Fix issues highlighted by @lmullen

- donttest examples should be happy now
- spelling fixed
- suggest, not import, dbplyr

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: arkdb
 Version: 0.0.0.9000
 Title: Archive and Unarchive Databases Using Flat Files
-Description: Flat text files provide a more robust, compressable,
+Description: Flat text files provide a more robust, compressible,
   and portable way to store tables.  This package provides convenient
   functions for exporting tables from relational database connections
   into compressed text files and streaming those text files back into
@@ -21,14 +21,14 @@ VignetteBuilder: knitr
 RoxygenNote: 6.0.1.9000
 Roxygen: list(markdown = TRUE)
 Imports: 
-    dbplyr,
     DBI,
     readr,
     progress,
     dplyr,
     methods,
     tools
 Suggests: 
+    dbplyr,
     nycflights13,
     testthat,
     knitr,

R/ark.R

@@ -2,7 +2,7 @@
 #' Archive tables from a database as flat files
 #' 
 #' @param db_con a database connection
-#' @param dir a directory where we will write the compressed text files ouput
+#' @param dir a directory where we will write the compressed text files output
 #' @param lines the number of lines to use in each single chunk
 #' @param compress file compression algorithm. Should be one of "bzip2" (default),
 #' "gzip" (faster write times, a bit less compression), or "xz".
@@ -13,7 +13,7 @@
 #' is probably why you are using a database in the first place!)  Compressed
 #' text files will likely take up much less space, making them easier to store and
 #' transfer over networks.  Compressed plain-text files are also more archival
-#' friendly, as they rely on widely avialable and long-established open source compression
+#' friendly, as they rely on widely available and long-established open source compression
 #' algorithms and plain text, making them less vulnerable to loss by changes in 
 #' database technology and formats. 
 #' 
@@ -23,17 +23,15 @@
 #' 
 #' @examples 
 #' \donttest{
-#' sql_path <- tempdir()
-#' db <- dbplyr::nycflights13_sqlite(sql_path)
-#' dir <- file.path(tempdir(), "nycflights")
-#' dir.create(dir)
+#' # setup
+#' library(dplyr)
+#' dir <- tempdir() 
+#' db <- dbplyr::nycflights13_sqlite(tempdir())
 #' 
+#' ## And here we go:
 #' ark(db, dir)
-#' list.files(dir)
 #' 
 #' 
-#' ## clean up
-#' unlink(dir, TRUE)
 #' }
 ark <- function(db_con, dir, lines = 10000L, 
                 compress = c("bzip2", "gzip", "xz")){

R/unark.R

@@ -5,7 +5,7 @@
 ## 1. consider supporting non tsv formats?
 
 
-#' Unarchive a list of compressed tsv files into a databaase
+#' Unarchive a list of compressed tsv files into a database
 #' @param files vector of filenames to be read in. Must be `tsv`
 #' format compressed using `bzip2`, `gzip`, `zip`, or `xz` format
 #' at present.
@@ -23,23 +23,22 @@
 #' @return a database connection (invisibly)
 #' 
 #' @examples \donttest{
-#'
 #' ## Setup: create an archive.
+#' library(dplyr)
 #' dir <- tempdir() 
 #' db <- dbplyr::nycflights13_sqlite(tempdir())
 #' 
 #' ## database -> .tsv.bz2 
-#' ark(db)
+#' ark(db, dir)
 #' 
 #' ## list all files in archive (full paths)
-#' files <- list.files(dir, "[.tsv.gz]", full.names = TRUE)
+#' files <- list.files(dir, "[.]tsv\\.bz2$", full.names = TRUE)
 #' 
 #' ## Read archived files into a new database (defaults to sqlite)
-#' new_db <- src_sqlite("local.sqlite", create=TRUE)
+#' new_db <- src_sqlite(file.path(dir, "local.sqlite"), create=TRUE)
 #' unark(files, new_db)
 #' 
 #' ## Prove table is returned successfully.
-#' library(dplyr)
 #' tbl(new_db, "flights")
 #' 
 #' }