---
title: tidydfidx
author: Yves Croissant
date: today
date-format: long
number-sections: true
format: 
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    theme: none
    minimal: true
    embed-resources: true
    css: custom.css
bibliography: ../inst/REFERENCES.bib
vignette: >
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{dfidx and tibbles}
  %\VignetteEngine{quarto::html}
---

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In some situations, series from a data frame have a natural
two-dimensional (tabular) representation because each observation can
be uniquely characterized by a combination of two indexes. Two major
cases of these situations in applied econometrics are:

- panel data, where the same individuals are observed for several time
  periods,
- random utility models, where each observation describes the features
  of an alternative among a set of alternatives for a given choice
  situation.

The idea of **dfidx** is to keep in the same object the data and
the information about its structure. A `dfidx` object is a
data frame with an `idx` column, which is a data frame that
contains the series that define the indexes.

From version 0.1-2, **dfidx** doesn't depend anymore on some of
**tidyverse** packages. If you want to use **dfidx** along with
**tidyverse** in order to use tibbles instead of ordinary data frames
and **dplyr**'s verbs, you should use the new **tidydfidx** package
instead of **dfidx**.

This vignette supersede the preceding vignette of the **dfidx**
package by showing the advantages of creating `dfidx` objects from a
tibble and not from an ordinary data frame. It also introduces a new
vector interface to define the indexes.


# Basic use of the `dfidx` function

The `dfidx` package is loaded using:

```{r }
#| label: setup
#| include: false
options(rmarkdown.html_vignette.check_title = FALSE)
knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE, widht = 50)
old_options <- options(width = 70,
                       tibble.print_max = 3,
                       tibble.print_min = 3,
                       dfidx.print_n = 3)
```

<!-- ```{r } -->
<!-- #| label: load_dfidx -->
<!-- sources <- FALSE -->
<!-- if (! sources){ -->
<!--     library(dfidx) -->
<!-- } else { -->
<!--     library(dplyr);library(Formula);library(pillar);library(vctrs);library(glue) -->
<!--     z <- lapply(system("ls ~/YvesPro2/R_github/dfidx/dfidx/R/*.R", intern = TRUE), source) -->
<!--     load("~/YvesPro2/R_github/dfidx/dfidx/data/munnell.rda") -->
<!--     load("~/YvesPro2/R_github/dfidx/dfidx/data/munnell_wide.rda") -->
<!-- }    -->
<!-- ``` -->


```{r }
#| label: load_dfidx
#| message: false
library(tidydfidx)
library(dplyr)
```

We also attach the **dplyr** package [@WICK:FRAN:23], which exports
functions from the **tibble** [@MULL:WICK:23] and the **margrittr**
[@BACH:WICK:22] packages because we'll use
throughout this vignette tibbles and not ordinary data frames and
we'll show how **dplyr**'s verbs can be used with `dfidx` objects
thanks to appropriate methods.

To illustrate the features of **dfidx**, we'll use the `munnell` data
set [@MUNN:90] that is used in @BALT:13' and is part of the **plm**
package as `Produc`. It contains several economic series for American
states from 1970 to 1986. We've added to the initial data set a
`president` series which indicates the name of the American president
in power for the given year. We convert it to a tibble:

```{r}
#| label: print.tibble
data("munnell", package = "dfidx")
munnell <- as_tibble(munnell)
```
The two indexes are `state` and `year` and both are nested in another
variable: `state` in `region` and `year` in `president`.  A `dfidx`
object is created with the `dfidx` function: the first argument should
be a data frame (or a tibble) and the second argument `idx` is used to
indicate the indexes. As, in the `munnell` data set, the first two
columns contain the two indexes, the `idx` argument is not mandatory
and a `dfidx` can be obtained from the `munnell` tibble simply by
using:

```{r}
#| label: print.dfidx
munnell %>% dfidx
```

The resulting object is of class `dfidx` and is a tibble with an `idx`
column, which is a tibble containing the two indexes.  Note that the
two indexes are now longer standalone series in the resulting tibble,
because the default value of the `drop.index` argument is `TRUE`. The
header of the tibble indicates the names and the cardinal of the two
indexes. It also indicated whether the data set is balanced ie, in
this panel data context, whether all the states are observed for the
same set of years (which is the case for the `munnell` data set).
The `idx` column can be retrieved using the `idx` function:

```{r }
#| label: extract_idx
munnell %>% dfidx %>% idx
```

If the first two columns don't contain the indexes, the `idx` argument
should be set. If the observations are ordered first by the first
index and then by the second one and if the data set is *balanced*,
`idx` can be an integer, the number of distinct values of the first
index:

```{r }
#| label: dfidx_integer
munnell %>% dfidx(48L)
```

Then the two indexes are created with the default names `id1` and
`id2`. More relevant names can be indicated using the `idnames`
argument and the values of the second index can be indicated, using
the `levels` argument.

```{r }
#| label: dfidx_integer_pretty
munnell %>% dfidx(48L, idnames = c("state", "year"), levels = 1970:1986)
```

The `idx` argument can also be a character of length one or two. In
the first case, only the first index is indicated:

```{r }
#| label: dfidx_one_index
munnell %>% dfidx("state", idnames = c(NA, "date"), levels = 1970:1986)
```

Note that we've only provided a name for the second index, the `NA` in
the first position of the `idnames` argument meaning that we want to
keep the original name for the first index.
Finally, if the `idx` argument is a character of length 2, it should
contain the name of the two indexes.

```{r }
#| label: dfidx_two_indexes
munnell %>% dfidx(c("state", "year"))
```

# More advanced use of `dfidx`

## Nesting structure

One or both of the indexes may be nested in another series. In this
case, the `idx` argument is still a character of length two, but the
nesting series is indicated as the name of the corresponding index:

```{r}
#| label: one_or_two_nests
mn <- munnell %>% dfidx(c(region = "state", "year"))
mn <- munnell %>% dfidx(c(region = "state", president = "year"))
mn
```

The `idx` column is now a tibble containing the two indexes and the
nesting variables.

```{r}
#| label: idx_two_nests
mn %>% idx
```

## Customized the name and the position of the `idx` column

By default, the column that contains the indexes is called `idx` and
is the first column of the returned data frame. The position and the
name of this column can be set using the `position` and `name`
arguments:


```{r position_name}
dfidx(munnell, idx = c(region = "state", president = "year"),
            name = "index", position = 4)
```

## Data frames in wide format

`dfidx` can deal with data frames in wide format, i.e., for which each
series for a given value of the second index is a column of the data
frame. This is the case of the `munnell_wide` tibble that contains two
series of the original data set (`gsp` and `unemp`).

```{r}
#| label: munnell_wide
data("munnell_wide", package = "dfidx")
munnell_wide <- munnell_wide %>% as_tibble
```

Each line is now an American state and, apart the indexes, there are
now 34 series with names obtained by the concatenation of the name of
the series and the year (for example `gsp_1988`). In this case a
supplementary argument called `varying` should be provided. It is a
vector of integers indicating the position of the columns that should
be merged in the resulting long formatted data frame. The
`stats::reshape` function is then used and the `sep` argument can be
also provided to indicate the separating character in the names of the
series (the default value being `"."`).

```{r}
#| label: varying
munnell_wide %>% dfidx(varying = 3:36, sep = "_")
```

Better results can be obtained using the `idx` and `idnames` previously described:

```{r}
#| label: varying_pretty
munnell_wide %>% dfidx(idx = c(region = "state"), varying = 3:36, 
                       sep = "_", idnames = c(NA, "year"))
```

# Getting the indexes or their names

The name (and the position) of the `idx` column can be obtained as a
named integer (the integer being the position of the column and the
name its name) using the `idx_name` function:

```{r}
#| label: idx_names
#| collapse: true
idx_name(mn)
```

To get the name of one of the indexes, the second argument, `n`, is
set either to 1 or 2 to get the first or the second index, ignoring
the nesting variables:

```{r }
#| label: one_idx
#| collapse: true
idx_name(mn, 2)
idx_name(idx(mn), 2)
```

Not that `idx_name` can be in this case applied to a `dfidx` or to a
`idx` object.  To get a nesting variable, the third argument, called
`m`, is set to 2:

```{r }
#| label: nested_idx
#| collapse: true
idx_name(mn, 1, 1)
idx_name(mn, 1, 2)
```

To extract one or all the indexes, the `idx` function is used. This
function has already been encountered when one wants to extract the
`idx` column of a `dfidx` object. 
The  same `n` and `m` arguments as for the `idx_name` function can be
used in order to extract a specific series. For example, to extract the
region index, which nests the state index:

```{r }
#| collapse: true
#| label: extract_index_with_idx
id_index1 <- idx(mn, n = 1, m = 2)
id_index2 <- idx(idx(mn), n = 1, m = 2)
head(id_index1)
identical(id_index1, id_index2)
```

# Data frames subsetting

Subsets of data frames are obtained using the `[` and the `[[`
operators. The former returns most of the time a data frame as the
second one always returns a series.

## Commands that return a data frame

Consider first the use of `[`. If one argument is provided, it
indicates the columns that should be selected. The result is always a
data frame, even if a single column is selected. If two arguments are
provided, the first one indicates the subset of lines and the second
one the subset of columns that should be returned. If only one column
is selected, the result depends on the value of the `drop`
argument. If `TRUE`, a series is returned and if `FALSE`, a one series
data frame is returned. An important difference between tibbles and
ordinary data frames is that the default value of `drop` is `FALSE`
for the former and `TRUE` for the later. Therefore, with tibbles, the
use of `[` will always by default return a data frame. 

A specific `dfidx` method is provided for one reason: the column that
contains the indexes should be "sticky" (we borrow this idea from the
`sf` package^[@PEBE:BIVA:23 and @PEBE:18.]), which means that it
should be always returned while using the extractor operator, even if
it is not explicitly selected.

```{r}
#| label: one_bracket
mn[mn$unemp > 10, ]
mn[mn$unemp > 10, c("highway", "utilities")]
mn[mn$unemp > 10, "highway"]
```

All the previous commands extract the observations where the
unemployment rate is greater than 10% and, in the first case all the
series, in the second case two of them and in the third case only one
series.

## Commands that return a series

A series can be extracted using any of the following commands:

```{r }
#| collapse: true
#| label: return_series
mn1 <- mn[, "highway", drop = TRUE]
mn2 <- mn[["highway"]]
mn3 <- mn$highway
c(identical(mn1, mn2), identical(mn1, mn3))
```

The result is a `xseries` which inherits the `idx` column from the
data frame it has been extracted from as an attribute :

```{r }
#| label: xseries
mn1 %>% print(n = 3)
class(mn1)
idx(mn1) %>% print(n = 3)
```

Note that, except when `dfidx` hasn't been used with `drop.index =
FALSE`, a series which defines the indexes is dropped from the data
frame (but is one of the column of the `idx` column of the data
frame). It can be therefore retrieved using:


```{r}
#| label: extract_index_1
mn$idx$president %>% head
```

or

```{r }
#| label: extract_index_2
idx(mn)$president %>% head
```

or more simply by applying the `$` operator as if the series were a
stand-alone series in the data frame :

```{r }
#| label: extract_index_3
mn$president %>% print(n = 3)
```
In this last case, the resulting series is a `xseries`, 
*ie* it inherits the index data frame as an attribute.

## User defined class for extracted series

While creating the `dfidx`, a `pkg` argument can be indicated, so that
the resulting `dfidx` object and its series are respectively of class
`c("dfidx_pkg", "dfidx")` and `c("xseries_pkg", "xseries")` which enables the
definition of special methods for `dfidx` and `xseries` objects. For
example, consider the hypothetical **pnl** package for panel data:

```{r }
#| label: pkg_series
#| collapse: true
mn <- dfidx(munnell, idx = c(region = "state", president = "year"), 
                                pkg = "pnl")
mn1 <- mn$gsp
class(mn)
class(mn1)
```
For example, we want to define a `lag` method for `xseries_pnl`
objects. While lagging there should be a `NA` not only on the first
position of the resulting vector like for time-series, but each time
we encounter a new individual. A minimal `lag` method could therefore be
written as:

```{r }
#| label: pnl_seriex
lag.xseries_pnl <- function(x, ...){
    .idx <- idx(x)
    class <- class(x)
    x <- unclass(x)
    id <- .idx[[1]]
    lgt <- length(id)
    lagid <- c("", id[- lgt])
    sameid <- lagid ==  id
    x <- c(NA, x[- lgt])
    x[! sameid] <- NA
    structure(x, class = class, idx = .idx)
}
lmn1 <- stats::lag(mn1)
lmn1 %>% print(n = 3)
class(lmn1)
rbind(mn1, lmn1)[, 1:20]
```

Note the use of `stats::lag` instead of `lag` which ensures that the
`stats::lag` function is used, even if the **dplyr** (or **tidyverse**)
package is attached.

# tidyverse

## dplyr

**dfidx** supports some of the verbs of **dplyr**, namely, for the
current version:

- `select` to select columns,
- `filter` to select some rows using logical conditions,
- `arrange` to sort the lines according to one or several variables,
- `mutate` and `transmute` for creating new series,
- `slice` to select some rows using their position.

**dplyr**'s verbs don't work with `dfidx` objects for two main
reasons:

- the first one is that with most of the verbs (`select` is an
  exception), the returned object is a `data.frame` (or a `tibble`)
  and not a `dfidx`,
- the second one is that the index column should be "sticky", which
  means that it should be always returned, even while selecting a
  subset of columns which doesn't include the index column or while
  using `transmute`.

Therefore, specific methods are provided for **dplyr**'s verb. The
general strategy consists on:

@. first save the original attributes of the argument (a `dfidx`
  object),
@. coerce to a data frame or a tibble using the `as.data.frame`
  method, 
@. use `dplyr`'s verb,
@. add the column containing the index if necessary (i.e., while
  using `transmute` or while `select`ing a subset of columns which
  doesn't contain the index column),
@. change some of the attributes if necessary, 
@. attach the attributes to the data frame and returns the result.

The following code illustrates the use of **dplyr**'s verbs applied to
`dfidx` objects.

```{r }
#| label: dplyr_verbs
select(mn, highway, utilities)
arrange(mn, desc(unemp))
mutate(mn, lgsp = log(gsp), lgsp2 = lgsp ^ 2)
transmute(mn, lgsp = log(gsp), lgsp2 = lgsp ^ 2)
filter(mn, unemp > 10, gsp > 150000)
slice(mn, 1:3)
mutate(mn, gsp = ifelse(gsp < 170000, 0, gsp))
```

To extract a series, the `pull` function can be used:

```{r}
#| label: pull
mn %>% pull(utilities)
```

# Model building

The two main steps in **R** in order to estimate a model are to use the
`model.frame` function to construct a data frame, using a formula
and a data frame and then to extract from it the matrix of
covariates using the `model.matrix` function.

## Model frame

The default method of `model.frame` has as first two arguments
`formula` and `data`. It returns a data frame with a `terms`
attribute. Some other methods exist in the **stats** package, for
example for `lm` and `glm` object with a first and main argument
called `formula`. This is quite unusual and misleading as for most of
the generic functions in **R**, the first argument is called either
`x` or `object`.

Another noticeable method for `model.frame` is provided by the
**Formula** package and, in this case, the first argument is a `Formula`
object, which is an extended formula which can contain several parts
on the left and/or on the right hand side of the formula.

We provide a `model.frame` method for `dfidx` objects, mainly
because the `idx` column should be returned in the resulting
data frame. This leads to an unusual order of the arguments, the
data frame first and then the formula. The method then first extract
(and subset if necessary the `idx` column), call the
`formula`/`Formula` method and then add to the resulting data frame
the `idx` column. The resulting data frame is a `dfidx` object.

```{r}
#| label: model_frame
mf_mn <- mn %>% model.frame(gsp ~ utilities + highway | unemp | labor,
                            subset = unemp > 10)
mf_mn
formula(mf_mn)
```

Note that the column that contains the indexes is at the end and not
at the begining of the returned data frame. This is because the
`stats::model.response` function, which is used to extract the
response of a model and is not generic consider that the first column
of the model frame is the response.

## Model matrix

`model.matrix` is a generic function and for the default method, the
first two arguments are a `terms` object and a data frame. In `lm`,
the `terms` attribute is extracted from the `model.frame` internally
constructed using the `model.frame` function. This means that, at
least in this context, `model.matrix` doesn't need a `formula`/`term`
argument and a `data.frame`, but only a data frame returned by the
model frame method, i.e., a data frame with a `terms` attribute.

We use this idea for the `model.matrix` method for `dfidx` object; the
only required argument is a `dfidx` returned by the `model.frame`
function. The formula is then extracted from the `dfidx` and the
`Formula` or default method is then called. The result is a matrix of
class `dfidx_matrix`, with a printing method that allows the use of
the `n` argument:

```{r}
#| label: model_matrix
mf_mn %>% model.matrix(rhs = 1) %>% print(n = 5)
mf_mn %>% model.matrix(rhs = 2:3) %>% print(n = 5)
```



```{r }
#| include: false
options(old_options)
```

# References
