Refactor sms metrics to produce a single file

Snakefile

@@ -9,11 +9,10 @@ rule all:
         expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"]),
         expand("data/interim/{pid}/phone_valid_sensed_days.csv", pid=config["PIDS"]),
-        expand("data/processed/{pid}/com_sms_{sms_type}_{day_segment}_{metric}.csv",
+        expand("data/processed/{pid}/sms_{sms_type}_{day_segment}.csv",
                             pid=config["PIDS"],
-                            sms_type = config["COM_SMS"]["SMS_TYPES"],
-                            day_segment = config["COM_SMS"]["DAY_SEGMENTS"],
-                            metric = config["COM_SMS"]["METRICS"]),
+                            sms_type = config["SMS"]["TYPES"],
+                            day_segment = config["SMS"]["DAY_SEGMENTS"]),
         expand("data/processed/{pid}/call_{call_type}_{segment}.csv",
                             pid=config["PIDS"], 
                             call_type=config["CALLS"]["TYPES"],

config.yaml

@@ -22,14 +22,15 @@ DOWNLOAD_DATASET:
 READABLE_DATETIME:
   FIXED_TIMEZONE: *timezone
 
-# Communication SMS features config
-COM_SMS:
-  SMS_TYPES : [received, sent]
+# Communication SMS features config, TYPES and METRICS keys need to match
+SMS:
+  TYPES : [received, sent]
+  METRICS: 
+    received: [count, distinctcontacts]
+    sent: [count, distinctcontacts]
   DAY_SEGMENTS: *day_segments  
-  METRICS: [count, distinctcontacts]
 
-# Communication call features config
-# Separate configurations for missed and taken calls
+# Communication call features config, TYPES and METRICS keys need to match
 CALLS:
   TYPES: [missed, incoming, outgoing]
   METRICS:

rules/features.snakefile

@@ -1,14 +1,14 @@
-rule communication_sms_metrics:
+rule sms_metrics:
     input: 
         "data/raw/{pid}/messages_with_datetime.csv"
     params:
         sms_type = "{sms_type}",
         day_segment = "{day_segment}",
-        metric = "{metric}"
+        metrics = lambda wildcards: config["SMS"]["METRICS"][wildcards.sms_type]
     output:
-        "data/processed/{pid}/com_sms_{sms_type}_{day_segment}_{metric}.csv"
+        "data/processed/{pid}/sms_{sms_type}_{day_segment}.csv"
     script:
-        "../src/features/communication_sms_metrics.R"
+        "../src/features/sms_metrics.R"
 
 rule call_metrics:
     input: 

src/features/communication_sms_metrics.R

@@ -1,23 +0,0 @@
-source("packrat/init.R")
-
-library(dplyr)
-
-sms <-  read.csv(snakemake@input[[1]])
-day_segment <- snakemake@params[["day_segment"]]
-metric <-  snakemake@params[["metric"]]
-sms_type <-  snakemake@params[["sms_type"]]
-output_file <- snakemake@output[[1]]
-
-metrics <- sms %>% filter(message_type == ifelse(sms_type == "received", "1", "2"))
-
-if(day_segment == "daily"){
-  metrics <- metrics %>% group_by(local_date)
-} else {
-  metrics <- metrics %>% filter(day_segment == local_day_segment) %>% group_by(local_date)
-}
-
-metrics <- switch(metric,
-       "count" = metrics %>% summarise(!!paste("com", "sms", sms_type, day_segment, metric, sep = "_") := n()),
-       "distinctcontacts" = metrics %>% summarise(!!paste("com", "sms", sms_type, day_segment, metric, sep = "_") := n_distinct(trace)))
-
-write.csv(na.omit(metrics), output_file, row.names = F)

src/features/sms_metrics.R

@@ -0,0 +1,33 @@
+source("packrat/init.R")
+
+library(dplyr)
+
+filter_by_day_segment <- function(data, day_segment) {
+  if(day_segment %in% c("morning", "afternoon", "evening", "night"))
+    data <- data %>% filter(local_day_segment == day_segment)
+
+  return(data %>% group_by(local_date))
+}
+
+compute_sms_feature <- function(sms, metric, day_segment){
+  sms <- sms %>% filter_by_day_segment(day_segment)
+  feature <- switch(metric,
+       "count" = sms %>% summarise(!!paste("com", "sms", sms_type, day_segment, metric, sep = "_") := n()),
+       "distinctcontacts" = sms %>% summarise(!!paste("com", "sms", sms_type, day_segment, metric, sep = "_") := n_distinct(trace)))
+  return(feature)
+}
+
+sms <-  read.csv(snakemake@input[[1]])
+day_segment <- snakemake@params[["day_segment"]]
+metrics <-  snakemake@params[["metrics"]]
+sms_type <-  snakemake@params[["sms_type"]]
+features = data.frame(local_date = character(), stringsAsFactors = FALSE)
+
+sms <- sms %>% filter(message_type == ifelse(sms_type == "received", "1", "2"))
+
+for(metric in metrics){
+  feature <- compute_sms_feature(sms, metric, day_segment)
+  features <- merge(features, feature, by="local_date", all = TRUE)
+}
+
+write.csv(features, snakemake@output[[1]], row.names = FALSE)