Using the package
First, load the package.
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.
ccode year month gdp population
RWA 1990 Jan 24180.77 14969.988
RWA 1990 Feb 23650.53 11791.464
RWA 1990 Mar 21860.14 30047.979
RWA 1990 Apr 20801.06 19853.556
RWA 1990 May 18702.84 5148.118
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.
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.
# ccode time_frame
# RWA 1990 - 1995
# AGO 1990 - 1992
# BEN 1995 - 1999
# GBR 1991, 1993, 1995, 1997, 1999
# FRA 1993, 1996, 1999overview_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.
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.