---
title: "All tables examples"
author: "Ewen Harrison"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{All tables examples}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
	collapse = TRUE,
	comment = "#>"
)
```

```{r, eval=FALSE}
install.packages("finalfit")
```


## 1 Cross tables

Two-way tables are used extensively in healthcare research, e.g. a 2x2 table comparing two factors with two levels each, or table 1 from a typical clinical study or trial

The main functions all take a `dependent` variable - the outcome (maximum of 5 levels) - and `explanatory` variables - predictors or exposures (any number categorical or continuous variables). 

### 1.01 Default

```{r}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
  summary_factorlist(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r"))
```

Note, chi-squared warnings will be generated when the expected count in any cell is less than 5. Fisher's exact test can be used as below, or go straight to a univariable logistic regression, e.g. `colon_s %>% finalfit(dependent, explanatory)`

### 1.02 Add or edit variable labels

```{r, warning = FALSE}
library(finalfit)
library(dplyr)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
	mutate(
		sex.factor = ff_label(sex.factor, "Gender")
	) %>% 
  summary_factorlist(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r"))
```

### 1.03 P-value for hypothesis test

Chi-squared for categorical, Kruskal-Wallis/Mann-Whitney for continuous

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
	summary_factorlist(dependent, explanatory, p = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r"))
```

### 1.04 With Fisher's exact test

```{r}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
  summary_factorlist(dependent, explanatory, p = TRUE, catTest = catTestfisher) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r"))
```

### 1.05 Median (interquartile range) instead of mean (standard deviation)

... for continuous variables.

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
	summary_factorlist(dependent, explanatory, p = TRUE, cont = "median") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r"))
```

### 1.06 Missing values for the explanatory variables

Always do this when describing your data.

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
	summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r"))
```

### 1.07 Column proportions (rather than row)

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
	summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
										 column = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r"))
```

### 1.08 Total column

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
	summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
										 column = TRUE, total_col = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r"))
```

### 1.09 Order a variable by total

This is intended for when there is only one `explanatory` variable.  

```{r, warning=FALSE}
library(finalfit)
explanatory = c("extent.factor")
dependent = "perfor.factor"
colon_s %>%
	summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
										 column = TRUE, total_col = TRUE, orderbytotal = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r"))
```

### 1.10 Label with `dependent` name

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
	summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
										 column = TRUE, total_col = TRUE, add_dependent_label = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r"))
```

The dependent name cannot be passed directly to the table intentionally. This is to avoid errors when code is copied and the name is not updated. Change the dependent label using the following. The prefix ("Dependent: ") and any suffix can be altered. 

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "perfor.factor"
colon_s %>%
  dplyr::mutate(
    perfor.factor = ff_label(perfor.factor, "Perforated cancer")
	) %>% 
  summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
    column = TRUE, total_col = TRUE, add_dependent_label = TRUE, dependent_label_prefix = "") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r"))
```

### 1.11 Dependent variable with any number of factor levels supported

```{r, warning=FALSE}
library(finalfit)
explanatory = c("age", "age.factor", "sex.factor", "obstruct.factor")
dependent = "extent.factor"
colon_s %>%
  dplyr::mutate(
    perfor.factor = ff_label(perfor.factor, "Perforated cancer")
  ) %>% 
  summary_factorlist(dependent, explanatory, p = TRUE, cont = "median", na_include = TRUE,
    column = TRUE, total_col = TRUE, add_dependent_label = TRUE, dependent_label_prefix = "") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 1.12 Explanatory variable defaults to factor when ≤5 distinct values

```{r, warning=FALSE}
library(finalfit)

# Here, `extent` is a continuous variable with 4 distinct values. 
# Any continuous variable with 5 or fewer unique values is converted silently to factor 
# e.g.
explanatory = c("extent")
dependent = "mort_5yr"
colon_s %>%
  summary_factorlist(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 1.13 Keep as continous variable when ≤5 distinct values

```{r, warning=FALSE}
library(finalfit)
explanatory = c("extent")
dependent = "mort_5yr"
colon_s %>%
  summary_factorlist(dependent, explanatory, cont_cut = 3) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 1.14 Stratified crosstables

I've been meaning to include support for table stratification for a while. I have delayed for a good reason. Perhaps the most straightforward way to implement stratificiation is with `dplyr::group_by()`. However, the non-standard evaluation required for multiple strata may confuse as it is not implemented else where in the package (doesn't work with `group_by_`). This translates to whether variable names are passed in quotes or not. Finally,. `dplyr::do()` is planned for deprecation, but there is no good alternative at the moment. Anyway, here is a solution, which while not that pretty, is very effective. 

```{r, warning=FALSE}
library(dplyr)
# Piped function to generate stratified crosstabs table
explanatory = c("age.factor", "sex.factor")
dependent = "rx.factor"

# Pick option below
split = "rx.factor"
split = c("perfor.factor", "node4.factor")

colon_s %>%
  group_by(!!! syms(split)) %>% #Looks awkward, but this keeps quoted var names (rather than unquoted)
  do(
    summary_factorlist(., dependent, explanatory, p = TRUE)
  ) %>%
  data.frame() %>%
  dependent_label(colon_s, dependent, prefix = "") %>%
  colname2label(split) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "l", "l", "r", "r", "r"))
```

## 2 Model tables with `finalfit()`

### 2.01 Default

Logistic regression first.

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.02 Hide reference levels

Most appropriate when all explanatory variables are continuous or well-known binary variables, such as sex. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age", "sex.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory, add_dependent_label = FALSE) %>% 
	ff_remove_ref() %>% 
	dependent_label(colon_s, dependent)-> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.03 Model metrics

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory, metrics = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t[[1]], row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
kable(t[[2]], row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"), col.names = "")
```

### 2.04 Model metrics can be applied to all supported base models

```{r, warning=FALSE, message=FALSE}
library(finalfit)
glm(mort_5yr ~ age.factor + sex.factor + obstruct.factor + perfor.factor, data = colon_s, family = "binomial") %>% 
	ff_metrics() -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"), col.names = "")
```

### 2.05 Reduced model

```{r, warning=FALSE, message=FALSE}
library(finalfit)
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) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.06 Include all models

```{r, warning=FALSE, message=FALSE}
library(finalfit)
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, metrics = TRUE, keep_models = TRUE) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t[[1]], row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
kable(t[[2]], row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"), col.names = "")
```

### 2.06 Interactions

Interactions can be specified in the normal way. Formatting the output is trickier. At the moment, we have left the default model output. This can be adjusted as necessary.

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.07 Interactions: create interaction variable with two factors

```{r, warning=FALSE, message=FALSE}
library(finalfit)
#explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
explanatory = c("obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	ff_interaction(age.factor, sex.factor) %>% 
	finalfit(dependent, c(explanatory, "age.factor__sex.factor")) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.08 Dependent name

The dependent name cannot be specified directly intentionally. This is to prevent errors when copying code. Re-label using `ff_label()`. The dependent prefix and suffix can also be altered. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	dplyr::mutate(
		mort_5yr = ff_label(mort_5yr, "5-year mortality")
	) %>% 
	finalfit(dependent, explanatory, dependent_label_prefix = "",
					 dependent_label_suffix = " (full model)") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.09 Estimate name

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory, estimate_name = "Odds ratio") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.10 Digits / decimal places

Number of digits to round to regression results. (1) estimate, (2) confidence interval limits, (3) p-value. Default is c(2,2,3). Trailing zeros are preserved. Number of decimal places for counts and mean (sd) / median (IQR) not currently supported. Defaults are senisble :)

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory, digits = c(3,3,4)) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.11 Confidence interval type

One of `c("profile", "default")` for GLM models (`confint.glm()`). Note, a little awkwardly, the 'default' setting is `profile`, rather than `default`. Profile levels are probably a little more accurate. Only go to default if taking a significant length of time for profile, i.e. data is greater than hundreds of thousands of lines.

For glmer/lmer models (`confint.merMod()`), `c("profile", "Wald", "boot")`. Not implemented for `lm()`, `coxph()` or `coxphlist`, which use default.

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory, confint_type = "default") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.12 Confidence interval level

Probably never change this :) Note, the p-value is intentionally not included for confidence levels other than 95% to avoid confusion.

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory, confint_level = 0.90) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.13 Confidence interval separation

Some like to avoid the hyphen so as not to confuse with minus sign. Obviously not an issue in logistic regression.   

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
colon_s %>%
	finalfit(dependent, explanatory, confint_sep = " to ") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.14 Mixed effects random-intercept model

At its simplest, a random-intercept model can be specified using a single quoted variable. In this example, it is the equivalent of quoting `random_effect = "(1 | hospital)"`. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
random_effect = "hospital"
colon_s %>%
	finalfit(dependent, explanatory, random_effect = random_effect,
					 dependent_label_suffix = " (random intercept)") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.15 Mixed effects random-slope model

In the example below, allow the effect of age on outcome to vary by hospital. Note, this specification must have parentheses included.

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
random_effect = "(age.factor | hospital)"
colon_s %>%
	finalfit(dependent, explanatory, random_effect = random_effect,
					 dependent_label_suffix = " (random slope: age)") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.16 Mixed effects random-slope model directly from `lme4`

Clearly, as models get more complex, parameters such as random effect group variances may require to be extracted directly from model outputs. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
random_effect = "(age.factor | hospital)"
colon_s %>% 
	lme4::glmer(mort_5yr ~ age.factor + (age.factor | hospital), family = "binomial", data = .) %>% 
	broom::tidy() -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.17 Exclude all missing data in final model from univariable analyses 

This can be useful if you want the numbers in the final table to match the final multivariable model. However, be careful to include a full explanation of this in the methods and the reason for exluding the missing data. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'mort_5yr'
colon_s %>%
	dplyr::select(explanatory, dependent) %>%
	na.omit() %>%
	finalfit(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.18 Linear regression

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = 'nodes'
colon_s %>%
	finalfit(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.19 Mixed effects random-intercept linear regression

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
random_effect = "hospital"
colon_s %>%
	finalfit(dependent, explanatory, random_effect = random_effect,
					 dependent_label_suffix = " (random intercept)") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.20 Mixed effects random-slope linear regression

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
random_effect = "(age.factor | hospital)"
colon_s %>%
	finalfit(dependent, explanatory, random_effect = random_effect,
					 dependent_label_suffix = " (random slope: age)") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.21 Cox proportional hazards model (survival / time to event)

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
	finalfit(dependent, explanatory) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 2.22 Cox proportional hazards model: change dependent label

As above, the dependent label cannot be specfied directly in the model to avoid errors. However, in survival modelling the surivial object specification can be long or awkward. Therefore, here is the work around. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "Surv(time, status)"
colon_s %>%
	finalfit(dependent, explanatory, add_dependent_label = FALSE) %>% 
	dplyr::rename("Overall survival" = label) %>% 
	dplyr::rename(" " = levels)	-> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

## 3 Model tables manually using `ff_merge()`

### 3.1 Basic table

Note `summary_factorlist()` needs argument, `fit_id = TRUE`. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"

## Crosstable
colon_s %>%
	summary_factorlist(dependent, explanatory, fit_id=TRUE) -> table_1

## Univariable
colon_s %>%
	glmuni(dependent, explanatory) %>%
	fit2df(estimate_suffix=" (univariable)") -> table_2

## Merge

table_1 %>% 
	ff_merge(table_2) %>% 
	select(-c(fit_id, index)) %>% 
	dependent_label(colon_s, dependent)-> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 3.2 Complex table (all in single pipe)

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
random_effect = "hospital"
dependent = "mort_5yr"

# All in one pipe

colon_s %>%
	## Crosstable
	summary_factorlist(dependent, explanatory, fit_id=TRUE)  %>% 
	
	## Add univariable
	ff_merge(
		glmuni(colon_s, dependent, explanatory) %>%
			fit2df(estimate_suffix=" (univariable)")
	) %>% 
	
	## Add multivariable
	ff_merge(
		glmmulti(colon_s, dependent, explanatory) %>%
			fit2df(estimate_suffix=" (multivariable)")
	) %>% 
	
	## Add mixed effects
	ff_merge(
		glmmixed(colon_s, dependent, explanatory, random_effect) %>%
			fit2df(estimate_suffix=" (multilevel)") 
	) %>% 
	select(-c(fit_id, index)) %>% 
	dependent_label(colon_s, dependent) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 3.3 Other GLM models

#### Poisson

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts = c(18,17,15,20,10,20,25,13,12)
outcome = gl(3,1,9)
treatment = gl(3,3)
d.AD <- data.frame(treatment, outcome, counts)

dependent = "counts"
explanatory = c("outcome", "treatment")

fit_uni = d.AD %>% 
	glmuni(dependent, explanatory, family = poisson) %>% 
	fit2df(estimate_name = "Rate ratio (univariable)")

fit_multi = d.AD %>% 
	glmmulti(dependent, explanatory, family = poisson) %>% 
	fit2df(estimate_name = "Rate ratio (multivariable)")

# All in one pipe
d.AD %>%
	## Crosstable
	summary_factorlist(dependent, explanatory, cont = "median", fit_id=TRUE)  %>% 
	
	## Add univariable
	ff_merge(fit_uni, estimate_name = "Rate ratio") %>% 
	
	## Add multivariable
	ff_merge(fit_multi, estimate_name = "Rate ratio") %>% 
	
	select(-c(fit_id, index)) %>% 
	dependent_label(d.AD, dependent) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

#### Gamma

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)

# A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
clotting <- data.frame(
    u = c(5,10,15,20,30,40,60,80,100),
    lot1 = c(118,58,42,35,27,25,21,19,18),
    lot2 = c(69,35,26,21,18,16,13,12,12))

dependent = "lot1"
explanatory = "log(u)"

fit_uni = clotting %>% 
	glmuni(dependent, explanatory, family = Gamma) %>% 
	fit2df(estimate_name = "Coefficient", exp = FALSE, digits = c(3,3,4))

# All in one pipe
clotting %>%
	## Crosstable
	summary_factorlist(dependent, explanatory, cont = "median", fit_id=TRUE)  %>% 
	
	## Add fit
	ff_merge(fit_uni) %>% 
	
	select(-c(fit_id, index)) %>% 
	dependent_label(colon_s, dependent) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 3.4 Weighted regression

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"
weights = runif(dim(colon_s)[1]) # random just for example

# All in one pipe
colon_s %>%
	## Crosstable
	summary_factorlist(dependent, explanatory, fit_id=TRUE)  %>% 
	
	## Add univariable
	ff_merge(
		glmuni(colon_s, dependent, explanatory, weights = weights, family = quasibinomial) %>%
			fit2df(estimate_suffix=" (univariable)")
	) %>% 
	
	## Add multivariable
	ff_merge(
		glmmulti(colon_s, dependent, explanatory, weights = weights, family = quasibinomial) %>%
			fit2df(estimate_suffix=" (multivariable)")
	) %>% 
	select(-c(fit_id, index)) %>% 
	dependent_label(colon_s, dependent) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 3.5 Using base R functions

Note `ff_formula()` convenience function to make multivariable formula (`y ~ x1 + x2 + x3` etc.) from a `dependent` and `explanatory` vector of names. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"

# All in one pipe

colon_s %>%
	## Crosstable
	summary_factorlist(dependent, explanatory, fit_id=TRUE)  %>% 
	
	## Add univariable
	ff_merge(
		glmuni(colon_s, dependent, explanatory) %>%
			fit2df(estimate_suffix=" (univariable)")
	) %>% 
	
	## Add multivariable
	ff_merge(
		glm(
			ff_formula(dependent, explanatory), data = colon_s, family = "binomial", weights = NULL
		) %>%
			fit2df(estimate_suffix=" (multivariable)")
	) %>% 
	
	select(-c(fit_id, index)) %>% 
	dependent_label(colon_s, dependent) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 3.6 Edit table rows

This can be done as any dataframe would be edited. 

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)
explanatory = c("age.factor*sex.factor", "obstruct.factor", "perfor.factor")
dependent = "mort_5yr"

# Run model for term test
fit <- glm(
	ff_formula(dependent, explanatory), 
	data=colon_s, family = binomial
)

# Not run
#term_test <- survey::regTermTest(fit, "age.factor:sex.factor")

# Run final table with results of term test
colon_s %>%
	finalfit(dependent, explanatory) %>%
	rbind(c(
		"age.factor:sex.factor (overall)",
		"Interaction",
		"-",
		"-",
		"-",
		paste0("p = 0.775")
	))-> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r"))
```

### 3.7 Base model + individual explanatory variables

This was an email enquiry about how to build on a base model. The example request was in a survival context.

This has been updated August 2019. We have left the original example of building the table from scratch as a comparison.

`ff_permute()` allows combinations of variables to be built on a base model. See options on help page to,

* include the base model and/or the full model,
* present the permuted variables at the top or bottom of the table,
* produce separate model tables, or the default which is a single table. 

```{r, warning=FALSE, message=FALSE}
library(dplyr)
mydata = colon_s
explanatory_base = c("age.factor", "sex.factor")
explanatory_permute = c("obstruct.factor", "perfor.factor", "node4.factor")
dependent = "Surv(time, status)"

mydata %>% 
	ff_permute(dependent, explanatory_base, explanatory_permute) %>% 
	rename("Overall survival" = `Dependent: Surv(time, status)`, # optional tidying
				 `n (%)` = "all") -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r", "r", "r"))
```

```{r, warning=FALSE, message=FALSE}
library(finalfit)
library(dplyr)

mydata = colon_s
base_explanatory = c("age.factor", "sex.factor")
explanatory = c("obstruct.factor", "perfor.factor", "node4.factor")
dependent = "Surv(time, status)"

mydata %>%
	# Counts
	summary_factorlist(dependent, c(base_explanatory,
																	explanatory),
										 column = TRUE,
										 fit_id = TRUE) %>% 
	
	# Univariable
	ff_merge(
		coxphuni(mydata, dependent, c(base_explanatory, explanatory)) %>% 
			fit2df(estimate_suffix = " (Univariable)")
	) %>% 
	
	# Base
	ff_merge(
		coxphmulti(mydata, dependent, base_explanatory) %>% 
			fit2df(estimate_suffix = " (Base model)")
	) %>% 
	
	# Model 1
	ff_merge(
		coxphmulti(mydata, dependent, c(base_explanatory, explanatory[1])) %>% 
			fit2df(estimate_suffix = " (Model 1)")
	) %>% 
	
	# Model 2
	ff_merge(
		coxphmulti(mydata, dependent, c(base_explanatory, explanatory[2])) %>% 
			fit2df(estimate_suffix = " (Model 2)")
	) %>% 
	
	# Model 3
	ff_merge(
		coxphmulti(mydata, dependent, c(base_explanatory, explanatory[3])) %>% 
			fit2df(estimate_suffix = " (Model 3)")
	) %>% 
	
	# Full
	ff_merge(
		coxphmulti(mydata, dependent, c(base_explanatory, explanatory)) %>% 
			fit2df(estimate_suffix = " (Full)")
	) %>% 
	
	# Tidy-up
	select(-c(fit_id, index)) %>% 
	rename("Overall survival" = label) %>% 
	rename(" " = levels) %>% 
	rename(`n (%)` = all) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r", "r", "r"))
```

## 4 Support for complex survey structures via `library(survey)`

### 4.1 Linear regression

Examples taken from `survey::svyglm()` help page.

```{r, warning=FALSE, message=FALSE}
library(survey)
library(dplyr)

data(api)
dependent = "api00"
explanatory = c("ell", "meals", "mobility")

# Label data frame
apistrat = apistrat %>%
  mutate(
  api00 = ff_label(api00, "API in 2000 (api00)"),
  ell = ff_label(ell, "English language learners (percent)(ell)"),
  meals = ff_label(meals, "Meals eligible (percent)(meals)"),
  mobility = ff_label(mobility, "First year at the school (percent)(mobility)"),
  sch.wide = ff_label(sch.wide, "School-wide target met (sch.wide)")
  )

# Linear example
dependent = "api00"
explanatory = c("ell", "meals", "mobility")

# Stratified design
dstrat = svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

# Univariable fit
fit_uni = dstrat %>%
  svyglmuni(dependent, explanatory) %>%
  fit2df(estimate_suffix = " (univariable)")

# Multivariable fit
fit_multi = dstrat %>%
  svyglmmulti(dependent, explanatory) %>%
  fit2df(estimate_suffix = " (multivariable)")

# Pipe together
apistrat %>%
  summary_factorlist(dependent, explanatory, fit_id = TRUE) %>%
  ff_merge(fit_uni) %>%
  ff_merge(fit_multi) %>%
  select(-fit_id, -index) %>%
  dependent_label(apistrat, dependent) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r", "r", "r"))
```

### 4.2 Binomial example

Note model family needs specified and exponentiation set to `TRUE` if desired.

```{r, warning=FALSE, message=FALSE}
library(survey)
library(dplyr)

data(api)
dependent = "sch.wide"
explanatory = c("ell", "meals", "mobility")

# Label data frame
apistrat = apistrat %>%
  mutate(
  api00 = ff_label(api00, "API in 2000 (api00)"),
  ell = ff_label(ell, "English language learners (percent)(ell)"),
  meals = ff_label(meals, "Meals eligible (percent)(meals)"),
  mobility = ff_label(mobility, "First year at the school (percent)(mobility)"),
  sch.wide = ff_label(sch.wide, "School-wide target met (sch.wide)")
  )
  
# Univariable fit
fit_uni = dstrat %>%
  svyglmuni(dependent, explanatory, family = "quasibinomial") %>%
  fit2df(exp = TRUE, estimate_name = "OR", estimate_suffix = " (univariable)")

# Multivariable fit
fit_multi = dstrat %>%
  svyglmmulti(dependent, explanatory, family = "quasibinomial") %>%
  fit2df(exp = TRUE, estimate_name = "OR", estimate_suffix = " (multivariable)")

# Pipe together
apistrat %>%
  summary_factorlist(dependent, explanatory, fit_id = TRUE) %>%
  ff_merge(fit_uni) %>%
  ff_merge(fit_multi) %>%
  select(-fit_id, -index) %>%
  dependent_label(apistrat, dependent) -> t
```

```{r, echo=FALSE}
library(knitr)
kable(t, row.names=FALSE, align = c("l", "l", "r", "r", "r", "r", "r", "r", "r", "r"))
```


