NHSRdatasets meets runcharter

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Data
Packages
Run charts
Statistics
Author

John MacKintosh

Published

February 12, 2020

Modified

July 11, 2024

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.

Installing from GitHub

Some packages, like {runcharter} are not on CRAN and can be installed using another package, in this case {remotes} also needs to be installed.

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).

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 to Stackoverflow.

Let’s have a look at the data:

Rows: 12,765
Columns: 6
$ period      <date> 2017-03-01, 2017-03-01, 2017-03-01, 2017-03-01, 2017-03-0…
$ org_code    <fct> RF4, RF4, RF4, R1H, R1H, R1H, AD913, RYX, RQM, RQM, RJ6, R…
$ type        <fct> 1, 2, other, 1, 2, other, other, other, 1, other, 1, other…
$ attendances <dbl> 21289, 813, 2850, 30210, 807, 11352, 4381, 19562, 17414, 7…
$ breaches    <dbl> 2879, 22, 6, 5902, 11, 136, 2, 258, 2030, 86, 1322, 140, 0…
$ admissions  <dbl> 5060, 0, 0, 6943, 0, 0, 0, 0, 3597, 0, 2202, 0, 0, 0, 3360…

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
        <ord>  <num>     <Date>     <Date>     <Date>    <char>
  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$sustained)
Data summary
Name p$sustained
Number of rows 210
Number of columns 6
Key NULL
_______________________
Column type frequency:
character 1
Date 3
factor 1
numeric 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
run_type 0 1 8 9 0 2 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
start_date 0 1 2016-04-01 2018-07-01 2016-04-01 9
end_date 0 1 2017-04-01 2019-03-01 2017-04-01 9
extend_to 0 1 2017-05-01 2019-03-01 2019-03-01 7

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
org_code 0 1 TRUE 139 RA4: 3, RDD: 3, RDE: 3, RGN: 3

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
median 0 1 9389.8 4317.54 3477 6468.25 8413 11311.25 29102 ▇▅▁▁▁

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 and so on

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
       <ord>  <num>     <Date>     <Date>     <Date>    <char>
 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.

This post was originally published on johnmackintosh.net but has kindly been re-posted to the NHS-R community blog.

It has also been formatted to remove Latin Abbreviations, edited for NHS-R Style and to ensure running of code in Quarto.

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Citation

For attribution, please cite this work as:
MacKintosh, John. 2020. “NHSRdatasets Meets Runcharter.” February 12, 2020. https://nhsrcommunity.com/blog/nhsrdatasets-meets-runcharter.html.