Package 'ExPanDaR' reference manual

Title:	Explore Your Data Interactively
Description:	Provides a shiny-based front end (the 'ExPanD' app) and a set of functions for exploratory data analysis. Run as a web-based app, 'ExPanD' enables users to assess the robustness of empirical evidence without providing them access to the underlying data. You can export a notebook containing the analysis of 'ExPanD' and/or use the functions of the package to support your exploratory data analysis workflow. Refer to the vignettes of the package for more information on how to use 'ExPanD' and/or the functions of this package.
Authors:	Joachim Gassen [aut, cre]
Maintainer:	Joachim Gassen <[email protected]>
License:	MIT + file LICENSE
Version:	0.5.3.9000
Built:	2024-12-10 05:00:35 UTC
Source:	https://github.com/joachim-gassen/expandar

Explore Your Data (ExPanD)

Description

A shiny based web app that uses ExPanDaR functionality for interactive data exploration. Designed for long-form panel data but works on simple cross-sectional data as well.

Usage

ExPanD(
  df = NULL,
  cs_id = NULL,
  ts_id = NULL,
  df_def = NULL,
  var_def = NULL,
  config_list = NULL,
  title = "ExPanD - Explore your data!",
  abstract = NULL,
  df_name = deparse(substitute(df)),
  long_def = TRUE,
  factor_cutoff = 10L,
  components = c(sample_selection = TRUE, subset_factor = TRUE, grouping = TRUE,
    bar_chart = TRUE, missing_values = TRUE, udvars = TRUE, descriptive_table = TRUE,
    histogram = TRUE, ext_obs = TRUE, by_group_bar_graph = TRUE, by_group_violin_graph =
    TRUE, trend_graph = TRUE, quantile_trend_graph = TRUE, by_group_trend_graph = TRUE,
    corrplot = TRUE, scatter_plot = TRUE, regression = TRUE),
  html_blocks = NULL,
  export_nb_option = FALSE,
  save_settings_option = TRUE,
  store_encrypted = FALSE,
  key_phrase = "What a wonderful key",
  debug = FALSE,
  ...
)
ExPanD(
  df = NULL,
  cs_id = NULL,
  ts_id = NULL,
  df_def = NULL,
  var_def = NULL,
  config_list = NULL,
  title = "ExPanD - Explore your data!",
  abstract = NULL,
  df_name = deparse(substitute(df)),
  long_def = TRUE,
  factor_cutoff = 10L,
  components = c(sample_selection = TRUE, subset_factor = TRUE, grouping = TRUE,
    bar_chart = TRUE, missing_values = TRUE, udvars = TRUE, descriptive_table = TRUE,
    histogram = TRUE, ext_obs = TRUE, by_group_bar_graph = TRUE, by_group_violin_graph =
    TRUE, trend_graph = TRUE, quantile_trend_graph = TRUE, by_group_trend_graph = TRUE,
    corrplot = TRUE, scatter_plot = TRUE, regression = TRUE),
  html_blocks = NULL,
  export_nb_option = FALSE,
  save_settings_option = TRUE,
  store_encrypted = FALSE,
  key_phrase = "What a wonderful key",
  debug = FALSE,
  ...
)

Arguments

`df`	A data frame or a list of data frames containing the data that you want to explore. If NULL, ExPanD will start up with a file upload dialog.
`cs_id`	A character vector containing the names of the variables that identify the cross-section in your data. If only `cs_id` and not `ts_id` is provided, the data is treated as cross-sectional, and only appropriate displays are included. `df_def` overrides if provided.
`ts_id`	A character scalar identifying the name of the variable that identifies the time series in your data. The according variable needs to be coercible to an ordered vector. If you provide a time series indicator that already is an ordered vector, ExPanD will verify that it has the same levels for each data frame and throw an error otherwise. If `cs_id` and `ts_id` are not provided either directly of by `df_def`, the data is treated as cross-sectional,observations are identified by row names and only appropriate displays are included. `df_def` overrides if provided.
`df_def`	An optional dataframe (or a list of dataframes) containing variable names, definitions and types. If NULL (the default) ExPanD uses `cs_id` and `ts_id` to identify the data structure and determines the variable types (factor, numeric, logical) based on the classes of the data. See the details section for further information.
`var_def`	If you specify here a dataframe containing variable names and variable definitions, ExPanD will use these on the provided sample(s) to create the analysis sample. See the details section for the structure of the `var_def` dataframe. If NULL (default) the sample(s) provided by `df` will be used as analysis sample(s) directly.
`config_list`	a list containing the startup configuration for ExPanD to display. Take a look at `data(ExPanD_config_russell_3000)` for the format. The easiest way to generate a config list is to customize the display within the app and then save the configuration locally.
`title`	the title to display in the shiny web app.
`abstract`	An introductory text to display in the shiny web app. Needs to be formatted as clean HTML.
`df_name`	A character string or a vector of character strings characterizing the dataframe(s) provided in `df` (will be used in the selection menu of the app)
`long_def`	If you set this to TRUE (default) and are providing a `var_def` then ExPanD will add the definitions of the used variables of the underlying dataframe to the definitions provided for the analysis sample to make these more informative to the user. If set to FALSE only the variable definitions provided in the `var_def` sample will be provided to the user.
`factor_cutoff`	ExPanD treats factors different from numerical variables. Factors are available for sub-sampling data and for certain plots. Each variable classified as such will be treated as a factor. In addition, ExPanD classifies all logical values and all numerical values with less or equal than `factor_cutoff` unique values as a factor.
`components`	A named logical vector indicating the components that you want ExPanD to generate and their order. See the function head of `ExpanD` for the list of available components. By default, all components are reported. You can also exclude selected components from the standard order by setting then to `FALSE`. In addition, you can include an arbitrary number of `html_block` components. Each block will render clean HTML code as contained in the `html_blocks` parameter below. This allows you to customize your ExPanD report.
`html_blocks`	A character vector containing the clean HTML code for each `html_block` that is included in `components`.
`export_nb_option`	Do you want to give the user the option to download your data and an R notebook containing code for the analyses that `ExPanD` displays? Defaults to `FALSE`.
`save_settings_option`	Do you want to give the user the option to save and/or load the settings of the ExPanD app to their local environment? Defaults to `TRUE`.
`store_encrypted`	Do you want the user-side saved config files to be encrypted? A security measure to avoid that users can inject arbitrary code in the config list. Probably a good idea when you are hosting sensitive data on a publicly available server.
`key_phrase`	The key phrase to use for encryption. Change this from the default if you want to encrypt the config files.
`debug`	Do you want ExPanD to echo some debug timing information to the console/log file and to store some diagnostics to the global environment? Probably not.
`...`	Additional parameters that are passed on to `runApp`.

Details

If you start ExPanD without any options, it will start with an upload dialog so that the user (e.g., you) can upload a data file for analysis. Supported formats are as provided by the rio package.

When you start ExPanD with a dataframe as the only parameter, it will assume the data to be cross-sectional and will use its row names as the cross-sectional identifier.

When you have panel data in long format, set the ts_id and cs_id parameters to identify the variables that determine the time series and cross-sectional dimensions.

If you provide variable definitions in df_def and/or var_def, ExPanD displays these as tooltips in the descriptive table of the ExPanD app. In this case, you need to identify the panel dimensions in the variable definitions (see below).

When you provide more than one data frame in df, make sure that all have the same variables and variable types defined. If not, ExPanD will throw an error. When you provide only one df_def for multiple data frames, df_def will be recycled.

When you provide var_def, ExPanD starts up in the "advanced mode". The advanced mode uses (a) base sample(s) (the one(s) you provide via df) and the variable definitions in var_def to generate an analysis sample based on the active base sample. In the advanced mode, the app user can delete variables from the analysis sample within the app.

A df_def or var_def dataframe can contain the following variables

"var_name": Required: The names of the variables that are provided by the base sample or are to be calculated for the analysis sample
"var_def": Required: For a var_def data frame, the code that is passed to the data frame (grouped by cross-sectional units) in calls to mutate as right hand side to calculate the respective variable. For a data_def data frame, a string describing the nature of the variable.
"type": Required: One of the strings "cs_id", "ts_id", "factor", "logical" or "numeric", indicating the type of the variable. Please note that at least one variable has to be assigned as a cross-sectional identifier ("cs_id") and exactly one variable that is coercible into an ordered factor has to be assigned as the time-series identifier ("ts_id").
"can_be_na": Optional: If included, then all variables with this value set to FALSE are required to be non missing in the data set. This reduces the number of observations. If missing, it defaults to being TRUE for all variables other than cs_id and ts_id.

Examples

## Not run: 
  ExPanD()

  # Use this if you want to read very large files via the file dialog
  options(shiny.maxRequestSize = 1024^3)
  ExPanD()

  # Explore cross-sectional data
  ExPanD(mtcars)

  # Include the option to download notebook code and data
  ExPanD(mtcars, export_nb_option = TRUE)

  # Use ExPanD on long-form panel data
  data(russell_3000)
  ExPanD(russell_3000, c("coid", "coname"), "period")
  ExPanD(russell_3000, df_def = russell_3000_data_def)
  ExPanD(russell_3000, df_def = russell_3000_data_def,
    components = c(ext_obs = T, descriptive_table = T, regression = T))
  ExPanD(russell_3000, df_def = russell_3000_data_def,
    components = c(missing_values = F, by_group_violin_graph = F))
  ExPanD(russell_3000, df_def = russell_3000_data_def,
    components = c(html_block = T, descriptive_table = T,
    html_block = T, regression = T),
    html_blocks = c(
    paste('<div class="col-sm-2"><h3>HTML Block 1</h3></div>',
    '<div class="col-sm-10">',
    "<p></p>This is a condensed variant of ExPanD with two additional HTML Blocks.",
    "</div>"),
    paste('<div class="col-sm-2"><h3>HTML Block 2</h3></div>',
    '<div class="col-sm-10">',
    "It contains only the descriptive table and the regression component.",
    "</div>")))
  data(ExPanD_config_russell_3000)
  ExPanD(df = russell_3000, df_def = russell_3000_data_def,
    config_list = ExPanD_config_russell_3000)
  exploratory_sample <- sample(nrow(russell_3000), round(0.5*nrow(russell_3000)))
  test_sample <- setdiff(1:nrow(russell_3000), exploratory_sample)
  ExPanD(df = list(russell_3000[exploratory_sample, ], russell_3000[test_sample, ]),
    df_def = russell_3000_data_def,
    df_name = c("Exploratory sample", "Test sample"))
  ExPanD(worldbank, df_def = worldbank_data_def, var_def = worldbank_var_def,
    config_list = ExPanD_config_worldbank)

    
## End(Not run)
## Not run: 
  ExPanD()

  # Use this if you want to read very large files via the file dialog
  options(shiny.maxRequestSize = 1024^3)
  ExPanD()

  # Explore cross-sectional data
  ExPanD(mtcars)

  # Include the option to download notebook code and data
  ExPanD(mtcars, export_nb_option = TRUE)

  # Use ExPanD on long-form panel data
  data(russell_3000)
  ExPanD(russell_3000, c("coid", "coname"), "period")
  ExPanD(russell_3000, df_def = russell_3000_data_def)
  ExPanD(russell_3000, df_def = russell_3000_data_def,
    components = c(ext_obs = T, descriptive_table = T, regression = T))
  ExPanD(russell_3000, df_def = russell_3000_data_def,
    components = c(missing_values = F, by_group_violin_graph = F))
  ExPanD(russell_3000, df_def = russell_3000_data_def,
    components = c(html_block = T, descriptive_table = T,
    html_block = T, regression = T),
    html_blocks = c(
    paste('<div class="col-sm-2"><h3>HTML Block 1</h3></div>',
    '<div class="col-sm-10">',
    "<p></p>This is a condensed variant of ExPanD with two additional HTML Blocks.",
    "</div>"),
    paste('<div class="col-sm-2"><h3>HTML Block 2</h3></div>',
    '<div class="col-sm-10">',
    "It contains only the descriptive table and the regression component.",
    "</div>")))
  data(ExPanD_config_russell_3000)
  ExPanD(df = russell_3000, df_def = russell_3000_data_def,
    config_list = ExPanD_config_russell_3000)
  exploratory_sample <- sample(nrow(russell_3000), round(0.5*nrow(russell_3000)))
  test_sample <- setdiff(1:nrow(russell_3000), exploratory_sample)
  ExPanD(df = list(russell_3000[exploratory_sample, ], russell_3000[test_sample, ]),
    df_def = russell_3000_data_def,
    df_name = c("Exploratory sample", "Test sample"))
  ExPanD(worldbank, df_def = worldbank_data_def, var_def = worldbank_var_def,
    config_list = ExPanD_config_worldbank)

    
## End(Not run)

Default Configuration to use with ExPanD and the Russell 3000 Data Set

Description

List to use as a list_config parameter when starting ExPanD.

Usage

data(ExPanD_config_russell_3000)
data(ExPanD_config_russell_3000)

Format

An object of class "list".

Examples

data(russell_3000)
data(russell_3000_data_def)
data(ExPanD_config_russell_3000)
## Not run: 
  ExPanD(russell_3000, df_def = russell_3000_data_def, config_list = ExPanD_config_russell_3000)

## End(Not run)
data(russell_3000)
data(russell_3000_data_def)
data(ExPanD_config_russell_3000)
## Not run: 
  ExPanD(russell_3000, df_def = russell_3000_data_def, config_list = ExPanD_config_russell_3000)

## End(Not run)

Default Configuration to Use with ExPanD and the `worldbank` Data Set

Description

List to use as a list_config parameter when starting ExPanD.

Usage

data(ExPanD_config_worldbank)
data(ExPanD_config_worldbank)

Format

An object of class "list".

Examples

data(worldbank)
data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank, df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)
data(worldbank)
data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank, df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)

ExPanDaR: Explore Panel Data with R

Description

ExPanDaR provides the code base for the ExPanD web app. ExPanD is a shiny based app supporting interactive exploratory data analysis.

Details

ExPanDaR has two main goals:

Enable users to assess the robustness of empirical evidence without providing them with access to the underlying data.
Provide a toolbox for researchers to explore panel data on the fly.

To learn more about ExPanDaR, start with the vignettes: browseVignettes(package = "ExPanDaR")

Prepares a by Group Bar Graph

Description

Reads a data frame containing a grouping factor and a numerical variable and plots a bar graph of a given statistic of the variable by the grouping factor.

Usage

prepare_by_group_bar_graph(
  df,
  by_var,
  var,
  stat_fun = mean,
  order_by_stat = FALSE,
  color = "red"
)
prepare_by_group_bar_graph(
  df,
  by_var,
  var,
  stat_fun = mean,
  order_by_stat = FALSE,
  color = "red"
)

Arguments

`df`	Data frame containing the grouping factor and the numerical variable to be plotted
`by_var`	a string containing the column name of the grouping factor
`var`	a string containing the column name of the numerical variable
`stat_fun`	a function to be called on the numerical variable. Will be called with `na.rm = TRUE` to ignore missing values
`order_by_stat`	a logical value indicating whether you want your bars to be ordered the value of the statistic (defaults to FALSE)
`color`	bar color

Value

A list containing two items:

"df": A data frame containing the statistics by group
"plot": The plot as returned by ggplot

Examples

data(russell_3000)
graph <- prepare_by_group_bar_graph(russell_3000, "sector", "ni_sales", median)
graph$plot
data(russell_3000)
graph <- prepare_by_group_bar_graph(russell_3000, "sector", "ni_sales", median)
graph$plot

Prepares a By Group Trend Graph

Description

Reads a data frame and line plots the selected variables (which need to be numeric) by group and an ordered factor (normally the time-series indicator).

Usage

prepare_by_group_trend_graph(
  df,
  ts_id,
  group_var,
  var,
  points = TRUE,
  error_bars = FALSE
)
prepare_by_group_trend_graph(
  df,
  ts_id,
  group_var,
  var,
  points = TRUE,
  error_bars = FALSE
)

Arguments

`df`	Data frame containing the ordered factor and a set of numerical variables to be plotted.
`ts_id`	a string containing the column name of the ordered factor (normally the time-series indicator).
`group_var`	a variable coercible into a factor to group the data on.
`var`	The name of the variable that you want to plot.
`points`	Do you want points to indicate the observations? Defaults to `TRUE`.
`error_bars`	Do you want error bars to be plotted? Defaults to `FALSE`.

Value

A list containing two items:

"df": A data frame containing the plotted means and standard errors by group
"plot: The plot as returned by ggplot

Examples

df <- worldbank
df$gdp_capita <- worldbank$NY.GDP.PCAP.KD
graph <- prepare_by_group_trend_graph(df, "year", "region", "gdp_capita")
graph$plot
df <- worldbank
df$gdp_capita <- worldbank$NY.GDP.PCAP.KD
graph <- prepare_by_group_trend_graph(df, "year", "region", "gdp_capita")
graph$plot

Prepares a by Group Violin Graph

Description

Reads a data frame containing a grouping factor and a numerical variable and plots a series of violin graphs by the grouping factor.

Usage

prepare_by_group_violin_graph(
  df,
  by_var,
  var,
  order_by_mean = FALSE,
  group_on_y = TRUE,
  ...
)
prepare_by_group_violin_graph(
  df,
  by_var,
  var,
  order_by_mean = FALSE,
  group_on_y = TRUE,
  ...
)

Arguments

`df`	Data frame containing the grouping factor and the numerical variable to be plotted
`by_var`	a string containing the column name of the grouping factor
`var`	a string containing the column name of the numerical variable
`order_by_mean`	a logical value indicating whether you want your violins to be ordered by group means (defaults to FALSE)
`group_on_y`	a logical value indicating whether you want your violins to be oriented horizontally (defaults to TRUE)
`...`	additional parameters that are passed to `geom_violin`

Value

The plot as returned by ggplot2

Examples

data(russell_3000)
df <- treat_outliers(russell_3000)
prepare_by_group_violin_graph(df, "sector", "nioa")
data(russell_3000)
df <- treat_outliers(russell_3000)
prepare_by_group_violin_graph(df, "sector", "nioa")

Prepares a Correlation Graph

Description

Reads a data frame and presents Pearson correlations above and Spearman correlations the diagonal using a fancy graph prepared by the package corrplot.

Usage

prepare_correlation_graph(df)
prepare_correlation_graph(df)

Arguments

`df`	Data frame containing at least two variables that are either numeric or logical and at least five observations.

Value

The function directly renders the graph as produced by corrplot. In addition, it returns a list containing three items:

"df_corr": A data frame containing the correlations
"df_prob": A data frame containing the p-values of the correlations
"df_n": A data frame containing the number of observations used for the correlations

Examples

prepare_correlation_graph(mtcars)
prepare_correlation_graph(mtcars)

Prepares a Correlation Table

Description

Reads a data frame and presents Pearson correlations above the diagonal and Spearman correlations below.

Usage

prepare_correlation_table(df, digits = 2, bold = 0.05, format = "html", ...)
prepare_correlation_table(df, digits = 2, bold = 0.05, format = "html", ...)

Arguments

`df`	Data frame containing at least two variables that are either numeric or logical and at least five observations.
`digits`	The number of digits that you want to report.
`bold`	Indicate the p-Value for for identifying significant correlations in bold print. Defaults to 0.05. If set to 0, no bold print is being used.
`format`	The format that you want `kable` to produce ("html" or "latex")
`...`	Additional parameters that are passed on to `kable`

Value

A list containing four items:

"df_corr": A data frame containing the correlations
"df_prob": A data frame containing the p-values of the correlations
"df_n": A data frame containing the number of observations used for the correlations
"kable_ret": The return value provided by kable containing the formatted table

Examples

t <- prepare_correlation_table(mtcars)
t$df_corr
t <- prepare_correlation_table(mtcars)
t$df_corr

Prepares a Table of Descriptive Statistics

Description

Reads a data frame and reports descriptive statistics (n, mean, standard deviation, minimum, first quartile, median, third quartile, maximum) for all members of the data frame that are either numeric or logical.

Usage

prepare_descriptive_table(
  df,
  digits = c(0, 3, 3, 3, 3, 3, 3, 3),
  format = "html"
)
prepare_descriptive_table(
  df,
  digits = c(0, 3, 3, 3, 3, 3, 3, 3),
  format = "html"
)

Arguments

`df`	Data frame containing at least one variable that is either numeric or logical and at least two observations.
`digits`	Number of decimal digits that you want to be displayed for each column. If you provide NA, then the column is omitted from the output.
`format`	character scalar that is handed over to `kable` (e.g., "html" or "latex").

Details

The digits parameter from prepare_descriptive_table() uses the default method of kable to format numbers, calling round. This implies that trailing zeroes are just omitted.

Value

A list containing two items.

"df": A data frame containing the descriptive table
"kable_ret": The return value provided by kable containing the formatted table

Examples

t <- prepare_descriptive_table(mtcars)
t$df
t <- prepare_descriptive_table(mtcars)
t$df

Prepares a Table Displaying Extreme Observations

Description

Reads a data frame, sorts it by the given variable and displays the top and bottom n observations.

Usage

prepare_ext_obs_table(
  df,
  n = 5,
  cs_id = NULL,
  ts_id = NULL,
  var = utils::tail(colnames(df[sapply(df, is.numeric) & (!colnames(df) %in% c(cs_id,
    ts_id))]), n = 1),
  ...
)
prepare_ext_obs_table(
  df,
  n = 5,
  cs_id = NULL,
  ts_id = NULL,
  var = utils::tail(colnames(df[sapply(df, is.numeric) & (!colnames(df) %in% c(cs_id,
    ts_id))]), n = 1),
  ...
)

Arguments

`df`	Data frame
`n`	The number of top/bottom observations that you want to report.
`cs_id`	The variable(s) identifying the cross-section in the data.
`ts_id`	The variable identifying the time-series in the data.
`var`	Variable to display. Defaults to the last numerical variable of the data frame.
`...`	Additional parameters that are passed to `kable`.

Details

When both cs_id and ts_id are omitted, all variables are tabulated. Otherwise, var is tabulated along with the identifiers. Infinite values in var are omitted. The default parameters for calling kable, are format = "html", digits = 3, format.args = list(big.mark = ','), row.names = FALSE.

Value

A list containing two items:

"df": A data frame containing the top/bottom n observations
"kable_ret": The return value provided by kable containing the formatted table

Examples

t <- prepare_ext_obs_table(russell_3000, n = 10,
                           cs_id = c("coid", "coname"),
                           ts_id = "period", var = "sales")
t$df
t <- prepare_ext_obs_table(russell_3000, n = 10,
                           cs_id = c("coid", "coname"),
                           ts_id = "period", var = "sales")
t$df

Prepares a Graph Displaying Missing Values in Panel Data

Description

Displays a heatmap of missing value frequency across the panel

Usage

prepare_missing_values_graph(df, ts_id, no_factors = FALSE, binary = FALSE)
prepare_missing_values_graph(df, ts_id, no_factors = FALSE, binary = FALSE)

Arguments

`df`	Data frame containing the data.
`ts_id`	A string containing the name of the variable indicating the time dimension. Needs to be coercible into an ordered factor.
`no_factors`	A logical value indicating whether you want to limit the plot to logical and numerical variables. Defaults to `FALSE`.
`binary`	If set to `TRUE`, the plot uses a binary scale only high-lightening whether values are missing or not. Defaults to `FALSE`.

Details

This was inspired by a blog post of Rense Nieuwenhuis. Thanks!

Value

A ggplot2 plot.

Examples

prepare_missing_values_graph(russell_3000, ts_id="period")
prepare_missing_values_graph(russell_3000, ts_id="period", binary = TRUE)
prepare_missing_values_graph(russell_3000, ts_id="period")
prepare_missing_values_graph(russell_3000, ts_id="period", binary = TRUE)

Prepares a Quantile Trend Graph

Description

Reads a data frame and plots the quantiles of the specified variable by an ordered factor (normally the time-series indicator)

Usage

prepare_quantile_trend_graph(
  df,
  ts_id,
  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  var = utils::tail(colnames(df[sapply(df, is.numeric) & colnames(df) != ts_id]), n =
    1),
  points = TRUE
)
prepare_quantile_trend_graph(
  df,
  ts_id,
  quantiles = c(0.05, 0.25, 0.5, 0.75, 0.95),
  var = utils::tail(colnames(df[sapply(df, is.numeric) & colnames(df) != ts_id]), n =
    1),
  points = TRUE
)

Arguments

`df`	Data frame containing the ordered factor and the numerical variable to be plotted
`ts_id`	a string containing the column name of the ordered factor (normally the time-series indicator)
`quantiles`	a numerical vector containing the quantiles that are to be plotted
`var`	a string containing the column name of the variable to be plotted. Defaults to the last numerical variable of the data frame that is not `ts_id`.
`points`	Do you want points to indicate the statistics? Defaults to `TRUE`.

Value

A list containing two items:

"df": A data frame containing the plotted quantiles
"plot": The plot as returned by ggplot

Examples

prepare_quantile_trend_graph(worldbank, "year", var = "SP.DYN.LE00.IN")$plot +
  ggplot2::ylab("Life expectancy at birth world-wide")
df <- data.frame(year = floor(stats::time(datasets::EuStockMarkets)),
                 DAX = datasets::EuStockMarkets[,"DAX"])
graph <- prepare_quantile_trend_graph(df, "year", c(0.05, 0.25, 0.5, 0.75, 0.95))
graph$plot
prepare_quantile_trend_graph(worldbank, "year", var = "SP.DYN.LE00.IN")$plot +
  ggplot2::ylab("Life expectancy at birth world-wide")
df <- data.frame(year = floor(stats::time(datasets::EuStockMarkets)),
                 DAX = datasets::EuStockMarkets[,"DAX"])
graph <- prepare_quantile_trend_graph(df, "year", c(0.05, 0.25, 0.5, 0.75, 0.95))
graph$plot

Prepares a Regression Table

Description

Builds a regression table based on a set of user-specified models or a single model and a partitioning variable.

Usage

prepare_regression_table(
  df,
  dvs,
  idvs,
  feffects = rep("", length(dvs)),
  clusters = rep("", length(dvs)),
  models = rep("auto", length(dvs)),
  byvar = "",
  format = "html"
)
prepare_regression_table(
  df,
  dvs,
  idvs,
  feffects = rep("", length(dvs)),
  clusters = rep("", length(dvs)),
  models = rep("auto", length(dvs)),
  byvar = "",
  format = "html"
)

Arguments

`df`	Data frame containing the data to estimate the models on.
`dvs`	A character vector containing the variable names for the dependent variable(s).
`idvs`	A character vector or a a list of character vectors containing the variable names of the independent variables.
`feffects`	A character vector or a a list of character vectors containing the variable names of the fixed effects.
`clusters`	A character vector or a a list of character vectors containing the variable names of the cluster variables.
`models`	A character vector indicating the model types to be estimated ('ols', 'logit', or 'auto')
`byvar`	A factorial variable to estimate the model on (only possible if only one model is being estimated).
`format`	A character scalar that is passed on `stargazer` as `type` to determine the presentation format ("html", "text", or "latex").

Details

This is a wrapper function calling the stargazer package. Depending on whether the dependent variable is numeric, logical or a factor with two levels, the models are estimated using felm (for numeric dependent variables) or glm (with family = binomial(link="logit")) (for two-level factors or logical variables). You can override this behavior by specifying the model with the models parameter. Multinomial logit models are not supported. For glm, clustered standard errors are estimated using cluster.vcov. For felm, it is being run with cmethod='reghdfe' to make clustered standard errors consistent with Stata's 'reghdfe'. If run with byvar, only levels that have more observations than coefficients are estimated.

Value

A list containing two items

"models": A list containing the model results and by values if appropriate
"table": The output of stargazer containing the table

Examples

df <- data.frame(year = as.factor(floor(stats::time(datasets::EuStockMarkets))),
                 datasets::EuStockMarkets)
dvs = c("DAX", "SMI", "CAC", "FTSE")
idvs = list(c("SMI", "CAC", "FTSE"),
            c("DAX", "CAC", "FTSE"),
            c("SMI", "DAX", "FTSE"),
            c("SMI", "CAC", "DAX"))
feffects = list("year", "year", "year", "year")
clusters = list("year", "year", "year", "year")
t <- prepare_regression_table(df, dvs, idvs, feffects, clusters, format = "text")
t$table
t <- prepare_regression_table(df, "DAX", c("SMI", "CAC", "FTSE"), byvar="year", format = "text")
print(t$table)
df <- data.frame(year = as.factor(floor(stats::time(datasets::EuStockMarkets))),
                 datasets::EuStockMarkets)
dvs = c("DAX", "SMI", "CAC", "FTSE")
idvs = list(c("SMI", "CAC", "FTSE"),
            c("DAX", "CAC", "FTSE"),
            c("SMI", "DAX", "FTSE"),
            c("SMI", "CAC", "DAX"))
feffects = list("year", "year", "year", "year")
clusters = list("year", "year", "year", "year")
t <- prepare_regression_table(df, dvs, idvs, feffects, clusters, format = "text")
t$table
t <- prepare_regression_table(df, "DAX", c("SMI", "CAC", "FTSE"), byvar="year", format = "text")
print(t$table)

Prepares a Scatter Plot

Description

Reads a data frame and prepares a scatter plot.

Usage

prepare_scatter_plot(
  df,
  x,
  y,
  color = "",
  size = "",
  loess = 0,
  alpha = min(1, 1/((1 + (max(0, log(nrow(df)) - log(100))))))
)
prepare_scatter_plot(
  df,
  x,
  y,
  color = "",
  size = "",
  loess = 0,
  alpha = min(1, 1/((1 + (max(0, log(nrow(df)) - log(100))))))
)

Arguments

`df`	Data frame containing the data
`x`	a string containing the column name of the x variable
`y`	a string containing the column name of the y variable
`color`	a string containing the column name of the variable providing the color aesthetic (can be numerical or a factor)
`size`	a string containing the column name of the variable providing the size aesthetic
`loess`	a numerical scalar 0 No loess curve 1 loess curve with equal weights 2 loess curve with weights based on `size` variable
`alpha`	The alpha value to be used. If missing, it calculates a default based on the sample size

Value

the plot as returned by ggplot

Examples

df <- data.frame(year = floor(stats::time(datasets::EuStockMarkets)),
                 datasets::EuStockMarkets[, c("DAX", "FTSE")])
prepare_scatter_plot(df, x="DAX", y="FTSE", color="year")
df <- data.frame(year = floor(stats::time(datasets::EuStockMarkets)),
                 datasets::EuStockMarkets[, c("DAX", "FTSE")])
prepare_scatter_plot(df, x="DAX", y="FTSE", color="year")

Prepares a Trend Graph

Description

Reads a data frame and line plots all variables (which need to be numeric) by an ordered factor (normally the time-series indicator).

Usage

prepare_trend_graph(
  df,
  ts_id,
  var = colnames(df[sapply(df, is.numeric) & colnames(df) != ts_id])
)
prepare_trend_graph(
  df,
  ts_id,
  var = colnames(df[sapply(df, is.numeric) & colnames(df) != ts_id])
)

Arguments

`df`	Data frame containing the ordered factor and a set of numerical variables to be plotted
`ts_id`	a string containing the column name of the ordered factor (normally the time-series indicator)
`var`	a character vector containing the column names of the variables that should be plotted. Defaults to all numeric variables of the data frame besides the one indicated by `ts_id`.

Value

A list containing two items:

"df": A data frame containing the plotted means and standard errors
"plot: The plot as returned by ggplot

Examples

df <- data.frame(year = floor(time(EuStockMarkets)), EuStockMarkets)
graph <- prepare_trend_graph(df, "year")
graph$plot
df <- data.frame(year = floor(time(EuStockMarkets)), EuStockMarkets)
graph <- prepare_trend_graph(df, "year")
graph$plot

Annual Financial Accounting and Stock Return Data for a Sample of Russell 3000 Firms (2013-2016)

Description

Data collected from Google Finance and Yahoo finance using the package tidyquant.

Usage

data(russell_3000)
data(russell_3000)

Format

An object of class "data.frame".

Source

Has been collected using the tidyquant::tq_get function family in Summer 2017. The code to generate this data is available in the github repository of this package. As the Google Finance API providing financial statement data is currently unavailable, the data cannot be replicated by running the code. Use in scientific studies is not advised without prior cleaning/checking.

Examples

data(russell_3000)
prepare_missing_values_graph(russell_3000, ts_id = "period")

data(russell_3000)
prepare_missing_values_graph(russell_3000, ts_id = "period")

Data Definitions for `russell_3000` Data Set

Description

A data frame containing variable definitions for the russell_3000 data set. The data definitions can be passed to ExPanD via the df_def parameter.

Usage

data(russell_3000_data_def)
data(russell_3000_data_def)

Format

An object of class "data.frame".

Details

Data definitions are provided by the package maintainer and are somewhat superficial to make them both, short and informative. User discretion is advised when using this data outside of its didactic purpose.

Examples

data(russell_3000)
data(russell_3000_data_def)
data(ExPanD_config_russell_3000)
## Not run: 
  ExPanD(russell_3000, df_def = russell_3000_data_def, config_list = ExPanD_config_russell_3000)

## End(Not run)
data(russell_3000)
data(russell_3000_data_def)
data(ExPanD_config_russell_3000)
## Not run: 
  ExPanD(russell_3000, df_def = russell_3000_data_def, config_list = ExPanD_config_russell_3000)

## End(Not run)

Treats Outliers in Numerical Data

Description

Treats numerical outliers either by winsorizing or by truncating.

Usage

treat_outliers(x, percentile = 0.01, truncate = FALSE, by = NULL, ...)
treat_outliers(x, percentile = 0.01, truncate = FALSE, by = NULL, ...)

Arguments

`x`	Data that is coercible into a numeric vector or matrix. If it is a data frame then all numerical variables of the data frame are coerced into a matrix.
`percentile`	A numeric scalar. The percentile below which observations are considered to be outliers. Is treated symmetrical so that `c(percentile, 1-percentile)` are used as boundaries. Defaults to 0.01 and needs to be > 0 and < 0.5.
`truncate`	A logical scalar. If TRUE then data are truncated (i.e., set to NA if out of bounds). Defaults to FALSE.
`by`	NULL or either a factor vector or a character string identifying a factor variable in the data frame provided by x. The factor indicated by 'by' is being used to identify groups by which the outlier treatment is applied. Defaults to NULL (no grouping). If provided, the resulting vector must not contain NAs and needs to be such so that `length(byvec) == nrows(as.matrix(x))`.
`...`	Additional parameters forwarded to quantile (notably, `type`)

Details

All members of the numerical matrix are checked for finiteness and are set to NA if they are not finite. NAs are removed when calculating the outlier cut-offs.

Value

A numeric vector or matrix containing the outlier-treated x. if a data frame was provided in x, a data frame with its numeric variables replaced by their outlier-treated values.

Examples

treat_outliers(seq(1:100), 0.05)
treat_outliers(seq(1:100), truncate = TRUE, 0.05)

# When you like the percentiles calculated like STATA's summary or pctile:
treat_outliers(seq(1:100), 0.05, type = 2)

df <- data.frame(a = seq(1:1000), b = rnorm(1000), c = sample(LETTERS[1:5], 1000, replace=TRUE))
winsorized_df <- treat_outliers(df)
summary(df)
summary(winsorized_df)

winsorized_df <- treat_outliers(df, 0.05, by="c")
by(df, df$c, summary)
by(winsorized_df, df$c, summary)

hist(treat_outliers(rnorm(1000)), breaks=100)
treat_outliers(seq(1:100), 0.05)
treat_outliers(seq(1:100), truncate = TRUE, 0.05)

# When you like the percentiles calculated like STATA's summary or pctile:
treat_outliers(seq(1:100), 0.05, type = 2)

df <- data.frame(a = seq(1:1000), b = rnorm(1000), c = sample(LETTERS[1:5], 1000, replace=TRUE))
winsorized_df <- treat_outliers(df)
summary(df)
summary(winsorized_df)

winsorized_df <- treat_outliers(df, 0.05, by="c")
by(df, df$c, summary)
by(winsorized_df, df$c, summary)

hist(treat_outliers(rnorm(1000)), breaks=100)

A Snapshot of Macroeconomic Data as Provided by the World Bank API (1960 - 2018)

Description

Data collected from the World Bank API using the package wbstats.

Usage

data(worldbank)
data(worldbank)

Format

An object of class "data.frame".

Source

Has been collected using the wbstats::wb() function from the World Bank API in Dec 2020. The code to generate this data is available in the github repository of this package. Use in scientific studies is not advised without prior cleaning/checking.

Examples

data(worldbank)
prepare_missing_values_graph(worldbank, ts_id = "year")

data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank, df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)

data(worldbank)
prepare_missing_values_graph(worldbank, ts_id = "year")

data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank, df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)

Data Definitions for `worldbank` Data Set

Description

A data frame containing variable definitions for the worldbank data set. The data definitions can be passed to ExPanD via the df_def parameter.

Usage

data(worldbank_data_def)
data(worldbank_data_def)

Format

An object of class "data.frame".

Details

Data definitions are as provided by the World Bank API and the code to generate them is available in the github repository of this package.

Examples

data(worldbank)
data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank,df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)
data(worldbank)
data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank,df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)

Variable Definitions to Construct an Analysis Sample Based on the `worldbank` Data Set

Description

A data frame containing variable definitions that can be passed to ExPanD via the var_def parameter.

Usage

data(worldbank_var_def)
data(worldbank_var_def)

Format

An object of class "data.frame".

Examples

data(worldbank)
data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank, df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)
data(worldbank)
data(worldbank_data_def)
data(worldbank_var_def)
data(ExPanD_config_worldbank)
## Not run: 
  ExPanD(worldbank, df_def = worldbank_data_def,
    var_def = worldbank_var_def, config_list = ExPanD_config_worldbank)

## End(Not run)

Package 'ExPanDaR'

Help Index

Explore Your Data (ExPanD)

Description

Usage

Arguments

Details

Examples

Default Configuration to use with ExPanD and the Russell 3000 Data Set

Description

Usage

Format

Examples

Default Configuration to Use with ExPanD and the worldbank Data Set

Description

Usage

Format

Examples

ExPanDaR: Explore Panel Data with R

Description

Details

Prepares a by Group Bar Graph

Description

Usage

Arguments

Value

Examples

Prepares a By Group Trend Graph

Description

Usage

Arguments

Value

Examples

Prepares a by Group Violin Graph

Description

Usage

Arguments

Value

Examples

Prepares a Correlation Graph

Description

Usage

Arguments

Value

Examples

Prepares a Correlation Table

Description

Usage

Arguments

Value

Examples

Prepares a Table of Descriptive Statistics

Description

Usage

Arguments

Details

Value

Examples

Prepares a Table Displaying Extreme Observations

Description

Usage

Arguments

Details

Value

Examples

Prepares a Graph Displaying Missing Values in Panel Data

Description

Usage

Arguments

Details

Value

Examples

Prepares a Quantile Trend Graph

Description

Usage

Arguments

Value

Examples

Prepares a Regression Table

Description

Default Configuration to Use with ExPanD and the `worldbank` Data Set

Data Definitions for `russell_3000` Data Set

Data Definitions for `worldbank` Data Set

Variable Definitions to Construct an Analysis Sample Based on the `worldbank` Data Set