-
Notifications
You must be signed in to change notification settings - Fork 1
/
global.R
205 lines (181 loc) · 7.97 KB
/
global.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
library(shiny)
library(shinydashboard)
library(plyr)
library(tidyverse)
library(readxl)
library(shinythemes)
library(RColorBrewer)
library(DT)
library(data.table)
library(Unicode)
library(leaflet)
library(cowplot)
library(shinyBS)
library(shinycssloaders)
library(shinyLP)
library(kableExtra)
library(shinyjs)
library(shinyWidgets)
library(formattable)
library(shinyalert, quietly = TRUE)
library(plotly)
library(sf)
library(ggbump)
#Store value for the most recent year data is available, this needs to be changed when data is refreshed annually
FrstYear <- "2010/11"
RcntYear <- "2021/22"
ProjYear <- "2024/25"
#First and last years for Duncan Index graphs
DIFrYr <- substr(FrstYear,1,4)
DIRcYr <- substr(RcntYear,1,4)
LblFrst <- "10/11"
LblRcnt <- "21/22"
LblProj <- "24/25"
SpPolysDF <- read_rds("data/Shapes_decs.rds")
SpPolysIZ <- read_rds("data/IZshapes_decs.rds")
SpPolysLA <- read_rds("data/LAShps.rds")
CPPdta <- read_csv("data/CPPcleandata.csv", show_col_types = FALSE)
CPP_Imp <- read_csv("data/Imp_rate_CPP.csv", show_col_types = FALSE)
IGZdta <- read_csv("data/IGZcleandata.csv", show_col_types = FALSE)
IGZ_latest <- read_csv("data/IGZ_latest.csv", show_col_types = FALSE)
IGZ_change <- read_csv("data/IGZ_change.csv", show_col_types = FALSE)
Metadata <- read_csv("data/Metadata.csv", show_col_types = FALSE)
# VulnComm <- read_csv("data/Formatted Vulnerable Communities.csv", show_col_types = FALSE)
#
# VulnComm$Most_Deprived_Comm[VulnComm$Most_Deprived_Comm == 6] <- ""
# VulnComm[VulnComm$AreaLabel == "CPP Average", c(4,7,10,13,16,19,22,25,28)] <- ""
# VulnComm <- as.data.frame(VulnComm)
#NEW VIZ DATA FORMAT (calculations in Other Code/7. Vulnerable community calcs.R)
vulnerable_communities_data <- read_csv("data/vulnerable_communities_outcomes_and_change.csv", show_col_types = FALSE)
#rename Edinburgh
SpPolysIZ[SpPolysIZ$council == "Edinburgh","council"] <- "Edinburgh, City of"
SpPolysDF[SpPolysDF$council == "Edinburgh","council"] <- "Edinburgh, City of"
#extract data and rename indicator columns (col names will be used directly in leaflet pop-ups in UI)
CPPMapDta <- SpPolysDF %>%
dplyr::rename("Children in Poverty (%)" = "X..of.children.in.poverty",
"Average Highest Attainment" = "Average.highest.attainment",
"Out of Work Benefits (%)" = "X..of.population..aged.16.64..in.receipt.of.out.of.work.benefits",
"Crimes per 10,000" = "Number.of.crimes.per.10.000.of.the.population",
"Emergency Admissions (65+) per 100,000" = "Emergency.admissions..65...per.100.000.population")
##convert to numeric
CPPMapDta[[15]] <- as.numeric(CPPMapDta[[15]])
CPPMapDta[[14]] <- as.numeric(CPPMapDta[[14]])
##read in Fife data for MyCommunity
IGZ_latest_Fife <- read_csv("data/IGZ_latest_Fife.csv", show_col_types = FALSE)
IGZ_change_Fife <- read_csv("data/IGZ_change_Fife.csv", show_col_types = FALSE)
#global variables (taken from server)------------
#list of indicators
indicators <- c("Healthy Birthweight", "Primary 1 Body Mass Index", "Child Poverty",
"Attainment", "Positive Destinations", "Employment Rate",
"Median Earnings", "Out of Work Benefits", "Business Survival",
"Crime Rate", "Dwelling Fires", "Carbon Emissions",
"Emergency Admissions", "Unplanned Hospital Attendances",
"Early Mortality", "Fragility", "Well-being", "Fuel Poverty"
)
#Create list of CPP names for use in UI
CPPNames <- unique(CPPdta[CPPdta$CPP != "Scotland", "CPP"])
##Read in Duncan Index Scores and calculate whether improving
DIdta <- read_csv("data/DuncanIndex.csv", show_col_types = FALSE)
DIdta <- DIdta[,-5]
DIdta <- gather(DIdta, "ind", "value",3:9)
DIdta <- na.omit(DIdta)
DIdta <- setDT(DIdta)[, ImprovementRate :=
(abs(last(value))/abs(first(value)))-1,
by = list(la, ind)
]
InqDta <-readRDS("data/DecileData.rds")
#functions ------------
# Map colour functions
# These render polygons red-green (or blue-yellow if the colour blindness button is checked).
# One of seven colours is ascribed depending on a geography's ranking score (1-7) for the given indicator
clrs <- brewer.pal(7, "RdYlGn")
clrsCB <- rev(brewer.pal(7, "YlGnBu"))
LowGoodColourBins <- colorBin(rev(clrs), 1:7)
LowGoodColourBinsCB <- colorBin(rev(clrsCB), 1:7)
HighGoodColourBins <- colorBin(clrs, 1:7)
HighGoodColourBinsCB <- colorBin(clrsCB, 1:7)
#renders a plot output with associated metadata pop up for a given indicator.
# This function is used for the CPP Overt Time, Compare All CPPs, and Compare Similar CPPs tabs.
plotWithMetadataPopup <- function (metadata, plotName, indicatorTitle, plc = "top", plotHeight = "25vh"){
indicatorMetadata <- filter(metadata, Indicator == indicatorTitle)
column(2,
style = paste0("margin-left:0px;margin-right:0px;padding-right:0px; padding-left:0px; height:", plotHeight,"!important"),
plotOutput(plotName, height= plotHeight),
bsPopover(id = plotName,
title = indicatorTitle,
content = paste(
"<b>Definition</b></p><p>",
indicatorMetadata[[1,2]],
"</p><p>",
"<b>Raw Time Period</b></p><p>",
indicatorMetadata[[1,3]],
"</p><p>",
"<b>Source</b></p><p>",
indicatorMetadata[[1,4]]
),
placement = plc,
trigger = "hover",
options = list(container = "body")
)
)
}
#determines the colour of the traffic light marker on each plot in tab "P1"
trafficLightMarkerColour <- function (data, selected_cpp, comparator_cpp) {
selected_cpp_data <- filter(data, CPP == selected_cpp)
comparator_cpp_data <- filter(data, CPP == comparator_cpp)
highIsPositive <- unique(data$`High is Positive`)
if_else(last(selected_cpp_data$value) > last(comparator_cpp_data$value),
if_else(last(selected_cpp_data$Improvement_Rate) > last(comparator_cpp_data$Improvement_Rate),
if_else(highIsPositive == "Yes",
"green",
"red"),
"yellow"),
if_else(last(selected_cpp_data$value) < last(comparator_cpp_data$value),
if_else(last(selected_cpp_data$Improvement_Rate) < last(comparator_cpp_data$Improvement_Rate),
if_else(highIsPositive == "Yes",
"red",
"green"),
"yellow"),
"black")
)
}
#adds on-click pop-ups to the data zone maps in the "Map2" tab
showDZpopup <- function(DZdata, group, lat, lng, map_ind, plotId) {
selectedDZ <- st_drop_geometry(DZdata[DZdata$DataZone == group,])
colIndex <- grep(map_ind, colnames(selectedDZ))
content <- as.character(tagList(
tags$h4(as.character(unique(selectedDZ$DataZone))),
sprintf(
"%s: %s\n",
names(selectedDZ[colIndex]),
round(unique(as.numeric(selectedDZ[colIndex])),2)
),
tags$br()
))
leafletProxy(plotId) %>% addPopups(lng, lat, content, layerId = group)
}
#clickable pop-ups for IZ in "Map1"
showIZPopup <- function(group, lat, lng){
selectedIZ <- SpPolysIZ[SpPolysIZ$InterZone == group,]
content <- as.character(tagList(
tags$h4(as.character(unique(selectedIZ$`IGZ.name`)))))
leafletProxy("communityMap") %>% addPopups(lng, lat, content, layerId = group)
}
addColourSchemeColumn <- function (dataset, colName, input1, input2 = NULL) {
colName <- enquo(colName)
if(is.null(input2))
{
dta <- dataset %>%
mutate(colourscheme = ifelse(!!colName == input1, "A", "C"))
}
else
{
dta <- dataset %>%
mutate(colourscheme = ifelse(!!colName == input1,
"A",
ifelse(!!colName == input2,
"B",
"C")))
}
return(dta)
}