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labelOptions = labelOptions(
style = list("font-weight" = "normal", padding = "3px 8px"),
textsize = "13px",
direction = "auto"
)
) %>%
addPolygons(
data=stp_spdf,
group="ICS boundary",
fillOpacity=0.1,
color='black',
weight=2,
label=mytext_ics
) %>%
#addLegend( data=ccg_spdf,pal=catpal, values=~Short.Status, opacity=0.9, title = "21/22 DSPT Status (CCG)", position = "bottomleft" ) %>%
leaflet::addLayersControl(
overlayGroups = c("CCG","ICS boundary"),  # add these layers
options = layersControlOptions(collapsed = FALSE)  # expand on hover?
) %>%
hideGroup(c("ICS boundary"))  # turn these off by default
m02_l <- m02 %>% addLegend( data=ccg_spdf,pal=catpal, values=~Short.Status, opacity=0.9, title = "21/22 DSPT Status (CCG)", position = "bottomleft" )
#############################################
# Mapping - CCGS + ICS + trust layer
#############################################
get_popup_content <- function(my_spdf) {
paste0(
"<b>Provider </b>",
"<br><b>- Provider code</b>:", my_spdf@data$ODS.Code,
"<br><b>- Provider name:</b> ", my_spdf@data$ODS.Org.Name,
#"<br><b>- STP/ICS (HQ postcode-based):</b> ", my_spdf@data$STP20NM,
#"<br><b>- Region:</b> ", my_spdf@data$`NHSER20NM`,
"<br><b>- DSPT status:</b> ", my_spdf@data$Status,
sep=""
)
}
m03 <- m02_l %>%
addCircleMarkers(data=trust_spdf_points,
group="Trusts",
label = ~ lapply(get_popup_content(trust_spdf_points), htmltools::HTML),
fillColor = ~catpal(Status),
color="black",
weight=2,
fillOpacity = 1,
stroke = T,
#clusterOptions = markerClusterOptions(),
radius= 6)
#m03
#adding the zoom toggle for trust level (trust layer appears between 9 and 20)
#m03 <- m03 %>%
#  groupOptions("Trusts", zoomLevels = 9:20)
#adding legend and layering CCG trusts and ICG boundary together
m03 <- m03 %>%
addLegend( data=trust_spdf_points,pal=catpal, values=~Status, opacity=0.9, title = "21/22 DSPT Status (trust)", position = "bottomright" ) %>%
leaflet::addLayersControl(
overlayGroups = c("ICS boundary","CCG","Trusts"),  # add these layers
options = layersControlOptions(collapsed = FALSE)  # expand on hover?
) %>%
hideGroup(c("ICS boundary","Trusts"))  # turn these off by default
#############################################
# Summary metric
#############################################
#get the stp codes for trusts from etr
etr = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/etr.csv', header = FALSE)
etr = etr[,c('V1', 'V4')]
etr = select(etr, 'ODS.Code' = 'V1', 'STP21CDH' = 'V4')
trusts_data2 = trusts_data
trusts_data2$Sector = 'Trust'
View(trusts_data2)
trusts_data_merged = left_join(trusts_data2, etr)
View(trusts_data_merged)
trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = left_join(trusts_data2, etr)
#trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = select(trusts_data_merged, c('ODS.Code', 'ODS.Org.Name', 'Status', 'Sector', 'STP21CDH'))
View(trusts_data_merged)
data_ccg = data_merged
stp_codes = select(data_ccg, 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM')
stp_codes = unique(stp_codes)
View(stp_codes)
trusts_data_merged = left_join(trusts_data_merged, stp_codes)
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified.csv')
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified_21_22.csv')
View(ect)
#merge the final 4 care trusts with trustsdatamerged
trusts_data_merged = left_join(trusts_data_merged, ect)
data_ccg = data_merged
stp_codes = select(data_ccg, 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM')
stp_codes = unique(stp_codes)
trusts_data_merged = left_join(trusts_data_merged, stp_codes)
#############################################
#get the stp codes for trusts from etr
etr = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/etr.csv', header = FALSE)
etr = etr[,c('V1', 'V4')]
etr = select(etr, 'ODS.Code' = 'V1', 'STP21CDH' = 'V4')
trusts_data2 = trusts_data
trusts_data2$Sector = 'Trust'
trusts_data_merged = left_join(trusts_data2, etr)
#trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = select(trusts_data_merged, c('ODS.Code', 'ODS.Org.Name', 'Status', 'Sector', 'STP21CDH'))
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified_21_22.csv')
ect = select(ect, 'ODS.Code', 'STP21CDH')
#merge the final 4 care trusts with trustsdatamerged
trusts_data_merged = left_join(trusts_data_merged, ect)
data_ccg = data_merged
stp_codes = select(data_ccg, 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM')
stp_codes = unique(stp_codes)
trusts_data_merged = left_join(trusts_data_merged, stp_codes)
#get the stp codes for trusts from etr
etr = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/etr.csv', header = FALSE)
etr = etr[,c('V1', 'V4')]
etr = select(etr, 'ODS.Code' = 'V1', 'STP21CDH' = 'V4')
trusts_data2 = trusts_data
trusts_data2$Sector = 'Trust'
trusts_data_merged = left_join(trusts_data2, etr)
#trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = select(trusts_data_merged, c('ODS.Code', 'ODS.Org.Name', 'Status', 'Sector', 'STP21CDH'))
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified_21_22.csv')
ect = select(ect, 'ODS.Code', 'STP21CDH')
#merge the final 4 care trusts with trustsdatamerged
trusts_data_merged = left_join(trusts_data_merged, ect)
#merge the final 4 care trusts with trustsdatamerged
trusts_data_merged = left_join(trusts_data_merged, ect, by = 'ODS.Code')
#############################################
#get the stp codes for trusts from etr
etr = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/etr.csv', header = FALSE)
etr = etr[,c('V1', 'V4')]
etr = select(etr, 'ODS.Code' = 'V1', 'STP21CDH' = 'V4')
trusts_data2 = trusts_data
trusts_data2$Sector = 'Trust'
trusts_data_merged = left_join(trusts_data2, etr)
#trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = select(trusts_data_merged, c('ODS.Code', 'ODS.Org.Name', 'Status', 'Sector', 'STP21CDH'))
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified_21_22.csv')
ect = select(ect, 'ODS.Code', 'STP21CDH')
#merge the final 4 care trusts with trustsdatamerged
trusts_data_merged = left_join(trusts_data_merged, ect, by = 'ODS.Code')
View(etr)
#get the stp codes for trusts from etr
etr = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/etr.csv', header = FALSE)
etr = etr[,c('V1', 'V4')]
etr = select(etr, 'ODS.Code' = 'V1', 'STP21CDH' = 'V4')
#get the final 4 stp codes of the 4 care trusts from ect (modified to change column names and select relevant columns)
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified_21_22.csv')
ect = select(ect, 'ODS.Code', 'STP21CDH')
etr_ect = rbind(etr, ect)
View(etr_ect)
trusts_data2 = trusts_data
trusts_data2$Sector = 'Trust'
trusts_data_merged = left_join(trusts_data2, etr_ect)
#trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = select(trusts_data_merged, c('ODS.Code', 'ODS.Org.Name', 'Status', 'Sector', 'STP21CDH'))
#merge the final 4 care trusts with trustsdatamerged
trusts_data_merged = left_join(trusts_data_merged, ect, by = 'ODS.Code')
data_ccg = data_merged
stp_codes = select(data_ccg, 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM')
stp_codes = unique(stp_codes)
trusts_data_merged = left_join(trusts_data_merged, stp_codes)
#get the stp codes for trusts from etr
etr = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/etr.csv', header = FALSE)
etr = etr[,c('V1', 'V4')]
etr = select(etr, 'ODS.Code' = 'V1', 'STP21CDH' = 'V4')
#get the final 4 stp codes of the 4 care trusts from ect (modified to change column names and select relevant columns)
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified_21_22.csv')
ect = select(ect, 'ODS.Code', 'STP21CDH')
etr_ect = rbind(etr, ect)
trusts_data2 = trusts_data
trusts_data2$Sector = 'Trust'
trusts_data_merged = left_join(trusts_data2, etr_ect)
#trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = select(trusts_data_merged, c('ODS.Code', 'ODS.Org.Name', 'Status', 'Sector', 'STP21CDH'))
data_ccg = data_merged
stp_codes = select(data_ccg, 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM')
stp_codes = unique(stp_codes)
trusts_data_merged = left_join(trusts_data_merged, stp_codes)
region_codes = unique(select(data_ccg, 'STP21CD' = 'STP21CD', 'NHSER22NM' = 'NHSER22NM'))
trusts_data_merged = left_join(trusts_data_merged, region_codes)
data_ccg$Sector = 'CCG'
data_ccg = select(data_ccg, 'ODS.Code' = 'ODS.Code', 'ODS.Org.Name' = 'LOC22NM', 'Status' = 'Status', 'Sector' = 'Sector', 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM', 'NHSER22NM' = 'NHSER22NM')
data_joint <- rbind(data_ccg, trusts_data_merged)
data_metric <- data_joint %>% filter(Sector %in% c("Trust","CCG"))
data_metric <- data_joint %>% filter(Sector %in% c("Trust"))
View(data_metric)
unique(data_metric$ODS.Code)
View(data_regions)
trusts_data_merged = left_join(trusts_data_merged, data_regions)
#############################################
#get the stp codes for trusts from etr
etr = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/etr.csv', header = FALSE)
etr = etr[,c('V1', 'V4')]
etr = select(etr, 'ODS.Code' = 'V1', 'STP21CDH' = 'V4')
#get the final 4 stp codes of the 4 care trusts from ect (modified to change column names and select relevant columns)
ect = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/ect_modified_21_22.csv')
ect = select(ect, 'ODS.Code', 'STP21CDH')
etr_ect = rbind(etr, ect)
trusts_data2 = trusts_data
trusts_data2$Sector = 'Trust'
trusts_data_merged = left_join(trusts_data2, etr_ect)
#trusts_data_merged = na.omit(trusts_data_merged)
trusts_data_merged = select(trusts_data_merged, c('ODS.Code', 'ODS.Org.Name', 'Status', 'Sector', 'STP21CDH'))
data_ccg = data_merged
stp_codes = select(data_ccg, 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM')
stp_codes = unique(stp_codes)
trusts_data_merged = left_join(trusts_data_merged, stp_codes)
#load in ccg-stp-region lookup data
regions_lookup = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/Clinical_Commissioning_Group_to_STP_and_NHS_England_(Region)_(April_2021)_Lookup_in_England.csv')
data_regions = unique(select(regions_lookup, 'STP21CD', 'STP21NM', 'NHSER21NM'))
trusts_data_merged = left_join(trusts_data_merged, data_regions)
unique(trusts_data_merged)
unique(trusts_data_merged$ODS.Code)
data_ccg$Sector = 'CCG'
data_ccg = select(data_ccg, 'ODS.Code' = 'ODS.Code', 'ODS.Org.Name' = 'LOC22NM', 'Status' = 'Status', 'Sector' = 'Sector', 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM', 'NHSER22NM' = 'NHSER22NM')
data_joint <- rbind(data_ccg, trusts_data_merged)
View(data_ccg)
data_ccg$Sector = 'CCG'
data_ccg = select(data_ccg, 'ODS.Code' = 'ODS.Code', 'ODS.Org.Name' = 'LOC22NM', 'Status' = 'Status', 'Sector' = 'Sector', 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM', 'NHSER21NM' = 'NHSER22NM')
data_ccg = data_merged
data_ccg$Sector = 'CCG'
data_ccg = select(data_ccg, 'ODS.Code' = 'ODS.Code', 'ODS.Org.Name' = 'LOC22NM', 'Status' = 'Status', 'Sector' = 'Sector', 'STP21CD' = 'STP21CD', 'STP21CDH' = 'STP21CDH', 'STP21NM' = 'STP21NM', 'NHSER21NM' = 'NHSER22NM')
data_joint <- rbind(data_ccg, trusts_data_merged)
data_metric <- data_joint %>% filter(Sector %in% c("Trust","CCG"))
data_metric <- data_joint %>% filter(Sector %in% c("Trust"))
gppopdata <- read_csv("https://files.digital.nhs.uk/62/79B56F/gp-reg-pat-prac-sing-age-regions.csv")
gppopdata_red <- gppopdata %>% filter(SEX=="ALL",AGE=="ALL",ORG_TYPE=="SUB_ICB_LOCATION") %>% select(c("ORG_CODE","NUMBER_OF_PATIENTS"))
# score mapping
data_metric <- data_metric %>% mutate(Status.Score=case_when(Status=="21/22 Standards Exceeded"~3,
Status=="21/22 Standards Met"~1,
Status=="21/22 Approaching Standards"~-1,
Status=="21/22 Standards Not Met"~-3,
Status=="21/22 Not Published"~-3))
NUMBER_OF_PATIENTS = gppopdata['NUMBER_OF_PATIENTS']
data_metric <- data_metric %>% left_join(gppopdata_red,by=c("ODS.Code"="ORG_CODE"))                                                             #TRUE~NA_integer_))
data_metric_ICS <- data_metric %>% group_by(STP21CDH, STP21CD, Sector) %>% summarise(Simple.Score=mean(Status.Score,na.rm=T),
Simple.n=n(),
Pop.Score=sum(NUMBER_OF_PATIENTS*Status.Score)/sum(NUMBER_OF_PATIENTS))
data_metric_ICS <- data_metric_ICS %>% pivot_wider(names_from=Sector,values_from=c("Simple.Score","Simple.n","Pop.Score"))
data_metric_ICS <- data_metric_ICS %>% mutate(metric_CCG_simple = Simple.Score_CCG,
metric_CCG_pop = Pop.Score_CCG,
metric_CCGTrust_simple = 0.5*Simple.Score_CCG+0.5*Simple.Score_Trust,
metric_CCGp_Trusts =0.5*Pop.Score_CCG+0.5*Simple.Score_Trust)
data_metric <- data_joint %>% filter(Sector %in% c("Trust","CCG"))
# GP practice population
# https://digital.nhs.uk/data-and-information/publications/statistical/patients-registered-at-a-gp-practice/september-2022
gppopdata <- read_csv("https://files.digital.nhs.uk/62/79B56F/gp-reg-pat-prac-sing-age-regions.csv")
gppopdata_red <- gppopdata %>% filter(SEX=="ALL",AGE=="ALL",ORG_TYPE=="SUB_ICB_LOCATION") %>% select(c("ORG_CODE","NUMBER_OF_PATIENTS"))
# score mapping
data_metric <- data_metric %>% mutate(Status.Score=case_when(Status=="21/22 Standards Exceeded"~3,
Status=="21/22 Standards Met"~1,
Status=="21/22 Approaching Standards"~-1,
Status=="21/22 Standards Not Met"~-3,
Status=="21/22 Not Published"~-3))
NUMBER_OF_PATIENTS = gppopdata['NUMBER_OF_PATIENTS']
data_metric <- data_metric %>% left_join(gppopdata_red,by=c("ODS.Code"="ORG_CODE"))                                                             #TRUE~NA_integer_))
data_metric_ICS <- data_metric %>% group_by(STP21CDH, STP21CD, Sector) %>% summarise(Simple.Score=mean(Status.Score,na.rm=T),
Simple.n=n(),
Pop.Score=sum(NUMBER_OF_PATIENTS*Status.Score)/sum(NUMBER_OF_PATIENTS))
data_metric_ICS <- data_metric_ICS %>% pivot_wider(names_from=Sector,values_from=c("Simple.Score","Simple.n","Pop.Score"))
data_metric_ICS <- data_metric_ICS %>% mutate(metric_CCG_simple = Simple.Score_CCG,
metric_CCG_pop = Pop.Score_CCG,
metric_CCGTrust_simple = 0.5*Simple.Score_CCG+0.5*Simple.Score_Trust,
metric_CCGp_Trusts =0.5*Pop.Score_CCG+0.5*Simple.Score_Trust)
stp_spdf@data <- stp_spdf@data %>% left_join(data_metric_ICS,by=c("STP21CD"="STP21CD"))
stp_data = stp_spdf@data
# Create a continuous palette function
# Create a continuous palette function in our desired range (+3 - 3)
# Create a continuous palette function in our desired range (+3 - 3)
pal_metric <- colorNumeric(
palette = "RdYlBu",
domain = range(-3:3))
#create the label text for the first composite map
mytext_ics_score <- paste(
"<b>STP code (ODS): </b>", stp_spdf@data$STP21CD,"<br/>",
"<b>STP name (ODS): </b>", stp_spdf@data$STP21NM,"<br/>",
"<b>ICS score (CCG+Trust simple), range [-3,3]: </b>",round(stp_spdf@data$metric_CCGp_Trusts,2),"<br/>",
sep="") %>%
lapply(htmltools::HTML)
#create the label text to display the stp info for each polygon
mytext_new <- paste(
"<b>STP code (ODS): </b>", stp_spdf@data$STP21CD,"<br/>",
"<b>STP name (ODS): </b>", stp_spdf@data$STP21NM,"<br/>",
"<b>Region: </b>", stp_spdf@data$NHSER21NM,"<br/>",
"<b>ICS score (CCG population + Trust simple), range [-3,3]: </b>",round(stp_spdf@data$metric_CCGp_Trusts,2),"<br/>",
sep="") %>%
lapply(htmltools::HTML)
#create the map using map panes
#add a layer for the ICS colour coded polygons
#add a layer for empty ICS polygons with just the labels (this has the highest zindex - will be the top layer)
#add a layer for the region boundaries to be displayed in blue
m04 = leaflet() %>%
addMapPane(name = "regionBorder", zIndex = 425) %>%
addMapPane(name = "ICS polygons", zIndex = 400) %>%
addMapPane(name = "ICS Labels", zIndex = 450) %>%
addPolygons(
data=regions_spdf,
group="Region boundary",
fillOpacity=0,
color='blue',
weight=5,
options = leafletOptions(pane = "regionBorder")
) %>%
addTiles()  %>%
setView( lat=53, lng=-2 , zoom=6) %>%
addPolygons(
data=stp_spdf,
group="ICS",
fillOpacity=1,
fillColor=~pal_metric(metric_CCGp_Trusts),
color="black",
weight=1,
options = leafletOptions(pane = "ICS polygons")) %>%
addPolygons(
data=stp_spdf,
group="ICS",
fillOpacity=0,
fillColor=~pal_metric(metric_CCGp_Trusts),
color="black",
weight= 0,
options = leafletOptions(pane = "ICS Labels"),
label = mytext_new ) %>%
addLegend("bottomright",pal=pal_metric,values=-3:3,title="ICS score - CCG population/Trust simple")
m04
#load in the data and filter for trusts changing the statuses to numerical values
data_trusts =  data_joint %>% filter(Sector %in% c("Trust"))
#data_trusts <- data_trusts[c("ODS.Code", "ODS.Org.Name", "STP21CD", "Status")]
#change all the instances of each dspt score to 1 so this can be summed as a numerical tally to make the charts
data_trusts<- data_trusts %>% mutate(Standards_Met = case_when(Status == "21/22 Standards Met"~1,
TRUE ~ 0))
data_trusts<- data_trusts %>% mutate(Standards_Exceeded = case_when(Status == "21/22 Standards Exceeded"~1,
TRUE ~ 0))
data_trusts<- data_trusts %>% mutate(Standards_Not_Met = case_when(Status == "21/22 Standards Not Met"~1,
TRUE ~ 0))
data_trusts<- data_trusts %>% mutate(Approaching_Standards = case_when(Status == "21/22 Approaching Standards"~1,
TRUE ~ 0))
#select the relevant columns only
data_trusts = data_trusts[,c("STP21CD", "Standards_Met", "Standards_Exceeded", "Standards_Not_Met", "Approaching_Standards")]
#get the sum of each DSPT metric in separate columns grouped by each STP
#data_trusts_aggregate = data_trusts %>% group_by(STP21CD) %>% summarise_each(funs(sum))
data_trusts_aggregate = data_trusts %>% group_by(STP21CD) %>% summarise_at(vars(Standards_Met, Standards_Exceeded, Standards_Not_Met, Approaching_Standards), funs(sum))
#data_trusts_aggregate = data_trusts_aggregate %>% rename("STP21CD" = 1)
#wrangle teh GP population data to be used as the diameter for each of the mini piecharts
stp_filter_numpatients <- gppopdata %>% filter(SEX=="ALL",AGE=="ALL",ORG_TYPE=="ICB")
#stp_filter_numpatients <- stp_filter_numpatients %>% rename("STP21CD" = 5)
stp_filter_numpatients <- stp_filter_numpatients[c("STP21CD", "NUMBER_OF_PATIENTS")]
#merge together the separated dspt data with the gp population data and stp spatial data frame for mapping
data_trust_spdf_pie = left_join(x = stp_spdf@data, y = data_trusts_aggregate, by = "STP21CD")
gppopdata_blue <- gppopdata %>% filter(SEX=="ALL",AGE=="ALL",ORG_TYPE=="ICB") %>% select(c("ONS_CODE","NUMBER_OF_PATIENTS"))
gppopdata_blue <- gppopdata_blue %>% rename("STP21CD" = 1)
stp_spdfdata <- stp_spdf@data
stp_spdfdata1 = merge(stp_spdfdata, gppopdata_blue, by = "STP21CD")
#stp_spdf$data <- stp_spdfdata1
num_patients_stp <- gppopdata_blue[c("NUMBER_OF_PATIENTS")]
minpatients <- min(num_patients_stp)
maxpatients <- max(num_patients_stp)
patients_stps <- stp_spdfdata$data.NUMBER_OF_PATIENTS
pal_metric2 <- colorNumeric(
palette = "Blues",
domain = range(minpatients:maxpatients))
m07 <- leaflet() %>%
addTiles %>%
addPolygons(
data=stp_spdf,
group="ICS Tiles",
fillOpacity=1,
fillColor=~pal_metric2(stp_spdfdata1$NUMBER_OF_PATIENTS), #grey out the ICS tiles so there is less unnecessary detail
weight=0,
label=mytext_ics) %>%
addPolygons(
data=stp_spdf,
group="ICS boundary",
fillOpacity=0,
color='black',
weight=5,
label=mytext_ics) %>%
addMinicharts(lng = data_trust_spdf_pie$LONG,
lat = data_trust_spdf_pie$LAT,
type = "pie",
chartdata = data_trust_spdf_pie[, c("Standards_Exceeded", "Standards_Met", "Approaching_Standards", "Standards_Not_Met")],
colorPalette = c("#129F8C", '#9FD0BA', "#F5FFBF", "#FF4227"),
width = 25,
transitionTime = 0) %>%
addLegend("topright",pal=pal_metric2, minpatients:maxpatients, title="ICS Patient Population Level")
m07
##################
View(data_trust_spdf_pie)
#data_trust_spdf_pie = merge(x = data_trust_spdf_pie, y = stp_filter_numpatients, by = "STP21CD")
data_trust_spdf_pie[41, 7]
#data_trust_spdf_pie = merge(x = data_trust_spdf_pie, y = stp_filter_numpatients, by = "STP21CD")
data_trust_spdf_pie[41, 7] = 51.0
gppopdata_blue <- gppopdata %>% filter(SEX=="ALL",AGE=="ALL",ORG_TYPE=="ICB") %>% select(c("ONS_CODE","NUMBER_OF_PATIENTS"))
gppopdata_blue <- gppopdata_blue %>% rename("STP21CD" = 1)
stp_spdfdata <- stp_spdf@data
stp_spdfdata1 = merge(stp_spdfdata, gppopdata_blue, by = "STP21CD")
#stp_spdf$data <- stp_spdfdata1
num_patients_stp <- gppopdata_blue[c("NUMBER_OF_PATIENTS")]
minpatients <- min(num_patients_stp)
maxpatients <- max(num_patients_stp)
patients_stps <- stp_spdfdata$data.NUMBER_OF_PATIENTS
pal_metric2 <- colorNumeric(
palette = "Blues",
domain = range(minpatients:maxpatients))
m07 <- leaflet() %>%
addTiles %>%
addPolygons(
data=stp_spdf,
group="ICS Tiles",
fillOpacity=1,
fillColor=~pal_metric2(stp_spdfdata1$NUMBER_OF_PATIENTS), #grey out the ICS tiles so there is less unnecessary detail
weight=0,
label=mytext_ics) %>%
addPolygons(
data=stp_spdf,
group="ICS boundary",
fillOpacity=0,
color='black',
weight=5,
label=mytext_ics) %>%
addMinicharts(lng = data_trust_spdf_pie$LONG,
lat = data_trust_spdf_pie$LAT,
type = "pie",
chartdata = data_trust_spdf_pie[, c("Standards_Exceeded", "Standards_Met", "Approaching_Standards", "Standards_Not_Met")],
colorPalette = c("#129F8C", '#9FD0BA', "#F5FFBF", "#FF4227"),
width = 25,
transitionTime = 0) %>%
addLegend("topright",pal=pal_metric2, minpatients:maxpatients, title="ICS Patient Population Level")
m07
data_trust_spdf_pie[41, 6] = 0.0
gppopdata_blue <- gppopdata %>% filter(SEX=="ALL",AGE=="ALL",ORG_TYPE=="ICB") %>% select(c("ONS_CODE","NUMBER_OF_PATIENTS"))
gppopdata_blue <- gppopdata_blue %>% rename("STP21CD" = 1)
stp_spdfdata <- stp_spdf@data
stp_spdfdata1 = merge(stp_spdfdata, gppopdata_blue, by = "STP21CD")
#stp_spdf$data <- stp_spdfdata1
num_patients_stp <- gppopdata_blue[c("NUMBER_OF_PATIENTS")]
minpatients <- min(num_patients_stp)
maxpatients <- max(num_patients_stp)
patients_stps <- stp_spdfdata$data.NUMBER_OF_PATIENTS
pal_metric2 <- colorNumeric(
palette = "Blues",
domain = range(minpatients:maxpatients))
m07 <- leaflet() %>%
addTiles %>%
addPolygons(
data=stp_spdf,
group="ICS Tiles",
fillOpacity=1,
fillColor=~pal_metric2(stp_spdfdata1$NUMBER_OF_PATIENTS), #grey out the ICS tiles so there is less unnecessary detail
weight=0,
label=mytext_ics) %>%
addPolygons(
data=stp_spdf,
group="ICS boundary",
fillOpacity=0,
color='black',
weight=5,
label=mytext_ics) %>%
addMinicharts(lng = data_trust_spdf_pie$LONG,
lat = data_trust_spdf_pie$LAT,
type = "pie",
chartdata = data_trust_spdf_pie[, c("Standards_Exceeded", "Standards_Met", "Approaching_Standards", "Standards_Not_Met")],
colorPalette = c("#129F8C", '#9FD0BA', "#F5FFBF", "#FF4227"),
width = 25,
transitionTime = 0) %>%
addLegend("topright",pal=pal_metric2, minpatients:maxpatients, title="ICS Patient Population Level")
m07
#load in the snapshot data and filter for the 5 relevant CSUs
data_snapshot = read.csv('/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/data/DSPT search results 09_09_2022 12_44_07.csv')
data_summary = data_snapshot %>% select('Organisation.Name', 'Status', 'Primary.Sector')
data_summary = data_summary %>% filter(Primary.Sector == "Commissioning Support Unit (CSU)")
data_csu = data_summary %>% filter(str_detect(Organisation.Name, 'CSU'))
#get the joint dataframe of CCGs and Trusts and merge with CSUs
data_joint$Primary.Sector = data_joint$Sector
data_joint$Organisation.Name = data_joint$ODS.Org.Name
data_s = rbind(select(data_joint, 'Organisation.Name', 'Status', 'Primary.Sector'), data_csu)
#data_s = data_summary %>% mutate(Short.Status = case_when(Status %in% c("20/21 Standards Met", "18/19 Standards Met", "19/20 Standards Met", "19/20 Approaching Standards", "19/20 Standards Exceeded", "20/21 Standards Exceeded", "20/21 Standards Not Met") ~ '21/22 Status Not Met',
#                                                    Status %in% c("22/23 Standards Met") ~ '21/22 Standards Met', TRUE ~ Status))
auxl <-data_s %>% group_by(Primary.Sector,Status) %>% summarise(n=n())
aux <- data_s %>% group_by(Primary.Sector,Status) %>% summarise(n=n()) %>% pivot_wider(names_from='Status',values_from='n')
org_type <-aux$Primary.Sector
fig_x <- auxl %>% plot_ly(x=~Primary.Sector,y= ~n,color=~Status,type='bar')
fig_x
data_ccg = data_joing %>% filter(Sector == 'CCG')
data_ccg = data_join %>% filter(Sector == 'CCG')
data_ccg = data_joint %>% filter(Sector == 'CCG')
View(data_ccg)
tbl_summary(select(data_s, 'Primary.Sector', 'Status'), by = (c('Status')))
ct_final = ctable(data_s$Primary.Sector, data_s$Status,
prop = "r", chisq = FALSE, headings = FALSE
)
ct_final %>% print(method="browser")
ct_final %>% print(file=paste0("/Users/muhammad-faaiz.shanawas/Documents/GitHub/open-cyber/cross_table_summary_21_22",Sys.Date(),".html"))
aux <- data_s %>% group_by(Primary.Sector,Status) %>% summarise(n=n()) %>% pivot_wider(names_from='Status',values_from='n')
View(aux)
colnames(aux)
aux <- select(aux, "21/22 Standards Exceeded", "21/22 Standards Met", "21/22 Approaching Standards", "21/22 Standards Not Met")
org_type <-aux$Primary.Sector
fig_x <- auxl %>% plot_ly(x=~Primary.Sector,y= ~n,color=~Status,type='bar')
fig_x
View(auxl)
fig_x <- aux %>% plot_ly(x=~Primary.Sector,y= ~n,color=~Status,type='bar')
fig_x
fig_x <- aux %>% plot_ly(x=~Primary.Sector,y= ~n,type='bar')
fig_x
fig_x <- auxl %>% plot_ly(x=~Primary.Sector,y= ~n,color=~Status,type='bar')%>%
layout(xaxis = list(categoryorder = "category ascending"))
fig_x
fig_x <- auxl %>% plot_ly(x=~Primary.Sector,y= ~n,color=~Status,type='bar')%>%
layout(xaxis = list(categoryorder = "category descending"))
fig_x
xform <- list(categoryorder = "array",
categoryarray = c("21/22 Standards Exceeded", "21/22 Standards Met", "21/22 Approaching Standards", "21/22 Standards Not Met"))
fig_x <- auxl %>% plot_ly(x=~Primary.Sector,y= ~n,color=~Status,type='bar')%>%
layout(xaxis = xform)
fig_x
org_type <-aux$Primary.Sector