#bivarate analusis install and load the package:
library(gtsummary)
#cross table
airquality %>%
tbl_summary(
by = Month)| Characteristic | 5 N = 311 |
6 N = 301 |
7 N = 311 |
8 N = 311 |
9 N = 301 |
|---|---|---|---|---|---|
| Ozone | 18 (11, 32) | 23 (20, 37) | 60 (35, 80) | 52 (28, 84) | 23 (16, 36) |
| Unknown | 5 | 21 | 5 | 5 | 1 |
| Solar.R | 194 (66, 290) | 189 (127, 273) | 253 (175, 274) | 198 (99, 233) | 192 (112, 236) |
| Unknown | 4 | 0 | 0 | 3 | 0 |
| Wind | 11.5 (8.6, 14.3) | 9.7 (8.0, 11.5) | 8.6 (6.9, 10.9) | 8.6 (6.3, 11.5) | 10.3 (7.4, 12.6) |
| Temp | 66 (59, 69) | 78 (76, 83) | 84 (81, 86) | 82 (79, 89) | 76 (71, 81) |
| Day | 16 (8, 24) | 16 (8, 23) | 16 (8, 24) | 16 (8, 24) | 16 (8, 23) |
| 1 Median (Q1, Q3) | |||||
NA##common statistics
airquality %>%
tbl_summary(
by = Month,
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n} / {N} ({p}%)"
)
)| Characteristic | 5 N = 311 |
6 N = 301 |
7 N = 311 |
8 N = 311 |
9 N = 301 |
|---|---|---|---|---|---|
| Ozone | 24 (22) | 29 (18) | 59 (32) | 60 (40) | 31 (24) |
| Unknown | 5 | 21 | 5 | 5 | 1 |
| Solar.R | 181 (115) | 190 (93) | 216 (81) | 172 (77) | 167 (79) |
| Unknown | 4 | 0 | 0 | 3 | 0 |
| Wind | 11.6 (3.5) | 10.3 (3.8) | 8.9 (3.0) | 8.8 (3.2) | 10.2 (3.5) |
| Temp | 66 (7) | 79 (7) | 84 (4) | 84 (7) | 77 (8) |
| Day | 16 (9) | 16 (9) | 16 (9) | 16 (9) | 16 (9) |
| 1 Mean (SD) | |||||
NA