scicalc
scicalc.RmdTo install the scicalc package you can run the following two commands:
options(repos = c('scicalc' = "https://a2-ai.github.io/gh-pkg-mirror/scicalc",
getOption("repos")))
install.packages("scicalc")Scicalc
Scicalc is a package of functions commonly used at A2-Ai. We hope this package will ease data assembly and allow for a more reproducible code across analysts and projects. It is in no way complete or all encompassing, but we hope this is a good start. Please let us know if there are additional functions we should include!
Reading in data
I’ll first try and read in data from
~/inst/extdata/data/source - in R packages extra files go
in the inst directory, but we can pretend this is just like
a Project starter data/source directory.
data_source <- file.path(here::here(), "inst", "extdata", "data", "source")
if (!dir.exists(data_source)) {
fs::dir_create(data_source)
}
list.files(data_source)
#> [1] "dm.sas7bdat" "lb.csv" "pc.parquet" "test_excel.xlsx"
#> [5] "test.txt" "vs.parquet"This is a contrived example, but there are several different file
types in source data representing different SDTM domains. As well as a
test.txt file. We can use the
read_file_with_hash function to read in all the files in
this source directory. No more creating
read_file_type_with_hash functions in each script!
raw_data <- purrr::map(
list.files(data_source, full.names = TRUE),
scicalc::read_file_with_hash) %>%
purrr::set_names(tools::file_path_sans_ext(list.files(data_source)))
#> dm.sas7bdat: 900d253fa720b67ad5bab4fa9aab8487
#> lb.csv: b5e4122d4df0ec79401eb6bb0d2d0d85
#> Rows: 12 Columns: 11
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (4): CREATU, CYSTCU, ASTU, BILIU
#> dbl (7): ID, CREAT, CYSTC, AST, ULNAST, BILI, ULNBILI
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> pc.parquet: 2fada724fc254792587bb16a7183145c
#> test_excel.xlsx: f665dce4222df738750216bb9cd53819
#> Sheets in test_excel.xlsx: model_summary, sheet with space, empty
#> Warning in .f(.x[[i]], ...): File type: txt not currently supported
#> vs.parquet: 8bb048faa17703eab465d500d393d1e5These hashes come from digest::digest’s default
algorithm md5, but we can alter this by supplying
algo = 'blake3' to the read_file_with_hash function.
raw_data <- purrr::map(
list.files(data_source, full.names = TRUE),
scicalc::read_file_with_hash, algo = "blake3") %>%
purrr::set_names(tools::file_path_sans_ext(list.files(data_source)))
#> dm.sas7bdat: ebc8f25b765f8be20063010da48947afc8e64ab0d3c9018b20d9d8792731badd
#> lb.csv: 577007ecd6292a122b6fdf48add5112ba0976d43db617bb087d8fca07da5b126
#> Rows: 12 Columns: 11
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (4): CREATU, CYSTCU, ASTU, BILIU
#> dbl (7): ID, CREAT, CYSTC, AST, ULNAST, BILI, ULNBILI
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> pc.parquet: 1fbb225647da2a6df6a14adf6dde22ae783054000ba5cc56255139caf87b87de
#> test_excel.xlsx: 99bf0f59e74683f3173aba85b083dd928f827d6619b01e1afe80dc1830e7859d
#> Sheets in test_excel.xlsx: model_summary, sheet with space, empty
#> Warning in .f(.x[[i]], ...): File type: txt not currently supported
#> vs.parquet: f0ad72f0b3ac385081baa07461dbbeba70bc8edf0df2bf91820a05b73657137b
names(raw_data)
#> [1] "dm" "lb" "pc" "test_excel" "test"
#> [6] "vs"We can see that each different file type was read with
read_file_with_hash and saved to raw_data. We also got a
warning for test.txt file was not currently supported. If
there are any other file types you commonly read and want to print a
hash besides csv, parquet, and
sas7bdat please reach out so we can add that functionality!
Behind the scenes of read_file_with_hash the extension of
the file is determined and a corresponding function such as
read_parquet_with_hash is called. You can call either, but
I would recommend using the general
read_file_with_hash.
Simple data analysis
Let’s do some bmi and bsa caclulations with the data in the vs domain
df <- raw_data$pc
df <- df %>%
dplyr::mutate(.data = raw_data$vs,
BBMI = scicalc::bbmi(WEIGHT, HEIGHT),
DBSA = scicalc::bsa(WEIGHT, HEIGHT),
MBSA = scicalc::bsa(WEIGHT, HEIGHT, method = "mosteller"))
df
#> # A tibble: 12 × 8
#> ID HEIGHT WEIGHT DV NTLD BBMI DBSA MBSA
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 174 70 10 0 23.1 1.84 1.84
#> 2 1 174 70 150 0.25 23.1 1.84 1.84
#> 3 1 174 70 70 1 23.1 1.84 1.84
#> 4 1 174 70 9 8 23.1 1.84 1.84
#> 5 2 201 80 7 0 19.8 2.16 2.11
#> 6 2 201 80 140 0.25 19.8 2.16 2.11
#> 7 2 201 80 60 1 19.8 2.16 2.11
#> 8 2 201 80 7 8 19.8 2.16 2.11
#> 9 3 190 75 11 0 20.8 2.02 1.99
#> 10 3 190 75 156 0.25 20.8 2.02 1.99
#> 11 3 190 75 70 1 20.8 2.02 1.99
#> 12 3 190 75 7 8 20.8 2.02 1.99Now we can verify the lab units before moving on - remember this is a contrived dataset, but the workflow should be somewhat similar.
dflb <- raw_data$lb %>%
dplyr::select(c("ID", "CREAT", "CYSTC", "AST", "BILI", )) %>%
dplyr::distinct() %>%
tidyr::pivot_longer(cols = c("CREAT", "CYSTC", "AST", "BILI"),
names_to = "LBTESTCD", values_to = "LBORRES")
dfu <- raw_data$lb %>%
dplyr::select(c("ID", "CREATU", "CYSTCU", "ASTU", "BILIU")) %>%
dplyr::distinct() %>%
tidyr::pivot_longer(cols = c("CREATU", "CYSTCU", "ASTU", "BILIU"),
names_to = "LBTESTCDU", values_to = "LBORRESU")
lb <- dflb %>%
dplyr::mutate(LBORRESU = dfu$LBORRESU)
lb
#> # A tibble: 12 × 4
#> ID LBTESTCD LBORRES LBORRESU
#> <dbl> <chr> <dbl> <chr>
#> 1 1 CREAT 1 mg/dL
#> 2 1 CYSTC 0.4 mg/L
#> 3 1 AST 15 U/L
#> 4 1 BILI 0.8 mg/dL
#> 5 2 CREAT 4 mg/dL
#> 6 2 CYSTC 0.9 mg/L
#> 7 2 AST 29 U/L
#> 8 2 BILI 1.9 mg/dL
#> 9 3 CREAT 3 mg/dL
#> 10 3 CYSTC 0.7 mg/L
#> 11 3 AST 38 U/L
#> 12 3 BILI 1.1 mg/dL
if (scicalc::check_for_unique_units(lb$LBTESTCD, lb$LBORRESU)) {
print("Units are 1:1!")
} else {
stop("Error with units!")
}
#> [1] "Units are 1:1!"
scicalc::get_unique_units_df(lb$LBTESTCD, lb$LBORRESU)
#> PARAM UNIT
#> 1 CREAT mg/dL
#> 2 CYSTC mg/L
#> 3 AST U/L
#> 4 BILI mg/dLNow we can use the lab data to calculate hepatic function.
df <- df %>%
dplyr::mutate(.data = raw_data$lb,
BHFC = scicalc::bhfc(AST, ULNAST, BILI, ULNBILI),
)
df
#> # A tibble: 12 × 19
#> ID CREAT CREATU CYSTC CYSTCU AST ULNAST BILI ULNBILI ASTU BILIU HEIGHT
#> <fct> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl>
#> 1 1 1 mg/dL 0.4 mg/L 15 33 0.8 1.2 U/L mg/dL 174
#> 2 1 1 mg/dL 0.4 mg/L 15 33 0.8 1.2 U/L mg/dL 174
#> 3 1 1 mg/dL 0.4 mg/L 15 33 0.8 1.2 U/L mg/dL 174
#> 4 1 1 mg/dL 0.4 mg/L 15 33 0.8 1.2 U/L mg/dL 174
#> 5 2 4 mg/dL 0.9 mg/L 29 33 1.9 1.2 U/L mg/dL 201
#> 6 2 4 mg/dL 0.9 mg/L 29 33 1.9 1.2 U/L mg/dL 201
#> 7 2 4 mg/dL 0.9 mg/L 29 33 1.9 1.2 U/L mg/dL 201
#> 8 2 4 mg/dL 0.9 mg/L 29 33 1.9 1.2 U/L mg/dL 201
#> 9 3 3 mg/dL 0.7 mg/L 38 33 1.1 1.2 U/L mg/dL 190
#> 10 3 3 mg/dL 0.7 mg/L 38 33 1.1 1.2 U/L mg/dL 190
#> 11 3 3 mg/dL 0.7 mg/L 38 33 1.1 1.2 U/L mg/dL 190
#> 12 3 3 mg/dL 0.7 mg/L 38 33 1.1 1.2 U/L mg/dL 190
#> # ℹ 7 more variables: WEIGHT <dbl>, DV <dbl>, NTLD <dbl>, BBMI <dbl>,
#> # DBSA <dbl>, MBSA <dbl>, BHFC <dbl>Now we’ll need the demographics needed for Creatinine clearance, estimated glomerular filtration rate, and renal function.
df <- df %>%
dplyr::mutate(.data = raw_data$dm,
CRCL = scicalc::crcl(scicalc::is_female(SEX), AGE, CREAT, WEIGHT),
BRFC = scicalc::brfc(CRCL),
ckdepi_2009_egfr = scicalc::egfr(
sexf = scicalc::is_female(SEX),
raceb = scicalc::is_black(RACE),
age = AGE,
creat = CREAT,
method = "CKDEPI 2009"),
ckdepi_2021_egfr = scicalc::egfr(
sexf = scicalc::is_female(SEX),
age = AGE,
creat = CREAT,
method = "CKDEPI 2021"),
ckdepi_2021_egfr_cystatin = scicalc::egfr(
sexf = scicalc::is_female(SEX),
age = AGE,
creat = CREAT,
cystc = CYSTC,
method = "CKDEPI 2021 CYSTATIN"),
mdrd_egfr = scicalc::egfr(
sexf = scicalc::is_female(SEX),
raceb = scicalc::is_black(RACE),
age = AGE,
creat = CREAT,
method = "MDRD"),
schwartz_egfr = scicalc::egfr(
height = HEIGHT,
creat = CREAT,
method = "Schwartz"
)
)
df
#> # A tibble: 12 × 29
#> ID SEX RACE AGE CREAT CREATU CYSTC CYSTCU AST ULNAST BILI ULNBILI
#> <fct> <chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 2 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 3 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 4 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 5 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 6 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 7 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 8 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 9 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 10 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 11 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 12 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> # ℹ 17 more variables: ASTU <chr>, BILIU <chr>, HEIGHT <dbl>, WEIGHT <dbl>,
#> # DV <dbl>, NTLD <dbl>, BBMI <dbl>, DBSA <dbl>, MBSA <dbl>, BHFC <dbl>,
#> # CRCL <dbl>, BRFC <dbl>, ckdepi_2009_egfr <dbl>, ckdepi_2021_egfr <dbl>,
#> # ckdepi_2021_egfr_cystatin <dbl>, mdrd_egfr <dbl>, schwartz_egfr <dbl>Writting data
We can use the write_file_with_hash to write
parquet files (We highly recommend you write all data to
parquet unless there is a specific need for other file type). We just
need to supply a path to the file if the directory the file should be
written to does not exist, this function will create it and all
directories needed on the path. This function won’t let us overwrite the
file unless we supply overwrite = TRUE. Like
read_file_with_hash we can choose which algorithm to use
for the digest.
data_derived <- file.path(here::here(), "inst", "extdata", "data", "derived")
if (!file.exists(file.path(data_derived, "pk_data_v01.parquet"))) {
scicalc::write_file_with_hash(
df, file.path(data_derived, "pk_data_v01.parquet"), algo = "blake3")
} else {
print("Overwriting data!")
scicalc::write_file_with_hash(
df, file.path(data_derived, "pk_data_v01.parquet"), overwrite = TRUE, algo = "blake3")
}
#> [1] "Overwriting data!"
#> /home/runner/work/scicalc/scicalc/inst/extdata/data/derived/pk_data_v01.parquet: 153bb3587d8082270a4e019364be974e8a5edd29d40b98df4ae8d5e973dd0eeaReading in derived data
Now if we want to read in the derived data and verify it is the data
we think it is we can use read_hashed_file. This function
will only let you ingest the data if the hash you supply matches the
hash of the contents of the file. Here I’ve supplied the
blake3 hash which does not match the md5 hash
digest uses by default. We have two options, tell
read_hashed_file to use algo = 'blake3' or
supply the md5 hash.
hash <- digest::digest(file.path(data_derived, "pk_data_v01.parquet")) # This is a hash of the string to the file type so it will not match the hash for contents of the file.
print(hash)
#> [1] "16719721ab4cf192479f02ae956dc51b"
#This hash is incorrect so the function errors
if (file.exists(file.path(data_derived, "pk_data_v01.parquet"))) {
testthat::expect_error(scicalc::read_hashed_file(file.path(data_derived, "pk_data_v01.parquet"), hash))
}- updated algo argument works:
correct_hash <- digest::digest(file = file.path(data_derived, "pk_data_v01.parquet"), algo = "blake3")
print(correct_hash)
#> [1] "153bb3587d8082270a4e019364be974e8a5edd29d40b98df4ae8d5e973dd0eea"
data <- scicalc::read_hashed_file(
file.path(data_derived, "pk_data_v01.parquet"),
algo = "blake3",
correct_hash)
data
#> # A tibble: 12 × 29
#> ID SEX RACE AGE CREAT CREATU CYSTC CYSTCU AST ULNAST BILI ULNBILI
#> <fct> <chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 2 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 3 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 4 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 5 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 6 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 7 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 8 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 9 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 10 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 11 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 12 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> # ℹ 17 more variables: ASTU <chr>, BILIU <chr>, HEIGHT <dbl>, WEIGHT <dbl>,
#> # DV <dbl>, NTLD <dbl>, BBMI <dbl>, DBSA <dbl>, MBSA <dbl>, BHFC <dbl>,
#> # CRCL <dbl>, BRFC <dbl>, ckdepi_2009_egfr <dbl>, ckdepi_2021_egfr <dbl>,
#> # ckdepi_2021_egfr_cystatin <dbl>, mdrd_egfr <dbl>, schwartz_egfr <dbl>- giving the
md5hash works
md5_hash <- digest::digest(file = file.path(data_derived, "pk_data_v01.parquet"))
print(md5_hash)
#> [1] "5ddba88109c5d84eed47f9d7376e58e6"
data <- scicalc::read_hashed_file(
file.path(data_derived, "pk_data_v01.parquet"),
md5_hash)
data
#> # A tibble: 12 × 29
#> ID SEX RACE AGE CREAT CREATU CYSTC CYSTCU AST ULNAST BILI ULNBILI
#> <fct> <chr> <chr> <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 2 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 3 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 4 1 F WHITE 24 1 mg/dL 0.4 mg/L 15 33 0.8 1.2
#> 5 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 6 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 7 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 8 2 M BLACK 22 4 mg/dL 0.9 mg/L 29 33 1.9 1.2
#> 9 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 10 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 11 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> 12 3 F ASIAN 35 3 mg/dL 0.7 mg/L 38 33 1.1 1.2
#> # ℹ 17 more variables: ASTU <chr>, BILIU <chr>, HEIGHT <dbl>, WEIGHT <dbl>,
#> # DV <dbl>, NTLD <dbl>, BBMI <dbl>, DBSA <dbl>, MBSA <dbl>, BHFC <dbl>,
#> # CRCL <dbl>, BRFC <dbl>, ckdepi_2009_egfr <dbl>, ckdepi_2021_egfr <dbl>,
#> # ckdepi_2021_egfr_cystatin <dbl>, mdrd_egfr <dbl>, schwartz_egfr <dbl>