2020_Tesis medicación y léntulo

#packages

library("tidyverse")

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✓ tibble  3.0.2     ✓ dplyr   1.0.0
✓ tidyr   1.1.0     ✓ stringr 1.4.0
✓ readr   1.3.1     ✓ forcats 0.5.0
── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

#abro df

df <- read.csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vQnW03NbVujy31CzAGJ-XZXNcXQ6WR5iw9JWYGcwytIca8ItQvJARfLUejUZSs3FXVGzxoEehcdy-lj/pub?gid=1934081665&single=true&output=csv")

#agrupo por tipo de instrumentacion y calculo medidas para tercio coronal

options(digits = 4)
df %>% 
  group_by(Tipo.de.instrumentacion) %>% 
  summarise(n=n(),  promedio = mean(Tercio_coronal), sd = sd(Tercio_coronal), mediana = median(Tercio_coronal))

`summarise()` ungrouping output (override with `.groups` argument)

#agrupo por tipo de instrumentacion y calculo medidas para tercio medio

options(digits = 4)
df %>% 
  group_by(Tipo.de.instrumentacion) %>% 
  summarise(n=n(), promedio = mean(Tercio_medio), sd = sd(Tercio_medio), mediana = median(Tercio_medio))

`summarise()` ungrouping output (override with `.groups` argument)

#agrupo por tipo de instrumentacion y calculo medidas para tercio apical

options(digits = 4)
df %>% 
  group_by(Tipo.de.instrumentacion) %>% 
  summarise(n=n(), promedio = mean(Tercio_apical), sd = sd(Tercio_apical), mediana = median(Tercio_apical))

`summarise()` ungrouping output (override with `.groups` argument)

#agrupo tipo instrumentacion por alcance(mm)

options(digits = 4)
df %>% 
  group_by(Tipo.de.instrumentacion) %>% 
  summarise(n=n(), promedio = mean(alcance.medicacion.mm.), sd = sd(alcance.medicacion.mm.), mediana = median(alcance.medicacion.mm.))

`summarise()` ungrouping output (override with `.groups` argument)

#agrupar los datos en una variable

df1 <- df %>% 
gather(key = "Tercio", value = "Puntaje", Tercio_coronal:Tercio_apical)

#agrupò

df1 %>% 
  group_by(Tercio,Tipo.de.instrumentacion, Puntaje) %>% 
  summarise(suma = n())  

`summarise()` regrouping output by 'Tercio', 'Tipo.de.instrumentacion' (override with `.groups` argument)

#existe diferencia al instrumentar y el alcance de la medicación ???

t.test(df$alcance.medicacion.mm.~df$Tipo.de.instrumentacion)

    Welch Two Sample t-test

data:  df$alcance.medicacion.mm. by df$Tipo.de.instrumentacion
t = -3.3, df = 84, p-value = 0.002
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.8993 -0.2198
sample estimates:
   mean in group Instrumentado mean in group Sin instrumentar 
                         1.857                          2.417 

#para coronal

t.test(df$Tercio_coronal~df$Tipo.de.instrumentacion)

    Welch Two Sample t-test

data:  df$Tercio_coronal by df$Tipo.de.instrumentacion
t = -4.1, df = 47, p-value = 2e-04
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.5608 -0.1892
sample estimates:
   mean in group Instrumentado mean in group Sin instrumentar 
                         1.000                          1.375 

#para medio

t.test(df$Tercio_medio~df$Tipo.de.instrumentacion)

    Welch Two Sample t-test

data:  df$Tercio_medio by df$Tipo.de.instrumentacion
t = -2.2, df = 88, p-value = 0.03
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.80687 -0.04432
sample estimates:
   mean in group Instrumentado mean in group Sin instrumentar 
                         1.429                          1.854 

#para apical

t.test(df$Tercio_apical~df$Tipo.de.instrumentacion)

    Welch Two Sample t-test

data:  df$Tercio_apical by df$Tipo.de.instrumentacion
t = -3.2, df = 88, p-value = 0.002
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.2187 -0.2932
sample estimates:
   mean in group Instrumentado mean in group Sin instrumentar 
                         2.286                          3.042 

#agrupar los datos

df2 <- df %>% 
gather(key = "Calidad", value = "Valor", Tercio_coronal:Tercio_apical)

#resultados

df2 %>% 
  group_by(Calidad, Valor, Tipo.de.instrumentacion) %>% 
  summarise(suma = n())  

`summarise()` regrouping output by 'Calidad', 'Valor' (override with `.groups` argument)

#graficas

df2 %>% 
  group_by(Calidad, Valor, Tipo.de.instrumentacion) %>% 
  summarise(suma = n())  %>% 
  ggplot(aes(x= Valor, y= suma, fill=Tipo.de.instrumentacion))+
  geom_col() +
  facet_wrap(~Calidad)+
  coord_flip() + 
  theme_classic() +
  ggtitle("Densidad de la medicacion por tercio segun frecuencia de puntaje") +
xlab("Puntaje segun densidad") + 
ylab("Frecuencia") +
labs(fill = "Tipo de instrumentacion")

`summarise()` regrouping output by 'Calidad', 'Valor' (override with `.groups` argument)

#para alcance

df %>% 

Warning messages:
1: In readChar(file, size, TRUE) : truncating string with embedded nuls
2: In readChar(file, size, TRUE) : truncating string with embedded nuls
3: In readChar(file, size, TRUE) : truncating string with embedded nuls
4: In readChar(file, size, TRUE) : truncating string with embedded nuls
5: In readChar(file, size, TRUE) : truncating string with embedded nuls
6: In readChar(file, size, TRUE) : truncating string with embedded nuls
7: In readChar(file, size, TRUE) : truncating string with embedded nuls
8: In readChar(file, size, TRUE) : truncating string with embedded nuls
9: In readChar(file, size, TRUE) : truncating string with embedded nuls
10: In readChar(file, size, TRUE) : truncating string with embedded nuls

  group_by(alcance.medicacion.mm., Tipo.de.instrumentacion) %>% 
  summarise(suma = n())  %>% 
  ggplot(aes(x= alcance.medicacion.mm., y= suma, fill= Tipo.de.instrumentacion))+
  geom_col()+
  theme_classic()+
  scale_fill_manual(values=c("lightblue", "indianred1")) + 
  coord_flip() +
  ggtitle("Alcance de medicacion en grupos de dientes segun frecuencia de puntaje") +
xlab("Puntaje segun alcance") + 
ylab("Frecuencia") +
labs(fill = "Tipo de instrumentacion")

`summarise()` regrouping output by 'alcance.medicacion.mm.' (override with `.groups` argument)