Read and return an R object from data on disk, from URL, or from packages.
read(
file,
type = NULL,
header = "#",
header.max = 50L,
skip = 0L,
locale = default_locale(),
lang = getOption("SciViews_lang", "en"),
lang_encoding = "UTF-8",
as_dataframe = FALSE,
as_labelled = FALSE,
comments = NULL,
package = NULL,
sidecar_file = TRUE,
fun_list = NULL,
hfun = NULL,
fun = NULL,
data,
cache_file = NULL,
method = "auto",
quiet = FALSE,
force = FALSE,
...
)
type_from_extension(file, full = FALSE)
hread_text(file, header.max, skip = 0L, locale = default_locale(), ...)
hread_xls(file, header.max, skip = 0L, locale = default_locale(), ...)
hread_xlsx(file, header.max, skip = 0L, locale = default_locale(), ...)
# S3 method for class 'read_function_subset'
.DollarNames(x, pattern = "")Arguments
- file
The path to the file to read, or the name of the dataset to get from an R package (in that case, you must provide the
package=argument).- type
The type (format) of data to read.
- header
The character to use for the header and other comments.
- header.max
The maximum of lines to consider for the header.
- skip
The number of lines to skip at the beginning of the file.
- locale
A readr locale object with all the data regarding required to correctly interpret country-related items. The default value matches R defaults as US English + UTF-8 encoding, and it is advised to be used as much as possible.
- lang
The language to use (mainly for comment, label and units), but also for factor levels or other character strings if a translation exists and if the language is spelled with uppercase characters (e.g.,
"FR"). The default value can be set with, e.g.,options(SciViews_lang = "fr")for French.- lang_encoding
Encoding used by R scripts for translation. They should all be encoded as
UTF-8, which is the default. However, this argument allows to specify a different encoding if needed.- as_dataframe
Deprecated: now use
options(SciViews.as_dtx = as_XXX)to specify if you want a data.frame (as_dtf), a data.table (as_dtt, by default), or a tibble (as_dtbl). Do we try to convert the resulting object into adataframe(inheriting fromdata.frame,tblandtbl_dbaliastibble)? IfFALSE, no conversion is attempted. Note that now, whatever you indicate, it is always assumed to beFALSEas part of the deprecation!- as_labelled
Are variable converted into 'labelled' objects. This allows to keep labels and units when the vector is manipulated, but it can lead to incompatibilities with some R code (hence, it is
FALSEby default).- comments
Comments to add in the created object.
- package
The package where to look for the dataset. If
file=is not provided, a list of available datasets in the package is displayed.- sidecar_file
If
TRUEand a file with same name asfile=+.Ris found in the same directory, it is considered as code to import these data and it is sourced withlocal = TRUE,chdir = TRUEandverbose = FALSE. That script must create an object nameddataset, which is the result that is returned by the function. It is advised to encode this script inUTF-8, which is the default value, but it is possible to specify a different encoding through thelang_encoding=parameter.- fun_list
The table with correspondence of the types, read, and write functions.
- hfun
The function to read the header (lines starting with a special mark, usually '#' at the beginning of the file). This function must have the same arguments as
hread_text()and should return a character string with the firstheader.maxlines.- fun
The function to delegate reading of the data. If
NULL(default), The function is chosen fromfun_list.- data
A synonym to
file=(the name makes more sense when the dataset is loaded from a package). You cannot usedata=andfile=at the same time.- cache_file
The path to a local file to use as a cache when file is downloaded (http://, https://, ftp://, or file:// protocols). If cache_file already exists, data are read from this cache, except if
force = TRUE, see here under. Otherwise, data are saved in it before being used. Ifcache_file = NULL(the default), a temporary file is used and data are read from the Internet every time. This cache mechanism is particularly useful to provide data associated with a git repository. Put cache_file in.gitignoreand usecache_file=in the code (andforce = FALSE). That way, the data are downloaded once in a freshly cloned repository, and they are not included in the versioning system (useful for large datasets).- method
The downloading method used (
"auto"by default), seeutils::download.file().- quiet
In case we have to download files, do it silently (
TRUE) or do we provide feedback and a progression bar (FALSE, by default)?- force
If
TRUEand an URL is provided forfile=and a path forcache_file=, then the content is downloaded all the time, even if the cache file already exists (it overwrites it). By default, it isFALSE, which is the most useful setting to make good use of the cache mechanism.- ...
Further arguments passed to the function
fun=.- full
Do we return the full extension, like
csv.tar.gz(TRUE), or only the main extension, likecsv(FALSE, by default).- x
An object.
- pattern
A regular expression to list matching names.
Value
An R object with the data (its class depends on the data being read).
Details
read() allows for a unique entry point to read various kinds of
data, but it delegates the actual work to various other functions dispatched
across several R packages. See getOption("read_write").
See also
Examples
# Use of read() as a more flexible substitute to data() (can change dataset
# name and syntax more similar to read R datasets and datasets from files)
read() # List all available datasets in your installed version of R
# List datasets in one particular package
read(package = "data.io")
# Read one dataset from this package, possibly changing its name
(urchin <- read("urchin_bio", package = "data.io"))
#> # A data.trame: [421 × 19]
#> origin diameter1 diameter2 height buoyant_weight weight solid_parts
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Fishery 9.9 10.2 5 NA 0.522 0.478
#> 2 Fishery 10.5 10.6 5.7 NA 0.642 0.589
#> 3 Fishery 10.8 10.8 5.2 NA 0.734 0.677
#> 4 Fishery 9.6 9.3 4.6 NA 0.370 0.344
#> 5 Fishery 10.4 10.7 4.8 NA 0.610 0.559
#> 6 Fishery 10.5 11.1 5 NA 0.610 0.551
#> 7 Fishery 11 11 5.2 NA 0.672 0.605
#> 8 Fishery 11.1 11.2 5.7 NA 0.703 0.628
#> 9 Fishery 9.4 9.2 4.6 NA 0.413 0.375
#> 10 Fishery 10.1 9.5 4.7 NA 0.449 0.398
#> # ℹ 411 more rows
#> # ℹ 12 more variables: integuments <dbl>, dry_integuments <dbl>,
#> # digestive_tract <dbl>, dry_digestive_tract <dbl>, gonads <dbl>,
#> # dry_gonads <dbl>, skeleton <dbl>, lantern <dbl>, test <dbl>, spines <dbl>,
#> # maturity <int>, sex <fct>
# Same, but using labels in French
(urchin <- read("urchin_bio", package = "data.io", lang = "fr"))
#> # A data.trame: [421 × 19]
#> origin diameter1 diameter2 height buoyant_weight weight solid_parts
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Fishery 9.9 10.2 5 NA 0.522 0.478
#> 2 Fishery 10.5 10.6 5.7 NA 0.642 0.589
#> 3 Fishery 10.8 10.8 5.2 NA 0.734 0.677
#> 4 Fishery 9.6 9.3 4.6 NA 0.370 0.344
#> 5 Fishery 10.4 10.7 4.8 NA 0.610 0.559
#> 6 Fishery 10.5 11.1 5 NA 0.610 0.551
#> 7 Fishery 11 11 5.2 NA 0.672 0.605
#> 8 Fishery 11.1 11.2 5.7 NA 0.703 0.628
#> 9 Fishery 9.4 9.2 4.6 NA 0.413 0.375
#> 10 Fishery 10.1 9.5 4.7 NA 0.449 0.398
#> # ℹ 411 more rows
#> # ℹ 12 more variables: integuments <dbl>, dry_integuments <dbl>,
#> # digestive_tract <dbl>, dry_digestive_tract <dbl>, gonads <dbl>,
#> # dry_gonads <dbl>, skeleton <dbl>, lantern <dbl>, test <dbl>, spines <dbl>,
#> # maturity <int>, sex <fct>
# ... and also the levels of factors in French (note: uppercase FR)
(urchin <- read("urchin_bio", package = "data.io", lang = "FR"))
#> # A data.trame: [421 × 19]
#> origin diameter1 diameter2 height buoyant_weight weight solid_parts
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Pêcherie 9.9 10.2 5 NA 0.522 0.478
#> 2 Pêcherie 10.5 10.6 5.7 NA 0.642 0.589
#> 3 Pêcherie 10.8 10.8 5.2 NA 0.734 0.677
#> 4 Pêcherie 9.6 9.3 4.6 NA 0.370 0.344
#> 5 Pêcherie 10.4 10.7 4.8 NA 0.610 0.559
#> 6 Pêcherie 10.5 11.1 5 NA 0.610 0.551
#> 7 Pêcherie 11 11 5.2 NA 0.672 0.605
#> 8 Pêcherie 11.1 11.2 5.7 NA 0.703 0.628
#> 9 Pêcherie 9.4 9.2 4.6 NA 0.413 0.375
#> 10 Pêcherie 10.1 9.5 4.7 NA 0.449 0.398
#> # ℹ 411 more rows
#> # ℹ 12 more variables: integuments <dbl>, dry_integuments <dbl>,
#> # digestive_tract <dbl>, dry_digestive_tract <dbl>, gonads <dbl>,
#> # dry_gonads <dbl>, skeleton <dbl>, lantern <dbl>, test <dbl>, spines <dbl>,
#> # maturity <int>, sex <fct>
# Read one dataset from another package, but with labels and comments
data(iris) # The R way: you got the initial datasets
# Same result, using read()
ir2 <- read("iris", package = "datasets", lang = NULL)
# ir2 records that it comes from datasets::iris
attr(comment(ir2), "src")
#> [1] "datasets::iris"
# otherwise, it is identical to iris, except is may be a data.table or a
# tibble, depending on user preferences
comment(ir2) <- NULL
# Force coercion into a data.frame
ir2 <- svBase::as_dtf(ir2)
identical(iris, ir2)
#> [1] TRUE
# More interesting: you can get an enhanced version of iris with read():
# (note that variable names ar in snake-case now!)
(ir3 <- read("iris", package = "datasets"))
#> # A data.trame: [150 × 5]
#> sepal_length sepal_width petal_length petal_width species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
class(ir3)
#> [1] "data.trame" "data.frame"
comment(ir3)
#> [1] "The 'iris' from 'datasets', but with variables names in snake_case"
#> [2] "(Sepal.Length -> sepal_length, Species -> species)."
#> attr(,"lang")
#> [1] "en"
#> attr(,"lang_encoding")
#> [1] "UTF-8"
#> attr(,"src")
#> [1] "datasets::iris"
ir3$sepal_length
#> [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
#> [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
#> [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
#> [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
#> [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
#> [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
#> [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
#> [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
#> [145] 6.7 6.7 6.3 6.5 6.2 5.9
#> attr(,"label")
#> [1] "Length of the sepals"
#> attr(,"units")
#> [1] "cm"
# ... and you can get it in French too!
(ir_fr <- read("iris", package = "datasets", lang = "fr"))
#> # A data.trame: [150 × 5]
#> sepal_length sepal_width petal_length petal_width species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
class(ir_fr)
#> [1] "data.trame" "data.frame"
comment(ir_fr)
#> [1] "Jeu de données 'iris' de 'datasets', mais avec noms de variables modifiées"
#> [2] "(Sepal.Length -> sepal_length, Species -> species)."
#> attr(,"lang")
#> [1] "fr"
#> attr(,"lang_encoding")
#> [1] "UTF-8"
#> attr(,"src")
#> [1] "datasets::iris"
ir_fr$sepal_length
#> [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1
#> [19] 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0
#> [37] 5.5 4.9 4.4 5.1 5.0 4.5 4.4 5.0 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5
#> [55] 6.5 5.7 6.3 4.9 6.6 5.2 5.0 5.9 6.0 6.1 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1
#> [73] 6.3 6.1 6.4 6.6 6.8 6.7 6.0 5.7 5.5 5.5 5.8 6.0 5.4 6.0 6.7 6.3 5.6 5.5
#> [91] 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 5.1 5.7 6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3
#> [109] 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 7.7 6.0 6.9 5.6 7.7 6.3 6.7 7.2
#> [127] 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.0 6.9 6.7 6.9 5.8 6.8
#> [145] 6.7 6.7 6.3 6.5 6.2 5.9
#> attr(,"label")
#> [1] "Longueur des sépales"
#> attr(,"units")
#> [1] "cm"
# Sometimes, datasets are more deeply reworked. For instance, trees has
# variables in imperial units (in, ft, and cubic ft), but it is automatically
# reworked by read() into metric variables (m or m^3):
data(trees)
head(trees)
#> Girth Height Volume
#> 1 8.3 70 10.3
#> 2 8.6 65 10.3
#> 3 8.8 63 10.2
#> 4 10.5 72 16.4
#> 5 10.7 81 18.8
#> 6 10.8 83 19.7
(trees2 <- read("trees", package = "datasets"))
#> # A data.trame: [31 × 3]
#> diameter height volume
#> <dbl> <dbl> <dbl>
#> 1 0.211 21.3 0.292
#> 2 0.218 19.8 0.292
#> 3 0.224 19.2 0.289
#> 4 0.267 21.9 0.464
#> 5 0.272 24.7 0.532
#> 6 0.274 25.3 0.558
#> 7 0.279 20.1 0.442
#> 8 0.279 22.9 0.515
#> 9 0.282 24.4 0.64
#> 10 0.284 22.9 0.563
#> # ℹ 21 more rows
comment(trees2)
#> [1] "The 'trees' from 'datasets' but with variables renamed and in m or m^3"
#> [2] "(Girth [in] -> diameter [m], Height [ft] -> height [m],"
#> [3] "Volume [ft^3] -> volume [m^3])."
#> attr(,"lang")
#> [1] "en"
#> attr(,"lang_encoding")
#> [1] "UTF-8"
#> attr(,"src")
#> [1] "datasets::trees"
trees2$volume
#> [1] 0.292 0.292 0.289 0.464 0.532 0.558 0.442 0.515 0.640 0.563 0.685 0.595
#> [13] 0.606 0.603 0.541 0.629 0.957 0.776 0.728 0.705 0.977 0.898 1.028 1.085
#> [25] 1.206 1.569 1.577 1.651 1.458 1.444 2.180
#> attr(,"label")
#> [1] "Volume of timber"
#> attr(,"units")
#> [1] "m^3"
# \donttest{
# Read from a Github Gist (need to specify the type here!)
# (ble <- read$csv("http://tinyurl.com/Biostat-Ble"))
# Various versions of the famous iris dataset
(iris <- read(data_example("iris.csv")))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris.csv.zip")))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris.csv.gz")))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris.csv.bz2")))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris.tsv")))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris.xls")))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris.xlsx")))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris.rds")))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
#(iris <- read(data_example("iris.syd"))) ##
#(iris <- read(data_example("iris.csvy"))) ##
#(iris <- read(data_example("iris.csvy.zip"))) ##
# A file with an header both in English (default) and in French
(iris <- read(data_example("iris_short_header.csv")))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <labelled> <labelled> <labelled> <labelled> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris_fr <- read(data_example("iris_short_header.csv"), lang = "fr"))
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <labelled> <labelled> <labelled> <labelled> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
# Headers are also recognized in xls/xlsx files
(iris_fr <- read(data_example("iris_short_header.xls"), lang = "fr"))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <labelled> <labelled> <labelled> <labelled> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
# Read a file with a sidecar file (same name + '.R')
(iris <- read(data_example("iris_sidecar.csv"))) # lang = "en" by default
#> # A data.trame: [150 × 5]
#> sepal_length sepal_width petal_length petal_width species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris_sidecar.csv"), lang = "EN")) # Full lang
#> # A data.trame: [150 × 5]
#> sepal_length sepal_width petal_length petal_width species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 I. setosa
#> 2 4.9 3 1.4 0.2 I. setosa
#> 3 4.7 3.2 1.3 0.2 I. setosa
#> 4 4.6 3.1 1.5 0.2 I. setosa
#> 5 5 3.6 1.4 0.2 I. setosa
#> 6 5.4 3.9 1.7 0.4 I. setosa
#> 7 4.6 3.4 1.4 0.3 I. setosa
#> 8 5 3.4 1.5 0.2 I. setosa
#> 9 4.4 2.9 1.4 0.2 I. setosa
#> 10 4.9 3.1 1.5 0.1 I. setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris_sidecar.csv"), lang = "en_us")) # US (in)
#> # A data.trame: [150 × 5]
#> sepal_length sepal_width petal_length petal_width species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 2.01 1.38 0.551 0.0787 setosa
#> 2 1.93 1.18 0.551 0.0787 setosa
#> 3 1.85 1.26 0.512 0.0787 setosa
#> 4 1.81 1.22 0.591 0.0787 setosa
#> 5 1.97 1.42 0.551 0.0787 setosa
#> 6 2.13 1.54 0.669 0.157 setosa
#> 7 1.81 1.34 0.551 0.118 setosa
#> 8 1.97 1.34 0.591 0.0787 setosa
#> 9 1.73 1.14 0.551 0.0787 setosa
#> 10 1.93 1.22 0.591 0.0394 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris_sidecar.csv"), lang = "fr")) # French
#> # A data.trame: [150 × 5]
#> sepal_length sepal_width petal_length petal_width species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris_sidecar.csv"), lang = "FR_BE")) # Belgian
#> # A data.trame: [150 × 5]
#> sepal_length sepal_width petal_length petal_width species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 I. setosa
#> 2 4.9 3 1.4 0.2 I. setosa
#> 3 4.7 3.2 1.3 0.2 I. setosa
#> 4 4.6 3.1 1.5 0.2 I. setosa
#> 5 5 3.6 1.4 0.2 I. setosa
#> 6 5.4 3.9 1.7 0.4 I. setosa
#> 7 4.6 3.4 1.4 0.3 I. setosa
#> 8 5 3.4 1.5 0.2 I. setosa
#> 9 4.4 2.9 1.4 0.2 I. setosa
#> 10 4.9 3.1 1.5 0.1 I. setosa
#> # ℹ 140 more rows
(iris <- read(data_example("iris_sidecar.csv"), lang = NULL)) # No labels
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
# Require the feather package
#(iris <- read(data_example("iris.feather"))) # Not available for all Win
# Challenging datasets from the readr package
library(readr)
(mtcars <- read(readr_example("mtcars.csv")))
#> # A data.trame: [32 × 11]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
(mtcars <- read(readr_example("mtcars.csv.zip")))
#> # A data.trame: [32 × 11]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
(mtcars <- read(readr_example("mtcars.csv.bz2")))
#> # A data.trame: [32 × 11]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
(challenge <- read(readr_example("challenge.csv")))
#> # A data.trame: [2,000 × 2]
#> x y
#> <dbl> <IDate>
#> 1 404 NA
#> 2 4172 NA
#> 3 3004 NA
#> 4 787 NA
#> 5 37 NA
#> 6 2332 NA
#> 7 2489 NA
#> 8 1449 NA
#> 9 3665 NA
#> 10 3863 NA
#> # ℹ 1,990 more rows
# Or using readr::read_csv()... There are differences!
(challenge2 <- read$csv_alt(readr_example("challenge.csv"), guess_max = 1001))
#> Rows: 2000 Columns: 2
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (1): x
#> date (1): y
#>
#> 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 data.trame: [2,000 × 2]
#> x y
#> <dbl> <date>
#> 1 404 NA
#> 2 4172 NA
#> 3 3004 NA
#> 4 787 NA
#> 5 37 NA
#> 6 2332 NA
#> 7 2489 NA
#> 8 1449 NA
#> 9 3665 NA
#> 10 3863 NA
#> # ℹ 1,990 more rows
sapply(challenge, class)
#> $x
#> [1] "numeric"
#>
#> $y
#> [1] "IDate" "Date"
#>
sapply(challenge2, class)
#> x y
#> "numeric" "Date"
(massey <- read(readr_example("massey-rating.txt")))
#> [1] "UCC PAY LAZ KPK RT COF BIH DII ENG ACU Rank Team Conf\n 1 1 1 1 1 1 1 1 1 1 1 Ohio St B10 \n 2 2 2 2 2 2 2 2 4 2 2 Oregon P12 \n 3 4 3 4 3 4 3 4 2 3 3 Alabama SEC \n 4 3 4 3 4 3 5 3 3 4 4 TCU B12 \n 6 6 6 5 5 7 6 5 6 11 5 Michigan St B10 \n 7 7 7 6 7 6 11 8 7 8 6 Georgia SEC \n 5 5 5 7 6 8 4 6 5 5 7 Florida St ACC \n 8 8 9 9 10 5 7 7 10 7 8 Baylor B12 \n 9 11 8 13 11 11 12 9 14 9 9 Georgia Tech ACC \n 13 10 13 11 8 9 10 11 9 10 10 Mississippi SEC \n"
# By default, the type cannot be guessed from the extension
# This is a space-separated vaules file (ssv)
(massey <- read(readr_example("massey-rating.txt"), type = "ssv"))
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> UCC = col_double(),
#> PAY = col_double(),
#> LAZ = col_double(),
#> KPK = col_double(),
#> RT = col_double(),
#> COF = col_double(),
#> BIH = col_double(),
#> DII = col_double(),
#> ENG = col_double(),
#> ACU = col_double(),
#> Rank = col_double(),
#> Team = col_character(),
#> Conf = col_character()
#> )
#> Warning: 10 parsing failures.
#> row col expected actual file
#> 1 -- 13 columns 15 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 2 -- 13 columns 14 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 3 -- 13 columns 14 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 4 -- 13 columns 14 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 5 -- 13 columns 15 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> ... ... .......... .......... .................................................................
#> See problems(...) for more details.
#> # A data.trame: [10 × 13]
#> UCC PAY LAZ KPK RT COF BIH DII ENG ACU Rank Team Conf
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 1 1 1 1 1 1 1 1 1 1 1 Ohio St
#> 2 2 2 2 2 2 2 2 2 4 2 2 Oreg… P12
#> 3 3 4 3 4 3 4 3 4 2 3 3 Alab… SEC
#> 4 4 3 4 3 4 3 5 3 3 4 4 TCU B12
#> 5 6 6 6 5 5 7 6 5 6 11 5 Mich… St
#> 6 7 7 7 6 7 6 11 8 7 8 6 Geor… SEC
#> 7 5 5 5 7 6 8 4 6 5 5 7 Flor… St
#> 8 8 8 9 9 10 5 7 7 10 7 8 Bayl… B12
#> 9 9 11 8 13 11 11 12 9 14 9 9 Geor… Tech
#> 10 13 10 13 11 8 9 10 11 9 10 10 Miss… SEC
# or ...
(massey <- read$ssv(readr_example("massey-rating.txt")))
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> UCC = col_double(),
#> PAY = col_double(),
#> LAZ = col_double(),
#> KPK = col_double(),
#> RT = col_double(),
#> COF = col_double(),
#> BIH = col_double(),
#> DII = col_double(),
#> ENG = col_double(),
#> ACU = col_double(),
#> Rank = col_double(),
#> Team = col_character(),
#> Conf = col_character()
#> )
#> Warning: 10 parsing failures.
#> row col expected actual file
#> 1 -- 13 columns 15 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 2 -- 13 columns 14 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 3 -- 13 columns 14 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 4 -- 13 columns 14 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> 5 -- 13 columns 15 columns '/home/runner/work/_temp/Library/readr/extdata/massey-rating.txt'
#> ... ... .......... .......... .................................................................
#> See problems(...) for more details.
#> # A data.trame: [10 × 13]
#> UCC PAY LAZ KPK RT COF BIH DII ENG ACU Rank Team Conf
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
#> 1 1 1 1 1 1 1 1 1 1 1 1 Ohio St
#> 2 2 2 2 2 2 2 2 2 4 2 2 Oreg… P12
#> 3 3 4 3 4 3 4 3 4 2 3 3 Alab… SEC
#> 4 4 3 4 3 4 3 5 3 3 4 4 TCU B12
#> 5 6 6 6 5 5 7 6 5 6 11 5 Mich… St
#> 6 7 7 7 6 7 6 11 8 7 8 6 Geor… SEC
#> 7 5 5 5 7 6 8 4 6 5 5 7 Flor… St
#> 8 8 8 9 9 10 5 7 7 10 7 8 Bayl… B12
#> 9 9 11 8 13 11 11 12 9 14 9 9 Geor… Tech
#> 10 13 10 13 11 8 9 10 11 9 10 10 Miss… SEC
(epa <- read$ssv(readr_example("epa78.txt"), col_names = FALSE))
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> X1 = col_character(),
#> X2 = col_character(),
#> X3 = col_character(),
#> X4 = col_character(),
#> X5 = col_double()
#> )
#> Warning: 17 parsing failures.
#> row col expected actual file
#> 2 -- 5 columns 10 columns '/home/runner/work/_temp/Library/readr/extdata/epa78.txt'
#> 3 -- 5 columns 6 columns '/home/runner/work/_temp/Library/readr/extdata/epa78.txt'
#> 4 -- 5 columns 3 columns '/home/runner/work/_temp/Library/readr/extdata/epa78.txt'
#> 5 -- 5 columns 8 columns '/home/runner/work/_temp/Library/readr/extdata/epa78.txt'
#> 6 -- 5 columns 8 columns '/home/runner/work/_temp/Library/readr/extdata/epa78.txt'
#> ... ... ......... .......... .........................................................
#> See problems(...) for more details.
#> # A data.trame: [20 × 5]
#> X1 X2 X3 X4 X5
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 ALFA ROMEO ALFA ROMEO 78010003
#> 2 ALFETTA 03 81 8 74
#> 3 SPIDER 2000 01 SPIDER 2000
#> 4 AMC AMC 78020002 NA NA
#> 5 GREMLIN 03 79 9 79
#> 6 PACER 04 89 11 89
#> 7 PACER WAGON 07 90 26
#> 8 CONCORD 04 88 12 90
#> 9 CONCORD WAGON 07 91 30
#> 10 MATADOR COUPE 05 97 14
#> 11 MATADOR SEDAN 06 110 20
#> 12 MATADOR WAGON 09 112 50
#> 13 ASTON MARTIN ASTON MARTIN 78040002
#> 14 ASTON MARTIN ASTON MARTIN 78040053
#> 15 AUDI AUDI 78050002 NA NA
#> 16 FOX 03 84 11 84
#> 17 FOX WAGON 07 83 40
#> 18 5000 04 90 15 90
#> 19 AVANTI AVANTI 78065002 NA NA
#> 20 AVANTI II 02 75 8
(example_log <- read(readr_example("example.log")))
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> X1 = col_character(),
#> X2 = col_logical(),
#> X3 = col_character(),
#> X4 = col_character(),
#> X5 = col_character(),
#> X6 = col_double(),
#> X7 = col_double()
#> )
#> # A data.trame: [2 × 7]
#> X1 X2 X3 X4 X5 X6 X7
#> <chr> <lgl> <chr> <chr> <chr> <dbl> <dbl>
#> 1 172.21.13.45 NA "Microsoft\\JohnDoe" 08/Apr/2001:17:39:0… GET … 200 3401
#> 2 127.0.0.1 NA "frank" 10/Oct/2000:13:55:3… GET … 200 2326
# There are different ways to specify columns for fixed-width files (fwf)
# See ?read_fwf in package readr
(fwf_sample <- read$fwf(readr_example("fwf-sample.txt"),
col_positions = fwf_cols(name = 20, state = 10, ssn = 12)))
#> Rows: 3 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#>
#> chr (3): name, state, ssn
#>
#> 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 data.trame: [3 × 3]
#> name state ssn
#> <chr> <chr> <chr>
#> 1 John Smith WA 418-Y11-4111
#> 2 Mary Hartford CA 319-Z19-4341
#> 3 Evan Nolan IL 219-532-c301
# Various examples of Excel datasets from readxl
library(readxl)
(xl <- read(readxl_example("datasets.xls")))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [32 × 11]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
(xl <- read(readxl_example("datasets.xlsx"), sheet = "mtcars"))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [32 × 11]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # ℹ 22 more rows
(xl <- read(readxl_example("datasets.xlsx"), sheet = 3))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [1,000 × 5]
#> lat long depth mag stations
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 -20.4 182. 562 4.8 41
#> 2 -20.6 181. 650 4.2 15
#> 3 -26 184. 42 5.4 43
#> 4 -18.0 182. 626 4.1 19
#> 5 -20.4 182. 649 4 11
#> 6 -19.7 184. 195 4 12
#> 7 -11.7 166. 82 4.8 43
#> 8 -28.1 182. 194 4.4 15
#> 9 -28.7 182. 211 4.7 35
#> 10 -17.5 180. 622 4.3 19
#> # ℹ 990 more rows
# Accomodate a column with disparate types via col_type = "list"
(clip <- read(readxl_example("clippy.xls"), col_types = c("text", "list")))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [4 × 2]
#> name value
#> <chr> <list>
#> 1 Name <chr [1]>
#> 2 Species <chr [1]>
#> 3 Approx date of death <dttm [1]>
#> 4 Weight in grams <dbl [1]>
(clip <- read(readxl_example("clippy.xlsx"), col_types = c("text", "list")))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [4 × 2]
#> name value
#> <chr> <list>
#> 1 Name <chr [1]>
#> 2 Species <chr [1]>
#> 3 Approx date of death <dttm [1]>
#> 4 Weight in grams <dbl [1]>
tibble::deframe(clip)
#> $Name
#> [1] "Clippy"
#>
#> $Species
#> [1] "paperclip"
#>
#> $`Approx date of death`
#> [1] "2007-01-01 UTC"
#>
#> $`Weight in grams`
#> [1] 0.9
#>
# Read from a specific range in a sheet
(xl <- read(readxl_example("datasets.xlsx"), range = "mtcars!B1:D5"))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [4 × 3]
#> cyl disp hp
#> <dbl> <dbl> <dbl>
#> 1 6 160 110
#> 2 6 160 110
#> 3 4 108 93
#> 4 6 258 110
(deaths <- read(readxl_example("deaths.xls"), range = cell_rows(5:15)))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [10 × 6]
#> Name Profession Age `Has kids` `Date of birth` `Date of death`
#> <chr> <chr> <dbl> <lgl> <dttm> <dttm>
#> 1 David Bo… musician 69 TRUE 1947-01-08 00:00:00 2016-01-10 00:00:00
#> 2 Carrie F… actor 60 TRUE 1956-10-21 00:00:00 2016-12-27 00:00:00
#> 3 Chuck Be… musician 90 TRUE 1926-10-18 00:00:00 2017-03-18 00:00:00
#> 4 Bill Pax… actor 61 TRUE 1955-05-17 00:00:00 2017-02-25 00:00:00
#> 5 Prince musician 57 TRUE 1958-06-07 00:00:00 2016-04-21 00:00:00
#> 6 Alan Ric… actor 69 FALSE 1946-02-21 00:00:00 2016-01-14 00:00:00
#> 7 Florence… actor 82 TRUE 1934-02-14 00:00:00 2016-11-24 00:00:00
#> 8 Harper L… author 89 FALSE 1926-04-28 00:00:00 2016-02-19 00:00:00
#> 9 Zsa Zsa … actor 99 TRUE 1917-02-06 00:00:00 2016-12-18 00:00:00
#> 10 George M… musician 53 FALSE 1963-06-25 00:00:00 2016-12-25 00:00:00
(deaths <- read(readxl_example("deaths.xlsx"), range = cell_rows(5:15)))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [10 × 6]
#> Name Profession Age `Has kids` `Date of birth` `Date of death`
#> <chr> <chr> <dbl> <lgl> <dttm> <dttm>
#> 1 David Bo… musician 69 TRUE 1947-01-08 00:00:00 2016-01-10 00:00:00
#> 2 Carrie F… actor 60 TRUE 1956-10-21 00:00:00 2016-12-27 00:00:00
#> 3 Chuck Be… musician 90 TRUE 1926-10-18 00:00:00 2017-03-18 00:00:00
#> 4 Bill Pax… actor 61 TRUE 1955-05-17 00:00:00 2017-02-25 00:00:00
#> 5 Prince musician 57 TRUE 1958-06-07 00:00:00 2016-04-21 00:00:00
#> 6 Alan Ric… actor 69 FALSE 1946-02-21 00:00:00 2016-01-14 00:00:00
#> 7 Florence… actor 82 TRUE 1934-02-14 00:00:00 2016-11-24 00:00:00
#> 8 Harper L… author 89 FALSE 1926-04-28 00:00:00 2016-02-19 00:00:00
#> 9 Zsa Zsa … actor 99 TRUE 1917-02-06 00:00:00 2016-12-18 00:00:00
#> 10 George M… musician 53 FALSE 1963-06-25 00:00:00 2016-12-25 00:00:00
(type_me <- read(readxl_example("type-me.xls"), sheet = "logical_coercion",
col_types = c("logical", "text")))
#> New names:
#> • `` -> `...1`
#> Warning: Expecting logical in A5 / R5C1: got a date
#> Warning: Expecting logical in A8 / R8C1: got 'cabbage'
#> # A data.trame: [10 × 2]
#> `maybe boolean?` description
#> <lgl> <chr>
#> 1 NA "empty"
#> 2 FALSE "0 (numeric)"
#> 3 TRUE "1 (numeric)"
#> 4 NA "datetime"
#> 5 TRUE "boolean true"
#> 6 FALSE "boolean false"
#> 7 NA "\"cabbage\""
#> 8 TRUE "the string \"true\""
#> 9 FALSE "the letter \"F\""
#> 10 FALSE "\"False\" preceded by single quote"
(type_me <- read(readxl_example("type-me.xlsx"), sheet = "numeric_coercion",
col_types = c("numeric", "text")))
#> New names:
#> • `` -> `...1`
#> Warning: Coercing boolean to numeric in A3 / R3C1
#> Warning: Coercing boolean to numeric in A4 / R4C1
#> Warning: Expecting numeric in A5 / R5C1: got a date
#> Warning: Coercing text to numeric in A6 / R6C1: '123456'
#> Warning: Expecting numeric in A8 / R8C1: got 'cabbage'
#> # A data.trame: [7 × 2]
#> `maybe numeric?` explanation
#> <dbl> <chr>
#> 1 NA "empty"
#> 2 1 "boolean true"
#> 3 0 "boolean false"
#> 4 40534 "datetime"
#> 5 123456 "the string \"123456\""
#> 6 123456 "the number 123456"
#> 7 NA "\"cabbage\""
(type_me <- read(readxl_example("type-me.xls"), sheet = "date_coercion",
col_types = c("date", "text")))
#> New names:
#> • `` -> `...1`
#> Warning: Expecting date in A5 / R5C1: got boolean
#> Warning: Expecting date in A6 / R6C1: got 'cabbage'
#> Warning: Coercing numeric to date in A7 / R7C1
#> Warning: Coercing numeric to date in A8 / R8C1
#> # A data.trame: [7 × 2]
#> `maybe a datetime?` explanation
#> <dttm> <chr>
#> 1 NA "empty"
#> 2 2016-05-23 00:00:00 "date only format"
#> 3 2016-04-28 11:30:00 "date and time format"
#> 4 NA "boolean true"
#> 5 NA "\"cabbage\""
#> 6 1904-01-05 07:12:00 "4.3 (numeric)"
#> 7 2012-01-02 00:00:00 "another numeric"
(type_me <- read(readxl_example("type-me.xlsx"), sheet = "text_coercion",
col_types = c("text", "text")))
#> New names:
#> • `` -> `...1`
#> # A data.trame: [6 × 2]
#> text explanation
#> <chr> <chr>
#> 1 NA "empty"
#> 2 cabbage "\"cabbage\""
#> 3 TRUE "boolean true"
#> 4 1.3 "numeric"
#> 5 41175 "datetime"
#> 6 36436153 "another numeric"
(xl <- read(readxl_example("geometry.xls"), col_names = FALSE))
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> • `` -> `...3`
#> # A data.trame: [4 × 3]
#> ...1 ...2 ...3
#> <chr> <chr> <chr>
#> 1 B3 C3 D3
#> 2 B4 C4 D4
#> 3 B5 C5 D5
#> 4 B6 C6 D6
(xl <- read(readxl_example("geometry.xlsx"), range = cell_rows(4:8)))
#> # A data.trame: [4 × 3]
#> B4 C4 D4
#> <chr> <chr> <chr>
#> 1 B5 C5 D5
#> 2 B6 C6 D6
#> 3 NA NA NA
#> 4 NA NA NA
# Various examples from haven
library(haven)
haven_example <- function(path)
system.file("examples", path, package = "haven", mustWork = TRUE)
(iris2 <- read(haven_example("iris.dta"))) # Stata v. 8-14
#> # A data.trame: [150 × 5]
#> sepallength sepalwidth petallength petalwidth species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.10 3.5 1.40 0.200 setosa
#> 2 4.90 3 1.40 0.200 setosa
#> 3 4.70 3.20 1.30 0.200 setosa
#> 4 4.60 3.10 1.5 0.200 setosa
#> 5 5 3.60 1.40 0.200 setosa
#> 6 5.40 3.90 1.70 0.400 setosa
#> 7 4.60 3.40 1.40 0.300 setosa
#> 8 5 3.40 1.5 0.200 setosa
#> 9 4.40 2.90 1.40 0.200 setosa
#> 10 4.90 3.10 1.5 0.100 setosa
#> # ℹ 140 more rows
(iris2 <- read(haven_example("iris.sav"))) # SPSS, TODO: labelled -> factor?
#> # A data.trame: [150 × 5]
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> <dbl> <dbl> <dbl> <dbl> <dbl+lbl>
#> 1 5.1 3.5 1.4 0.2 1 [setosa]
#> 2 4.9 3 1.4 0.2 1 [setosa]
#> 3 4.7 3.2 1.3 0.2 1 [setosa]
#> 4 4.6 3.1 1.5 0.2 1 [setosa]
#> 5 5 3.6 1.4 0.2 1 [setosa]
#> 6 5.4 3.9 1.7 0.4 1 [setosa]
#> 7 4.6 3.4 1.4 0.3 1 [setosa]
#> 8 5 3.4 1.5 0.2 1 [setosa]
#> 9 4.4 2.9 1.4 0.2 1 [setosa]
#> 10 4.9 3.1 1.5 0.1 1 [setosa]
#> # ℹ 140 more rows
(pbc <- read(data_example("pbc.por"))) # SPSS, POR format
#> # A data.trame: [418 × 20]
#> AGE ALB ALKPHOS ASCITES BILI CHOL EDEMA EDTRT HEPMEG TIME PLATELET
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 58.8 2.6 1718 1 14.5 261 1 1 1 400 190
#> 2 56.4 4.14 7395. 0 1.1 302 0 0 1 4500 221
#> 3 70.1 3.48 516 0 1.4 176 1 0.5 0 1012 151
#> 4 54.7 2.54 6122. 0 1.8 244 1 0.5 1 1925 183
#> 5 38.1 3.53 671 0 3.4 279 0 0 1 1504 136
#> 6 66.3 3.98 944 0 0.8 248 0 0 1 2503 -9
#> 7 55.5 4.09 824 0 1 322 0 0 1 1832 204
#> 8 53.1 4 4651. 0 0.3 280 0 0 0 2466 373
#> 9 42.5 3.08 2276 0 3.2 562 0 0 0 2400 251
#> 10 70.6 2.74 918 1 12.6 200 1 1 0 51 302
#> # ℹ 408 more rows
#> # ℹ 9 more variables: PROTIME <dbl>, SEX <dbl>, SGOT <dbl>, SPIDERS <dbl>,
#> # STAGE <dbl>, STATUS <dbl>, TRT <dbl>, TRIG <dbl>, COPPER <dbl>
(iris2 <- read$sas(haven_example("iris.sas7bdat"))) # SAS file
#> # A data.trame: [150 × 5]
#> Sepal_Length Sepal_Width Petal_Length Petal_Width Species
#> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 5.1 3.5 1.4 0.2 setosa
#> 2 4.9 3 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5 3.6 1.4 0.2 setosa
#> 6 5.4 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> # ℹ 140 more rows
(afalfa <- read(data_example("afalfa.xpt"))) # SAS transport file
#> # A data.trame: [40 × 6]
#> POP SAMPLE REP SEEDWT HARV1 HARV2
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 min 0 1 64 172. 180.
#> 2 min 1 1 54 138. 151.
#> 3 min 2 1 40 146. 129.
#> 4 min 3 1 45 170. 191.
#> 5 min 4 1 64 125. 173.
#> 6 MAX 5 1 75 179 235.
#> 7 MAX 6 1 45 166. 174.
#> 8 MAX 7 1 63 170. 156.
#> 9 MAX 8 1 65 193. 178.
#> 10 MAX 9 1 59 186. 179.
#> # ℹ 30 more rows
# Note that where completion is available, you have a completion list of file
# format after typing read$<tab>
# }