Update README

alwinw · May 4, 2022 · d19a00b · d19a00b
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epocakir
 Title: Clinical Coding of Patients with Kidney Disease
-Version: 0.9.7
+Version: 0.9.8
 Authors@R:
     c(person(given = "Alwin",
              family = "Wang",
@@ -16,7 +16,7 @@ Description: Clinical coding and diagnosis of patients with kidney using
     clinical practice guidelines. The guidelines used are the evidence-based
     KDIGO guidelines, see <https://kdigo.org/guidelines/> for more information.
     This package covers acute kidney injury (AKI), anemia, and
-    chronic liver disease (CKD).
+    chronic kidney disease (CKD).
 License: MIT + file LICENSE
 URL: https://github.com/alwinw/epocakir
 BugReports: https://github.com/alwinw/epocakir/issues

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,12 @@
+# epocakir 0.9.8
+
+Update README and formatting
+
+## Bug Fixes
+
+- Update readme and description
+- Addressed comments from `goodpractice::gp()`
+
 # epocakir 0.9.7
 
 CRAN re-submission

diff --git a/R/aki.R b/R/aki.R
@@ -86,8 +86,7 @@ aki_staging.data.frame <- function(.data,
 
 #' @rdname aki_staging
 #' @export
-aki_staging.units <- function(
-                              SCr = NULL,
+aki_staging.units <- function(SCr = NULL,
                               bCr = NULL,
                               UO = NULL,
                               dttm = NULL,
@@ -107,8 +106,7 @@ aki_staging.units <- function(
 
 #' @rdname aki_staging
 #' @export
-aki_staging.numeric <- function(
-                                SCr = NULL,
+aki_staging.numeric <- function(SCr = NULL,
                                 bCr = NULL,
                                 UO = NULL,
                                 dttm = NULL,

diff --git a/R/ckd.R b/R/ckd.R
@@ -58,8 +58,7 @@ eGFR <- function(...) {
   UseMethod("eGFR")
 }
 
-eGFR_internal <- function(
-                          SCr,
+eGFR_internal <- function(SCr,
                           SCysC,
                           Age,
                           height,
@@ -125,8 +124,7 @@ eGFR.data.frame <- function(.data,
 
 #' @rdname eGFR
 #' @export
-eGFR.units <- function(
-                       SCr = NULL,
+eGFR.units <- function(SCr = NULL,
                        SCysC = NULL,
                        Age = NULL,
                        height = NULL,
@@ -161,8 +159,7 @@ eGFR.units <- function(
 
 #' @rdname eGFR
 #' @export
-eGFR.numeric <- function(
-                         SCr = NULL,
+eGFR.numeric <- function(SCr = NULL,
                          SCysC = NULL,
                          Age = NULL,
                          height = NULL,

diff --git a/README.Rmd b/README.Rmd
@@ -32,7 +32,7 @@ knitr::opts_chunk$set(
 
 The *epocakir* package makes clinical coding of patients with kidney disease using clinical practice guidelines easy.
 The guidelines used are the evidence-based [KDIGO guidelines](https://kdigo.org/guidelines/).
-This package covers acute kidney injury (AKI), anemia, and chronic liver disease(CKD):
+This package covers acute kidney injury (AKI), anemia, and chronic kidney disease (CKD):
 
 - `aki_staging()`: Classification of AKI staging (`aki_stages`) with automatic selection of:
 
@@ -57,7 +57,7 @@ This package covers acute kidney injury (AKI), anemia, and chronic liver disease
   - `eGFR_child_SCysC()`: eGFR based on the pediatric cystatin C-based equation
 
 - `GFR_staging()`: Staging of GFR (`GFR_stages`)
-  
+
 - Multiple utility functions including:
 
   - `conversion_factors`: Conversion factors used throughout the KDIGO guidelines

diff --git a/README.md b/README.md
@@ -27,54 +27,54 @@ stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://
 The *epocakir* package makes clinical coding of patients with kidney
 disease using clinical practice guidelines easy. The guidelines used are
 the evidence-based [KDIGO guidelines](https://kdigo.org/guidelines/).
-This package covers acute kidney injury (AKI), anemia, and chronic liver
-disease(CKD):
+This package covers acute kidney injury (AKI), anemia, and chronic
+kidney disease (CKD):
 
-  - `aki_staging()`: Classification of AKI staging (`aki_stages`) with
+-   `aki_staging()`: Classification of AKI staging (`aki_stages`) with
     automatic selection of:
-
-      - `aki_bCr()`: AKI based on baseline creatinine
-      - `aki_SCr()`: AKI based on changes in serum creatinine
-      - `aki_UO()`: AKI based on urine output
 
-  - `anemia()`: Classification of anemia
+    -   `aki_bCr()`: AKI based on baseline creatinine
+    -   `aki_SCr()`: AKI based on changes in serum creatinine
+    -   `aki_UO()`: AKI based on urine output
 
-  - Classification of albuminuria (`Albuminuria_stages`)
-
-      - `Albuminuria_staging_ACR()`: Albuminuria based on Albumin
+-   `anemia()`: Classification of anemia
+
+-   Classification of albuminuria (`Albuminuria_stages`)
+
+    -   `Albuminuria_staging_ACR()`: Albuminuria based on Albumin
         excretion rate
-      - `Albuminuria_staging_AER()`: Albuminuria based on
+    -   `Albuminuria_staging_AER()`: Albuminuria based on
         Albumin-to-creatinine ratio
 
-  - `eGFR()`: Estimation of glomerular filtration rate with automatic
+-   `eGFR()`: Estimation of glomerular filtration rate with automatic
     selection of:
-    
-      - `eGFR_adult_SCr()`: eGFR based on the 2009 CKD-EPI creatinine
+
+    -   `eGFR_adult_SCr()`: eGFR based on the 2009 CKD-EPI creatinine
         equation
-      - `eGFR_adult_SCysC()`: eGFR based on the 2012 CKD-EPI cystatin C
+    -   `eGFR_adult_SCysC()`: eGFR based on the 2012 CKD-EPI cystatin C
         equation
-      - `eGFR_adult_SCr_SCysC()`: eGFR based on the 2012 CKD-EPI
+    -   `eGFR_adult_SCr_SCysC()`: eGFR based on the 2012 CKD-EPI
         creatinine-cystatin C equation
-      - `eGFR_child_SCr()`: eGFR based on the pediatric creatinine-based
+    -   `eGFR_child_SCr()`: eGFR based on the pediatric creatinine-based
         equation
-      - `eGFR_child_SCr_BUN()`: eGFR based on the pediatric
+    -   `eGFR_child_SCr_BUN()`: eGFR based on the pediatric
         creatinine-BUN equation
-      - `eGFR_child_SCysC()`: eGFR based on the pediatric cystatin
+    -   `eGFR_child_SCysC()`: eGFR based on the pediatric cystatin
         C-based equation
 
-  - `GFR_staging()`: Staging of GFR (`GFR_stages`)
+-   `GFR_staging()`: Staging of GFR (`GFR_stages`)
+
+-   Multiple utility functions including:
 
-  - Multiple utility functions including:
-
-      - `conversion_factors`: Conversion factors used throughout the
+    -   `conversion_factors`: Conversion factors used throughout the
         KDIGO guidelines
-      - `as_metric()`: Conversion of a measured value into metric units
-      - `dob2age()`: Calculation of age from a date of birth
-      - `binary2factor()`: Conversion of binary data into factors based
+    -   `as_metric()`: Conversion of a measured value into metric units
+    -   `dob2age()`: Calculation of age from a date of birth
+    -   `binary2factor()`: Conversion of binary data into factors based
         on a column name
-      - `combine_date_time_cols()`: Combining separate date and time
+    -   `combine_date_time_cols()`: Combining separate date and time
         columns into a single date and time column
-      - `combn_changes`: Generating changes between measurements
+    -   `combn_changes`: Generating changes between measurements
 
 ## Installation
 
@@ -131,13 +131,13 @@ glimpse(tidy_obvs)
 #> Rows: 3
 #> Columns: 8
 #> $ `Patient Number`     <chr> "p10001", "p10002", "p10003"
-#> $ `Admission DateTime` <dttm> 2020-03-05 14:01:00, 2020-03-06 09:10:00, 2020-03…
-#> $ Discharge_DateTime   <dttm> 2020-03-10 16:34:00, 2020-03-16 18:51:00, 2020-0…
+#> $ `Admission DateTime` <dttm> 2020-03-05 14:01:00, 2020-03-06 09:10:00, 202...
+#> $ Discharge_DateTime   <dttm> 2020-03-10 16:34:00, 2020-03-16 18:51:00, 202...
 #> $ `Date of Birth`      <chr> "1956-01-09", "1997-12-04", "1973-05-28"
 #> $ Male                 <ord> Male, Not_Male, Male
 #> $ Height               [m] 1.82 [m], 1.61 [m], 1.68 [m]
 #> $ Surgery              <ord> Not_Surgery, Not_Surgery, Surgery
-#> $ Age                  <Duration> 2076624000s (~65.8 years), 754272000s (~23.9 yea…
+#> $ Age                  <Duration> 2092780800s (~66.32 years), 770428800s (~...
 ```
 
 Make sure to use `set_units()` from the `units` package to convert all
@@ -152,15 +152,15 @@ possible to classify AKI using `aki_bCr()`, `aki_SCr()` or `aki_UO().`
 
 ``` r
 head(aki_pt_data)
-#> # A tibble: 6 × 7
-#>      SCr_    bCr_ pt_id_ dttm_      UO_ aki_staging_type aki_       
-#>   [mg/dl] [mg/dl] <chr>  <dttm> [ml/kg] <chr>            <ord>      
-#> 1     2       1.5 <NA>   NA          NA aki_bCr          No AKI     
-#> 2     2.5     1.5 <NA>   NA          NA aki_bCr          AKI Stage 1
-#> 3     3       1.5 <NA>   NA          NA aki_bCr          AKI Stage 2
-#> 4     3.5     1.5 <NA>   NA          NA aki_bCr          AKI Stage 2
-#> 5     4       1.5 <NA>   NA          NA aki_bCr          AKI Stage 3
-#> 6     4.5     1.5 <NA>   NA          NA aki_bCr          AKI Stage 3
+#> # A tibble: 6 x 7
+#>       SCr_    bCr_ pt_id_ dttm_                   UO_ aki_staging_type aki_     
+#>    [mg/dl] [mg/dl] <chr>  <dttm>              [ml/kg] <chr>            <ord>    
+#> 1      2.0     1.5 <NA>   NA                       NA aki_bCr          No AKI   
+#> 2      2.5     1.5 <NA>   NA                       NA aki_bCr          AKI Stag~
+#> 3      3.0     1.5 <NA>   NA                       NA aki_bCr          AKI Stag~
+#> 4      3.5     1.5 <NA>   NA                       NA aki_bCr          AKI Stag~
+#> 5      4.0     1.5 <NA>   NA                       NA aki_bCr          AKI Stag~
+#> 6      4.5     1.5 <NA>   NA                       NA aki_bCr          AKI Stag~
 
 aki_staging(aki_pt_data,
   SCr = "SCr_", bCr = "bCr_", UO = "UO_",
@@ -179,40 +179,40 @@ aki_pt_data %>%
     dttm = dttm_, pt_id = pt_id_
   )) %>%
   select(pt_id_, SCr_:dttm_, aki)
-#> # A tibble: 27 × 5
+#> # A tibble: 27 x 5
 #>    pt_id_    SCr_    bCr_ dttm_               aki        
 #>    <chr>  [mg/dl] [mg/dl] <dttm>              <ord>      
-#>  1 <NA>       2       1.5 NA                  No AKI     
+#>  1 <NA>       2.0     1.5 NA                  No AKI     
 #>  2 <NA>       2.5     1.5 NA                  AKI Stage 1
-#>  3 <NA>       3       1.5 NA                  AKI Stage 2
+#>  3 <NA>       3.0     1.5 NA                  AKI Stage 2
 #>  4 <NA>       3.5     1.5 NA                  AKI Stage 2
-#>  5 <NA>       4       1.5 NA                  AKI Stage 3
+#>  5 <NA>       4.0     1.5 NA                  AKI Stage 3
 #>  6 <NA>       4.5     1.5 NA                  AKI Stage 3
-#>  7 pt1        3.4    NA   2020-10-23 09:00:00 No AKI     
-#>  8 pt1        3.9    NA   2020-10-25 21:00:00 No AKI     
-#>  9 pt1        3      NA   2020-10-20 09:00:00 AKI Stage 1
-#> 10 pt2        3.4    NA   2020-10-18 22:00:00 No AKI     
-#> # … with 17 more rows
+#>  7 pt1        3.4      NA 2020-10-23 09:00:00 No AKI     
+#>  8 pt1        3.9      NA 2020-10-25 21:00:00 No AKI     
+#>  9 pt1        3.0      NA 2020-10-20 09:00:00 AKI Stage 1
+#> 10 pt2        3.4      NA 2020-10-18 22:00:00 No AKI     
+#> # ... with 17 more rows
 
 aki_pt_data %>%
   mutate(aki = aki_SCr(
     SCr = SCr_, dttm = dttm_, pt_id = pt_id_
   )) %>%
   select(pt_id_, SCr_:dttm_, aki)
-#> # A tibble: 27 × 5
+#> # A tibble: 27 x 5
 #>    pt_id_    SCr_    bCr_ dttm_               aki        
 #>    <chr>  [mg/dl] [mg/dl] <dttm>              <ord>      
-#>  1 <NA>       2       1.5 NA                  No AKI     
+#>  1 <NA>       2.0     1.5 NA                  No AKI     
 #>  2 <NA>       2.5     1.5 NA                  No AKI     
-#>  3 <NA>       3       1.5 NA                  No AKI     
+#>  3 <NA>       3.0     1.5 NA                  No AKI     
 #>  4 <NA>       3.5     1.5 NA                  No AKI     
-#>  5 <NA>       4       1.5 NA                  No AKI     
+#>  5 <NA>       4.0     1.5 NA                  No AKI     
 #>  6 <NA>       4.5     1.5 NA                  No AKI     
-#>  7 pt1        3.4    NA   2020-10-23 09:00:00 No AKI     
-#>  8 pt1        3.9    NA   2020-10-25 21:00:00 No AKI     
-#>  9 pt1        3      NA   2020-10-20 09:00:00 AKI Stage 1
-#> 10 pt2        3.4    NA   2020-10-18 22:00:00 No AKI     
-#> # … with 17 more rows
+#>  7 pt1        3.4      NA 2020-10-23 09:00:00 No AKI     
+#>  8 pt1        3.9      NA 2020-10-25 21:00:00 No AKI     
+#>  9 pt1        3.0      NA 2020-10-20 09:00:00 AKI Stage 1
+#> 10 pt2        3.4      NA 2020-10-18 22:00:00 No AKI     
+#> # ... with 17 more rows
 ```
 
 Similarly, `eGFR()` offers the ability to automatically select the
@@ -223,15 +223,16 @@ particular formula is required, then `eGFR_adult_SCr`,
 
 ``` r
 head(eGFR_pt_data)
-#> # A tibble: 6 × 10
-#>    SCr_ SCysC_  Age_ male_ black_ height_  BUN_ eGFR_calc_type_ eGFR_ pediatric_
-#>   [mg/dl] [mg/L] [years] <lgl> <lgl>      [m] [mg/dl] <chr>           [mL/1.73m2/min] <lgl>     
-#> 1   0.5   NA      20 FALSE FALSE       NA    NA eGFR_adult_SCr   139. FALSE     
-#> 2  NA      0.4    20 FALSE FALSE       NA    NA eGFR_adult_SCy…  162. FALSE     
-#> 3   0.5    0.4    20 FALSE FALSE       NA    NA eGFR_adult_SCr…  167. FALSE     
-#> 4   0.5   NA      30 FALSE TRUE        NA    NA eGFR_adult_SCr   150. FALSE     
-#> 5  NA      0.4    30 FALSE TRUE        NA    NA eGFR_adult_SCy…  155. FALSE     
-#> 6   0.5    0.4    30 FALSE TRUE        NA    NA eGFR_adult_SCr…  171. FALSE
+#> # A tibble: 6 x 10
+#>       SCr_   SCysC_     Age_ male_ black_  height_     BUN_ eGFR_calc_type_
+#>    [mg/dl]   [mg/L]  [years] <lgl> <lgl>       [m]  [mg/dl] <chr>          
+#> 1      0.5       NA       20 FALSE FALSE        NA       NA eGFR_adult_SCr 
+#> 2       NA      0.4       20 FALSE FALSE        NA       NA eGFR_adult_SCy~
+#> 3      0.5      0.4       20 FALSE FALSE        NA       NA eGFR_adult_SCr~
+#> 4      0.5       NA       30 FALSE TRUE         NA       NA eGFR_adult_SCr 
+#> 5       NA      0.4       30 FALSE TRUE         NA       NA eGFR_adult_SCy~
+#> 6      0.5      0.4       30 FALSE TRUE         NA       NA eGFR_adult_SCr~
+#> # ... with 2 more variables: eGFR_ [mL/1.73m2/min], pediatric_ <lgl>
 
 eGFR(eGFR_pt_data,
   SCr = "SCr_", SCysC = "SCysC_",
@@ -255,42 +256,42 @@ eGFR_pt_data %>%
     male = male_, black = black_, pediatric = pediatric_
   )) %>%
   select(SCr_:pediatric_, eGFR)
-#> # A tibble: 51 × 11
-#>       SCr_ SCysC_    Age_ male_ black_ height_    BUN_ eGFR_calc_type_     eGFR_
-#>    [mg/dl] [mg/L] [years] <lgl> <lgl>      [m] [mg/dl] <chr>             [mL/1.73m2/min]
-#>  1     0.5   NA        20 FALSE FALSE       NA      NA eGFR_adult_SCr      139. 
-#>  2    NA      0.4      20 FALSE FALSE       NA      NA eGFR_adult_SCysC    162. 
-#>  3     0.5    0.4      20 FALSE FALSE       NA      NA eGFR_adult_SCr_S…   167. 
-#>  4     0.5   NA        30 FALSE TRUE        NA      NA eGFR_adult_SCr      150. 
-#>  5    NA      0.4      30 FALSE TRUE        NA      NA eGFR_adult_SCysC    155. 
-#>  6     0.5    0.4      30 FALSE TRUE        NA      NA eGFR_adult_SCr_S…   171. 
-#>  7     0.5   NA        20 FALSE FALSE       NA      NA eGFR_adult_SCr      139. 
-#>  8    NA      1.2      20 FALSE FALSE       NA      NA eGFR_adult_SCysC     66.8
-#>  9     0.5    1.2      20 FALSE FALSE       NA      NA eGFR_adult_SCr_S…    96.4
-#> 10     0.5   NA        30 FALSE TRUE        NA      NA eGFR_adult_SCr      150. 
-#> # … with 41 more rows, and 2 more variables: pediatric_ <lgl>,
-#> #   eGFR [mL/1.73m2/min]
+#> # A tibble: 51 x 11
+#>        SCr_   SCysC_     Age_ male_ black_  height_     BUN_ eGFR_calc_type_
+#>     [mg/dl]   [mg/L]  [years] <lgl> <lgl>       [m]  [mg/dl] <chr>          
+#>  1      0.5       NA       20 FALSE FALSE        NA       NA eGFR_adult_SCr 
+#>  2       NA      0.4       20 FALSE FALSE        NA       NA eGFR_adult_SCy~
+#>  3      0.5      0.4       20 FALSE FALSE        NA       NA eGFR_adult_SCr~
+#>  4      0.5       NA       30 FALSE TRUE         NA       NA eGFR_adult_SCr 
+#>  5       NA      0.4       30 FALSE TRUE         NA       NA eGFR_adult_SCy~
+#>  6      0.5      0.4       30 FALSE TRUE         NA       NA eGFR_adult_SCr~
+#>  7      0.5       NA       20 FALSE FALSE        NA       NA eGFR_adult_SCr 
+#>  8       NA      1.2       20 FALSE FALSE        NA       NA eGFR_adult_SCy~
+#>  9      0.5      1.2       20 FALSE FALSE        NA       NA eGFR_adult_SCr~
+#> 10      0.5       NA       30 FALSE TRUE         NA       NA eGFR_adult_SCr 
+#> # ... with 41 more rows, and 3 more variables: eGFR_ [mL/1.73m2/min],
+#> #   pediatric_ <lgl>, eGFR [mL/1.73m2/min]
 
 eGFR_pt_data %>%
   dplyr::mutate(eGFR = eGFR_adult_SCr(
     SCr = SCr_, Age = Age_, male = male_, black = black_
   )) %>%
   select(SCr_:pediatric_, eGFR)
-#> # A tibble: 51 × 11
-#>       SCr_ SCysC_    Age_ male_ black_ height_    BUN_ eGFR_calc_type_     eGFR_
-#>    [mg/dl] [mg/L] [years] <lgl> <lgl>      [m] [mg/dl] <chr>             [mL/1.73m2/min]
-#>  1     0.5   NA        20 FALSE FALSE       NA      NA eGFR_adult_SCr      139. 
-#>  2    NA      0.4      20 FALSE FALSE       NA      NA eGFR_adult_SCysC    162. 
-#>  3     0.5    0.4      20 FALSE FALSE       NA      NA eGFR_adult_SCr_S…   167. 
-#>  4     0.5   NA        30 FALSE TRUE        NA      NA eGFR_adult_SCr      150. 
-#>  5    NA      0.4      30 FALSE TRUE        NA      NA eGFR_adult_SCysC    155. 
-#>  6     0.5    0.4      30 FALSE TRUE        NA      NA eGFR_adult_SCr_S…   171. 
-#>  7     0.5   NA        20 FALSE FALSE       NA      NA eGFR_adult_SCr      139. 
-#>  8    NA      1.2      20 FALSE FALSE       NA      NA eGFR_adult_SCysC     66.8
-#>  9     0.5    1.2      20 FALSE FALSE       NA      NA eGFR_adult_SCr_S…    96.4
-#> 10     0.5   NA        30 FALSE TRUE        NA      NA eGFR_adult_SCr      150. 
-#> # … with 41 more rows, and 2 more variables: pediatric_ <lgl>,
-#> #   eGFR [mL/1.73m2/min]
+#> # A tibble: 51 x 11
+#>        SCr_   SCysC_     Age_ male_ black_  height_     BUN_ eGFR_calc_type_
+#>     [mg/dl]   [mg/L]  [years] <lgl> <lgl>       [m]  [mg/dl] <chr>          
+#>  1      0.5       NA       20 FALSE FALSE        NA       NA eGFR_adult_SCr 
+#>  2       NA      0.4       20 FALSE FALSE        NA       NA eGFR_adult_SCy~
+#>  3      0.5      0.4       20 FALSE FALSE        NA       NA eGFR_adult_SCr~
+#>  4      0.5       NA       30 FALSE TRUE         NA       NA eGFR_adult_SCr 
+#>  5       NA      0.4       30 FALSE TRUE         NA       NA eGFR_adult_SCy~
+#>  6      0.5      0.4       30 FALSE TRUE         NA       NA eGFR_adult_SCr~
+#>  7      0.5       NA       20 FALSE FALSE        NA       NA eGFR_adult_SCr 
+#>  8       NA      1.2       20 FALSE FALSE        NA       NA eGFR_adult_SCy~
+#>  9      0.5      1.2       20 FALSE FALSE        NA       NA eGFR_adult_SCr~
+#> 10      0.5       NA       30 FALSE TRUE         NA       NA eGFR_adult_SCr 
+#> # ... with 41 more rows, and 3 more variables: eGFR_ [mL/1.73m2/min],
+#> #   pediatric_ <lgl>, eGFR [mL/1.73m2/min]
 ```
 
 ## References
@@ -308,7 +309,7 @@ commit](https://img.shields.io/github/last-commit/alwinw/epocakir)
 bytes](https://img.shields.io/github/repo-size/alwinw/epocakir) ![Total
 Lines](https://img.shields.io/tokei/lines/github/alwinw/epocakir)
 
------
+------------------------------------------------------------------------
 
 See <https://alwinw.github.io/epocakir/reference/index.html> for more
 usage details and package reference.