CSV for Seattle library checkouts - #103

.gitignore

@@ -1,3 +1,4 @@
 .Rproj.user
 .Rhistory
 .RData
+data-raw/seattle-library-checkouts.csv

data-raw/seattle-library-checkouts.R

@@ -0,0 +1,122 @@
+# Dataset used in the chapter arrow.qmd from R4DS 2nd Ed.----------------
+
+# Downloading the dataset
+if(!file.exists("data/seattle-library-checkouts.csv")) {
+  dir.create("data-raw/", showWarnings = FALSE)
+  curl::multi_download(
+    "https://r4ds.s3.us-west-2.amazonaws.com/seattle-library-checkouts.csv",
+    "data-raw/seattle-library-checkouts.csv",
+    resume = TRUE
+  )
+}
+
+# Reading the CSV file (We started with some rows)
+seattle_library_checkouts <- vroom::vroom("data-raw/seattle-library-checkouts.csv",
+                                          n_max =  10000)
+
+
+
+# First, we want to know the name of the columns:
+
+names(seattle_library_checkouts)
+ # [1] "UsageClass"      "CheckoutType"    "MaterialType"    "CheckoutYear"
+ # [5] "CheckoutMonth"   "Checkouts"       "Title"           "ISBN"
+ # [9] "Creator"         "Subjects"        "Publisher"       "PublicationYear"
+
+# Translating the Subjects:
+# Translating the subjects - There are a lot! I just started.
+# After that, we will need to join this translated list with the original
+# dataset
+
+seattle_library_checkouts |>
+  dplyr::select(Subjects) |>
+  tidyr::separate_longer_delim(Subjects, ",") |>
+  dplyr::mutate(Subjects = stringr::str_trim(Subjects)) |>
+  dplyr::count(Subjects, sort = TRUE) |>
+  dplyr::mutate(Subjects_pt = dplyr::case_match(
+    Subjects,
+    "Fiction" ~ "Ficção",
+    "Juvenile Fiction" ~ "Ficção juvenil",
+    "Historical Fiction" ~ "Ficção histórica",
+    "Mystery" ~ "Mistério",
+    "Nonfiction" ~ "Não ficção",
+    "Romance" ~ "Romance",
+    "Graphic novels" ~ "Graphic novel",
+    "Feature films" ~ "Longa-metragem",
+    "Video recordings for the hearing impaired" ~ "Gravações de vídeo para deficientes auditivos",
+  ))
+
+
+
+seattle_library_checkouts_pt <- seattle_library_checkouts |>
+
+# Now, we want to translate the values of the columns:
+  dplyr::mutate(
+    # UsageClass
+    UsageClass = dplyr::case_match(
+      UsageClass,
+      "Physical" ~ "Físico",
+      "Digital" ~ "Digital",
+      .default = paste0("TRADUZIR: ", UsageClass) # UsageClass
+    ),
+    # CheckoutType - I'm not sure about this one
+
+    # MaterialType
+    MaterialType = dplyr::case_match(
+      MaterialType,
+      "BOOK" ~ "Livro",
+      "EBOOK" ~ "Ebook",
+      "SOUNDDISC" ~ "Disco de som",
+      "AUDIOBOOK" ~ "Audiolivro",
+      "VIDEODISC" ~ "Disco de vídeo",
+      "SONG" ~ "Canção",
+      "MUSIC" ~ "Música",
+      "SOUNDREC" ~ "Gravação de som",
+      "MOVIE" ~ "Filme",
+      "TELEVISION" ~ "Televisão",
+      "MAP" ~ "Mapa",
+      "MIXED" ~ "Misto",
+      "REGPRINT" ~ "Impressão regular",
+      "VIDEO" ~ "Vídeo",
+      "MAGAZINE" ~ "Revista",
+      "VIDEOCASS" ~ "Videocassete",
+      "ER" ~ "TRADUZIR: ER", #?
+      "CR" ~ "TRADUZIR: CR", #?
+      "VISUAL" ~ "Visual", #?
+      "VIDEOCART" ~ "Videocartão", #?
+      .default = paste0("TRADUZIR: ", MaterialType) # MaterialType
+    )
+  ) |>
+# Rename columns to Brasilian Portuguese
+  dplyr::rename(
+    classeUso = UsageClass,
+    tipoEmprestimo = CheckoutType,
+    tipoMaterial = MaterialType,
+    anoEmprestimo = CheckoutYear,
+    mesEmprestimo = CheckoutMonth,
+    Emprestimos = Checkouts,
+    Titulo = Title,
+    ISBN = ISBN,
+    Autoria = Creator,
+    Temas = Subjects,
+    Editora = Publisher,
+    AnoPublicacao = PublicationYear
+  )
+
+dplyr::glimpse(seattle_library_checkouts_pt)
+
+
+# Checking if there are any values that still need to be translated
+
+seattle_library_checkouts_pt |>
+  dplyr::filter(stringr::str_detect(classeUso, "TRADUZIR"))
+
+seattle_library_checkouts_pt |>
+  dplyr::filter(stringr::str_detect(tipoMaterial, "TRADUZIR")) |>
+  dplyr::count(tipoMaterial, sort = TRUE)
+
+# TO DO:
+# After we finish, we will need to:
+# export the csv file
+# upload the dataset to the S3 Bucket
+