Start to better classify system apps.

presentation/ApplicationCategories.R

@@ -34,7 +34,45 @@ df_app_categories <- tbl(con, "app_categories") %>%
 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")
+
 # Correct some mistakes
+# And classify 'not_found'
 df_app_categories %<>%
   mutate(
     play_store_genre = {
@@ -48,16 +86,17 @@ df_app_categories %<>%
       }
     }(play_store_genre)
   ) %>%
-  select(-package_name) %>% 
+  select(-package_name) %>%
   rename(
     genre = play_store_genre,
-    package_name = package_hash)
+    package_name = package_hash
+  )
 
 table(df_app_categories$genre)
 
-df_app_categories %>% 
-  group_by(genre) %>% 
-  count() %>% 
+df_app_categories %>%
+  group_by(genre) %>%
+  count() %>%
   arrange(desc(n)) %>%
   write_csv("play_store_categories_count.csv")