overviewR 
You can access the CheatSheet for overviewR here
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
two functions (overview_tab and overview_crosstab) that generate a
tabular overview of the general sample as well as a conditional 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. 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. The function
overview_print converts this output of both overview_tab and
overview_crosstab into LaTeX code and/or directly into a .tex file.
The output of overview_tab and overview_crosstab are also compatible
with other packages such as
xtable,
flextable, or
knitr.
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")Example
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
#> 1 RWA 1990 Jan 24180.77 14969.988
#> 2 RWA 1990 Feb 23650.53 11791.464
#> 3 RWA 1990 Mar 21860.14 30047.979
#> 4 RWA 1990 Apr 20801.06 19853.556
#> 5 RWA 1990 May 18702.84 5148.118
#> 6 RWA 1990 Jun 30272.37 48625.140There 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.
overview_tab
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
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_print
To generate an easily usable LaTeX output for the generated
overview_tab and overview_crosstab objects, overviewR offers the
function overview_print. The following illustrate this using the
output_table object from overview_tab.
overview_print(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_print(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_print(
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}
With save_out = TRUE the function exports the output as a .tex file
and stores it on the device.
overview_print(obj = output_table, save_out = TRUE, path = "SET-YOUR-PATH",
file = "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.
data(toydata)
overview_plot(dat = toydata, id = ccode, time = year)
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.
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.
overview_na(toydata_with_na)
overview_na(toydata_with_na, perc = FALSE)
Compatibilities with other packages
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)
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)
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 |
Credits
The hex sticker is generated by ourselves using the
hexSticker package.