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tests/testthat/_snaps/data_codebook.md
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data_codebook iris
Code
data_codebook(iris)
Output
iris (150 rows and 5 variables, 5 shown)
ID | Name | Type | Missings | Values | N
---+--------------+-------------+----------+------------+-----------
1 | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150
---+--------------+-------------+----------+------------+-----------
2 | Sepal.Width | numeric | 0 (0.0%) | [2, 4.4] | 150
---+--------------+-------------+----------+------------+-----------
3 | Petal.Length | numeric | 0 (0.0%) | [1, 6.9] | 150
---+--------------+-------------+----------+------------+-----------
4 | Petal.Width | numeric | 0 (0.0%) | [0.1, 2.5] | 150
---+--------------+-------------+----------+------------+-----------
5 | Species | categorical | 0 (0.0%) | setosa | 50 (33.3%)
| | | | versicolor | 50 (33.3%)
| | | | virginica | 50 (33.3%)
--------------------------------------------------------------------
data_codebook iris, reordered
Code
data_codebook(iris[c(1, 2, 5, 3, 4)])
Output
iris[c(1, 2, 5, 3, 4)] (150 rows and 5 variables, 5 shown)
ID | Name | Type | Missings | Values | N
---+--------------+-------------+----------+------------+-----------
1 | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150
---+--------------+-------------+----------+------------+-----------
2 | Sepal.Width | numeric | 0 (0.0%) | [2, 4.4] | 150
---+--------------+-------------+----------+------------+-----------
3 | Species | categorical | 0 (0.0%) | setosa | 50 (33.3%)
| | | | versicolor | 50 (33.3%)
| | | | virginica | 50 (33.3%)
---+--------------+-------------+----------+------------+-----------
4 | Petal.Length | numeric | 0 (0.0%) | [1, 6.9] | 150
---+--------------+-------------+----------+------------+-----------
5 | Petal.Width | numeric | 0 (0.0%) | [0.1, 2.5] | 150
--------------------------------------------------------------------
data_codebook NaN and Inf
Code
data_codebook(d)
Output
d (9 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+-----------+--------+----------
1 | x | numeric | 2 (22.2%) | 1 | 3 (42.9%)
| | | | 2 | 1 (14.3%)
| | | | 4 | 2 (28.6%)
| | | | Inf | 1 (14.3%)
----------------------------------------------------
Code
data_codebook(d)
Output
d (102 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+---------+------------
1 | x | numeric | 0 (0.0%) | [1, 15] | 102 (98.1%)
| | | | Inf | 2 ( 1.9%)
------------------------------------------------------
Code
data_codebook(d, range_at = 100)
Output
d (102 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+--------+-----------
1 | x | numeric | 0 (0.0%) | 1 | 4 ( 4.0%)
| | | | 2 | 5 ( 5.0%)
| | | | 3 | 6 ( 6.0%)
| | | | 4 | 5 ( 5.0%)
| | | | 5 | 8 ( 8.0%)
| | | | 6 | 10 (10.0%)
| | | | 7 | 6 ( 6.0%)
| | | | 8 | 3 ( 3.0%)
| | | | 9 | 13 (13.0%)
| | | | 10 | 7 ( 7.0%)
| | | | (...) |
----------------------------------------------------
Code
data_codebook(d, range_at = 100, max_values = 4)
Output
d (102 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+--------+---------
1 | x | numeric | 0 (0.0%) | 1 | 4 (4.0%)
| | | | 2 | 5 (5.0%)
| | | | 3 | 6 (6.0%)
| | | | 4 | 5 (5.0%)
| | | | (...) |
--------------------------------------------------
data_codebook iris, select
Code
data_codebook(iris, select = starts_with("Sepal"))
Output
iris (150 rows and 5 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+--------------+---------+----------+------------+----
1 | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150
---+--------------+---------+----------+------------+----
2 | Sepal.Width | numeric | 0 (0.0%) | [2, 4.4] | 150
---------------------------------------------------------
data_codebook iris, select, ID
Code
data_codebook(iris, select = starts_with("Petal"))
Output
iris (150 rows and 5 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+--------------+---------+----------+------------+----
3 | Petal.Length | numeric | 0 (0.0%) | [1, 6.9] | 150
---+--------------+---------+----------+------------+----
4 | Petal.Width | numeric | 0 (0.0%) | [0.1, 2.5] | 150
---------------------------------------------------------
data_codebook efc
Code
data_codebook(efc)
Output
efc (100 rows and 5 variables, 5 shown)
ID | Name | Label | Type | Missings | Values | Value Labels | N
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
1 | c12hour | average number of hours of care per week | numeric | 2 (2.0%) | [5, 168] | | 98
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
2 | e16sex | elder's gender | numeric | 0 (0.0%) | 1 | male | 46 (46.0%)
| | | | | 2 | female | 54 (54.0%)
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
3 | e42dep | elder's dependency | categorical | 3 (3.0%) | 1 | independent | 2 ( 2.1%)
| | | | | 2 | slightly dependent | 4 ( 4.1%)
| | | | | 3 | moderately dependent | 28 (28.9%)
| | | | | 4 | severely dependent | 63 (64.9%)
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
4 | c172code | carer's level of education | numeric | 10 (10.0%) | 1 | low level of education | 8 ( 8.9%)
| | | | | 2 | intermediate level of education | 66 (73.3%)
| | | | | 3 | high level of education | 16 (17.8%)
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
5 | neg_c_7 | Negative impact with 7 items | numeric | 3 (3.0%) | [7, 28] | | 97
---------------------------------------------------------------------------------------------------------------------------------------------
data_codebook efc, variable_label_width
Code
data_codebook(efc, variable_label_width = 30)
Output
efc (100 rows and 5 variables, 5 shown)
ID | Name | Label | Type | Missings | Values | Value Labels | N
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
1 | c12hour | average number of hours of | numeric | 2 (2.0%) | [5, 168] | | 98
| | care per week | | | | |
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
2 | e16sex | elder's gender | numeric | 0 (0.0%) | 1 | male | 46 (46.0%)
| | | | | 2 | female | 54 (54.0%)
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
3 | e42dep | elder's dependency | categorical | 3 (3.0%) | 1 | independent | 2 ( 2.1%)
| | | | | 2 | slightly dependent | 4 ( 4.1%)
| | | | | 3 | moderately dependent | 28 (28.9%)
| | | | | 4 | severely dependent | 63 (64.9%)
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
4 | c172code | carer's level of education | numeric | 10 (10.0%) | 1 | low level of education | 8 ( 8.9%)
| | | | | 2 | intermediate level of education | 66 (73.3%)
| | | | | 3 | high level of education | 16 (17.8%)
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
5 | neg_c_7 | Negative impact with 7 items | numeric | 3 (3.0%) | [7, 28] | | 97
---------------------------------------------------------------------------------------------------------------------------------
data_codebook efc, value_label_width
Code
data_codebook(efc, variable_label_width = 30, value_label_width = 15)
Output
efc (100 rows and 5 variables, 5 shown)
ID | Name | Label | Type | Missings | Values | Value Labels | N
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
1 | c12hour | average number of hours of | numeric | 2 (2.0%) | [5, 168] | | 98
| | care per week | | | | |
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
2 | e16sex | elder's gender | numeric | 0 (0.0%) | 1 | male | 46 (46.0%)
| | | | | 2 | female | 54 (54.0%)
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
3 | e42dep | elder's dependency | categorical | 3 (3.0%) | 1 | independent | 2 ( 2.1%)
| | | | | 2 | slightly... | 4 ( 4.1%)
| | | | | 3 | moderately... | 28 (28.9%)
| | | | | 4 | severely... | 63 (64.9%)
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
4 | c172code | carer's level of education | numeric | 10 (10.0%) | 1 | low level of... | 8 ( 8.9%)
| | | | | 2 | intermediate... | 66 (73.3%)
| | | | | 3 | high level of... | 16 (17.8%)
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
5 | neg_c_7 | Negative impact with 7 items | numeric | 3 (3.0%) | [7, 28] | | 97
------------------------------------------------------------------------------------------------------------------
data_codebook truncated data
Code
data_codebook(d, max_values = 5)
Output
d (100 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+-----------+----------+---------+---------
1 | a | integer | 0 (0.0%) | [1, 15] | 100
---+------+-----------+----------+---------+---------
2 | b | character | 0 (0.0%) | a | 4 (4.0%)
| | | | b | 3 (3.0%)
| | | | c | 5 (5.0%)
| | | | d | 4 (4.0%)
| | | | e | 3 (3.0%)
| | | | (...) |
-----------------------------------------------------
data_codebook mixed numeric lengths
Code
data_codebook(d)
Output
d (100 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+---------+-----------
1 | a | integer | 0 (0.0%) | 1 | 28 (28.0%)
| | | | 2 | 26 (26.0%)
| | | | 3 | 29 (29.0%)
| | | | 4 | 17 (17.0%)
---+------+---------+----------+---------+-----------
2 | b | integer | 0 (0.0%) | [5, 15] | 100
-----------------------------------------------------
data_codebook mixed range_at
Code
data_codebook(d, range_at = 3)
Output
d (100 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+---------+----
1 | a | integer | 0 (0.0%) | [1, 4] | 100
---+------+---------+----------+---------+----
2 | b | integer | 0 (0.0%) | [5, 15] | 100
----------------------------------------------
data_codebook logicals
Code
data_codebook(d)
Output
d (100 rows and 3 variables, 3 shown)
ID | Name | Type | Missings | Values | N
---+------+-----------+----------+---------+-----------
1 | a | integer | 0 (0.0%) | [1, 15] | 100
---+------+-----------+----------+---------+-----------
2 | b | character | 0 (0.0%) | a | 26 (26.0%)
| | | | b | 38 (38.0%)
| | | | c | 36 (36.0%)
---+------+-----------+----------+---------+-----------
3 | c | logical | 0 (0.0%) | FALSE | 42 (42.0%)
| | | | TRUE | 58 (58.0%)
-------------------------------------------------------
data_codebook labelled data exceptions
Code
data_codebook(d)
Output
d (100 rows and 3 variables, 3 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+------------+--------+--------------+-----------
1 | f1 | integer | 17 (17.0%) | 1 | One | 21 (25.3%)
| | | | 2 | Two | 20 (24.1%)
| | | | 3 | Three | 23 (27.7%)
| | | | 5 | Five | 19 (22.9%)
---+------+---------+------------+--------+--------------+-----------
2 | f2 | integer | 0 (0.0%) | 1 | One | 25 (25.0%)
| | | | 2 | Two | 20 (20.0%)
| | | | 3 | Three | 14 (14.0%)
| | | | 4 | 4 | 17 (17.0%)
| | | | 5 | Five | 24 (24.0%)
---+------+---------+------------+--------+--------------+-----------
3 | f3 | integer | 0 (0.0%) | 1 | One | 21 (21.0%)
| | | | 2 | Two | 24 (24.0%)
| | | | 3 | Three | 16 (16.0%)
| | | | 4 | Four | 14 (14.0%)
| | | | 5 | Five | 25 (25.0%)
---------------------------------------------------------------------
data_codebook labelled data factors
Code
data_codebook(d)
Output
d (100 rows and 3 variables, 3 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+-------------+----------+--------+--------------+-----------
1 | f1 | categorical | 0 (0.0%) | a | A | 35 (35.0%)
| | | | b | Bee | 32 (32.0%)
| | | | c | Cee | 33 (33.0%)
---+------+-------------+----------+--------+--------------+-----------
2 | f2 | categorical | 0 (0.0%) | a | A | 30 (30.0%)
| | | | b | Bee | 38 (38.0%)
| | | | c | Cee | 32 (32.0%)
---+------+-------------+----------+--------+--------------+-----------
3 | f3 | categorical | 0 (0.0%) | a | A | 23 (23.0%)
| | | | b | Bee | 28 (28.0%)
| | | | c | Cee | 49 (49.0%)
-----------------------------------------------------------------------
data_codebook works with numbers < 1
Code
data_codebook(d)
Output
d (6 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+--------+----------
1 | a | numeric | 0 (0.0%) | 1 | 2 (33.3%)
| | | | 2 | 2 (33.3%)
| | | | 3 | 2 (33.3%)
---+------+---------+----------+--------+----------
2 | b | numeric | 0 (0.0%) | 0 | 3 (50.0%)
| | | | 1 | 2 (33.3%)
| | | | 2 | 1 (16.7%)
---------------------------------------------------
data_codebook, big marks
Code
data_codebook(d)
Output
d (1,000,000 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+-------------+----------+--------+----------------
1 | f1 | categorical | 0 (0.0%) | a | 333,238 (33.3%)
| | | | b | 332,910 (33.3%)
| | | | c | 333,852 (33.4%)
---+------+-------------+----------+--------+----------------
2 | f2 | categorical | 0 (0.0%) | 1 | 333,285 (33.3%)
| | | | 2 | 333,358 (33.3%)
| | | | 3 | 333,357 (33.3%)
-------------------------------------------------------------
data_codebook, tagged NA
Code
data_codebook(data.frame(x))
Output
data.frame(x) (26 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+------------+--------+--------------+----------
1 | x | numeric | 12 (46.2%) | 1 | Agreement | 4 (15.4%)
| | | | 2 | 2 | 4 (15.4%)
| | | | 3 | 3 | 4 (15.4%)
| | | | 4 | Disagreement | 2 ( 7.7%)
| | | | NA(a) | Refused | 4 (15.4%)
| | | | NA(c) | First | 5 (19.2%)
| | | | NA(z) | Not home | 3 (11.5%)
--------------------------------------------------------------------
Code
data_codebook(data.frame(x))
Output
data.frame(x) (23 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+-----------+--------+--------------+----------
1 | x | numeric | 9 (39.1%) | 1 | Agreement | 4 (17.4%)
| | | | 2 | 2 | 4 (17.4%)
| | | | 3 | 3 | 4 (17.4%)
| | | | 4 | Disagreement | 2 ( 8.7%)
| | | | NA(a) | Refused | 4 (17.4%)
| | | | NA(c) | First | 5 (21.7%)
-------------------------------------------------------------------
data_codebook, negative label values #334
Code
data_codebook(data.frame(x1, x2))
Output
data.frame(x1, x2) (4 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+----------+--------+--------------+----------
1 | x1 | integer | 0 (0.0%) | 1 | Agreement | 1 (25.0%)
| | | | 2 | 2 | 1 (25.0%)
| | | | 3 | 3 | 1 (25.0%)
| | | | 4 | Disagreement | 1 (25.0%)
---+------+---------+----------+--------+--------------+----------
2 | x2 | numeric | 0 (0.0%) | -9 | Missing | 1 (25.0%)
| | | | 1 | Agreement | 1 (25.0%)
| | | | 2 | 2 | 1 (25.0%)
| | | | 3 | 3 | 1 (25.0%)
------------------------------------------------------------------
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data_codebook iris
Code
data_codebook(iris)
Output
iris (150 rows and 5 variables, 5 shown)
ID | Name | Type | Missings | Values | N
---+--------------+-------------+----------+------------+-----------
1 | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150
---+--------------+-------------+----------+------------+-----------
2 | Sepal.Width | numeric | 0 (0.0%) | [2, 4.4] | 150
---+--------------+-------------+----------+------------+-----------
3 | Petal.Length | numeric | 0 (0.0%) | [1, 6.9] | 150
---+--------------+-------------+----------+------------+-----------
4 | Petal.Width | numeric | 0 (0.0%) | [0.1, 2.5] | 150
---+--------------+-------------+----------+------------+-----------
5 | Species | categorical | 0 (0.0%) | setosa | 50 (33.3%)
| | | | versicolor | 50 (33.3%)
| | | | virginica | 50 (33.3%)
--------------------------------------------------------------------
data_codebook iris, reordered
Code
data_codebook(iris[c(1, 2, 5, 3, 4)])
Output
iris[c(1, 2, 5, 3, 4)] (150 rows and 5 variables, 5 shown)
ID | Name | Type | Missings | Values | N
---+--------------+-------------+----------+------------+-----------
1 | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150
---+--------------+-------------+----------+------------+-----------
2 | Sepal.Width | numeric | 0 (0.0%) | [2, 4.4] | 150
---+--------------+-------------+----------+------------+-----------
3 | Species | categorical | 0 (0.0%) | setosa | 50 (33.3%)
| | | | versicolor | 50 (33.3%)
| | | | virginica | 50 (33.3%)
---+--------------+-------------+----------+------------+-----------
4 | Petal.Length | numeric | 0 (0.0%) | [1, 6.9] | 150
---+--------------+-------------+----------+------------+-----------
5 | Petal.Width | numeric | 0 (0.0%) | [0.1, 2.5] | 150
--------------------------------------------------------------------
data_codebook NaN and Inf
Code
data_codebook(d)
Output
d (9 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+-----------+--------+----------
1 | x | numeric | 2 (22.2%) | 1 | 3 (42.9%)
| | | | 2 | 1 (14.3%)
| | | | 4 | 2 (28.6%)
| | | | Inf | 1 (14.3%)
----------------------------------------------------
Code
data_codebook(d)
Output
d (102 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+---------+------------
1 | x | numeric | 0 (0.0%) | [1, 15] | 102 (98.1%)
| | | | Inf | 2 ( 1.9%)
------------------------------------------------------
Code
data_codebook(d, range_at = 100)
Output
d (102 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+--------+-----------
1 | x | numeric | 0 (0.0%) | 1 | 4 ( 4.0%)
| | | | 2 | 5 ( 5.0%)
| | | | 3 | 6 ( 6.0%)
| | | | 4 | 5 ( 5.0%)
| | | | 5 | 8 ( 8.0%)
| | | | 6 | 10 (10.0%)
| | | | 7 | 6 ( 6.0%)
| | | | 8 | 3 ( 3.0%)
| | | | 9 | 13 (13.0%)
| | | | 10 | 7 ( 7.0%)
| | | | (...) |
----------------------------------------------------
Code
data_codebook(d, range_at = 100, max_values = 4)
Output
d (102 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+--------+---------
1 | x | numeric | 0 (0.0%) | 1 | 4 (4.0%)
| | | | 2 | 5 (5.0%)
| | | | 3 | 6 (6.0%)
| | | | 4 | 5 (5.0%)
| | | | (...) |
--------------------------------------------------
data_codebook iris, select
Code
data_codebook(iris, select = starts_with("Sepal"))
Output
iris (150 rows and 5 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+--------------+---------+----------+------------+----
1 | Sepal.Length | numeric | 0 (0.0%) | [4.3, 7.9] | 150
---+--------------+---------+----------+------------+----
2 | Sepal.Width | numeric | 0 (0.0%) | [2, 4.4] | 150
---------------------------------------------------------
data_codebook iris, select, ID
Code
data_codebook(iris, select = starts_with("Petal"))
Output
iris (150 rows and 5 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+--------------+---------+----------+------------+----
3 | Petal.Length | numeric | 0 (0.0%) | [1, 6.9] | 150
---+--------------+---------+----------+------------+----
4 | Petal.Width | numeric | 0 (0.0%) | [0.1, 2.5] | 150
---------------------------------------------------------
data_codebook efc
Code
data_codebook(efc)
Output
efc (100 rows and 5 variables, 5 shown)
ID | Name | Label | Type | Missings | Values | Value Labels | N
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
1 | c12hour | average number of hours of care per week | numeric | 2 (2.0%) | [5, 168] | | 98
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
2 | e16sex | elder's gender | numeric | 0 (0.0%) | 1 | male | 46 (46.0%)
| | | | | 2 | female | 54 (54.0%)
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
3 | e42dep | elder's dependency | categorical | 3 (3.0%) | 1 | independent | 2 ( 2.1%)
| | | | | 2 | slightly dependent | 4 ( 4.1%)
| | | | | 3 | moderately dependent | 28 (28.9%)
| | | | | 4 | severely dependent | 63 (64.9%)
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
4 | c172code | carer's level of education | numeric | 10 (10.0%) | 1 | low level of education | 8 ( 8.9%)
| | | | | 2 | intermediate level of education | 66 (73.3%)
| | | | | 3 | high level of education | 16 (17.8%)
---+----------+------------------------------------------+-------------+------------+----------+---------------------------------+-----------
5 | neg_c_7 | Negative impact with 7 items | numeric | 3 (3.0%) | [7, 28] | | 97
---------------------------------------------------------------------------------------------------------------------------------------------
data_codebook efc, variable_label_width
Code
data_codebook(efc, variable_label_width = 30)
Output
efc (100 rows and 5 variables, 5 shown)
ID | Name | Label | Type | Missings | Values | Value Labels | N
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
1 | c12hour | average number of hours of | numeric | 2 (2.0%) | [5, 168] | | 98
| | care per week | | | | |
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
2 | e16sex | elder's gender | numeric | 0 (0.0%) | 1 | male | 46 (46.0%)
| | | | | 2 | female | 54 (54.0%)
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
3 | e42dep | elder's dependency | categorical | 3 (3.0%) | 1 | independent | 2 ( 2.1%)
| | | | | 2 | slightly dependent | 4 ( 4.1%)
| | | | | 3 | moderately dependent | 28 (28.9%)
| | | | | 4 | severely dependent | 63 (64.9%)
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
4 | c172code | carer's level of education | numeric | 10 (10.0%) | 1 | low level of education | 8 ( 8.9%)
| | | | | 2 | intermediate level of education | 66 (73.3%)
| | | | | 3 | high level of education | 16 (17.8%)
---+----------+------------------------------+-------------+------------+----------+---------------------------------+-----------
5 | neg_c_7 | Negative impact with 7 items | numeric | 3 (3.0%) | [7, 28] | | 97
---------------------------------------------------------------------------------------------------------------------------------
data_codebook efc, value_label_width
Code
data_codebook(efc, variable_label_width = 30, value_label_width = 15)
Output
efc (100 rows and 5 variables, 5 shown)
ID | Name | Label | Type | Missings | Values | Value Labels | N
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
1 | c12hour | average number of hours of | numeric | 2 (2.0%) | [5, 168] | | 98
| | care per week | | | | |
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
2 | e16sex | elder's gender | numeric | 0 (0.0%) | 1 | male | 46 (46.0%)
| | | | | 2 | female | 54 (54.0%)
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
3 | e42dep | elder's dependency | categorical | 3 (3.0%) | 1 | independent | 2 ( 2.1%)
| | | | | 2 | slightly... | 4 ( 4.1%)
| | | | | 3 | moderately... | 28 (28.9%)
| | | | | 4 | severely... | 63 (64.9%)
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
4 | c172code | carer's level of education | numeric | 10 (10.0%) | 1 | low level of... | 8 ( 8.9%)
| | | | | 2 | intermediate... | 66 (73.3%)
| | | | | 3 | high level of... | 16 (17.8%)
---+----------+------------------------------+-------------+------------+----------+------------------+-----------
5 | neg_c_7 | Negative impact with 7 items | numeric | 3 (3.0%) | [7, 28] | | 97
------------------------------------------------------------------------------------------------------------------
data_codebook truncated data
Code
data_codebook(d, max_values = 5)
Output
d (100 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+-----------+----------+---------+---------
1 | a | integer | 0 (0.0%) | [1, 15] | 100
---+------+-----------+----------+---------+---------
2 | b | character | 0 (0.0%) | a | 4 (4.0%)
| | | | b | 3 (3.0%)
| | | | c | 5 (5.0%)
| | | | d | 4 (4.0%)
| | | | e | 3 (3.0%)
| | | | (...) |
-----------------------------------------------------
data_codebook mixed numeric lengths
Code
data_codebook(d)
Output
d (100 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+---------+-----------
1 | a | integer | 0 (0.0%) | 1 | 28 (28.0%)
| | | | 2 | 26 (26.0%)
| | | | 3 | 29 (29.0%)
| | | | 4 | 17 (17.0%)
---+------+---------+----------+---------+-----------
2 | b | integer | 0 (0.0%) | [5, 15] | 100
-----------------------------------------------------
data_codebook mixed range_at
Code
data_codebook(d, range_at = 3)
Output
d (100 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+---------+----
1 | a | integer | 0 (0.0%) | [1, 4] | 100
---+------+---------+----------+---------+----
2 | b | integer | 0 (0.0%) | [5, 15] | 100
----------------------------------------------
data_codebook logicals
Code
data_codebook(d)
Output
d (100 rows and 3 variables, 3 shown)
ID | Name | Type | Missings | Values | N
---+------+-----------+----------+---------+-----------
1 | a | integer | 0 (0.0%) | [1, 15] | 100
---+------+-----------+----------+---------+-----------
2 | b | character | 0 (0.0%) | a | 26 (26.0%)
| | | | b | 38 (38.0%)
| | | | c | 36 (36.0%)
---+------+-----------+----------+---------+-----------
3 | c | logical | 0 (0.0%) | FALSE | 42 (42.0%)
| | | | TRUE | 58 (58.0%)
-------------------------------------------------------
data_codebook labelled data exceptions
Code
data_codebook(d)
Output
d (100 rows and 3 variables, 3 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+------------+--------+--------------+-----------
1 | f1 | integer | 17 (17.0%) | 1 | One | 21 (25.3%)
| | | | 2 | Two | 20 (24.1%)
| | | | 3 | Three | 23 (27.7%)
| | | | 5 | Five | 19 (22.9%)
---+------+---------+------------+--------+--------------+-----------
2 | f2 | integer | 0 (0.0%) | 1 | One | 25 (25.0%)
| | | | 2 | Two | 20 (20.0%)
| | | | 3 | Three | 14 (14.0%)
| | | | 4 | 4 | 17 (17.0%)
| | | | 5 | Five | 24 (24.0%)
---+------+---------+------------+--------+--------------+-----------
3 | f3 | integer | 0 (0.0%) | 1 | One | 21 (21.0%)
| | | | 2 | Two | 24 (24.0%)
| | | | 3 | Three | 16 (16.0%)
| | | | 4 | Four | 14 (14.0%)
| | | | 5 | Five | 25 (25.0%)
---------------------------------------------------------------------
data_codebook labelled data factors
Code
data_codebook(d)
Output
d (100 rows and 3 variables, 3 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+-------------+----------+--------+--------------+-----------
1 | f1 | categorical | 0 (0.0%) | a | A | 35 (35.0%)
| | | | b | Bee | 32 (32.0%)
| | | | c | Cee | 33 (33.0%)
---+------+-------------+----------+--------+--------------+-----------
2 | f2 | categorical | 0 (0.0%) | a | A | 30 (30.0%)
| | | | b | Bee | 38 (38.0%)
| | | | c | Cee | 32 (32.0%)
---+------+-------------+----------+--------+--------------+-----------
3 | f3 | categorical | 0 (0.0%) | a | A | 23 (23.0%)
| | | | b | Bee | 28 (28.0%)
| | | | c | Cee | 49 (49.0%)
-----------------------------------------------------------------------
data_codebook works with numbers < 1
Code
data_codebook(d)
Output
d (6 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+---------+----------+--------+----------
1 | a | numeric | 0 (0.0%) | 1 | 2 (33.3%)
| | | | 2 | 2 (33.3%)
| | | | 3 | 2 (33.3%)
---+------+---------+----------+--------+----------
2 | b | numeric | 0 (0.0%) | 0 | 3 (50.0%)
| | | | 1 | 2 (33.3%)
| | | | 2 | 1 (16.7%)
---------------------------------------------------
data_codebook, big marks
Code
data_codebook(d)
Output
d (1,000,000 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | N
---+------+-------------+----------+--------+----------------
1 | f1 | categorical | 0 (0.0%) | a | 333,238 (33.3%)
| | | | b | 332,910 (33.3%)
| | | | c | 333,852 (33.4%)
---+------+-------------+----------+--------+----------------
2 | f2 | categorical | 0 (0.0%) | 1 | 333,285 (33.3%)
| | | | 2 | 333,358 (33.3%)
| | | | 3 | 333,357 (33.3%)
-------------------------------------------------------------
data_codebook, tagged NA
Code
data_codebook(data.frame(x))
Output
data.frame(x) (26 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+------------+--------+--------------+----------
1 | x | numeric | 12 (46.2%) | 1 | Agreement | 4 (15.4%)
| | | | 2 | 2 | 4 (15.4%)
| | | | 3 | 3 | 4 (15.4%)
| | | | 4 | Disagreement | 2 ( 7.7%)
| | | | NA(a) | Refused | 4 (15.4%)
| | | | NA(c) | First | 5 (19.2%)
| | | | NA(z) | Not home | 3 (11.5%)
--------------------------------------------------------------------
Code
data_codebook(data.frame(x))
Output
data.frame(x) (23 rows and 1 variables, 1 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+-----------+--------+--------------+----------
1 | x | numeric | 9 (39.1%) | 1 | Agreement | 4 (17.4%)
| | | | 2 | 2 | 4 (17.4%)
| | | | 3 | 3 | 4 (17.4%)
| | | | 4 | Disagreement | 2 ( 8.7%)
| | | | NA(a) | Refused | 4 (17.4%)
| | | | NA(c) | First | 5 (21.7%)
-------------------------------------------------------------------
data_codebook, negative label values #334
Code
data_codebook(data.frame(x1, x2))
Output
data.frame(x1, x2) (4 rows and 2 variables, 2 shown)
ID | Name | Type | Missings | Values | Value Labels | N
---+------+---------+----------+--------+--------------+----------
1 | x1 | integer | 0 (0.0%) | 1 | Agreement | 1 (25.0%)
| | | | 2 | 2 | 1 (25.0%)
| | | | 3 | 3 | 1 (25.0%)
| | | | 4 | Disagreement | 1 (25.0%)
---+------+---------+----------+--------+--------------+----------
2 | x2 | numeric | 0 (0.0%) | -9 | Missing | 1 (25.0%)
| | | | 1 | Agreement | 1 (25.0%)
| | | | 2 | 2 | 1 (25.0%)
| | | | 3 | 3 | 1 (25.0%)
------------------------------------------------------------------
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