trial run with NHSRdatasets package and runcharter

nhsr_datasets.R

library(NHSRdatasets) # install from github for latest dataset
library(runcharter)
library(data.table)
library(magrittr)


# Load 
data("ae_attendances")

DT <-   setDT(ae_attendances)

# wrangle
DT1 <- DT[order(period),][type == 1,] # filter for type == 1 and order by date


setnames(DT1, old = c("period","org_code"),new = c("date","grp")) #rename cols

DT1[,y := (attendances),] # create new column, in this case copying attendances
# this keeps the DT flexible so we can group by another metric later


# runcharter - returns a list with plot and data frame ("sustained").
attends <- runcharter(DT1,med_rows = 9, direction = "above")

# uncomment to show the plot
# attends$runchart
# whoa 146 charts!!

# there are too many locations for my laptop screen, so let's cut it down a bit.
# we want to only plot areas where signals occur
# so get a list of unique groups (org_codes) from the sustained data frame

changes <- unique(attends$sustained$grp)


# take the runchart data and use that for filtering 
attends <- setDT(attends$runchart$data) # make sure it's a data.table
attends <- attends[grp %chin% changes,] #filter it for locations with signals


# the dataset now has extra columns that might cause a problem when plotting
# with runcharter, so let's just take what we need


# columns we want: 
selected <- c("grp","date","y")

# SDcols is a subset of the data.table
# basically all the rows, .SD = subset the data, then specify the SD columns
attends <- attends[ , .SD, .SDcols = selected] 


# now analyse and plot the filtered dataset with runcharter

p <- runcharter(attends, med_rows = 9, direction = "above",
           chart_title = "Signals demonstrating rise in A&E Attendances",
           facet_cols = 5)
p <- p$runchart + ggplot2::facet_wrap( ~ grp, 
                                      scales = "free_y", 
                                      ncol = 5)
p

# now down to 50