The `finafit`

package brings together the day-to-day functions we use to generate final results tables and plots when modelling. I spent many years repeatedly manually copying results from R analyses and built these functions to automate our standard healthcare data workflow. It is particularly useful when undertaking a large study involving multiple different regression analyses. When combined with RMarkdown, the reporting becomes entirely automated. Its design follows Hadley Wickham’s tidy tool manifesto.

### Installation and Documentation

The full documentation is now here: finalfit.org

The code lives on GitHub.

You can install `finalfit`

from CRAN with:

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install.packages("finalfit") |

It is recommended that this package is used together with `dplyr`

, which is a dependent.

Some of the functions require `rstan`

and `boot`

. These have been left as `Suggests`

rather than `Depends`

to avoid unnecessary installation. If needed, they can be installed in the normal way:

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install.packages("rstan") install.packages("boot") |

To install off-line (or in a Safe Haven), download the zip file and use `devtools::install_local()`

.

### Main Features

#### 1. Summarise variables/factors by a categorical variable

`summary_factorlist()`

is a wrapper used to aggregate any number of explanatory variables by a single **variable of interest**. This is often “Table 1” of a published study. When categorical, the variable of interest can have a maximum of five levels. It uses `Hmisc::summary.formula()`

.

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library(finalfit) library(dplyr) # Load example dataset, modified version of survival::colon data(colon_s) # Table 1 - Patient demographics by variable of interest ---- explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor") dependent = "perfor.factor" # Bowel perforation colon_s %>% summary_factorlist(dependent, explanatory, p=TRUE, add_dependent_label=TRUE) |

See other options relating to inclusion of missing data, mean vs. median for continuous variables, column vs. row proportions, include a total column etc.

`summary_factorlist()`

is also commonly used to summarise any number of variables by an **outcome variable** (say dead yes/no).

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# Table 2 - 5 yr mortality ---- explanatory = c("age.factor", "sex.factor", "obstruct.factor") dependent = 'mort_5yr' colon_s %>% summary_factorlist(dependent, explanatory, p=TRUE, add_dependent_label=TRUE) |

Tables can be knitted to PDF, Word or html documents. We do this in RStudio from a .Rmd document. Example chunk:

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```{r, echo = FALSE, results='asis'} knitr::kable(example_table, row.names=FALSE, align=c("l", "l", "r", "r", "r", "r")) ``` |

#### 2. Summarise regression model results in final table format

The second main feature is the ability to create final tables for linear (`lm()`

), logistic (`glm()`

), hierarchical logistic (`lme4::glmer()`

) and

Cox proportional hazards (`survival::coxph()`

) regression models.

The `finalfit()`

“all-in-one” function takes a single dependent variable with a vector of explanatory variable names (continuous or categorical variables) to produce a final table for publication including summary statistics, univariable and multivariable regression analyses. The first columns are those produced by `summary_factorist()`

. The appropriate regression model is chosen on the basis of the dependent variable type and other arguments passed.

##### Logistic regression: glm()

Of the form: `glm(depdendent ~ explanatory, family="binomial")`

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explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% finalfit(dependent, explanatory) |

##### Logistic regression with reduced model: glm()

Where a multivariable model contains a subset of the variables included specified in the full univariable set, this can be specified.

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explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") explanatory_multi = c("age.factor", "obstruct.factor") dependent = 'mort_5yr' colon_s %>% finalfit(dependent, explanatory, explanatory_multi) |

##### Mixed effects logistic regression: lme4::glmer()

Of the form: `lme4::glmer(dependent ~ explanatory + (1 | random_effect), family="binomial")`

Hierarchical/mixed effects/multilevel logistic regression models can be specified using the argument `random_effect`

. At the moment it is just set up for random intercepts (i.e. `(1 | random_effect)`

, but in the future I’ll adjust this to accommodate random gradients if needed (i.e. `(variable1 | variable2)`

.

1 2 3 4 5 6 7 8 |
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") explanatory_multi = c("age.factor", "obstruct.factor") random_effect = "hospital" dependent = 'mort_5yr' colon_s %>% finalfit(dependent, explanatory, explanatory_multi, random_effect) |

##### Cox proportional hazards: survival::coxph()

Of the form: `survival::coxph(dependent ~ explanatory)`

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explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "Surv(time, status)" colon_s %>% finalfit(dependent, explanatory) |

##### Add common model metrics to output

`metrics=TRUE`

provides common model metrics. The output is a list of two dataframes. Note chunk specification for output below.

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explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% finalfit(dependent, explanatory, metrics=TRUE) |

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```{r, echo=FALSE, results="asis"} knitr::kable(table7[[1]], row.names=FALSE, align=c("l", "l", "r", "r", "r")) knitr::kable(table7[[2]], row.names=FALSE) ``` |

Rather than going all-in-one, any number of subset models can be manually added on to a `summary_factorlist()`

table using `finalfit_merge()`

. This is particularly useful when models take a long-time to run or are complicated.

Note the requirement for `fit_id=TRUE`

in `summary_factorlist()`

. `fit2df`

extracts, condenses, and add metrics to supported models.

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explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") explanatory_multi = c("age.factor", "obstruct.factor") random_effect = "hospital" dependent = 'mort_5yr' # Separate tables colon_s %>% summary_factorlist(dependent, explanatory, fit_id=TRUE) -> example.summary colon_s %>% glmuni(dependent, explanatory) %>% fit2df(estimate_suffix=" (univariable)") -> example.univariable colon_s %>% glmmulti(dependent, explanatory) %>% fit2df(estimate_suffix=" (multivariable)") -> example.multivariable colon_s %>% glmmixed(dependent, explanatory, random_effect) %>% fit2df(estimate_suffix=" (multilevel)") -> example.multilevel # Pipe together example.summary %>% finalfit_merge(example.univariable) %>% finalfit_merge(example.multivariable) %>% finalfit_merge(example.multilevel) %>% select(-c(fit_id, index)) %>% # remove unnecessary columns dependent_label(colon_s, dependent, prefix="") # place dependent variable label |

##### Bayesian logistic regression: with `stan`

Our own particular `rstan`

models are supported and will be documented in the future. Broadly, if you are running (hierarchical) logistic regression models in Stan with coefficients specified as a vector labelled `beta`

, then `fit2df()`

will work directly on the `stanfit`

object in a similar manner to if it was a `glm`

or `glmerMod`

object.

### 3. Summarise regression model results in plot

Models can be summarized with odds ratio/hazard ratio plots using `or_plot`

, `hr_plot`

and `surv_plot`

.

#### OR plot

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# OR plot explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = 'mort_5yr' colon_s %>% or_plot(dependent, explanatory) # Previously fitted models (`glmmulti()` or # `glmmixed()`) can be provided directly to `glmfit` |

#### HR plot

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# HR plot explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor") dependent = "Surv(time, status)" colon_s %>% hr_plot(dependent, explanatory, dependent_label = "Survival") # Previously fitted models (`coxphmulti`) can be provided directly using `coxfit` |

#### Kaplan-Meier survival plots

KM plots can be produced using the `library(survminer)`

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# KM plot explanatory = c("perfor.factor") dependent = "Surv(time, status)" colon_s %>% surv_plot(dependent, explanatory, xlab="Time (days)", pval=TRUE, legend="none") |

### Notes

Use `Hmisc::label()`

to assign labels to variables for tables and plots.

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label(colon_s$age.factor) = "Age (years)" |

Export dataframe tables directly or to R Markdown `knitr::kable()`

.

Note wrapper `summary_missing()`

is also useful. Wraps `mice::md.pattern`

.

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colon_s %>% summary_missing(dependent, explanatory) |

Development will be on-going, but any input appreciated.

Nice, thanks for sharing! Hope to use this as an inspiration to make something similar based around financial statement analysis.

Hi. I really appreciate the time you took to provide these elegant regression examples.

I am a novice R ursR.

I am able to follow your examples using my own data.

However, I wonder, would you mind providing me with a sample code of how you used R Markdown and/or knitr to complete the any of these splendid examples of table formatting.

I have reviewed these sites:

https://yihui.name/knitr/options/

https://rmarkdown.rstudio.com/lesson-1.html

Yet I cannot figure out how it all works together.

Any example would be most appreciated.

Thanks Bill. Are you using RStudio? I can post something up.

Everything you need should be in this previous post Bill:

http://www.datasurg.net/2018/05/22/finalfit-knitr-and-r-markdown-for-quick-results/