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Some (usually older) data sets are only available in fixed-width
ASCII files (.txt or .dat) that have an .sps (SPSS) or .sas (SAS) setup
file explaining to the software how to read that file. This package
allows you to read in the data if you have both the fixed-width file and
its accompanying setup file. These parameters data and
setup_file are the only ones requires to run the package
though three optional parameters allow you to customize results.
data - A string containing the name of the data file
setup_file - A string containing the name of the data
file
Both files must be in your working directory or the string must contain the path to the file. Below is an example of reading in the example dataset - the original data and setup files can be found here.
Please note that I am only using system.file() here so
the vignette builds in the package even not on my own computer. You will
not use this in the function. Instead you’d simply input
data = "example_data.zip" and
setup_file = "example_setup.sps". The data file does not
have to be in a zip folder, it is only in a zip folder here to reduce
the size of this package. In most cases it will be a .dat or a .txt
file.
data <- system.file("extdata", "example_data.zip",
package = "asciiSetupReader")
setup_file <- system.file("extdata", "example_setup.sps",
package = "asciiSetupReader")
example <- asciiSetupReader::read_ascii_setup(data = data,
setup_file = setup_file)
example[1:6, 1:4] # Look at first 6 rows and first 4 columns## IDENTIFIER_CODE NUMERIC_STATE_CODE ORI_CODE GROUP
## 1 SHR master file Alabama AL00112 Cit 50,000-99,999
## 2 SHR master file Alabama AL00112 Cit 50,000-99,999
## 3 SHR master file Alabama AL00112 Cit 50,000-99,999
## 4 SHR master file Arizona AZ00189 Cit < 2,500
## 5 SHR master file Arizona AZ00189 Cit < 2,500
## 6 SHR master file Arizona AZ00189 Cit < 2,500There are three optional parameters: use_value_labels,
use_clean_names, and select_columns.
use_value_labelsFixed-width delimited text files are designed to be as compressed as
possible. One way of doing this is having letters or numbers represent
values. For example, instead of writing “male” or “female” in a column
about gender, it will be “0” or “1” (or “M” and “F”). The setup file
gives the actual value of these representations. When the parameter
use_value_labels is TRUE (which it is by default) it will
give the value labels; otherwise it will give only the representation.
This parameter is the most time consuming part of the function so if you
have a very large dataset but only a few variables you are interested
in, it may be wise to set it as FALSE (or use the parameter
select_columns to get only those columns).
example <- asciiSetupReader::read_ascii_setup(data = data,
setup_file = setup_file,
use_value_labels = FALSE)
example[1:6, 1:4] # Look at first 6 rows and first 4 columns## IDENTIFIER_CODE NUMERIC_STATE_CODE ORI_CODE GROUP
## 1 6 1 AL00112 3
## 2 6 1 AL00112 3
## 3 6 1 AL00112 3
## 4 6 2 AZ00189 7
## 5 6 2 AZ00189 7
## 6 6 2 AZ00189 7use_clean_namesColumn names are similar to how there are both value representations
and value labels for values in a column. The columns may have a
non-descriptive name (e.g. V1, V2) or a descriptive one (e.g. CITY,
GENDER). When use_clean_names is TRUE (which it is by), the
descriptive name is given; otherwise the non-descriptive name is
given.
example <- asciiSetupReader::read_ascii_setup(data = data,
setup_file = setup_file,
use_clean_names = FALSE)
example[1:6, 1:4] # Look at first 6 rows and first 4 columns## V1 V2 V3 V4
## 1 SHR master file Alabama AL00112 Cit 50,000-99,999
## 2 SHR master file Alabama AL00112 Cit 50,000-99,999
## 3 SHR master file Alabama AL00112 Cit 50,000-99,999
## 4 SHR master file Arizona AZ00189 Cit < 2,500
## 5 SHR master file Arizona AZ00189 Cit < 2,500
## 6 SHR master file Arizona AZ00189 Cit < 2,500select_columnsThis parameter allows you to return only the specific columns you want. It is very useful when dealing with a large file which you only want part of. It accepts 3 inputs: column numbers, the non-descriptive column names, or the descriptive column names - you can only choose one input type, cannot mix them together. To get the column names and numbers, consult with the g documentation.
This gets only the first two columns of data and specifies the columns by number.
example <- asciiSetupReader::read_ascii_setup(data = data,
setup_file = setup_file,
select_columns = 1:2) # Gets only the first 2 columns
head(example)## IDENTIFIER_CODE NUMERIC_STATE_CODE
## 1 SHR master file Alabama
## 2 SHR master file Alabama
## 3 SHR master file Alabama
## 4 SHR master file Arizona
## 5 SHR master file Arizona
## 6 SHR master file ArizonaThis gets only the first two columns of data and specifies the columns by descriptive names.
example <- asciiSetupReader::read_ascii_setup(data = data,
setup_file = setup_file,
select_columns = c("IDENTIFIER_CODE", "NUMERIC_STATE_CODE")) # Gets only the first 2 columns
head(example)## IDENTIFIER_CODE NUMERIC_STATE_CODE
## 1 SHR master file Alabama
## 2 SHR master file Alabama
## 3 SHR master file Alabama
## 4 SHR master file Arizona
## 5 SHR master file Arizona
## 6 SHR master file ArizonaThis gets only the first column of data and specifies the column by non-descriptive names.
example <- asciiSetupReader::read_ascii_setup(data = data,
setup_file = setup_file,
select_columns = "V1") # Gets only the first columnss
head(example)## IDENTIFIER_CODE
## 1 SHR master file
## 2 SHR master file
## 3 SHR master file
## 4 SHR master file
## 5 SHR master file
## 6 SHR master fileThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.