readr will guess column types from the data if the user does not specify the types. The
guess_max parameter controls how many rows of the input file are used to form these guesses. Ideally, the column types would be completely obvious from the first non-header row and we could use
guess_max = 1. That would be very efficient! But the situation is rarely so clear-cut.
By default, readr consults 1000 rows when type-guessing, i.e.
guess_max = 1000. Note that readr never consults rows that won’t be part of the import, so the actual default is
guess_max = min(1000, n_max).
Sometimes you want to convey “use all of the data we’re going to import to guess the column types”, often without even knowing or specifying
n_max. How should you say that? It’s also worth discussing the possible downsides of such a request.
readr got a new parsing engine in version 2.0.0 (released July 2021). In this so-called second edition, readr calls
vroom::vroom(), by default. The vroom package and, therefore, the second edition of readr supports a very natural expression of “use all the data to guess”: namely
guess_max = Inf.
read_csv("path/to/your/file", ..., guess_max = Inf)
Why isn’t this the default? Why not do this all the time? Because column type guessing basically adds another pass through the data, in addition to the main parsing.
If you routinely use
guess_max = Inf, you’re basically processing every file twice, in its entirety. If you only work with small files, this is fine. But for larger files, this can be very costly and for relatively little benefit. Often the column types guessed based on a subset of the file are “good enough”.
Note also that
guess_max = n, for finite
n, works better in the second edition parser. Due to its different design, vroom is able to sub-sample
n rows throughout the file and it always includes the last row, whereas earlier versions of readr just consulted the first
n rows. In practice, the result is that the default of
guess_max = min(1000, n_max) produces better guessed column types that it used to. It should feel less necessary to fiddle with
As always, remember that the best strategy is to provide explicit column types as any data analysis project matures past the exploratory phase.
The parsing engine in readr versions prior to 2.0.0 is now called the first edition. If you’re using readr >= 2.0.0, you can still access first edition parsing via the functions
local_edition(). And, obviously, if you’re using readr < 2.0.0, you will get first edition parsing, by definition, because that’s all there is.
The first edition parser doesn’t have a perfect way to convey “use all of the data to guess the column types”. (This is one of several reasons to prefer readr >= 2.0.0.)
Let’s set up a slightly tricky file, so we can demonstrate different approaches. The column
x is mostly empty, but has some numeric data at the very end, in row 1001.
<- tibble::tibble( tricky_dat x = rep(c("", "2"), c(1000, 1)), y = "y" )<- tempfile("tricky-column-type-guessing-", fileext = ".csv") tfile write_csv(tricky_dat, tfile)
First, note that the second edition parser guesses the right type for
x, even with the default
tail(read_csv(tfile)) #> Rows: 1001 Columns: 2 #> ── Column specification ──────────────────────────────────────────────────────── #> Delimiter: "," #> chr (1): y #> dbl (1): x #> #> ℹ Use `spec()` to retrieve the full column specification for this data. #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. #> # A tibble: 6 × 2 #> x y #> <dbl> <chr> #> 1 NA y #> 2 NA y #> 3 NA y #> 4 NA y #> 5 NA y #> 6 2 y
In contrast, the first edition parser doesn’t guess the right type for
x with the
x is imported as logical and the
2 becomes an
with_edition( 1, tail(read_csv(tfile)) )#> #> ── Column specification ──────────────────────────────────────────────────────── #> cols( #> x = col_logical(), #> y = col_character() #> ) #> Warning: 1 parsing failure. #> row col expected actual file #> 1001 x 1/0/T/F/TRUE/FALSE 2 '/tmp/Rtmpfcobik/tricky-column-type-guessing-37e431c8f3c9.csv' #> # A tibble: 6 × 2 #> x y #> <lgl> <chr> #> 1 NA y #> 2 NA y #> 3 NA y #> 4 NA y #> 5 NA y #> 6 NA y
There are three ways to proceed, each of which has some downside:
guess_max = Inf, just like we do for the second edition parser.
Since readr does not know how much data it will be processing, the first edition engine pre-allocates a large amount of memory in the face of this uncertainty. This means that reading with
guess_max = Inf can be extremely slow and might even crash your R session.
with_edition( 1, tail(read_csv(tfile, guess_max = Inf)) )#> Warning: `guess_max` is a very large value, setting to `21474836` to avoid #> exhausting memory #> #> ── Column specification ──────────────────────────────────────────────────────── #> cols( #> x = col_double(), #> y = col_character() #> ) #> # A tibble: 6 × 2 #> x y #> <dbl> <chr> #> 1 NA y #> 2 NA y #> 3 NA y #> 4 NA y #> 5 NA y #> 6 2 y
Specify an actual, non-infinite value for
This is an awkward suggestion, because if you knew how many rows there were, we wouldn’t be having this conversation in the first place. But sometimes you have a decent estimate and can choose a value of
guess_max that is “big enough”. This usually results in much better performance than
guess_max = Inf.
with_edition( 1, tail(read_csv(tfile, guess_max = 1200)) )#> #> ── Column specification ──────────────────────────────────────────────────────── #> cols( #> x = col_double(), #> y = col_character() #> ) #> # A tibble: 6 × 2 #> x y #> <dbl> <chr> #> 1 NA y #> 2 NA y #> 3 NA y #> 4 NA y #> 5 NA y #> 6 2 y
Read all columns as character, then use
type_convert(). This is a bit clunky, since this obligates you to post-processing once you’ve brought you data into R.
<- with_edition( dat_chr 1, read_csv(tfile, col_types = cols(.default = col_character())) )tail(dat_chr) #> # A tibble: 6 × 2 #> x y #> <chr> <chr> #> 1 <NA> y #> 2 <NA> y #> 3 <NA> y #> 4 <NA> y #> 5 <NA> y #> 6 2 y <- type_convert(dat_chr) dat #> #> ── Column specification ──────────────────────────────────────────────────────── #> cols( #> x = col_double(), #> y = col_character() #> ) tail(dat) #> # A tibble: 6 × 2 #> x y #> <dbl> <chr> #> 1 NA y #> 2 NA y #> 3 NA y #> 4 NA y #> 5 NA y #> 6 2 y
Clean up the temporary tricky csv file.
file.remove(tfile) #>  TRUE