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AllFunctions.R
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library(pacman)
p_load(here,
tidyverse,
magrittr,
httr,
glue,
jsonlite,
RColorBrewer)
fnNHSOpenData <- function(dataset = c("PCA", "EPD", "SCMD"), Year, Month, strSelect = "*", strWhere = "", strGroupBy = "") {
strURL <- "https://opendata.nhsbsa.net/api/3/action/datastore_search_sql?resource_id={res}&sql="
if (dataset=="SCMD") dataset <- paste0("SCMD_", c("FINAL", "WIP", "PROVISIONAL"))
res <- paste0(dataset, "_", Year, sprintf("%02d", Month))
strSQL <- paste("SELECT", strSelect, "FROM `{res}`")
if (strWhere != "") strSQL <- paste(strSQL, "WHERE", strWhere)
if (strGroupBy != "") strSQL <- paste(strSQL, "GROUP BY", strGroupBy)
url <- glue(strURL, strSQL) |>
URLencode()
for (i in 1:length(url)) {
suppressWarnings(xx <- tryCatch(fromJSON(url[i]), error=function(e) e))
if (!inherits(xx, "error")) break
}
if (!is.null(xx$result$records_truncated)) {
gz <- tempfile()
httr::GET(url = xx$result$gc_urls$url,
config = httr::write_disk(gz))
tibble(Dataset = res[i]) |>
bind_cols(gzfile(gz) |>
read.csv() |>
dplyr::as_tibble())
} else {
tibble(Dataset = res[i]) |>
bind_cols(xx$result$result$records |>
as_tibble())
}
}
fnReadLifeTable <- function(path, sheet, range, yr) {
suppressMessages(
read_excel(path, sheet, range) %>%
mutate(yr = sheet)
)
}
fnRateToProb <- function(r, t=1) 1 - exp(-r * t)
fnProbToRate <- function(p, t=1) -log(1 - p) / t
fnProbToProb <- function(p, t=1) 1 - (1 - p) ^ t
fnONSLifeTable<- function(years = 1980:2018,
ages = 0:100,
sexes = c("Male", "Female"),
countries = c("UK", "Eng", "Sco", "Wal", "EngWal"),
spans = c("3-year", "single year"),
extrapolate = FALSE,
maxExtrap = 120,
widthExtrap = 10) {
countries <- match.arg(countries)
spans <- match.arg(spans)
url <- paste0(switch(spans,
"3-year" = "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fbirthsdeathsandmarriages%2flifeexpectancies%2fdatasets%2fnationallifetables",
"single year" = "https://www.ons.gov.uk/file?uri=%2fpeoplepopulationandcommunity%2fbirthsdeathsandmarriages%2flifeexpectancies%2fdatasets%2fsingleyearlifetablesuk1980to2018%2fsingleyearlifetables"),
switch(spans,
"3-year" = switch(countries,
"Eng" = "englandreferencetables%2fcurrent/nationallifetables3yreng.xlsx",
"Sco" = "scotlandreferencetables%2fcurrent/nationallifetables3yrsco.xlsx",
"Wal" = "walesreferencetables%2fcurrent/nationallifetables3yrwal.xlsx",
"EngWal" = "englandandwalesreferencetables%2fcurrent/nationallifetables3yrew.xlsx",
"unitedkingdomreferencetables%2fcurrent/nationallifetables3yruk.xlsx"),
"single year" = switch(countries,
"Eng" = "england/singleyearlifetablese.xlsx",
"Sco" = "scotland/singleyearlifetabless.xlsx",
"Wal" = "wales/singleyearlifetablesw1.xlsx",
"EngWal" = "englandandwales/singleyearlifetablesew.xlsx",
"uk/singleyearlifetablesuk.xlsx")))
GET(url, write_disk(tmp <- tempfile(fileext = ".xlsx")))
tblYrs <- tibble(path = tmp,
sheet = tmp %>%
excel_sheets() %>%
str_subset(pattern = if_else(spans=="3-year", "\\d{4}-\\d{4}", "\\d{4}")),
range = "A6:M107")
tblLT <- tblYrs %>%
filter(substr(sheet, 1, 4) %in% years) %>%
pmap_dfr(fnReadLifeTable)
tblLT %<>%
dplyr::select(!c(...7, age...8)) %>%
rename(age = age...1) %>%
pivot_longer(cols = !c(age, yr),
names_to = "tmp",
values_to = "val") %>%
separate(col = tmp,
into = c("par", "sex")) %>%
mutate(sex = ifelse(as.numeric(sex)<7, "Male", "Female"),
year_from = as.numeric(substr(yr, 1, 4)),
year_to = year_from + if_else(spans=="3-year", 2, 0)) %>%
pivot_wider(id_cols = c(age, year_from, year_to, sex),
values_from = val,
names_from = par) %>%
rowwise() %>%
mutate(lx = ifelse(min(ages)==0, lx, NA),
dx = ifelse(min(ages)==0, dx, NA)) %>%
filter(year_from %in% years,
age %in% ages,
sex %in% sexes) %>%
arrange(-year_from, sex, age) %>%
group_by(sex, year_from, year_to)
if (extrapolate) {
maxA <- max(tblLT$age)
tblLT %<>%
slice_tail(n=widthExtrap) %>%
nest() %>%
mutate(lm_obj = map(data, ~ lm(log(mx)~log(age), data=.x)),
preddat = list(tibble(age = (maxA+1):(maxExtrap+1))),
pred = map2(lm_obj, preddat, predict)) %>%
unnest(c(preddat, pred)) %>%
mutate(mx = exp(pred)) %>%
select(!c(data, lm_obj, pred)) %>%
bind_rows(tblLT)
}
if (extrapolate | min(ages)>0) {
tblLT %<>%
arrange(-year_from, sex, age) %>%
mutate(qx = coalesce(qx, fnRateToProb(mx)),
llx = if_else(row_number()==1, 100000, lag(100000*cumprod(1-qx), 1)),
lx = coalesce(lx, llx),
dx = coalesce(dx, lx*qx),
Lx = (lx+lead(lx,1))/2,
Tx = rev(cumsum(rev(coalesce(Lx, 0)))),
Ex = Tx/lx,
ex = coalesce(ex, Ex)) %>%
filter(age<=maxExtrap) %>%
dplyr::select(!c(llx, Lx, Tx, Ex))
}
tblLT %>%
ungroup()
}
fnSurvFromLT <- function(LT, Yr, Sex, minAge, maxAge, CyclesPerYr = 1, probVar = qx) {
if (missing(Yr)) {Yr <- LT %>% distinct(year_from) %>% pull()}
if (missing(Sex)) {Sex <- LT %>% distinct(sex) %>% pull()}
if (missing(minAge)) {minAge <- min(LT$age)}
if (missing(maxAge)) {maxAge <- max(LT$age)}
probVar <- enquo(probVar)
numCycles <- (maxAge-minAge)*CyclesPerYr+1
tibble(Cycle = 0:(numCycles-1)) %>%
crossing(Year_from = Yr, Sex) %>%
mutate(Yrs = Cycle / CyclesPerYr,
Age = minAge + Yrs) %>%
left_join(LT %>%
filter(year_from %in% Yr,
sex %in% Sex,
age >= minAge,
age <= maxAge) %>%
select(age, sex, year_from, !!probVar),
by = c("Age" = "age", "Sex" = "sex", "Year_from" = "year_from")) %>%
mutate(qxPC = fnProbToProb(!!probVar, 1/CyclesPerYr)) %>%
group_by(Sex, Year_from) %>%
fill(qxPC) %>%
mutate(St = if_else(Cycle==0, 1, lag(1*cumprod(1-qxPC), 1)),
Year_to = Year_from + 2) %>%
select(Year_from, Year_to, Sex, Cycle, Yrs, Age, TP = qxPC, St) %>%
arrange(Year_from, Sex, Cycle)
}
fnGetZipCSV <- function(url, csv, subzip = "") {
print("Downloading...")
zip <- tempfile()
GET(url = url,
config = write_disk(zip),
progress())
if (subzip != "") {
print("Unzipping subzip...")
zip <- unzip(zipfile = zip,
files = subzip,
exdir = dirname(tempdir()),
junkpaths = T)
}
print("Unzipping...")
csv <- unzip(zipfile = zip,
files = csv,
exdir = dirname(tempdir()),
junkpaths = T)
unlink(zip)
fread(file = csv)
}
fnParseRefCostsCSV <- function(csv, Yr, blnXS = TRUE) {
print("Parsing...")
csv %<>%
clean_names(case = "upper_camel") %>%
rename(any_of(c(CurrencyCode = "CurrenyCode",
Mean = "NationalMean",
DeptCode = "DepartmentCode",
Activity = "TotalActivity",
OrgCode = "ProviderCode"))) %>%
mutate(across(.cols = any_of(c("Activity", "Mean", "ExpectedCost")),
.fns = ~ as.numeric(na_if(as.character(.x), "*"))),
across(.cols = any_of(c("ServiceCode")),
.fns = ~ as.character(.x)),
DeptCode = str_replace_all(DeptCode,
c("TEI$|EI$" = "EL",
"TEIXS$|EI_XS$" = "EL_XS",
"TNEI_L$|NEI_L$" = "NEL",
"TNEI_L_XS$|NEI_L_XS$" = "NEL_XS",
"TNEI_S$|NEI_S$" = "NES",
"TDC$" = "DC",
"TOPROC" = "OPROC",
"\\GTCL" = "CL",
"\\GTNCL" = "NCL"))) %>%
select(-any_of(c("Memo1", "ScaledMff", "OrganisationName", "DepartmentName", "ServiceName", "CurrencyName"))) %>%
add_column(Yr = Yr) %>%
as_tibble()
if (Yr==2008) {
csv %<>%
rename(any_of(c(Activity = "Fce",
Activity = "ActivityP1"))) %>%
select(-any_of(c("Flag", "ActivityP2", "ActivityP3", "ActivityP4")))
if ("BedDays" %in% names(csv)) {
csv %<>%
pivot_longer(cols = contains(c("UnitCost", "Activity")),
names_to = c("tmp1", "tmp2"),
names_pattern = "(.*)(UnitCost|Activity)") %>%
drop_na(value) %>%
mutate(DeptCode = paste0(DeptCode, ifelse(tmp1 == "ExcessBedDays", "_XS", "")),
BedDays = ifelse(tmp1 == "ExcessBedDays", NA, BedDays)) %>%
pivot_wider(names_from = tmp2,
values_from = value) %>%
select(-tmp1)
}
csv %<>%
mutate(ActualCost = UnitCost * Activity)
}
csv %<>%
bind_rows(tibble(OrgType = character(),
SupplierType = character(),
UnitCost = numeric(),
BedDays = numeric()))
}
fnDownloadParseCSV <- function(url, subzip, csv, Yr) {
print(Yr)
fnGetZipCSV(url = url,
subzip = subzip,
csv = csv) %!>%
fnParseRefCostsCSV(Yr = Yr)
}
fnReadSchedXL <- function(Yr, fil, sht){
read_excel(path = fil,
sheet = sht,
skip = ifelse(Yr<2011, 8, 3),
guess_max = 99999) %>%
as_tibble() %>%
clean_names(case = "upper_camel") %>%
rename_with(~ str_replace_all(., c("Name" = "Description",
"Fce|FcEs" = "FCEs",
"TotalCosts" = "TotalCost"))) %>%
rename_with(~ str_remove_all(., "NumberOf|NoOf|Number|No")) %>%
mutate(across(.cols = any_of(c("FCEs", "NationalAverageUnitCost", "Procedures", "Attendances")),
.fns = ~ as.numeric(na_if(as.character(.x), "*"))),
across(.cols = any_of(c("ServiceCode")),
.fns = ~ as.character(.x))) %>%
select(-starts_with("X"))
}
pfnReadSchedXL <- possibly(fnReadSchedXL, NULL)
fnGetSched <- function(Yr, url, zip = "") {
print(Yr)
print("Downloading")
GET(url, progress(), write_disk(tmp <- tempfile()))
if (zip != "") {
print("Unzipping")
tmp <- unzip(zipfile = tmp,
files = zip,
exdir = dirname(tempdir()))
}
tblT <- tibble(Yr = Yr, DeptCode = c("EL", "TEI", "EI",
"EL_XS", "TEIXS", "EI_XS",
"NEL", "TNEI_L", "NEI_L",
"NEL_XS", "TNEI_L_XS", "NEI_L_XS",
"NES", "TNEI_S", "NEI_S",
"DC", "TDC",
"OPROC", "TOPROC",
"CL", "NCL",
"TCLFUSFF", "TCLFASFF", "TCLFUSNFF", "TCLFASNFF", "TCLFUMFF", "TCLFAMFF", "TCLFUMNFF", "TCLFAMNFF", "TNCLFUSFF", "TNCLFASFF", "TNCLFUSNFF", "TNCLFASNFF", "TNCLFUMFF", "TNCLFAMFF", "TNCLFUMNFF", "TNCLFAMNFF",
"CLFUSFF", "CLFASFF", "CLFUSNFF", "CLFASNFF", "CLFUMFF", "CLFAMFF", "CLFUMNFF", "CLFAMNFF", "NCLFUSFF", "NCLFASFF", "NCLFUSNFF", "NCLFASNFF", "NCLFUMFF", "NCLFAMFF", "NCLFUMNFF", "NCLFAMNFF",
"APC", "OP"
)) # this list can be extended to extract data recorded under other department codes (i.e. in other sheets of the Excel workbooks)
print("Extracting")
tblT %>%
mutate(d = pmap(.l = list(Yr, sht = DeptCode, fil = tmp),
.f = pfnReadSchedXL)) %>%
filter(!is.null(d)) %>%
unnest(d) %>%
select(-any_of("d"))
}