The blaise package aims to provide an interface between the blaise software and R by enabling the reading and writing of blaise datafile sin a transparent manner. The aim is for an average user to be able to read or write such a datafile with a single command. Defaults are always set in such a way that the data is not changed if a user reads a datafile to a dataframe and immediately writes it to a blaise datafile afterwards.
For the purpose of this vignette we need to create some small examples.
model1 = "
DATAMODEL Test
FIELDS
A : STRING[1]
B : INTEGER[1]
C : REAL[3,1]
D : REAL[3]
E : (Male, Female)
F : 1..20
G : 1.00..100.00
ENDMODEL
"
model2 = "
DATAMODEL Test
FIELDS
A : STRING[1]
B : INTEGER[1]
C : REAL[3,1]
D : REAL[3]
E : (Male (1), Female (2), Unknown (9))
F : 1..20
G : 1.00..100.00
ENDMODEL
"
data1 =
"A12.30.11 1 1.00
B23.41.2210 20.20
C34.50.0120100.00"
data2 =
"A12,30,11 1 1,00
B23,41,2210 20,20
C34,50,0920100,00"
blafile1 = tempfile('testbla1', fileext = '.bla')
datafile1 = tempfile('testdata1', fileext = '.asc')
blafile2 = tempfile('testbla2', fileext = '.bla')
datafile2 = tempfile('testdata2', fileext = '.asc')
writeLines(data1, con = datafile1)
writeLines(model1, con = blafile1)
writeLines(data2, con = datafile2)
writeLines(model2, con = blafile2)These file can then be simply read to a dataframe by using
read_fwf_blaise.
df = read_fwf_blaise(datafile1, blafile1)
df
#> # A tibble: 3 × 7
#> A B C D E F G
#> <chr> <int> <dbl> <dbl> <fct> <int> <dbl>
#> 1 A 1 2.3 0.1 Male 1 1
#> 2 B 2 3.4 1.2 Female 10 20.2
#> 3 C 3 4.5 0 Male 20 100If you try to read the second datafile with model you will however get some warnings and the resulting dataframe will not look as expected.
df_comma = read_fwf_blaise(datafile2, blafile2)
#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
#> e.g.:
#> dat <- vroom(...)
#> problems(dat)
df_comma
#> # A tibble: 3 × 7
#> A B C D E F G
#> <chr> <int> <dbl> <dbl> <fct> <int> <dbl>
#> 1 A 1 NA NA 1 1 NA
#> 2 B 2 NA NA 2 10 NA
#> 3 C 3 NA NA 9 20 NAThe blaise package uses readr to actually read the file into memory.
Reading problems can therefore be analysed by using
readr::problems()
readr::problems(df_comma)
#> # A tibble: 9 × 5
#> row col expected actual file
#> <int> <int> <chr> <chr> <chr>
#> 1 1 3 a double 2,3 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 2 1 3 a double 3,4 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 3 1 3 a double 4,5 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 4 1 4 a double 0,1 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 5 1 4 a double 1,2 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 6 1 4 a double 0,0 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 7 1 7 a double 1,00 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 8 1 7 a double 20,20 /tmp/Rtmp8qJr4B/testdata213897ddc0370.asc
#> 9 1 7 a double 100,00 /tmp/Rtmp8qJr4B/testdata213897ddc0370.ascThese results are somewhat easier to parse but still hard to
interpret. In this case it is clear that the comma is an unexpected
character. This is because the locale is set to expect “.” as a decimal
separator by default. This setting (and others, such as date format,
encoding, etc.) can be changed by supplying a readr locale object using
readr::locale().
The second datamodel contains a numbered enum and is therefore read
as a factor with number labels. By interpreting it thus the file will be
written out exactly the same as can be seen later. This behaviour can be
overwritten by using the option numbered_enum = FALSE. If
the resulting dataframe is written back to blaise using
write_fwf_blaise it will however write the integers in the
set 1,2,3 instead of 1,2,9.
Finally, instead of reading the file into memory, a LaF object can be
returned instead. For details see the documentation for the
LaF package.
df_laf = read_fwf_blaise(datafile1, blafile1, output = "laf")
df_laf
#> Connection to fixed width ASCII file
#> Filename: /tmp/Rtmp8qJr4B/testdata113896a3fabf5.asc
#> Column 1: name = A, type = string, internal type = character, column width = 1
#> Column 2: name = B, type = integer, internal type = integer, column width = 1
#> Column 3: name = C, type = double, internal type = numeric, column width = 3
#> Column 4: name = D, type = double, internal type = numeric, column width = 3
#> Column 5: name = E, type = integer, internal type = integer, column width = 1
#> Column 6: name = F, type = integer, internal type = integer, column width = 2
#> Column 7: name = G, type = double, internal type = numeric, column width = 6
df_laf$E
#> Column number 5 of fixed width ASCII file
#> Filename: /tmp/Rtmp8qJr4B/testdata113896a3fabf5.asc
#> Column name = E
#> Column type = integer
#> Internal type = integer
#> Column width = 1
#> Showing first 10 elements:
#> [1] Male Female Male
#> Levels: Male FemaleDataframes can also be written out as blaise datafiles. By default this will also write a corresponding blaise datamodel with the same filename and a .bla extension
outfile = tempfile(fileext = ".asc")
outbla = sub(".asc", ".bla", outfile)
write_fwf_blaise(df, outfile)
readr::read_lines(outfile)
#> [1] "A12.30.11 1 1.0" "B23.41.2210 20.2" "C34.50.0120100.0"
readr::read_lines(outbla)
#> [1] "DATAMODEL df" "FIELDS" " A : STRING[1]" " B : INTEGER[1]"
#> [5] " C : REAL[3]" " D : REAL[3]" " E : (Male," " Female)"
#> [9] " F : INTEGER[2]" " G : REAL[5]" "ENDMODEL"As can be seen, this is equivalent to the input data and model. An
optional name for the datamodel can be given with
output_model or the writing of a model can be entirely
suppressed by using write_model = FALSE. For further
options see the help file. Implicit conversions from R types to blaise
types are as follows:
Note that information about the labels in the datamodel is lost for
the numbered enum type. One way to solve this is by providing an
existing datamodel and using write_fwf_blaise_with_model as
follows.
outfile_model = tempfile(fileext = ".asc")
write_fwf_blaise_with_model(df_enum, outfile_model, blafile2)
readr::read_lines(outfile_model)
#> [1] "A12.30.11 1 1.00" "B23.41.2210 20.20" "C34.50.0920100.00"This results in the same datafile here, but ensures conformity to the datamodel. One could for instance also force a different model on the same data.
model3 = "
DATAMODEL Test
FIELDS
A : (A, B, C)
B : (Male (1), Female (2), Unknown (3))
ENDMODEL
"
blafile3 = tempfile('testbla3', fileext = '.bla')
writeLines(model3, con = blafile3)
outfile_new_model = tempfile(fileext = ".asc")
write_fwf_blaise_with_model(df_enum, outfile_new_model, blafile3)
readr::read_lines(outfile_new_model)
#> [1] "11" "22" "33"This explicitly checks for conformity, so if the data can not be converted an error will be shown and nothing will be written to disk.
model4 = "
DATAMODEL Test
FIELDS
A : (A, B)
B : (Male (1), Female (2))
ENDMODEL
"
blafile4 = tempfile('testbla4', fileext = '.bla')
writeLines(model4, con = blafile4)
outfile_wrong_model = tempfile(fileext = ".asc")
write_fwf_blaise_with_model(df_enum, outfile_wrong_model, blafile4)
#> Error in cast_type(variables(model)[zonder_dummy][[i]], df[[i]]): numbers in dataframe column (A;B;C) do not correspond to range of indices in model (A;B) for variable A