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 the following functions:
overview_tabgenerates 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_crosstabgenerates 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_latexconverts the output of bothoverview_tabandoverview_crosstabinto LaTeX code and/or directly into a .tex file.overview_plotis an alternative to visualize the sample (a way to present results fromoverview_tab)overview_crossplotis an alternative to visualize a cross table (a way to present results fromoverview_crosstab)overview_heatplots a heat map of your time lineoverview_naplots an overview of missing values by variable (both by row and by column)overview_overlapplots 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,
flextable, or
knitr.
We present a short step-by-step guide as well as the functions in more detail below.
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) # Tools to Make Developing R Packages Easier # Tools to Make Developing R Packages Easier
devtools::install_github("cosimameyer/overviewR")First, load the package.
library(overviewR) # Easily Extracting Information About Your Data # Easily Extracting Information About Your DataThe 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 6There 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
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
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).
With overviewR v 0.0.11.999 we introduced
overview_latexinstead ofoverview_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. These functions are currently supported only in the
development version of the package.
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"
)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.
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.
# 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”.
# Plot using the `color` argument
overview_plot(
dat = toydata,
id = ccode,
time = year,
dot_size = 5
)
To visualize also the cross table, overview_crossplot does the job.
overview_crossplot(
toydata,
id = ccode,
time = year,
cond1 = gdp,
cond2 = population,
threshold1 = 25000,
threshold2 = 27000,
color = TRUE,
label = TRUE
)
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 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)
overview_na(toydata_with_na, perc = FALSE)
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, by = "ccode"
#> Warning: Removed 1 rows containing missing values (geom_bar).
Or a Venn diagram
overview_overlap(
dat1 = toydata,
dat2 = toydata2,
dat1_id = ccode,
dat2_id = ccode,
plot_type = "venn"
)
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 |
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() 
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()
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}
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 |
✓ |
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}
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 | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
Simply open an issue on GitHub.
If you have an idea (but no code yet), open an issue 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.
The easiest way is to open an issue - this way, your question is also visible to others who may face similar problems.
The hex sticker is generated by ourselves using the
hexSticker package.