---
title: "Format and Combine"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Format and Combine}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```
### How to use fapply2()

The `fapply2()` function applies two formats to two different vectors, and combines
them when complete.  This function can be used to collapse two columns into one.
The `fapply2()` function is convenient to use when preparing your data for 
reporting.  Here is an example:
```{r eval=FALSE, echo=TRUE} 
# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)

# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)

# Format and Combine
dat$CntPct <- fapply2(dat$Counts, dat$Percents, "%d", "(%.1f%%)")

# View results
dat
#   Counts Percents     CntPct
# 1     27 18.24324 27 (18.2%)
# 2     43 29.05405 43 (29.1%)
# 3     22 14.86486 22 (14.9%)
# 4     56 37.83784 56 (37.8%)

```

### Use with *dplyr*

The `fapply2()` function is suitable for use with the *dplyr* package.
Here is the same example as above, but using *dplyr* `mutate()` instead of Base R:
```{r eval=FALSE, echo=TRUE} 
library(dplyr)

# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)

# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)

# Format and Combine
dat <- dat |> 
  mutate(CntPct = fapply2(dat$Counts, dat$Percents, "%d", "(%.1f%%)"))

# View results
dat
#   Counts Percents     CntPct
# 1     27 18.24324 27 (18.2%)
# 2     43 29.05405 43 (29.1%)
# 3     22 14.86486 22 (14.9%)
# 4     56 37.83784 56 (37.8%)

```

### Use with *datastep*

The `fapply2()` function is also compatible with the `datastep()` function
from the *libr* package.
Here is the example again with the `datastep()`: 
```{r eval=FALSE, echo=TRUE} 
library(libr)

# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)

# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)

# Format and Combine
dat <- datastep(dat,
         {
           CntPct <- fapply2(Counts, Percents, "%d", "(%.1f%%)")
         })
  
# View results
dat
#   Counts Percents     CntPct
# 1     27 18.24324 27 (18.2%)
# 2     43 29.05405 43 (29.1%)
# 3     22 14.86486 22 (14.9%)
# 4     56 37.83784 56 (37.8%)

```


## Using Assigned Formats

Note that `fapply2()` will use formats assigned to the data frame columns
if they are available.  Assigning the formats to the columns first can 
simplify use of the function and promote format reuse.
To assign the formats to the columns, use the 
`formats()` function, like so:
```{r eval=FALSE, echo=TRUE} 
# Create sample vectors
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)

# Create data frame
dat <- data.frame("Counts" = v1, "Percents" = v2)

formats(dat) <- list(Counts = "%d", Percents = "(%.1f%%)")

# Format and Combine - Formats already assigned
dat$CntPct <- fapply2(dat$Counts, dat$Percents)

# View results
dat
#   Counts Percents     CntPct
# 1     27 18.24324 27 (18.2%)
# 2     43 29.05405 43 (29.1%)
# 3     22 14.86486 22 (14.9%)
# 4     56 37.83784 56 (37.8%)

```

## Format Catalog with `datastep()`

The ability to use any formats assigned to the columns makes the `fapply()` 
function very useful when combined with format catalogs and the `datastep()`
function.  When the format catalog is assigned to the `datastep()`, it will
automatically assign the formats in the catalog to any corresponding columns
on the input data frame.  This feature allows you to quickly assign saved formats
to a new dataset, and use those formats to combine columns in the desired way.
Observe:
```{r eval=FALSE, echo=TRUE} 
library(libr)

# Create sample vectors
grp <- c("Group1", "Group2", "Group3", "Group4")
v1 <- c(27, 43, 22, 56)
v2 <- c(18.24324, 29.05405, 14.86486, 37.83784)
v3 <- c(5.24883, 8.83724, 2.39483, 9.12542)
v4 <- c(2.97632, 3.32845, 0.29784, 4.22156)

# Create data frame
dat <- data.frame("Group" = grp, "Counts" = v1, "Percents" = v2, 
                  "Mean" = v3, "SD" = v4)

# View original data
dat
#    Group Counts Percents    Mean      SD
# 1 Group1     27 18.24324 5.24883 2.97632
# 2 Group2     43 29.05405 8.83724 3.32845
# 3 Group3     22 14.86486 2.39483 0.29784
# 4 Group4     56 37.83784 9.12542 4.22156

# Create format catalog
fc <- fcat(Counts = "%d", Percents = "(%03.1f%%)",
           Mean = "%.1f", SD = "(%04.2f)")

# Format and Combine columns using Format catalog
dat2 <- datastep(dat, format = fc,
                 keep = v(Group, CntPct, MeanSD),
                 {
                   
                   CntPct <- fapply2(Counts, Percents)
                   MeanSD <- fapply2(Mean, SD)
                   
                 })
# View results
dat2
#    Group     CntPct     MeanSD
# 1 Group1 27 (18.2%) 5.2 (2.98)
# 2 Group2 43 (29.1%) 8.8 (3.33)
# 3 Group3 22 (14.9%) 2.4 (0.30)
# 4 Group4 56 (37.8%) 9.1 (4.22)

```

The above technique points to a method for sharing formats between programs
and ensuring that statistical results are formatted consistently across programs. 

## Use of Other Format Types

Note that the `fapply2()` function will
accept any type of format supported by the *fmtr* package. That means you can use
numeric formats, date formats, vector lookups, user-defined formats, 
and vectorized functions.  The combination of these format types allows you
to format and combine data in a powerful way that will enhance the impact 
of your analysis.


Next: [Convenience Functions](fmtr-convenience.html)
