library(readxl)
library(dplyr)
## Warning: replacing previous import 'vctrs::data_frame' by 'tibble::data_frame'
## when loading 'dplyr'
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(janitor)
##
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
##
## chisq.test, fisher.test
library(skimr)
volvulos <- read_excel("C:/Users/eyase/Downloads/volvulos.xlsx",
sheet = "Volvulus SILS", col_types = c("numeric",
"text", "numeric", "text", "text",
"text", "text", "text", "text", "text",
"text", "text", "text", "text", "text",
"text", "text", "text", "text", "numeric",
"numeric", "text", "text", "numeric"))
volvulos
## # A tibble: 24 x 24
## n genero edad dm2 hta esquizofrenia deficitcog dolorabd distension
## <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 13 Mujer~ 30 No No Sí No Sí No
## 2 17 Hombr~ 29 No No Sí No No No
## 3 19 Mujer~ 78 Sí Sí No No No No
## 4 21 Hombr~ 74 No Sí No No Sí No
## 5 2 Mujer~ 65 Sí Sí No No Sí Sí
## 6 6 Hombr~ 18 No No Sí No Sí No
## 7 7 Hombr~ 44 Sí No No No No No
## 8 10 Mujer~ 55 No No No Sí Sí No
## 9 9 Mujer~ 80 No Sí No No No No
## 10 1 Hombr~ 38 No No Sí No Sí No
## # ... with 14 more rows, and 15 more variables: obstruccion <chr>,
## # rxabdomen <chr>, tac <chr>, isquemia <chr>, devovulacion <chr>,
## # tuboendoanal <chr>, cxtresdias <chr>, cxcincodias <chr>,
## # anastomosistt <chr>, anastomosisll <chr>, tiempoqx <dbl>, sangrado <dbl>,
## # complicaciones <chr>, fuganastomotica <chr>, estanciahospital <dbl>
names(volvulos)
## [1] "n" "genero" "edad" "dm2"
## [5] "hta" "esquizofrenia" "deficitcog" "dolorabd"
## [9] "distension" "obstruccion" "rxabdomen" "tac"
## [13] "isquemia" "devovulacion" "tuboendoanal" "cxtresdias"
## [17] "cxcincodias" "anastomosistt" "anastomosisll" "tiempoqx"
## [21] "sangrado" "complicaciones" "fuganastomotica" "estanciahospital"
###### Baseline Characteristics
volvulos %>%
tabyl(genero)
## genero n percent
## Hombres 14 0.5833333
## Mujeres 10 0.4166667
volvulos %>%
skim(edad)
Data summary
| Name |
Piped data |
| Number of rows |
24 |
| Number of columns |
24 |
| _______________________ |
|
| Column type frequency: |
|
| numeric |
1 |
| ________________________ |
|
| Group variables |
|
Variable type: numeric
| edad |
0 |
1 |
46.83 |
17.12 |
18 |
34.5 |
43 |
58.25 |
80 |
▃▇▅▃▃ |
volvulos %>%
tabyl(dm2)
## dm2 n percent
## No 20 0.8333333
## Sí 4 0.1666667
volvulos %>%
tabyl(hta)
## hta n percent
## No 16 0.6666667
## Sí 8 0.3333333
volvulos %>%
tabyl(esquizofrenia)
## esquizofrenia n percent
## No 17 0.7083333
## Sí 7 0.2916667
volvulos %>%
tabyl(deficitcog)
## deficitcog n percent
## No 21 0.875
## Sí 3 0.125
volvulos %>%
tabyl(deficitcog)
## deficitcog n percent
## No 21 0.875
## Sí 3 0.125
volvulos %>%
tabyl(dolorabd)
## dolorabd n percent
## No 12 0.5
## Sí 12 0.5
volvulos %>%
tabyl(distension)
## distension n percent
## No 17 0.7083333
## Sí 7 0.2916667
volvulos %>%
tabyl(obstruccion)
## obstruccion n percent
## No 15 0.625
## Sí 9 0.375
volvulos %>%
tabyl(rxabdomen)
## rxabdomen n percent
## Sí 24 1
volvulos %>%
tabyl(tac)
## tac n percent
## No 20 0.8333333
## Sí 4 0.1666667
volvulos %>%
tabyl(isquemia)
## isquemia n percent
## No 24 1
volvulos %>%
tabyl(devovulacion)
## devovulacion n percent
## Sí 24 1
volvulos %>%
tabyl(tuboendoanal)
## tuboendoanal n percent
## No 13 0.5416667
## Sí 11 0.4583333
volvulos %>%
tabyl(cxtresdias)
## cxtresdias n percent
## No 13 0.5416667
## Sí 11 0.4583333
volvulos %>%
tabyl(cxcincodias)
## cxcincodias n percent
## No 11 0.4583333
## Sí 13 0.5416667
volvulos %>%
tabyl(anastomosistt)
## anastomosistt n percent
## No 14 0.5833333
## Sí 10 0.4166667
volvulos %>%
tabyl(anastomosisll)
## anastomosisll n percent
## No 10 0.4166667
## Sí 14 0.5833333
volvulos %>%
skim(tiempoqx)
Data summary
| Name |
Piped data |
| Number of rows |
24 |
| Number of columns |
24 |
| _______________________ |
|
| Column type frequency: |
|
| numeric |
1 |
| ________________________ |
|
| Group variables |
|
Variable type: numeric
| tiempoqx |
0 |
1 |
131.25 |
32.88 |
90 |
112.5 |
120 |
150 |
180 |
▆▇▁▅▅ |
volvulos %>%
skim(sangrado)
Data summary
| Name |
Piped data |
| Number of rows |
24 |
| Number of columns |
24 |
| _______________________ |
|
| Column type frequency: |
|
| numeric |
1 |
| ________________________ |
|
| Group variables |
|
Variable type: numeric
| sangrado |
0 |
1 |
62.5 |
29.23 |
30 |
30 |
60 |
90 |
120 |
▇▇▁▆▂ |
volvulos %>%
tabyl(complicaciones)
## complicaciones n percent
## No 23 0.95833333
## Sí 1 0.04166667
volvulos %>%
tabyl(fuganastomotica)
## fuganastomotica n percent
## No 23 0.95833333
## Sí 1 0.04166667
volvulos %>%
skim(estanciahospital)
Data summary
| Name |
Piped data |
| Number of rows |
24 |
| Number of columns |
24 |
| _______________________ |
|
| Column type frequency: |
|
| numeric |
1 |
| ________________________ |
|
| Group variables |
|
Variable type: numeric
| estanciahospital |
0 |
1 |
4.58 |
3.08 |
3 |
3 |
3 |
5 |
17 |
▇▂▁▁▁ |
#### TIPO DE ANASTOMOSIS
volvulos %>%
tabyl(genero, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## genero No Sí Total
## Hombres 6 (42.9%) 8 (80.0%) 14 (58.3%)
## Mujeres 8 (57.1%) 2 (20.0%) 10 (41.7%)
volvulos %>%
tabyl(genero, anastomosistt) %>%
chisq.test()
## Warning in stats::chisq.test(., ...): Chi-squared approximation may be incorrect
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: .
## X-squared = 1.9592, df = 1, p-value = 0.1616
volvulos %>%
group_by(anastomosistt) %>%
skim(edad)
Data summary
| Name |
Piped data |
| Number of rows |
24 |
| Number of columns |
24 |
| _______________________ |
|
| Column type frequency: |
|
| numeric |
1 |
| ________________________ |
|
| Group variables |
anastomosistt |
Variable type: numeric
| edad |
No |
0 |
1 |
45.57 |
14.77 |
22 |
38.75 |
43.0 |
53 |
80 |
▃▇▃▁▁ |
| edad |
Sí |
0 |
1 |
48.60 |
20.69 |
18 |
33.50 |
44.5 |
65 |
78 |
▅▇▂▅▅ |
with(data = volvulos, wilcox.test(edad ~ anastomosistt))
## Warning in wilcox.test.default(x = c(30, 65, 44, 55, 80, 38, 41, 56, 22, :
## cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: edad by anastomosistt
## W = 67.5, p-value = 0.9066
## alternative hypothesis: true location shift is not equal to 0
volvulos %>%
tabyl(dm2, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## dm2 No Sí Total
## No 11 (78.6%) 9 (90.0%) 20 (83.3%)
## Sí 3 (21.4%) 1 (10.0%) 4 (16.7%)
volvulos %>%
tabyl(genero, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 0.1041
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.0153816 1.5585265
## sample estimates:
## odds ratio
## 0.2017943
volvulos %>%
tabyl(hta, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## hta No Sí Total
## No 9 (64.3%) 7 (70.0%) 16 (66.7%)
## Sí 5 (35.7%) 3 (30.0%) 8 (33.3%)
volvulos %>%
tabyl(hta, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.08894922 5.81813753
## sample estimates:
## odds ratio
## 0.7797679
volvulos %>%
tabyl(esquizofrenia, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## esquizofrenia No Sí Total
## No 11 (78.6%) 6 (60.0%) 17 (70.8%)
## Sí 3 (21.4%) 4 (40.0%) 7 (29.2%)
volvulos %>%
tabyl(esquizofrenia, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 0.3926
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.2883631 21.9874807
## sample estimates:
## odds ratio
## 2.35086
volvulos %>%
tabyl(deficitcog, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## deficitcog No Sí Total
## No 12 (85.7%) 9 (90.0%) 21 (87.5%)
## Sí 2 (14.3%) 1 (10.0%) 3 (12.5%)
volvulos %>%
tabyl(deficitcog, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.01019117 14.99701641
## sample estimates:
## odds ratio
## 0.6776633
volvulos %>%
tabyl(dolorabd, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## dolorabd No Sí Total
## No 4 (28.6%) 8 (80.0%) 12 (50.0%)
## Sí 10 (71.4%) 2 (20.0%) 12 (50.0%)
volvulos %>%
tabyl(dolorabd, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 0.03607
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.008101526 0.896925044
## sample estimates:
## odds ratio
## 0.1121872
volvulos %>%
tabyl(distension, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## distension No Sí Total
## No 10 (71.4%) 7 (70.0%) 17 (70.8%)
## Sí 4 (28.6%) 3 (30.0%) 7 (29.2%)
volvulos %>%
tabyl(distension, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1175924 8.7547429
## sample estimates:
## odds ratio
## 1.068345
volvulos %>%
tabyl(tuboendoanal, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## tuboendoanal No Sí Total
## No 8 (57.1%) 5 (50.0%) 13 (54.2%)
## Sí 6 (42.9%) 5 (50.0%) 11 (45.8%)
volvulos %>%
tabyl(tuboendoanal, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1963085 9.0577124
## sample estimates:
## odds ratio
## 1.317346
volvulos %>%
tabyl(cxtresdias, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## cxtresdias No Sí Total
## No 9 (64.3%) 4 (40.0%) 13 (54.2%)
## Sí 5 (35.7%) 6 (60.0%) 11 (45.8%)
volvulos %>%
tabyl(cxtresdias, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 0.4081
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3866882 19.7763559
## sample estimates:
## odds ratio
## 2.585674
volvulos %>%
tabyl(cxcincodias, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## cxcincodias No Sí Total
## No 5 (35.7%) 6 (60.0%) 11 (45.8%)
## Sí 9 (64.3%) 4 (40.0%) 13 (54.2%)
volvulos %>%
tabyl(cxtresdias, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 0.4081
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.3866882 19.7763559
## sample estimates:
## odds ratio
## 2.585674
volvulos %>%
tabyl(complicaciones, anastomosistt) %>%
adorn_totals(c("col")) %>%
adorn_percentages(denominator = "col") %>%
adorn_pct_formatting() %>%
adorn_ns(position = "front") %>%
adorn_title()
## anastomosistt
## complicaciones No Sí Total
## No 13 (92.9%) 10 (100.0%) 23 (95.8%)
## Sí 1 (7.1%) 0 (0.0%) 1 (4.2%)
volvulos %>%
tabyl(complicaciones, anastomosistt) %>%
fisher.test()
##
## Fisher's Exact Test for Count Data
##
## data: .
## p-value = 1
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.00000 54.55431
## sample estimates:
## odds ratio
## 0
volvulos %>%
group_by(anastomosistt) %>%
skim(estanciahospital)
Data summary
| Name |
Piped data |
| Number of rows |
24 |
| Number of columns |
24 |
| _______________________ |
|
| Column type frequency: |
|
| numeric |
1 |
| ________________________ |
|
| Group variables |
anastomosistt |
Variable type: numeric
| estanciahospital |
No |
0 |
1 |
4.29 |
3.73 |
3 |
3 |
3 |
3.00 |
17 |
▇▁▁▁▁ |
| estanciahospital |
Sí |
0 |
1 |
5.00 |
1.94 |
3 |
3 |
5 |
6.75 |
8 |
▇▃▂▃▂ |
wilcox.test( estanciahospital ~ anastomosistt, data = volvulos)
## Warning in wilcox.test.default(x = c(3, 3, 3, 17, 5, 3, 3, 3, 3, 3, 3, 5, :
## cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: estanciahospital by anastomosistt
## W = 42, p-value = 0.06328
## alternative hypothesis: true location shift is not equal to 0