tests/testthat/_snaps/data_tabulate.md
In easystats/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$weights))
Output
[1] "Table: elder's dependency (efc$e42dep) (categorical)"
[2] ""
[3] "|Value | N| Raw %| Valid %| Cumulative %|"
[4] "|:-----|--:|-----:|-------:|------------:|"
[5] "|1 | 3| 2.86| 3.00| 3.00|"
[6] "|2 | 4| 3.81| 4.00| 7.00|"
[7] "|3 | 26| 24.76| 26.00| 33.00|"
[8] "|4 | 67| 63.81| 67.00| 100.00|"
[9] "|(NA) | 5| 4.76| (NA)| (NA)|"
[10] "total N=105 valid N=100 (weighted)\n\n"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
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$weights))
Output
[1] "Table: Frequency Table (weighted)"
[2] ""
[3] "|Variable | Value| N| Raw %| Valid %| Cumulative %|"
[4] "|:--------|-----:|--:|-----:|-------:|------------:|"
[5] "|e42dep | 1| 3| 2.86| 3.00| 3.00|"
[6] "| | 2| 4| 3.81| 4.00| 7.00|"
[7] "| | 3| 26| 24.76| 26.00| 33.00|"
[8] "| | 4| 67| 63.81| 67.00| 100.00|"
[9] "| | (NA)| 5| 4.76| (NA)| (NA)|"
[10] "| | | | | | |"
[11] "|e16sex | 1| 50| 47.62| 47.62| 47.62|"
[12] "| | 2| 55| 52.38| 52.38| 100.00|"
[13] "| | (NA)| 0| 0.00| (NA)| (NA)|"
[14] "| | | | | | |"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
data_tabulate print
Code
data_tabulate(efc$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>
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, markdown
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full"))
Output
[1] "|efc$c172code | male| female| (NA) | Total|"
[2] "|:------------|----------:|----------:|:--------|-----:|"
[3] "|1 | 5 (5.0%)| 2 (2.0%)|1 (1.0%) | 8|"
[4] "|2 | 31 (31.0%)| 33 (33.0%)|2 (2.0%) | 66|"
[5] "|3 | 4 (4.0%)| 11 (11.0%)|1 (1.0%) | 16|"
[6] "|(NA) | 5 (5.0%)| 4 (4.0%)|1 (1.0%) | 10|"
[7] "| | | | | |"
[8] "|Total | 45| 50| 5 | 100|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE))
Output
[1] "|efc$c172code | male| female| Total|"
[2] "|:------------|----------:|----------:|-----:|"
[3] "|1 | 5 (5.8%)| 2 (2.3%)| 7|"
[4] "|2 | 31 (36.0%)| 33 (38.4%)| 64|"
[5] "|3 | 4 (4.7%)| 11 (12.8%)| 15|"
[6] "| | | | |"
[7] "|Total | 40| 46| 86|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
weights = efc$weights))
Output
[1] "|efc$c172code | male| female| (NA) | Total|"
[2] "|:------------|----------:|----------:|:--------|-----:|"
[3] "|1 | 5 (4.8%)| 3 (2.9%)|2 (1.9%) | 10|"
[4] "|2 | 32 (30.5%)| 32 (30.5%)|3 (2.9%) | 67|"
[5] "|3 | 3 (2.9%)| 11 (10.5%)|1 (1.0%) | 15|"
[6] "|(NA) | 8 (7.6%)| 5 (4.8%)|1 (1.0%) | 14|"
[7] "| | | | | |"
[8] "|Total | 48| 51| 7 | 105|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE, weights = efc$weights))
Output
[1] "|efc$c172code | male| female| Total|"
[2] "|:------------|----------:|----------:|-----:|"
[3] "|1 | 5 (5.8%)| 3 (3.5%)| 8|"
[4] "|2 | 32 (37.2%)| 32 (37.2%)| 64|"
[5] "|3 | 3 (3.5%)| 11 (12.8%)| 14|"
[6] "| | | | |"
[7] "|Total | 40| 46| 86|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
[1] "|c172code | male| female| Total|"
[2] "|:--------|----------:|----------:|-----:|"
[3] "|1 | 5 (12.5%)| 3 (6.5%)| 8|"
[4] "|2 | 32 (80.0%)| 32 (69.6%)| 64|"
[5] "|3 | 3 (7.5%)| 11 (23.9%)| 14|"
[6] "| | | | |"
[7] "|Total | 40| 46| 86|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex",
proportions = "row"))
Output
[1] "|Variable | Value| male| female| (NA)| Total|"
[2] "|:--------|-----:|----------:|----------:|----------:|-----:|"
[3] "|c172code | 1| 5 (62.5%)| 2 (25.0%)| 1 (12.5%)| 8|"
[4] "|c172code | 2| 31 (47.0%)| 33 (50.0%)| 2 (3.0%)| 66|"
[5] "|c172code | 3| 4 (25.0%)| 11 (68.8%)| 1 (6.2%)| 16|"
[6] "|c172code | NA| 5 (50.0%)| 4 (40.0%)| 1 (10.0%)| 10|"
[7] "|e42dep | 1| 2 (100.0%)| 0 (0.0%)| 0 (0.0%)| 2|"
[8] "|e42dep | 2| 2 (50.0%)| 2 (50.0%)| 0 (0.0%)| 4|"
[9] "|e42dep | 3| 8 (28.6%)| 18 (64.3%)| 2 (7.1%)| 28|"
[10] "|e42dep | 4| 32 (50.8%)| 28 (44.4%)| 3 (4.8%)| 63|"
[11] "|e42dep | NA| 1 (33.3%)| 2 (66.7%)| 0 (0.0%)| 3|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
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
easystats/datawizard documentation built on April 17, 2025, 5:32 a.m.
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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
[1] "Table: elder's dependency (efc$e42dep) (categorical)"
[2] ""
[3] "|Value | N| Raw %| Valid %| Cumulative %|"
[4] "|:-----|--:|-----:|-------:|------------:|"
[5] "|1 | 3| 2.86| 3.00| 3.00|"
[6] "|2 | 4| 3.81| 4.00| 7.00|"
[7] "|3 | 26| 24.76| 26.00| 33.00|"
[8] "|4 | 67| 63.81| 67.00| 100.00|"
[9] "|(NA) | 5| 4.76| (NA)| (NA)|"
[10] "total N=105 valid N=100 (weighted)\n\n"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print(data_tabulate(efc, c("e42dep", "e16sex"), collapse = TRUE, weights = efc$
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$weights))
Output
[1] "Table: Frequency Table (weighted)"
[2] ""
[3] "|Variable | Value| N| Raw %| Valid %| Cumulative %|"
[4] "|:--------|-----:|--:|-----:|-------:|------------:|"
[5] "|e42dep | 1| 3| 2.86| 3.00| 3.00|"
[6] "| | 2| 4| 3.81| 4.00| 7.00|"
[7] "| | 3| 26| 24.76| 26.00| 33.00|"
[8] "| | 4| 67| 63.81| 67.00| 100.00|"
[9] "| | (NA)| 5| 4.76| (NA)| (NA)|"
[10] "| | | | | | |"
[11] "|e16sex | 1| 50| 47.62| 47.62| 47.62|"
[12] "| | 2| 55| 52.38| 52.38| 100.00|"
[13] "| | (NA)| 0| 0.00| (NA)| (NA)|"
[14] "| | | | | | |"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
data_tabulate print
Code
data_tabulate(efc$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>
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, markdown
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full"))
Output
[1] "|efc$c172code | male| female| (NA) | Total|"
[2] "|:------------|----------:|----------:|:--------|-----:|"
[3] "|1 | 5 (5.0%)| 2 (2.0%)|1 (1.0%) | 8|"
[4] "|2 | 31 (31.0%)| 33 (33.0%)|2 (2.0%) | 66|"
[5] "|3 | 4 (4.0%)| 11 (11.0%)|1 (1.0%) | 16|"
[6] "|(NA) | 5 (5.0%)| 4 (4.0%)|1 (1.0%) | 10|"
[7] "| | | | | |"
[8] "|Total | 45| 50| 5 | 100|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE))
Output
[1] "|efc$c172code | male| female| Total|"
[2] "|:------------|----------:|----------:|-----:|"
[3] "|1 | 5 (5.8%)| 2 (2.3%)| 7|"
[4] "|2 | 31 (36.0%)| 33 (38.4%)| 64|"
[5] "|3 | 4 (4.7%)| 11 (12.8%)| 15|"
[6] "| | | | |"
[7] "|Total | 40| 46| 86|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
weights = efc$weights))
Output
[1] "|efc$c172code | male| female| (NA) | Total|"
[2] "|:------------|----------:|----------:|:--------|-----:|"
[3] "|1 | 5 (4.8%)| 3 (2.9%)|2 (1.9%) | 10|"
[4] "|2 | 32 (30.5%)| 32 (30.5%)|3 (2.9%) | 67|"
[5] "|3 | 3 (2.9%)| 11 (10.5%)|1 (1.0%) | 15|"
[6] "|(NA) | 8 (7.6%)| 5 (4.8%)|1 (1.0%) | 14|"
[7] "| | | | | |"
[8] "|Total | 48| 51| 7 | 105|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc$c172code, by = efc$e16sex, proportions = "full",
remove_na = TRUE, weights = efc$weights))
Output
[1] "|efc$c172code | male| female| Total|"
[2] "|:------------|----------:|----------:|-----:|"
[3] "|1 | 5 (5.8%)| 3 (3.5%)| 8|"
[4] "|2 | 32 (37.2%)| 32 (37.2%)| 64|"
[5] "|3 | 3 (3.5%)| 11 (12.8%)| 14|"
[6] "| | | | |"
[7] "|Total | 40| 46| 86|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc, "c172code", by = "e16sex", proportions = "column",
remove_na = TRUE, weights = "weights"))
Output
[1] "|c172code | male| female| Total|"
[2] "|:--------|----------:|----------:|-----:|"
[3] "|1 | 5 (12.5%)| 3 (6.5%)| 8|"
[4] "|2 | 32 (80.0%)| 32 (69.6%)| 64|"
[5] "|3 | 3 (7.5%)| 11 (23.9%)| 14|"
[6] "| | | | |"
[7] "|Total | 40| 46| 86|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
Code
print_md(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex",
proportions = "row"))
Output
[1] "|Variable | Value| male| female| (NA)| Total|"
[2] "|:--------|-----:|----------:|----------:|----------:|-----:|"
[3] "|c172code | 1| 5 (62.5%)| 2 (25.0%)| 1 (12.5%)| 8|"
[4] "|c172code | 2| 31 (47.0%)| 33 (50.0%)| 2 (3.0%)| 66|"
[5] "|c172code | 3| 4 (25.0%)| 11 (68.8%)| 1 (6.2%)| 16|"
[6] "|c172code | NA| 5 (50.0%)| 4 (40.0%)| 1 (10.0%)| 10|"
[7] "|e42dep | 1| 2 (100.0%)| 0 (0.0%)| 0 (0.0%)| 2|"
[8] "|e42dep | 2| 2 (50.0%)| 2 (50.0%)| 0 (0.0%)| 4|"
[9] "|e42dep | 3| 8 (28.6%)| 18 (64.3%)| 2 (7.1%)| 28|"
[10] "|e42dep | 4| 32 (50.8%)| 28 (44.4%)| 3 (4.8%)| 63|"
[11] "|e42dep | NA| 1 (33.3%)| 2 (66.7%)| 0 (0.0%)| 3|"
attr(,"format")
[1] "pipe"
attr(,"class")
[1] "knitr_kable" "character"
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
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