The objective of MSNAdashboard is to offer a well-organized repository, making it simpler and less time-consuming to design the dashboard.
You can download the repo from github
- Clean dataset: It should be located in the directory data-raw/cleaned_data and include two columns: one for the population group information and another for stratification details.
- Validated analysis: Must have key_index and stat column. It must be validated from HQ. It needs to be placed within the directory `data-raw/validated_analysis/`.
- Kobo tool: It needs to be placed within the directory data-raw/.
- Sample frame: It needs to be placed within the directory data-raw/.
- Admin three level shape file: It needs to be placed within the directory data-raw/admin_boundary/.
Please put the admin boundaries under data-raw/admin_boundary. You can download the ocha adim boundaries by using following code.
admin_boundary <- MSNAdashboard::download_hdx_adm(country_code = "lbn",admin_level = 2,df_index = 2) ## Downloading OCHA-COD admin 2 data for LebanonYou can create the Data Analysis plan write_dap_for_dashboard()
function.
list_of_dataset <- list(
HH_data = HH_data,
INDV_data = INDV_data
)
write_dap_for_dashboard(list_of_dataset = list_of_dataset,
kobo_survey = survey,
column_name_for_populaion_group = "pop_group")The out should look like-
openxlsx::read.xlsx("data-raw/DAP_for_dashboard.xlsx") |> head()
#> sector indicator main_variable
#> 1 <NA> <NA> today
#> 2 Demographic What is the name of the population group? pop_group
#> 3 <NA> <NA> data_collection_mode
#> 4 <NA> <NA> governorate
#> 5 <NA> <NA> district
#> 6 <NA> <NA> point_accessible
#> analysis_level label::English
#> 1 HH_data today
#> 2 HH_data What is the name of the population group?
#> 3 HH_data Is this interview conducted in-person or over the phone?
#> 4 HH_data In which governorate is the household located?
#> 5 HH_data In which district is the household located?
#> 6 HH_data Is the point accessible?
#> label::Arabic
#> 1 اليوم
#> 2 ما هي الفئة السكانية؟
#> 3 هل أجريت هذه المقابلة وجهاً لوجه أم عبر الهاتف؟
#> 4 في أي محافظة تقع الأسرة؟
#> 5 في أي قضاء تقع الأسرة؟
#> 6 هل المنطقة يمكن الوصول إليها؟Please choose the sector from the drop down menu and enter the indicator
label in the indicator column.
The function check_validated_analysis() will assess whether the
validated analysis contains any potential errors or not.
validated_analysis |> check_validated_analysis()To join the map data with MSNA data, the strata name from the dataset
should be same as admin boundary name in the OCHA boundary. You can use
write_mismatched_strata() to compare the the admin names.
write_mismatched_strata(ocha_cod_level_3 = cntry_adm_3,
cod_admin1_name = "admin1Name",
cod_admin2_name = "admin2Name",
cod_admin3_name = "admin3Name",
validated_analysis = base_file,
admin_level_in_validated_analysis = "admin_level",
hh_data=df,
pop_group_in_validated_analysis = "pop_group" )This will create a file called admin_mismatch_fix.xlsx under data-raw
folder. The file will have a list with mismatched admin names. The user
should fill the ocha_cod_name from the drop down menu.
Once you fill the output file from write_mismatched_strata function
then we will need to apply the changes to either dataset or to OCHA
cod.However as MSNA data mostly contain multiple loop so its better to
rename the ocha boundaries. You can apply
write_renamed_ocha_admin_name so write the renamed OCHA boundary as
shapefile to use in the dashboard.
MSNAdashboard::write_renamed_ocha_admin_name(ocha_cod_level_3 = cntry_adm_3,
cod_admin1_name = "admin1Name",
cod_admin2_name = "admin2Name",
cod_admin3_name = "admin3Name",
mistached_list = mistached_list,
output_path = "data-raw/admin_boundary/OCHA_renamed_adm3.shp")The function will create a shapefile under data-raw folder named
OCHA_renamed_adm3. At this stage you can dissolve the admin3 to
admin2,admin1,and admin0 by following code:
OCHA_renamed_adm3 <- sf::st_read( "data-raw/admin_boundary/OCHA_renamed_adm3.shp")
## admin 2
OCHA_renamed_adm2 <- OCHA_renamed_adm3 |> dplyr::group_by(admin0Name,admin1Name,admin2Name) |> dplyr::summarise()
|> rmapshaper::ms_simplify()
sf::st_write(OCHA_renamed_adm2,"data-raw/admin_boundary/OCHA_renamed_adm2.shp")
## admin 1
OCHA_renamed_adm1 <- OCHA_renamed_adm2 |> dplyr::group_by(admin0Name,admin1Name) |> dplyr::summarise()
|> rmapshaper::ms_simplify()
sf::st_write(OCHA_renamed_adm1,"data-raw/admin_boundary/OCHA_renamed_adm1.shp")
## admin 0
OCHA_renamed_adm0 <- OCHA_renamed_adm1 |> dplyr::group_by(admin0Name) |> dplyr::summarise()
|> rmapshaper::ms_simplify()
sf::st_write(OCHA_renamed_adm0,"data-raw/admin_boundary/OCHA_renamed_adm0.shp")create_text_input() is a function that generates an Excel file, which
should be completed with detailed information such as an overview,
methodology, limitations, and contact person details. Additionally,
please note that the function requires an improved version and a
comprehensive README for proper usage and understanding.
After filling and updating all the files under the data-raw/
directory, the subsequent step involves checking and preparing the file
to be used for the dashboard. Before running app.R, it is essential to
execute data_preparation.R. Both scripts contain comments, so their
functionalities are adequately explained within the code.
In case you want to customize the dashboard, you can follow the following steps -
To customized the dashboard, you first need to clone the repository from the github.
Once you are done with the repo, now you need to understand the
structure of repo. The repository is created using golem package.
The golem package is a framework for building and deploying
production-ready Shiny applications in R. Shiny is a web application
framework for R that allows users to create interactive web applications
using R code. However, while Shiny is great for creating interactive
prototypes, it is not always well-suited for building production-ready
applications with robust performance, security, and scalability.
This is wheregolem comes in. Golem provides a framework for
organizing and structuring your Shiny code to create scalable and
maintainable applications. It includes a set of best practices and
conventions for building Shiny applications, such as separating the UI
(user interface) and server logic into separate files, using reactive
programming to minimize data processing, and leveraging package
management to simplify dependency management.
In addition to these best practices, golem also provides a set of
tools for testing, debugging, and deploying Shiny applications. For
example, it includes a command-line interface for creating and managing
application templates, as well as tools for managing application
configuration, logging, and error handling.
Overall, the golem package is a powerful tool for building robust,
scalable, and maintainable Shiny applications in R.
The Golem package in R is designed to facilitate the creation of production-ready Shiny applications. It is organized into several subdirectories, each of which serves a specific purpose:
-
R/:This directory contains the R code for your application. You should place your application logic, including any functions you write, in this directory. -
data/:This directory is used to store any data files that your application needs to function. You can also store any other resources that your application requires in this directory. -
www/:This directory is used to store any static files that your application requires, such as images, stylesheets, or JavaScript files. -
inst/:This directory is used to store any additional files that your application needs to function, such as configuration files or documentation. -
tests/:This directory is used to store any test files for your application. You can write unit tests for your application logic in this directory. -
man/:This directory contains the documentation for your application. You should document your functions and other objects in this directory using the Roxygen2 syntax. -
NAMESPACE:This file specifies the package’s exported functions and other objects. -
DESCRIPTION:This file contains metadata about your package, including its name, version, and dependencies. -
README.md:This file contains information about your package, including how to install and use it
#> .
#> ├── R
#> │ ├── app_config.R
#> │ ├── app_server.R
#> │ ├── app_ui.R
#> │ ├── run_app.R
#> │ ├── golem_utils_server.R
#> │ ├── golem_utils_ui.R
#> │ ├── mod_Graph.R
#> │ ├── mod_introduction.R
#> │ ├── mod_map.R
#> │ ├── global.R
#> │ ├── creating_base_file.R
#> │ ├── create_sample_frame.R
#> │ ├── utils-pipe.R
#> │ ├── create_overview_map.R
#> │ ├── download_hdx_admin.R
#> │ ├── create_dap_for_dashboard.R
#> │ ├── identifying_mismatch_strata_name_with_ocha_cod.R
#> │ ├── check_validated_analysis.R
#> │ ├── rename_cod.R
#> │ └── create_text_file.R
#> ├── MSNAdashboard.Rproj
#> ├── dev
#> │ ├── 01_start.R
#> │ ├── 02_dev.R
#> │ ├── 03_deploy.R
#> │ └── run_dev.R
#> ├── inst
#> │ ├── app
#> │ │ └── www
#> │ │ ├── favicon.ico
#> │ │ └── reach_logo.png
#> │ └── golem-config.yml
#> ├── man
#> │ ├── creating_base_file.Rd
#> │ ├── create_dashboard.Rd
#> │ ├── create_overview_map.Rd
#> │ ├── create_sample_frame_file.Rd
#> │ ├── pipe.Rd
#> │ └── figures
#> │ └── README-pressure-1.png
#> ├── DESCRIPTION
#> ├── NAMESPACE
#> ├── tests
#> │ ├── testthat
#> │ │ ├── test-golem_utils_server.R
#> │ │ └── test-golem_utils_ui.R
#> │ └── testthat.R
#> ├── data-raw
#> │ ├── admin_boundary
#> │ │ ├── OCHA_renamed_adm0.dbf
#> │ │ ├── OCHA_renamed_adm0.prj
#> │ │ ├── OCHA_renamed_adm0.shp
#> │ │ ├── OCHA_renamed_adm0.shx
#> │ │ ├── OCHA_renamed_adm1.dbf
#> │ │ ├── OCHA_renamed_adm1.prj
#> │ │ ├── OCHA_renamed_adm1.shp
#> │ │ ├── OCHA_renamed_adm1.shx
#> │ │ ├── OCHA_renamed_adm2.dbf
#> │ │ ├── OCHA_renamed_adm2.prj
#> │ │ ├── OCHA_renamed_adm2.shp
#> │ │ ├── OCHA_renamed_adm2.shx
#> │ │ ├── OCHA_renamed_adm3.dbf
#> │ │ ├── OCHA_renamed_adm3.prj
#> │ │ ├── OCHA_renamed_adm3.shp
#> │ │ └── OCHA_renamed_adm3.shx
#> │ ├── validated_analysis
#> │ │ └── Validated_analysis.csv
#> │ ├── cleaned_data
#> │ │ └── cleaned_data.xlsx
#> │ ├── text_file.xlsx
#> │ ├── kobo_tool.xlsx
#> │ ├── sample_frame.csv
#> │ ├── DAP_for_dashboard.xlsx
#> │ └── admin_mismatch_fix.xlsx
#> ├── style.css
#> ├── app.R
#> ├── data
#> │ ├── base_file.rda
#> │ ├── text_file.rda
#> │ ├── overview.rda
#> │ ├── methodology.rda
#> │ ├── limitation.rda
#> │ ├── contact_name.rda
#> │ ├── contact.rda
#> │ ├── admin_three_sf.rda
#> │ ├── admin_two_sf.rda
#> │ ├── admin_one_sf.rda
#> │ ├── admin_zero_sf.rda
#> │ ├── sample_frame_for_dashboard.rda
#> │ └── overview_map.rda
#> ├── LICENSE
#> ├── LICENSE.md
#> ├── data_preparation.R
#> ├── README.Rmd
#> └── README.md
With in the R/ folder you will scripts started with mod_ which I
will be calling module. Each module represent a tab in the dashboard.
Within the scripts, each module have two parts, 1. user interface (ui)
2. server.
If you want to remove any module/tab from the dashboard, you can easily
do it from app_ui.R and app_server.R. All you have to do is delete
the module from both app_ui and app_server
If you want to add a new tab then you will need to create a module first
and then add the module to the app_ui.R and app_server.R
## to create the module
golem::add_module(name = "name_of_module1", with_test = TRUE) # Name of the moduleYou can change a specific tab without creating error in out tab by
editing existing module. just make the changes in mod_name_of_module1
and then you are all set. [Make sure you have made changes in both ui
and server]
If you made any changes in the dashboard, please make sure you have run
the tests before deploying the app. It will make sure the the changes
didn’t change anything in other tab or in the app. You can hit
Ctrl + Shift + T to run the tests
It is also important to run the CDM check. If your CDM check fails then
the app may runs in local but in the shinyserver it might not run. You
can hit Ctrl + Shift + E to run the CMD checks
MSNAdashboard::create_dashboard(country = "Iraq",
assessment_name = "McNA",
year = 2024
)