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Partner Progress Report

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The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.

Create the PPR dataset

  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")
  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line
  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.

#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13
  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===

Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Partner Progress Report
Build Status
The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.
Create the PPR dataset
  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")

  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line

  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.
#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13

  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===
Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.
About
Monitoring of partner progress towards their COP targets
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Readme
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Stars
1 star
Watchers
3 watching
Forks
5 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Partner Progress Report
Build Status
The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.
Create the PPR dataset
  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")

  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line

  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.
#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13

  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===
Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.
About
Monitoring of partner progress towards their COP targets
Resources
Readme
Activity
Stars
1 star
Watchers
3 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Partner Progress Report
Build Status
The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.
Create the PPR dataset
  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")

  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line

  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.
#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13

  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===
Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.
About
Monitoring of partner progress towards their COP targets
Resources
Readme
Activity
Stars
1 star
Watchers
3 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Partner Progress Report
Build Status
The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.
Create the PPR dataset
  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")

  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line

  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.
#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13

  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===
Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.
About
Monitoring of partner progress towards their COP targets
Resources
Readme
Activity
Stars
1 star
Watchers
3 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Partner Progress Report
Build Status
The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.
Create the PPR dataset
  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")

  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line

  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.
#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13

  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===
Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Partner Progress Report
Build Status
The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.
Create the PPR dataset
  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")

  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line

  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.
#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13

  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===
Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.
About
Monitoring of partner progress towards their COP targets
Resources
Readme
Activity
Stars
1 star
Watchers
3 watching
Forks
5 forks
Report repository
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Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Partner Progress Report
Build Status
The package is used to create the data output that underlies the ICPI Partner Progress Reports (PPRs) which are generated twice a quarter (initial and post-cleaning). Historically, the data was created in Stata and required slight tweaks every quarter. Starting with FY18Q1, the scripts were converted into a automated package. The output is then used to populate an Excel template via VBA and the reports are then posted to PEPFAR Sharepoint.
Create the PPR dataset
  1. Install the package
#install
install.packages("devtools")
devtools::install_github("achafetz/PartnerProgress")
#load package
library("genPPR")

  1. Setup folder - Clone this repository to your local machine to have most of the folder structure and some data.
#setup any missing folders
initialize_fldr(projectname = "PartnerProgress", #don't change this line
projectpath"~/GitHub", #change to match where you want the project folder created locally
"RawData", "Documents", "R", "ExcelOutput", "Reports") #don't change this line

  1. Download the current MER Structured PSNUxIM dataset from the PEPFAR Panorama. You will need to convert the txt file to an rds file prior to running this package (use read_msd() from ICPI/ICPIutilities). You should save the rds file in the RawData folder.
#convert MSD from .txt to .rds
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::read_msd("~/Data/ICPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1")

If you want to run this with an inprocess dataset from DATIM Genie, you will need to run the match_msd() function from ICPIUtilities to have the correct file name and saved as an rds.
#ALTERNATIVE: IF USING IN PROCESS DATA FROM DATIM
devtools::install_github("ICPI/ICPIutilities")
ICPIutilities::match_msd("~/Downloads/PEPFAR-Data-Genie-OUByIMs-2018-11-13.zip")
PEPFAR-Data-Genie-OUByIMs-2018-11-13

  1. Create the PPR dataset - run the main script, genPPR() from the package to create the underlying dataset that feeds into the template
#create global and OU output
filepath <- "~/ICPI/Data/CPI_MER_Structured_Dataset_OU_IM_FY17-18_20180515_v1_1.rds"
genPPR(filepath, folderpath_output = "~/ExcelOutput")
#alternatively, you can just create a specific OU output
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput",
output_subset_type = "ou", "Kenya")
#or generate "global" files that contain one or many implementing partners
genPPR(filepath, output_global = FALSE, output_ctry_all = FALSE, df_return = FALSE, folderpath_output = "~/ExcelOutput", output_subset_type = "mechid", c("18045", "17097")
)
#or just the global output
genPPR(filepath, output_ctry_all = FALSE, folderpath_output = "~/ExcelOutput",)

===
Disclaimer: The findings, interpretation, and conclusions expressed herein are those of the authors and do not necessarily reflect the views of United States Agency for International Development, Centers for Disease Control and Prevention, Department of State, Department of Defense, Peace Corps, or the United States Government. All errors remain our own.
About
Monitoring of partner progress towards their COP targets
Resources
Readme
Activity
Stars
1 star
Watchers
3 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages