The parse_setup function is what this packages uses to
convert the .sps or .sas setup files into an usable format for R.
This will return a list of length 3 containing the objects “setup”, “value_labels”, and “missing”.
# Using the example .sps setup file included with the package
sps_name <- system.file("extdata", "example_setup.sps",
package = "asciiSetupReader")
example <- asciiSetupReader::parse_setup(sps_name)The first object in the returned list is a data.frame with 4 columns
and as many rows as there are columns in the data. The “column_number”
column is the non-descriptive name of the column while the “column_name”
is the descriptive name of the column. In read_ascii_setup,
setting use_clean_names to TRUE will set the data column
names to the “column_name” names, otherwise it will remain as the
“column_number” names. Since the data is in fixed-width format, you need
to know the location of each column. The “begin” and “end” columns in
this object provide that location for each column in the data.
To make the data more compact, the data often provides values that represent a label. For example, in a column about participant’s gender it may only include “M” and “F” which stands for “Male” and “Female”. The setup file will say the M = Male and F = Female. The value labels tell us that we need to convert M to Male in the given column. This is a list of named vectors indicating the value and its corresponding label. If there are no value labels this object will be NULL.
example$value_labels[1:3]
#> $V1
#> SHR master file
#> "6"
#>
#> $V2
#> Alabama Arizona Arkansas California Colorado
#> "1" "2" "3" "4" "5"
#> Connecticut Delaware Washington, D.C Florida Georgia
#> "6" "7" "8" "9" "10"
#> Idaho Illinois Indiana Iowa Kansas
#> "11" "12" "13" "14" "15"
#> Kentucky Louisiana Maine Maryland Massachusetts
#> "16" "17" "18" "19" "20"
#> Michigan Minnesota Mississippi Missouri Montana
#> "21" "22" "23" "24" "25"
#> Nebraska Nevada New Hampshire New Jersey New Mexico
#> "26" "27" "28" "29" "30"
#> New York North Carolina North Dakota Ohio Oklahoma
#> "31" "32" "33" "34" "35"
#> Oregon Pennsylvania Rhode Island South Carolina South Dakota
#> "36" "37" "38" "39" "40"
#> Tennessee Texas Utah Vermont Virginia
#> "41" "42" "43" "44" "45"
#> Washington West Virginia Wisconsin Wyoming Alaska
#> "46" "47" "48" "49" "50"
#> Hawaii Canal Zone Puerto Rico American Samoa Guam
#> "51" "52" "53" "54" "55"
#> Virgin Islands
#> "62"
#>
#> $V4
#> Possessions ALL cit 250,000 + Cit 100,000-249,999
#> "0" "1" "2"
#> Cit 50,000-99,999 Cit 25,000-49,999 Cit 10,000-24,999
#> "3" "4" "5"
#> Cit 2,500-9,999 Cit < 2,500 Non-MSA co.
#> "6" "7" "8"
#> MSA counties Cit 1,000,000 + Cit 500,000-999,999
#> "9" "1A" "1B"
#> Cit 250,000-499,999 Non-MSA co. 100,000 + Non-MSA co. 25,000-99,999
#> "1C" "8A" "8B"
#> Non-MSA co. 10,000-24,999 Non-MSA co. < 10,000 Non-MSA St Police
#> "8C" "8D" "8E"
#> MSA co. 100,000 + MSA co. 25,000-99,999 MSA co. 10,000-24,999
#> "9A" "9B" "9C"
#> MSA co. < 10,000 MSA St Police
#> "9D" "9E"There is one named vector for each column in the data that has value
labels. We can see how many there are using length().
The final object in the list a data.frame with two columns and as many rows as there are missing values in the data. The column “variable” indicates the column in the data and the column “values” says that the value in that row is to be replaced with NA. For example, if there are 10 columns in the data with missing values and each column has two missing values (e.g. -8 and -9) there will be 20 rows in this data.frame. A missing value is when the data includes a value that should be replaced with NA. For example, data often includes negative values such as -8 or -9 mean that that value is missing and should be NA. If there are no missing values this object will be NULL.