - calculate percentages
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simdr12_ecs_percent<- prop.table(simdr12_ecs)
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simdr12_transition_percent<- prop.table(simdr12_transition)
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simdr12_permaculture_percent<- prop.table(simdr12_permaculture)
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simdr12_dtas_percent<- prop.table(simdr12_dtas)
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incr12_ecs_percent<- prop.table(incr12_ecs)
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incr12_transition_percent<- prop.table(incr12_transition)
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incr12_permaculture_percent<- prop.table(incr12_permaculture)
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incr12_dtas_percent<- prop.table(incr12_dtas)
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empr12_ecs_percent<- prop.table(empr12_ecs)
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empr12_transition_percent<- prop.table(empr12_transition)
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empr12_permaculture_percent<- prop.table(empr12_permaculture)
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empr12_dtas_percent<- prop.table(empr12_dtas)
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her12_ecs_percent<- prop.table(her12_ecs)
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her12_transition_percent<- prop.table(her12_transition)
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her12_permaculture_percent<- prop.table(her12_permaculture)
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her12_dtas_percent<- prop.table(her12_dtas)
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estr12_ecs_percent<- prop.table(estr12_ecs)
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estr12_transition_percent<- prop.table(estr12_transition)
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estr12_permaculture_percent<- prop.table(estr12_permaculture)
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estr12_dtas_percent<- prop.table(estr12_dtas)
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# STAGE 4b, generate data frame using integer sets
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simd = data.frame(simdr12_ecs, simdr12_ecs_percent, incr12_ecs, incr12_ecs_percent, empr12_ecs, empr12_ecs_percent, her12_ecs, her12_ecs_percent, estr12_ecs, estr12_ecs_percent, simdr12_transition, simdr12_transition_percent, incr12_transition, incr12_transition_percent, empr12_transition, empr12_transition_percent, her12_transition, her12_transition_percent, estr12_transition, estr12_transition_percent, simdr12_permaculture, simdr12_permaculture_percent, incr12_permaculture, incr12_permaculture_percent, empr12_permaculture, empr12_permaculture_percent, her12_permaculture, her12_permaculture_percent, estr12_permaculture, estr12_permaculture_percent, simdr12_dtas, simdr12_dtas_percent, incr12_dtas, incr12_dtas_percent, empr12_dtas, empr12_dtas_percent, her12_dtas, her12_dtas_percent, estr12_dtas, estr12_dtas_percent)
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write.csv(simd, "derivedData/simd.csv", row.names=FALSE)
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simd_percents_only = data.frame(simd_rownames, simdr12_ecs_percent, incr12_ecs_percent, empr12_ecs_percent, her12_ecs_percent, estr12_ecs_percent, simdr12_transition_percent, incr12_transition_percent, empr12_transition_percent, her12_transition_percent, estr12_transition_percent, simdr12_permaculture_percent, incr12_permaculture_percent, empr12_permaculture_percent, her12_permaculture_percent, estr12_permaculture_percent, simdr12_dtas_percent, incr12_dtas_percent, empr12_dtas_percent, her12_dtas_percent, estr12_dtas_percent)
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write.csv(simd_percents_only, "derivedData/simd_percents_only.csv", row.names=FALSE)
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allgroups_gathered <- gather(allgroups_simd, key = "simd_category", value = "rank", Overal_SIMD16_Rank, Income_Domain_2016_Rank, Employment_Domain_2016_Rank, Health_Domain_2016_Rank, Education_Domain_2016_Rank, Geographic_Access_Domain_2016_Rank, Crime_Domain_2016_Rank, Housing_Domain_2016_Rank)
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```
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```{r create_simd_barplot}
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# STAGE 5, generate cool charts
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# Run plots
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# Faceted stacked bar plot
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# See here: http://theduke.at/blog/science/beginners-guide-to-creating-grouped-and-stacked-bar-charts-in-r-with-ggplot2/
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# Reference here: https://ggplot2.tidyverse.org/reference/geom_bar.html
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ggplot(data=allgroups_gathered, aes(x=simd_category, y=rank)) +
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geom_bar(stat="identity") +
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facet_grid(~group_type)
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# TODO modify fill = quantiles via cut_interval() or mutate ntile()
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# cut_interval(x, n = NULL, length = NULL, ...)
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write.csv(simd_percents_only, "derivedData/simd_percents_only.csv", row.names=FALSE)
|
||||
```
|
||||
|
||||
```{r create_simd_boxplot}
|
||||
|
||||
# simd boxplot
|
||||
|
||||
simd_df <- data.frame(simd)
|
||||
ggplot(simd, aes(x=cond, y=rating, fill=cond)) + geom_boxplot()
|
||||
|
||||
|
||||
# comvert admin back to dataframe for analysis
|
||||
urbanrural.df <- data.frame(urbanrural)
|
||||
|
||||
# Need to flatten urbanrural based on all the count columns and generate using ggplot
|
||||
|
||||
urbanrural_gathered <- gather(data.frame(urbanrural), key="group_type", value="number", ecs_count, transition_count, dtas_count, permaculture_count)
|
||||
|
||||
# TODO: change to ur category column
|
||||
ggplot(admin_gathered, aes(fill=group_type, y=number, x=name)) + geom_bar(position="dodge", stat="identity") + coord_flip() + labs(title = "Figure 7", subtitle="Comparison of Groups by UrbanRural category", fill = "Groups")
|
||||
# Work in progress below - uncomment when ready.
|
||||
# simd_df <- data.frame(simd)
|
||||
# ggplot(simd, aes(x=cond, y=rating, fill=cond)) + geom_boxplot()
|
||||
|
||||
# clustered bar charts
|
||||
# convert to long format
|
||||
|
||||
library(reshape2)
|
||||
simd_percents_only_long <- melt(simd_percents_only, id.vars = "simd_rownames",
|
||||
measure.vars = grep("^12", names(simd_percents_only), value = TRUE))
|
||||
# library(reshape2)
|
||||
# simd_percents_only_long <- melt(simd_percents_only, id.vars = "simd_rownames", measure.vars = grep("^12", names(simd_percents_only), value = TRUE))
|
||||
|
||||
qplot(data=simd_percents_only_long , geom="bar", fill=(factor(simd_rownames)))
|
||||
# qplot(data=simd_percents_only_long , geom="bar", fill=(factor(simd_rownames)))
|
||||
|
||||
# jitterplot option, from Teutonico 2015, p. 63
|
||||
# https://ggplot2.tidyverse.org/reference/geom_jitter.html
|
||||
|
|
|
|||
Loading…
Reference in a new issue