Context

This package has been commissioned by the NHS-R community and is intended to be used to web scrape the NHS Data Dictionary website for useful look up tables. The NHS-R community have been pivotal in getting this package off the ground.

The package is maintained by Gary Hutson - Head of Advanced Analytics at Arden and GEM Commissioning Support Unit and to contact the maintainer directly you can navigate to this site.

Additionally, the package has been developed with generic web scraping functionality to allow other websites containing data tables and elements to be scraped.

Loading the package

To load the package, you can use the below command:

library(NHSDataDictionaRy)
library(dplyr)
library(magrittr)
library(tibble)

This brings in the functions needed to work with the package. The below sub sections will show how to use the package, as intended.

Text manipulation of the tibble

The NHSDataDictionaRy package provides a couple of Microsoft Excel convenience functions for working with text data. These are:

• left_xl()
• right_xl()
• mid_xl()
• len_xl()

I will demonstrate how these can be used on the tibble extracted from the previous example in the following sub sections.

left_xl() function

To utilise the left_xl function it expects two parameters - the first is the text to work with and the second is the number of characters to left trim by:

#Grab a sub set of the data frame
df <- nhs_tibble[10,]
result <- NHSDataDictionaRy::left_xl(df$link_name, 22) print(result) #> [1] "ACCESSIBLE INFORMATION" class(result) #> [1] "character" right_xl() function This works the same way as the left function, but trims from the right of the text inward: #Grab a sub set of the data frame df <- nhs_tibble[10,] result <- NHSDataDictionaRy::right_xl(df$link_name, 23)
print(result)
#> [1] "FORMAT CODE (SNOMED CT)"
class(result)
#> [1] "character"

mid_xl() function

This function takes a slightly different approach and expects 3 input parameter, the first being the text to trim, the second being where to start trimming and the third parameter is the termination point i.e. where to stop the trimming of the string:

#Grab a sub set of the data frame
df <- nhs_tibble[10,]
original <- df$link_name #Original string result <- NHSDataDictionaRy::mid_xl(df$link_name, 12, 20)
print(original); print(result)
#> [1] "ACCESSIBLE INFORMATION SPECIFIC INFORMATION FORMAT CODE (SNOMED CT)"
#> [1] "INFORMATION SPECIFIC"
class(result)
#> [1] "character"

len_xl() function

This is a simple, but useful function, as it gets the length of the string:

#Grab a sub set of the data frame
df <- nhs_tibble[10,]
#Original string
xpath = reduced_tibble$xpath_nat_code, title = "NHS Hospital Activity Treatment Function National Codes") # The query has returned results, if the url does not have a lookup table an error will be thrown print(head(national_codes,10)) #> # A tibble: 10 x 4 #> Code Description Dict_Type DttmExtracted #> <chr> <chr> <chr> <dttm> #> 1 100 General Surgery Service NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 2 101 Urology Service NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 3 102 Transplant Surgery Serv~ NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 4 103 Breast Surgery Service NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 5 104 Colorectal Surgery Serv~ NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 6 105 Hepatobiliary and Pancr~ NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 7 106 Upper Gastrointestinal ~ NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 8 107 Vascular Surgery Service NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 9 108 Spinal Surgery Service NHS Hospital Activity Tre~ 2021-07-09 12:38:04 #> 10 109 Bariatric Surgery Servi~ NHS Hospital Activity Tre~ 2021-07-09 12:38:04 Not all lookups will have associated national code tables, if they are not returned you will receive a message saying the lookup table is not available for this NHS Data Dictionary type. Using my lookup with NHS data There are common lookups that are needed, and this is one such mapping between specialty code, to get the description of the specialty unit description. I will show an example with a made up data frame to illustrate the use case for these lookups and to have up to date lookups:  act_aggregations <- tibble(SpecCode = as.character(c(101,102,103, 104, 105)), ActivityCounts = round(rnorm(5,250,3),0), Month = rep("May", 5)) # Use dplyr to join the NHS activity by specialty code if(is.null(national_codes)){ print("The NHS tibble has not loaded, this could be due to internet connection issues.") } else{ act_aggregations %>% left_join(national_codes, by = c("SpecCode"="Code")) } #> # A tibble: 5 x 6 #> SpecCode ActivityCounts Month Description Dict_Type DttmExtracted #> <chr> <dbl> <chr> <chr> <chr> <dttm> #> 1 101 248 May Urology Servi~ NHS Hospital~ 2021-07-09 12:38:04 #> 2 102 247 May Transplant Su~ NHS Hospital~ 2021-07-09 12:38:04 #> 3 103 256 May Breast Surger~ NHS Hospital~ 2021-07-09 12:38:04 #> 4 104 248 May Colorectal Su~ NHS Hospital~ 2021-07-09 12:38:04 #> 5 105 250 May Hepatobiliary~ NHS Hospital~ 2021-07-09 12:38:04 # This easily joins the lookup on to your data  The benefit of having it in an R package is that you can instantaneously have a lookup of the most relevant and up to date NHS lookups, replacing the need to have a massive data warehouse to capture this information. nhs_table_findeR function This function allows you to perform the steps above in one consolidated function. This means that there is no need to call the nhs_data_elements() function and tableR functions separately, they are all nested in this nice convenience function. This is how you would use it: nhs_table_findeR("ACCOMMODATION STATUS CODE", title="Accomodation Status Code National Code Lookup") #> # A tibble: 54 x 4 #> Code Description Dict_Type DttmExtracted #> <chr> <chr> <chr> <dttm> #> 1 MA00 Mainstream Housing Accomodation Statu~ 2021-07-09 12:38:05 #> 2 MA01 Owner occupier Accomodation Statu~ 2021-07-09 12:38:05 #> 3 MA02 Settled mainstream housing wit~ Accomodation Statu~ 2021-07-09 12:38:05 #> 4 MA03 Shared ownership scheme e.g. S~ Accomodation Statu~ 2021-07-09 12:38:05 #> 5 MA04 Tenant - Local Authority/Arms ~ Accomodation Statu~ 2021-07-09 12:38:05 #> 6 MA05 Tenant - Housing Association Accomodation Statu~ 2021-07-09 12:38:05 #> 7 MA06 Tenant - private landlord Accomodation Statu~ 2021-07-09 12:38:05 #> 8 MA09 Other mainstream housing (not ~ Accomodation Statu~ 2021-07-09 12:38:05 #> 9 HM00 Homeless Accomodation Statu~ 2021-07-09 12:38:05 #> 10 HM01 Rough sleeper Accomodation Statu~ 2021-07-09 12:38:05 #> # ... with 44 more rows #Lower case still works glimpse(nhs_table_findeR("accommodation status code")) #> Rows: 54 #> Columns: 4 #>$ Code          <chr> "MA00", "MA01", "MA02", "MA03", "MA04", "MA05", "MA06", ~
#> $Description <chr> "Mainstream Housing", "Owner occupier", "Settled mainstr~ #>$ Dict_Type     <chr> "Not Specified", "Not Specified", "Not Specified", "Not ~
#> $DttmExtracted <dttm> 2021-07-09 12:38:05, 2021-07-09 12:38:05, 2021-07-09 12~ xpathTextR function This function has been provided to return elements from a website, other than html tables, as these functions predominately work with tables. The below example shows how this can be implemented, but requires the retrieval of the xpath via the Inspect command in Google Chrome (CTRL + SHIFT + I):  url <- "https://datadictionary.nhs.uk/data_elements/abbreviated_mental_test_score.html" xpath_element <- '//*[@id="element_abbreviated_mental_test_score.description"]' # Run the xpathTextR function to retrieve details of the element retrieved result_list <- NHSDataDictionaRy::xpathTextR(url, xpath_element) print(result_list) #>$result
#> [1] "Description\n  \n  \n  \n    \n      ABBREVIATED MENTAL TEST SCORE\n is the \n              PERSON SCORE\n where the \n              ASSESSMENT TOOL TYPE\n is \n              'Abbreviated Mental Test Score'.        \n    The score is in the range 0 to 10.\n  \n\n"
#>
#> $website_passed #> [1] "https://datadictionary.nhs.uk/data_elements/abbreviated_mental_test_score.html" #> #>$xpath_passed
#> [1] "//*[@id=\"element_abbreviated_mental_test_score.description\"]"
#>
#> $html_node_result #> {html_document} #> <html xmlns="http://www.w3.org/1999/xhtml" xmlns:whc="http://www.oxygenxml.com/webhelp/components" xml:lang="en" lang="en" whc:version="21.1"> #> [1] <head>\n<link rel="shortcut icon" href="../oxygen-webhelp%5Ctemplate%5Cre ... #> [2] <body class="wh_topic_page frmBody">\n <a href="#wh_topic_body" cl ... #> #>$datetime_access
#> [1] "2021-07-09 12:38:05 BST"
#>
#> $person_accessed #> [1] "GARYH - LAPTOP-GE3S96EI" This provides details of the result, the text retrieved live from the website - this would need some cleaning, the website passed to the function, the xpath included, the result of the node search, the date and time the list was generated and the person and domain accessing this. Cleaning the text example The example below shows how the text could be cleaned once it is retrieved: # Use the returned result and do some text processing clean_text <- trimws(unlist(result_list$result))
clean_text <- clean_text %>%
gsub("[\r\n]", "", .) %>% #Remove new line and breaks
trimws() %>% #Get rid of any white space
as.character() #Cast to a character vector

print(clean_text)
#> [1] "Description                ABBREVIATED MENTAL TEST SCORE is the               PERSON SCORE where the               ASSESSMENT TOOL TYPE is               'Abbreviated Mental Test Score'.            The score is in the range 0 to 10."

I have used the trim white space function to extract the result element from the returned list from the previous function and now I use piping to a gsub function to remove newlines and spaces, I use the trimws() command again to make sure the spacing is sorted and then I convert (cast) this into a character string. Finally, the results are printed.

Getting data from OpenSafely

A contribution has been added to the package to allow for the OpenSafely data to be examined. To get the OpenSafely data you can specify the code list required and this will pull it into a list. To do this follow the below example:

# Check if the connection has returned any values
if(is.null(result_list)){
print("There is an issue with the internet. This function cannot be used until the internet is available.")
} else{
os_list <- NHSDataDictionaRy::openSafely_listR("opensafely/ace-inhibitor-medications")
glimpse(os_list)
}
#> Rows: 1,096
#> Columns: 6
#> $type <chr> "amp", "amp", "amp", "amp", "amp", "amp", "amp", "amp", ~ #>$ id            <chr> "2.191211e+16", "2.192711e+16", "2.998391e+16", "2.19124~
#> $bnf_code <chr> "0205051AAAAAAAA", "0205051AAAAAAAA", "0205051AAAAAAAA",~ #>$ nm            <chr> "Perindopril tosilate 2.5mg tablets (Teva UK Ltd)", "Per~
#> $Dict_Type <chr> "Not Specified", "Not Specified", "Not Specified", "Not ~ #>$ DttmExtracted <dttm> 2021-07-09 12:38:06, 2021-07-09 12:38:06, 2021-07-09 12~

This extends the functionality of the tableR wrapper to pull back the HTML tables, and has been added as its specific function for convenience in working with the OpenSafely site.

Wrapping up

There are lots of use cases for this, but I would like to keep iterating this tool so please contact me with suggestions of what could be included in future versions.