Import and Export CSV Data with a YAML Metadata Header.
Import and Export CSV Data With a YAML Metadata Header
CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.). The CSVY file specification is simple: place a YAML header on top of a regular CSV. The yaml header is formatted according to the Table Schema of a Tabular Data Package.
A CSVY file looks like this:
#---
#profile: tabular-data-resource
#name: my-dataset
#path: https://raw.githubusercontent.com/csvy/csvy.github.io/master/examples/example.csvy
#title: Example file of csvy
#description: Show a csvy sample file.
#format: csvy
#mediatype: text/vnd.yaml
#encoding: utf-8
#schema:
# fields:
# - name: var1
# type: string
# - name: var2
# type: integer
# - name: var3
# type: number
#dialect:
# csvddfVersion: 1.0
# delimiter: ","
# doubleQuote: false
# lineTerminator: "\r\n"
# quoteChar: "\""
# skipInitialSpace: true
# header: true
#sources:
#- title: The csvy specifications
# path: http://csvy.org/
# email: ''
#licenses:
#- name: CC-BY-4.0
# title: Creative Commons Attribution 4.0
# path: https://creativecommons.org/licenses/by/4.0/
#---
var1,var2,var3
A,1,2.0
B,3,4.3
Which we can read into R like this:
library("csvy")
str(read_csvy(system.file("examples", "example1.csvy", package = "csvy")))
## 'data.frame': 2 obs. of 3 variables:
## $ var1: chr "A" "B"
## $ var2: int 1 3
## $ var3: num 2 4.3
## - attr(*, "profile")= chr "tabular-data-resource"
## - attr(*, "title")= chr "Example file of csvy"
## - attr(*, "description")= chr "Show a csvy sample file."
## - attr(*, "name")= chr "my-dataset"
## - attr(*, "format")= chr "csvy"
## - attr(*, "sources")=List of 1
## ..$ :List of 3
## .. ..$ name : chr "CC-BY-4.0"
## .. ..$ title: chr "Creative Commons Attribution 4.0"
## .. ..$ path : chr "https://creativecommons.org/licenses/by/4.0/"
Optional comment characters on the YAML lines make the data readable with any standard CSV parser while retaining the ability to import and export variable- and file-level metadata. The CSVY specification does not use these, but the csvy package for R does so that you (and other users) can continue to rely on utils::read.csv() or readr::read_csv() as usual. The import() function in rio supports CSVY natively.
Export
To create a CSVY file from R, just do:
library("csvy")
library("datasets")
write_csvy(iris, "iris.csvy")
It is also possible to export the metadata to separate YAML or JSON file (and then also possible to import from those separate files) by specifying the metadata field in write_csvy() and read_csvy().
Import
To read a CSVY into R, just do:
d1 <- read_csvy("iris.csvy")
str(d1)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : chr "setosa" "setosa" "setosa" "setosa" ...
## ..- attr(*, "levels")= chr "setosa" "versicolor" "virginica"
## - attr(*, "profile")= chr "tabular-data-package"
## - attr(*, "name")= chr "iris"
or use any other appropriate data import function to ignore the YAML metadata:
d2 <- utils::read.table("iris.csvy", sep = ",", header = TRUE)
str(d2)
## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
Package Installation
The package is available on CRAN and can be installed directly in R using:
install.packages("csvy")
The latest development version on GitHub can be installed using devtools:
if(!require("remotes")){
install.packages("remotes")
}
remotes::install_github("leeper/csvy")
