read_adl_delim: Read a delim file from Azure Data Lake
In alexhallam/flyrod: Read and Write Files to 'Azure Data Lake Storage (Gen 1)'

Description Usage Arguments Value Examples

View source: R/read_adl_delim.R

Read a deliminated from Azure Data Lake given a path to the file. 'set_adl_token()' must be run before any file may be read. Also, the parameters for this function are the same as the parameters found in readr::read_delim(). Thus the documentation that follows is identical to the readr documentation.

read_adl_delim(adl_file_path, delim, quote = "\"",
  escape_backslash = FALSE, escape_double = TRUE, col_names = TRUE,
  col_types = NULL, locale = default_locale(), na = c("", "NA"),
  quoted_na = TRUE, comment = "", trim_ws = FALSE, skip = 0,
  n_max = Inf, guess_max = min(1000, n_max),
  progress = show_progress(), skip_empty_rows = TRUE)

`adl_file_path`	A string representing the adl file path. _Required parameter._
`delim`	A string representing the seperator such as pipe "\|" _Required parameter._
`quote`	Single character used to quote strings.
`escape_backslash`	Does the file use backslashes to escape special characters? This is more general than ‘escape_double’ as backslashes can be used to escape the delimiter character, the quote character, or to add special characters like ‘\n’.
`escape_double`	Does the file escape quotes by doubling them? i.e. If this option is ‘TRUE’, the value ‘""""’ represents a single quote, ‘\"’.
`col_names`	Either 'TRUE', 'FALSE' or a character vector of column names. If 'TRUE', the first row of the input will be used as the column names, and will not be included in the data frame. If 'FALSE', column names will be generated automatically: X1, X2, X3 etc. If 'col_names' is a character vector, the values will be used as the names of the columns, and the first row of the input will be read into the first row of the output data frame. Missing ('NA') column names will generate a warning, and be filled in with dummy names 'X1', 'X2' etc. Duplicate column names will generate a warning and be made unique with a numeric prefix.
`col_types`	One of 'NULL', a [cols()] specification, or a string. See 'vignette("readr")' for more details. If 'NULL', all column types will be imputed from the first 1000 rows on the input. This is convenient (and fast), but not robust. If the imputation fails, you'll need to supply the correct types yourself. If a column specification created by [cols()], it must contain one column specification for each column. If you only want to read a subset of the columns, use [cols_only()]. Alternatively, you can use a compact string representation where each character represents one column: - c = character - i = integer - n = number - d = double - l = logical - f = factor - D = date - T = date time - t = time - ? = guess - _ or - = skip
`locale`	The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use [locale()] to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names.
`na`	Character vector of strings to interpret as missing values. Set this option to ‘character()’ to indicate no missing values.
`quoted_na`	Should missing values inside quotes be treated as missing values (the default) or strings.
`comment`	A string used to identify comments. Any text after the comment characters will be silently ignored.
`trim_ws`	Should leading and trailing whitespace be trimmed from each field before parsing it?
`skip`	Number of lines to skip before reading data.
`n_max`	Maximum number of records to read.
`guess_max`	Maximum number of records to use for guessing column types.
`progress`	Display a progress bar? By default it will only display in an interactive session and not while knitting a document. The display is updated every 50,000 values and will only display if estimated reading time is 5 seconds or more. The automatic progress bar can be disabled by setting option 'readr.show_progress' to 'FALSE'.
`skip_empty_rows`	Should blank rows be ignored altogether? i.e. If this option is ‘TRUE’ then blank rows will not be represented at all. If it is ‘FALSE’ then they will be represented by ‘NA’ values in all the columns.

a tibble

 set_adl_token(tenant = "abc123", client_id = "abc123", client_secret = "abc123")
 df <- read_adl_delim(
       adl_file_path =  "adl://<storename>.azuredatalakestore.net/path/to/file.csv",
       delim = "|")