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Background
The NHSRdatasets package made it to CRAN recently, and as it is designed for use by NHS data analysts, and I am an NHS data analyst, let’s take a look at it. Thanks to Chris Mainey and Tom Jemmett for getting this together.
Load packages and data
As above let’s load what we need for this session. The runcharter package is built using data.table, but I’m using dplyr in this main section to show that you don’t need to know data.table to use it.
library(NHSRdatasets)
library(runcharter) # remotes::install_github("johnmackintosh/runcharter)
library(dplyr)
library(skimr)
However- seriously, do take a look at data.table. It’s not as hard to understand as some might have you believe. A little bit of effort pays off. You can load the runcharter package from github using the remotes package. (I’ve managed to install it on Windows and Ubuntu. Mac OS? No idea, I have no experience of that, hopefully it works OK).
ae <- data("ae_attendances") # a promise
ae <- ae_attendances # a string
rm(ae_attendances) # just typing 'ae' brings it to life in the environment
That felt a bit glitchy. There has to be a sleeker way to load and assign a built in dataset but I couldn’t find one. Cursory google here.
Let’s have a look at the data:
glimpse(ae) ## Observations: 12,765 ## Variables: 6 ## $ period <date> 2017-03-01, 2017-03-01, 2017-03-01, 2017-03-01, 2... ## $ org_code <fct> RF4, RF4, RF4, R1H, R1H, R1H, AD913, RYX, RQM, RQM... ## $ type <fct> 1, 2, other, 1, 2, other, other, other, 1, other, ... ## $ attendances <dbl> 21289, 813, 2850, 30210, 807, 11352, 4381, 19562, ... ## $ breaches <dbl> 2879, 22, 6, 5902, 11, 136, 2, 258, 2030, 86, 1322... ## $ admissions <dbl> 5060, 0, 0, 6943, 0, 0, 0, 0, 3597, 0, 2202, 0, 0,...
Lot’s of factors. I’m actually very grateful for this package, as it caused me major issues when I first tried to plot this data using an earlier version of runcharter. I hadn’t considered factors as a possible grouping variable, which was a pretty big miss, as all the facets were out of order. All sorted now.
There’s way too much data for my tiny laptop screen, so I will filter the data for type 1 departments – the package help gives us a great link to explain what this means
type1 <- ae %>%
filter(type == 1) %>%
arrange(period)
# plot attendances
p <- runcharter(type1,
med_rows = 13, # median of first 13 points
runlength = 9, # find a run of 9 consecutive points
direction = "above", # find run above the original median
datecol = "period",
grpvar = "org_code",
yval = "attendances")
The runcharter function returns both a plot, and a data.table/ data.frame showing a summary of any runs in the desired direction (I’m assuming folk reading this have a passing knowledge of run charts, but if not, you can look at the package vignette, which is the cause of most of my commits!!)
Don’t try loading the plot right now, because it is huge, and takes ages. If we look at the summary dataframe,we can see 210 rows, a fairly decent portion of which relate to significant increases above the original median value
p$sustained ## org_code median start_date end_date extend_to run_type ## 1: R0A 21430 2017-10-01 2018-10-01 2019-03-01 baseline ## 2: R1F 3477 2016-04-01 2017-04-01 2017-05-01 baseline ## 3: R1H 28843 2016-04-01 2017-04-01 2019-03-01 baseline ## 4: R1K 11733 2016-04-01 2017-04-01 2019-03-01 baseline ## 5: RA2 5854 2016-04-01 2017-04-01 2018-03-01 baseline ## --- ## 206: RGN 12473 2018-05-01 2019-01-01 2019-03-01 sustained ## 207: RLT 6977 2018-03-01 2018-11-01 2019-03-01 sustained ## 208: RQ8 8456 2018-03-01 2018-11-01 2019-03-01 sustained ## 209: RTE 12610 2018-05-01 2019-01-01 2019-03-01 sustained ## 210: RVV 14582 2018-03-01 2018-11-01 2019-03-01 sustained
Let’s use skimr to get a sense of this
skimr::skim(p$sustained) ## Skim summary statistics ## n obs: 210 ## n variables: 6 ## ## -- Variable type:character ----------------------------------------------------------- ## variable missing complete n min max empty n_unique ## run_type 0 210 210 8 9 0 2 ## ## -- Variable type:Date ---------------------------------------------------------------- ## variable missing complete n min max median n_unique ## end_date 0 210 210 2017-04-01 2019-03-01 2017-04-01 9 ## extend_to 0 210 210 2017-05-01 2019-03-01 2019-03-01 7 ## start_date 0 210 210 2016-04-01 2018-07-01 2016-04-01 9 ## ## -- Variable type:factor -------------------------------------------------------------- ## variable missing complete n n_unique top_counts ## org_code 0 210 210 139 RA4: 3, RDD: 3, RDE: 3, RGN: 3 ## ordered ## TRUE ## ## -- Variable type:numeric ------------------------------------------------------------- ## variable missing complete n mean sd p0 p25 p50 p75 ## median 0 210 210 9389.8 4317.54 3477 6468.25 8413 11311.25 ## p100 hist ## 29102 <U+2586><U+2587><U+2585><U+2581><U+2581><U+2581><U+2581><U+2581>
To keep this manageable, I’m going to filter out for areas that have median admissions > 10000 (based on the first 13 data points)
high_admits <- p$sustained %>% filter(median > 10000 & run_type == "sustained") %>% select(org_code)
Then I change the org_code from factor to character, and pull out unique values. I’m sure there is a slicker way of doing this, but it’s getting late, and I don’t get paid for this..
I use the result to create a smaller data frame
high_admits$org_code <- as.character(high_admits$org_code) type1_high <- type1 %>% filter(org_code %in% high_admits$org_code)
And now I can produce a plot that fits on screen. I’ve made the individual scales free along the y axis, and added titles etc
p2 <- runcharter(type1_high,
med_rows = 13, # median of first 13 points as before
runlength = 9, # find a run of 9 consecutive points
direction = "above",
datecol = "period",
grpvar = "org_code",
yval = "attendances",
facet_scales = "free_y",
facet_cols = 4,
chart_title = "Increased attendances in selected Type 1 AE depts",
chart_subtitle = "Data covers 2016/17 to 2018/19",
chart_caption = "Source : NHSRDatasets",
chart_breaks = "6 months")
Let’s look at the sustained dataframe
p2$sustained ## org_code median start_date end_date extend_to run_type ## 1: RCB 9121 2016-04-01 2017-04-01 2018-03-01 baseline ## 2: RDD 11249 2016-04-01 2017-04-01 2017-05-01 baseline ## 3: RDE 7234 2016-04-01 2017-04-01 2017-05-01 baseline ## 4: RGN 7912 2016-04-01 2017-04-01 2017-05-01 baseline ## 5: RJ1 12240 2016-04-01 2017-04-01 2018-03-01 baseline ## 6: RJE 14568 2016-04-01 2017-04-01 2018-05-01 baseline ## 7: RJL 11262 2016-04-01 2017-04-01 2018-03-01 baseline ## 8: RQM 16478 2016-04-01 2017-04-01 2018-03-01 baseline ## 9: RRK 9584 2016-04-01 2017-04-01 2018-03-01 baseline ## 10: RTE 11303 2016-04-01 2017-04-01 2017-05-01 baseline ## 11: RTG 11344 2016-04-01 2017-04-01 2018-07-01 baseline ## 12: RTR 10362 2016-04-01 2017-04-01 2018-03-01 baseline ## 13: RVV 12700 2016-04-01 2017-04-01 2017-05-01 baseline ## 14: RW6 22114 2016-04-01 2017-04-01 2017-05-01 baseline ## 15: RWE 12275 2016-04-01 2017-04-01 2017-05-01 baseline ## 16: RWF 11939 2016-04-01 2017-04-01 2018-03-01 baseline ## 17: RWP 9976 2016-04-01 2017-04-01 2018-03-01 baseline ## 18: RXC 9396 2016-04-01 2017-04-01 2018-03-01 baseline ## 19: RXH 12494 2016-04-01 2017-04-01 2018-03-01 baseline ## 20: RXP 10727 2016-04-01 2017-04-01 2017-05-01 baseline ## 21: RYR 11578 2016-04-01 2017-04-01 2018-03-01 baseline ## 22: RCB 10062 2018-03-01 2018-11-01 2019-03-01 sustained ## 23: RDD 12093 2017-05-01 2018-01-01 2018-03-01 sustained ## 24: RDE 7637 2017-05-01 2018-01-01 2018-03-01 sustained ## 25: RGN 11896 2017-05-01 2018-01-01 2018-05-01 sustained ## 26: RJ1 13570 2018-03-01 2018-11-01 2019-03-01 sustained ## 27: RJE 15183 2018-05-01 2019-01-01 2019-03-01 sustained ## 28: RJL 11972 2018-03-01 2018-11-01 2019-03-01 sustained ## 29: RQM 18560 2018-03-01 2018-11-01 2019-03-01 sustained ## 30: RRK 29102 2018-03-01 2018-11-01 2019-03-01 sustained ## 31: RTE 11772 2017-05-01 2018-01-01 2018-05-01 sustained ## 32: RTG 17169 2018-07-01 2019-03-01 2019-03-01 sustained ## 33: RTR 10832 2018-03-01 2018-11-01 2019-03-01 sustained ## 34: RVV 13295 2017-05-01 2018-01-01 2018-03-01 sustained ## 35: RW6 22845 2017-05-01 2018-01-01 2019-03-01 sustained ## 36: RWE 18173 2017-05-01 2018-01-01 2019-03-01 sustained ## 37: RWF 12793 2018-03-01 2018-11-01 2019-03-01 sustained ## 38: RWP 10358 2018-03-01 2018-11-01 2019-03-01 sustained ## 39: RXC 10279 2018-03-01 2018-11-01 2019-03-01 sustained ## 40: RXH 13158 2018-03-01 2018-11-01 2019-03-01 sustained ## 41: RXP 11314 2017-05-01 2018-01-01 2019-03-01 sustained ## 42: RYR 11970 2018-03-01 2018-11-01 2019-03-01 sustained ## 43: RDD 12776 2018-03-01 2018-11-01 2019-03-01 sustained ## 44: RDE 15322 2018-03-01 2018-11-01 2019-03-01 sustained ## 45: RGN 12473 2018-05-01 2019-01-01 2019-03-01 sustained ## 46: RTE 12610 2018-05-01 2019-01-01 2019-03-01 sustained ## 47: RVV 14582 2018-03-01 2018-11-01 2019-03-01 sustained ## org_code median start_date end_date extend_to run_type
And of course, the plot itself
p2$runchart
I haven’t looked into the actual data too much, but there are some interesting little facets here – what’s the story with RDE, RRK and RTG for example? I don’t know which Trusts these codes represent, but they show a marked increase. Of course, generally, all areas show an increase at some point.
The RGN (top right) and RVV (mid left) show the reason why I worked on this package – we can see that there has been more than one run above the median. . Performing this analysis in Excel is not much fun after a while.
There is a lot more I can look at with this package, and we in the NHS-R community are always happy to receive more datasets for inclusion, so please contribute if you can.
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