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tests/testthat/_snaps/data_tabulate.md
In datawizard: Easy Data Wrangling and Statistical Transformations
data_tabulate, weights
Code
print(data_tabulate(efc$e42dep, weights = efc$weights))
Output
elder's dependency (efc$e42dep) <categorical>
# total N=105 valid N=100 (weighted)
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 3 | 2.86 | 3.00 | 3.00
2 | 4 | 3.81 | 4.00 | 7.00
3 | 26 | 24.76 | 26.00 | 33.00
4 | 67 | 63.81 | 67.00 | 100.00
<NA> | 5 | 4.76 | <NA> | <NA>
Code
print_md(data_tabulate(efc$e42dep, weights = efc$weights))
Output
Table: elder's dependency (efc$e42dep) (categorical)
|Value | N| Raw %| Valid %| Cumulative %|
|:-----|--:|-----:|-------:|------------:|
|1 | 3| 2.86| 3.00| 3.00|
|2 | 4| 3.81| 4.00| 7.00|
|3 | 26| 24.76| 26.00| 33.00|
|4 | 67| 63.81| 67.00| 100.00|
|(NA) | 5| 4.76| (NA)| (NA)|
total N=105 valid N=100 (weighted)
Code
display(data_tabulate(efc$e42dep, weights = efc$weights))
Output
Table: elder's dependency (efc$e42dep) (categorical)
|Value | N| Raw %| Valid %| Cumulative %|
|:-----|--:|-----:|-------:|------------:|
|1 | 3| 2.86| 3.00| 3.00|
|2 | 4| 3.81| 4.00| 7.00|
|3 | 26| 24.76| 26.00| 33.00|
|4 | 67| 63.81| 67.00| 100.00|
|(NA) | 5| 4.76| (NA)| (NA)|
total N=105 valid N=100 (weighted)
Code
print(data_tabulate(efc, c("e42dep", "e16sex"), collapse = TRUE, weights = efc$
weights))
Output
# Frequency Table (weighted)
Variable | Value | N | Raw % | Valid % | Cumulative %
---------+-------+----+-------+---------+-------------
e42dep | 1 | 3 | 2.86 | 3.00 | 3.00
| 2 | 4 | 3.81 | 4.00 | 7.00
| 3 | 26 | 24.76 | 26.00 | 33.00
| 4 | 67 | 63.81 | 67.00 | 100.00
| <NA> | 5 | 4.76 | <NA> | <NA>
---------+-------+----+-------+---------+-------------
e16sex | 1 | 50 | 47.62 | 47.62 | 47.62
| 2 | 55 | 52.38 | 52.38 | 100.00
| <NA> | 0 | 0.00 | <NA> | <NA>
------------------------------------------------------
Code
print_md(data_tabulate(efc, c("e42dep", "e16sex"), weights = efc$weights))
Output
Table: Frequency Table (weighted)
|Variable | Value| N| Raw %| Valid %| Cumulative %|
|:--------|-----:|--:|-----:|-------:|------------:|
|e42dep | 1| 3| 2.86| 3.00| 3.00|
| | 2| 4| 3.81| 4.00| 7.00|
| | 3| 26| 24.76| 26.00| 33.00|
| | 4| 67| 63.81| 67.00| 100.00|
| | (NA)| 5| 4.76| (NA)| (NA)|
| | | | | | |
|e16sex | 1| 50| 47.62| 47.62| 47.62|
| | 2| 55| 52.38| 52.38| 100.00|
| | (NA)| 0| 0.00| (NA)| (NA)|
| | | | | | |
Code
display(data_tabulate(efc, c("e42dep", "e16sex"), weights = efc$weights))
Output
Table: Frequency Table (weighted)
|Variable | Value| N| Raw %| Valid %| Cumulative %|
|:--------|-----:|--:|-----:|-------:|------------:|
|e42dep | 1| 3| 2.86| 3.00| 3.00|
| | 2| 4| 3.81| 4.00| 7.00|
| | 3| 26| 24.76| 26.00| 33.00|
| | 4| 67| 63.81| 67.00| 100.00|
| | (NA)| 5| 4.76| (NA)| (NA)|
| | | | | | |
|e16sex | 1| 50| 47.62| 47.62| 47.62|
| | 2| 55| 52.38| 52.38| 100.00|
| | (NA)| 0| 0.00| (NA)| (NA)|
| | | | | | |
data_tabulate print
Code
data_tabulate(efc$e42dep)
Output
elder's dependency (efc$e42dep) <categorical>
# total N=100 valid N=97
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 2 | 2.00 | 2.06 | 2.06
2 | 4 | 4.00 | 4.12 | 6.19
3 | 28 | 28.00 | 28.87 | 35.05
4 | 63 | 63.00 | 64.95 | 100.00
<NA> | 3 | 3.00 | <NA> | <NA>
data_tabulate print multiple
Code
data_tabulate(efc, c("c172code", "e16sex"))
Output
carer's level of education (c172code) <numeric>
# total N=100 valid N=90
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 8 | 8.00 | 8.89 | 8.89
2 | 66 | 66.00 | 73.33 | 82.22
3 | 16 | 16.00 | 17.78 | 100.00
<NA> | 10 | 10.00 | <NA> | <NA>
elder's gender (e16sex) <numeric>
# total N=100 valid N=100
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 46 | 46.00 | 46.00 | 46.00
2 | 54 | 54.00 | 54.00 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
data_tabulate big numbers
Code
data_tabulate(x)
Output
x <integer>
# total N=10,000,000 valid N=10,000,000
Value | N | Raw % | Valid % | Cumulative %
------+-----------+-------+---------+-------------
1 | 1,998,318 | 19.98 | 19.98 | 19.98
2 | 1,998,338 | 19.98 | 19.98 | 39.97
3 | 2,001,814 | 20.02 | 20.02 | 59.98
4 | 1,999,423 | 19.99 | 19.99 | 79.98
5 | 2,002,107 | 20.02 | 20.02 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
Code
print(data_tabulate(x), big_mark = "-")
Output
x <integer>
# total N=10-000-000 valid N=10-000-000
Value | N | Raw % | Valid % | Cumulative %
------+-----------+-------+---------+-------------
1 | 1-998-318 | 19.98 | 19.98 | 19.98
2 | 1-998-338 | 19.98 | 19.98 | 39.97
3 | 2-001-814 | 20.02 | 20.02 | 59.98
4 | 1-999-423 | 19.99 | 19.99 | 79.98
5 | 2-002-107 | 20.02 | 20.02 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
Code
print(data_tabulate(x), big_mark = "")
Output
x <integer>
# total N=10000000 valid N=10000000
Value | N | Raw % | Valid % | Cumulative %
------+---------+-------+---------+-------------
1 | 1998318 | 19.98 | 19.98 | 19.98
2 | 1998338 | 19.98 | 19.98 | 39.97
3 | 2001814 | 20.02 | 20.02 | 59.98
4 | 1999423 | 19.99 | 19.99 | 79.98
5 | 2002107 | 20.02 | 20.02 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
data_tabulate print multiple, collapse
Code
data_tabulate(efc, c("c172code", "e16sex"), collapse = TRUE)
Output
# Frequency Table
Variable | Value | N | Raw % | Valid % | Cumulative %
---------+-------+----+-------+---------+-------------
c172code | 1 | 8 | 8.00 | 8.89 | 8.89
| 2 | 66 | 66.00 | 73.33 | 82.22
| 3 | 16 | 16.00 | 17.78 | 100.00
| <NA> | 10 | 10.00 | <NA> | <NA>
---------+-------+----+-------+---------+-------------
e16sex | 1 | 46 | 46.00 | 46.00 | 46.00
| 2 | 54 | 54.00 | 54.00 | 100.00
| <NA> | 0 | 0.00 | <NA> | <NA>
------------------------------------------------------
data_tabulate print grouped data
Code
data_tabulate(poorman::group_by(efc, e16sex), "c172code")
Output
carer's level of education (c172code) <numeric>
Grouped by e16sex (1)
# total N=46 valid N=41
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 5 | 10.87 | 12.20 | 12.20
2 | 32 | 69.57 | 78.05 | 90.24
3 | 4 | 8.70 | 9.76 | 100.00
<NA> | 5 | 10.87 | <NA> | <NA>
carer's level of education (c172code) <numeric>
Grouped by e16sex (2)
# total N=54 valid N=49
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 3 | 5.56 | 6.12 | 6.12
2 | 34 | 62.96 | 69.39 | 75.51
3 | 12 | 22.22 | 24.49 | 100.00
<NA> | 5 | 9.26 | <NA> | <NA>
data_tabulate print, collapse groups
Code
data_tabulate(poorman::group_by(efc, e16sex), "c172code", collapse = TRUE)
Output
# Frequency Table
Variable | Group | Value | N | Raw % | Valid % | Cumulative %
---------+------------+-------+----+-------+---------+-------------
c172code | e16sex (1) | 1 | 5 | 10.87 | 12.20 | 12.20
| | 2 | 32 | 69.57 | 78.05 | 90.24
| | 3 | 4 | 8.70 | 9.76 | 100.00
| | <NA> | 5 | 10.87 | <NA> | <NA>
---------+------------+-------+----+-------+---------+-------------
c172code | e16sex (2) | 1 | 3 | 5.56 | 6.12 | 6.12
| | 2 | 34 | 62.96 | 69.39 | 75.51
| | 3 | 12 | 22.22 | 24.49 | 100.00
| | <NA> | 5 | 9.26 | <NA> | <NA>
-------------------------------------------------------------------
data_tabulate print, collapse groups, drop levels
Code
data_tabulate(poorman::group_by(efc, e16sex), "e42dep", collapse = TRUE,
drop_levels = TRUE)
Output
# Frequency Table
Variable | Group | Value | N | Raw % | Valid % | Cumulative %
---------+------------+-------+----+-------+---------+-------------
e42dep | e16sex (1) | 1 | 2 | 4.35 | 4.44 | 4.44
| | 2 | 2 | 4.35 | 4.44 | 8.89
| | 3 | 8 | 17.39 | 17.78 | 26.67
| | 4 | 33 | 71.74 | 73.33 | 100.00
| | <NA> | 1 | 2.17 | <NA> | <NA>
---------+------------+-------+----+-------+---------+-------------
e42dep | e16sex (2) | 2 | 2 | 3.70 | 3.85 | 3.85
| | 3 | 20 | 37.04 | 38.46 | 42.31
| | 4 | 30 | 55.56 | 57.69 | 100.00
| | <NA> | 2 | 3.70 | <NA> | <NA>
-------------------------------------------------------------------
data_tabulate, cross tables
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full"))
Output
efc$c172code | male | female | <NA> | Total
-------------+------------+------------+----------+------
1 | 5 (5.0%) | 2 (2.0%) | 1 (1.0%) | 8
2 | 31 (31.0%) | 33 (33.0%) | 2 (2.0%) | 66
3 | 4 (4.0%) | 11 (11.0%) | 1 (1.0%) | 16
<NA> | 5 (5.0%) | 4 (4.0%) | 1 (1.0%) | 10
-------------+------------+------------+----------+------
Total | 45 | 50 | 5 | 100
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE))
Output
efc$c172code | male | female | Total
-------------+------------+------------+------
1 | 5 (5.8%) | 2 (2.3%) | 7
2 | 31 (36.0%) | 33 (38.4%) | 64
3 | 4 (4.7%) | 11 (12.8%) | 15
-------------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
weights = efc$weights))
Output
efc$c172code | male | female | <NA> | Total
-------------+------------+------------+----------+------
1 | 5 (4.8%) | 3 (2.9%) | 2 (1.9%) | 10
2 | 32 (30.5%) | 32 (30.5%) | 3 (2.9%) | 67
3 | 3 (2.9%) | 11 (10.5%) | 1 (1.0%) | 15
<NA> | 8 (7.6%) | 5 (4.8%) | 1 (1.0%) | 14
-------------+------------+------------+----------+------
Total | 48 | 51 | 7 | 105
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE, weights = efc$weights))
Output
efc$c172code | male | female | Total
-------------+------------+------------+------
1 | 5 (5.8%) | 3 (3.5%) | 8
2 | 32 (37.2%) | 32 (37.2%) | 64
3 | 3 (3.5%) | 11 (12.8%) | 14
-------------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row"))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (62.5%) | 2 (25.0%) | 1 (12.5%) | 8
2 | 31 (47.0%) | 33 (50.0%) | 2 (3.0%) | 66
3 | 4 (25.0%) | 11 (68.8%) | 1 (6.2%) | 16
<NA> | 5 (50.0%) | 4 (40.0%) | 1 (10.0%) | 10
---------+------------+------------+-----------+------
Total | 45 | 50 | 5 | 100
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row",
remove_na = TRUE))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (71.4%) | 2 (28.6%) | 7
2 | 31 (48.4%) | 33 (51.6%) | 64
3 | 4 (26.7%) | 11 (73.3%) | 15
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row",
weights = efc$weights))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (50.0%) | 3 (30.0%) | 2 (20.0%) | 10
2 | 32 (47.8%) | 32 (47.8%) | 3 (4.5%) | 67
3 | 3 (20.0%) | 11 (73.3%) | 1 (6.7%) | 15
<NA> | 8 (57.1%) | 5 (35.7%) | 1 (7.1%) | 14
---------+------------+------------+-----------+------
Total | 48 | 51 | 7 | 105
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row",
remove_na = TRUE, weights = efc$weights))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (62.5%) | 3 (37.5%) | 8
2 | 32 (50.0%) | 32 (50.0%) | 64
3 | 3 (21.4%) | 11 (78.6%) | 14
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column"))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (11.1%) | 2 (4.0%) | 1 (20.0%) | 8
2 | 31 (68.9%) | 33 (66.0%) | 2 (40.0%) | 66
3 | 4 (8.9%) | 11 (22.0%) | 1 (20.0%) | 16
<NA> | 5 (11.1%) | 4 (8.0%) | 1 (20.0%) | 10
---------+------------+------------+-----------+------
Total | 45 | 50 | 5 | 100
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (12.5%) | 2 (4.3%) | 7
2 | 31 (77.5%) | 33 (71.7%) | 64
3 | 4 (10.0%) | 11 (23.9%) | 15
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
weights = "weights"))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (10.4%) | 3 (5.9%) | 2 (28.6%) | 10
2 | 32 (66.7%) | 32 (62.7%) | 3 (42.9%) | 67
3 | 3 (6.2%) | 11 (21.6%) | 1 (14.3%) | 15
<NA> | 8 (16.7%) | 5 (9.8%) | 1 (14.3%) | 14
---------+------------+------------+-----------+------
Total | 48 | 51 | 7 | 105
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (12.5%) | 3 (6.5%) | 8
2 | 32 (80.0%) | 32 (69.6%) | 64
3 | 3 (7.5%) | 11 (23.9%) | 14
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex", proportions = "row"))
Output
Variable | Value | male | female | <NA> | Total
---------+-------+------------+------------+------------+------
c172code | 1 | 5 (62.5%) | 2 (25.0%) | 1 (12.5%) | 8
c172code | 2 | 31 (47.0%) | 33 (50.0%) | 2 (3.0%) | 66
c172code | 3 | 4 (25.0%) | 11 (68.8%) | 1 (6.2%) | 16
c172code | <NA> | 5 (50.0%) | 4 (40.0%) | 1 (10.0%) | 10
e42dep | 1 | 2 (100.0%) | 0 (0.0%) | 0 (0.0%) | 2
e42dep | 2 | 2 (50.0%) | 2 (50.0%) | 0 (0.0%) | 4
e42dep | 3 | 8 (28.6%) | 18 (64.3%) | 2 (7.1%) | 28
e42dep | 4 | 32 (50.8%) | 28 (44.4%) | 3 (4.8%) | 63
e42dep | <NA> | 1 (33.3%) | 2 (66.7%) | 0 (0.0%) | 3
data_tabulate, cross tables, grouped df
Code
print(data_tabulate(grp, "c172code", by = "e16sex", proportions = "row"))
Output
Grouped by e42dep (1)
Variable | Value | male | female | <NA> | Total
---------+-------+------------+--------+------------+------
c172code | 2 | 2 (100.0%) | <NA> | 0 (0.0%) | 2
| <NA> | 0 (0%) | <NA> | 0 (0%) | 0
Grouped by e42dep (2)
Variable | Value | male | female | <NA> | Total
---------+-------+-----------+-----------+-----------+------
c172code | 2 | 2 (50.0%) | 2 (50.0%) | 0 (0.0%) | 4
| <NA> | 0 (0%) | 0 (0%) | 0 (0%) | 0
Grouped by e42dep (3)
Variable | Value | male | female | <NA> | Total
---------+-------+-----------+------------+-----------+------
c172code | 1 | 2 (50.0%) | 2 (50.0%) | 0 (0.0%) | 4
| 2 | 4 (25.0%) | 11 (68.8%) | 1 (6.2%) | 16
| 3 | 1 (16.7%) | 5 (83.3%) | 0 (0.0%) | 6
| <NA> | 1 (50.0%) | 0 (0.0%) | 1 (50.0%) | 2
Grouped by e42dep (4)
Variable | Value | male | female | <NA> | Total
---------+-------+------------+------------+-----------+------
c172code | 1 | 3 (75.0%) | 0 (0.0%) | 1 (25.0%) | 4
| 2 | 23 (54.8%) | 18 (42.9%) | 1 (2.4%) | 42
| 3 | 3 (30.0%) | 6 (60.0%) | 1 (10.0%) | 10
| <NA> | 3 (42.9%) | 4 (57.1%) | 0 (0.0%) | 7
Grouped by e42dep (NA)
Variable | Value | male | female | <NA> | Total
---------+-------+------------+------------+------------+------
c172code | 2 | 0 (0.0%) | 2 (100.0%) | 0 (0.0%) | 2
| <NA> | 1 (100.0%) | 0 (0.0%) | 0 (0.0%) | 1
data_tabulate, cross tables, print/format works
Code
print(x)
Output
Variable | Value | 3 | 4 | 5 | <NA> | Total
---------+-------+----+---+---+------+------
cyl | 4 | 1 | 8 | 2 | 0 | 11
cyl | 6 | 2 | 4 | 1 | 0 | 7
cyl | 8 | 12 | 0 | 2 | 0 | 14
cyl | <NA> | 0 | 0 | 0 | 0 | 0
am | 0 | 15 | 4 | 0 | 0 | 19
am | 1 | 0 | 8 | 5 | 0 | 13
am | <NA> | 0 | 0 | 0 | 0 | 0
data_tabulate, cross tables, markdown
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full"))
Output
|efc$c172code | male| female| (NA) | Total|
|:------------|----------:|----------:|:--------|-----:|
|1 | 5 (5.0%)| 2 (2.0%)|1 (1.0%) | 8|
|2 | 31 (31.0%)| 33 (33.0%)|2 (2.0%) | 66|
|3 | 4 (4.0%)| 11 (11.0%)|1 (1.0%) | 16|
|(NA) | 5 (5.0%)| 4 (4.0%)|1 (1.0%) | 10|
| | | | | |
|Total | 45| 50| 5 | 100|
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE))
Output
|efc$c172code | male| female| Total|
|:------------|----------:|----------:|-----:|
|1 | 5 (5.8%)| 2 (2.3%)| 7|
|2 | 31 (36.0%)| 33 (38.4%)| 64|
|3 | 4 (4.7%)| 11 (12.8%)| 15|
| | | | |
|Total | 40| 46| 86|
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
weights = efc$weights))
Output
|efc$c172code | male| female| (NA) | Total|
|:------------|----------:|----------:|:--------|-----:|
|1 | 5 (4.8%)| 3 (2.9%)|2 (1.9%) | 10|
|2 | 32 (30.5%)| 32 (30.5%)|3 (2.9%) | 67|
|3 | 3 (2.9%)| 11 (10.5%)|1 (1.0%) | 15|
|(NA) | 8 (7.6%)| 5 (4.8%)|1 (1.0%) | 14|
| | | | | |
|Total | 48| 51| 7 | 105|
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE, weights = efc$weights))
Output
|efc$c172code | male| female| Total|
|:------------|----------:|----------:|-----:|
|1 | 5 (5.8%)| 3 (3.5%)| 8|
|2 | 32 (37.2%)| 32 (37.2%)| 64|
|3 | 3 (3.5%)| 11 (12.8%)| 14|
| | | | |
|Total | 40| 46| 86|
Code
print_md(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
|c172code | male| female| Total|
|:--------|----------:|----------:|-----:|
|1 | 5 (12.5%)| 3 (6.5%)| 8|
|2 | 32 (80.0%)| 32 (69.6%)| 64|
|3 | 3 (7.5%)| 11 (23.9%)| 14|
| | | | |
|Total | 40| 46| 86|
Code
print_md(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex",
proportions = "row"))
Output
|Variable | Value| male| female| (NA)| Total|
|:--------|-----:|----------:|----------:|----------:|-----:|
|c172code | 1| 5 (62.5%)| 2 (25.0%)| 1 (12.5%)| 8|
|c172code | 2| 31 (47.0%)| 33 (50.0%)| 2 (3.0%)| 66|
|c172code | 3| 4 (25.0%)| 11 (68.8%)| 1 (6.2%)| 16|
|c172code | (NA)| 5 (50.0%)| 4 (40.0%)| 1 (10.0%)| 10|
|e42dep | 1| 2 (100.0%)| 0 (0.0%)| 0 (0.0%)| 2|
|e42dep | 2| 2 (50.0%)| 2 (50.0%)| 0 (0.0%)| 4|
|e42dep | 3| 8 (28.6%)| 18 (64.3%)| 2 (7.1%)| 28|
|e42dep | 4| 32 (50.8%)| 28 (44.4%)| 3 (4.8%)| 63|
|e42dep | (NA)| 1 (33.3%)| 2 (66.7%)| 0 (0.0%)| 3|
Code
display(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
|c172code | male| female| Total|
|:--------|----------:|----------:|-----:|
|1 | 5 (12.5%)| 3 (6.5%)| 8|
|2 | 32 (80.0%)| 32 (69.6%)| 64|
|3 | 3 (7.5%)| 11 (23.9%)| 14|
| | | | |
|Total | 40| 46| 86|
Code
display(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex", proportions = "row"))
Output
|Variable | Value| male| female| (NA)| Total|
|:--------|-----:|----------:|----------:|----------:|-----:|
|c172code | 1| 5 (62.5%)| 2 (25.0%)| 1 (12.5%)| 8|
|c172code | 2| 31 (47.0%)| 33 (50.0%)| 2 (3.0%)| 66|
|c172code | 3| 4 (25.0%)| 11 (68.8%)| 1 (6.2%)| 16|
|c172code | (NA)| 5 (50.0%)| 4 (40.0%)| 1 (10.0%)| 10|
|e42dep | 1| 2 (100.0%)| 0 (0.0%)| 0 (0.0%)| 2|
|e42dep | 2| 2 (50.0%)| 2 (50.0%)| 0 (0.0%)| 4|
|e42dep | 3| 8 (28.6%)| 18 (64.3%)| 2 (7.1%)| 28|
|e42dep | 4| 32 (50.8%)| 28 (44.4%)| 3 (4.8%)| 63|
|e42dep | (NA)| 1 (33.3%)| 2 (66.7%)| 0 (0.0%)| 3|
data_tabulate, correct 0% for proportions
Code
print(out[[1]])
Output
c172code | male | female | <NA> | Total
---------+------------+------------+--------+------
1 | 5 (10.9%) | 3 (5.6%) | 0 (0%) | 8
2 | 32 (69.6%) | 34 (63.0%) | 0 (0%) | 66
3 | 4 (8.7%) | 12 (22.2%) | 0 (0%) | 16
<NA> | 5 (10.9%) | 5 (9.3%) | 0 (0%) | 10
---------+------------+------------+--------+------
Total | 46 | 54 | 0 | 100
data_tabulate, table methods
Code
as.table(x)
Output
[[1]]
4 6 8
11 7 14
Code
as.table(x)
Output
[[1]]
4 6 8
11 7 14
Code
as.table(x, remove_na = FALSE)
Output
[[1]]
4 6 8 <NA>
11 7 14 0
Code
as.table(x)
Output
[[1]]
4 6 8
11 7 14
[[2]]
3 4 5
15 12 5
Code
as.table(x)
Output
[[1]]
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x, simplify = TRUE)
Output
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x)
Output
[[1]]
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x, simplify = TRUE)
Output
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x)
Output
[[1]]
3 4 5
0 15 4 0
1 0 8 5
[[2]]
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x)
Output
$`am (0)`
3 4
4 1 2
6 2 2
8 12 0
$`am (1)`
4 5
4 6 2
6 2 1
8 0 2
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data_tabulate, weights
Code
print(data_tabulate(efc$e42dep, weights = efc$weights))
Output
elder's dependency (efc$e42dep) <categorical>
# total N=105 valid N=100 (weighted)
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 3 | 2.86 | 3.00 | 3.00
2 | 4 | 3.81 | 4.00 | 7.00
3 | 26 | 24.76 | 26.00 | 33.00
4 | 67 | 63.81 | 67.00 | 100.00
<NA> | 5 | 4.76 | <NA> | <NA>
Code
print_md(data_tabulate(efc$e42dep, weights = efc$weights))
Output
Table: elder's dependency (efc$e42dep) (categorical)
|Value | N| Raw %| Valid %| Cumulative %|
|:-----|--:|-----:|-------:|------------:|
|1 | 3| 2.86| 3.00| 3.00|
|2 | 4| 3.81| 4.00| 7.00|
|3 | 26| 24.76| 26.00| 33.00|
|4 | 67| 63.81| 67.00| 100.00|
|(NA) | 5| 4.76| (NA)| (NA)|
total N=105 valid N=100 (weighted)
Code
display(data_tabulate(efc$e42dep, weights = efc$weights))
Output
Table: elder's dependency (efc$e42dep) (categorical)
|Value | N| Raw %| Valid %| Cumulative %|
|:-----|--:|-----:|-------:|------------:|
|1 | 3| 2.86| 3.00| 3.00|
|2 | 4| 3.81| 4.00| 7.00|
|3 | 26| 24.76| 26.00| 33.00|
|4 | 67| 63.81| 67.00| 100.00|
|(NA) | 5| 4.76| (NA)| (NA)|
total N=105 valid N=100 (weighted)
Code
print(data_tabulate(efc, c("e42dep", "e16sex"), collapse = TRUE, weights = efc$
weights))
Output
# Frequency Table (weighted)
Variable | Value | N | Raw % | Valid % | Cumulative %
---------+-------+----+-------+---------+-------------
e42dep | 1 | 3 | 2.86 | 3.00 | 3.00
| 2 | 4 | 3.81 | 4.00 | 7.00
| 3 | 26 | 24.76 | 26.00 | 33.00
| 4 | 67 | 63.81 | 67.00 | 100.00
| <NA> | 5 | 4.76 | <NA> | <NA>
---------+-------+----+-------+---------+-------------
e16sex | 1 | 50 | 47.62 | 47.62 | 47.62
| 2 | 55 | 52.38 | 52.38 | 100.00
| <NA> | 0 | 0.00 | <NA> | <NA>
------------------------------------------------------
Code
print_md(data_tabulate(efc, c("e42dep", "e16sex"), weights = efc$weights))
Output
Table: Frequency Table (weighted)
|Variable | Value| N| Raw %| Valid %| Cumulative %|
|:--------|-----:|--:|-----:|-------:|------------:|
|e42dep | 1| 3| 2.86| 3.00| 3.00|
| | 2| 4| 3.81| 4.00| 7.00|
| | 3| 26| 24.76| 26.00| 33.00|
| | 4| 67| 63.81| 67.00| 100.00|
| | (NA)| 5| 4.76| (NA)| (NA)|
| | | | | | |
|e16sex | 1| 50| 47.62| 47.62| 47.62|
| | 2| 55| 52.38| 52.38| 100.00|
| | (NA)| 0| 0.00| (NA)| (NA)|
| | | | | | |
Code
display(data_tabulate(efc, c("e42dep", "e16sex"), weights = efc$weights))
Output
Table: Frequency Table (weighted)
|Variable | Value| N| Raw %| Valid %| Cumulative %|
|:--------|-----:|--:|-----:|-------:|------------:|
|e42dep | 1| 3| 2.86| 3.00| 3.00|
| | 2| 4| 3.81| 4.00| 7.00|
| | 3| 26| 24.76| 26.00| 33.00|
| | 4| 67| 63.81| 67.00| 100.00|
| | (NA)| 5| 4.76| (NA)| (NA)|
| | | | | | |
|e16sex | 1| 50| 47.62| 47.62| 47.62|
| | 2| 55| 52.38| 52.38| 100.00|
| | (NA)| 0| 0.00| (NA)| (NA)|
| | | | | | |
data_tabulate print
Code
data_tabulate(efc$e42dep)
Output
elder's dependency (efc$e42dep) <categorical>
# total N=100 valid N=97
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 2 | 2.00 | 2.06 | 2.06
2 | 4 | 4.00 | 4.12 | 6.19
3 | 28 | 28.00 | 28.87 | 35.05
4 | 63 | 63.00 | 64.95 | 100.00
<NA> | 3 | 3.00 | <NA> | <NA>
data_tabulate print multiple
Code
data_tabulate(efc, c("c172code", "e16sex"))
Output
carer's level of education (c172code) <numeric>
# total N=100 valid N=90
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 8 | 8.00 | 8.89 | 8.89
2 | 66 | 66.00 | 73.33 | 82.22
3 | 16 | 16.00 | 17.78 | 100.00
<NA> | 10 | 10.00 | <NA> | <NA>
elder's gender (e16sex) <numeric>
# total N=100 valid N=100
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 46 | 46.00 | 46.00 | 46.00
2 | 54 | 54.00 | 54.00 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
data_tabulate big numbers
Code
data_tabulate(x)
Output
x <integer>
# total N=10,000,000 valid N=10,000,000
Value | N | Raw % | Valid % | Cumulative %
------+-----------+-------+---------+-------------
1 | 1,998,318 | 19.98 | 19.98 | 19.98
2 | 1,998,338 | 19.98 | 19.98 | 39.97
3 | 2,001,814 | 20.02 | 20.02 | 59.98
4 | 1,999,423 | 19.99 | 19.99 | 79.98
5 | 2,002,107 | 20.02 | 20.02 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
Code
print(data_tabulate(x), big_mark = "-")
Output
x <integer>
# total N=10-000-000 valid N=10-000-000
Value | N | Raw % | Valid % | Cumulative %
------+-----------+-------+---------+-------------
1 | 1-998-318 | 19.98 | 19.98 | 19.98
2 | 1-998-338 | 19.98 | 19.98 | 39.97
3 | 2-001-814 | 20.02 | 20.02 | 59.98
4 | 1-999-423 | 19.99 | 19.99 | 79.98
5 | 2-002-107 | 20.02 | 20.02 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
Code
print(data_tabulate(x), big_mark = "")
Output
x <integer>
# total N=10000000 valid N=10000000
Value | N | Raw % | Valid % | Cumulative %
------+---------+-------+---------+-------------
1 | 1998318 | 19.98 | 19.98 | 19.98
2 | 1998338 | 19.98 | 19.98 | 39.97
3 | 2001814 | 20.02 | 20.02 | 59.98
4 | 1999423 | 19.99 | 19.99 | 79.98
5 | 2002107 | 20.02 | 20.02 | 100.00
<NA> | 0 | 0.00 | <NA> | <NA>
data_tabulate print multiple, collapse
Code
data_tabulate(efc, c("c172code", "e16sex"), collapse = TRUE)
Output
# Frequency Table
Variable | Value | N | Raw % | Valid % | Cumulative %
---------+-------+----+-------+---------+-------------
c172code | 1 | 8 | 8.00 | 8.89 | 8.89
| 2 | 66 | 66.00 | 73.33 | 82.22
| 3 | 16 | 16.00 | 17.78 | 100.00
| <NA> | 10 | 10.00 | <NA> | <NA>
---------+-------+----+-------+---------+-------------
e16sex | 1 | 46 | 46.00 | 46.00 | 46.00
| 2 | 54 | 54.00 | 54.00 | 100.00
| <NA> | 0 | 0.00 | <NA> | <NA>
------------------------------------------------------
data_tabulate print grouped data
Code
data_tabulate(poorman::group_by(efc, e16sex), "c172code")
Output
carer's level of education (c172code) <numeric>
Grouped by e16sex (1)
# total N=46 valid N=41
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 5 | 10.87 | 12.20 | 12.20
2 | 32 | 69.57 | 78.05 | 90.24
3 | 4 | 8.70 | 9.76 | 100.00
<NA> | 5 | 10.87 | <NA> | <NA>
carer's level of education (c172code) <numeric>
Grouped by e16sex (2)
# total N=54 valid N=49
Value | N | Raw % | Valid % | Cumulative %
------+----+-------+---------+-------------
1 | 3 | 5.56 | 6.12 | 6.12
2 | 34 | 62.96 | 69.39 | 75.51
3 | 12 | 22.22 | 24.49 | 100.00
<NA> | 5 | 9.26 | <NA> | <NA>
data_tabulate print, collapse groups
Code
data_tabulate(poorman::group_by(efc, e16sex), "c172code", collapse = TRUE)
Output
# Frequency Table
Variable | Group | Value | N | Raw % | Valid % | Cumulative %
---------+------------+-------+----+-------+---------+-------------
c172code | e16sex (1) | 1 | 5 | 10.87 | 12.20 | 12.20
| | 2 | 32 | 69.57 | 78.05 | 90.24
| | 3 | 4 | 8.70 | 9.76 | 100.00
| | <NA> | 5 | 10.87 | <NA> | <NA>
---------+------------+-------+----+-------+---------+-------------
c172code | e16sex (2) | 1 | 3 | 5.56 | 6.12 | 6.12
| | 2 | 34 | 62.96 | 69.39 | 75.51
| | 3 | 12 | 22.22 | 24.49 | 100.00
| | <NA> | 5 | 9.26 | <NA> | <NA>
-------------------------------------------------------------------
data_tabulate print, collapse groups, drop levels
Code
data_tabulate(poorman::group_by(efc, e16sex), "e42dep", collapse = TRUE,
drop_levels = TRUE)
Output
# Frequency Table
Variable | Group | Value | N | Raw % | Valid % | Cumulative %
---------+------------+-------+----+-------+---------+-------------
e42dep | e16sex (1) | 1 | 2 | 4.35 | 4.44 | 4.44
| | 2 | 2 | 4.35 | 4.44 | 8.89
| | 3 | 8 | 17.39 | 17.78 | 26.67
| | 4 | 33 | 71.74 | 73.33 | 100.00
| | <NA> | 1 | 2.17 | <NA> | <NA>
---------+------------+-------+----+-------+---------+-------------
e42dep | e16sex (2) | 2 | 2 | 3.70 | 3.85 | 3.85
| | 3 | 20 | 37.04 | 38.46 | 42.31
| | 4 | 30 | 55.56 | 57.69 | 100.00
| | <NA> | 2 | 3.70 | <NA> | <NA>
-------------------------------------------------------------------
data_tabulate, cross tables
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full"))
Output
efc$c172code | male | female | <NA> | Total
-------------+------------+------------+----------+------
1 | 5 (5.0%) | 2 (2.0%) | 1 (1.0%) | 8
2 | 31 (31.0%) | 33 (33.0%) | 2 (2.0%) | 66
3 | 4 (4.0%) | 11 (11.0%) | 1 (1.0%) | 16
<NA> | 5 (5.0%) | 4 (4.0%) | 1 (1.0%) | 10
-------------+------------+------------+----------+------
Total | 45 | 50 | 5 | 100
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE))
Output
efc$c172code | male | female | Total
-------------+------------+------------+------
1 | 5 (5.8%) | 2 (2.3%) | 7
2 | 31 (36.0%) | 33 (38.4%) | 64
3 | 4 (4.7%) | 11 (12.8%) | 15
-------------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
weights = efc$weights))
Output
efc$c172code | male | female | <NA> | Total
-------------+------------+------------+----------+------
1 | 5 (4.8%) | 3 (2.9%) | 2 (1.9%) | 10
2 | 32 (30.5%) | 32 (30.5%) | 3 (2.9%) | 67
3 | 3 (2.9%) | 11 (10.5%) | 1 (1.0%) | 15
<NA> | 8 (7.6%) | 5 (4.8%) | 1 (1.0%) | 14
-------------+------------+------------+----------+------
Total | 48 | 51 | 7 | 105
Code
print(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE, weights = efc$weights))
Output
efc$c172code | male | female | Total
-------------+------------+------------+------
1 | 5 (5.8%) | 3 (3.5%) | 8
2 | 32 (37.2%) | 32 (37.2%) | 64
3 | 3 (3.5%) | 11 (12.8%) | 14
-------------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row"))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (62.5%) | 2 (25.0%) | 1 (12.5%) | 8
2 | 31 (47.0%) | 33 (50.0%) | 2 (3.0%) | 66
3 | 4 (25.0%) | 11 (68.8%) | 1 (6.2%) | 16
<NA> | 5 (50.0%) | 4 (40.0%) | 1 (10.0%) | 10
---------+------------+------------+-----------+------
Total | 45 | 50 | 5 | 100
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row",
remove_na = TRUE))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (71.4%) | 2 (28.6%) | 7
2 | 31 (48.4%) | 33 (51.6%) | 64
3 | 4 (26.7%) | 11 (73.3%) | 15
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row",
weights = efc$weights))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (50.0%) | 3 (30.0%) | 2 (20.0%) | 10
2 | 32 (47.8%) | 32 (47.8%) | 3 (4.5%) | 67
3 | 3 (20.0%) | 11 (73.3%) | 1 (6.7%) | 15
<NA> | 8 (57.1%) | 5 (35.7%) | 1 (7.1%) | 14
---------+------------+------------+-----------+------
Total | 48 | 51 | 7 | 105
Code
print(data_tabulate(efc, "c172code", by = efc$e16sex, proportions = "row",
remove_na = TRUE, weights = efc$weights))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (62.5%) | 3 (37.5%) | 8
2 | 32 (50.0%) | 32 (50.0%) | 64
3 | 3 (21.4%) | 11 (78.6%) | 14
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column"))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (11.1%) | 2 (4.0%) | 1 (20.0%) | 8
2 | 31 (68.9%) | 33 (66.0%) | 2 (40.0%) | 66
3 | 4 (8.9%) | 11 (22.0%) | 1 (20.0%) | 16
<NA> | 5 (11.1%) | 4 (8.0%) | 1 (20.0%) | 10
---------+------------+------------+-----------+------
Total | 45 | 50 | 5 | 100
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (12.5%) | 2 (4.3%) | 7
2 | 31 (77.5%) | 33 (71.7%) | 64
3 | 4 (10.0%) | 11 (23.9%) | 15
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
weights = "weights"))
Output
c172code | male | female | <NA> | Total
---------+------------+------------+-----------+------
1 | 5 (10.4%) | 3 (5.9%) | 2 (28.6%) | 10
2 | 32 (66.7%) | 32 (62.7%) | 3 (42.9%) | 67
3 | 3 (6.2%) | 11 (21.6%) | 1 (14.3%) | 15
<NA> | 8 (16.7%) | 5 (9.8%) | 1 (14.3%) | 14
---------+------------+------------+-----------+------
Total | 48 | 51 | 7 | 105
Code
print(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
c172code | male | female | Total
---------+------------+------------+------
1 | 5 (12.5%) | 3 (6.5%) | 8
2 | 32 (80.0%) | 32 (69.6%) | 64
3 | 3 (7.5%) | 11 (23.9%) | 14
---------+------------+------------+------
Total | 40 | 46 | 86
Code
print(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex", proportions = "row"))
Output
Variable | Value | male | female | <NA> | Total
---------+-------+------------+------------+------------+------
c172code | 1 | 5 (62.5%) | 2 (25.0%) | 1 (12.5%) | 8
c172code | 2 | 31 (47.0%) | 33 (50.0%) | 2 (3.0%) | 66
c172code | 3 | 4 (25.0%) | 11 (68.8%) | 1 (6.2%) | 16
c172code | <NA> | 5 (50.0%) | 4 (40.0%) | 1 (10.0%) | 10
e42dep | 1 | 2 (100.0%) | 0 (0.0%) | 0 (0.0%) | 2
e42dep | 2 | 2 (50.0%) | 2 (50.0%) | 0 (0.0%) | 4
e42dep | 3 | 8 (28.6%) | 18 (64.3%) | 2 (7.1%) | 28
e42dep | 4 | 32 (50.8%) | 28 (44.4%) | 3 (4.8%) | 63
e42dep | <NA> | 1 (33.3%) | 2 (66.7%) | 0 (0.0%) | 3
data_tabulate, cross tables, grouped df
Code
print(data_tabulate(grp, "c172code", by = "e16sex", proportions = "row"))
Output
Grouped by e42dep (1)
Variable | Value | male | female | <NA> | Total
---------+-------+------------+--------+------------+------
c172code | 2 | 2 (100.0%) | <NA> | 0 (0.0%) | 2
| <NA> | 0 (0%) | <NA> | 0 (0%) | 0
Grouped by e42dep (2)
Variable | Value | male | female | <NA> | Total
---------+-------+-----------+-----------+-----------+------
c172code | 2 | 2 (50.0%) | 2 (50.0%) | 0 (0.0%) | 4
| <NA> | 0 (0%) | 0 (0%) | 0 (0%) | 0
Grouped by e42dep (3)
Variable | Value | male | female | <NA> | Total
---------+-------+-----------+------------+-----------+------
c172code | 1 | 2 (50.0%) | 2 (50.0%) | 0 (0.0%) | 4
| 2 | 4 (25.0%) | 11 (68.8%) | 1 (6.2%) | 16
| 3 | 1 (16.7%) | 5 (83.3%) | 0 (0.0%) | 6
| <NA> | 1 (50.0%) | 0 (0.0%) | 1 (50.0%) | 2
Grouped by e42dep (4)
Variable | Value | male | female | <NA> | Total
---------+-------+------------+------------+-----------+------
c172code | 1 | 3 (75.0%) | 0 (0.0%) | 1 (25.0%) | 4
| 2 | 23 (54.8%) | 18 (42.9%) | 1 (2.4%) | 42
| 3 | 3 (30.0%) | 6 (60.0%) | 1 (10.0%) | 10
| <NA> | 3 (42.9%) | 4 (57.1%) | 0 (0.0%) | 7
Grouped by e42dep (NA)
Variable | Value | male | female | <NA> | Total
---------+-------+------------+------------+------------+------
c172code | 2 | 0 (0.0%) | 2 (100.0%) | 0 (0.0%) | 2
| <NA> | 1 (100.0%) | 0 (0.0%) | 0 (0.0%) | 1
data_tabulate, cross tables, print/format works
Code
print(x)
Output
Variable | Value | 3 | 4 | 5 | <NA> | Total
---------+-------+----+---+---+------+------
cyl | 4 | 1 | 8 | 2 | 0 | 11
cyl | 6 | 2 | 4 | 1 | 0 | 7
cyl | 8 | 12 | 0 | 2 | 0 | 14
cyl | <NA> | 0 | 0 | 0 | 0 | 0
am | 0 | 15 | 4 | 0 | 0 | 19
am | 1 | 0 | 8 | 5 | 0 | 13
am | <NA> | 0 | 0 | 0 | 0 | 0
data_tabulate, cross tables, markdown
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full"))
Output
|efc$c172code | male| female| (NA) | Total|
|:------------|----------:|----------:|:--------|-----:|
|1 | 5 (5.0%)| 2 (2.0%)|1 (1.0%) | 8|
|2 | 31 (31.0%)| 33 (33.0%)|2 (2.0%) | 66|
|3 | 4 (4.0%)| 11 (11.0%)|1 (1.0%) | 16|
|(NA) | 5 (5.0%)| 4 (4.0%)|1 (1.0%) | 10|
| | | | | |
|Total | 45| 50| 5 | 100|
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE))
Output
|efc$c172code | male| female| Total|
|:------------|----------:|----------:|-----:|
|1 | 5 (5.8%)| 2 (2.3%)| 7|
|2 | 31 (36.0%)| 33 (38.4%)| 64|
|3 | 4 (4.7%)| 11 (12.8%)| 15|
| | | | |
|Total | 40| 46| 86|
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
weights = efc$weights))
Output
|efc$c172code | male| female| (NA) | Total|
|:------------|----------:|----------:|:--------|-----:|
|1 | 5 (4.8%)| 3 (2.9%)|2 (1.9%) | 10|
|2 | 32 (30.5%)| 32 (30.5%)|3 (2.9%) | 67|
|3 | 3 (2.9%)| 11 (10.5%)|1 (1.0%) | 15|
|(NA) | 8 (7.6%)| 5 (4.8%)|1 (1.0%) | 14|
| | | | | |
|Total | 48| 51| 7 | 105|
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE, weights = efc$weights))
Output
|efc$c172code | male| female| Total|
|:------------|----------:|----------:|-----:|
|1 | 5 (5.8%)| 3 (3.5%)| 8|
|2 | 32 (37.2%)| 32 (37.2%)| 64|
|3 | 3 (3.5%)| 11 (12.8%)| 14|
| | | | |
|Total | 40| 46| 86|
Code
print_md(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
|c172code | male| female| Total|
|:--------|----------:|----------:|-----:|
|1 | 5 (12.5%)| 3 (6.5%)| 8|
|2 | 32 (80.0%)| 32 (69.6%)| 64|
|3 | 3 (7.5%)| 11 (23.9%)| 14|
| | | | |
|Total | 40| 46| 86|
Code
print_md(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex",
proportions = "row"))
Output
|Variable | Value| male| female| (NA)| Total|
|:--------|-----:|----------:|----------:|----------:|-----:|
|c172code | 1| 5 (62.5%)| 2 (25.0%)| 1 (12.5%)| 8|
|c172code | 2| 31 (47.0%)| 33 (50.0%)| 2 (3.0%)| 66|
|c172code | 3| 4 (25.0%)| 11 (68.8%)| 1 (6.2%)| 16|
|c172code | (NA)| 5 (50.0%)| 4 (40.0%)| 1 (10.0%)| 10|
|e42dep | 1| 2 (100.0%)| 0 (0.0%)| 0 (0.0%)| 2|
|e42dep | 2| 2 (50.0%)| 2 (50.0%)| 0 (0.0%)| 4|
|e42dep | 3| 8 (28.6%)| 18 (64.3%)| 2 (7.1%)| 28|
|e42dep | 4| 32 (50.8%)| 28 (44.4%)| 3 (4.8%)| 63|
|e42dep | (NA)| 1 (33.3%)| 2 (66.7%)| 0 (0.0%)| 3|
Code
display(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
|c172code | male| female| Total|
|:--------|----------:|----------:|-----:|
|1 | 5 (12.5%)| 3 (6.5%)| 8|
|2 | 32 (80.0%)| 32 (69.6%)| 64|
|3 | 3 (7.5%)| 11 (23.9%)| 14|
| | | | |
|Total | 40| 46| 86|
Code
display(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex", proportions = "row"))
Output
|Variable | Value| male| female| (NA)| Total|
|:--------|-----:|----------:|----------:|----------:|-----:|
|c172code | 1| 5 (62.5%)| 2 (25.0%)| 1 (12.5%)| 8|
|c172code | 2| 31 (47.0%)| 33 (50.0%)| 2 (3.0%)| 66|
|c172code | 3| 4 (25.0%)| 11 (68.8%)| 1 (6.2%)| 16|
|c172code | (NA)| 5 (50.0%)| 4 (40.0%)| 1 (10.0%)| 10|
|e42dep | 1| 2 (100.0%)| 0 (0.0%)| 0 (0.0%)| 2|
|e42dep | 2| 2 (50.0%)| 2 (50.0%)| 0 (0.0%)| 4|
|e42dep | 3| 8 (28.6%)| 18 (64.3%)| 2 (7.1%)| 28|
|e42dep | 4| 32 (50.8%)| 28 (44.4%)| 3 (4.8%)| 63|
|e42dep | (NA)| 1 (33.3%)| 2 (66.7%)| 0 (0.0%)| 3|
data_tabulate, correct 0% for proportions
Code
print(out[[1]])
Output
c172code | male | female | <NA> | Total
---------+------------+------------+--------+------
1 | 5 (10.9%) | 3 (5.6%) | 0 (0%) | 8
2 | 32 (69.6%) | 34 (63.0%) | 0 (0%) | 66
3 | 4 (8.7%) | 12 (22.2%) | 0 (0%) | 16
<NA> | 5 (10.9%) | 5 (9.3%) | 0 (0%) | 10
---------+------------+------------+--------+------
Total | 46 | 54 | 0 | 100
data_tabulate, table methods
Code
as.table(x)
Output
[[1]]
4 6 8
11 7 14
Code
as.table(x)
Output
[[1]]
4 6 8
11 7 14
Code
as.table(x, remove_na = FALSE)
Output
[[1]]
4 6 8 <NA>
11 7 14 0
Code
as.table(x)
Output
[[1]]
4 6 8
11 7 14
[[2]]
3 4 5
15 12 5
Code
as.table(x)
Output
[[1]]
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x, simplify = TRUE)
Output
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x)
Output
[[1]]
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x, simplify = TRUE)
Output
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x)
Output
[[1]]
3 4 5
0 15 4 0
1 0 8 5
[[2]]
3 4 5
4 1 8 2
6 2 4 1
8 12 0 2
Code
as.table(x)
Output
$`am (0)`
3 4
4 1 2
6 2 2
8 12 0
$`am (1)`
4 5
4 6 2
6 2 1
8 0 2
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