# Preparing data for finalfit

#### Ewen Harrison

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")
    #> Rows: 205 Columns: 7
    #> ── Column specification ────────────────────────────────────────────────────────
    #> Delimiter: ","
    #> dbl (7): time, status, sex, age, year, thickness, ulcer
    #>
    #> ℹ Use `spec()` to retrieve the full column specification for this data.
    #> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 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)
    #> $Continuous
    #>               label var_type   n missing_n missing_percent   mean     sd    min
    #> time           time    <dbl> 205         0             0.0 2152.8 1122.1   10.0
    #> status       status    <dbl> 205         0             0.0    1.8    0.6    1.0
    #> sex             sex    <dbl> 205         0             0.0    0.4    0.5    0.0
    #> age             age    <dbl> 205         0             0.0   52.5   16.7    4.0
    #> year           year    <dbl> 205         0             0.0 1969.9    2.6 1962.0
    #> thickness thickness    <dbl> 205         0             0.0    2.9    3.0    0.1
    #> ulcer         ulcer    <dbl> 205         0             0.0    0.4    0.5    0.0
    #>           quartile_25 median quartile_75    max
    #> time           1525.0 2005.0      3042.0 5565.0
    #> status            1.0    2.0         2.0    3.0
    #> sex               0.0    0.0         1.0    1.0
    #> age              42.0   54.0        65.0   95.0
    #> year           1968.0 1970.0      1972.0 1977.0
    #> thickness         1.0    1.9         3.6   17.4
    #> ulcer             0.0    0.0         1.0    1.0
    #>
    #> $Categorical
    #> data frame with 0 columns and 205 rows

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)
    #>               label var_type   n missing_n missing_percent
    #> time           time    <dbl> 205         0             0.0
    #> status       status    <dbl> 205         0             0.0
    #> sex             sex    <dbl> 205         0             0.0
    #> age             age    <dbl> 205         0             0.0
    #> year           year    <dbl> 205         0             0.0
    #> thickness thickness    <dbl> 205         0             0.0
    #> ulcer         ulcer    <dbl> 205         0             0.0

## 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)
    #> $Continuous
    #>               label var_type   n missing_n missing_percent   mean     sd    min
    #> time           time    <dbl> 205         0             0.0 2152.8 1122.1   10.0
    #> status       status    <dbl> 205         0             0.0    1.8    0.6    1.0
    #> sex             sex    <dbl> 205         0             0.0    0.4    0.5    0.0
    #> age             age    <dbl> 205         0             0.0   52.5   16.7    4.0
    #> year           year    <dbl> 205         0             0.0 1969.9    2.6 1962.0
    #> thickness thickness    <dbl> 205         0             0.0    2.9    3.0    0.1
    #> ulcer         ulcer    <dbl> 205         0             0.0    0.4    0.5    0.0
    #>           quartile_25 median quartile_75    max
    #> time           1525.0 2005.0      3042.0 5565.0
    #> status            1.0    2.0         2.0    3.0
    #> sex               0.0    0.0         1.0    1.0
    #> age              42.0   54.0        65.0   95.0
    #> year           1968.0 1970.0      1972.0 1977.0
    #> thickness         1.0    1.9         3.6   17.4
    #> ulcer             0.0    0.0         1.0    1.0
    #>
    #> $Categorical
    #>                label var_type   n missing_n missing_percent levels_n
    #> status.factor Status    <fct> 205         0             0.0        3
    #> sex.factor       Sex    <fct> 205         0             0.0        2
    #> ulcer.factor   Ulcer    <fct> 205         0             0.0        2
    #>                                                                             levels
    #> status.factor "Died from melanoma", "Alive", "Died from other causes", "(Missing)"
    #> sex.factor                                           "Male", "Female", "(Missing)"
    #> ulcer.factor                                      "Present", "Absent", "(Missing)"
    #>               levels_count   levels_percent
    #> status.factor  57, 134, 14 27.8, 65.4,  6.8
    #> sex.factor         79, 126           39, 61
    #> ulcer.factor       90, 115           44, 56

Everything looks good and you are ready to start analysis.