Low-level file read and write — low-level-read-write • ieegio

Interfaces to read from or write to files with common formats.

Usage

io_read_fst(
  con,
  method = c("proxy", "data_table", "data_frame", "header_only"),
  ...,
  old_format = FALSE
)

io_write_fst(x, con, compress = 50, ...)

io_read_ini(con, ...)

io_read_json(con, ...)

io_write_json(
  x,
  con = stdout(),
  ...,
  digits = ceiling(-log10(.Machine$double.eps)),
  pretty = TRUE,
  serialize = TRUE
)

io_read_mat(
  con,
  method = c("auto", "R.matlab", "pymatreader", "mat73"),
  verbose = TRUE,
  on_convert_error = c("warning", "error", "ignore"),
  ...
)

io_write_mat(x, con, method = c("R.matlab", "scipy"), ...)

io_read_yaml(con, ...)

io_write_yaml(x, con, ..., sorted = FALSE)

Arguments

con

connection or file

method

method to read table. For 'fst', the choices are

'proxy': do not read data to memory, query the table when needed;
'data_table': read as data.table;
'data_frame': read as data.frame;
'header_only': read 'fst' table header.

For 'mat', the choices are

'auto': automatically try the native option, and then 'pymatreader' if fails;
'R.matlab': use the native method (provided by readMat); only support 'MAT 5.0' format;
'pymatreader': use 'Python' library 'pymatreader';
'mat73': use 'Python' library 'mat73'.

...

passed to internal function calls

old_format

see fst

x

data to write to disk

compress

compress level from 0 to 100; default is 50

digits, pretty

for writing numeric values to 'json' format

serialize

set to TRUE to serialize the data to 'json' format (with the data types, default); or FALSE to save the values without types

verbose

whether to print out the process

on_convert_error

for reading 'mat' files with 'Python' modules, the results will be converted to R objects in the end. Not all objects can be converted. This input defines the behavior when the conversion fails; choices are "error", "warning", or "ignore"

sorted

whether to sort the list; default is FALSE

Value

The reader functions returns the data extracted from files, mostly as R objects, with few exceptions on some 'Matlab' files. When reading a 'Matlab' file requires using 'Python' modules, io_read_mat will try its best effort to convert 'Python' objects to R. However, such conversion might fail. In this case, the result might partially contain 'Python' objects with warnings.

Examples


# ---- fst ----------------------------------------------------------------


f <- tempfile(fileext = ".fst")
x <- data.frame(
  a = 1:10,
  b = rnorm(10),
  c = letters[1:10]
)

io_write_fst(x, con = f)

# default reads in proxy
io_read_fst(f)
#> <fst file>
#> 10 rows, 3 columns (file18a3598ce91.fst)
#> 
#>            a            b           c
#>    <integer>     <double> <character>
#> 1          1 -1.400043517           a
#> 2          2  0.255317055           b
#> 3          3 -2.437263611           c
#> 4          4 -0.005571287           d
#> 5          5  0.621552721           e
#> 6          6  1.148411606           f
#> 7          7 -1.821817661           g
#> 8          8 -0.247325302           h
#> 9          9 -0.244199607           i
#> 10        10 -0.282705449           j

# load as data.table
io_read_fst(f, "data_table")
#>         a            b      c
#>     <int>        <num> <char>
#>  1:     1 -1.400043517      a
#>  2:     2  0.255317055      b
#>  3:     3 -2.437263611      c
#>  4:     4 -0.005571287      d
#>  5:     5  0.621552721      e
#>  6:     6  1.148411606      f
#>  7:     7 -1.821817661      g
#>  8:     8 -0.247325302      h
#>  9:     9 -0.244199607      i
#> 10:    10 -0.282705449      j

# load as data.frame
io_read_fst(f, "data_frame")
#>     a            b c
#> 1   1 -1.400043517 a
#> 2   2  0.255317055 b
#> 3   3 -2.437263611 c
#> 4   4 -0.005571287 d
#> 5   5  0.621552721 e
#> 6   6  1.148411606 f
#> 7   7 -1.821817661 g
#> 8   8 -0.247325302 h
#> 9   9 -0.244199607 i
#> 10 10 -0.282705449 j

# get header
io_read_fst(f, "header_only")
#> <fst file>
#> 10 rows, 3 columns (file18a3598ce91.fst)
#> 
#> * 'a': integer
#> * 'b': double
#> * 'c': character

# clean up
unlink(f)



# ---- json ---------------------------------------------------------------
f <- tempfile(fileext = ".json")

x <- list(a = 1L, b = 2.3, c = "a", d = 1+1i)

# default is serialize
io_write_json(x, f)

io_read_json(f)
#> $a
#> [1] 1
#> 
#> $b
#> [1] 2.3
#> 
#> $c
#> [1] "a"
#> 
#> $d
#> [1] 1+1i
#> 

cat(readLines(f), sep = "\n")
#> {
#>   "type": "list",
#>   "attributes": {
#>     "names": {
#>       "type": "character",
#>       "attributes": {},
#>       "value": ["a", "b", "c", "d"]
#>     }
#>   },
#>   "value": [
#>     {
#>       "type": "integer",
#>       "attributes": {},
#>       "value": [1]
#>     },
#>     {
#>       "type": "double",
#>       "attributes": {},
#>       "value": [2.2999999999999998]
#>     },
#>     {
#>       "type": "character",
#>       "attributes": {},
#>       "value": ["a"]
#>     },
#>     {
#>       "type": "complex",
#>       "attributes": {},
#>       "value": ["1+1i"]
#>     }
#>   ]
#> }

# just values
io_write_json(x, f, serialize = FALSE, pretty = FALSE)

io_read_json(f)
#> $a
#> [1] 1
#> 
#> $b
#> [1] 2.3
#> 
#> $c
#> [1] "a"
#> 
#> $d
#> [1] "1+1i"
#> 

cat(readLines(f), sep = "\n")
#> {"a":[1],"b":[2.2999999999999998],"c":["a"],"d":["1+1i"]}

# clean up
unlink(f)



# ---- Matlab .mat --------------------------------------------------------

if (FALSE) { # \dontrun{

f <- tempfile(fileext = ".mat")

x <- list(a = 1L, b = 2.3, c = "a", d = 1+1i)

# save as MAT 5.0
io_write_mat(x, f)

io_read_mat(f)

# require setting up Python environment

io_read_mat(f, method = "pymatreader")

# MAT 7.3 example
sample_data <- ieegio_sample_data("mat_v73.mat")
io_read_mat(sample_data)

# clean up
unlink(f)

} # }



# ---- yaml ---------------------------------------------------------------

f <- tempfile(fileext = ".yaml")

x <- list(a = 1L, b = 2.3, c = "a")
io_write_yaml(x, f)

io_read_yaml(f)
#> $a
#> [1] 1
#> 
#> $b
#> [1] 2.3
#> 
#> $c
#> [1] "a"
#> 

# clean up
unlink(f)