ExPanD.RdA 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.
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, store_encrypted = FALSE, key_phrase = "What a wonderful key", debug = FALSE, ... )
| 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 |
| 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 |
| df_def | An optional dataframe (or a list of dataframes) containing
variable names, definitions and types. If NULL (the default) ExPanD
uses |
| 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 |
| config_list | a list containing the startup configuration for ExPanD to
display. Take a look at |
| 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 |
| long_def | If you set this to TRUE (default) and are providing a
|
| 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 |
| components | A named logical vector indicating the components that you want
ExPanD to generate and their order. See the function head of |
| html_blocks | A character vector containing the clean HTML code for each
|
| 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 |
| 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
|
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
Required: The names of the variables that are provided by the base sample or are to be calculated for the analysis sample
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.
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").
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.
# 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) # }