refactor calculate_performance(); fix #115

R/muac_at_admission.R

@@ -9,8 +9,8 @@
 #' @param na_values A vector of values in `muac` that are to be considered as
 #'   NA values. Default to NULL which will use NA.
 #' 
-#' @returns A numeric value (if `index = NULL`) or vector of values for median
-#'   mid-upper arm circumference.
+#' @returns A numeric value (if `index = NULL`) or named vector of values for 
+#'   median mid-upper arm circumference with names from each level of `index`.
 #'
 #' @examples
 #' calculate_median_muac(otp_beneficiaries$muac)

R/performance.R

@@ -41,27 +41,29 @@ calculate_performance <- function(df,
                                   add = TRUE) {
   ## Get vars
   if (is.null(vars)) {
-    ## Find variables names in .data
-    .vars <- c(find_var_names(df, vars = c("cure", "recover")),
-               find_var_names(df, vars = c("dead", "death")),
-               find_var_names(df, vars = c("default", "def")),
-               find_var_names(df, vars = c("response", "respond")))
+    ## Find variables names in df
+    .vars <- c(
+      find_var_names(df, vars = c("cure", "recover", "rec")),
+      find_var_names(df, vars = c("dead", "death")),
+      find_var_names(df, vars = c("default", "def")),
+      find_var_names(df, vars = c("response", "respond", "nr"))
+    )
 
     ##
-    if (any(is.null(.vars))) {
-      message(
-        "No variables in `df` match with usual performance indicators data
-        variable names. First 4 columns of df will be used."
+    if (all(is.null(.vars))) {
+      stop(
+        "No variables in `df` match with usual performance indicators data ",
+        "variable names. Please check `df` or specify appropriate `vars`.",
+        call. = TRUE
       )
-
-      .vars <- names(df)[1:4]
     }
   } else {
     ## check if vars found in names of df
-    if (!all(vars %in% names(df))) {
+    if (any(!vars %in% names(df))) {
       stop(
-        "Variables in .data does not match with specified variable names in
-        vars. Try again.", call. = TRUE
+        "Some or all variables in `df` does not match with specified variable ",
+        "names in `vars`. Please try again.", 
+        call. = TRUE
       )
     }
 

README.Rmd

@@ -148,17 +148,17 @@ calculate_los_median(otp_beneficiaries$admDate, otp_beneficiaries$disDate)
 
 ### CMAM programme coverage
 
-The `{squeacr}` provides functions to calculate programme coverage. These functions implement the single coverage estimator approach[^1]. In this approach, treatment coverage is calculated in such a way that estimates $r_out$ or the number of severe acute malnutrition (SAM) cases that have not been enrolled in the programme but have been recovering without treatment.
+The `{squeacr}` provides functions to calculate programme coverage. These functions implement the single coverage estimator approach[^1]. In this approach, treatment coverage is calculated in such a way that estimates the number of severe acute malnutrition (SAM) cases that have not been enrolled in the programme but have been recovering without treatment (`r_out`).
 
-For example, if a coverage survey yielded 5 SAM cases in the programme, 25 cases not in the programme, and 5 recovering cases in the programme, $r_out$ can be calculated as follows:
+For example, if a coverage survey yielded 5 SAM cases in the programme, 25 cases not in the programme, and 5 recovering cases in the programme, `r_out` can be calculated as follows:
 
 ```{r}
 calculate_rout(cin = 5, cout = 25, rin = 5)
 ```
 
 Note here that the `calculate_rout()` function has another argument named `k` which is a correction factor representing the ratio of the mean length of an untreated episode to the mean length of a CMAM treatment episode. This, by default, is set to `k = 3` in the function. However, this should be adjusted based on programme data to estimate the mean length of a SAM treatment episode.
 
-This calculation for $r_out$ is used within `calculate_tc()` to estimate treatment coverage:
+This calculation for `r_out` is used within `calculate_tc()` to estimate treatment coverage:
 
 ```{r}
 calculate_tc(cin = 5, cout = 25, rin = 5)
@@ -178,9 +178,6 @@ Feedback, bug reports, and feature requests are welcome; file issues or seek sup
 
 This project is released with a [Contributor Code of Conduct](https://nutriverse.io/squeacr/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
 
-
 <br/>
 
-## Footnotes
-
 [^1]: Safari Balegamire, Katja Siling, Jose Luis Alvarez Moran, Ernest Guevarra, Sophie Woodhead, Alison Norris, Lionella Fieschi, Paul Binns, and Mark Myatt (2015). A single coverage estimator for use in SQUEAC, SLEAC, and other CMAM coverage assessments. Field Exchange 49, March 2015. p81. <www.ennonline.net/fex/49/singlecoverage>

README.md

@@ -133,18 +133,18 @@ monitoring |>
 which results in the following:
 
     #> # A tibble: 72 × 7
-    #>    State      Year  total_discharge cure_rate default_rate death_rate
-    #>    <chr>      <chr>           <dbl>     <dbl>        <dbl>      <dbl>
-    #>  1 Blue Nile  2016             9693     0.889       0.0906    0.0151 
-    #>  2 Blue Nile  2017            10286     0.948       0.0399    0.00972
-    #>  3 Blue Nile  2018             8807     0.947       0.0404    0.00863
-    #>  4 Blue Nile  2019             9882     0.953       0.0366    0.00708
-    #>  5 Central D… 2016            13313     0.921       0.0440    0.0174 
-    #>  6 Central D… 2017            18098     0.935       0.0421    0.00912
-    #>  7 Central D… 2018            17600     0.939       0.0364    0.00955
-    #>  8 Central D… 2019            18573     0.952       0.0260    0.00549
-    #>  9 East Darf… 2016             9895     0.929       0.0550    0.0104 
-    #> 10 East Darf… 2017            12611     0.956       0.0327    0.00690
+    #>    State        Year  total_discharge cure_rate default_rate death_rate
+    #>    <chr>        <chr>           <dbl>     <dbl>        <dbl>      <dbl>
+    #>  1 Blue Nile    2016             9693     0.889       0.0906    0.0151 
+    #>  2 Blue Nile    2017            10286     0.948       0.0399    0.00972
+    #>  3 Blue Nile    2018             8807     0.947       0.0404    0.00863
+    #>  4 Blue Nile    2019             9882     0.953       0.0366    0.00708
+    #>  5 Central Dar… 2016            13313     0.921       0.0440    0.0174 
+    #>  6 Central Dar… 2017            18098     0.935       0.0421    0.00912
+    #>  7 Central Dar… 2018            17600     0.939       0.0364    0.00955
+    #>  8 Central Dar… 2019            18573     0.952       0.0260    0.00549
+    #>  9 East Darfur  2016             9895     0.929       0.0550    0.0104 
+    #> 10 East Darfur  2017            12611     0.956       0.0327    0.00690
     #> # ℹ 62 more rows
     #> # ℹ 1 more variable: non_response_rate <dbl>
 
@@ -221,16 +221,13 @@ which gives the following results:
 The `{squeacr}` provides functions to calculate programme coverage.
 These functions implement the single coverage estimator approach\[1\].
 In this approach, treatment coverage is calculated in such a way that
-estimates
-![r\_out](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;r_out
-"r_out") or the number of severe acute malnutrition (SAM) cases that
-have not been enrolled in the programme but have been recovering without
-treatment.
+estimates the number of severe acute malnutrition (SAM) cases that have
+not been enrolled in the programme but have been recovering without
+treatment (`r_out`).
 
 For example, if a coverage survey yielded 5 SAM cases in the programme,
 25 cases not in the programme, and 5 recovering cases in the programme,
-![r\_out](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;r_out
-"r_out") can be calculated as follows:
+`r_out` can be calculated as follows:
 
 ``` r
 calculate_rout(cin = 5, cout = 25, rin = 5)
@@ -244,9 +241,8 @@ treatment episode. This, by default, is set to `k = 3` in the function.
 However, this should be adjusted based on programme data to estimate the
 mean length of a SAM treatment episode.
 
-This calculation for
-![r\_out](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;r_out
-"r_out") is used within `calculate_tc()` to estimate treatment coverage:
+This calculation for `r_out` is used within `calculate_tc()` to estimate
+treatment coverage:
 
 ``` r
 calculate_tc(cin = 5, cout = 25, rin = 5)
@@ -266,8 +262,8 @@ citation("squeacr")
 #>   Allison, Guerrero, Saul, Schofield, Lilly, Jones, Daniel,
 #>   Emru, Ephrem, Sadler, Kate (2012). _Semi-Quantitative
 #>   Evaluation of Access and Coverage (SQUEAC)/Simplified Lot
-#>   Quality Assurance Sampling Evaluation of Access and
-#>   Coverage (SLEAC) Technical Reference_.
+#>   Quality Assurance Sampling Evaluation of Access and Coverage
+#>   (SLEAC) Technical Reference_.
 #>   <https://www.fantaproject.org/sites/default/files/resources/SQUEAC-SLEAC-Technical-Reference-Oct2012_0.pdf>.
 #> 
 #> A BibTeX entry for LaTeX users is
@@ -296,8 +292,6 @@ contributing to this project, you agree to abide by its terms.
 
 <br/>
 
-## Footnotes
-
 1.  Safari Balegamire, Katja Siling, Jose Luis Alvarez Moran, Ernest
     Guevarra, Sophie Woodhead, Alison Norris, Lionella Fieschi, Paul
     Binns, and Mark Myatt (2015). A single coverage estimator for use in

pkgdown/_pkgdown.yml

@@ -8,6 +8,7 @@ development:
 template:
   bootstrap: 5
   theme: breeze-light
+  math-rendering: katex
   ganalytics: "G-C6TYX2E6RD"
   bslib:
     bootswatch: pulse

tests/testthat/test-performance.R

@@ -75,7 +75,7 @@ test_that("warning/error is activated", {
       df = monitoring, vars = c("healed", "died", "missing", "no_recovery")
     )
   )
-  expect_message(calculate_performance(df = x))
+  expect_error(calculate_performance(df = x))
   expect_error(calculate_performance(y1))
   expect_error(calculate_performance(y2))
   expect_error(calculate_performance(y3))

vignettes/squeacr.Rmd

@@ -12,6 +12,7 @@ vignette: >
 ```{r, include = FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE,
+  message = FALSE,
   comment = "#>"
 )
 ```
@@ -140,20 +141,21 @@ calculate_los_median(otp_beneficiaries$admDate, otp_beneficiaries$disDate)
 
 ### CMAM programme coverage
 
-The `{squeacr}` provides functions to calculate programme coverage. These functions implement the single coverage estimator approach[^1]. In this approach, treatment coverage is calculated in such a way that estimates $r_{out}$ or the number of severe acute malnutrition (SAM) cases that have not been enrolled in the programme but have been recovering without treatment.
+The `{squeacr}` provides functions to calculate programme coverage. These functions implement the single coverage estimator approach[^1]. In this approach, treatment coverage is calculated in such a way that estimates the number of severe acute malnutrition (SAM) cases that have not been enrolled in the programme but have been recovering without treatment (`r_out`).
 
-For example, if a coverage survey yielded 5 SAM cases in the programme, 25 cases not in the programme, and 5 recovering cases in the programme, $r_{out}$ can be calculated as follows:
+For example, if a coverage survey yielded 5 SAM cases in the programme, 25 cases not in the programme, and 5 recovering cases in the programme, `r_out` can be calculated as follows:
 
 ```{r}
 calculate_rout(cin = 5, cout = 25, rin = 5)
 ```
 
 Note here that the `calculate_rout()` function has another argument named `k` which is a correction factor representing the ratio of the mean length of an untreated episode to the mean length of a CMAM treatment episode. This, by default, is set to `k = 3` in the function. However, this should be adjusted based on programme data to estimate the mean length of a SAM treatment episode.
 
-This calculation for $r_{out}$ is used within `calculate_tc()` to estimate treatment coverage:
+This calculation for `r_out` is used within `calculate_tc()` to estimate treatment coverage:
 
 ```{r}
 calculate_tc(cin = 5, cout = 25, rin = 5)
 ```
 
 
+[^1]: Safari Balegamire, Katja Siling, Jose Luis Alvarez Moran, Ernest Guevarra, Sophie Woodhead, Alison Norris, Lionella Fieschi, Paul Binns, and Mark Myatt (2015). A single coverage estimator for use in SQUEAC, SLEAC, and other CMAM coverage assessments. Field Exchange 49, March 2015. p81. <www.ennonline.net/fex/49/singlecoverage>