| Type: | Package |
| Title: | Analyse 'EuFMDiS' Output Files via a Shiny App |
| Version: | 0.1.0 |
| Author: | Ian Kopacka [aut, cre], Tatiana Marschik [aut], Elena Sassu [aut], Annette Nigsch [aut], Food and Agriculture Organization of the United Nations (FAO) [cph, fnd] |
| Maintainer: | Ian Kopacka <ian.kopacka@ages.at> |
| Description: | Analyses 'EuFMDiS' output files in a Shiny App. The distributions of relevant output parameters are described in form of tables (quantiles) and plots. The App is called using eufmdis.adapt::run_adapt(). |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| Imports: | graphics, stats, utils, rlang, grDevices, magrittr, dplyr, tibble, tidyselect, ggplot2, shiny, shinydashboard, shinyWidgets, htmltools, DT |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2023-09-11 12:42:35 UTC; ian30 |
| Repository: | CRAN |
| Date/Publication: | 2023-09-12 06:10:09 UTC |
Check if list items are empty
Description
The function argument is a list of data frames that are required for some subsequent analysis. If any of the data frames are empty (i.e. the data have not been uploaded to the app), a message is returned as HTML code listing the names of the required data frames (= names of list items).
Usage
check_availability(list_data)
Arguments
list_data |
Named list of data frames |
Details
In the ADAPT app, individual analyses can only be performed if the
necessary output files are uploaded. If certain files are not uploaded,
the app produces empty data frames. In the app, the function
check_availability() is used with the necessary data frames to check if they
have been uploaded and displays a message of the form "To generate this
analysis, please upload the following reports: x, y" otherwise.
Value
Possibly empty HTML text, listing names of required data frames.
Author(s)
Ian Kopacka
Clean up a vector of column names
Description
The function takes a vector of column names as an argument and returns a cleaned up version of it.
Usage
cleanup_names(x)
Arguments
x |
A character vector |
Details
The following changes are made: - names are converted to lower case - dots are replaced by underscores - underscores in the beginning and end of a string are removed - multiple underscores are replaced by a single one
Value
A character vector
Author(s)
Ian Kopacka
Compute p value for freedom from disease sample
Description
Compute the probability of drawing no positives in a sample of n items from a Population of N containing n_dis positives.
Usage
compute_p_value(N, n, n_dis)
Arguments
N |
Integer; size of the population |
n |
Integer; size of the sample |
n_dis |
Integer; number of positives in the population |
Details
The probability is computed using the hypergeometric distribution. This
function is used in compute_sample_size.
Value
Returns the probability of not finding any positives in the sample as a numeric between 0 and 1
Author(s)
Ian Kopacka
See Also
Compute sample size for freedom from disease
Description
Compute sample size for a one stage freedom from disease survey for given Population size, design prevalence and accuracy, assuming a perfect diagnostic test.
Usage
compute_sample_size(N, prev, accuracy)
Arguments
N |
Integer containing the Size of the population |
prev |
Numeric between 0 and 1; design prevalence |
accuracy |
Numeric between 0 and 1; accuracy of the survey (i.e. detection probability) |
Details
The function finds the optimal sample size using a bisection method.
Value
Sample size (integer).
Author(s)
Ian Kopacka
Compute sample size for freedom from disease (vectorised)
Description
Compute sample size for a one stage freedom from disease survey for given
Population size, design prevalence and accuracy, assuming a perfect
diagnostic test. Vectorised version of compute_sample_size.
Usage
compute_sample_size_vectorised(N, prev, accuracy)
Arguments
N |
Integer vector containing the population sizes |
prev |
Numeric between 0 and 1; design prevalence |
accuracy |
Numeric between 0 and 1; accuracy of the survey (i.e. detection probability) |
Details
Uses vapply to vectorise compute_sample_size over the
population size N. prev and accuracy must be scalars.
For the sake of efficiency, the sample size is only computed once for every
different value of N, even if they apper multiple times in the vector.
Value
Sample size (integer vector).
Author(s)
Ian Kopacka
See Also
Data analysis for diagnostic samples during the control phase
Description
Function to perform the data analysis, necessary for the analysis of the diagnostic samples during the control phase
Usage
create_diag_control(
herd_summary,
farm_summary,
par_diag_control_ffd_prev,
par_diag_control_ffd_certainty,
par_diag_control_edta,
par_diag_control_serum,
par_diag_control_bulk_milk,
par_diag_control_lesions_smrum,
par_diag_control_lesions_pigs,
par_diag_control_lesions_cattle,
rel_cols_farm_summary_dc,
rel_cols_herd_summary_dc,
herd_types_dairy,
herd_types_small_ruminants,
herd_types_pigs,
herd_types_cattle
)
Arguments
herd_summary |
Data frame; EuFMDIS output file "Herd summary" |
farm_summary |
Data frame; EuFMDIS output file "Farm summary" |
par_diag_control_ffd_prev |
numeric between 0 and 100; design prevalence for the computation of the sample size according to freedom from disease |
par_diag_control_ffd_certainty |
numeric between 0 and 100; desired accuracy for the computation of the sample size according to freedom from disease |
par_diag_control_edta |
positive integer; Number of blood samples (EDTA) per symptomatic suspect holding |
par_diag_control_serum |
positive integer; Number of blood samples (serum) per symptomatic suspect holding |
par_diag_control_bulk_milk |
positive integer; Number of bulk milk samples per dairy farm |
par_diag_control_lesions_smrum |
positive integer; Number of acute lesion samples for small ruminants per farm |
par_diag_control_lesions_pigs |
positive integer; Number of acute lesion samples for pigs per farm |
par_diag_control_lesions_cattle |
positive integer; Number of acute lesion samples for cattle per farm |
rel_cols_farm_summary_dc |
character vector of column names of the
data frame |
rel_cols_herd_summary_dc |
character vector of column names of the
data frame |
herd_types_dairy |
character vector listing the different herd types that are associated with dairy herds |
herd_types_small_ruminants |
character vector listing the different herd types that are associated with small ruminant herds |
herd_types_pigs |
character vector listing the different herd types that are associated with pig herds |
herd_types_cattle |
character vector listing the different herd types that are associated with cattle herds |
Details
This function is used internally to prepare the input data for the output (tables and plots) in the sub menu "Diagnostic tests control phase" of the ADAPT App.
Value
Returns an aggregated data frame with one line per simulation run. The data frame contains auxilliary variables needed to appriximate the number of diagnostic samples required during the control phase as well as the estimated values for number of bulk milk samples (n_bulk_milk), acute lesions (n_acute_lesion), swabs (n_swabs), blood samples for edta analysis (n_blood_edta) and serum analysis (n_blood_serum).
Reshape wide data frame with combined column names
Description
The function identifies columns whose name contains a combination of two categorical characteristics (e.g. farm type and output parameter), splits them up and reshapes the data to a long format.
Usage
create_long_data_frame(dat, categories, name_categories, starts_with = FALSE)
Arguments
dat |
Data frame with combined column names (e.g. |
categories |
Character vector of possible values of categories in the
column names (e.g. |
name_categories |
Character; name of the newly created column that contains the categories in the long data frame |
starts_with |
Logical; Flag indicating how the combined columns should
be identified. |
Details
The function looks for combined columns based on the category names provided
in the argument categories. Two modes of searching are possible:
starts_with = FALSE (=default) looks for all columns whose name
contains the strings in categories, whereas starts_with = TRUE
only includes columns whose name starts with the string. Relevant
combinations of #' values that are not found in the wide data frame are
filled with NA in the ling data frame.
Value
A long data frame where the combined columns have been split up
Author(s)
Ian Kopacka
Inverse of Cumulative Sum
Description
Computes the inverse of the cumsum function
Usage
discumulate_data(value_cum)
Arguments
value_cum |
numeric vector; usually the result of cumulating values. |
Value
A vector of the same length as value_cum
Author(s)
Ian Kopacka
Safe wrapper for DT::formatCurrency
Wrapper for DT::formatCurrency that returns NULL when the input table
is NULL (instead of throwing an error).
Description
Safe wrapper for DT::formatCurrency
Wrapper for DT::formatCurrency that returns NULL when the input table
is NULL (instead of throwing an error).
Usage
format_numbers_DT(x, ...)
Arguments
x |
A table object created from |
... |
other arguments passed to |
Value
Behaves the same output as DT::formatCurrency except when
x is NULL. Then NULL is returned and no error is thrown.
Author(s)
Ian Kopacka
Import data from csv file in Shiny App
Description
The function is used in the ADAPT app to import data from uploaded csv files into a data frame. Only relevant columns are returned, the column names are matched and unified, so that data produced by different versions of the EuFMDiS software can be used.
Usage
import_data_file(
pattern,
names_files,
paths_files,
def_columns,
transpose = FALSE
)
Arguments
pattern |
Character string containing a regular expression to identify the correct file by its name. |
names_files |
Character vector of file names as they were uploaded (= file name on the original file system from which they were uploaded) |
paths_files |
Character vector of file names + absolute paths of the
files in the local hard drive to which they were uploaded. Each entry of
|
def_columns |
Data frame of meta information containing the possible
column names in the different versions of the EuFMDiS output files. The data
frame must contain columns |
transpose |
Logical flag (default = FALSE). COntrols whether the data frame should be transposed prior to any data manipulation/extraction. |
Value
Data frame containing the columns defined in def_columns for
the relevant Datensatz according to the argument pattern.
Plot bar chart with error bars
Description
Creates a bar chart with error bars using ggplot2::geom_col.
Usage
plot_barchart(x)
Arguments
x |
Data frame with columns |
Value
Returns an object of class ggplot and prints it to the
graphics device.
Plot bar chart with error bars an Euro notation
Description
Creates a bar chart with error bars using ggplot2::geom_col.
Usage
plot_barchart_euros(x, country)
Arguments
x |
Data frame with columns |
country |
Character to be displayed in the Plot title. |
Value
Returns an object of class ggplot and prints it to the
graphics device.
See Also
Plot the distribution of a variable
Description
Creates a histogram of the value along with a horizontal boxplot above it to show the distribution of a variable.
Usage
plot_distribution(x, parameter, main = "")
Arguments
x |
A numeric vector |
parameter |
Character to use as label of the x-axis |
main |
(optional) character to use as plot title |
Value
No return value. Creates a plot.
Author(s)
Ian Kopacka
Plot graph of a time series with daily error margin
Description
Creates a line plot with a shaded polygon showing daily error margins (uncertainty ranges)
Usage
plot_time_series(x, parameter, main = "")
Arguments
x |
Data frame with columns |
parameter |
Character to use as label of the y-axis |
main |
(optional) character to use as plot title |
Value
No return value. Creates a plot.
Author(s)
Ian Kopacka
Run ADAPT Shiny App
Description
This function runs the Shiny App "ADAPT" to analyse 'EuFMDiS' output files.
Usage
run_adapt()
Details
Upload the relevant csv output files via the "Upload files" dialog to trigger the analysis.
Value
no return value; starts a Shiny app
Author(s)
Ian Kopacka
Examples
if (interactive()) {
run_adapt()
}