Long code chunks when knitting to pdf

[meesew]

I have tried all other solutions online, including those posted by the author of the knitr package. For some reason, every time I knit to pdf, it will not format the code correctly, and all the code falls off the page. I have been working on this for two weeks and have spent about 2-3 hours a day trying to solve it. Does anyone have an idea of why it isn't working?

I tried the styler package options, formatR options, etc. Below you can see the code using the solution for setting linewidth options in knitr, https://github.com/yihui/knitr-examples/blob/master/077-wrap-output.Rmd. I tried uploading the data file for anyone to reproduce the problem, but I can't upload an .rds file.

--- title: "Online Supplement" author: "Anonymous for Review" date: "r Sys.Date()`" output: pdf_document: default

set.seed(42)
library(car)
library(lattice)
library(effects)
library(emmeans)
library(psych)
library(tidyverse)
library(flexplot)
library(lme4)
library(kableExtra)
library(stargazer)
library(papaja)
library(confintr)
library(ppcor)
library(jtools)
library(interactions)
library(patchwork)
library(ggstance)
library(ggExtra)
library(gghalves)
library(colorspace)
library(viridis)
library(formatR)
library(styler)

pcor_ci.test <-
function (x, y, z, method = c("pearson", "kendall", "spearman"), conf.level = 0.95, ...) {
    d1 <- deparse(substitute(x))
    d2 <- deparse(substitute(y))
    d3 <- deparse(substitute(z))
    data.name <- paste0(d1, " and ", d2, "; controlling: ", d3)
    method <- match.arg(method)
    Method <- paste0("Partial correlation (", method, ")")
    alternative <- "true partial correlation is not equal to 0"

    x <- as.vector(x)
    y <- as.vector(y)
    z <- as.data.frame(z)
    xyz <- data.frame(x, y, z)
    pcor <- ppcor::pcor(xyz, method = method)
    estimate <- pcor$est[1, 2]
    p.value <- pcor$p.value[1, 2]
    parameter <- c(n = pcor$n, gp = pcor$gp)
    statistic <- c(Stat = pcor$statistic[1, 2])

    fit1 <- lm(x ~ z, data = xyz)
    fit2 <- lm(y ~ z, data = xyz)
    cortest <- cor.test(resid(fit1), resid(fit2), method = method, conf.level = conf.level, ...)
    ci <- cortest$conf.int

    ht <- list(
        statistic = statistic,
        parameter = parameter,
        p.value = p.value,
        estimate = c(partial.cor = estimate),
        alternative = alternative,
        method = Method,
        data.name = data.name,
        conf.int = ci
    )
    class(ht) <- "htest"
    ht
}

theme_minimalism <- function(){ theme_minimal() + theme(panel.grid.major.y=element_blank(),
panel.grid.minor.y=element_blank(),
panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
text=element_text(size=14),
axis.text=element_text(size=12),
axis.title=element_text(size=14, face="bold"))
}

library(knitr)
hook_output = knit_hooks$get('output')
knit_hooks$set(output = function(x, options) {
  # this hook is used only when the linewidth option is not NULL
  if (!is.null(n <- options$linewidth)) {
    x = xfun::split_lines(x)
    # any lines wider than n should be wrapped
    if (any(nchar(x) > n)) x = strwrap(x, width = n)
    x = paste(x, collapse = '\n')
  }
  hook_output(x, options)
})

Study 1a

Data Prep

study1a <- read_rds("study1a.rds")
keys_list_study1a <- list(
  infoavoid = c("Q103_4", "Q103_5", "-Q103_6", "Q103_7", "-Q103_8", "Q103_9", "-Q103_10", "-Q103_11"),
  derog = c("-Q63_1", "Q63_5", "Q63_8", "Q63_9", "Q63_10"),
  heartavoid = c("Q104_4", "Q104_5", "-Q104_6", "Q104_7", "-Q104_8", "Q104_9", "-Q104_10", "-Q104_11")
) #keys created identify items to be scored for each scale.
my_scales_study1a <- scoreItems(keys_list_study1a,
                                study1a,
                                totals = FALSE,
                                min = 1, max = 7, impute = "none") #Score items using keys list to create a new variable representing one score, the mean, for each scale for each person
#print(my_scales_study1a, short = TRUE)
study1a_scores <- my_scales_study1a$scores  
study1a <- as_tibble(cbind.data.frame(study1a, study1a_scores))#add new variables to data set
study1a$derog[study1a$TestingDecision == 1] <- NA

x <- study1a %>% filter(TestingDecision == 0) # Data frame for correlations between predisposition to avoid and feedback derogation includes only those who received feedback (did not avoid)

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