Package 'glamr'

Description

negate '

Usage

... %ni% NA

Description

‘clean_countries' is used to adjust Natural Earth country names to match PEPFAR’s Operatingunit / country. This function can also be used to shorten OU/Country names by setting the parameter short to TRUE.

Usage

clean_countries(.data, colname = "admin", language = "en", short = TRUE)

Arguments

Value

Cleaned DataFrame

Examples

## Not run: 
 library(sf)
 library(rnaturalearth)
 library(glamr)

 spdf <- ne_countries(type = "sovereignty", scale = 110, returnclass = "sf") %>%
   sf::st_drop_geometry() %>%
   dplyr::select(sovereignt, admin, name, adm0_a3) %>%
   glamr::clean_countries(colname = "admin") 
## End(Not run)

Description

This function is primarily useful for removing any apostrophe from the filename since this will get rejected by Google Drive, but also includes features like replacing spaces with an underscore, converting to all lowercase, and adding a date prefix or suffix.

Usage

clean_filename(
  x,
  rm_apostrophe = TRUE,
  rp_space = FALSE,
  mk_lower = FALSE,
  add_date = NULL
)

Arguments

Value

clean filename

Examples

## Not run: 
file <- "Submission_Coted'Ivoire_data.csv"
new_file <- clean_filename(file, rm_apostrophe = TRUE, add_date = 'prefix') 
## End(Not run)

Description

Clean and connect parts of text together

Usage

connect_text(txt, connections = "[^a-zA-Z0-9]", connector = "_")

Arguments

Value

cleaned text

Examples

## Not run: 
connect_text("THIS - is complex (very bad)")
## End(Not run)

Description

Convert Date to FY Quarter/Period

Usage

convert_date_to_qtr(date)

Arguments

Value

vector of FY period, eg FY22Q1

See Also

Other period: convert_datim_pd_to_qtr(), convert_fy_qtr_to_pd(), convert_qtr_to_date()

Examples

## Not run: 
dates <- c("2021-10-01", "2021-11-15")
convert_date_to_qtr(dates) 
## End(Not run)

Description

Convert a period a DATIM API in the format of FY22Q1 or FY22 (for targets/cumulative). This function is built into 'extract_datim'.

Usage

convert_datim_pd_to_qtr(df, pd_col = "Period")

Arguments

Value

Convert periods from long CY dates to PEPFAR standard FY

See Also

[set_datim()] to store DATIM authentication; [load_secrets()] to store DATIM authentication

Other period: convert_date_to_qtr(), convert_fy_qtr_to_pd(), convert_qtr_to_date()

Examples

## Not run: 
df <- tibble::tibble(Periods = c("October - December 2023",
                                 "January - March 2024",
                                 "October 2023 to September 2024"))
df <- convert_datim_pd_to_qtr(df) 
## End(Not run)

Description

Using 'gophr::reshape_msd()' often creates the a perfable long dataset when working with the MSD, but may restrict the user to certain default during the process. Creating a clean period (eg FY22Q1) requires a number of lines of code to get right, so this function provides stopgap when you are working with a long dataset that has a fiscal year and quarter column and desire a period variable.

Usage

convert_fy_qtr_to_pd(df, fy_ind = "fiscal_year", qtr_ind = "qtr")

Arguments

Value

united period column combining and cleaning fiscal year and quarter

See Also

Other period: convert_date_to_qtr(), convert_datim_pd_to_qtr(), convert_qtr_to_date()

Examples

df_summary <- df_msd %>%
       filter(indicator == "TX_CURR",
              standardizeddisaggregate == "Total Numerator",
              operatingunit == "Jupiter") %>%
       group_by(mech_code, fiscal_year) %>%
       summarise(across(starts_with("qtr"), sum, na.rm = TRUE),
                 .groups = "drop")

df_summary <- df_summary %>%
       pivot_longer(-c(mech_code, fiscal_year), names_to = "qtrs")

df_summary <- convert_fy_qtr_to_pd(df_summary)

Description

Convert a period (from reshape_msd()) in the format of FY22Q1 or FY22 (for targets/cumulative).

Usage

convert_qtr_to_date(period, type = "start")

Arguments

Value

date vector

See Also

Other period: convert_date_to_qtr(), convert_datim_pd_to_qtr(), convert_fy_qtr_to_pd()

Examples

## Not run: 
df <- read_msd(path)
df <- df %>%
  filter(df,
         operatingunit == "Jupiter",
         indicator == "TX_NEW",
         standarddisaggregate == "Total Numerator") %>%
  group_by(fiscal_year, primepartner) %>%
  summarize(across(start_with("qtr"), sum, na.rm = TRUE)) %>%
  ungroup()
df <- df %>%
  reshape_msd() %>%
  mutate(date = convert_qtr_to_date(period), .after = period) 
## End(Not run)

Description

Get formatted current date

Usage

curr_date(fmt = "%Y-%m-%d")

Arguments

Examples

## Not run: 
 curr_date()
 curr_date(fmt = "%m/%d/%Y")

## End(Not run)

Description

To setup/store, run 'glamr::set_datim()'.

Usage

datim_pwd()

Value

access DATIM password from keyring

See Also

Other authentication: datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
load_secrets()
ou_table <- datim_outable(datim_user(), datim_pwd()) 
## End(Not run)

Description

To setup/store, run 'glamr::set_datim()'.

Usage

datim_user()

Value

access DATIM username from keyring

See Also

Other authentication: datim_pwd(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
load_secrets()
ou_table <- datim_outable(datim_user(), datim_pwd()) 
## End(Not run)

Description

'r lifecycle::badge("experimental")'

'export_drivefile() is designed to move files googledrive'

Usage

export_drivefile(
  filename,
  to_drive,
  to_folder = NULL,
  add_folder = TRUE,
  overwrite = TRUE,
  ...
)

Arguments

Value

Googledrive file(s) id(s)

Examples

## Not run: 
 library(glamr)

 list.files("./Graphics", "NIGERIA", full.names = TRUE) %>%
      export_drivefile(filename = .,
                       to_drive = "<path-id>",
                       to_folder = "FY99Q4/VL Suppression",
                       add_folder = TRUE)


## End(Not run)

Description

Extract data from excel link

Usage

extract_excel_data(src_page, link_id, file_sheet = 2, file_ext = "xlsx")

Arguments

Value

File content as a data frame

Description

Extract table data from web page

Usage

extract_tbl_data(src_url, tbl_id)

Arguments

Value

A data frme

Description

Extract text

Usage

extract_text(txt, limits = "()")

Arguments

Value

text within limits

Examples

## Not run: 
 extract_text(txt = "Saint Mary Hopital (SMH)")
 extract_text(txt = "TDB [Placeholder - New Mechanism]") 
## End(Not run)

Description

'folder_setup()' creates an organizational structure that is common across OHA/SI projects so every analyst knows what to expect and where when picking up a new project or one cloned from a co-worker. This function can be used as a stand alone function but primarily serves 'si_setup()'

Usage

folder_setup(
  folder_list = list("Data", "Images", "Scripts", "AI", "Dataout", "Data_public", "GIS",
    "Documents", "Graphics", "markdown")
)

Arguments

Details

The standard setup provides the following folders for these uses: * Data - where any raw/input data (**xlsx/csv/rds**) specific to the project are stored * Dataout - where any intermediary or final data (**xlsx/csv/rds**) are output as a product of your code * Data_public - where all public data lives * * Scripts - where all the code (**R/py**) are stored (if there is a local order, make sure to add prefixes to each script, e.g. 00_init.R, 01_data-access.R, 02_data-munging.R, ...) * Images - any **png/jpeg** visual outputs from your code * Graphics - any **svg/pdf** visual outputs that will be edited in vector graphics editor, eg Adobe Illustrator or Inkscape * AI - any **ai** files or other files from a graphics editor (exported pngs products will be stored in Images) * GIS - any **shp** files or other GIS releated inputs * Documents - any **docx/xlsx/pptx/pdf** documents that relate to the process or are final outputs * markdown - exported **md** files from a knitr report

See Also

Other project setup: setup_gitignore(), setup_readme(), si_setup()

Examples

## Not run: 
#standard
  folder_setup()
#specific
  fldrs <- c("Data", "Tableau", "AI")
  folder_setup(fldrs)

## End(Not run)

Description

Get id of googledrive folder

Usage

gdrive_folder(name, path, add = FALSE, ...)

Arguments

Value

Googledrive folder item it or NULL for non existing folder

Note

This function will create a new folder if add is set to TRUE

Examples

## Not run: 
 library(glamr)

 gdrive_folder("Test-Folder", "ID-adfdfsdfdfdfs")

## End(Not run)

Description

Google API provides extra metadata stored as a list in the dribble returned, eg modified time, permissions, owner, etc.

Usage

gdrive_metadata(df, show_details = FALSE)

Arguments

Value

adds extra meta data to data frame

Examples

## Not run: 
library(googledrive)
drive_auth()
fldr <- as_id("<google-folder-id>")
drive_ls(fldr) %>% gdrive_metadata()

## End(Not run)

Description

This function returns a unique reference id that can be used in scripts and cited in associated plots to help find the associated code on GitHub.

Usage

gen_ref_id()

Details

A best practice would be to store the character string output as an object called 'ref_id' in the top matter of your script. If you run 'gophr::get_metadata' after this is stored as an object, it will automatically store this for use in 'metadata$caption'.

Value

8 character string

Examples

## Not run: 
library(glamr)
library(gophr)
library(ggplot2)
library(glue)
#create a reference id to include in a plot
gen_ref_id()
ref_id <- "1e64716c"
get_metadata()
#plot with ref id
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() +
  labs(caption = glue("Source: Edgar Anderson's Iris Data | Ref id: {ref_id}"))
Or
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point() +
  labs(caption = metadata$caption)

## End(Not run)

Description

Get account details

Usage

get_account(name)

Arguments

Value

key / value pair as list containing details of the account (invisible)

Note

Inspired by 'grabr::lazy_secrets()'

See Also

Other authentication: datim_pwd(), datim_user(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
get_account(name = 's3')
## End(Not run)

Description

Get value of service key name

Usage

get_key(service, name)

Arguments

Value

key value

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
get_key(service = '<service-name>', name = '<key-name>')
## End(Not run)

Description

Get Service Keys

Usage

get_keys(service)

Arguments

Value

list of key names for active services

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
get_keys('<service-name>')
## End(Not run)

Description

'get_s3key' retrieves your S3 keys using the 'keyring' package. Set name to 'access' for 'Access Key', 'name' to 'secret' for 'Secret Access Key'

Usage

get_s3key(name = "access")

Arguments

Value

stored key

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
get_s3key(name = "access") 
## End(Not run)

Description

Get Services

Usage

get_services()

Value

list of active services

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
get_services()
## End(Not run)

Description

'import_drivefile' is a wrapper around 'googledrive::drive_download', useful for pulling multiple files from a given Google Drive folder (with a Google ID provided) to download by default to the Data folder of a project.

Usage

import_drivefile(drive_folder, filename, folderpath = "Data", zip = TRUE)

Arguments

Value

stores file from Google Drive as a zipped file

Examples

## Not run: 
library(googledrive)
googledrive::drive_auth()
fldr <- "Spp-y8DYsdRTrzDqUmK4fX5v"
import_drivefile(fldr, "TestFile.csv")

## End(Not run)

Description

Test if service is stored in credential manager

Usage

is_stored(service = c("datim", "email", "pano", "s3", "pdap"))

Arguments

Value

A boolean

Description

'load_secrets' should be set at the beginning of a script to store your email and DATIM user name under Options for the current session. This allows analysts to more easily share their scripts without having to manually update or remove use names.

Usage

load_secrets(service = c("email", "datim", "pano", "s3", "pdap"))

Arguments

Details

To initially store your credentials, you will first need to run 'set_email()', 'set_datim()', 'set_pano()', and/or 'set_key' (for s3)

'load_secrets' utilizes 'keyring' package to access the OS credentials store. Storing in a centralized, secure location allows analysts to other analysts code without having to manually change user names/email address to access DATIM or Google Drive.

Value

stores Google, DATIM, PEFPFAR Panorama, s3, and PDAP credentials in session

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
load_secrets()
ou_table <- datim_outable(datim_user(), datim_pwd()) 
## End(Not run)

Description

Lookup official Country name

Usage

lookup_country(country, language = "en")

Arguments

Value

cleaned country name

Examples

{
 cntry <- "Cote d'Ivoire"

 name <- lookup_country(cntry)

 name
}

Description

Open directory explorer or files

Usage

open_path(path)

Arguments

Note

This assumes default applications are set for various file type

Examples

## Not run: 
 dir_name <- "C:/Users/<username>/Downloads"
 open_path(dir_name)

 file_name <- "C:/Users/<username>/Downloads/test.csv"
 open_path(file_name)

## End(Not run)

Description

To setup/store, run 'glamr::set_pano()'.

Usage

pano_pwd()

Value

access Panorama password from keyring

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Description

To setup/store, run 'glamr::set_pano()'.

Usage

pano_user()

Value

access Panorama username from keyring

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Description

When working with PDAP, you will need to access data from either the read or write buckets and need the credentials to do so. This function stores the Access Key associated with your account, 'Sys.getenv("AWS_ACCESS_KEY_ID")'. To use locally, the user will need to store ‘set_key(’pdap', 'access')', which securely stores this information with 'keyring' (we can only write, not read from a local machine).

Usage

pdap_access()

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
library(grabr)
s3_upload(upload_file_path,
          bucket = pdap_bucket("write"),
          prefix = "usaid/",
          access_key = pdap_access(),
          secret_key = pdap_secret())

#identify path to dataset uploaded
  path_wrkbnch <- s3_objects(bucket = pdap_bucket("write"),
                             prefix = "usaid/",
                             access_key = pdap_access(),
                             secret_key = pdap_secret()) %>%
    filter(str_detect(key, "Moz")) %>%
    pull(key)

#read
df_msd <- s3read_using(read_psd,
                       bucket = pdap_bucket("write"),
                       object = path_wrkbnch) 
## End(Not run)

Description

When working with PDAP, you will need to access data from either the read or write buckets. The read bucket ("S3_READ") is where PEPFAR Systems stores the MSDs and the write bucket ("S3_WRITE") is where users can upload files (USAID users will have access and write to the "usaid/" sub bucket).

Usage

pdap_bucket(type = c("read", "write"))

Arguments

Details

When access from PDAP Posit Workbench, the function will access the system environment variables 'Sys.getenv("S3_READ")' or 'Sys.getenv("S3_WRITE")' where as it accessing locally, the user will need to store the read bucket location with 'set_key()', which securely stores this information with 'keyring' (we can only write, not read from a local machine).

Value

character string of AWS bucket location

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
library(grabr)
s3_upload(upload_file_path,
          bucket = pdap_bucket("write"),
          prefix = "usaid/",
          access_key = pdap_access(),
          secret_key = pdap_secret())

#identify path to dataset uploaded
  path_wrkbnch <- s3_objects(bucket = pdap_bucket("write"),
                             prefix = "usaid/",
                             access_key = pdap_access(),
                             secret_key = pdap_secret()) %>%
    filter(str_detect(key, "Moz")) %>%
    pull(key)

#read
df_msd <- s3read_using(read_psd,
                       bucket = pdap_bucket("write"),
                       object = path_wrkbnch) 
## End(Not run)

Description

When working with PDAP, you will need to access data from either the read or write buckets and need the credentials to do so. This function stores the Secret Access Key associated with your account, 'Sys.getenv("AWS_SECRET_ACCESS_KEY")'. To use locally, the user will need to store ‘set_key(’pdap', 'secret')', which securely stores this information with 'keyring' (we can only write, not read from a local machine).

Usage

pdap_secret()

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), set_account(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
library(grabr)
s3_upload(upload_file_path,
          bucket = pdap_bucket("write"),
          prefix = "usaid/",
          access_key = pdap_access(),
          secret_key = pdap_secret())

#identify path to dataset uploaded
  path_wrkbnch <- s3_objects(bucket = pdap_bucket("write"),
                             prefix = "usaid/",
                             access_key = pdap_access(),
                             secret_key = pdap_secret()) %>%
    filter(str_detect(key, "Moz")) %>%
    pull(key)

#read
df_msd <- s3read_using(read_psd,
                       bucket = pdap_bucket("write"),
                       object = path_wrkbnch) 
## End(Not run)

Description

A dataset PEPFAR Operating Units and Countries along with their ISO codes. This is a useful dataset for having a full set of PEPFAR countries or to align ISO codes with external datasources. Pulled from DATIM and the FSD.

Usage

pepfar_country_list

Format

A data frame with 55 rows and 7 variables:

operatingunit

PEPFAR Operating Unit (countries + 3 regional programs)

operatingunit_iso

PEPFAR Operating Unit ISO-3

operatingunit_uid

PEPFAR Operating Unit unique id from DATIM

country

PEPFAR Country Name

country_iso

PEPFAR Country Name ISO-3

country_uid

PEPFAR Country unique id from DATIM

pepfar_accel

Is the country prioritized in the 8+1+1 group defined by SGAC?

Details

The list of PEPFAR acceleration countries was defined by Amb. Nkengasong during a DP's retreat for the [Zaidi 2023-06-08 re: Moving countries to green!]. These are countries "where enhanced attention and focus might help 'move the dial' on achieving and sustaining the UNAIDS 95-95-95 targets by 2025"

Source

https://final.datim.org/

Description

A dataset PEPFAR Operating Units and Countries along with their ISO codes, alternative names from other sources. This is a useful dataset designed to help with data cleaning / matching from different sources.

Usage

pepfar_country_xwalk

Format

A data frame with 55 rows and 29 variables:

iso3c

ISO-3 Code

continent

Continent name

region

World Region Name

region23

Alternative name for World Region

un_region_name

US Region Name

iso_name_en

ISO Country name in English

iso_name_fr

SO Country name in French

country_name_de

Regular Expression of Country name in German

country_name_de_regex

Country name in German

country_name_en

Country name in English

country_name_en_regex

Regular Expression of Country name in English

country_name_fr

Regular Expression of Country name in French

country_name_fr_regex

Country name in French

country_name_it

Regular Expression of Country name in Italian

country_name_it_regex

Country name in Italian

un_name_ar

UN Name in Arabic

un_name_en

UN Name in English

un_name_es

UN Name in Spanish

un_name_fr

UN Name in French

un_name_ru

UN Name in ru

un_name_zh

UN Name in zh

sovereignt

rnaturalearth sovereign territory name

admin

rnaturalearth administrative unit name

name

rnaturalearth country name

operatingunit

PEPFAR Operating Unit (countries + 3 regional programs)

operatingunit_iso

PEPFAR Operating Unit ISO-3

operatingunit_uid

PEPFAR Operating Unit unique id from DATIM

country

PEPFAR Country Name

country_uid

PEPFAR Country unique id from DATIM

Source

https://final.datim.org/ https://www.naturalearthdata.com/ https://vincentarelbundock.github.io/countrycode/

Description

A dataset the dates for the release of the MER Structured Dataset

Usage

pepfar_data_calendar

Format

A data frame with 16 rows and 6 variables:

fiscal_year

fiscal year, start = October

quarter

fiscal quarter, integer 1-4

type

data release type, initial or clean

entry_open

date entry begins into DATIM

entry_close

date DATIM is closed and data are frozen

msd_release

date the MSD is released on PEPFAR Panorma

Source

https://datim.zendesk.com/hc/en-us/articles/115001940503-PEPFAR-Data-Calendar

Description

'prinf' is a wrapper around 'print' that returns all rows rather than just the first 10 by default.

Usage

prinf(df, ...)

Arguments

Value

prints out all rows rather than default 10 rows.

Examples

## Not run: 
df_geo %>% prinf() 
## End(Not run)

Description

'return_latest' checks for a pattern in a folder and provides the most recent file bases on the time the file was modified

Usage

return_latest(folderpath, pattern, quiet = FALSE, ...)

Arguments

Value

a vector of the full filepath for the most recent version of a file stub

Examples

## Not run: 
file_stub <- "MER_Structured_Datasets_OU_IM_FY18-20"
filepath <- return_latest("Data", file_stub)
df <- read_rds(filepath) 
## End(Not run)

Description

Create / Update account

Usage

set_account(name, keys = c("username", "password"), update = FALSE)

Arguments

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_datim(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
set_account(name = 's3', keys = c("access", "secret"))
## End(Not run)

Description

'set_datim' stores your DATIM credentials email using the 'keyring' package. This will only need to done once. After running 'set_datim(user)', you will be promoted to enter your password through the RStudio API which will then store the username and password in your OS credential store using 'keyring'.

Usage

set_datim(datim_username)

Arguments

Details

The 'keyring' package utilized the OS credentials store. Storing in a centralized, secure location allows analysts to other analysts code without having to manually change user names/email address to access DATIM or Google Drive.

After 'set_datim' has been run once, an analyst can set 'load_secrets' at the beginning of a script, storing their email and DATIM username under Options for the current session.

Value

stores USAID email in using keyring

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_email(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
set_datim("rshah") 
## End(Not run)

Description

'set_email' stores your USAID email using the 'keyring' package. This will only need to run once.

Usage

set_email(usaid_email)

Arguments

Details

The 'keyring' package utilized the OS credentials store. Storing in a centralized, secure location allows analysts to other analysts code without having to manually change user names/email address to access DATIM or Google Drive.

After 'set_email' has been run once, an analyst can set 'load_secrets' at the beginning of a script, storing their email and DATIM username under Options for the current session.

This function also stores the email locally in your .Rprofile, allowing to be used automatically as the default for 'googledrive::drive_auth()' and 'googlesheets4::gs4_auth()'

Value

stores USAID email using keyring and .Rprofile

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_key(), set_pano(), set_s3keys()

Examples

## Not run: 
set_email("[email protected]") 
## End(Not run)

Description

Set value for service name

Usage

set_key(service, name)

Arguments

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_pano(), set_s3keys()

Examples

## Not run: 
set_key(service = '<service-name>', name = '<key-name>')
## End(Not run)

Description

'set_pano' stores your PEPFAR Panoram credentials email using the 'keyring' package. This will only need to done once. After running 'set_pano(user)', you will be promoted to enter your password through the RStudio API which will then store the username and password in your OS credential store using 'keyring'.

Usage

set_pano(pano_username)

Arguments

Details

The 'keyring' package utilized the OS credentials store. Storing in a centralized, secure location allows analysts to other analysts code without having to manually change user names/email address to access DATIM, Panorama, or Google Drive.

After 'set_pano' has been run once, an analyst can set 'load_secrets' at the beginning of a script, storing their PEPFAR Panorama credentials under Options for the current session.

Value

stores Panorama username and password in using keyring

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_s3keys()

Examples

## Not run: 
set_pano("[email protected]") 
## End(Not run)

Description

'set_paths' stores store local folder paths where larger data are stored centrally and outside of projects. Accessed through use of 'si_path()'.

Usage

set_paths(
  folderpath_msd,
  folderpath_datim,
  folderpath_raster,
  folderpath_vector,
  folderpath_downloads
)

Arguments

Value

code chunk to paste into .Rprofile

See Also

Other stored paths: si_path()

Examples

## Not run: 
set_paths <- set_path(folderpath_msd = "C:/Users/rshah/Documents/Data") 
## End(Not run)

Description

'set_s3keys' stores your s3 keys using the 'keyring' package. This will only need to done once. After running 'set_s3keys(access, secret)', RStudio API which will then store the keys in your OS credential store using 'keyring'.

Usage

set_s3keys(access, secret)

Arguments

Value

stored access key

See Also

Other authentication: datim_pwd(), datim_user(), get_account(), get_key(), get_keys(), get_s3key(), get_services(), load_secrets(), pano_pwd(), pano_user(), pdap_access(), pdap_bucket(), pdap_secret(), set_account(), set_datim(), set_email(), set_key(), set_pano()

Examples

## Not run: 
set_s3access("ABDCEDFF", "MLIZD998SD") 
## End(Not run)

Description

'setup_gitignore()' creates a .gitignore file (or appends to an existing) one) the standard file types/folders that should not be published to GitHub due to the sensitive nature of PEPFAR data. This function can be used as a stand alone function but primarily serves 'si_setup()'.

Usage

setup_gitignore()

Value

adds standard gitignore, plus specific ignores

See Also

Other project setup: folder_setup(), setup_readme(), si_setup()

Examples

## Not run: 
  setup_gitignore() 
## End(Not run)

Description

'setup_readme()' establishes a README.md with the standard USAID disclaimer (or appends to one that currently exists). This function can be used as a stand alone function but primarily serves 'si_setup()'.

Usage

setup_readme(add_disclaimer = TRUE)

Arguments

Value

adds/appends disclaimer to README

See Also

Other project setup: folder_setup(), setup_gitignore(), si_setup()

Examples

## Not run: 
#standard (appends dislaimer if README exists)
  setup_readme() 
## End(Not run)

Description

'si_path' accesses folder paths stored in global options to make it easier work across analysts/machines. Analysts will first setup the paths using 'set_paths()' which then store local folder paths where larger data are stored centrally and outside of projects. This will also work on PEPFAR Workbench to return the location of the MSD, 'Sys.getenv("S3_READ")'.

Usage

si_path(type = "path_msd")

Arguments

Value

folderpath stored in global options

See Also

Other stored paths: set_paths()

Examples

## Not run: 
#old
  list.files("C:/Users/rshah/Documents/Data", "OU_IM", full.names = TRUE)
#new
  list.files(si_path("path_msd"), "OU_IM", full.names = TRUE)

## End(Not run)

Description

'si_setup()' combines three function - 'folder_setup()', 'setup_gitignore()', and 'setup_readme()' to create the base OHA/SI project structure with the necessary folders, disclaimers, and ignored files.

Usage

si_setup()

Value

creates folders, readme, and gitignore

See Also

Other project setup: folder_setup(), setup_gitignore(), setup_readme()

Description

'temp_folder' created a temporary folder in your AppData directory, which will be automatically removed after you close your RStudio session.

Usage

temp_folder(launch = FALSE, quiet = FALSE)

Arguments

Value

creates a temp directory and stores it as 'folderpath_tmp'

Examples

## Not run: 
load_secrets()
temp_folder(launch = TRUE)
purrr::walk2(.x = df_googlefiles$id,
             .y = df_googlefiles$filename,
             .f = ~googledrive::drive_download(googledrive::as_id(.x),
                                               file.path(folderpath_tmp, .y))) 
## End(Not run)