libr (version 1.2.1)

specs: Create an Import Spec Collection

Description

A function to capture a set of import specifications for a directory of data files. These specs can be used on the libname function to correctly assign the data types for imported data files. The import engines will guess at the data types for any columns that are not explicitly defined in the import specifications. Import specifications are defined with the import_spec function. The import spec syntax is the same for all data engines.

Note that the na and trim_ws parameters on the specs function will be applied globally to all files in the library. These global settings can be overridden on the import_spec for any particular data file.

Also note that the specs collection is defined as an object so it can be stored and reused. See the write.specs and read.specs functions for additional information on saving specs.

Usage

specs(..., na = c("", "NA"), trim_ws = TRUE)

Arguments

...

Named input specs. The name should correspond to the file name, without the file extension. The spec is defined as an import_spec object. See the import_spec function for additional information on parameters for that object.

na

A vector of values to be treated as NA. For example, the vector c('', ' ') will cause empty strings and single blanks to be converted to NA values. For most file types, empty strings and the string 'NA' ('', 'NA') are considered NA. For SAS<U+00AE> datasets and transport files, a single blank and a single dot c(" ", ".") are considered NA. The value of the na parameter on the specs function can be overridden by the na parameter on the import_spec function.

trim_ws

Whether or not to trim white space from the input data values. Valid values are TRUE, and FALSE. Default is TRUE. The value of the trim_ws parameter on the specs function can be overridden by the trim_ws parameter on the import_spec function.

Value

The import specifications object.

See Also

libname to create a data library, dictionary for generating a data dictionary, and import_spec for additional information on defining an import spec.

Other specs: import_spec(), print.specs(), read.specs(), write.specs()

Examples

# NOT RUN {
library(readr)

# Create temp path
tmp <- file.path(tempdir(), "mtcars.csv")

# Create data for illustration purposes
df <- data.frame(vehicle = rownames(mtcars), mtcars[c("mpg", "cyl", "disp")],
                 stringsAsFactors = FALSE)

# Kill rownames
rownames(df) <- NULL

# Add some columns
df <- datastep(df[1:10, ], {

        recdt <- "10JUN1974"

        if (mpg >= 20)
          mpgcat <- "High"
        else 
          mpgcat <- "Low"
      
        if (cyl == 8)
          cyl8 <- TRUE
  })
  
df
#              vehicle  mpg cyl  disp     recdt mpgcat cyl8
# 1          Mazda RX4 21.0   6 160.0 10JUN1974   High   NA
# 2      Mazda RX4 Wag 21.0   6 160.0 10JUN1974   High   NA
# 3         Datsun 710 22.8   4 108.0 10JUN1974   High   NA
# 4     Hornet 4 Drive 21.4   6 258.0 10JUN1974   High   NA
# 5  Hornet Sportabout 18.7   8 360.0 10JUN1974    Low TRUE
# 6            Valiant 18.1   6 225.0 10JUN1974    Low   NA
# 7         Duster 360 14.3   8 360.0 10JUN1974    Low TRUE
# 8          Merc 240D 24.4   4 146.7 10JUN1974   High   NA
# 9           Merc 230 22.8   4 140.8 10JUN1974   High   NA
# 10          Merc 280 19.2   6 167.6 10JUN1974    Low   NA

# Save to temp directory for this example
write_csv(df, tmp)

## Start Example ##

# Define import spec
spcs <- specs(mtcars = import_spec(vehicle = "character",
                                   cyl = "integer",
                                   recdt = "date=%d%b%Y",
                                   mpgcat = "guess",
                                   cyl8 = "logical"))
                                   
# Create library
libname(dat, tempdir(), "csv", import_specs = spcs)
# $mtcars
# library 'dat': 1 items
# - attributes: csv not loaded
# - path: C:\Users\User\AppData\Local\Temp\RtmpqAMV6L
# - items:
#     Name Extension Rows Cols   Size        LastModified
# 1 mtcars       csv   10    7 9.3 Kb 2020-11-29 09:47:52

# View data types
dictionary(dat)
# # A tibble: 7 x 10
#   Name   Column  Class     Label Description Format Width Justify  Rows   NAs
#   <chr>  <chr>   <chr>     <chr> <chr>       <lgl>  <int> <chr>   <int> <int>
# 1 mtcars vehicle character NA    NA          NA        17 NA         10     0
# 2 mtcars mpg     numeric   NA    NA          NA        NA NA         10     0
# 3 mtcars cyl     integer   NA    NA          NA        NA NA         10     0
# 4 mtcars disp    numeric   NA    NA          NA        NA NA         10     0
# 5 mtcars mpgcat  character NA    NA          NA         4 NA         10     0
# 6 mtcars recdt   Date      NA    NA          NA        NA NA         10     0
# 7 mtcars cyl8    logical   NA    NA          NA        NA NA         10     8

# Clean up
lib_delete(dat)
# }