---
title: "Introduction to concstats"
author: "Andreas Schneider"
date: "Last updated `r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: TRUE
    toc_depth: 3
vignette: >
  %\VignetteIndexEntry{Introduction to concstats}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(collapse = TRUE,
                      comment = "#>",
                      fig.align = "center",
                      warning = FALSE,
                      screenshot.force = FALSE)
```


The goal of the `concstats` package is to offer a set of alternative and/or
additional measures to better determine a given market structure and therefore
reduce uncertainty with respect to a given market situation. Various functions
or groups of functions are available to achieve the desired goal. 

## Installation
You can install `concstats` directly from CRAN or the latest development version
from  github (requires `remotes` or `devtools`)
remotes::install_github("ropensci/concstats")

Then, load the package.
```{r, message=FALSE, warning=FALSE}
library(concstats)
```

## Data
The following examples use mainly fictitious data to present the functions.
However, if you want to test the functionality in more detail, the package comes
with a small data set of real Paraguayan credit cooperatives (creditcoops). There
are 22 paired observations for real Paraguayan credit cooperatives 
(with assets > 11 Mio. USD) for 2016 and 2018 with their respective total loans 
granted. For a better visualization there is an additional column with the 
transformed total loans. For further information on the data please see the
`creditcoops`
[help](https://docs.ropensci.org/concstats/reference/creditcoops.html)
file. For a practical implementation you might be interested
to read the following [article](https://doi.org/10.1515/zfgg-2022-0002).

```{r}
data("creditcoops")
head(creditcoops)
```
<!--```-->
<!--  # A tibble: 6 x 5                                   -->
<!--  coop_id   year  total_loans paired total_loans_log  -->
<!--    <dbl>   <fct>       <dbl>  <int>           <dbl>  -->
<!--       1    2016    173892358      1            19.0  -->
<!--       1    2018    199048199      1            19.1  -->
<!--       2    2016    323892456      2            19.6  -->
<!--       2    2018    461609439      2            20.0  -->
<!--       3    2016    179981404      3            19.0  -->
<!--       3    2018    227232008      3            19.2  -->
<!--```-->

## Group wrapper overview
At the moment, there are the following groups of functions available:    
- `concstats_mstruct()` is a wrapper for market structure measures    
- `concstats_inequ()` is a wrapper for inequality and diversity measures    
- `concstats_comp()` is a wrapper with different concentration measures    
- `concstats_concstats()` is a function which calculates a set of pre-selected
                      measures in a one step procedure to get a quick overview
                      of a given market structure    

The functions will be presented in more details in the following short
step-by-step guide. 

## Examples
We will use a vector which represents market participants with their respective 
market shares (in decimal form): 

### concstats_mstruct
The wrapper includes the following arguments to calculate individual functions:
``firm``, ``nrs_eq``, ``top``, ``top3``, ``top5``, and ``all``. You can also
pass the additional argument ``digits`` to ``all``, which controls for the
number of digits to be printed.  
All individual functions can be accessed directly with the prefix "concstats_"
(e.g. "concstats_firm" or "concstats_all_mstruct").
```{r example-1}
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_top5 <- concstats_top5(test_share) # top 5 market share in percentage
test_share_top5
```

You should have noticed that the market shares are in decimal form. However, you
can use integers or floating point numeric types to express market shares. All
`concstats` functions will take care of this and convert theses vectors into
decimal form. There are eight market participants, however, two have no market
shares, by default `concstats` treats 0 as NA. The result is a top 5 market
share of 96 %.  
You can also access each function through their respective argument in the 
group wrapper:

```{r example-2}
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_top <- concstats_mstruct(test_share, type = "top") # top market share
test_share_top
```

Or, just calculate all measures of the group wrapper, and store it in a named
object.
```{r examples-mstruct-all}
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_mstruct <- concstats_mstruct(test_share, type = "all", digits = 3)
test_share_mstruct
```

The result is a data frame of market structure measures.

### concstats_inequ
The inequality and diversity group has the following arguments: ```entropy```, 
``gini``, ``simpson``, ``palma``, ``grs``, and ``all``. They can also
be accessed as individual functions.

```{r examples-entropy, echo=TRUE}
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04)
test_share_entropy <- concstats_entropy(test_share)
test_share_entropy

# and as a non-normalized value
test_share_entropy2 <- concstats_entropy(test_share, normalized = FALSE)
test_share_entropy2
```

### concstats_comp
The group wrapper for competition measures includes the following arguments to
calculate ``hhi``, ``hhi_d``, ``hhi_min``, ``dom``, ``sten``, and ``all``.

```{r examples-hhi, echo=TRUE}
test_share <- c(0.35, 0.4, 0.05, 0.1, 0.06, 0.04, 0, 0)
test_share_hhi <- concstats_hhi(test_share)
test_share_hhi

# a normalized value
test_share_hhi2 <- concstats_hhi(test_share, normalized = TRUE)
test_share_hhi2

# the min average of the hhi
test_share_hhi3 <- concstats_comp(test_share, type = "hhi_min")
test_share_hhi3
```

### concstats_concstats
A single function which calculates a set of eight pre-selected measures in a 
one step procedure for a first overview of a given market structure. The
resulting data frame contains eight measures, which are: number of firms with
market share, numbers equivalent, the cumulative share of the top
(top 3 and top 5) firm(s) in percentage, the hhi index, the entropy index, and
the palma ratio.

```{r examples-concstats, echo=TRUE}
test_share <- c(0.2, 0.3, 0.5)
test_share_conc <- concstats_concstats(test_share, digit = 2)
test_share_conc
```

## Visualization
The scope of the package is to calculate market structure and concentration 
measures to get a quick and more informed overview of a given market situation.
However, it is good practice to visualize your data in an exploratory step or in
reporting your results. The package `concstats` works fine with other
Exploratory Data Analysis (EDA) packages or data visualization packages e.g.
[```overviewR```](https://CRAN.R-project.org/package=overviewR),
[```dataexplorer```](https://CRAN.R-project.org/package=DataExplorer), [```kableExtra```](https://CRAN.R-project.org/package=kableExtra) or [```ggplot2```](https://CRAN.R-project.org/package=ggplot2) to name a few.

Some examples on how you can accomplish this.
Let us assume one would like to use the group measure for e.g. market structure,
and keep the resulting data frame. We can refine the table
using `kableExtra` which works nice with `knitr`.

This time, we will use our ``creditcoops`` data set again, which comes with
the package.

```{r}
data("creditcoops")
head(creditcoops)
```
<!--```-->
<!--  # A tibble: 6 x 5                                   -->
<!--  coop_id   year  total_loans paired total_loans_log  -->
<!--    <dbl>   <fct>       <dbl>  <int>           <dbl>  -->
<!--       1    2016    173892358      1            19.0  -->
<!--       1    2018    199048199      1            19.1  -->
<!--       2    2016    323892456      2            19.6  -->
<!--       2    2018    461609439      2            20.0  -->
<!--       3    2016    179981404      3            19.0  -->
<!--       3    2018    227232008      3            19.2  -->
<!--```-->

You will need the following two packages. Make sure you have these packages
installed.
```{r visualization-prep, message=FALSE, eval=FALSE}
library(dplyr)
library(kableExtra)
```

Now, we will filter out data for the year 2016.
```{r visualization-tab, echo=TRUE, eval=FALSE}
coops_2016 <- creditcoops %>% dplyr::filter(year == 2016)
head(coops_2016)

coops_2016 <- coops_2016[["total_loans"]] # atomic vector of total loans
coops_2016 <- coops_2016 / sum(coops_2016)  # market shares in decimal form

# We then use the new object `coops_2016` to calculate the market structure
# measures as a group in a one-step-procedure and capture the results in a
# printed table:
coops_2016_mstruct <- concstats_mstruct(coops_2016, type = "all", digits = 2)
coops_2016_mstruct_tab <- coops_2016_mstruct %>%
  kableExtra::kbl(caption = "Market structure 2016", digits = 2,
                  booktabs = TRUE, align = "r") %>%
  kableExtra::kable_classic(full_width = FALSE, html_font = "Arial")
coops_2016_mstruct_tab

```

The result is a nice reusable table.

<center>

![](mstruct_tab.png){width="400" height="200"}

</center>

Now, let's go a step further. We will compare the two samples for 2016 and 2018.
For this purpose, we will select from our ``creditcoops`` data set the relevant
columns (coop_id, year, paired, and total_loans_log) and make a new data frame.
```{r visualization-sample-prep, echo=FALSE, eval=FALSE}
df_shares <- creditcoops %>%
  dplyr::select(coop_id, year, paired, total_loans_log)

```
Make sure you have the `ggplot2` package installed. Load the package.
```{r, echo=TRUE, eval=FALSE}
library(ggplot2) # Data Visualizations Using the Grammar of Graphics

df_shares_plot <- df_shares %>%
  ggplot(aes(year, total_loans_log, fill = year)) +
  geom_boxplot() +
  geom_point() +
  geom_line(aes(group = paired)) +
  labs(title = "Credit cooperatives (type A)", y = "Total loans (log)",
       caption = "Source: Andreas Schneider with data from INCOOP") +
  theme(legend.position = "none")
df_shares_plot
```
<center>

![](coops_paired.png){width="469" height="521"}

</center>

Having a look a the output, we see a box plot with paired values of the
cooperatives and the evolution of their respective total loans over time for the
two sample years 2016 and 2018.