library(conflicted)
library(yaml)
library(RPostgreSQL)
library(tidyverse)
conflicts_prefer(
  dplyr::filter,
  dplyr::lag
)
library(magrittr)

# read the password from file
credentials <- yaml.load_file("../rapids/credentials.yaml")
pw <- credentials$PSQL_STRAW$password

# load the PostgreSQL driver
drv <- RPostgres::Postgres()

# creates a connection to the postgres database
# note that "con" will be used later in each connection to the database
con <- RPostgres::dbConnect(drv,
  dbname = "staw",
  host = "eol.ijs.si", port = 5432,
  user = "staw_db", password = pw
)

rm(pw, credentials) # removes the password

# check for the bluetooth table, an example
dbExistsTable(con, "app_categories")

df_app_categories <- tbl(con, "app_categories") %>%
  collect()

head(df_app_categories)
table(df_app_categories$play_store_genre)

df_app_categories %>%
  filter(play_store_genre == "not_found") %>%
  group_by(play_store_response) %>%
  count()
# All "not_found" have an HTTP status of 404.

df_app_categories %>%
  filter(play_store_genre == "not_found") %>%
  group_by(package_name) %>%
  count() %>%
  arrange(desc(n))
# All "not_found" apps are unique.

# Exclude phone manufacturers, custom ROM names and similar.
manufacturers <- c(
  "samsung",
  "oneplus",
  "huawei",
  "xiaomi",
  "lge",
  "motorola",
  "miui",
  "lenovo",
  "oppo",
  "mediatek"
)
custom_rom <- c("coloros", "lineageos", "myos", "cyanogenmod", "foundation.e")
other <- c("android", "wssyncmldm")

grep_pattern <- paste(c(manufacturers, custom_rom, other), collapse = "|")

rows_os_manufacturer <- grepl(grep_pattern, df_app_categories$package_name)

# Explore what remains after excluding above.
df_app_categories[!rows_os_manufacturer, ] %>%
  filter(play_store_genre == "not_found")

# Manually classify apps
df_app_categories %<>% 
  mutate(
    play_store_genre = 
      case_when(
        str_detect(str_to_lower(package_name), grep_pattern) ~ "System",
        str_detect(str_to_lower(package_name), "straw") ~ "STRAW",
        str_detect(str_to_lower(package_name), "chromium") ~ "Communication", # Same as chrome.
        str_detect(str_to_lower(package_name), "skype") ~ "Communication", # Skype Lite not classified.
        str_detect(str_to_lower(package_name), "imsservice") ~ "Communication", # IP Multimedia Subsystem
        str_detect(str_to_lower(package_name), paste(c("covid", "empatica"), collapse = "|")) ~ "Medical",
        str_detect(str_to_lower(package_name), paste(c("libri", "tachiyomi"), collapse = "|")) ~ "Books & Reference",
        str_detect(str_to_lower(package_name), paste(c("bricks", "chess"), collapse = "|")) ~ "Casual",
        str_detect(str_to_lower(package_name), "weather") ~ "Weather",
        str_detect(str_to_lower(package_name), "excel") ~ "Productivity",
        str_detect(str_to_lower(package_name), paste(c("qr", "barcode", "archimedes", "mixplorer", "winrar", "filemanager", "shot", "faceunlock", "signin", "milink"), collapse = "|")) ~ "Tools",
        str_detect(str_to_lower(package_name), "stupeflix") ~ "Photography",
        str_detect(str_to_lower(package_name), "anyme") ~ "Entertainment",
        str_detect(str_to_lower(package_name), "vanced") ~ "Video Players & Editors",
        str_detect(str_to_lower(package_name), paste(c("music", "radio", "dolby"), collapse = "|")) ~ "Music & Audio",
        str_detect(str_to_lower(package_name), paste(c("tensorflow", "object_detection"), collapse = "|")) ~ "Education",
        .default = play_store_genre
      )
  )

# Explore what remains after classifying above.
df_app_categories %>%
  filter(play_store_genre == "not_found")

# After this, 13 applications remain, which I will classify as "Other".

# Correct some mistakes
# And classify 'not_found'
df_app_categories %<>%
  mutate(
    play_store_genre = {
      function(x) {
        case_when(
          x == "Education,Education" ~ "Education",
          x == "EducationEducation" ~ "Education",
          x == "not_found" ~ "Other",
          .default = x
        )
      }
    }(play_store_genre)
  ) %>%
  select(-package_name) %>%
  rename(
    genre = play_store_genre,
    package_name = package_hash
  )

table(df_app_categories$genre)

df_app_categories %>%
  group_by(genre) %>%
  count() %>%
  arrange(desc(n)) %>%
  write_csv("play_store_categories_count.csv")

write_csv(
  x = select(df_app_categories, c(package_name, genre)),
  file = "play_store_application_genre_catalogue.csv"
)

dbDisconnect(con)