Author: Cosima Meyer and Dennis Hammerschmidt
Date: 2026-05-05

# overviewR: Easily Extracting Information About Your Data

#### Cosima Meyer and Dennis Hammerschmidt

#### 2026-05-05

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_markdown` converts the output of both `overview_tab` and `overview_crosstab` into Markdown and/or directly into a .md 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 (column-wise and row-wise)

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`, `flextable`, or `knitr`.

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:

    install.packages("overviewR", force = TRUE)

To install the latest development version of `overviewR` directly from GitHub use:

    library(devtools)
    devtools::install_github("cosimameyer/overviewR")

# Tabular overview

## `overview_tab`

First, load the package.

    library(overviewR)

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.

    data(toydata)
    head(toydata)
    #>   ccode year month      gdp population day
    #> 1   RWA 1990   Jan 24180.77  14969.988   1
    #> 2   RWA 1990   Jan 23650.53  11791.464   2
    #> 3   RWA 1990   Jan 21860.14  30047.979   3
    #> 4   RWA 1990   Jan 20801.06  19853.556   4
    #> 5   RWA 1990   Jan 18702.84   5148.118   5
    #> 6   RWA 1990   Jan 30272.37  48625.140   6

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()` to get to the format.

Generate some general overview of the data set using the time and scope conditions with `overview_tab`.

    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`.

    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)`.

    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:

    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.

    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:

    #   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`.

    overview_latex(obj = output_table)

LaTeX output

    % 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).

    overview_latex(
      obj = output_table,
      id = "Countries",
      time = "Years",
      title = "Cool new title for our awesome table"
    )

LaTeX output

    % 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.

    overview_latex(
      obj = output_crosstab,
      title = "Cross table of the sample",
      crosstab = TRUE,
      cond1 = "GDP",
      cond2 = "Population"
    )

LaTeX output

    % 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.

    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.

    overview_latex(
      obj = output_table,
      save_out = TRUE,
      file_path = "SET-YOUR-PATH/output.tex"
    )

## `overview_markdown`

`overview_markdown` mirrors the functionality of `overview_latex` but produces Markdown output — useful for R Markdown documents, GitHub READMEs, or any Markdown-based workflow.

    overview_markdown(obj = output_table)

    ## Time and scope of the sample
    | Sample | Time frame |
    |--------|------------|
    | RWA | 1990 - 1995 |
    | AGO | 1990 - 1992 |
    | BEN | 1995 - 1999 |
    | GBR | 1991, 1993, 1995, 1997, 1999 |
    | FRA | 1993, 1996, 1999 |

The column headers can be customised with `id` and `time`, and the title with `title`.

    overview_markdown(
      obj = output_table,
      id = "Countries",
      time = "Years",
      title = "My custom title"
    )

For cross-tab objects, set `crosstab = TRUE` and provide condition labels:

    overview_markdown(
      obj = output_crosstab,
      crosstab = TRUE,
      cond1 = "GDP",
      cond2 = "Population"
    )

Use `save_out = TRUE` together with `file_path` to write the table directly to a `.md` file:

    overview_markdown(
      obj = output_table,
      save_out = TRUE,
      file_path = "output/overview.md"
    )

# Visual overview

## `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.

    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`.

    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.

    library(overviewR) # Easily Extracting Information About Your Data
    library(magrittr) # A Forward-Pipe Operator for R

    # 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
    )

Since `overview_plot` returns a ggplot object, you can apply any ggplot2 color scale directly by adding a layer. This works for any number of categories.

    library(ggplot2)

    # Explicit colors via scale_color_manual
    overview_plot(
      dat = toydata,
      id = ccode,
      time = year,
      color = before
    ) +
      ggplot2::scale_color_manual(values = c("#E84646", "#4E84C4"))

    # RColorBrewer palette via scale_color_brewer
    overview_plot(
      dat = toydata,
      id = ccode,
      time = year,
      color = before
    ) +
      ggplot2::scale_color_brewer(palette = "Set1")

The development version also allows to change the dot size using the `dot_size` argument. The default is “2”.

    # Plot using the `dot_size` argument
    overview_plot(
      dat = toydata,
      id = ccode,
      time = year,
      dot_size = 5
    )

## `overview_crossplot`

To visualize the cross table, `overview_crossplot` does the job. It places each observation in one of four quadrants defined by `threshold1` (x-axis) and `threshold2` (y-axis).

Basic plot — points only, no coloring or labels:

    overview_crossplot(
      toydata,
      id = ccode,
      time = year,
      cond1 = gdp,
      cond2 = population,
      threshold1 = 25000,
      threshold2 = 27000
    )

With quadrant coloring (`color = TRUE`) — each quadrant gets a distinct color, making it easy to see how observations cluster:

    overview_crossplot(
      toydata,
      id = ccode,
      time = year,
      cond1 = gdp,
      cond2 = population,
      threshold1 = 25000,
      threshold2 = 27000,
      color = TRUE
    )

With labels (`label = TRUE`) — adds id–time labels to each point using `ggrepel` to avoid overlaps:

    overview_crossplot(
      toydata,
      id = ccode,
      time = year,
      cond1 = gdp,
      cond2 = population,
      threshold1 = 25000,
      threshold2 = 27000,
      label = TRUE
    )

With both coloring and labels — the most informative variant:

    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`.

    toydata_red <- toydata[-sample(seq_len(nrow(toydata)), 64), ]

    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`.

    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)
      )

    overview_na(toydata_with_na)
    #> Warning in overview_na(toydata_with_na): Missing values detected in
    #> time-related column(s): month. Consider using `row_wise = TRUE` or inspecting
    #> these columns before drawing conclusions about time coverage.

    overview_na(toydata_with_na, perc = FALSE)
    #> Warning in overview_na(toydata_with_na, perc = FALSE): Missing values detected
    #> in time-related column(s): month. Consider using `row_wise = TRUE` or
    #> inspecting these columns before drawing conclusions about time coverage.

### Row-wise NA overview

By default, `overview_na` summarises NAs column-wise (one bar per variable). Setting `row_wise = TRUE` switches to a row-wise perspective: each observation on the y-axis, NA count on the x-axis. This is useful to spot rows that are largely empty.

    overview_na(toydata_with_na, row_wise = TRUE)

    overview_na(toydata_with_na, row_wise = TRUE, perc = FALSE)

You can also append the row-wise NA count and percentage directly to your original data frame with `add = TRUE`. This returns a data frame rather than a plot.

    toydata_extended <- overview_na(toydata_with_na, row_wise = TRUE, add = TRUE)
    head(toydata_extended)

## `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:

    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
    )
    #> Joining with `by = join_by(ccode)`
    #> Warning: Removed 1 row containing missing values or values outside the scale range
    #> (`geom_bar()`).

Or a Venn diagram

    overview_overlap(
      dat1 = toydata,
      dat2 = toydata2,
      dat1_id = ccode,
      dat2_id = ccode,
      plot_type = "venn"
    )

## Customizing plots: `ggplot2`

The plot functions are fully `ggplot2` based. While a theme is pre-defined, this can easily be overwritten.

A classical `ggplot2` theme alternative

    library(ggplot2) # Create Elegant Data Visualisations
                     # Using the Grammar of Graphics

    overview_na(toydata_with_na) +
      ggplot2::theme_minimal()
    #> Warning in overview_na(toydata_with_na): Missing values detected in
    #> time-related column(s): month. Consider using `row_wise = TRUE` or inspecting
    #> these columns before drawing conclusions about time coverage.

# Compatibilities with other packages

## 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

    library(dplyr) # A Grammar of Data Manipulation # A Grammar of Data Manipulation

    toydata_with_na %>%
      dplyr::filter(year > 1993) %>%
      overview_na()
    #> Warning in overview_na(.): Missing values detected in time-related column(s):
    #> month. Consider using `row_wise = TRUE` or inspecting these columns before
    #> drawing conclusions about time coverage.

Using mutate to generate meaningful country names

    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

    # 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}

## Presenting tables: `flextable`, `xtable`, and `kable`

The outputs of `overview_tab` and `overview_crosstab` are also compatible with other functions such as `xtable`, `flextable`, or `kable` from `knitr`.

Two examples are shown below:

    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")

    library(knitr) # A General-Purpose Package for Dynamic Report Generation in R
    knitr::kable(output_table)

ccode | time_frame
--- | ---
RWA | 1990-1995
AGO | 1990-1992
BEN | 1995-1999
GBR | 1991, 1993, 1995, 1997, 1999
FRA | 1993, 1996, 1999

A classical `ggplot2` theme alternative

    library(ggplot2) # Create Elegant Data Visualisations Using the
    # Grammar of Graphics

    overview_na(toydata_with_na) +
      ggplot2::theme_minimal()
    #> Warning in overview_na(toydata_with_na): Missing values detected in
    #> time-related column(s): month. Consider using `row_wise = TRUE` or inspecting
    #> these columns before drawing conclusions about time coverage.

## 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.

    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:

    head(dat_full)

    #> # A tibble: 5 x 3
    #> # Groups:   ccode [5]
    #>   ccode time_dat1                    time_dat2
    #>   <chr> <chr>                        <chr>
    #> 1 AGO   1990 - 1992                  <NA>
    #> 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.

    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}