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.
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")
Note the output shows how the columns/variables have been parsed. For full details see ?readr::cols()
.
col_integer()
col_double()
col_factor()
col_character()
col_logical()
col_date()
col_time()
col_datetime()
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)
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.
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