TABLA 1

library(readxl)
BASE <- read_excel("HERNIA OCULTA.xlsx")
library(dplyr)
#names(BASE)
BASE <- BASE |> select(Sexo:`Diagnóstico manometría`)

library(stringr)
BASE$`Diagnostico pH (es el diagnóstico de comparación)` <- str_to_title(BASE$`Diagnostico pH (es el diagnóstico de comparación)`)
BASE$`RESERVA CONTRACTIL` <- str_to_title(BASE$`RESERVA CONTRACTIL`)
BASE$`RESERVA CONTRACTIL` <- case_when(
  BASE$`RESERVA CONTRACTIL`%in% c("adecuado", "adecuada", "Adecuado", "Adecuada", "Adecuda") ~ "Adecuado",
  BASE$`RESERVA CONTRACTIL` %in% c("inadecuado", "inadecuada", "Inadecuado", "Inadecuada", "Inadecauda") ~ "Inadecuado",
  TRUE ~ BASE$`RESERVA CONTRACTIL`
)

BASE$`HERNIA OCULTA` <- trimws(tolower(BASE$`HERNIA OCULTA`))
table(BASE$`HERNIA OCULTA`)
## 
## no si 
## 80 46
library(gtsummary)

#cat(names(BASE), sep = ",")
tabla1 <- BASE %>%
   dplyr::select(Sexo,Edad,`IMC kg/m2`,Diabetes,HTA,`Tiempo de exposición ácida % (PH)`,`N de eventos de reflujo`, `Diagnostico pH (es el diagnóstico de comparación)`,`Impedancia del segmento contractil distal Ohms`,`Integral contractil mmHg/cm`,`DCI mmHg/cm/seg`,`ACLARAMIENTO ESOFAGICO`,`PRESION EII`, `RESERVA CONTRACTIL`, `HERNIA OCULTA`,`Diagnóstico manometría`) %>%
  tbl_summary(
    by = `Diagnostico pH (es el diagnóstico de comparación)`,
    type = list(
      all_continuous()                    ~ "continuous2",
      c(Sexo, Diabetes, HTA, `RESERVA CONTRACTIL`, `HERNIA OCULTA`,`Diagnóstico manometría`)            ~ "categorical"
    ),
    label = list(
      Sexo ~ "Sexo",
      Edad ~ "Edad",
      `IMC kg/m2` ~ "IMC kg/m2",
      Diabetes ~ "Diabetes",
      HTA ~ "HTA",
      `Tiempo de exposición ácida % (PH)` ~ "Tiempo exposición ácida% (PH)",
      `N de eventos de reflujo` ~ "Eventos de reflujo", 
      `Impedancia del segmento contractil distal Ohms` ~"Impedancia segmento contractil distal (OHMS)",
      `Integral contractil mmHg/cm` ~ "Integral contractil (mmHg/cm)",
      `DCI mmHg/cm/seg` ~ "DCI mmHg/CM/SEG",
      `ACLARAMIENTO ESOFAGICO` ~ "Aclaramiento esofágico",
      `PRESION EII` ~ "Presión EEI", 
      `RESERVA CONTRACTIL` ~ "Reserva Contractil", 
      `HERNIA OCULTA` ~ "Hernia Oculta",
      `Diagnóstico manometría` ~ "Diagnóstico manometría"
    ),
    statistic = list(
      all_continuous2()  ~ c(
        "{mean} ({sd})",
        "{median} ({p25}, {p75})",
        "{min} - {max}"
      ),
      all_categorical() ~ "{n} ({p}%)"
    ),
    digits = list(
      all_continuous()  ~ 2,
      all_categorical() ~ 1
    ),
    missing = "ifany",
    missing_text = "Sin dato"
  ) %>%
  add_p(
    test = list(
      all_continuous2()  ~ "kruskal.test",  
      all_categorical() ~ "chisq.test"
    ),
    pvalue_fun = ~ style_pvalue(.x, digits = 3)
  ) %>%
  add_overall() %>%
  modify_header(label ~ "**Variable**") %>%
  bold_labels() %>%
  bold_p(t = 0.05) %>%
  modify_caption("**Tabla 1**")

tabla1
Tabla 1
Variable Overall
N = 1261		 Nerd
N = 591		 Normal
N = 671		 p-value2
Sexo


0.620
    Femenino 85.0 (67.5%) 38.0 (64.4%) 47.0 (70.1%)
    Masculino 41.0 (32.5%) 21.0 (35.6%) 20.0 (29.9%)
Edad


0.967
    Mean (SD) 54.88 (15.23) 54.63 (15.94) 55.10 (14.69)
    Median (Q1, Q3) 53.50 (44.00, 66.00) 55.00 (42.00, 67.00) 53.00 (44.00, 65.00)
    Min - Max 19.00 - 88.00 19.00 - 88.00 28.00 - 84.00
IMC kg/m2


0.011
    Mean (SD) 23.85 (3.41) 23.13 (3.04) 24.49 (3.60)
    Median (Q1, Q3) 23.40 (21.40, 25.40) 22.50 (21.20, 24.50) 24.50 (21.90, 27.20)
    Min - Max 18.10 - 32.80 18.50 - 30.80 18.10 - 32.80
Diabetes


<0.001
    0 87.0 (69.0%) 26.0 (44.1%) 61.0 (91.0%)
    1 39.0 (31.0%) 33.0 (55.9%) 6.0 (9.0%)
HTA


<0.001
    0 97.0 (77.0%) 37.0 (62.7%) 60.0 (89.6%)
    1 29.0 (23.0%) 22.0 (37.3%) 7.0 (10.4%)
Tiempo exposición ácida% (PH)


<0.001
    Mean (SD) 4.64 (4.03) 8.45 (2.44) 1.29 (1.06)
    Median (Q1, Q3) 3.45 (1.10, 7.50) 7.50 (6.90, 9.30) 1.20 (0.40, 2.00)
    Min - Max 0.00 - 16.90 6.00 - 16.90 0.00 - 3.70
Eventos de reflujo


<0.001
    Mean (SD) 49.90 (33.87) 64.73 (38.14) 36.85 (22.90)
    Median (Q1, Q3) 39.00 (28.00, 64.00) 54.00 (38.00, 87.00) 34.00 (21.00, 50.00)
    Min - Max 1.00 - 185.00 15.00 - 185.00 1.00 - 140.00
Impedancia segmento contractil distal (OHMS)


<0.001
    Mean (SD) 2,165.60 (597.29) 1,572.83 (214.54) 2,687.58 (211.01)
    Median (Q1, Q3) 2,351.50 (1,532.00, 2,721.00) 1,526.00 (1,467.00, 1,610.00) 2,679.00 (2,546.00, 2,769.00)
    Min - Max 1,230.00 - 3,200.00 1,230.00 - 2,356.00 2,156.00 - 3,200.00
Integral contractil (mmHg/cm)


<0.001
    Mean (SD) 33.67 (7.09) 29.55 (5.57) 37.29 (6.29)
    Median (Q1, Q3) 32.95 (28.60, 38.20) 28.90 (25.30, 32.30) 35.60 (32.70, 42.60)
    Min - Max 20.50 - 48.70 20.50 - 45.80 24.80 - 48.70
DCI mmHg/CM/SEG


0.005
    Mean (SD) 1,562.24 (1,275.76) 1,195.51 (853.92) 1,885.18 (1,488.49)
    Median (Q1, Q3) 1,233.00 (668.00, 1,982.00) 1,033.00 (573.00, 1,669.00) 1,426.00 (836.00, 2,566.00)
    Min - Max 130.00 - 7,175.00 130.00 - 3,555.00 150.00 - 7,175.00
Aclaramiento esofágico


0.109
    Mean (SD) 68.49 (35.26) 62.37 (38.03) 73.88 (31.95)
    Median (Q1, Q3) 80.00 (40.00, 100.00) 80.00 (30.00, 100.00) 80.00 (60.00, 100.00)
    Min - Max 0.00 - 100.00 0.00 - 100.00 0.00 - 100.00
Presión EEI


0.009
    Mean (SD) 23.05 (12.98) 19.97 (11.66) 25.75 (13.56)
    Median (Q1, Q3) 20.05 (13.70, 32.40) 15.90 (11.10, 29.90) 21.30 (16.00, 35.20)
    Min - Max 1.30 - 61.70 2.10 - 42.80 1.30 - 61.70
Reserva Contractil


0.927
    Adecuado 71.0 (56.3%) 34.0 (57.6%) 37.0 (55.2%)
    Inadecuado 55.0 (43.7%) 25.0 (42.4%) 30.0 (44.8%)
Hernia Oculta


<0.001
    no 80.0 (63.5%) 25.0 (42.4%) 55.0 (82.1%)
    si 46.0 (36.5%) 34.0 (57.6%) 12.0 (17.9%)
Diagnóstico manometría


>0.999
    MEI 19.0 (15.1%) 9.0 (15.3%) 10.0 (14.9%)
    Normal 107.0 (84.9%) 50.0 (84.7%) 57.0 (85.1%)
1 n (%)
2 Pearson’s Chi-squared test; Kruskal-Wallis rank sum test 
library(flextable)
tabla1 %>%
  as_flex_table() %>%
  save_as_docx(path = "tabla1.docx")

Modelo multivariado

Se buscan variables con un valor p igual o menos a 0.2 para ser consideradas dentro del modelo multivariado.

#cat(names(BASE), sep = ",")
tabla2 <- BASE %>%
   dplyr::select(Sexo,Edad,`IMC kg/m2`,Diabetes,HTA,`Tiempo de exposición ácida % (PH)`,`N de eventos de reflujo`, `Diagnostico pH (es el diagnóstico de comparación)`,`Impedancia del segmento contractil distal Ohms`,`Integral contractil mmHg/cm`,`DCI mmHg/cm/seg`,`ACLARAMIENTO ESOFAGICO`,`PRESION EII`, `RESERVA CONTRACTIL`, `HERNIA OCULTA`,`Diagnóstico manometría`) %>%
  tbl_summary(
    by = `HERNIA OCULTA`,
    type = list(
      all_continuous()                    ~ "continuous2",
      c(Sexo, Diabetes, HTA, `RESERVA CONTRACTIL`,`Diagnóstico manometría`)            ~ "categorical"
    ),
    label = list(
      Sexo ~ "Sexo",
      Edad ~ "Edad",
      `IMC kg/m2` ~ "IMC kg/m2",
      Diabetes ~ "Diabetes",
      HTA ~ "HTA",
      `Tiempo de exposición ácida % (PH)` ~ "Tiempo exposición ácida% (PH)",
      `N de eventos de reflujo` ~ "Eventos de reflujo", 
      `Impedancia del segmento contractil distal Ohms` ~"Impedancia segmento contractil distal (OHMS)",
      `Integral contractil mmHg/cm` ~ "Integral contractil (mmHg/cm)",
      `DCI mmHg/cm/seg` ~ "DCI mmHg/CM/SEG",
      `ACLARAMIENTO ESOFAGICO` ~ "Aclaramiento esofágico",
      `PRESION EII` ~ "Presión EEI", 
      `RESERVA CONTRACTIL` ~ "Reserva Contractil", 
      `Diagnóstico manometría` ~ "Diagnóstico manometría",
       `Diagnostico pH (es el diagnóstico de comparación)` ~"DX"
    ),
    statistic = list(
      all_continuous2()  ~ c(
        "{mean} ({sd})",
        "{median} ({p25}, {p75})",
        "{min} - {max}"
      ),
      all_categorical() ~ "{n} ({p}%)"
    ),
    digits = list(
      all_continuous()  ~ 2,
      all_categorical() ~ 1
    ),
    missing = "ifany",
    missing_text = "Sin dato"
  ) %>%
  add_p(
    test = list(
      all_continuous2()  ~ "kruskal.test",  
      all_categorical() ~ "chisq.test"
    ),
    pvalue_fun = ~ style_pvalue(.x, digits = 3)
  ) %>%
  add_overall() %>%
  modify_header(label ~ "**Variable**") %>%
  bold_labels() %>%
  bold_p(t = 0.05) %>%
  modify_caption("**Tabla 2**")

tabla2
Tabla 2
Variable Overall
N = 1261		 no
N = 801		 si
N = 461		 p-value2
Sexo


0.834
    Femenino 85.0 (67.5%) 55.0 (68.8%) 30.0 (65.2%)
    Masculino 41.0 (32.5%) 25.0 (31.3%) 16.0 (34.8%)
Edad


0.461
    Mean (SD) 54.88 (15.23) 55.83 (14.19) 53.24 (16.91)
    Median (Q1, Q3) 53.50 (44.00, 66.00) 54.50 (45.00, 66.50) 51.50 (40.00, 66.00)
    Min - Max 19.00 - 88.00 28.00 - 84.00 19.00 - 88.00
IMC kg/m2


0.475
    Mean (SD) 23.85 (3.41) 24.14 (3.87) 23.36 (2.36)
    Median (Q1, Q3) 23.40 (21.40, 25.40) 23.40 (21.15, 26.90) 23.40 (21.50, 24.70)
    Min - Max 18.10 - 32.80 18.10 - 32.80 18.50 - 30.80
Diabetes


<0.001
    0 87.0 (69.0%) 66.0 (82.5%) 21.0 (45.7%)
    1 39.0 (31.0%) 14.0 (17.5%) 25.0 (54.3%)
HTA


0.400
    0 97.0 (77.0%) 64.0 (80.0%) 33.0 (71.7%)
    1 29.0 (23.0%) 16.0 (20.0%) 13.0 (28.3%)
Tiempo exposición ácida% (PH)


<0.001
    Mean (SD) 4.64 (4.03) 3.47 (3.55) 6.68 (4.04)
    Median (Q1, Q3) 3.45 (1.10, 7.50) 1.85 (0.65, 6.45) 7.30 (3.60, 8.90)
    Min - Max 0.00 - 16.90 0.10 - 14.50 0.00 - 16.90
Eventos de reflujo


0.168
    Mean (SD) 49.90 (33.87) 48.06 (35.48) 53.11 (30.97)
    Median (Q1, Q3) 39.00 (28.00, 64.00) 37.50 (24.50, 58.50) 42.00 (33.00, 73.00)
    Min - Max 1.00 - 185.00 1.00 - 185.00 12.00 - 148.00
DX


<0.001
    Nerd 59.0 (46.8%) 25.0 (31.3%) 34.0 (73.9%)
    Normal 67.0 (53.2%) 55.0 (68.8%) 12.0 (26.1%)
Impedancia segmento contractil distal (OHMS)


<0.001
    Mean (SD) 2,165.60 (597.29) 2,352.31 (559.57) 1,840.87 (521.07)
    Median (Q1, Q3) 2,351.50 (1,532.00, 2,721.00) 2,578.50 (1,699.50, 2,749.50) 1,534.00 (1,489.00, 2,356.00)
    Min - Max 1,230.00 - 3,200.00 1,230.00 - 3,200.00 1,423.00 - 2,876.00
Integral contractil (mmHg/cm)


<0.001
    Mean (SD) 33.67 (7.09) 36.17 (6.95) 29.32 (4.93)
    Median (Q1, Q3) 32.95 (28.60, 38.20) 35.40 (32.45, 41.65) 29.00 (27.30, 30.40)
    Min - Max 20.50 - 48.70 20.50 - 48.70 22.20 - 45.80
DCI mmHg/CM/SEG


0.186
    Mean (SD) 1,562.24 (1,275.76) 1,680.74 (1,358.00) 1,356.15 (1,102.30)
    Median (Q1, Q3) 1,233.00 (668.00, 1,982.00) 1,465.50 (740.00, 2,177.00) 1,102.50 (637.00, 1,688.00)
    Min - Max 130.00 - 7,175.00 150.00 - 7,175.00 130.00 - 5,786.00
Aclaramiento esofágico


0.636
    Mean (SD) 68.49 (35.26) 70.13 (33.70) 65.65 (38.04)
    Median (Q1, Q3) 80.00 (40.00, 100.00) 80.00 (50.00, 100.00) 80.00 (30.00, 100.00)
    Min - Max 0.00 - 100.00 0.00 - 100.00 0.00 - 100.00
Presión EEI


0.341
    Mean (SD) 23.05 (12.98) 23.90 (13.65) 21.57 (11.73)
    Median (Q1, Q3) 20.05 (13.70, 32.40) 20.45 (14.40, 32.55) 17.80 (12.50, 31.00)
    Min - Max 1.30 - 61.70 1.30 - 61.70 2.50 - 42.80
Reserva Contractil


0.829
    Adecuado 71.0 (56.3%) 44.0 (55.0%) 27.0 (58.7%)
    Inadecuado 55.0 (43.7%) 36.0 (45.0%) 19.0 (41.3%)
Diagnóstico manometría


0.458
    MEI 19.0 (15.1%) 14.0 (17.5%) 5.0 (10.9%)
    Normal 107.0 (84.9%) 66.0 (82.5%) 41.0 (89.1%)
1 n (%)
2 Pearson’s Chi-squared test; Kruskal-Wallis rank sum test

Modelo inicial incluye variables: diabetes, tiempo exposición ácida, Eventos de reflujo, diagnóstico de comapración, impedancia segmento contractil, integral contractil, DCI ,

BASE$HERNIA_bin <- ifelse(trimws(tolower(BASE$`HERNIA OCULTA`)) == "si", 1, 0)
BASE$Diabetes <- factor(BASE$Diabetes, levels = c(0,1), labels = c("No", "Si"))
table(BASE$HERNIA_bin)
## 
##  0  1 
## 80 46
modelo <- glm(HERNIA_bin ~ `Diagnostico pH (es el diagnóstico de comparación)` + Diabetes + `Tiempo de exposición ácida % (PH)` + `N de eventos de reflujo` + `Impedancia del segmento contractil distal Ohms` + `Integral contractil mmHg/cm` + `DCI mmHg/cm/seg`,
              data = BASE,
              family = binomial(link = "logit"))
summary(modelo)
## 
## Call:
## glm(formula = HERNIA_bin ~ `Diagnostico pH (es el diagnóstico de comparación)` + 
##     Diabetes + `Tiempo de exposición ácida % (PH)` + `N de eventos de reflujo` + 
##     `Impedancia del segmento contractil distal Ohms` + `Integral contractil mmHg/cm` + 
##     `DCI mmHg/cm/seg`, family = binomial(link = "logit"), data = BASE)
## 
## Coefficients:
##                                                             Estimate Std. Error
## (Intercept)                                                7.5410905  2.6008121
## `Diagnostico pH (es el diagnóstico de comparación)`Normal  0.8849141  1.5310997
## DiabetesSi                                                 1.4548042  0.5463815
## `Tiempo de exposición ácida % (PH)`                       -0.0553295  0.1177008
## `N de eventos de reflujo`                                 -0.0048610  0.0079789
## `Impedancia del segmento contractil distal Ohms`          -0.0014932  0.0010663
## `Integral contractil mmHg/cm`                             -0.1608477  0.0486873
## `DCI mmHg/cm/seg`                                         -0.0001005  0.0002149
##                                                           z value Pr(>|z|)    
## (Intercept)                                                 2.900 0.003737 ** 
## `Diagnostico pH (es el diagnóstico de comparación)`Normal   0.578 0.563291    
## DiabetesSi                                                  2.663 0.007754 ** 
## `Tiempo de exposición ácida % (PH)`                        -0.470 0.638293    
## `N de eventos de reflujo`                                  -0.609 0.542364    
## `Impedancia del segmento contractil distal Ohms`           -1.400 0.161420    
## `Integral contractil mmHg/cm`                              -3.304 0.000954 ***
## `DCI mmHg/cm/seg`                                          -0.468 0.639943    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 165.38  on 125  degrees of freedom
## Residual deviance: 120.30  on 118  degrees of freedom
## AIC: 136.3
## 
## Number of Fisher Scoring iterations: 5
BASE$`Diagnostico pH (es el diagnóstico de comparación)` <- factor(
  BASE$`Diagnostico pH (es el diagnóstico de comparación)`,
  levels = c("Normal", "Nerd")
)
modelo1 <- glm(HERNIA_bin ~ `Diagnostico pH (es el diagnóstico de comparación)` + Diabetes + `Integral contractil mmHg/cm`,
              data = BASE,
              family = binomial(link = "logit"))

summary(modelo1)
## 
## Call:
## glm(formula = HERNIA_bin ~ `Diagnostico pH (es el diagnóstico de comparación)` + 
##     Diabetes + `Integral contractil mmHg/cm`, family = binomial(link = "logit"), 
##     data = BASE)
## 
## Coefficients:
##                                                         Estimate Std. Error
## (Intercept)                                              3.94467    1.60357
## `Diagnostico pH (es el diagnóstico de comparación)`Nerd  0.34042    0.55600
## DiabetesSi                                               1.27730    0.52098
## `Integral contractil mmHg/cm`                           -0.15760    0.04615
##                                                         z value Pr(>|z|)    
## (Intercept)                                               2.460 0.013896 *  
## `Diagnostico pH (es el diagnóstico de comparación)`Nerd   0.612 0.540365    
## DiabetesSi                                                2.452 0.014216 *  
## `Integral contractil mmHg/cm`                            -3.415 0.000639 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 165.38  on 125  degrees of freedom
## Residual deviance: 123.22  on 122  degrees of freedom
## AIC: 131.22
## 
## Number of Fisher Scoring iterations: 5

ORs

library(sjPlot)
library(sjmisc)
library(sjlabelled)
library(gtsummary)

tbl_regression(modelo1, exponentiate = TRUE, add_estimate_to_reference_rows=TRUE)  %>% add_global_p()
Characteristic OR 95% CI p-value
Diagnostico pH (es el diagnóstico de comparación)

0.5
    Normal 1.00 —
    Nerd 1.41 0.46, 4.16
Diabetes

0.013
    No 1.00 —
    Si 3.59 1.31, 10.2
Integral contractil mmHg/cm 0.85 0.77, 0.93 <0.001
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Análisis de correlación

Correlacion Spearman

library(ggplot2)
library(ggpubr)

#CORRELACION 
# shapiro.test(BASE$`Integral contractil mmHg/cm`) #distribucion no es normal
cor_test <- cor.test(BASE$`Integral contractil mmHg/cm`, BASE$`Impedancia del segmento contractil distal Ohms`, method = "spearman")
png("COR1.png", width = 600, height = 550)
cor_1 <- ggplot(BASE, aes(x = `Integral contractil mmHg/cm`, y = `Impedancia del segmento contractil distal Ohms`)) +
  geom_point(color = "#2C3E50", size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "#E74C3C", fill = "salmon", alpha = 0.3, size = 1.1) +
  labs(
    title = "Correlación entre Integral contractil e Impedancia del segmento contractil",
    x = "Integral contractil",
    y = "Impendancia segmento contractil",
    caption = paste0("r = ", round(cor_test$estimate, 3),
                     ", p = ", format.pval(cor_test$p.value, digits = 2, eps = .001))
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5, size = 10),
    plot.caption = element_text(face = "italic", size = 12, hjust = 0.5)
  )
cor_1 
dev.off()
## png 
##   2
cor_1

Hernia oculta y aclaramiento esofagico

Correlacion biserial puntual

library(ltm)
library(ggplot2)

cor_test2 <- biserial.cor(BASE$`ACLARAMIENTO ESOFAGICO`, 
                           BASE$`HERNIA OCULTA`, 
                           use = "complete.obs",
                           level = 2)  # level indica cuál nivel de la dicotómica es "1"

png("COR2.png", width = 600, height = 550)
cor_2 <- ggplot(BASE, aes(x = `HERNIA OCULTA`, y = `ACLARAMIENTO ESOFAGICO`)) +
  geom_jitter(color = "#2C3E50", size = 2, alpha = 0.7, width = 0.1) +
  geom_boxplot(aes(fill = `HERNIA OCULTA`), alpha = 0.4, outlier.shape = NA) +
  scale_fill_manual(values = c("#3498DB", "#E74C3C")) +
  labs(
    title = "Correlación entre Hernia oculta y Aclaramiento esofágico",
    x = "Hernia oculta",
    y = "Aclaramiento esofágico",
    caption = paste0("r = ", round(cor_test2, 3))
  ) +
  theme_minimal(base_size = 14) +
  theme(
    plot.title   = element_text(face = "bold", hjust = 0.5, size = 10),
    plot.caption = element_text(face = "italic", size = 12, hjust = 0.5),
    legend.position = "none"
  )
cor_2
dev.off()
## png 
##   2
cor_2