148 lines
4.7 KiB
R
148 lines
4.7 KiB
R
library(conflicted)
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library(yaml)
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library(RPostgreSQL)
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library(tidyverse)
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conflicts_prefer(
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dplyr::filter,
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dplyr::lag
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)
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library(magrittr)
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# read the password from file
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credentials <- yaml.load_file("../rapids/credentials.yaml")
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pw <- credentials$PSQL_STRAW$password
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# load the PostgreSQL driver
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drv <- RPostgres::Postgres()
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# creates a connection to the postgres database
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# note that "con" will be used later in each connection to the database
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con <- RPostgres::dbConnect(drv,
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dbname = "staw",
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host = "eol.ijs.si", port = 5432,
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user = "staw_db", password = pw
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)
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rm(pw, credentials) # removes the password
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# check for the bluetooth table, an example
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dbExistsTable(con, "app_categories")
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df_app_categories <- tbl(con, "app_categories") %>%
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collect()
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head(df_app_categories)
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table(df_app_categories$play_store_genre)
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df_app_categories %>%
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filter(play_store_genre == "not_found") %>%
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group_by(play_store_response) %>%
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count()
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# All "not_found" have an HTTP status of 404.
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df_app_categories %>%
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filter(play_store_genre == "not_found") %>%
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group_by(package_name) %>%
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count() %>%
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arrange(desc(n))
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# All "not_found" apps are unique.
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# Exclude phone manufacturers, custom ROM names and similar.
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manufacturers <- c(
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"samsung",
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"oneplus",
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"huawei",
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"xiaomi",
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"lge",
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"motorola",
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"miui",
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"lenovo",
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"oppo",
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"mediatek"
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)
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custom_rom <- c("coloros", "lineageos", "myos", "cyanogenmod", "foundation.e")
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other <- c("android", "wssyncmldm")
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grep_pattern <- paste(c(manufacturers, custom_rom, other), collapse = "|")
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rows_os_manufacturer <- grepl(grep_pattern, df_app_categories$package_name)
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# Explore what remains after excluding above.
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df_app_categories[!rows_os_manufacturer, ] %>%
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filter(play_store_genre == "not_found")
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# Also check the relationship between is_system_app and System category.
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tbl(con, "applications") %>%
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filter(is_system_app, play_store_genre != "System") %>%
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count()
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# They are perfectly correlated.
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# Manually classify apps
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df_app_categories[df_app_categories$play_store_genre == "not_found",] <-
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df_app_categories %>%
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filter(play_store_genre == "not_found") %>%
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mutate(
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play_store_genre =
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case_when(
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str_detect(str_to_lower(package_name), grep_pattern) ~ "System",
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str_detect(str_to_lower(package_name), "straw") ~ "STRAW",
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str_detect(str_to_lower(package_name), "chromium") ~ "Communication", # Same as chrome.
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str_detect(str_to_lower(package_name), "skype") ~ "Communication", # Skype Lite not classified.
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str_detect(str_to_lower(package_name), "imsservice") ~ "Communication", # IP Multimedia Subsystem
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str_detect(str_to_lower(package_name), paste(c("covid", "empatica"), collapse = "|")) ~ "Medical",
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str_detect(str_to_lower(package_name), paste(c("libri", "tachiyomi"), collapse = "|")) ~ "Books & Reference",
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str_detect(str_to_lower(package_name), paste(c("bricks", "chess"), collapse = "|")) ~ "Casual",
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str_detect(str_to_lower(package_name), "weather") ~ "Weather",
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str_detect(str_to_lower(package_name), "excel") ~ "Productivity",
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str_detect(str_to_lower(package_name), paste(c("qr", "barcode", "archimedes", "mixplorer", "winrar", "filemanager", "shot", "faceunlock", "signin", "milink"), collapse = "|")) ~ "Tools",
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str_detect(str_to_lower(package_name), "stupeflix") ~ "Photography",
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str_detect(str_to_lower(package_name), "anyme") ~ "Entertainment",
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str_detect(str_to_lower(package_name), "vanced") ~ "Video Players & Editors",
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str_detect(str_to_lower(package_name), paste(c("music", "radio", "dolby"), collapse = "|")) ~ "Music & Audio",
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str_detect(str_to_lower(package_name), paste(c("tensorflow", "object_detection"), collapse = "|")) ~ "Education",
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.default = play_store_genre
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)
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)
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# Explore what remains after classifying above.
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df_app_categories %>%
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filter(play_store_genre == "not_found")
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# After this, 13 applications remain, which I will classify as "Other".
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# Correct some mistakes
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# And classify 'not_found'
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df_app_categories %<>%
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mutate(
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play_store_genre = {
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function(x) {
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case_when(
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x == "Education,Education" ~ "Education",
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x == "EducationEducation" ~ "Education",
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x == "not_found" ~ "Other",
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.default = x
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)
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}
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}(play_store_genre)
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) %>%
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select(-package_name) %>%
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rename(
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genre = play_store_genre,
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package_name = package_hash
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)
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table(df_app_categories$genre)
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df_app_categories %>%
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group_by(genre) %>%
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count() %>%
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arrange(desc(n)) %>%
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write_csv("play_store_categories_count.csv")
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write_csv(
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x = select(df_app_categories, c(package_name, genre)),
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file = "play_store_application_genre_catalogue.csv"
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)
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dbDisconnect(con)
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