expss (version 0.10.7)

write_labelled_csv: Write labelled data to file or export file to SPSS syntax.

Description

• write_labelled_csv and read_labelled_csv writes csv file with labels. By default labels are stored in the commented lines at the beginning of the file before the data part. *_csv2 write and read data with a semicolon separator and comma as decimal delimiter. *_tab/*_tab2 write and read data with 'tab' separator and "."/"," as decimal delimiter.

• write_labelled_xlsx and read_labelled_xlsx write and read labelled 'xlsx' format. It is a simple Excel file with data and labels on separate sheets. It can help you with labelled data exchange in the corporate environment.

• write_labelled_fst and read_labelled_fst write and read labelled data in the 'fst' format. See Fst Package. Data and labels are stored in the separate files. With 'fst' format you can read and write a huge amount of data very quickly.

• write_labelled_spss write 'csv' file with SPSS syntax for reading it. You can use it for the data exchange with SPSS.

• create_dictionary and apply_dictionary make data.frame with dictionary, e. g. variable and value labels for each variable. See format description in the 'Details' section.

• write_labels and write_labels_spss Write R code and SPSS syntax for labelling data. It allows to extract labels from *.sav files that come without accompanying syntax.

• old_write_labelled_csv and old_read_labelled_csv Read and write labelled 'csv' in format of the 'expss' version before 0.9.0.

Usage

write_labelled_csv(
  x,
  filename,
  remove_new_lines = TRUE,
  single_file = TRUE,
  ...
)
write_labelled_csv2(
  x,
  filename,
  remove_new_lines = TRUE,
  single_file = TRUE,
  ...
)
write_labelled_tab(
  x,
  filename,
  remove_new_lines = TRUE,
  single_file = TRUE,
  ...
)
write_labelled_tab2(
  x,
  filename,
  remove_new_lines = TRUE,
  single_file = TRUE,
  ...
)
write_labelled_xlsx(
  x,
  filename,
  data_sheet = "data",
  dict_sheet = "dictionary",
  remove_repeated = FALSE,
  use_references = TRUE
)
write_labelled_fst(x, filename, ...)
read_labelled_csv(filename, undouble_quotes = TRUE, ...)
read_labelled_csv2(filename, undouble_quotes = TRUE, ...)
read_labelled_tab(filename, undouble_quotes = TRUE, ...)
read_labelled_tab2(filename, undouble_quotes = TRUE, ...)
read_labelled_xlsx(filename, data_sheet = 1, dict_sheet = "dictionary")
read_labelled_fst(filename, ...)
write_labelled_spss(
  x,
  filename,
  fileEncoding = "",
  remove_new_lines = TRUE,
  ...
)
write_labels_spss(x, filename)
write_labels(x, filename, fileEncoding = "")
create_dictionary(x, remove_repeated = FALSE, use_references = TRUE)
apply_dictionary(x, dict)
old_write_labelled_csv(
  x,
  filename,
  fileEncoding = "",
  remove_new_lines = TRUE,
  ...
)
old_read_labelled_csv(filename, fileEncoding = "", undouble_quotes = TRUE, ...)

Arguments

x

data.frame to be written/data.frame whose labels to be written

filename

the name of the file which the data are to be read from/write to.

remove_new_lines

A logical indicating should we replace new lines with spaces in the character variables. TRUE by default.

single_file

logical. TRUE by default. Should we write labels into the same file as data? If it is FALSE dictionary will be written in the separate file.

...

additional arguments for fwrite/fread, e. g. column separator, decimal separator, encoding and etc.

data_sheet

character "data" by default. Where data will be placed in the '*.xlsx' file.

dict_sheet

character "dictionary" by default. Where dictionary will be placed in the '*.xlsx' file.

remove_repeated

logical. FALSE by default. If TRUE then we remove repeated variable names. It makes a dictionary to look nicer for humans but less convenient for usage.

use_references

logical. When TRUE (default) then if the variable has the same value labels as the previous variable, we use reference to this variable. It makes dictionary significantly more compact for datasets with many variables with the same value labels.

undouble_quotes

A logical indicating should we undouble quotes which were escaped by doubling. TRUE by default. Argument will be removed when data.table issue #1109 will be fixed.

fileEncoding

character string: if non-empty declares the encoding to be used on a file (not a connection) so the character data can be re-encoded as they are written. Used for writing dictionary. See file.

dict

data.frame with labels - a result of create_dictionary.

Value

Functions for writing invisibly return NULL. Functions for reading return labelled data.frame.

Details

Dictionary is a data.frame with the following columns:

• variable variable name in the data set. It can be omitted (NA). In this case name from the previous row will be taken.

• value code for label in the column 'label'.

• label in most cases it is value label but its meaning can be changed by the column 'meta'.

• meta if it is NA then we have value label in the 'label' column. If it is 'varlab', then there is a variable label in the 'label' column and column 'value' is ignored. If it is 'reference', then there is a variable name in the 'label' column and we use value labels from this variable, column 'value' is ignored.

Examples

# NOT RUN {
data(mtcars)
mtcars = modify(mtcars,{
                var_lab(mpg) = "Miles/(US) gallon"
                var_lab(cyl) = "Number of cylinders"
                var_lab(disp) = "Displacement (cu.in.)"
                var_lab(hp) = "Gross horsepower"
                var_lab(drat) = "Rear axle ratio"
                var_lab(wt) = "Weight (lb/1000)"
                var_lab(qsec) = "1/4 mile time"
                var_lab(vs) = "Engine"
                val_lab(vs) = c("V-engine" = 0, 
                                "Straight engine" = 1) 
                var_lab(am) = "Transmission"
                val_lab(am) = c(automatic = 0, 
                                manual=1)
                var_lab(gear) = "Number of forward gears"
                var_lab(carb) = "Number of carburetors"
})

write_labelled_csv(mtcars, "mtcars.csv")
new_mtcars = read_labelled_csv("mtcars.csv")
str(new_mtcars)

# identically, for xlsx
write_labelled_xlsx(mtcars, "mtcars.xlsx")
new_mtcars = read_labelled_xlsx("mtcars.xlsx")
str(new_mtcars)

# to SPSS syntax
write_labelled_spss(mtcars, "mtcars.csv")

# }