Merge pull request #10 from Ben-Sacks/main

update compute_auc and tests to fit new dAUC column names

Author: Ben-Sacks

R/compute_auc.R

@@ -217,7 +217,7 @@ compute_auc <- function(df_prep, verbose = TRUE) {
       dplyr::select(-Exchanges)
 
     output_auc <- dplyr::left_join(all_domain_df, all_domain_df_s,
-                                   by = "Event_ID", suffix = c("_raw", "_scaled100")) %>%
+                                   by = "Event_ID", suffix = c("_raw", "_scaled50")) %>%
       dplyr::select(c(Event_ID, Exchanges, dplyr::everything()))
 
 
@@ -244,7 +244,7 @@ compute_auc <- function(df_prep, verbose = TRUE) {
   # sub out negative lag colnames for lead
   colnames(combined_lag_df) <- gsub("_Lag-", "_Lead", colnames(combined_lag_df))
   # set the names of zero lag columsn to immediate
-  immediate_ind <- grep(".*(raw|scaled100)$", colnames(combined_lag_df), ignore.case = F)
+  immediate_ind <- grep(".*(raw|scaled50)$", colnames(combined_lag_df), ignore.case = F)
   #print(immediate_ind)
   colnames(combined_lag_df)[immediate_ind] <- sapply(colnames(combined_lag_df)[immediate_ind], function(x){
     paste0(x, "_Immediate")

tests/testthat/test-compute_auc.R

@@ -9,8 +9,8 @@ test_that("auc formats properly", {
   # computed previously
   expected_auc_df <- data.frame(
     Event_ID = "LittleLamb", Exchanges = 50, Talked_First = as.factor("Mary"),
-    AUC_emo_anger_raw_Immediate = 1.485925, AUC_emo_anger_scaled100_Immediate = 1.485925,
-    AUC_emo_anger_raw_Lag1 = 1.4556, AUC_emo_anger_scaled100_Lag1 = 1.4556
+    AUC_emo_anger_raw_Immediate = 1.485925, AUC_emo_anger_scaled50_Immediate = 1.485925,
+    AUC_emo_anger_raw_Lag1 = 1.4556, AUC_emo_anger_scaled50_Lag1 = 1.4556
   )
   expected_auc_df$Talked_First <- factor(expected_auc_df$Talked_First, levels = c("Dave", "Mary"))
   # compare to computed df
@@ -23,8 +23,8 @@ one_rhyme_small <- one_rhyme[one_rhyme$Exchange_Count < 3, ]
 test_that("auc fills with na when under 3 exchanges", {
   expected_small_auc_df <- data.frame(
     Event_ID = "LittleLamb", Exchanges = 2, Talked_First = as.factor("Mary"),
-    AUC_emo_anger_raw_Immediate = as.double(NA), AUC_emo_anger_scaled100_Immediate = as.double(NA),
-    AUC_emo_anger_raw_Lag1 = as.double(NA), AUC_emo_anger_scaled100_Lag1 = as.double(NA)
+    AUC_emo_anger_raw_Immediate = as.double(NA), AUC_emo_anger_scaled50_Immediate = as.double(NA),
+    AUC_emo_anger_raw_Lag1 = as.double(NA), AUC_emo_anger_scaled50_Lag1 = as.double(NA)
   )
   expected_small_auc_df$Talked_First <- factor(expected_small_auc_df$Talked_First, levels = c("Dave", "Mary"))
   # compare to computed df

tests/testthat/test-summarize_dyads.R

@@ -10,6 +10,6 @@ test_that("required column names are present", {
   # define list of required names and evaluate
   col_vec <- c("Event_ID", "Participant_ID", "Dimension", "Dimension_Mean", "AUC_raw", "AUC_scaled100", "Talked_First")
   col_vec <- c("Event_ID", "Participant_ID", "Talked_First", "Dimension", "Dimension_Mean", "AUC_raw_Immediate",
-               "AUC_scaled100_Immediate", "AUC_raw_Lag1", "AUC_scaled100_Lag1", "TurnCorr_Lead2", "TurnCorr_Immediate", "TurnCorr_Lag2")
+               "AUC_scaled50_Immediate", "AUC_raw_Lag1", "AUC_scaled50_Lag1", "TurnCorr_Lead2", "TurnCorr_Immediate", "TurnCorr_Lag2")
   expect_in(col_vec, colnames(output))
 })