Fix countmostfrequentcontact bug

pull/95/head
JulioV 2020-05-29 14:29:35 -04:00
parent c1e25ac1ad
commit cf272793a2
1 changed files with 6 additions and 3 deletions

View File

@ -1,7 +1,10 @@
filter_by_day_segment <- function(data, day_segment) { filter_by_day_segment <- function(data, day_segment) {
if(day_segment %in% c("morning", "afternoon", "evening", "night")) if(day_segment %in% c("morning", "afternoon", "evening", "night"))
data <- data %>% filter(local_day_segment == day_segment) data <- data %>% filter(local_day_segment == day_segment)
return(data) else if(day_segment == "daily")
return(data)
else
return(data %>% head(0))
} }
base_sms_features <- function(sms, sms_type, day_segment, requested_features){ base_sms_features <- function(sms, sms_type, day_segment, requested_features){
@ -15,7 +18,7 @@ base_sms_features <- function(sms, sms_type, day_segment, requested_features){
features_to_compute <- intersect(base_features_names, requested_features) features_to_compute <- intersect(base_features_names, requested_features)
# Filter rows that belong to the sms type and day segment of interest # Filter rows that belong to the sms type and day segment of interest
sms <- sms %>% filter(message_type == ifelse(sms_type == "received", "1", "2")) %>% sms <- sms %>% filter(message_type == ifelse(sms_type == "received", "1", ifelse(sms_type == "sent", 2, NA))) %>%
filter_by_day_segment(day_segment) filter_by_day_segment(day_segment)
# If there are not features or data to work with, return an empty df with appropiate columns names # If there are not features or data to work with, return an empty df with appropiate columns names
@ -33,7 +36,7 @@ base_sms_features <- function(sms, sms_type, day_segment, requested_features){
filter(N == max(N)) %>% filter(N == max(N)) %>%
head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only
group_by(local_date) %>% group_by(local_date) %>%
summarise(!!paste("sms", sms_type, day_segment, feature_name, sep = "_") := n()) %>% summarise(!!paste("sms", sms_type, day_segment, feature_name, sep = "_") := N) %>%
replace(is.na(.), 0) replace(is.na(.), 0)
features <- merge(features, feature, by="local_date", all = TRUE) features <- merge(features, feature, by="local_date", all = TRUE)