R datasets of modest size are routinely stored as flat files and retrieved as data frames. Unfortunately, the classic storage formats (comma delimited, tab delimited) do not have obvious mechanisms for storing data about the data: i.e., metadata such as column labels, units, and meanings of categorical codes. In many cases we hold such information in our heads and hard-code it in our scripts as axis labels, figure legends, or table enhancements. That’s probably fine for simple cases but does not scale well in production settings where the same metadata is re-used extensively. Is there a better way to store, retrieve, and bind table metadata for consistent reuse?
yamlet is a storage format for table metadata, implemented as an R package.
It was designed to be:
Although intended mainly to document (or pre-specify!) data column labels and units, there are few restrictions on the types of metadata that can be stored. In fact, the only real restriction is that the stored form must be valid yaml. Below, we use yamlet to indicate the paradigm or package, and
yamlet to indicate stored instances.
yamlet (think: “just a little yaml”) is a special case of
yaml that stores column attributes in one record per column. For instance, to store the fact that data for an imaginary drug trial has a column called ‘ID’, pop open a text file and write
This in itself is valid
yaml! But if you know a label to go with ID, you can add it:
ID: subject identifier
If you have (or expect) a second column with units, you can add it below.
ID: subject identifier CONC: concentration, ng/mL
A couple of notes here.
To get a sequence, just add square brackets. For instance, above we have said that ‘CONC’ has the label ‘concentration, ng/mL’ but what we really intend is that it has label ‘concentration’ and unit ‘ng/mL’ so we rewrite it as
ID: subject identifier CONC: [ concentration, ng/mL ]
Now label and units are two different things. Notice we have not explicitly named them. Unless we say otherwise, the yamlet package will treat the first two un-named items as ‘label’ (a short description) and ‘guide’ (a hint about how to interpret the values). ‘guide’ might be units for continuous variables, levels (and possibly labels) for categorical values, format strings for dates and times, or perhaps something else.
The yamlet package gives you five ways of controlling how data items are identified (see details for
?as_yamlet.character). The most direct way is to supply explicit
ID: [ label: subject identifier ] CONC: [ label: concentration, guide: ng/mL ]
We see that rather complex data can be expressed using only colons, commas, and square brackets.
yaml itself also uses curly braces to express “maps”, but for purposes here they are unnecessary.
Note above that we had to add square brackets for ‘ID’ when introducing the second colon (can’t really have two colons at the same level, so to speak). Note also that sequences can be nested arbitrarily deep. We take advantage of this principle to transform ‘guide’ into a set of categorical levels.
ID: [ label: subject identifier ] CONC: [ label: concentration, guide: ng/mL ] RACE: [ label: race, guide: [ 0, 1, 2 ]]
or more simply (taking advantage of default keys)
ID: subject identifier CONC: [ concentration, ng/mL ] RACE: [ race, [ 0, 1, 2 ]]
So now we have ‘codes’ (levels) for our dataset that represent races. What do these codes mean? We supply ‘decodes’ (labels) as keys.
ID: subject identifier CONC: [ concentration, ng/mL ] RACE: [ race, [ white: 0, black: 1, asian: 2 ]]
yaml (and therefore yamlet) gives us a way to represent a code even if we don’t know the decode, and a way to represent a decode even though we don’t know the code. Imagine a dataset is under collaborative development, and we already know that there are some ‘RACE’ values of 0 but we’re not sure what they mean. We also know that there will be some ‘asian’ race values, but we haven’t assigned a code yet. We can write:
ID: subject identifier CONC: [ concentration, ng/mL ] RACE: [ race, [ 0, black: 1, ? asian ]]
The whole point of this exercise (and I’m getting a little ahead of myself) is to have some stored metadata that we can read into R and apply to a data frame as column attributes. If typing square brackets isn’t your thing, you can actually do this backwards by supplying column attributes to a data frame and writing them out!
suppressMessages(library(dplyr)) library(magrittr) library(yamlet) #> #> Attaching package: 'yamlet' #> The following object is masked from 'package:stats': #> #> filter x <- data.frame( ID = 1, CONC = 1, RACE = 1 ) x$ID %<>% structure(label = 'subject identifier') x$CONC %<>% structure(label = 'concentration', guide = 'ng/mL') x$RACE %<>% structure(label = 'race', guide = list(white = 0, black = 1, asian = 2)) x %>% as_yamlet %>% as.character %>% writeLines #> ID: subject identifier #> CONC: [ concentration, ng/mL ] #> RACE: [ race, [ white: 0, black: 1, asian: 2 ]] # or x %>% as_yamlet %>% as.character %>% writeLines(file.path(tempdir(), 'drug.yaml'))
Let’s take advantage of that last example to show how we can read yamlet into R.
meta is just a named list of column attributes.
decorate() loads them onto columns of a data frame.
x <- data.frame(ID = 1, CONC = 1, RACE = 1) x <- decorate(x, meta = meta) str(x) #> Classes 'decorated' and 'data.frame': 1 obs. of 3 variables: #> $ ID : num 1 #> ..- attr(*, "label")= chr "subject identifier" #> $ CONC: num 1 #> ..- attr(*, "label")= chr "concentration" #> ..- attr(*, "guide")= chr "ng/mL" #> $ RACE: num 1 #> ..- attr(*, "label")= chr "race" #> ..- attr(*, "guide")=List of 3 #> .. ..$ white: int 0 #> .. ..$ black: int 1 #> .. ..$ asian: int 2
We saw earlier that
as_yamlet() can pull “decorations” off a data frame and present them as yamlet.
as_yamlet() on its primary argument, and sends the result to a connection of our choice.
A useful convention is to store metadata in a file next to the file it describes, with the same name but the ‘yaml’ extension.
decorate() expects this, and if given a file path to a CSV file, it will look for a ’*.yaml’ file nearby. To “decorate” a CSV path means to read it, read its
yamlet (if any) and apply the
yamlet as attributes on the resulting data frame.
library(csv) # see ?Quinidine in package nlme file <- system.file(package = 'yamlet', 'extdata','quinidine.csv') a <- decorate(file) as_yamlet(a)[1:3] #> - Subject #> - label: patient identifier #> - time #> - label: time since start of study #> - guide: h #> - conc #> - label: quinidine serum concentration #> - guide: mg/L
Another way to achieve the same thing is with
io_csv(). It is a toggle function that returns a path if given a file to store, and returns a decorated data frame if given a path to read (same for
io_table(), which has all the formatting options of
write.table()). The path is just the path to the primary data, but the path to the metadata is implied as well.
options(csv_source = FALSE) # see ?as.csv file <- system.file(package = 'yamlet', 'extdata','quinidine.csv') x <- decorate(file) out <- file.path(tempdir(), 'out.csv') io_csv(x, out) y <- io_csv(out) identical(x, y) # lossless 'round-trip' #>  TRUE file.exists(out) #>  TRUE meta <- sub('csv','yaml', out) file.exists(meta) #>  TRUE meta %>% readLines %>% head %>% writeLines #> Subject: patient identifier #> time: [ time since start of study, h ] #> conc: [ quinidine serum concentration, mg/L ] #> dose: [ quinidine administered dose, mg ] #> interval: [ inter-dose interval, h ] #> Age: [ baseline subject age, year ] options(csv_source = TRUE) # restore
Metadata can be used prospectively or retrospectively. Early in the data life cycle, it can be used prospectively to guide table development in a collaborative setting (i.e. as a data specification). Later in the life cycle, metadata can be used retrospectively to consistently inform report elements such as figures and tables.
For example, The yamlet package provides an experimental wrapper for ggplot that uses column attributes to automatically generate informative axis labels and legends.
The table1 package uses labels and units stored as attributes to enrich table output. In the example below, we use
resolve() to re-implement guides as units and factor levels, which is what
|baseline subject age (year)|
|Mean (SD)||67.1 (8.32)||67.9 (10.1)||65.5 (8.48)||66.9 (8.91)|
|Median [Min, Max]||68.0 [50.0, 87.0]||65.5 [46.0, 92.0]||64.0 [42.0, 88.0]||66.0 [42.0, 92.0]|
|subject body weight (kg)|
|Mean (SD)||81.7 (16.9)||78.6 (13.4)||77.6 (15.9)||79.6 (15.6)|
|Median [Min, Max]||80.0 [41.0, 119]||80.0 [55.0, 106]||74.0 [47.0, 112]||79.0 [41.0, 119]|
|Caucasian||40 (71.4%)||26 (65.0%)||25 (62.5%)||91 (66.9%)|
|Latin||13 (23.2%)||12 (30.0%)||10 (25.0%)||35 (25.7%)|
|Black||3 (5.4%)||2 (5.0%)||5 (12.5%)||10 (7.4%)|
It is a well-known problem that many table manipulations in R cause column attributes to be dropped. Binding of metadata is best done at a point in a workflow where few or no such manipulations remain. Else, precautions should be taken to preserve or restore attributes as necessary.
The yamlet package implements a metadata storage syntax that is easy to write, read, and bind to data frame columns. Systematic curation of metadata enriches and simplifies efforts to create and describe tables stored in flat files. Conforming tools can take advantage of internal and external yamlet representations to enhance data development and reporting.