https://github.com/cosimameyer/overviewR
🔦 Easily Extracting Information About Your Data in R
https://github.com/cosimameyer/overviewR
package-development
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🔦 Easily Extracting Information About Your Data in R
- Host: GitHub
- URL: https://github.com/cosimameyer/overviewR
- Owner: cosimameyer
- License: gpl-3.0
- Created: 2020-06-06T11:05:28.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-01-12T17:01:45.000Z (over 1 year ago)
- Last Synced: 2024-11-16T23:46:32.997Z (5 months ago)
- Topics: package-development
- Language: R
- Homepage: https://cosimameyer.github.io/overviewR/
- Size: 19.4 MB
- Stars: 52
- Watchers: 3
- Forks: 11
- Open Issues: 7
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- License: LICENSE.md
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README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/",
out.width = "100%"
)
```
```{R,echo=FALSE,results='hide',message=FALSE,include=FALSE, eval=TRUE}
## Packages
pkgs <- c("knitr",
"tidyverse",
"dplyr",
"magrittr",
"countrycode",
"xtable")
## Install uninstalled packages
lapply(pkgs[!(pkgs %in% installed.packages())], install.packages)
## Load all packages to library
lapply(pkgs, library, character.only = TRUE)
```
# overviewR 
[](https://github.com/cosimameyer/overviewR/actions)
[](https://app.codecov.io/gh/cosimameyer/overviewR?branch=master)
[**You can access the CheatSheet for overviewR here**](https://github.com/cosimameyer/overviewR/blob/master/man/figures/CheatSheet_overviewR.pdf)
The goal of overviewR is to make it easy to get an overview of a data set by displaying relevant sample information. At the moment, there are the following functions:
- `overview_tab` generates a tabular overview of the sample. The general sample plots a two-column table that provides information on an id in the left column and a the time frame on the right column.
- `overview_crosstab` generates a cross table. The conditional column allows to disaggregate the overview table by specifying two conditions, hence resulting a 2x2 table. This way, it is easy to visualize the time and scope conditions as well as theoretical assumptions with examples from the data set.
- `overview_latex` converts the output of both `overview_tab` and `overview_crosstab` into LaTeX code and/or directly into a .tex file.
- `overview_plot` is an alternative to visualize the sample (a way to present results from `overview_tab`)
- `overview_crossplot` is an alternative to visualize a cross table (a way to present results from `overview_crosstab`)
- `overview_heat` plots a heat map of your time line
- `overview_na` plots an overview of missing values by variable (both by row and by column)
- `overview_overlap` plots comparison plots (bar graph and Venn diagram) to compare to data frames
The plots can be saved using the `ggsave()` command. The output of `overview_tab` and `overview_crosstab` are also compatible with other packages such as [```xtable```](https://CRAN.R-project.org/package=xtable), [```flextable```](https://CRAN.R-project.org/package=flextable), or [`knitr`](https://bookdown.org/yihui/rmarkdown-cookbook/kable.html).
We present a short step-by-step guide as well as the functions in more detail below.
## Installation
A stable version of `overviewR` can be directly accessed on CRAN:
```{r, eval=FALSE}
install.packages("overviewR", force = TRUE)
```
To install the latest development version of `overviewR` directly from [GitHub](https://github.com/cosimameyer/overviewR) use:
```{r, message=FALSE, warning=FALSE, results = "hide", eval=FALSE}
library(devtools) # Tools to Make Developing R Packages Easier # Tools to Make Developing R Packages Easier
devtools::install_github("cosimameyer/overviewR")
```
## Example
First, load the package.
```{r, include = FALSE, eval = FALSE}
install.packages("overviewR")
```
```{r, message=FALSE, warning=FALSE}
library(overviewR) # Easily Extracting Information About Your Data # Easily Extracting Information About Your Data
```
The following examples use a toy data set (`toydata`) that comes with the package. This data contains artificially generated information in a cross-sectional format on 5 countries, covering the period 1990-1999.
```{r}
data(toydata)
head(toydata)
```
There are 264 observations for 5 countries (Angola, Benin, France, Rwanda, and UK) stored in the ```ccode``` variable, over a time period between 1990 to 1999 (```year```) with additional information for the month (```month```). Additionally, two artificially generated fake variables for GDP (```gdp```) and population size (```population```) are included to illustrate of conditions.
The following functions work best on data sets that have an id-time-structure, in the case of `toydata` this corresponds to country-year with `ccode` and `year`.
If the data set does not have this format yet, consider using [```pivot_wider()``` or ```pivot_longer()```](https://tidyr.tidyverse.org/reference/pivot_longer.html) to get to the format.
### ```overview_tab```
Generate some general overview of the data set using the time and scope conditions with ```overview_tab```.
```{r, message=FALSE, warning=FALSE, eval=FALSE}
output_table <- overview_tab(dat = toydata, id = ccode, time = year)
```
The resulting data frame collapses the time condition for each id by taking into account potential gaps in the time frame. Note that the column name for the time frame is set by default to `time_frame` and internally generated when using `overview_tab`.
```{r, message=FALSE, eval=FALSE}
output_table
```
```
# ccode time_frame
# RWA 1990 - 1995
# AGO 1990 - 1992
# BEN 1995 - 1999
# GBR 1991, 1993, 1995, 1997, 1999
# FRA 1993, 1996, 1999
```
### Multiple time arguments
As of overviewR version 0.0.7 you can also use multiple time objects in `overview_tab`. You can now pass a list containing multiple time variables with the following format: `time = list(year = NULL, month = NULL, day = NULL)`.
```{r, message=FALSE, eval=FALSE}
output_table_complex <- overview_tab(dat = toydata, id = ccode, time = list(year = toydata$year,
month = toydata$month, day = toydata$day), complex_date = TRUE)
```
The resulting data frame collapses again the time condition for each id by taking into account potential gaps in the time frame. Note that the column name for the time frame is set by default to `time_frame` and internally generated when using `overview_tab`. The output resembles something like this:
```{r, message=FALSE, eval=FALSE}
output_table_complex
```
```
ccode time_frame # AGO 1990-01-01, 1990-02-02, 1990-03-03, 1990-04-04, 1990-05-05, 1990-06-06, ...
# BEN 1995-01-01, 1995-02-02, 1995-03-03, 1995-04-04, 1995-05-05, 1995-06-06, ...
# FRA 1993-01-01, 1993-02-02, 1993-03-03, 1993-04-04, 1993-05-05, 1993-06-06, ...
# GBR 1991-01-01, 1991-02-02, 1991-03-03, 1991-04-04, 1991-05-05, 1991-06-06, ...
# RWA 1990-01-01 - 1990-01-12, 1991-01-01 - 1991-01-12, 1992-01-01 - 1992-01-12, 1993-01-01 - 1993-01-12, 1994-01-01
```
### ```overview_crosstab```
To generate a cross table that divides the data based on two conditions, for instance GDP and population size, ```overview_crosstab``` can be used. `threshold1` and `threshold2` thereby indicate the cut point for the two conditions (`cond1` and `cond2`), respectively.
```{r, message=FALSE, warning=FALSE, eval=FALSE}
output_crosstab <- overview_crosstab(
dat = toydata,
cond1 = gdp,
cond2 = population,
threshold1 = 25000,
threshold2 = 27000,
id = ccode,
time = year
)
```
The data frame output looks as follows:
```{r, message=FALSE, echo=FALSE, eval=FALSE}
output_crosstab
```
```
# part1 part2
# 1 AGO (1990, 1992), FRA (1993), GBR (1997) BEN (1996, 1999), FRA (1999), GBR (1993), RWA (1992, 1994)
# 2 BEN (1997), RWA (1990) AGO (1991), BEN (1995, 1998), FRA (1996), GBR (1991, 1995, 1999), RWA (1991, 1993, 1995)
```
Note, if a data set is used that has multiple observations on the id-time unit, the function automatically aggregates the data set using the mean of condition 1 (`cond1`) and condition 2 (`cond2`).
### ```overview_latex```
> With overviewR v 0.0.11.999 we introduced `overview_latex` instead of `overview_print`
To generate an easily usable LaTeX output for the generated `overview_tab` and `overview_crosstab` objects, `overviewR` offers the function `overview_latex`.
The following illustrate this using the `output_table` object from `overview_tab`.
```{r, eval=FALSE}
overview_latex(obj = output_table)
```
LaTeX output
```{r, eval=FALSE, include=FALSE}
overview_latex(obj = output_table)
```
```
% Overview table generated in R version 4.0.0 (2020-04-24) using overviewR
% Table created on 2020-06-21
\begin{table}[ht]
\centering
\caption{Time and scope of the sample}
\begin{tabular}{ll}
\hline
Sample & Time frame \\
\hline
RWA & 1990 - 1995 \\
AGO & 1990 - 1992 \\
BEN & 1995 - 1999 \\
GBR & 1991, 1993, 1995, 1997, 1999 \\
FRA & 1993, 1996, 1999 \\
\hline
\end{tabular}
\end{table}
```
The default already provides a title ("Time and scope of the sample") that can be modified in the argument `title`. The same holds for the column names ("Sample" and "Time frame" are set by default but can be modified as shown below).
```{r, eval = FALSE, eval=FALSE}
overview_latex(
obj = output_table,
id = "Countries",
time = "Years",
title = "Cool new title for our awesome table"
)
```
LaTeX output
```{r, eval=FALSE, include=FALSE}
overview_latex(
obj = output_table,
id = "Countries",
time = "Years",
title = "Cool new title for our awesome table"
)
```
```
% Overview table generated in R version 4.0.0 (2020-04-24) using overviewR
% Table created on 2020-06-21
\begin{table}[ht]
\centering
\caption{Cool new title for our awesome table}
\begin{tabular}{ll}
\hline
Countries & Years \\
\hline
RWA & 1990 - 1995 \\
AGO & 1990 - 1992 \\
BEN & 1995 - 1999 \\
GBR & 1991, 1993, 1995, 1997, 1999 \\
FRA & 1993, 1996, 1999 \\
\hline
\end{tabular}
\end{table}
```
The same function can also be used for outputs from the `overview_crosstab` function by using the argument `crosstab = TRUE`. There are also options to label the respective conditions (```cond1``` and ```cond2```). Note that this should correspond to the conditions (```cond1``` and ```cond2```) specified in the `overview_crosstab` function.
```{r, eval = FALSE}
overview_latex(
obj = output_crosstab,
title = "Cross table of the sample",
crosstab = TRUE,
cond1 = "GDP",
cond2 = "Population"
)
```
LaTeX output
```{r, eval=FALSE, include=FALSE}
overview_latex(
obj = output_crosstab,
title = "Cross table of the sample",
crosstab = TRUE,
cond1 = "GDP",
cond2 = "Population"
)
```
```
% Overview table generated in R version 4.0.0 (2020-04-24) using overviewR
% Table created on 2020-06-21
% Please add the following packages to your document preamble:
% \usepackage{multirow}
% \usepackage{tabularx}
% \newcolumntype{b}{X}
% \newcolumntype{s}{>{\hsize=.5\hsize}X}
\begin{table}[ht]
\caption{Cross table of the sample}
\begin{tabularx}{\textwidth}{ssbb}
\hline & & \multicolumn{2}{c}{\textbf{GDP}} \\
& & \textbf{Fulfilled} & \textbf{Not fulfilled} \\
\hline \\
\multirow{2}{*}{\textbf{Population}} & \textbf{Fulfilled} &
AGO (1990, 1992), FRA (1993), GBR (1997) & BEN (1996, 1999), FRA (1999), GBR (1993), RWA (1992, 1994)\\
\\ \hline \\
& \textbf{Not fulfilled} & BEN (1997), RWA (1990) & AGO (1991), BEN (1995, 1998), FRA (1996), GBR (1991, 1995, 1999), RWA (1991, 1993, 1995)\\ \hline \\
\end{tabularx}
\end{table}
```
`overview_latex` further allows more specifications such as the font size or a a label. *These functions are currently supported only in the development version of the package.*
```{r, eval = FALSE}
overview_latex(obj = output_table,
fontsize = "scriptsize",
label = "tab:overview")
```
With ```save_out = TRUE``` the function exports the output as a ```.tex``` file and stores it on the device.
```{r, eval = FALSE}
overview_latex(
obj = output_table,
save_out = TRUE,
file_path = "SET-YOUR-PATH/output.tex"
)
```
### ```overview_plot```
In addition to tables, `overviewR` also provides plots to illustrate the structure of your data.
`overview_plot` illustrates the information that is generated in `overview_table` in a ggplot graphic. All scope objects (e.g., countries) are listed on the y-axis where horizontal lines indicate the coverage across the entire time frame of the data (x-axis). This helps to spot gaps in the data for specific scope objects and outlines at what time point they occur.
```{r, out.width = '50%', fig.align='center'}
data(toydata)
overview_plot(dat = toydata, id = ccode, time = year)
```
The results are sorted alphabetically by default. The order can also be reversed by setting `asc` to `FALSE`.
```{r, out.width = '50%', fig.align='center'}
library(magrittr) # A Forward-Pipe Operator for R
overview_plot(
dat = toydata,
id = ccode,
time = year,
asc = FALSE
)
```
There is also an option to color the time lines conditionally. Here, we introduce a dummy variable that indicates whether the year was before 1995 or not. We use this dummy to color the time lines using the `color` argument.
```{r, out.width = '50%', fig.align='center', include = TRUE, results = TRUE, message = FALSE, warning=FALSE}
# Code whether a year was before 1995
toydata <- toydata %>%
dplyr::mutate(before = ifelse(year < 1995, 1, 0))
# Plot using the `color` argument
overview_plot(
dat = toydata,
id = ccode,
time = year,
color = before
)
```
The development version also allows to change the dot size using the `dot_size` argument. The default is "2".
```{r, eval = FALSE}
# Plot using the `color` argument
overview_plot(
dat = toydata,
id = ccode,
time = year,
dot_size = 5
)
```
### ```overview_crossplot```
To visualize also the cross table, `overview_crossplot` does the job.
```{r, out.width = '50%', fig.align='center', include = TRUE, results = TRUE, message = FALSE, warning=FALSE}
overview_crossplot(
toydata,
id = ccode,
time = year,
cond1 = gdp,
cond2 = population,
threshold1 = 25000,
threshold2 = 27000,
color = TRUE,
label = TRUE
)
```
### ```overview_heat```
`overview_heat` takes a closer look at the time and scope conditions by visualizing the data coverage for each time and scope combination in a ggplot heat map. This function is best explained using an example. Suppose you have a dataset with monthly data for different countries and want to know if data is available for each country in every month. `overview_heat` intuitively does this by plotting a heat map where each cell indicates the coverage for that specific combination of time and scope (e,g., country-year). As illustrated below, the darker the cell is, the more coverage it has. The plot also indicates the relative or absolute coverage of each cell. For instance, Angola ("AGO") in 1991 shows the coverage of 75%. This means that of all potential 12 months of coverage (12 months for one year), only 9 are covered.
For this purpose, we first artificially reduced the `toydata`.
```{r, include = TRUE}
toydata_red <- toydata[-sample(seq_len(nrow(toydata)), 64),]
```
```{r, out.width = '50%', fig.align='center'}
overview_heat(toydata_red,
ccode,
year,
perc = TRUE,
exp_total = 12)
```
### ```overview_na```
`overview_na` is a simple function that provides information about the content of all variables in your data, not only the time and scope conditions. It returns a horizontal ggplot bar plot that indicates the amount of missing data (NAs) for each variable (on the y-axis). You can choose whether to display the relative amount of NAs for each variable in percentage (the default) or the total number of NAs.
For this purpose, we first artificially reduced our `toydata`.
```{r, include=TRUE}
toydata_with_na <- toydata %>%
dplyr::mutate(
year = ifelse(year < 1992, NA, year),
month = ifelse(month %in% c("Jan", "Jun", "Aug"), NA, month),
gdp = ifelse(gdp < 20000, NA, gdp)
)
```
```{r, out.width = '50%', fig.align='center'}
overview_na(toydata_with_na)
```
```{r, out.width = '50%', fig.align='center'}
overview_na(toydata_with_na, perc = FALSE)
```
### ```overview_overlap```
This function allows to compare two data sets. We are currently working on an extended version that allows comparing >2 data sets.
At the current development stage, the function works as follows:
```{r, eval=TRUE, out.width = '50%', fig.align='center'}
library(dplyr)
# Subset one data set for comparison
toydata2 <- toydata %>% dplyr::filter(year > 1992)
overview_overlap(
dat1 = toydata,
dat2 = toydata2,
dat1_id = ccode,
dat2_id = ccode,
plot_type = "bar" # This is the default
)
```
Or a Venn diagram
```{r, eval = TRUE,out.width = '50%', fig.align='center'}
overview_overlap(
dat1 = toydata,
dat2 = toydata2,
dat1_id = ccode,
dat2_id = ccode,
plot_type = "venn"
)
```
## Compatibilities with other packages
### Presenting tables: `flextable`, `xtable`, and `kable`
The outputs of `overview_tab` and `overview_crosstab` are also compatible with other functions such as [`xtable`](https://CRAN.R-project.org/package=xtable), [`flextable`](https://CRAN.R-project.org/package=flextable), or [`kable`](https://bookdown.org/yihui/rmarkdown-cookbook/kable.html) from [`knitr`](https://yihui.org/knitr/).
Two examples are shown below:
```{r, eval=FALSE}
library(flextable) # not installed on this machine
table_output <- qflextable(output_table)
table_output <-
set_header_labels(table_output,
ccode = "Countries",
time_frame = "Time frame")
set_table_properties(table_output,
width = .4,
layout = "autofit")
```
```{r, echo=FALSE, include=FALSE}
output_table <-
data.frame(
ccode = c("RWA", "AGO", "BEN", "GBR", "FRA"),
time_frame = c(
"1990-1995",
"1990-1992",
"1995-1999",
"1991, 1993, 1995, 1997, 1999",
"1993, 1996, 1999"
)
)
```
```{r}
library(knitr) # A General-Purpose Package for Dynamic Report Generation in R
knitr::kable(output_table)
```
### Customizing plots: `ggplot2` and other packages
The plot functions are fully `ggplot2` based. While a theme is pre-defined, this can easily be overwritten.
A classical `ggplot2` theme alternative
```{r, out.width = '50%', fig.align='center'}
library(ggplot2) # Create Elegant Data Visualisations Using the Grammar of Graphics
overview_na(toydata_with_na) +
ggplot2::theme_minimal()
```
### Workflow: `tidyverse`
All functions are further easily accessible using a common `tidyverse` workflow. Here are just three examples -- the possibilities are endless.
Using a filter function
```{r, out.width = '50%', fig.align='center'}
library(dplyr) # A Grammar of Data Manipulation # A Grammar of Data Manipulation
toydata_with_na %>%
dplyr::filter(year > 1993) %>%
overview_na()
```
Using mutate to generate meaningful country names
```{r, out.width = '50%', fig.align='center'}
library(countrycode) # Convert Country Names and Country Codes
library(dplyr) # A Grammar of Data Manipulation # A Grammar of Data Manipulation
toydata %>%
# Transform the country code (ISO3 character code) into a country name using the `countrycode` package
dplyr::mutate(country = countrycode::countrycode(ccode, "iso3c", "country.name")) %>%
overview_plot(id = country, time = year)
```
Using different `overviewR` functions after each other to generate a workflow
```{r, eval = FALSE}
# Produces a printable LaTeX output
toydata %>%
overview_tab(id = ccode, time = year) %>%
overview_latex()
```
```
% Overview table generated in R version 4.0.2 (2020-06-22) using overviewR
% Table created on 2020-12-30
\begin{table}[ht]
\centering
\caption{Time and scope of the sample}
\label{tab:tab1}
\begin{tabular}{ll}
\hline
Sample & Time frame \\
\hline
AGO & 1990 - 1992 \\
BEN & 1995 - 1999 \\
FRA & 1993, 1996, 1999 \\
GBR & 1991, 1993, 1995, 1997, 1999 \\
RWA & 1990 - 1995 \\
\hline
\end{tabular}
\end{table}
```
## Overview of functional add-ons
| | Works with `data.frame` objects | Works with `data.table` | Can take multiple time arguments (year, month, day) |
|-----------------------|---------------------------------|-------------------------|-----------------------------------------------------|
| `overview_tab` | ✓ | ✓ | ✓ |
| `overview_na` | ✓ | ✓ | |
| `overview_plot` | ✓ | | |
| `overview_crossplot` | ✓ | | |
| `overview_crosstab` | ✓ | | |
| `overview_heat` | ✓ | | |
| `overview_overlap` | ✓ | | |
## Extensions
If you wish to compare two data sets using `overview_tab`, this is not (yet) implemented in `overviewR` but there is currently a workaround.
```{r, eval=TRUE}
library(overviewR)
library(dplyr)
library(xtable)
# Load data
data(toydata)
# Restrict the data so that we have something to compare :-)
toydata_res <- toydata %>%
dplyr::filter(year > 1992)
# Generate two overview_tab objects
dat1 <- overview_tab(toydata, id = ccode, time = year)
dat2 <- overview_tab(toydata_res, id = ccode, time = year)
# And now we use full_join to combine both
dat_full <- dat1 %>%
dplyr::full_join(dat2, by = "ccode") %>%
dplyr::rename(time_dat1 = time_frame.x,
time_dat2 = time_frame.y)
```
Having a look at the output, we see that this is exactly what we wanted to have:
```{r, eval=FALSE}
head(dat_full)
```
```
#> # A tibble: 5 x 3
#> # Groups: ccode [5]
#> ccode time_dat1 time_dat2
#>
#> 1 AGO 1990 - 1992
#> 2 BEN 1995 - 1999 1995 - 1999
#> 3 FRA 1993, 1996, 1999 1993, 1996, 1999
#> 4 GBR 1991, 1993, 1995, 1997, 1999 1993, 1995, 1997, 1999
#> 5 RWA 1990 - 1995 1993 - 1995
```
`overview_latex` cannot handle this object (yet), so we use `xtable` instead which gives us the LaTeX output.
```{r, eval=FALSE}
print(xtable(dat_full), include.rownames = FALSE)
```
```
% latex table generated in R 4.0.2 by xtable 1.8-4 package
% Tue Feb 16 18:20:51 2021
\begin{table}[ht]
\centering
\begin{tabular}{lll}
\hline
ccode & time\_dat1 & time\_dat2 \\
\hline
AGO & 1990 - 1992 & \\
BEN & 1995 - 1999 & 1995 - 1999 \\
FRA & 1993, 1996, 1999 & 1993, 1996, 1999 \\
GBR & 1991, 1993, 1995, 1997, 1999 & 1993, 1995, 1997, 1999 \\
RWA & 1990 - 1995 & 1993 - 1995 \\
\hline
\end{tabular}
\end{table}
```
# What's unique about overviewR?
With a specific focus on time-series cross-sectional data, it is unique in its coverage. The details are outlined in the table below:
| Key functionalities | overviewR (0.0.11) | DataExplorer (0.8.2) | dlookR (0.6.0) | gtsummary (1.6.1) | Hmisc (4.7-1) | naniar (0.6.1) | skimr (2.1.4) | smartEDA (0.3.8) | summarytools (1.0.1) |
|-------------------------------------------------------------------------------------|--------------------|----------------------|----------------|-------------------|---------------|----------------|---------------|------------------|----------------------|
| Shows time-series cross-sectional data coverage | ✓ | | | | | | | | |
| Allows to quickly report time-series cross-sectional data coverage (figure/table) | ✓ | | | | | | | | |
| Shows NAs | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Shows NAs (in a figure) | ✓ | ✓ | ✓ | | | ✓ | | | |
| Shows overlap between two data frames (based on time and id) | ✓ | | | | | | | | |
| Shows cross-table | ✓ | | | ✓ | | | | ✓ | ✓ |
| Shows cross-table based on country-year units | ✓ | | | | | | | | |
| Shows cross-tables (in a figure) | ✓ | | | | | | | | |
| Reports descriptive statistics | | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Shows value type | | ✓ | ✓ | ✓ | | ✓ | ✓ | ✓ | ✓ |
# How to reach out?
### Where do I report bugs?
Simply [open an issue](https://github.com/cosimameyer/overviewR/issues/new) on GitHub.
### How do I contribute to the package?
If you have an idea (but no code yet), [open an issue](https://github.com/cosimameyer/overviewR/issues/new) on GitHub. If you want to contribute with a specific feature and have the code ready, fork the repository, add your code, and create a pull request.
### Do you need support?
The easiest way is to [open an issue](https://github.com/cosimameyer/overviewR/issues/new) - this way, your question is also visible to others who may face similar problems.
# Credits
The hex sticker is generated by ourselves using the [```hexSticker```](https://github.com/GuangchuangYu/hexSticker) package.