Summarize coefficients and degrees of freedom for logistic regression - r

Question

Please see the code below to fit logistic regression model:
data = filter(msleep,vore =='carni' | vore == 'herbi')
data$vore = ifelse(data$vore == 'carni',0,1)
up1 =glm(formula = vore~sleep_total, data,family=binomial())
up2 <- round((summary(up1)$coefficients),4)
up2[ , 1]
Output gives only coefficients:
(Intercept) sleep_total
0.9112 -0.0392
Output desired (coefficients and degrees of freedom):
Coefficients:
(Intercept) sleep_total
0.9112 -0.0392
Degrees of Freedom: 50 Total (i.e. Null); 49 Residual
Null Deviance: 67.35
Residual Deviance: 66.95 AIC: 70.95

Answer 1

You can use capture.output as indicated by Roland in comment. The function is capturing output of a function and store it in a character vector. Then you can choose lines which are of interest to you.
library(dplyr)
library(ggplot2)
data <- filter(msleep,vore =='carni' | vore == 'herbi')
data$vore <- ifelse(data$vore == 'carni', 0, 1)
up1 <- glm(formula = vore ~ sleep_total, data,family = binomial())
out <- capture.output(summary(up1))
cat(paste0(out[c(9:12, 16:18)], "\n"))
Output:
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.91118 0.68692 1.326 0.185
sleep_total -0.03920 0.06187 -0.634 0.526
Null deviance: 67.350 on 50 degrees of freedom
Residual deviance: 66.945 on 49 degrees of freedom
AIC: 70.945