In RaymondBalise/rUM: R Templates from the University of Miami

knitr::opts_chunk$set(
                      ########## set global options ############
    echo = FALSE,     # don't show code
    collapse = TRUE,  # keep code from blocks together (if shown)
    message = TRUE,   # show messages
    warning = TRUE,   # show warnings
    error = TRUE,     # show error messages
    comment = "",     # don't show ## with printed output
    dpi = 100,        # image resolution (typically 300 for publication)
    fig.width = 6.5,  # figure width
    fig.height = 4.0  # figure height
)

# R's default rounding is to show 7 digits. This rounds results to 3 digits.
options(digits = 3)

library(conflicted)
suppressPackageStartupMessages(library(tidymodels))
tidymodels_prefer()
suppressPackageStartupMessages(library(tidyverse))

# suppress "`summarise()` has grouped output by " messages
options(dplyr.summarise.inform = FALSE)

library(glue)  # for glue()
library(rUM)  # needed for the bibliography to include the package
library(rio)  # for import() 

# gtsummary for tbl_summary(), add_n(), add_p(), modify_header(), bold_labels()
suppressPackageStartupMessages(library(gtsummary)) 

# If you want to import a dataset which is not an R file, put its name inside 
# the "" below.  For example:
# raw_data <- import("the_excel.xlsx")

# If your datasets is an Excel file saved in the data folder, add a path like:
# raw_data <- import("data/the_excel.xlsx")

# preprocess your data and ultimately make a dataset called analysis
analysis <- mtcars |> 
  mutate(auto_man = if_else(am == 0, "Automatic", "Manual")) |> 
  select(mpg, auto_man)

Abstract

Introduction

Methods

Analyses were conducted with r stringr::word(R.Version()$version.string, 1, 3) with the tidyverse (r packageVersion("tidyverse")), rUM (r packageVersion("rUM")), gtsummary (r packageVersion("gtsummary")) packages used to preprocess and summarize data. [@R-base; @R-tidyverse; @tidyverse2019; @R-rUM; @R-gtsummary]

Results

# To learn how to use tbl_summary look https://www.danieldsjoberg.com/gtsummary/
analysis |> 
  tbl_summary(
    include = c(everything()), # choose your variables here
    # change auto_man to the name of your column variable or delete by = auto_man
    by = auto_man, # split table by group
    missing = "no" # don't list missing data separately
  ) |>
  # add_n() |> # add column with total number of non-missing observations
  # add_p() |> # test for a difference between groups
  modify_header(label = "") |> # update the column header to be blank
  bold_labels() |> 
  modify_caption(
    "Cross references to tables start with tab: then the code chunk label."
  ) 

As can be seen in Table \@ref(tab:table-1) or Table 1

#| fig.cap: "Your real caption belongs here.  Remember figure references begin with #fig:"

# To learn how to use ggplot start here: https://ggplot2.tidyverse.org/#learning-ggplot2
analysis |> 
  ggplot() +
        labs(
            title = "Your short title goes here.",
            caption = "Your data sources/citation goes here."
        ) +
        geom_blank()
        # remove geom_blank() and add details here

See Figure \@ref(fig:figure1) or Figure 1

Discussion

References {-}

# automatically create a bib database for loaded R packages & rUM
knitr::write_bib(
  c(
    .packages(),
    "rUM"
  ),
  "packages.bib"
)

RaymondBalise/rUM documentation built on April 14, 2025, 11:48 a.m.