In ewenharrison/finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)

This vignette shows you how to upload and prepare any dataset for use with finalfit. The demonstration will use the boot::melanoma. Use ?boot::melanoma to see the help page with data description. I will use library(tidyverse) methods. First I'll write_csv() the data just to demonstrate reading it.

Read data

Note the various options in read_csv(), including providing column names, variable type, missing data identifier etc.

library(readr)

# Save example
write_csv(boot::melanoma, "boot.csv")

# Read data
melanoma = read_csv("boot.csv")

Column types

Note the output shows how the columns/variables have been parsed. For full details see ?readr::cols().

Continuous data

• Integer (whole numbers) - col_integer()
• Double or numeric (real numbers; the name comes from "double-precision floating point") - col_double()

Categorical data

• Factor (a fixed set of names/strings or numbers) - col_factor()
• Character (sequences letters, numbers, and symbols) - col_character()
• Logical (containing only TRUE or FALSE) - col_logical()

Dates and times

• Date - col_date()
• Time - col_time()
• Date-time - col_datetime()

Check data

ff_glimpse() provides a convenient overview of all data in a tibble or data frame. It is particularly important that factors are correctly specified. Hence, ff_glimpse() separates variables into continuous and categorcial. As expected, no factors are yet specified in the melanoma dataset.

library(finalfit)
ff_glimpse(melanoma)

If you wish to see the variables in the order in which they appear in the data frame or tibble, missing_glimpse() or tibble::glimpse() are useful.

missing_glimpse(melanoma)

Specify factors

Use an original description of the data (often called a data dictionary) to correctly assign and label any factor variables. This can be done in a single pipe.

library(dplyr)
melanoma %>% 
  mutate(
    status.factor = factor(status, levels = c(1, 2, 3), 
      labels = c("Died from melanoma", "Alive", "Died from other causes")) %>% 
    ff_label("Status"),
    sex.factor = factor(sex, levels = c(1, 0),
      labels = c("Male", "Female")) %>% 
    ff_label("Sex"),
    ulcer.factor = factor(ulcer, levels = c(1, 0),
      labels = c("Present", "Absent")) %>% 
    ff_label("Ulcer")
  ) -> melanoma

ff_glimpse(melanoma)

Everything looks good and you are ready to start analysis.

ewenharrison/finalfit documentation built on March 29, 2025, 10:34 a.m.