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.

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.

```
library(concstats)
```

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
file. For a practical implementation you might be interested
to read the following article.

data("creditcoops") head(creditcoops)

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.

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

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").

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:

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.

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.

The inequality and diversity group has the following arguments: `entropy`

,
`gini`

, `simpson`

, `palma`

, `grs`

, and `all`

. They can also
be accessed as individual functions.

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

The group wrapper for competition measures includes the following arguments to
calculate `hhi`

, `hhi_d`

, `hhi_min`

, `dom`

, `sten`

, and `all`

.

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

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.

test_share <- c(0.2, 0.3, 0.5) test_share_conc <- concstats_concstats(test_share, digit = 2) test_share_conc

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`

,
`dataexplorer`

, `kableExtra`

or `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.

data("creditcoops") head(creditcoops)

You will need the following two packages. Make sure you have these packages installed.

library(dplyr) library(kableExtra)

Now, we will filter out data for the year 2016.

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.

{width="400" height="200"}

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.

df_shares <- creditcoops %>% dplyr::select(coop_id, year, paired, total_loans_log)

Make sure you have the `ggplot2`

package installed. Load the package.

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

{width="469" height="521"}

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.

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.