---
title: "collapse for tidyverse Users"
author: "Sebastian Krantz"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true

vignette: >
  %\VignetteIndexEntry{collapse for tidyverse Users}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  
---

```{css, echo=FALSE}
pre {
  max-height: 500px;
  overflow-y: auto;
}

pre[class] {
  max-height: 500px;
}
```

```{r, echo=FALSE}
oldopts <- options(width = 100L)
```

```{r, echo = FALSE, message = FALSE, warning=FALSE}
knitr::opts_chunk$set(error = FALSE, message = FALSE, warning = FALSE, 
                      comment = "#", tidy = FALSE, cache = TRUE, collapse = TRUE,
                      fig.width = 8, fig.height = 5, 
                      out.width = '100%')
```


*collapse* is a C/C++ based package for data transformation and statistical computing in R that aims to enable greater performance and statistical complexity in data manipulation tasks and offers a stable, class-agnostic, and lightweight API. It is part of the core [*fastverse*](https://fastverse.github.io/fastverse/), a suite of lightweight packages with similar objectives. 

The [*tidyverse*](https://www.tidyverse.org/) set of packages provides a rich, expressive, and consistent syntax for data manipulation in R centering on the *tibble* object and tidy data principles (each observation is a row, each variable is a column). 

*collapse* fully supports the *tibble* object and provides many *tidyverse*-like functions for data manipulation. It can thus be used to write *tidyverse*-like data manipulation code that, thanks to low-level vectorization of many statistical operations and optimized R code, typically runs much faster than native *tidyverse* code, in addition to being much more lightweight in dependencies. 

Its aim is not to create a faster *tidyverse*, i.e., it does not implements all aspects of the rich *tidyverse* grammar or changes to it^[Notably, tidyselect, lambda expressions, and many of the smaller helper functions are left out.], and also takes inspiration from other leading data manipulation libraries to serve broad aims of performance, parsimony, complexity, and robustness in data manipulation for R. 


## Namespace and Global Options

*collapse* data manipulation functions familiar to *tidyverse* users include `fselect`, `fgroup_by`, `fsummarise`, `fmutate`, `across`, `frename`, `fslice`, and `fcount`. Other functions like `fsubset`, `ftransform`, and `get_vars` are inspired by base R, while again other functions like `join`, `pivot`, `roworder`, `colorder`, `rowbind`, etc. are inspired by other data manipulation libraries such as *data.table* and *polars*. 

By virtue of the f- prefixes, the *collapse* namespace has no conflicts with the *tidyverse*, and these functions can easily be substituted in a *tidyverse* workflow. 

R users willing to replace the *tidyverse* have the additional option to mask functions and eliminate the prefixes with `set_collapse`. For example 

```{r}
library(collapse)
set_collapse(mask = "manip") # version >= 2.0.0 
```

makes available functions `select`, `group_by`, `summarise`, `mutate`, `rename`, `count`, `subset`, `slice`, and `transform` in the *collapse* namespace and detaches and re-attaches the package, such that the following code is executed by *collapse*:

```{r}
mtcars |>
  subset(mpg > 11) |>
  group_by(cyl, vs, am) |>
  summarise(across(c(mpg, carb, hp), mean), 
            qsec_wt = weighted.mean(qsec, wt))
```

*Note* that the correct documentation still needs to be called with prefixes, i.e., `?fsubset`. See `?set_collapse` for further options to the package, which also includes optimization options such as `nthreads`, `na.rm`, `sort`, and `stable.algo`. *Note* also that if you use *collapse*'s namespace masking, you can use `fastverse::fastverse_conflicts()` to check for namespace conflicts with other packages. 

## Using the *Fast Statistical Functions*

A key feature of *collapse* is that it not only provides functions for data manipulation, but also a full set of statistical functions and algorithms to speed up statistical calculations and perform more complex statistical operations (e.g. involving weights or time series data). 

Notably among these, the [*Fast Statistical Functions*](https://sebkrantz.github.io/collapse/reference/fast-statistical-functions.html) is a consistent set of S3-generic statistical functions providing fully vectorized statistical operations in R. 

Specifically, operations such as calculating the mean via the S3 generic `fmean()` function are vectorized across columns and groups and may also involve weights or transformations of the original data:

```{r}
fmean(mtcars$mpg)     # Vector
fmean(EuStockMarkets) # Matrix
fmean(mtcars)         # Data Frame

fmean(mtcars$mpg, w = mtcars$wt)  # Weighted mean
fmean(mtcars$mpg, g = mtcars$cyl) # Grouped mean
fmean(mtcars$mpg, g = mtcars$cyl, w = mtcars$wt)   # Weighted group mean
fmean(mtcars[5:10], g = mtcars$cyl, w = mtcars$wt) # Of data frame
fmean(mtcars$mpg, g = mtcars$cyl, w = mtcars$wt, TRA = "fill") # Replace data by weighted group mean
# etc...
```

The data manipulation functions of *collapse* are integrated with these *Fast Statistical Functions* to enable vectorized statistical operations. For example, the following code 

```{r}
mtcars |>
  subset(mpg > 11) |>
  group_by(cyl, vs, am) |>
  summarise(across(c(mpg, carb, hp), fmean), 
            qsec_wt = fmean(qsec, wt))
```

gives exactly the same result as above, but the execution is much faster (especially on larger data), because with *Fast Statistical Functions*, the data does not need to be split by groups, and there is no need to call `lapply()` inside the `across()` statement: `fmean.data.frame()` is simply applied to a subset of the data containing columns `mpg`, `carb` and `hp`. 

The *Fast Statistical Functions* also have a method for grouped data, so if we did not want to calculate the weighted mean of `qsec`, the code would simplify as follows:

```{r}
mtcars |>
  subset(mpg > 11) |>
  group_by(cyl, vs, am) |>
  select(mpg, carb, hp) |> 
  fmean()
```

Note that all functions in *collapse*, including the *Fast Statistical Functions*, have the default `na.rm = TRUE`, i.e., missing values are skipped in calculations. This can be changed using `set_collapse(na.rm = FALSE)` to give behavior more consistent with base R. 

Another thing to be aware of when using *Fast Statistical Functions* inside data manipulation functions is that they toggle vectorized execution wherever they are used. E.g.

```{r}
mtcars |> group_by(cyl) |> summarise(mpg = fmean(mpg) + min(qsec)) # Vectorized
```

calculates a grouped mean of `mpg` but adds the overall minimum of `qsec` to the result, i.e., it is equivalent to `fmean(mpg, g = cyl) + min(qsec)`. On the other hand 

```{r}
mtcars |> group_by(cyl) |> summarise(mpg = fmean(mpg) + fmin(qsec)) # Vectorized
mtcars |> group_by(cyl) |> summarise(mpg = mean(mpg) + min(qsec))   # Not vectorized
```

both give the mean + the minimum within each group, but calculated in different ways: the former is equivalent to `fmean(mpg, g = cyl) + fmin(qsec, g = cyl)`, whereas the latter is equal to `sapply(gsplit(mpg, cyl), function(x) mean(x) + min(x))`. 

See `?fsummarise` and `?fmutate` for more detailed examples. This *eager vectorization* approach is intentional as it allows users to vectorize complex expressions and fall back to base R if this is not desired. [This blog post](https://andrewghazi.github.io/posts/collapse_is_sick/sick.html) by Andrew Ghazi provides an excellent example of computing a p-value test statistic by groups. *Note* that only expressions typed out can be vectorized; expressions inside functions such as `mean_plus_min <- function(x) fmean(x) + fmin(x)` are not vectorized.^[*collapse* can only read what you type, e.g. `exp <- substitute(fmean(mpg) + min(mpg))`, then `all_funs(exp)` gives `c("+", "fmean", "min")`, and `any(all_funs(exp) %in% .FAST_STAT_FUN)` returns `TRUE`, signifying to `fsummarise()` that the expression should be executed only once with the grouping object passed to the `g` argument of `fmean()`, instead of it being executed once for every group.] To take full advantage of *collapse*, it is thus highly recommended to use the *Fast Statistical Functions* as much as possible.

<!-- To take full advantage of *collapse*, it is highly recommended to use the *Fast Statistical Functions* as much as possible. You can also set `set_collapse(mask = "all")` to replace statistical functions in base R like `sum` and `mean` with the collapse versions (toggling vectorized execution in all cases), but this may affect other parts of your code^[When doing this, make sure to refer to base R functions explicitly using `::` e.g. `base::mean`.]. -->

## Writing Efficient Code

It is also performance-critical to correctly sequence operations and limit excess computations. *tidyverse* code is often inefficient simply because the *tidyverse* allows you to do everything. For example, `mtcars |> group_by(cyl) |> filter(mpg > 13) |> arrange(mpg)` is permissible but inefficient code as it filters and reorders grouped data, requiring modifications to both the data frame and the attached grouping object. *collapse* does not allow calls to `fsubset()` on grouped data, and messages about it in `roworder()`, encouraging you to write more efficient code. 

The above example can also be optimized because we are subsetting the whole frame and then doing computations on a subset of columns. It would be more efficient to select all required columns during the subset operation: 

```{r}
mtcars |>
  subset(mpg > 11, cyl, vs, am, mpg, carb, hp, qsec, wt) |>
  group_by(cyl, vs, am) |>
  summarise(across(c(mpg, carb, hp), fmean), 
            qsec_wt = fmean(qsec, wt))
```

Without the weighted mean of `qsec`, this would simplify to 

```{r}
mtcars |>
  subset(mpg > 11, cyl, vs, am, mpg, carb, hp) |>
  group_by(cyl, vs, am) |> 
  fmean()
```

Finally, we could set the following options to toggle unsorted grouping, no missing value skipping, and multithreading across the three columns for more efficient execution.

```{r}
mtcars |>
  subset(mpg > 11, cyl, vs, am, mpg, carb, hp) |>
  group_by(cyl, vs, am, sort = FALSE) |> 
  fmean(nthreads = 3, na.rm = FALSE)
```

Setting these options globally using `set_collapse(sort = FALSE, nthreads = 3, na.rm = FALSE)` avoids the need to set them repeatedly.

### Using Internal Grouping 

Another key to writing efficient code with *collapse* is to avoid `fgroup_by()` where possible, especially for mutate operations. *collapse* does not implement `.by` arguments to manipulation functions like *dplyr*, but instead allows ad-hoc grouped transformations through its statistical functions. For example, the easiest and fastest way to computed the median of `mpg` by `cyl`, `vs`, and `am` is

```{r}
mtcars |>
  mutate(mpg_median = fmedian(mpg, list(cyl, vs, am), TRA = "fill")) |> 
  head(3)
```

For the common case of averaging and centering data, *collapse* also provides functions `fbetween()` for averaging and `fwithin()` for centering, i.e., `fbetween(mpg, list(cyl, vs, am))` is the same as `fmean(mpg, list(cyl, vs, am), TRA = "fill")`. There is also `fscale()` for (grouped) scaling and centering. 

This also applies to multiple columns, where we can use `fmutate(across(...))` or `ftransformv()`, i.e. 

```{r}
mtcars |>
  mutate(across(c(mpg, disp, qsec), fmedian, list(cyl, vs, am), TRA = "fill")) |> 
  head(2)

# Or 
mtcars |>
  transformv(c(mpg, disp, qsec), fmedian, list(cyl, vs, am), TRA = "fill") |> 
  head(2)
```

Of course, if we want to apply different functions using the same grouping, `fgroup_by()` is sensible, but for mutate operations it also has the argument `return.groups = FALSE`, which avoids materializing the unique grouping columns, saving some memory. 

```{r}
mtcars |>
  group_by(cyl, vs, am, return.groups = FALSE) |> 
  mutate(mpg_median = fmedian(mpg), 
         mpg_mean = fmean(mpg), # Or fbetween(mpg)
         mpg_demean = fwithin(mpg), # Or fmean(mpg, TRA = "-")
         mpg_scale = fscale(mpg), 
         .keep = "used") |>
  ungroup() |>
  head(3)
```

The `TRA` argument supports a whole array of operations, see `?TRA`. For example `fsum(mtcars, TRA = "/")` turns the column vectors into proportions. As an application of this, consider a generated dataset of sector-level exports.

```{r, include = FALSE}
set.seed(101)
```
```{r}
# c = country, s = sector, y = year, v = value
exports <- expand.grid(c = paste0("c", 1:8), s = paste0("s", 1:8), y = 1:15) |>
           mutate(v = round(abs(rnorm(length(c), mean = 5)), 2)) |>
           subset(-sample.int(length(v), 360)) # Making it unbalanced and irregular
head(exports)
nrow(exports)
```

It is very easy then to compute Balassa's (1965) Revealed Comparative Advantage (RCA) index, which is the share of a sector in country exports divided by the share of the sector in world exports. An index above 1 indicates that a RCA of country c in sector s. 

```{r}
# Computing Balassa's (1965) RCA index: fast and memory efficient
# settfm() modifies exports and assigns it back to the global environment
settfm(exports, RCA = fsum(v, list(c, y), TRA = "/") %/=% fsum(fsum(v, y, TRA = "/"), list(s, y), TRA = "fill", set = TRUE))
```

Note that this involved a single expression with two different grouped operations, which is only possible by incorporating grouping into statistical functions themselves. Let's summarise this dataset using `pivot()` to aggregate the RCA index across years. Here `"mean"` calls a highly efficient internal mean function. 

```{r}
pivot(exports, ids = "c", values = "RCA", names = "s", 
      how = "wider", FUN = "mean", sort = TRUE)
```

We may also wish to investigate the growth rate of RCA. This can be done using `fgrowth()`. Since the panel is irregular, i.e., not every sector is observed in every year, it is critical to also supply the time variable. 

```{r}
exports |> 
  mutate(RCA_growth = fgrowth(RCA, g = list(c, s), t = y)) |> 
  pivot(ids = "c", values = "RCA_growth", names = "s", 
        how = "wider", FUN = fmedian, sort = TRUE)
```

Lastly, since the panel is unbalanced, we may wish to create an RCA index for only the last year, but balance the dataset a bit more by taking the last available trade within the last three years. This can be done using a single subset call

```{r}
# Taking the latest observation within the last 3 years
exports_latest <- subset(exports, y > 12 & y == fmax(y, list(c, s), "fill"), -y)
# How many sectors do we observe for each country in the last 3 years?
with(exports_latest, fndistinct(s, c))
```

We can then compute the RCA index on this data

```{r}
exports_latest |>
    mutate(RCA = fsum(v, c, TRA = "/") %/=% fsum(proportions(v), s, TRA = "fill")) |>
    pivot("c", "RCA", "s", how = "wider", sort = TRUE)
```

To summarise, *collapse* provides many options for ad-hoc or limited grouping, which are faster than a full `fgroup_by()`, and also syntactically efficient. Further efficiency gains are possible using operations by reference, e.g., `%/=%` instead of `/` to avoid an intermediate copy. It is also possible to transform by reference using fast statistical functions by passing the `set = TRUE` argument, e.g., `with(mtcars, fmean(mpg, cyl, TRA = "fill", set = TRUE))` replaces `mpg` by its group-averaged version (the transformed vector is returned invisibly). 

## Conclusion 

*collapse* enhances R both statistically and computationally and is a good option for *tidyverse* users searching for more efficient and lightweight solutions to data manipulation and statistical computing problems in R. For more information, I recommend starting with the short vignette on [*Documentation Resources*](https://sebkrantz.github.io/collapse/articles/collapse_documentation.html). 

R users willing to write efficient/lightweight code and completely replace the *tidyverse* in their workflow are also encouraged to closely examine the [*fastverse*](https://fastverse.github.io/fastverse/) suite of packages. *collapse* alone may not always suffice, but 99% of *tidyverse* code can be replaced with an efficient and lightweight *fastverse* solution. 

```{r, echo=FALSE}
options(oldopts)
```



