2022_Precisión de implantes digitalmente guiados

#abro el df

df <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vSisVhxo5wPyMYfOPwVgrfDi1BKfjG8Q0OmDiGkrLunYSZ0uTtih2ydNZlEF_bh55Vzwo9rZcy45heD/pub?gid=381433022&single=true&output=csv")

There were 12 warnings (use warnings() to see them)

#packges

library("tidyverse")
library("ggpubr")
library("ggsignif")

#ver

glimpse(df)

Rows: 60
Columns: 6
$ N        <chr> "#16", "#14", "#12", "#22", "#24", "#26", "#16", "#14", "#12", "#22", "#24"…
$ Corte    <chr> "Sagital", "Sagital", "Sagital", "Sagital", "Sagital", "Sagital", "Sagital"…
$ apical   <dbl> 0.00, 0.00, 0.86, 2.19, 2.29, 1.12, 0.00, 0.60, 1.24, 3.50, 0.33, 0.00, 1.8…
$ cervical <dbl> 1.42, 0.00, 0.68, 0.63, 0.44, 0.00, 0.00, 0.40, 0.50, 0.42, 0.41, 0.00, 0.6…
$ angular  <dbl> 5.55, 0.00, 5.70, 10.67, 9.00, 0.00, 0.00, 1.55, 4.24, 3.22, 1.53, 0.00, 9.…
$ depth    <dbl> 0.00, 2.30, 0.00, 0.00, 0.00, 0.99, 1.50, 0.90, 0.70, 0.60, 0.50, 1.41, 1.1…

#agrupo

df%>% 
  group_by(Corte) %>% 
  summarise(n=n(),  promedio_apical= mean(apical), sd_apical = sd(apical), promedio_cervical=mean(cervical), sd_cervical=sd(cervical), promedio_angulo= mean(angular), sd_angulo = sd(angular), promedio_profundidad = mean(depth), sd_profundidad = sd(depth))

#grafica sin outlier para apical

#grafica para cervical (sin outlier)

#grafica para angulación(sin outlier)

#grafica para profundidad

#agrupo corte y diente

#Calulo de intervalos de confianza

#intervalos segun dientes para corte sagital (df1) en apical

ci.mean(df$apical~df$N, data=df) 

 df$N mean CI-95%     
 #12  0.93 [0.59;1.27]
 #14  0.63 [0.14;1.13]
 #16  0.86 [0.35;1.38]
 #22  2.02 [1.23;2.81]
 #24  0.69 [0.06;1.31]
 #26  1.03 [0.44;1.63]

#intervalos segun dientes para corte sagital (df1) en cervical

ci.mean(df$cervical~df$N, data=df) 

 df$N mean CI-95%     
 #12  0.93 [0.41;1.46]
 #14  0.47 [0.09;0.85]
 #16  0.58 [0.27;0.90]
 #22  1.11 [0.48;1.73]
 #24  0.67 [0.20;1.14]
 #26  0.83 [0.07;1.60]

#intervalos segun dientes para corte sagital (df1) en angulo

ci.mean(df$angular~df$N, data=df) 

 df$N mean CI-95%      
 #12  3.37 [1.78;4.95] 
 #14  1.75 [0.29;3.21] 
 #16  4.34 [1.72;6.95] 
 #22  5.76 [1.52;10.00]
 #24  1.97 [-0.12;4.05]
 #26  0.39 [-0.15;0.94]

#los outliers

boxplot.stats(df$apical)

$stats
[1] 0.000 0.365 0.830 1.360 2.700

$n
[1] 60

$conf
[1] 0.6270428 1.0329572

$out
[1] 3.5 3.5

boxplot.stats(df$cervical)

$stats
[1] 0.000 0.210 0.515 1.290 2.100

$n
[1] 60

$conf
[1] 0.2947047 0.7352953

$out
[1] 3.00 3.02

boxplot.stats(df$angular)

$stats
[1]  0.000  0.000  1.745  4.640 10.670

$n
[1] 60

$conf
[1] 0.7985461 2.6914539

$out
[1] 20.1

#grafica correlacion apical cervical

#grafica correlacion apical angular

#grafica apical profundidad