Tukukkk

I have successfully created a plot of a binomial glm using example data. https://sciences.ucf.edu/biology/d4lab/wp-content/uploads/sites/125/2018/11/parasites.txt

The predictors of the model include 3 predictors (one categorical, 2 continuous)

The code works fine but I have been wanting to try and incorporating more dplyr functions and pipes to streamline code. Ultimately, I want to make my block of code into a function that works with any model with the same type and number of predictors for a binomial glm. Are there better ways to carry out my code with more tidyverse/dplyr code?

#import parasites file



df<-parasites

m1<-glm(data=df, infected~age+weight+sex, family = "binomial")

summary(m1)

age_grid <- round(seq(min(df$age), max(df$age), length.out = 15))

weight_grid <- round(seq(min(df$weight), max(df$weight), length.out = 15))

newdat <- expand.grid(weight =weight_grid,

                      age = age_grid, sex = c("female", "male")) 



pred <- predict.glm(m1, newdata = newdat, type="link", se=TRUE)

ymin <- m1$family$linkinv(pred$fit - 1.96 * pred$se.fit)

ymax <- m1$family$linkinv(pred$fit + 1.96 * pred$se.fit)

fit <- m1$family$linkinv(pred$fit) 

z <- matrix(fit, length(age_grid))

ci.low <- matrix(ymin, length(age_grid))

ci.up <- matrix(ymax, length(age_grid))



x<-data.frame(pred = fit,

              low = ymin,

              high = ymax,

              newdat) %>% mutate(category=cut(age, breaks=c(0, 69, 138, 206), labels = 

                                                c("0-69", "70-139", "139-206")))



x$age<-as.factor(x$age)



library(ggplot2)

finalgraph<-ggplot(data=x)+

  geom_line(aes(x = weight, y = pred, color = age))+

  geom_ribbon(aes(x = weight, ymin = low, ymax = high, fill = age), alpha = 0.1) +

  facet_grid(category~sex) +theme(panel.grid.major = element_blank(),

                                  panel.grid.minor = element_blank())+

  ylab(expression(bold(y = "Infection Probability"))) + xlab(expression(bold("Weight"))) +

  theme(legend.position = "right",strip.text.x = element_text(face = "bold", size=12),

        strip.text.y = element_text(size=10),

        axis.text.y = element_text(size=10, face = "bold"), axis.text.x = element_text(size=10),

        axis.title = element_text(size=12), 

        legend.text=element_text(size=10), legend.title = element_text(size=12, face="bold"))+ 

  labs(linetype="Age (months)", colour="Age (months)", fill = "Age (months)")

finalgraph

Code notes:
Essentially I made a model, created a bunch of values from my predictors (age_grid, v_grid) and made all possible combinations of these values along with the categorical variable of sex using expand.grid.

Then I just used the predict.glm function to extract predicted values based off of expand.grid object. I also extracted std. errors and calculated confidence intervals (ci.up and ci. low). Then I used some dplyr functions to create a dataframe with all this information and also made a new column called category. Category breaks down one of my variables (age) into four distinct groups based of f of breaks I decided on and labelled as decided as well. Then I plotted all of this data using ggplot2.