Merge pull request #7 from ubeattie/feat/style_updates

minor updates

R/profrep.R

@@ -11,17 +11,17 @@
 #'           Column 1 is the unique identifier of an individual animal or sample 
 #'           Column 2 is the time of the sample
 #'           Column 3-N are the columns of replicate data.
-#' @param n_trials The number of rows an individual sample will have. For example,
+#' @param n_timepoints The number of rows an individual sample will have. For example,
 #'                 if the replicates were collected for individual 1 at times 15
 #'                 and 30, for replicates A and B, the data frame would look like:
 #'                 
 #'                 | id | time | A | B |
 #'                 |:--:|:----:|:-:|:-:|
 #'                 | 1  | 15 | 1 | 2 |
 #'                 | 1  | 30 | 3 | 4 |
-#'                 
-#' @param verbose A boolean parameter that defaults to FALSE. Determines whether messages are printed.
+#'
 #' @param sort A boolean parameter that defaults to TRUE. If TRUE, sorts the returned data frame by score. If FALSE, returns the data in the individual order it was provided in
+#' @param verbose A boolean parameter that defaults to FALSE. Determines whether messages are printed.
 #' 
 #' @returns Returns a data frame of the results, in the following form:
 #' 
@@ -40,16 +40,16 @@
 #' 
 #' @examples
 #' test_data <- profrep::example_two_point_data
-#' results <- profrep::profrep(df=test_data, n_trials=2)
+#' results <- profrep::profrep(df=test_data, n_timepoints=2)
 #' print(results)
 #' 
 #' @export
-profrep <- function(df, n_trials, verbose=FALSE, sort=TRUE) {
+profrep <- function(df, n_timepoints, sort=TRUE, verbose=FALSE) {
   if (verbose) {message("Welcome to profrep!")}
   n_cols <- ncol(df)  # Number of columns in whole data frame
   if (verbose) {message("Number of columns in input dataframe: ", n_cols)}
 
-  n_individuals = nrow(df) / n_trials  # Number of individuals
+  n_individuals = nrow(df) / n_timepoints  # Number of individuals
   if (verbose) {message("Number of individuals: ", n_individuals)}
 
   n_replicates <- n_cols - 2  # Number of replicates (because of how the df is defined)
@@ -66,7 +66,7 @@ profrep <- function(df, n_trials, verbose=FALSE, sort=TRUE) {
 
   # Pass the list through the do_ordering function
   ordered_df <- do_ordering(
-    n_trials=n_trials, 
+    n_trials=n_timepoints, 
     id_list=ids, 
     df_list=individuals, 
     n_replicates=n_replicates,

tests/testthat/test-profrep.R

@@ -8,7 +8,7 @@ test_that("profrep calculates correctly for given data examples", {
     final_score = c(0.9581, 0.9356, 0.7876, 0.6719, 0.4809),
     rank = c(1, 2, 3, 4, 5)
   )
-  observed_two_point <- profrep::profrep(df=profrep::example_two_point_data, n_trials=2)
+  observed_two_point <- profrep::profrep(df=profrep::example_two_point_data, n_timepoints=2)
   expect_equal(observed_two_point, expected_two_point)
 
   expected_three_point <- data.frame(
@@ -20,6 +20,6 @@ test_that("profrep calculates correctly for given data examples", {
     final_score = c(0.9548, 0.8901, 0.8651, 0.8601, 0.8512, 0.8388, 0.7132, 0.6268, 0.5004, 0.3390, 0.0505, 0.0099),
     rank = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
   )
-  observed_three_point <- profrep::profrep(df=profrep::sparrow_repeatability_three_point, n_trials=3)
+  observed_three_point <- profrep::profrep(df=profrep::sparrow_repeatability_three_point, n_timepoints=3)
   expect_equal(observed_three_point, expected_three_point)
 })

vignettes/profrep.Rmd

@@ -44,7 +44,7 @@ Here, we see that there are no missing replicates for any individual at any time
 Since we know how many trials we performed, we can easily perform the profile repeatability calculation with 
 
 ```{r}
-pr_score_df <- profrep::profrep(df=synthetic_data, n_trials=4)
+pr_score_df <- profrep::profrep(df=synthetic_data, n_timepoints=4)
 ```
 
 If we wanted logs to be displayed in the terminal, we would have changed the default verbosity factor from `FALSE` to `TRUE` with `verbose = True`.