R/melt_table.R
melt_table.RdFor certain non-rectangular data formats, it can be useful to parse the data into a melted format where each row represents a single token.
melt_table() and melt_table2() are designed to read the type of textual
data where each column is separated by one (or more) columns of space.
melt_table2() allows any number of whitespace characters between columns,
and the lines can be of different lengths.
melt_table() is more strict, each line must be the same length,
and each field is in the same position in every line. It first finds empty
columns and then parses like a fixed width file.
melt_table(
file,
locale = default_locale(),
na = "NA",
skip = 0,
n_max = Inf,
guess_max = min(n_max, 1000),
progress = show_progress(),
comment = "",
skip_empty_rows = FALSE
)
melt_table2(
file,
locale = default_locale(),
na = "NA",
skip = 0,
n_max = Inf,
progress = show_progress(),
comment = "",
skip_empty_rows = FALSE
)Either a path to a file, a connection, or literal data (either a single string or a raw vector).
Files ending in .gz, .bz2, .xz, or .zip will
be automatically uncompressed. Files starting with http://,
https://, ftp://, or ftps:// will be automatically
downloaded. Remote gz files can also be automatically downloaded and
decompressed.
Literal data is most useful for examples and tests. To be recognised as
literal data, the input must be either wrapped with I(), be a string
containing at least one new line, or be a vector containing at least one
string with a new line.
Using a value of clipboard() will read from the system clipboard.
The locale controls defaults that vary from place to place.
The default locale is US-centric (like R), but you can use
locale() to create your own locale that controls things like
the default time zone, encoding, decimal mark, big mark, and day/month
names.
Character vector of strings to interpret as missing values. Set this
option to character() to indicate no missing values.
Number of lines to skip before reading data.
Maximum number of lines to read.
Maximum number of lines to use for guessing column types.
See vignette("column-types", package = "readr") for more details.
Display a progress bar? By default it will only display
in an interactive session and not while knitting a document. The automatic
progress bar can be disabled by setting option readr.show_progress to
FALSE.
A string used to identify comments. Any text after the comment characters will be silently ignored.
Should blank rows be ignored altogether? i.e. If this
option is TRUE then blank rows will not be represented at all. If it is
FALSE then they will be represented by NA values in all the columns.
A tibble() of four columns:
row, the row that the token comes from in the original file
col, the column that the token comes from in the original file
data_type, the data type of the token, e.g. "integer", "character",
"date", guessed in a similar way to the guess_parser() function.
value, the token itself as a character string, unchanged from its
representation in the original file.
If there are parsing problems, a warning tells you
how many, and you can retrieve the details with problems().
melt_fwf() to melt fixed width files where each column
is not separated by whitespace. melt_fwf() is also useful for reading
tabular data with non-standard formatting. readr::read_table() is the
conventional way to read tabular data from whitespace-separated files.
# One corner from http://www.masseyratings.com/cf/compare.htm
massey <- meltr_example("massey-rating.txt")
cat(readLines(massey))
#> UCC PAY LAZ KPK RT COF BIH DII ENG ACU Rank Team Conf 1 1 1 1 1 1 1 1 1 1 1 Ohio St B10 2 2 2 2 2 2 2 2 4 2 2 Oregon P12 3 4 3 4 3 4 3 4 2 3 3 Alabama SEC 4 3 4 3 4 3 5 3 3 4 4 TCU B12 6 6 6 5 5 7 6 5 6 11 5 Michigan St B10 7 7 7 6 7 6 11 8 7 8 6 Georgia SEC 5 5 5 7 6 8 4 6 5 5 7 Florida St ACC 8 8 9 9 10 5 7 7 10 7 8 Baylor B12 9 11 8 13 11 11 12 9 14 9 9 Georgia Tech ACC 13 10 13 11 8 9 10 11 9 10 10 Mississippi SEC
melt_table(massey)
#> # A tibble: 143 × 4
#> row col data_type value
#> <dbl> <dbl> <chr> <chr>
#> 1 1 1 character UCC
#> 2 1 2 character PAY
#> 3 1 3 character LAZ
#> 4 1 4 character KPK
#> 5 1 5 character RT
#> 6 1 6 character COF
#> 7 1 7 character BIH
#> 8 1 8 character DII
#> 9 1 9 character ENG
#> 10 1 10 character ACU
#> # ℹ 133 more rows
# Sample of 1978 fuel economy data from
# http://www.fueleconomy.gov/feg/epadata/78data.zip
epa <- meltr_example("epa78.txt")
writeLines(readLines(epa))
#> ALFA ROMEO ALFA ROMEO 78010003
#> ALFETTA 03 81 8 74 7 89 9 ALFETTA 78010053
#> SPIDER 2000 01 SPIDER 2000 78010103
#> AMC AMC 78020002
#> GREMLIN 03 79 9 79 9 GREMLIN 78020053
#> PACER 04 89 11 89 11 PACER 78020103
#> PACER WAGON 07 90 26 91 26 PACER WAGON 78020153
#> CONCORD 04 88 12 90 11 90 11 83 16 CONCORD 78020203
#> CONCORD WAGON 07 91 30 91 30 CONCORD WAGON 78020253
#> MATADOR COUPE 05 97 14 97 14 MATADOR COUPE 78020303
#> MATADOR SEDAN 06 110 20 110 20 MATADOR SEDAN 78020353
#> MATADOR WAGON 09 112 50 112 50 MATADOR WAGON 78020403
#> ASTON MARTIN ASTON MARTIN 78040002
#> ASTON MARTIN ASTON MARTIN 78040053
#> AUDI AUDI 78050002
#> FOX 03 84 11 84 11 84 11 FOX 78050053
#> FOX WAGON 07 83 40 83 40 FOX WAGON 78050103
#> 5000 04 90 15 90 15 5000 78050153
#> AVANTI AVANTI 78065002
#> AVANTI II 02 75 8 75 8 AVANTI II 78065053
melt_table(epa)
#> # A tibble: 240 × 4
#> row col data_type value
#> <dbl> <dbl> <chr> <chr>
#> 1 1 1 character "ALFA ROMEO"
#> 2 1 2 empty ""
#> 3 1 3 empty ""
#> 4 1 4 empty ""
#> 5 1 5 empty ""
#> 6 1 6 empty ""
#> 7 1 7 empty ""
#> 8 1 8 empty ""
#> 9 1 9 empty ""
#> 10 1 10 empty ""
#> # ℹ 230 more rows