Reading and Writing Data Files • egnyte

While eg_read() and eg_write() handle raw file transfers, you probably spend most of your time working with data files that you want to load directly into R as data frames. egnyte provides a set of functions that handle the download-read or write-upload workflow in a single step.

Prerequisites

Before you can read or write files, you need to authenticate. See vignette("configuration") or vignette("authorization") if you haven’t set that up yet.

library(egnyte)

eg_auth()

Reading Data Files

egnyte provides read functions for common data formats. Each function:

Downloads the file from Egnyte to a temporary location
Reads it using the appropriate R package
Returns the data as a data frame (or tibble)
Cleans up the temporary file

CSV Files

# Read a CSV file
dat <- eg_read_csv("/Shared/Data/analysis.csv")

This uses readr::read_csv() under the hood. You can pass any additional arguments that read_csv() accepts:

# Specify column types
dat <- eg_read_csv(
  "/Shared/Data/analysis.csv",
  col_types = cols(
    id = col_integer(),
    name = col_character(),
    value = col_double()
  )
)

# Skip rows, select columns, etc.
dat <- eg_read_csv(
  "/Shared/Data/analysis.csv",
  skip = 2,
  col_select = c(id, name, value)
)

Delimited Files

For files with delimiters other than commas:

# Tab-delimited file (default)
dat <- eg_read_delim("/Shared/Data/data.tsv")

# Pipe-delimited file
dat <- eg_read_delim("/Shared/Data/data.txt", delim = "|")

# Semicolon-delimited (common in European CSVs)
dat <- eg_read_delim("/Shared/Data/european.csv", delim = ";")

Excel Files

# Read an Excel file (first sheet by default)
dat <- eg_read_excel("/Shared/Data/workbook.xlsx")

# Read a specific sheet by name
dat <- eg_read_excel("/Shared/Data/workbook.xlsx", sheet = "Summary")

# Read a specific sheet by position
dat <- eg_read_excel("/Shared/Data/workbook.xlsx", sheet = 3)

Additional readxl::read_excel() arguments work here too:

# Specify a range
dat <- eg_read_excel(
  "/Shared/Data/workbook.xlsx",
  range = "B2:F100"
)

# Skip rows
dat <- eg_read_excel(
  "/Shared/Data/workbook.xlsx",
  skip = 5
)

SAS Files

# Read a SAS7BDAT file
dat <- eg_read_sas("/Shared/Data/dataset.sas7bdat")

# Read a SAS transport file (.xpt)
dat <- eg_read_xpt("/Shared/Data/dataset.xpt")

These use haven::read_sas() and haven::read_xpt(). Haven preserves SAS attributes like variable labels and formats.

Stata Files

# Read a Stata file
dat <- eg_read_stata("/Shared/Data/dataset.dta")

SPSS Files

# Read an SPSS file
dat <- eg_read_spss("/Shared/Data/dataset.sav")

R Objects (RDS)

# Read an RDS file
obj <- eg_read_rds("/Shared/Data/model.rds")

RDS files can contain any R object, not just data frames. This is useful for saving fitted models, lists, or other complex objects.

Writing Data Files

The write functions work in reverse - they take an R object, write it to a temporary file, and upload it to Egnyte.

CSV Files

# Write a data frame to CSV
eg_write_csv(dat, "/Shared/Data/results.csv")

By default, this will fail if the file already exists:

# Overwrite an existing file
eg_write_csv(dat, "/Shared/Data/results.csv", overwrite = TRUE)

You can pass additional arguments to readr::write_csv():

# Don't include column names
eg_write_csv(dat, "/Shared/Data/results.csv", col_names = FALSE)

# Use a different NA representation
eg_write_csv(dat, "/Shared/Data/results.csv", na = ".")

Delimited Files

# Write a tab-delimited file
eg_write_delim(dat, "/Shared/Data/results.tsv")

# Write with a different delimiter
eg_write_delim(dat, "/Shared/Data/results.txt", delim = "|")

Excel Files

# Write a single sheet
eg_write_excel(dat, "/Shared/Data/results.xlsx")

# Write multiple sheets by passing a named list
eg_write_excel(
  list(
    "Summary" = summary_df,
    "Details" = details_df,
    "Raw" = raw_df
  ),
  "/Shared/Data/workbook.xlsx"
)

SAS Transport Files

# Write a SAS transport file
eg_write_xpt(dat, "/Shared/Data/results.xpt")

Note: egnyte can write XPT files but not native SAS7BDAT files. If you need SAS7BDAT, you’ll need to use SAS itself or another tool.

Stata Files

# Write a Stata file
eg_write_stata(dat, "/Shared/Data/results.dta")

SPSS Files

# Write an SPSS file
eg_write_spss(dat, "/Shared/Data/results.sav")

R Objects (RDS)

# Save any R object
eg_write_rds(fitted_model, "/Shared/Data/model.rds")

# Control compression
eg_write_rds(large_data, "/Shared/Data/data.rds", compress = "xz")

Optional Dependencies

The format-specific functions require additional packages that aren’t installed by default with egnyte:

Function	Required Package
`eg_read_csv()`, `eg_write_csv()`	readr
`eg_read_delim()`, `eg_write_delim()`	readr
`eg_read_excel()`	readxl
`eg_write_excel()`	writexl
`eg_read_sas()`, `eg_read_xpt()`, `eg_write_xpt()`	haven
`eg_read_stata()`, `eg_write_stata()`	haven
`eg_read_spss()`, `eg_write_spss()`	haven
`eg_read_rds()`, `eg_write_rds()`	(base R - no extra package)

If you try to use a function without the required package installed, egnyte will prompt you to install it:

The readr package is required for this function.
Would you like to install it? (yes/no)

A Few Tips

Working with Large Files

The read functions download the entire file before reading it. For large files, this can take a while and use significant memory. A few suggestions:

If you only need specific columns, use the col_select argument (for CSV/delim files) to avoid loading unnecessary data
Consider whether you really need the whole file, or if you can work with a sample
For very large datasets, you might want to use eg_read() to download the file once, then work with it locally

Passing Arguments Through

All the format-specific functions accept ... arguments that get passed to the underlying read/write function. If you’re familiar with readr::read_csv() or haven::read_sas(), you can use all the same options:

# readr options
dat <- eg_read_csv(
  "/Shared/Data/data.csv",
  col_types = "ccdd",
  locale = locale(decimal_mark = ","),
  na = c("", "NA", "N/A", ".")
)

# haven options
dat <- eg_read_sas(
  "/Shared/Data/data.sas7bdat",
  encoding = "latin1"
)

File Extensions Matter

egnyte uses the file extension to determine the format when downloading. Make sure your files have the correct extension:

.csv for CSV files
.xlsx or .xls for Excel files
.sas7bdat for SAS data files
.xpt for SAS transport files
.dta for Stata files
.sav for SPSS files
.rds for R objects

Comparison with Base File Transfer

Here’s the difference between using the format-specific functions vs. the base eg_read()/eg_write():

Using eg_read_csv() (recommended for data files):

# One step - download and read
dat <- eg_read_csv("/Shared/Data/data.csv")

Using eg_read() (manual approach):

# Two steps - download, then read
temp_file <- eg_read("/Shared/Data/data.csv")
dat <- readr::read_csv(temp_file)
unlink(temp_file)  # Clean up

The format-specific functions handle all this for you and clean up temporary files automatically.

Next Steps

Learn about authentication options in vignette("authorization")
See raw file transfer in vignette("file-transfer")