Nothing
tests/testthat/_snaps/analyze_variables.md
In tern: Create Common TLGs Used in Clinical Trials
s_summary return NA for x length 0L
Code
res
Output
$n
n
0
$sum
sum
NA
$mean
mean
NA
$sd
sd
NA
$se
se
NA
$mean_sd
mean sd
NA NA
$mean_se
mean se
NA NA
$mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
NA
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
NA
$mad
mad
NA
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
NA NA
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
NA
$range
min max
NA NA
$min
min
NA
$max
max
NA
$median_range
median min max
NA NA NA
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
NA
$geom_mean
geom_mean
NaN
$geom_mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
NA
s_summary handles NA
Code
res
Output
$n
n
1
$sum
sum
1
$mean
mean
1
$sd
sd
NA
$se
se
NA
$mean_sd
mean sd
1 NA
$mean_se
mean se
1 NA
$mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
NA
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
1
$mad
mad
0
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
1 1
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
0
$range
min max
1 1
$min
min
1
$max
max
1
$median_range
median min max
1 1 1
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
NA
$geom_mean
geom_mean
1
$geom_mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
NA
Code
res
Output
$n
n
2
$sum
sum
NA
$mean
mean
NA
$sd
sd
NA
$se
se
NA
$mean_sd
mean sd
NA NA
$mean_se
mean se
NA NA
$mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
NA
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
NA
$mad
mad
NA
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
NA NA
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
NA
$range
min max
NA NA
$min
min
NA
$max
max
NA
$median_range
median min max
NA NA NA
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
NA
$geom_mean
geom_mean
NA
$geom_mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
NA
s_summary returns right results for n = 2
Code
res
Output
$n
n
2
$sum
sum
3
$mean
mean
1.5
$sd
sd
0.7071068
$se
se
0.5
$mean_sd
mean sd
1.5000000 0.7071068
$mean_se
mean se
1.5 0.5
$mean_ci
mean_ci_lwr mean_ci_upr
-4.853102 7.853102
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
1 2
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
0.7928932 2.2071068
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
0.2048328
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
1.5
$mad
mad
0
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
1 2
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
1
$range
min max
1 2
$min
min
1
$max
max
2
$median_range
median min max
1.5 1.0 2.0
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
47.14045
$geom_mean
geom_mean
1.414214
$geom_mean_ci
mean_ci_lwr mean_ci_upr
0.01729978 115.60839614
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
52.10922
s_summary returns right results for n = 8
Code
res
Output
$n
n
8
$sum
sum
48
$mean
mean
6
$sd
sd
3.207135
$se
se
1.133893
$mean_sd
mean sd
6.000000 3.207135
$mean_se
mean se
6.000000 1.133893
$mean_ci
mean_ci_lwr mean_ci_upr
3.318768 8.681232
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
4.866107 7.133893
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
2.792865 9.207135
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
0.001133783
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
6.5
$mad
mad
0
$median_ci
median_ci_lwr median_ci_upr
1 10
attr(,"conf_level")
[1] 0.9921875
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
3.5 8.5
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
5
$range
min max
1 10
$min
min
1
$max
max
10
$median_range
median min max
6.5 1.0 10.0
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
53.45225
$geom_mean
geom_mean
4.842534
$geom_mean_ci
mean_ci_lwr mean_ci_upr
2.456211 9.547283
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
96.61307
s_summary works with factors
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.0000000 0.2222222
$count_fraction$Male
[1] 3.0000000 0.3333333
$count_fraction$Unknown
[1] 4.0000000 0.4444444
$n_blq
[1] 0
s_summary works when factors have NA levels
Code
res
Output
$n
[1] 7
$count
$count$Female
[1] 2
$count$Male
[1] 2
$count$Unknown
[1] 2
$count$`NA`
[1] 1
$count_fraction
$count_fraction$Female
[1] 2.0000000 0.2857143
$count_fraction$Male
[1] 2.0000000 0.2857143
$count_fraction$Unknown
[1] 2.0000000 0.2857143
$count_fraction$`NA`
[1] 1.0000000 0.1428571
$n_blq
[1] 0
s_summary works with factors with NA values handled and correctly removes them by default
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.0000000 0.2222222
$count_fraction$Male
[1] 3.0000000 0.3333333
$count_fraction$Unknown
[1] 4.0000000 0.4444444
$n_blq
[1] 0
s_summary works with length 0 factors that have levels
Code
res
Output
$n
[1] 0
$count
$count$a
[1] 0
$count$b
[1] 0
$count$c
[1] 0
$count_fraction
$count_fraction$a
[1] 0 0
$count_fraction$b
[1] 0 0
$count_fraction$c
[1] 0 0
$n_blq
[1] 0
s_summary works with factors and different denominator choices
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.0 0.1
$count_fraction$Male
[1] 3.00 0.15
$count_fraction$Unknown
[1] 4.0 0.2
$n_blq
[1] 0
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.00000000 0.06666667
$count_fraction$Male
[1] 3.0 0.1
$count_fraction$Unknown
[1] 4.0000000 0.1333333
$n_blq
[1] 0
s_summary works with characters by converting to character and handling empty strings
Code
res
Output
$n
[1] 10
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count$`NA`
[1] 1
$count_fraction
$count_fraction$Female
[1] 2.0 0.2
$count_fraction$Male
[1] 3.0 0.3
$count_fraction$Unknown
[1] 4.0 0.4
$count_fraction$`NA`
[1] 1.0 0.1
$n_blq
[1] 0
s_summary works with logical vectors
Code
res
Output
$n
[1] 6
$count
[1] 4
$count_fraction
[1] 4.0000000 0.6666667
$n_blq
[1] 0
s_summary works with length 0 logical vectors
Code
res
Output
$n
[1] 0
$count
[1] 0
$count_fraction
[1] 0 0
$n_blq
[1] 0
s_summary works with logical vectors and by default removes NA
Code
res
Output
$n
[1] 6
$count
[1] 4
$count_fraction
[1] 4.0000000 0.6666667
$n_blq
[1] 0
s_summary works with logical vectors and by if requested does not remove NA from n
Code
res
Output
$n
[1] 8
$count
[1] 4
$count_fraction
[1] 4.0 0.5
$n_blq
[1] 0
a_summary work with healthy input.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 10 0 n
2 Sum 1.3 0 Sum
3 Mean 0.1 0 Mean
4 SD 0.8 0 SD
5 SE 0.2 0 SE
6 Mean (SD) 0.1 (0.8) 0 Mean (SD)
7 Mean (SE) 0.1 (0.2) 0 Mean (SE)
8 Mean 95% CI (-0.43, 0.69) 0 Mean 95% CI
9 Mean -/+ 1xSE (-0.11, 0.38) 0 Mean -/+ 1xSE
10 Mean -/+ 1xSD (-0.65, 0.91) 0 Mean -/+ 1xSD
11 Mean p-value (H0: mean = 0) 0.6052 0 Mean p-value (H0: mean = 0)
12 Median 0.3 0 Median
13 Median Absolute Deviation -0.0 0 Median Absolute Deviation
14 Median 95% CI (-0.82, 0.74) 0 Median 95% CI
15 25% and 75%-ile -0.6 - 0.6 0 25% and 75%-ile
16 IQR 1.2 0 IQR
17 Min - Max -0.8 - 1.6 0 Min - Max
18 Minimum -0.8 0 Minimum
19 Maximum 1.6 0 Maximum
20 Median (Min - Max) 0.3 (-0.8 - 1.6) 0 Median (Min - Max)
21 CV (%) 590.4 0 CV (%)
22 Geometric Mean NA 0 Geometric Mean
23 Geometric Mean 95% CI NA 0 Geometric Mean 95% CI
24 CV % Geometric Mean NA 0 CV % Geometric Mean
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 a 3 0 a
3 b 1 0 b
4 c 1 0 c
5 a 3 (60%) 0 a
6 b 1 (20%) 0 b
7 c 1 (20%) 0 c
8 a 3 (60.0%) 0 a
9 b 1 (20.0%) 0 b
10 c 1 (20.0%) 0 c
11 n_blq 0 0 n_blq
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 4 0 n
2 A 2 0 A
3 B 1 0 B
4 C 1 0 C
5 A 2 (50%) 0 A
6 B 1 (25%) 0 B
7 C 1 (25%) 0 C
8 A 2 (50.0%) 0 A
9 B 1 (25.0%) 0 B
10 C 1 (25.0%) 0 C
11 n_blq 0 0 n_blq
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 count 3 0 count
3 count_fraction 3 (60%) 0 count_fraction
4 count_fraction 3 (60.0%) 0 count_fraction
5 n_blq 0 0 n_blq
a_summary works with custom input.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 std. dev 1 3 std. dev
2 Median 95% CI -0.62 - 1.12 3 Median 95% CI
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 number of records 5.00 -1 number of records
2 a 2 5 a
3 b 1 5 b
4 c 1 5 c
5 NA 1 5 NA
6 a 2 (40%) 0 a
7 b 1 (20%) 0 b
8 c 1 (20%) 0 c
9 NA 1 (20%) 0 NA
10 a 2 (40.0%) 0 a
11 b 1 (20.0%) 0 b
12 c 1 (20.0%) 0 c
13 NA 1 (20.0%) 0 NA
14 n_blq 0 0 n_blq
a_summary works with healthy input when compare = TRUE.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 10 0 n
2 Sum 51.3 0 Sum
3 Mean 5.1 0 Mean
4 SD 0.8 0 SD
5 SE 0.2 0 SE
6 Mean (SD) 5.1 (0.8) 0 Mean (SD)
7 Mean (SE) 5.1 (0.2) 0 Mean (SE)
8 Mean 95% CI (4.57, 5.69) 0 Mean 95% CI
9 Mean -/+ 1xSE (4.89, 5.38) 0 Mean -/+ 1xSE
10 Mean -/+ 1xSD (4.35, 5.91) 0 Mean -/+ 1xSD
11 Mean p-value (H0: mean = 0) <0.0001 0 Mean p-value (H0: mean = 0)
12 Median 5.3 0 Median
13 Median Absolute Deviation -0.0 0 Median Absolute Deviation
14 Median 95% CI (4.18, 5.74) 0 Median 95% CI
15 25% and 75%-ile 4.4 - 5.6 0 25% and 75%-ile
16 IQR 1.2 0 IQR
17 Min - Max 4.2 - 6.6 0 Min - Max
18 Minimum 4.2 0 Minimum
19 Maximum 6.6 0 Maximum
20 Median (Min - Max) 5.3 (4.2 - 6.6) 0 Median (Min - Max)
21 CV (%) 15.2 0 CV (%)
22 Geometric Mean 5.1 0 Geometric Mean
23 Geometric Mean 95% CI (4.56, 5.66) 0 Geometric Mean 95% CI
24 CV % Geometric Mean 15.2 0 CV % Geometric Mean
25 p-value (t-test) <0.0001 0 p-value (t-test)
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 a 3 0 a
3 b 1 0 b
4 c 1 0 c
5 a 3 (60%) 0 a
6 b 1 (20%) 0 b
7 c 1 (20%) 0 c
8 a 3 (60.0%) 0 a
9 b 1 (20.0%) 0 b
10 c 1 (20.0%) 0 c
11 n_blq 0 0 n_blq
12 p-value (chi-squared test) 0.9560 0 p-value (chi-squared test)
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 4 0 n
2 A 2 0 A
3 B 1 0 B
4 C 1 0 C
5 A 2 (50%) 0 A
6 B 1 (25%) 0 B
7 C 1 (25%) 0 C
8 A 2 (50.0%) 0 A
9 B 1 (25.0%) 0 B
10 C 1 (25.0%) 0 C
11 n_blq 0 0 n_blq
12 p-value (chi-squared test) 0.9074 0 p-value (chi-squared test)
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 count 3 0 count
3 count_fraction 3 (60%) 0 count_fraction
4 count_fraction 3 (60.0%) 0 count_fraction
5 n_blq 0 0 n_blq
6 p-value (chi-squared test) 0.8091 0 p-value (chi-squared test)
a_summary works with custom input when compare = TRUE.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 pvalue <0.0001 3 pvalue
2 Median 95% CI -0.41 - 1.10 3 Median 95% CI
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 number of records 5.00 -1 number of records
2 a 2 5 a
3 b 1 5 b
4 c 1 5 c
5 NA 1 5 NA
6 a 2 (40%) 0 a
7 b 1 (20%) 0 b
8 c 1 (20%) 0 c
9 NA 1 (20%) 0 NA
10 a 2 (40.0%) 0 a
11 b 1 (20.0%) 0 b
12 c 1 (20.0%) 0 c
13 NA 1 (20.0%) 0 NA
14 n_blq 0 0 n_blq
15 p-value (chi-squared test) 0.8254 0 p-value (chi-squared test)
analyze_vars works with healthy input, default na.rm = TRUE.
Code
res
Output
all obs
—————————————————————
n 4
Mean (SD) 2.5 (1.3)
Median 2.5
Min - Max 1.0 - 4.0
analyze_vars works with healthy input, and control function.
Code
res
Output
all obs
——————————————————————————————
n 9
Mean (SD) 5.0 (2.7)
Mean (SE) 5.0 (0.9)
Mean 90% CI (3.30, 6.70)
10% and 90%-ile 1.0 - 9.0
analyze_vars works with healthy input, alternative na.rm = FALSE
Code
res
Output
all obs
———————————————————
n 6
Mean (SD) NA
Median NA
Min - Max NA
analyze_vars works with healthy factor input
Code
res
Output
all obs
—————————————
n 3
a 2 (66.7%)
b 1 (33.3%)
analyze_vars works with healthy factor input, alternative na.rm = FALSE
Code
res
Output
all obs
————————————
n 5
a 2 (40%)
b 1 (20%)
NA 2 (40%)
Code
res
Output
all obs
———————————————————
n 5
a 2 (40%)
b 1 (20%)
<Missing> 2 (40%)
analyze_vars works with factors and different denominators
Code
res
Output
A: Drug X B: Placebo C: Combination
(N=121) (N=106) (N=129)
——————————————————————————————————————————————————————————————————————————————————————
F (N=187)
n 70 56 61
ASIAN 44 (23.5%) 37 (19.8%) 40 (21.4%)
BLACK OR AFRICAN AMERICAN 18 (9.6%) 12 (6.4%) 13 (7%)
WHITE 8 (4.3%) 7 (3.7%) 8 (4.3%)
AMERICAN INDIAN OR ALASKA NATIVE 0 0 0
MULTIPLE 0 0 0
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 0 0
OTHER 0 0 0
UNKNOWN 0 0 0
M (N=169)
n 51 50 68
ASIAN 35 (20.7%) 31 (18.3%) 44 (26%)
BLACK OR AFRICAN AMERICAN 10 (5.9%) 12 (7.1%) 14 (8.3%)
WHITE 6 (3.6%) 7 (4.1%) 10 (5.9%)
AMERICAN INDIAN OR ALASKA NATIVE 0 0 0
MULTIPLE 0 0 0
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 0 0
OTHER 0 0 0
UNKNOWN 0 0 0
analyze_vars works in demographic table example
Code
res
Output
A: Drug X B: Placebo C: Combination
————————————————————————————————————————————————————————————————————————————————————
ASIAN
CHN 39 (49.4%) 32 (47.1%) 39 (46.4%)
USA 7 (8.9%) 9 (13.2%) 13 (15.5%)
BRA 6 (7.6%) 8 (11.8%) 6 (7.1%)
PAK 6 (7.6%) 5 (7.4%) 9 (10.7%)
NGA 9 (11.4%) 3 (4.4%) 8 (9.5%)
RUS 7 (8.9%) 4 (5.9%) 4 (4.8%)
JPN 2 (2.5%) 6 (8.8%) 3 (3.6%)
GBR 1 (1.3%) 1 (1.5%) 1 (1.2%)
CAN 2 (2.5%) 0 1 (1.2%)
CHE 0 0 0
BLACK OR AFRICAN AMERICAN
CHN 14 (50%) 10 (41.7%) 18 (66.7%)
USA 4 (14.3%) 3 (12.5%) 3 (11.1%)
BRA 3 (10.7%) 4 (16.7%) 1 (3.7%)
PAK 1 (3.6%) 2 (8.3%) 2 (7.4%)
NGA 0 1 (4.2%) 1 (3.7%)
RUS 1 (3.6%) 1 (4.2%) 0
JPN 3 (10.7%) 0 1 (3.7%)
GBR 1 (3.6%) 2 (8.3%) 1 (3.7%)
CAN 1 (3.6%) 1 (4.2%) 0
CHE 0 0 0
WHITE
CHN 9 (64.3%) 6 (42.9%) 12 (66.7%)
USA 2 (14.3%) 2 (14.3%) 1 (5.6%)
BRA 0 1 (7.1%) 0
PAK 1 (7.1%) 1 (7.1%) 1 (5.6%)
NGA 1 (7.1%) 1 (7.1%) 0
RUS 1 (7.1%) 0 2 (11.1%)
JPN 0 2 (14.3%) 1 (5.6%)
GBR 0 0 0
CAN 0 1 (7.1%) 1 (5.6%)
CHE 0 0 0
AMERICAN INDIAN OR ALASKA NATIVE
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
MULTIPLE
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
OTHER
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
UNKNOWN
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
analyze_vars works with logical input
Code
res
Output
all obs
————————————————————————
n 5
count_fraction 3 (60%)
analyze_vars works with healthy logical input, alternative na.rm = FALSE
Code
res
Output
all obs
———————————————
n 5
FALSE 1 (20%)
TRUE 2 (40%)
NA 2 (40%)
Code
res
Output
all obs
———————————————————
n 5
FALSE 1 (20%)
TRUE 2 (40%)
<Missing> 2 (40%)
analyze_vars works with empty named numeric variables
Code
res
Output
a b c
——————————————————————————————————————
n 0 2 2
Mean (SD) NA 3.5 (0.7) 5.5 (0.7)
Median NA 3.5 5.5
Min - Max NA 3.0 - 4.0 5.0 - 6.0
analyze_vars 'na_str' argument works as expected
Code
res
Output
A B C
———————————————————————————————————————
V1
n 2 1 0
Mean (SD) 7.5 (2.1) 3.0 (-) -
Median 7.5 3.0 -
Min - Max 6.0 - 9.0 3.0 - 3.0 -
V2
n 2 1 0
Mean (SD) 6.5 (2.1) 2.0 (-) -
Median 6.5 2.0 -
Min - Max 5.0 - 8.0 2.0 - 2.0 -
V3
n 2 1 0
Mean (SD) 5.5 (2.1) 1.0 (-) -
Median 5.5 1.0 -
Min - Max 4.0 - 7.0 1.0 - 1.0 -
control_analyze_vars works with customized parameters
Code
res
Output
$conf_level
[1] 0.9
$quantiles
[1] 0.1 0.9
$quantile_type
[1] 2
$test_mean
[1] 0
analyze_vars works correctly with auto formats
Code
res
Output
all obs
—————————————————————————————
n 5
Mean 1.4
Mean (SD) 1.44042 (1.91481)
Min - Max 0.0010 - 4.0000
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s_summary return NA for x length 0L
Code
res
Output
$n
n
0
$sum
sum
NA
$mean
mean
NA
$sd
sd
NA
$se
se
NA
$mean_sd
mean sd
NA NA
$mean_se
mean se
NA NA
$mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
NA
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
NA
$mad
mad
NA
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
NA NA
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
NA
$range
min max
NA NA
$min
min
NA
$max
max
NA
$median_range
median min max
NA NA NA
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
NA
$geom_mean
geom_mean
NaN
$geom_mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
NA
s_summary handles NA
Code
res
Output
$n
n
1
$sum
sum
1
$mean
mean
1
$sd
sd
NA
$se
se
NA
$mean_sd
mean sd
1 NA
$mean_se
mean se
1 NA
$mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
NA
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
1
$mad
mad
0
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
1 1
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
0
$range
min max
1 1
$min
min
1
$max
max
1
$median_range
median min max
1 1 1
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
NA
$geom_mean
geom_mean
1
$geom_mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
NA
Code
res
Output
$n
n
2
$sum
sum
NA
$mean
mean
NA
$sd
sd
NA
$se
se
NA
$mean_sd
mean sd
NA NA
$mean_se
mean se
NA NA
$mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
NA NA
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
NA
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
NA
$mad
mad
NA
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
NA NA
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
NA
$range
min max
NA NA
$min
min
NA
$max
max
NA
$median_range
median min max
NA NA NA
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
NA
$geom_mean
geom_mean
NA
$geom_mean_ci
mean_ci_lwr mean_ci_upr
NA NA
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
NA
s_summary returns right results for n = 2
Code
res
Output
$n
n
2
$sum
sum
3
$mean
mean
1.5
$sd
sd
0.7071068
$se
se
0.5
$mean_sd
mean sd
1.5000000 0.7071068
$mean_se
mean se
1.5 0.5
$mean_ci
mean_ci_lwr mean_ci_upr
-4.853102 7.853102
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
1 2
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
0.7928932 2.2071068
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
0.2048328
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
1.5
$mad
mad
0
$median_ci
median_ci_lwr median_ci_upr
NA NA
attr(,"conf_level")
[1] NA
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
1 2
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
1
$range
min max
1 2
$min
min
1
$max
max
2
$median_range
median min max
1.5 1.0 2.0
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
47.14045
$geom_mean
geom_mean
1.414214
$geom_mean_ci
mean_ci_lwr mean_ci_upr
0.01729978 115.60839614
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
52.10922
s_summary returns right results for n = 8
Code
res
Output
$n
n
8
$sum
sum
48
$mean
mean
6
$sd
sd
3.207135
$se
se
1.133893
$mean_sd
mean sd
6.000000 3.207135
$mean_se
mean se
6.000000 1.133893
$mean_ci
mean_ci_lwr mean_ci_upr
3.318768 8.681232
attr(,"label")
[1] "Mean 95% CI"
$mean_sei
mean_sei_lwr mean_sei_upr
4.866107 7.133893
attr(,"label")
[1] "Mean -/+ 1xSE"
$mean_sdi
mean_sdi_lwr mean_sdi_upr
2.792865 9.207135
attr(,"label")
[1] "Mean -/+ 1xSD"
$mean_pval
p_value
0.001133783
attr(,"label")
[1] "Mean p-value (H0: mean = 0)"
$median
median
6.5
$mad
mad
0
$median_ci
median_ci_lwr median_ci_upr
1 10
attr(,"conf_level")
[1] 0.9921875
attr(,"label")
[1] "Median 95% CI"
$quantiles
quantile_0.25 quantile_0.75
3.5 8.5
attr(,"label")
[1] "25% and 75%-ile"
$iqr
iqr
5
$range
min max
1 10
$min
min
1
$max
max
10
$median_range
median min max
6.5 1.0 10.0
attr(,"label")
[1] "Median (Min - Max)"
$cv
cv
53.45225
$geom_mean
geom_mean
4.842534
$geom_mean_ci
mean_ci_lwr mean_ci_upr
2.456211 9.547283
attr(,"label")
[1] "Geometric Mean 95% CI"
$geom_cv
geom_cv
96.61307
s_summary works with factors
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.0000000 0.2222222
$count_fraction$Male
[1] 3.0000000 0.3333333
$count_fraction$Unknown
[1] 4.0000000 0.4444444
$n_blq
[1] 0
s_summary works when factors have NA levels
Code
res
Output
$n
[1] 7
$count
$count$Female
[1] 2
$count$Male
[1] 2
$count$Unknown
[1] 2
$count$`NA`
[1] 1
$count_fraction
$count_fraction$Female
[1] 2.0000000 0.2857143
$count_fraction$Male
[1] 2.0000000 0.2857143
$count_fraction$Unknown
[1] 2.0000000 0.2857143
$count_fraction$`NA`
[1] 1.0000000 0.1428571
$n_blq
[1] 0
s_summary works with factors with NA values handled and correctly removes them by default
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.0000000 0.2222222
$count_fraction$Male
[1] 3.0000000 0.3333333
$count_fraction$Unknown
[1] 4.0000000 0.4444444
$n_blq
[1] 0
s_summary works with length 0 factors that have levels
Code
res
Output
$n
[1] 0
$count
$count$a
[1] 0
$count$b
[1] 0
$count$c
[1] 0
$count_fraction
$count_fraction$a
[1] 0 0
$count_fraction$b
[1] 0 0
$count_fraction$c
[1] 0 0
$n_blq
[1] 0
s_summary works with factors and different denominator choices
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.0 0.1
$count_fraction$Male
[1] 3.00 0.15
$count_fraction$Unknown
[1] 4.0 0.2
$n_blq
[1] 0
Code
res
Output
$n
[1] 9
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count_fraction
$count_fraction$Female
[1] 2.00000000 0.06666667
$count_fraction$Male
[1] 3.0 0.1
$count_fraction$Unknown
[1] 4.0000000 0.1333333
$n_blq
[1] 0
s_summary works with characters by converting to character and handling empty strings
Code
res
Output
$n
[1] 10
$count
$count$Female
[1] 2
$count$Male
[1] 3
$count$Unknown
[1] 4
$count$`NA`
[1] 1
$count_fraction
$count_fraction$Female
[1] 2.0 0.2
$count_fraction$Male
[1] 3.0 0.3
$count_fraction$Unknown
[1] 4.0 0.4
$count_fraction$`NA`
[1] 1.0 0.1
$n_blq
[1] 0
s_summary works with logical vectors
Code
res
Output
$n
[1] 6
$count
[1] 4
$count_fraction
[1] 4.0000000 0.6666667
$n_blq
[1] 0
s_summary works with length 0 logical vectors
Code
res
Output
$n
[1] 0
$count
[1] 0
$count_fraction
[1] 0 0
$n_blq
[1] 0
s_summary works with logical vectors and by default removes NA
Code
res
Output
$n
[1] 6
$count
[1] 4
$count_fraction
[1] 4.0000000 0.6666667
$n_blq
[1] 0
s_summary works with logical vectors and by if requested does not remove NA from n
Code
res
Output
$n
[1] 8
$count
[1] 4
$count_fraction
[1] 4.0 0.5
$n_blq
[1] 0
a_summary work with healthy input.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 10 0 n
2 Sum 1.3 0 Sum
3 Mean 0.1 0 Mean
4 SD 0.8 0 SD
5 SE 0.2 0 SE
6 Mean (SD) 0.1 (0.8) 0 Mean (SD)
7 Mean (SE) 0.1 (0.2) 0 Mean (SE)
8 Mean 95% CI (-0.43, 0.69) 0 Mean 95% CI
9 Mean -/+ 1xSE (-0.11, 0.38) 0 Mean -/+ 1xSE
10 Mean -/+ 1xSD (-0.65, 0.91) 0 Mean -/+ 1xSD
11 Mean p-value (H0: mean = 0) 0.6052 0 Mean p-value (H0: mean = 0)
12 Median 0.3 0 Median
13 Median Absolute Deviation -0.0 0 Median Absolute Deviation
14 Median 95% CI (-0.82, 0.74) 0 Median 95% CI
15 25% and 75%-ile -0.6 - 0.6 0 25% and 75%-ile
16 IQR 1.2 0 IQR
17 Min - Max -0.8 - 1.6 0 Min - Max
18 Minimum -0.8 0 Minimum
19 Maximum 1.6 0 Maximum
20 Median (Min - Max) 0.3 (-0.8 - 1.6) 0 Median (Min - Max)
21 CV (%) 590.4 0 CV (%)
22 Geometric Mean NA 0 Geometric Mean
23 Geometric Mean 95% CI NA 0 Geometric Mean 95% CI
24 CV % Geometric Mean NA 0 CV % Geometric Mean
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 a 3 0 a
3 b 1 0 b
4 c 1 0 c
5 a 3 (60%) 0 a
6 b 1 (20%) 0 b
7 c 1 (20%) 0 c
8 a 3 (60.0%) 0 a
9 b 1 (20.0%) 0 b
10 c 1 (20.0%) 0 c
11 n_blq 0 0 n_blq
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 4 0 n
2 A 2 0 A
3 B 1 0 B
4 C 1 0 C
5 A 2 (50%) 0 A
6 B 1 (25%) 0 B
7 C 1 (25%) 0 C
8 A 2 (50.0%) 0 A
9 B 1 (25.0%) 0 B
10 C 1 (25.0%) 0 C
11 n_blq 0 0 n_blq
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 count 3 0 count
3 count_fraction 3 (60%) 0 count_fraction
4 count_fraction 3 (60.0%) 0 count_fraction
5 n_blq 0 0 n_blq
a_summary works with custom input.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 std. dev 1 3 std. dev
2 Median 95% CI -0.62 - 1.12 3 Median 95% CI
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 number of records 5.00 -1 number of records
2 a 2 5 a
3 b 1 5 b
4 c 1 5 c
5 NA 1 5 NA
6 a 2 (40%) 0 a
7 b 1 (20%) 0 b
8 c 1 (20%) 0 c
9 NA 1 (20%) 0 NA
10 a 2 (40.0%) 0 a
11 b 1 (20.0%) 0 b
12 c 1 (20.0%) 0 c
13 NA 1 (20.0%) 0 NA
14 n_blq 0 0 n_blq
a_summary works with healthy input when compare = TRUE.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 10 0 n
2 Sum 51.3 0 Sum
3 Mean 5.1 0 Mean
4 SD 0.8 0 SD
5 SE 0.2 0 SE
6 Mean (SD) 5.1 (0.8) 0 Mean (SD)
7 Mean (SE) 5.1 (0.2) 0 Mean (SE)
8 Mean 95% CI (4.57, 5.69) 0 Mean 95% CI
9 Mean -/+ 1xSE (4.89, 5.38) 0 Mean -/+ 1xSE
10 Mean -/+ 1xSD (4.35, 5.91) 0 Mean -/+ 1xSD
11 Mean p-value (H0: mean = 0) <0.0001 0 Mean p-value (H0: mean = 0)
12 Median 5.3 0 Median
13 Median Absolute Deviation -0.0 0 Median Absolute Deviation
14 Median 95% CI (4.18, 5.74) 0 Median 95% CI
15 25% and 75%-ile 4.4 - 5.6 0 25% and 75%-ile
16 IQR 1.2 0 IQR
17 Min - Max 4.2 - 6.6 0 Min - Max
18 Minimum 4.2 0 Minimum
19 Maximum 6.6 0 Maximum
20 Median (Min - Max) 5.3 (4.2 - 6.6) 0 Median (Min - Max)
21 CV (%) 15.2 0 CV (%)
22 Geometric Mean 5.1 0 Geometric Mean
23 Geometric Mean 95% CI (4.56, 5.66) 0 Geometric Mean 95% CI
24 CV % Geometric Mean 15.2 0 CV % Geometric Mean
25 p-value (t-test) <0.0001 0 p-value (t-test)
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 a 3 0 a
3 b 1 0 b
4 c 1 0 c
5 a 3 (60%) 0 a
6 b 1 (20%) 0 b
7 c 1 (20%) 0 c
8 a 3 (60.0%) 0 a
9 b 1 (20.0%) 0 b
10 c 1 (20.0%) 0 c
11 n_blq 0 0 n_blq
12 p-value (chi-squared test) 0.9560 0 p-value (chi-squared test)
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 4 0 n
2 A 2 0 A
3 B 1 0 B
4 C 1 0 C
5 A 2 (50%) 0 A
6 B 1 (25%) 0 B
7 C 1 (25%) 0 C
8 A 2 (50.0%) 0 A
9 B 1 (25.0%) 0 B
10 C 1 (25.0%) 0 C
11 n_blq 0 0 n_blq
12 p-value (chi-squared test) 0.9074 0 p-value (chi-squared test)
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 n 5 0 n
2 count 3 0 count
3 count_fraction 3 (60%) 0 count_fraction
4 count_fraction 3 (60.0%) 0 count_fraction
5 n_blq 0 0 n_blq
6 p-value (chi-squared test) 0.8091 0 p-value (chi-squared test)
a_summary works with custom input when compare = TRUE.
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 pvalue <0.0001 3 pvalue
2 Median 95% CI -0.41 - 1.10 3 Median 95% CI
Code
res
Output
RowsVerticalSection (in_rows) object print method:
----------------------------
row_name formatted_cell indent_mod row_label
1 number of records 5.00 -1 number of records
2 a 2 5 a
3 b 1 5 b
4 c 1 5 c
5 NA 1 5 NA
6 a 2 (40%) 0 a
7 b 1 (20%) 0 b
8 c 1 (20%) 0 c
9 NA 1 (20%) 0 NA
10 a 2 (40.0%) 0 a
11 b 1 (20.0%) 0 b
12 c 1 (20.0%) 0 c
13 NA 1 (20.0%) 0 NA
14 n_blq 0 0 n_blq
15 p-value (chi-squared test) 0.8254 0 p-value (chi-squared test)
analyze_vars works with healthy input, default na.rm = TRUE.
Code
res
Output
all obs
—————————————————————
n 4
Mean (SD) 2.5 (1.3)
Median 2.5
Min - Max 1.0 - 4.0
analyze_vars works with healthy input, and control function.
Code
res
Output
all obs
——————————————————————————————
n 9
Mean (SD) 5.0 (2.7)
Mean (SE) 5.0 (0.9)
Mean 90% CI (3.30, 6.70)
10% and 90%-ile 1.0 - 9.0
analyze_vars works with healthy input, alternative na.rm = FALSE
Code
res
Output
all obs
———————————————————
n 6
Mean (SD) NA
Median NA
Min - Max NA
analyze_vars works with healthy factor input
Code
res
Output
all obs
—————————————
n 3
a 2 (66.7%)
b 1 (33.3%)
analyze_vars works with healthy factor input, alternative na.rm = FALSE
Code
res
Output
all obs
————————————
n 5
a 2 (40%)
b 1 (20%)
NA 2 (40%)
Code
res
Output
all obs
———————————————————
n 5
a 2 (40%)
b 1 (20%)
<Missing> 2 (40%)
analyze_vars works with factors and different denominators
Code
res
Output
A: Drug X B: Placebo C: Combination
(N=121) (N=106) (N=129)
——————————————————————————————————————————————————————————————————————————————————————
F (N=187)
n 70 56 61
ASIAN 44 (23.5%) 37 (19.8%) 40 (21.4%)
BLACK OR AFRICAN AMERICAN 18 (9.6%) 12 (6.4%) 13 (7%)
WHITE 8 (4.3%) 7 (3.7%) 8 (4.3%)
AMERICAN INDIAN OR ALASKA NATIVE 0 0 0
MULTIPLE 0 0 0
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 0 0
OTHER 0 0 0
UNKNOWN 0 0 0
M (N=169)
n 51 50 68
ASIAN 35 (20.7%) 31 (18.3%) 44 (26%)
BLACK OR AFRICAN AMERICAN 10 (5.9%) 12 (7.1%) 14 (8.3%)
WHITE 6 (3.6%) 7 (4.1%) 10 (5.9%)
AMERICAN INDIAN OR ALASKA NATIVE 0 0 0
MULTIPLE 0 0 0
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 0 0
OTHER 0 0 0
UNKNOWN 0 0 0
analyze_vars works in demographic table example
Code
res
Output
A: Drug X B: Placebo C: Combination
————————————————————————————————————————————————————————————————————————————————————
ASIAN
CHN 39 (49.4%) 32 (47.1%) 39 (46.4%)
USA 7 (8.9%) 9 (13.2%) 13 (15.5%)
BRA 6 (7.6%) 8 (11.8%) 6 (7.1%)
PAK 6 (7.6%) 5 (7.4%) 9 (10.7%)
NGA 9 (11.4%) 3 (4.4%) 8 (9.5%)
RUS 7 (8.9%) 4 (5.9%) 4 (4.8%)
JPN 2 (2.5%) 6 (8.8%) 3 (3.6%)
GBR 1 (1.3%) 1 (1.5%) 1 (1.2%)
CAN 2 (2.5%) 0 1 (1.2%)
CHE 0 0 0
BLACK OR AFRICAN AMERICAN
CHN 14 (50%) 10 (41.7%) 18 (66.7%)
USA 4 (14.3%) 3 (12.5%) 3 (11.1%)
BRA 3 (10.7%) 4 (16.7%) 1 (3.7%)
PAK 1 (3.6%) 2 (8.3%) 2 (7.4%)
NGA 0 1 (4.2%) 1 (3.7%)
RUS 1 (3.6%) 1 (4.2%) 0
JPN 3 (10.7%) 0 1 (3.7%)
GBR 1 (3.6%) 2 (8.3%) 1 (3.7%)
CAN 1 (3.6%) 1 (4.2%) 0
CHE 0 0 0
WHITE
CHN 9 (64.3%) 6 (42.9%) 12 (66.7%)
USA 2 (14.3%) 2 (14.3%) 1 (5.6%)
BRA 0 1 (7.1%) 0
PAK 1 (7.1%) 1 (7.1%) 1 (5.6%)
NGA 1 (7.1%) 1 (7.1%) 0
RUS 1 (7.1%) 0 2 (11.1%)
JPN 0 2 (14.3%) 1 (5.6%)
GBR 0 0 0
CAN 0 1 (7.1%) 1 (5.6%)
CHE 0 0 0
AMERICAN INDIAN OR ALASKA NATIVE
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
MULTIPLE
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
OTHER
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
UNKNOWN
CHN 0 0 0
USA 0 0 0
BRA 0 0 0
PAK 0 0 0
NGA 0 0 0
RUS 0 0 0
JPN 0 0 0
GBR 0 0 0
CAN 0 0 0
CHE 0 0 0
analyze_vars works with logical input
Code
res
Output
all obs
————————————————————————
n 5
count_fraction 3 (60%)
analyze_vars works with healthy logical input, alternative na.rm = FALSE
Code
res
Output
all obs
———————————————
n 5
FALSE 1 (20%)
TRUE 2 (40%)
NA 2 (40%)
Code
res
Output
all obs
———————————————————
n 5
FALSE 1 (20%)
TRUE 2 (40%)
<Missing> 2 (40%)
analyze_vars works with empty named numeric variables
Code
res
Output
a b c
——————————————————————————————————————
n 0 2 2
Mean (SD) NA 3.5 (0.7) 5.5 (0.7)
Median NA 3.5 5.5
Min - Max NA 3.0 - 4.0 5.0 - 6.0
analyze_vars 'na_str' argument works as expected
Code
res
Output
A B C
———————————————————————————————————————
V1
n 2 1 0
Mean (SD) 7.5 (2.1) 3.0 (-) -
Median 7.5 3.0 -
Min - Max 6.0 - 9.0 3.0 - 3.0 -
V2
n 2 1 0
Mean (SD) 6.5 (2.1) 2.0 (-) -
Median 6.5 2.0 -
Min - Max 5.0 - 8.0 2.0 - 2.0 -
V3
n 2 1 0
Mean (SD) 5.5 (2.1) 1.0 (-) -
Median 5.5 1.0 -
Min - Max 4.0 - 7.0 1.0 - 1.0 -
control_analyze_vars works with customized parameters
Code
res
Output
$conf_level
[1] 0.9
$quantiles
[1] 0.1 0.9
$quantile_type
[1] 2
$test_mean
[1] 0
analyze_vars works correctly with auto formats
Code
res
Output
all obs
—————————————————————————————
n 5
Mean 1.4
Mean (SD) 1.44042 (1.91481)
Min - Max 0.0010 - 4.0000
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