How to build a dictionary file
Jürgen Wilbert
2026-03-04
build_dic_file.RmdIn the following examples I will lay out the construction of a dictionary file.
First, we need an example data.frame to work with (i.e. the
ex_scaledic_data example dataset):
| Table ex_scaledic_data |
|||||||||||
| rel_1 | rel_2 | rel_3 | rel_4 | rel_5 | sui_1 | sui_2 | sui_3 | sui_4 | sui_5 | gender | age |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 6 | 1 | 2 | 1 | 2 | 2 | 2 | 3 | 0 | f | 10.5 |
| 3 | 1 | 1 | 2 | 1 | 4 | 1 | 4 | 1 | 0 | d | 6.5 |
| 1 | 66 | 2 | 3 | 4 | 3 | 2 | 1 | 3 | 3 | d | 10.5 |
| 2 | 2 | 5 | 3 | 1 | 0 | 2 | 1 | 2 | 3 | f | 13.0 |
| 5 | 5 | 4 | 2 | 3 | 1 | 3 | 1 | 1 | 3 | f | 8.0 |
| 3 | 4 | 5 | 5 | 1 | 3 | 2 | 3 | 1 | 1 | d | 6.0 |
| 3 | 2 | 2 | 2 | 3 | 0 | 4 | 3 | 4 | 2 | f | 6.0 |
| 4 | 3 | 4 | 3 | 3 | 0 | 0 | 4 | 3 | 3 | f | 10.0 |
| 2 | 6 | 2 | 11 | 1 | 1 | -999 | 4 | 2 | 1 | f | 8.5 |
| 6 | 6 | 4 | 5 | 3 | 55 | 4 | 0 | 3 | 1 | f | 8.5 |
| 6 | 1 | 3 | 1 | 66 | 4 | 0 | 2 | 3 | 4 | m | 7.5 |
| 6 | 1 | 2 | 5 | 3 | 1 | 3 | 1 | 3 | 0 | m | -999.0 |
| 3 | 1 | 5 | 5 | 5 | 2 | 2 | 1 | 3 | 0 | m | 7.0 |
| 6 | 1 | -999 | 1 | 1 | 4 | 2 | 3 | 3 | 0 | m | 7.5 |
| 2 | 2 | 1 | 5 | 3 | 2 | 3 | 2 | 2 | 0 | d | 8.5 |
| 3 | 5 | 5 | 4 | 2 | 1 | 0 | 0 | 1 | 2 | d | 13.0 |
| 4 | 4 | 5 | 5 | 1 | 3 | 1 | 4 | 66 | 0 | -999 | 10.5 |
| 5 | 5 | 4 | -999 | 4 | 1 | 4 | 2 | 0 | 1 | m | 10.0 |
| 6 | 5 | 4 | 2 | 4 | 0 | 0 | 2 | 3 | 3 | m | 8.0 |
| 2 | 3 | 5 | 2 | 3 | 1 | 3 | 2 | 4 | 3 | m | 6.0 |
Second, we need a dictionary file that is also a
data.frame. Each row of a dictionary file addresses a
variable (also called item in this context) and each column
holds a specific attribute. These attributes can be divided in two
classes. Firstly, predefined attributes with a specific name
and additional attributed that can have any name. The
predefined attributes are identified by their specific names.
Theses are: item_name, item_label,
values, value_labels, missing,
weight, and type.
Additional attributes can have any name and are mainly used for selecting items or displaying additional information of an item (e.g., the scale and subscale an item belongs to; the reference and the author of an item; the translation of an item).
Building a dictionary step by step
We start the example with a very simple dictionary file containing
only item labels and the corresponding item names (i.e, the
corresponding variable names in our data frame)1. The dictionary file
has two columns, item_name and item_label:
| Table Dictionary file |
|
| item_name | item_label |
|---|---|
| rel_1 | How often do you attend church or other religious meetings? |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life |
| rel_5 | I try hard to carry my religion over into all other dealings in life |
| sui_1 | Did you feel tense in the last week? |
| sui_2 | Did you feel blue in the last week? |
| sui_3 | Did you feel irritated in the last week? |
| sui_4 | Did you feel inferior in the last week? |
| sui_5 | Did you have problems falling asleep in the last week? |
| gender | gender |
| age | age |
We combine the data file and the dic file with the
apply_dic() function:
dat_dic <- apply_dic(ex_scaledic_data, dic_file)
[33m!
[39m Type of rel_1 is missing and is estimated as 'numeric'.
[33m!
[39m Type of rel_2 is missing and is estimated as 'numeric'.
[33m!
[39m Type of rel_3 is missing and is estimated as 'numeric'.
[33m!
[39m Type of rel_4 is missing and is estimated as 'numeric'.
[33m!
[39m Type of rel_5 is missing and is estimated as 'numeric'.
[33m!
[39m Type of sui_1 is missing and is estimated as 'numeric'.
[33m!
[39m Type of sui_2 is missing and is estimated as 'numeric'.
[33m!
[39m Type of sui_3 is missing and is estimated as 'numeric'.
[33m!
[39m Type of sui_4 is missing and is estimated as 'numeric'.
[33m!
[39m Type of sui_5 is missing and is estimated as 'numeric'.
[33m!
[39m Type of gender is missing and is estimated as 'character'.
[33m!
[39m Type of age is missing and is estimated as 'numeric'.We get the message that the dic file does not contain information of
the datatype (e.g. numeric, factor,
character). The type attribute is used in
various scaledic functions. For example when the data is
checked for invalid values, missing values are imputed, or scales are
scored. A variable can be one of the following types:
integer for numbers without decimals, numeric
for numbers with or without decimals, character for
variables with text, and factor for variables with text or
numbers that are levels of a factor. Scaledic will estimate the
data type from the given data when the type attribute is
not provided in the dic file.
Let us add type information to the dic file:
| Table Dictionary file |
||
| item_name | item_label | type |
|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | numeric |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | numeric |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | numeric |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | numeric |
| rel_5 | I try hard to carry my religion over into all other dealings in life | numeric |
| sui_1 | Did you feel tense in the last week? | numeric |
| sui_2 | Did you feel blue in the last week? | numeric |
| sui_3 | Did you feel irritated in the last week? | numeric |
| sui_4 | Did you feel inferior in the last week? | numeric |
| sui_5 | Did you have problems falling asleep in the last week? | numeric |
| gender | gender | factor |
| age | age | numeric |
Most item values are of type numeric with the exception
of gender which is of type factor (i.e. it has
a nominal scale with several levels).
dat_dic <- apply_dic(ex_scaledic_data, dic_file)Now we can add weight information to the dic file. The
weight attribute tells various scaledic functions
a) whether the item is inverted or not, and b) how the values of an item
are weighted. If a weight value is unsigned (e.g. 1), the
item is not inverted. If a weight value has a negative sign
(e.g. -1), the item is inverted. If the (absolute) value is
not 1, the item will be weighted when calculating the item
scores (e.g. 1.5 will give an item a weight of 1.5).
| Table Dictionary file |
|||
| item_name | item_label | weight | type |
|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1 | numeric |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1 | numeric |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1 | numeric |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1 | numeric |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1 | numeric |
| sui_1 | Did you feel tense in the last week? | 1 | numeric |
| sui_2 | Did you feel blue in the last week? | 1 | numeric |
| sui_3 | Did you feel irritated in the last week? | 1 | numeric |
| sui_4 | Did you feel inferior in the last week? | 1 | numeric |
| sui_5 | Did you have problems falling asleep in the last week? | 1 | numeric |
| gender | gender | 1 | factor |
| age | age | 1 | numeric |
In this example, all weights are 1 and no
item is inverted.
Again, we join dic and data file:
dat_dic <- apply_dic(ex_scaledic_data, dic_file)Now we add the values attribute to the dic file.
values defines the valid values that a variable can take.
Explicitly defining the values makes it possible to
automatically identify invalid values in a data frame (e.g. typos).
values are also necessary to reverse score an inverted
item. For coding the values, you provide the possible
values separated with a comma (e.g., 1, 2, 3, 4 for the
integers 1 to 4). It is also possible to use a colon to define a range
of integers (e.g., 5:11 indicated all integers starting
with 5 and ending with 11). For type float, values
represent the maximum and the minimum of valid values
(e.g. 5, 11 for all values within 5 and 11 including
decimal numbers). For variables of type factor or
character you may want to provide text values. in that
case, put these values within quotes (e.g., 'm', 'f', 'd'
indicates three valid text entries).
Here is the dic file with added values:
| Table Dictionary file |
||||
| item_name | item_label | values | weight | type |
|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1:6 | 1 | numeric |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1:6 | 1 | numeric |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1:5 | 1 | numeric |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1:5 | 1 | numeric |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1:5 | 1 | numeric |
| sui_1 | Did you feel tense in the last week? | 0:4 | 1 | numeric |
| sui_2 | Did you feel blue in the last week? | 0:4 | 1 | numeric |
| sui_3 | Did you feel irritated in the last week? | 0:4 | 1 | numeric |
| sui_4 | Did you feel inferior in the last week? | 0:4 | 1 | numeric |
| sui_5 | Did you have problems falling asleep in the last week? | 0:4 | 1 | numeric |
| gender | gender | 'm', 'f', 'd' | 1 | factor |
| age | age | 5, 11 | 1 | numeric |
dat_dic <- apply_dic(ex_scaledic_data, dic_file)
[33m!
[39m Set 1 NA in factor 'gender' for value -999Factor ‘gender’ has a value (-999) which is not provided in the
values attribute. For factors, these are replaced with
NA.
Invalid values for all other data types can be replaced automatically
with the check_values() function.
dat_dic <- check_values(dat_dic, replace = NA)
[33m!
[39m rel_2' invalid at row 3 (is 66) -> set as NA
[33m!
[39m rel_3' invalid at row 14 (is -999) -> set as NA
[33m!
[39m rel_4' invalid at rows 9, 18 (is 11, -999) -> set as NA
[33m!
[39m rel_5' invalid at row 11 (is 66) -> set as NA
[33m!
[39m sui_1' invalid at row 10 (is 55) -> set as NA
[33m!
[39m sui_2' invalid at row 9 (is -999) -> set as NA
[33m!
[39m sui_4' invalid at row 17 (is 66) -> set as NA
[33m!
[39m age' invalid at rows 4, 12, 16 (is 13, -999, 13) -> set as NA| Table data frame with replaced invalid values |
|||||||||||
| rel_1 | rel_2 | rel_3 | rel_4 | rel_5 | sui_1 | sui_2 | sui_3 | sui_4 | sui_5 | gender | age |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 6 | 1 | 2 | 1 | 2 | 2 | 2 | 3 | 0 | f | 10.5 |
| 3 | 1 | 1 | 2 | 1 | 4 | 1 | 4 | 1 | 0 | d | 6.5 |
| 1 | NA | 2 | 3 | 4 | 3 | 2 | 1 | 3 | 3 | d | 10.5 |
| 2 | 2 | 5 | 3 | 1 | 0 | 2 | 1 | 2 | 3 | f | NA |
| 5 | 5 | 4 | 2 | 3 | 1 | 3 | 1 | 1 | 3 | f | 8.0 |
| 3 | 4 | 5 | 5 | 1 | 3 | 2 | 3 | 1 | 1 | d | 6.0 |
| 3 | 2 | 2 | 2 | 3 | 0 | 4 | 3 | 4 | 2 | f | 6.0 |
| 4 | 3 | 4 | 3 | 3 | 0 | 0 | 4 | 3 | 3 | f | 10.0 |
| 2 | 6 | 2 | NA | 1 | 1 | NA | 4 | 2 | 1 | f | 8.5 |
| 6 | 6 | 4 | 5 | 3 | NA | 4 | 0 | 3 | 1 | f | 8.5 |
| 6 | 1 | 3 | 1 | NA | 4 | 0 | 2 | 3 | 4 | m | 7.5 |
| 6 | 1 | 2 | 5 | 3 | 1 | 3 | 1 | 3 | 0 | m | NA |
| 3 | 1 | 5 | 5 | 5 | 2 | 2 | 1 | 3 | 0 | m | 7.0 |
| 6 | 1 | NA | 1 | 1 | 4 | 2 | 3 | 3 | 0 | m | 7.5 |
| 2 | 2 | 1 | 5 | 3 | 2 | 3 | 2 | 2 | 0 | d | 8.5 |
| 3 | 5 | 5 | 4 | 2 | 1 | 0 | 0 | 1 | 2 | d | NA |
| 4 | 4 | 5 | 5 | 1 | 3 | 1 | 4 | NA | 0 | NA | 10.5 |
| 5 | 5 | 4 | NA | 4 | 1 | 4 | 2 | 0 | 1 | m | 10.0 |
| 6 | 5 | 4 | 2 | 4 | 0 | 0 | 2 | 3 | 3 | m | 8.0 |
| 2 | 3 | 5 | 2 | 3 | 1 | 3 | 2 | 4 | 3 | m | 6.0 |
Next, we add value_labels. Value labels give longer
labels for all or some of the values. Value labels are coded in the form
value = label with a semicolon separating each entry.
m = male; f = female; d = diverse codes three value label
(quotes are not necessary).
Here is the dic file with added value_labels:
| item_name | item_label | values | value_labels | weight | type |
|---|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1:6 | 1 = Never; 2 = Once a year or less; 3 = A few times a year; 4 = A few times a month; 5 = Once a week; 6 = More than once/week | 1 | numeric |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1:6 | 1 = Rarely or never; 2 = A few times a month; 3 = Once a week; 4 = Two or more times/week; 5 = Daily; 6 = More than once a day | 1 | numeric |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | numeric |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | numeric |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | numeric |
| sui_1 | Did you feel tense in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric |
| sui_2 | Did you feel blue in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric |
| sui_3 | Did you feel irritated in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric |
| sui_4 | Did you feel inferior in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric |
| sui_5 | Did you have problems falling asleep in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric |
| gender | gender | 'm', 'f', 'd' | m = male; f = female; d = diverse | 1 | factor |
| age | age | 5, 11 | 5 = min; 11 = max | 1 | numeric |
dat_dic <- ex_scaledic_data |>
apply_dic(dic_file) |>
check_values(replace = NA)
[33m!
[39m Set 1 NA in factor 'gender' for value -999
[33m!
[39m rel_2' invalid at row 3 (is 66) -> set as NA
[33m!
[39m rel_3' invalid at row 14 (is -999) -> set as NA
[33m!
[39m rel_4' invalid at rows 9, 18 (is 11, -999) -> set as NA
[33m!
[39m rel_5' invalid at row 11 (is 66) -> set as NA
[33m!
[39m sui_1' invalid at row 10 (is 55) -> set as NA
[33m!
[39m sui_2' invalid at row 9 (is -999) -> set as NA
[33m!
[39m sui_4' invalid at row 17 (is 66) -> set as NA
[33m!
[39m age' invalid at rows 4, 12, 16 (is 13, -999, 13) -> set as NANow lets see the coding for some of the variables:
dat_dic$rel_1
║How often do you attend church or other religious meetings?
║Data type is numeric
║Valid values: From 1 to 6
║Value labels:
║ 1 = Never
║ 2 = Once a year or less
║ 3 = A few times a year
║ 4 = A few times a month
║ 5 = Once a week
║ 6 = More than once/week
║
║Length is 20 (0 NA; 0 invalid)
║ [1] 1 3 1 2 5 3 3 4 2 6 6 6 3 6 2 3 4 5 6 2Valid values are all integers from 1 to 6 and each value has a label.
dat_dic$sui_1
║Did you feel tense in the last week?
║Data type is numeric
║Valid values: From 0 to 4
║Value labels:
║ 0 = not at all
║ 4 = extremely
║
║Length is 20 (1 NA; 0 invalid)
║ [1] 2 4 3 0 1 3 0 0 1 NA 4 1 2 4 2 1 3 1 0 1Here, valid values are 0, 1, 2, 3, 4 (short
0:4) but only the poles (0 and 4)
have labels.
dat_dic$gender
║gender
║Data type is factor
║Value labels:
║ m = male
║ f = female
║ d = diverse
║
║Length is 20 (1 NA; 0 invalid)
║ [1] female diverse diverse female female diverse female female female
║[10] female male male male male diverse diverse <NA> male
║[19] male male
║Levels: male female diversegender is of type factor. The valid values
'm', 'f', 'd' have been turned into three factor levels
with the corresponding labels.
Caution: The labels provided in the
value_labelsattribute are not automtically turned into the levels of a factor. This is because R internally would turn the values (here'm', 'f', 'd') into integers (here1, 2, 3)
dat_dic$age
║age
║Data type is numeric
║Valid values: From 5 to 11
║Value labels:
║ 5 = min
║ 11 = max
║
║Length is 20 (3 NA; 0 invalid)
║ [1] 10.5 6.5 10.5 NA 8.0 6.0 6.0 10.0 8.5 8.5 7.5 NA 7.0 7.5 8.5
║[16] NA 10.5 10.0 8.0 6.0age can take all numbers from 5 to 11 (minimum is 5 and
maximum is 11).
It is a common practice to code missing values with a specific number
(rather than just leaving an empty entry on a datasheet). In the example
dataset used here, -999 is used as a missing value. To
account for this, we add the missing attribute to the
dic file containing the missing number (e.g.,
-999). Multiple missing values are separated by commas
(e.g. -99, -77).
| Table Dictionary file |
||||||
| item_name | item_label | values | value_labels | weight | type | missing |
|---|---|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | 1:6 | 1 = Never; 2 = Once a year or less; 3 = A few times a year; 4 = A few times a month; 5 = Once a week; 6 = More than once/week | 1 | numeric | -999 |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 1:6 | 1 = Rarely or never; 2 = A few times a month; 3 = Once a week; 4 = Two or more times/week; 5 = Daily; 6 = More than once a day | 1 | numeric | -999 |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | numeric | -999 |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | numeric | -999 |
| rel_5 | I try hard to carry my religion over into all other dealings in life | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | 1 | numeric | -999 |
| sui_1 | Did you feel tense in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric | -999 |
| sui_2 | Did you feel blue in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric | -999 |
| sui_3 | Did you feel irritated in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric | -999 |
| sui_4 | Did you feel inferior in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric | -999 |
| sui_5 | Did you have problems falling asleep in the last week? | 0:4 | 0 = not at all; 4 = extremely | 1 | numeric | -999 |
| gender | gender | 'm', 'f', 'd' | m = male; f = female; d = diverse | 1 | factor | |
| age | age | 5, 11 | 5 = min; 11 = max | 1 | numeric | -999 |
The values within the missing attributes are
automatically replaced with NA when joining a data with a dic file:
dat_dic <- apply_dic(ex_scaledic_data, dic_file)
[33m!
[39m Set 1 NA in factor 'gender' for value -999
[33m!
[39m Replaced 1 missing value in 'rel_3' with NA
[33m!
[39m Replaced 1 missing value in 'rel_4' with NA
[33m!
[39m Replaced 1 missing value in 'sui_2' with NA
[33m!
[39m Replaced 1 missing value in 'age' with NATo turn off this behavior, set the argument
replace_missing = FALSE.
In the last step of this tutorial we will add information about the
scales that the items belong to. Therefore, we add a new attribute
called scale to the dic file and another attribute
scale_label for a longer description (we could have named
these attributes in any other way as they are not predefined
attributes):
| Table Dictionary file |
||||||||
| item_name | item_label | scale | scale_label | values | value_labels | type | weight | missing |
|---|---|---|---|---|---|---|---|---|
| rel_1 | How often do you attend church or other religious meetings? | rel | Religious beliefs | 1:6 | 1 = Never; 2 = Once a year or less; 3 = A few times a year; 4 = A few times a month; 5 = Once a week; 6 = More than once/week | numeric | 1 | -999 |
| rel_2 | How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | rel | Religious beliefs | 1:6 | 1 = Rarely or never; 2 = A few times a month; 3 = Once a week; 4 = Two or more times/week; 5 = Daily; 6 = More than once a day | numeric | 1 | -999 |
| rel_3 | In my life, I experience the presence of the Divine (i.e., God) | rel | Religious beliefs | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | numeric | 1 | -999 |
| rel_4 | My religious beliefs are what really lie behind my whole approach to life | rel | Religious beliefs | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | numeric | 1 | -999 |
| rel_5 | I try hard to carry my religion over into all other dealings in life | rel | Religious beliefs | 1:5 | 1 = Definitely not true; 2 = Tends not to be true; 3 = Unsure; 4 = Tends to be true; 5 = Definitely true of me | numeric | 1 | -999 |
| sui_1 | Did you feel tense in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | numeric | 1 | -999 |
| sui_2 | Did you feel blue in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | numeric | 1 | -999 |
| sui_3 | Did you feel irritated in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | numeric | 1 | -999 |
| sui_4 | Did you feel inferior in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | numeric | 1 | -999 |
| sui_5 | Did you have problems falling asleep in the last week? | sui | Suicide tendency | 0:4 | 0 = not at all; 4 = extremely | numeric | 1 | -999 |
| gender | gender | misc | Miscellaneous | 'm', 'f', 'd' | m = male; f = female; d = diverse | factor | 1 | |
| age | age | misc | Miscellaneous | 5, 11 | 5 = min; 11 = max | numeric | 1 | -999 |
dat_dic <- apply_dic(ex_scaledic_data, dic_file, check_values = TRUE)
[33m!
[39m Set 1 NA in factor 'gender' for value -999
[33m!
[39m Replaced 1 missing value in 'rel_3' with NA
[33m!
[39m Replaced 1 missing value in 'rel_4' with NA
[33m!
[39m Replaced 1 missing value in 'sui_2' with NA
[33m!
[39m Replaced 1 missing value in 'age' with NA
[33m!
[39m rel_2' invalid at row 3 (is 66) -> set as NA
[33m!
[39m rel_4' invalid at row 9 (is 11) -> set as NA
[33m!
[39m rel_5' invalid at row 11 (is 66) -> set as NA
[33m!
[39m sui_1' invalid at row 10 (is 55) -> set as NA
[33m!
[39m sui_4' invalid at row 17 (is 66) -> set as NA
[33m!
[39m age' invalid at rows 4, 16 (is 13, 13) -> set as NAYou can use the scale attribute to select items:
dat_dic |>
select_items(scale == "rel") |>
wmisc::nice_descriptives(round = 2)| Table Descriptive statistics |
|||||||||
| Variable | Valid | Missing | Mean | SD | Min | Max | Range | Median | MAD |
|---|---|---|---|---|---|---|---|---|---|
| How often do you attend church or other religious meetings? | 20 | 0 | 3.65 | 1.76 | 1 | 6 | 5 | 3 | 1.48 |
| How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 19 | 1 | 3.32 | 1.92 | 1 | 6 | 5 | 3 | 2.97 |
| In my life, I experience the presence of the Divine (i.e., God) | 19 | 1 | 3.37 | 1.54 | 1 | 5 | 4 | 4 | 1.48 |
| My religious beliefs are what really lie behind my whole approach to life | 18 | 2 | 3.17 | 1.50 | 1 | 5 | 4 | 3 | 1.48 |
| I try hard to carry my religion over into all other dealings in life | 19 | 1 | 2.47 | 1.31 | 1 | 5 | 4 | 3 | 1.48 |
| Note. MAD is the median average deviation with a consistency adjustment. | |||||||||
and the rename_items() function to get the item
labels:
dat_dic |>
select_items(scale == "rel") |>
rename_items() |>
wmisc::nice_descriptives(round = 2)| Table Descriptive statistics |
|||||||||
| Variable | Valid | Missing | Mean | SD | Min | Max | Range | Median | MAD |
|---|---|---|---|---|---|---|---|---|---|
| How often do you attend church or other religious meetings? | 20 | 0 | 3.65 | 1.76 | 1 | 6 | 5 | 3 | 1.48 |
| How often do you spend time in private religious activities, such as prayer, meditation or Bible study? | 19 | 1 | 3.32 | 1.92 | 1 | 6 | 5 | 3 | 2.97 |
| In my life, I experience the presence of the Divine (i.e., God) | 19 | 1 | 3.37 | 1.54 | 1 | 5 | 4 | 4 | 1.48 |
| My religious beliefs are what really lie behind my whole approach to life | 18 | 2 | 3.17 | 1.50 | 1 | 5 | 4 | 3 | 1.48 |
| I try hard to carry my religion over into all other dealings in life | 19 | 1 | 2.47 | 1.31 | 1 | 5 | 4 | 3 | 1.48 |
| Note. MAD is the median average deviation with a consistency adjustment. | |||||||||