Package 'grabr' reference manual

Title:	OHA/SI APIs Package
Description:	Provides a series of base functions useful to the GH OHA SI team. These function extend the utility functions in glamr, focusing primarily on API utility functions.
Authors:	Aaron Chafetz [aut, cre], Tim Essam [aut], Baboyma Kagniniwa [aut]
Maintainer:	Aaron Chafetz <[email protected]>
License:	MIT + file LICENSE
Version:	2.1.2
Built:	2024-11-22 06:24:25 UTC
Source:	https://github.com/USAID-OHA-SI/grabr

Get dimension / item id

Description

Get dimension / item id

Usage

datim_dim_item(
  dimension,
  name,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
datim_dim_item(
  dimension,
  name,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`dimension`	Dimension name
`name`	Item name
`username`	DATIM Account Username
`password`	DATIM Account password
`baseurl`	DATIM API end point

Value

UID of item

Examples

## Not run: 
  library(grabr)

  datim_dim_item(dimension = "Funding Agency", name = "USAID")

  datim_dim_item(dimension = "Targets / Results", name = "MER Results")
  datim_dim_item(dimension = "Targets / Results", name = "MER Targets")

## End(Not run)

## Not run: 
  library(grabr)

  datim_dim_item(dimension = "Funding Agency", name = "USAID")

  datim_dim_item(dimension = "Targets / Results", name = "MER Results")
  datim_dim_item(dimension = "Targets / Results", name = "MER Targets")

## End(Not run)

Get PEPFAR/DATIM Dimension Items

Description

Get PEPFAR/DATIM Dimension Items

Usage

datim_dim_items(
  dimension,
  username,
  password,
  var = NULL,
  fields = NULL,
  baseurl = "https://final.datim.org/"
)
datim_dim_items(
  dimension,
  username,
  password,
  var = NULL,
  fields = NULL,
  baseurl = "https://final.datim.org/"
)

Arguments

`dimension`	Dimension name
`username`	DATIM Account Username
`password`	DATIM Account passward
`var`	column name to pull values from, id or item
`fields`	list of column names to return, this will overwrite 'var'
`baseurl`	DATIM API end point

Value

Dimension's items as tibble or vector

Examples

## Not run: 
  library(grabr)

  datim_dim_items(dimension = "Funding Agency")
  datim_dim_items(dimension = "Funding Agency", var = "item")

## End(Not run)

## Not run: 
  library(grabr)

  datim_dim_items(dimension = "Funding Agency")
  datim_dim_items(dimension = "Funding Agency", var = "item")

## End(Not run)

Build PEPFAR/DATIM Query dimension

Description

Build PEPFAR/DATIM Query dimension

Usage

datim_dim_url(
  dimension,
  items = NULL,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
datim_dim_url(
  dimension,
  items = NULL,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`dimension`	Dimension name
`items`	Item name
`username`	DATIM Account Username
`password`	DATIM Account passward
`baseurl`	DATIM API End point

Value

Valid DATIM Query Params url

Examples

## Not run: 
  library(grabr)

  datim_dim_url(dimension = "Sex")

  datim_dim_url(
    dimension = "Disaggregation Type",
    items = "Age/Sex/HIVStatus"
  )

  datim_dim_url(
    dimension = "Disaggregation Type",
    items = c("Age/Sex", "Age/Sex/HIVStatus")
  )


## End(Not run)


## Not run: 
  library(grabr)

  datim_dim_url(dimension = "Sex")

  datim_dim_url(
    dimension = "Disaggregation Type",
    items = "Age/Sex/HIVStatus"
  )

  datim_dim_url(
    dimension = "Disaggregation Type",
    items = c("Age/Sex", "Age/Sex/HIVStatus")
  )


## End(Not run)

Get PEPFAR/DATIM Dimension ID

Description

Get PEPFAR/DATIM Dimension ID

Usage

datim_dimension(name, username, password, baseurl = "https://final.datim.org/")
datim_dimension(name, username, password, baseurl = "https://final.datim.org/")

Arguments

`name`	Dimension name
`username`	DATIM Account Username, recommended using glamr::datim_user()'
`password`	DATIM Account passward, recommended using glamr::datim_pwd()'
`baseurl`	DATIM API End point, default value is 'https://final.datim.org/'

Value

dimension uid

Examples

## Not run: 
  library(grabr)
  datim_dimension("OU Level")

## End(Not run)

## Not run: 
  library(grabr)
  datim_dimension("OU Level")

## End(Not run)

Get PEPFAR/DATIM dimensions

Description

Get PEPFAR/DATIM dimensions

Usage

datim_dimensions(
  username,
  password,
  var = NULL,
  baseurl = "https://final.datim.org"
)
datim_dimensions(
  username,
  password,
  var = NULL,
  baseurl = "https://final.datim.org"
)

Arguments

`username`	DATIM Account Username
`password`	DATIM Account passward
`var`	Column name to pull all values from, default is NULL, options are: id, dimension
`baseurl`	DATIM API End point, , default value is 'https://final.datim.org'

Value

Dimensions as tibble or list of ids / dimension names

Examples

## Not run: 
  library(grabr)

  datim_dimensions()

## End(Not run)

## Not run: 
  library(grabr)

  datim_dimensions()

## End(Not run)

Execute Datim Query

Description

Execute Datim Query

Usage

datim_execute_query(url, username, password, flatten = FALSE)
datim_execute_query(url, username, password, flatten = FALSE)

Arguments

`url`	API Base url & all query parameters
`username`	Datim username, recommend using 'glamr::datim_user()'
`password`	Datim password, recommend using 'glamr::datim_pwd()'
`flatten`	Should query json result be flatten? Default is false

Value

returns query results as json object, or NULL when error occurs.

Examples

## Not run: 
  library(grabr)

  datim_execute_query(
    url = 'https://www.datim.org/api/sqlViews/<uid>?format=json',
    username =glamr::datim_user(),
    password =glamr::datim_pwd(),
    flatten = TRUE
  )

## End(Not run)

## Not run: 
  library(grabr)

  datim_execute_query(
    url = 'https://www.datim.org/api/sqlViews/<uid>?format=json',
    username =glamr::datim_user(),
    password =glamr::datim_pwd(),
    flatten = TRUE
  )

## End(Not run)

Extract Mechanisms infos from Datim

Description

Extract Mechanisms infos from Datim

Usage

datim_mechs(cntry, username, password, agency = "USAID", baseurl = NULL)
datim_mechs(cntry, username, password, agency = "USAID", baseurl = NULL)

Arguments

`cntry`	Country name
`username`	Datim username
`password`	Datim password
`agency`	Agency name
`baseurl`	Datim API Base URL

Value

OU Mechanisms as data frame

Examples

## Not run: 
  library(grabr)

  datim_mechs(
   cntry = "Mozambique",
   username = glamr::datim_user(),
   password = glamr::datim_pwd()
  )

## End(Not run)
## Not run: 
  library(grabr)

  datim_mechs(
   cntry = "Mozambique",
   username = glamr::datim_user(),
   password = glamr::datim_pwd()
  )

## End(Not run)

Pull Orgunits SQLView

Description

Pull Orgunits SQLView

Usage

datim_orgunits(
  cntry,
  username,
  password,
  reshape = FALSE,
  baseurl = "https://final.datim.org/"
)
datim_orgunits(
  cntry,
  username,
  password,
  reshape = FALSE,
  baseurl = "https://final.datim.org/"
)

Arguments

`cntry`	Country name
`username`	Datim username
`password`	Datim password
`reshape`	Unpack parent org units
`baseurl`	Datim API Base URL, default to https://final.datim.org/

Value

OU/Country Orgunits as a data frame

Examples

## Not run: 
  library(grabr)

  datim_orgunits(
   cntry = "Mozambique",
   username = glamr::datim_user(),
   password = glamr::datim_pwd()
  )

## End(Not run)
## Not run: 
  library(grabr)

  datim_orgunits(
   cntry = "Mozambique",
   username = glamr::datim_user(),
   password = glamr::datim_pwd()
  )

## End(Not run)

Extract PLHIV and General POP Estimates from datim

Description

Extract PLHIV and General POP Estimates from datim

Usage

datim_pops(
  ou,
  username,
  password,
  level = "country",
  fy = NULL,
  hierarchy = FALSE,
  baseurl = "https://final.datim.org/"
)
datim_pops(
  ou,
  username,
  password,
  level = "country",
  fy = NULL,
  hierarchy = FALSE,
  baseurl = "https://final.datim.org/"
)

Arguments

`ou`	Operatingunit
`username`	Datim account username
`password`	Datim account password
`level`	Organization level
`fy`	Fiscal Year
`hierarchy`	Should additional organizational hierarchy be added?, default is FALSE
`baseurl`	DATIM API base url

Value

PLHIV and POP_EST Data

Examples

## Not run: 
  library(grabr)

  datim_pops(ou = "Nigeria")

  datim_pops(ou = "Nigeria", fy = 2021)

  datim_pops(ou = "Nigeria", level = "psnu", fy = 2021, hierarchy = TRUE)

## End(Not run)
## Not run: 
  library(grabr)

  datim_pops(ou = "Nigeria")

  datim_pops(ou = "Nigeria", fy = 2021)

  datim_pops(ou = "Nigeria", level = "psnu", fy = 2021, hierarchy = TRUE)

## End(Not run)

Process Datim Query results

Description

Process Datim Query results

Usage

datim_process_query(url, username, password)
datim_process_query(url, username, password)

Arguments

`url`	Datim API Call url
`username`	Datim username, recommend using 'glamr::datim_user()'
`password`	Datim password, recommend using 'glamr::datim_pwd()'

Value

Data as tibble or NULL when error occurs.

Examples

## Not run: 
 library(grabr)
 datim_process_query("<full-api-call-url>")

## End(Not run)

## Not run: 
 library(grabr)
 datim_process_query("<full-api-call-url>")

## End(Not run)

Extract PEPFAR Org Hierarchy

Description

Extract PEPFAR Org Hierarchy

Usage

datim_pull_hierarchy(
  ou_uid,
  username,
  password,
  add_geom = FALSE,
  baseurl = "https://final.datim.org/",
  folderpath_output = NULL
)
datim_pull_hierarchy(
  ou_uid,
  username,
  password,
  add_geom = FALSE,
  baseurl = "https://final.datim.org/",
  folderpath_output = NULL
)

Arguments

`ou_uid`	UID for the country, recommend using identify_ouuids
`username`	DATIM username, recommend using datim_user
`password`	DATIM password, recommend using datim_pwd
`add_geom`	Add geometry column to the output, default is false
`baseurl`	API base url, default = https://final.datim.org/
`folderpath_output`	provide the full path to the folder for saving

Note

This function is migrated from Wavelength and is similar to datim_orgunits

Examples

## Not run: 
#get OU UID
  ouuid <- identify_ouuids() %>% dplyr::filter(country == "Kenya")
#pull hierarchy (paths are all UIDs)
  df <- datim_pull_hierarchy(ouuid, username = myuser, password = mypwd(myuser)) 
## End(Not run)
## Not run: 
#get OU UID
  ouuid <- identify_ouuids() %>% dplyr::filter(country == "Kenya")
#pull hierarchy (paths are all UIDs)
  df <- datim_pull_hierarchy(ouuid, username = myuser, password = mypwd(myuser)) 
## End(Not run)

Query PEPFAR/DATIM targets/results data

Description

Query PEPFAR/DATIM targets/results data

Usage

datim_query(
  ou,
  username,
  password,
  level = "prioritization",
  pe = "THIS_FINANCIAL_YEAR",
  ta = "PLHIV",
  value = NULL,
  disaggs = NULL,
  dimensions = NULL,
  property = "SHORTNAME",
  metadata = TRUE,
  hierarchy = TRUE,
  baseurl = "https://final.datim.org/",
  verbose = FALSE
)
datim_query(
  ou,
  username,
  password,
  level = "prioritization",
  pe = "THIS_FINANCIAL_YEAR",
  ta = "PLHIV",
  value = NULL,
  disaggs = NULL,
  dimensions = NULL,
  property = "SHORTNAME",
  metadata = TRUE,
  hierarchy = TRUE,
  baseurl = "https://final.datim.org/",
  verbose = FALSE
)

Arguments

`ou`	Operatingunit
`username`	Datim username
`password`	Datim username
`level`	Organization hierarchy level
`pe`	Reporting period. This can be expressed as relative or fixed periods Eg.: "THIS_FINANCIAL_YEAR", "2020Oct", "QUARTERS_THIS_YEAR", "2021Q2" default is "THIS_FINANCIAL_YEAR"
`ta`	Technical Area, valid option can be obtain from glamr::datim_dim_items("Technical Area")'
`value`	Type of value to return, MER Targets or Results or both
`disaggs`	Disaggregation Types. This depends on the value of ta
`dimensions`	Additional dimensions and/or columns. This depends on values of ta and disaggs
`property`	Type of name
`metadata`	Should metadata be included
`hierarchy`	Should additional hirarchy level be included
`baseurl`	DATIM API End point url
`verbose`	Display all notifications

Value

data as tibble

Examples

## Not run: 
 library(grabr)

 datim_query(ou = "Nigeria", ta = "PLHIV")

 datim_query(ou = "Nigeria", ta = "POP_EST")

 datim_query(ou = "Nigeria",
             level = "country",
             ta = "TX_CURR",
             disaggs = "Age/Sex/HIVStatus",
             dimensions = c("Age: <15/15+  (Coarse)", "Sex"))

## End(Not run)

## Not run: 
 library(grabr)

 datim_query(ou = "Nigeria", ta = "PLHIV")

 datim_query(ou = "Nigeria", ta = "POP_EST")

 datim_query(ou = "Nigeria",
             level = "country",
             ta = "TX_CURR",
             disaggs = "Age/Sex/HIVStatus",
             dimensions = c("Age: <15/15+  (Coarse)", "Sex"))

## End(Not run)

Query Datim SQLViews

Description

Query Datim SQLViews

Usage

datim_sqlviews(
  username,
  password,
  view_name = NULL,
  dataset = FALSE,
  datauid = NULL,
  query = NULL,
  baseurl = NULL
)
datim_sqlviews(
  username,
  password,
  view_name = NULL,
  dataset = FALSE,
  datauid = NULL,
  query = NULL,
  baseurl = NULL
)

Arguments

`username`	Datim username
`password`	Datim password
`view_name`	Datim SQLView name
`dataset`	Return SQLView dataset or uid? Default is false
`datauid`	Data UID
`query`	SQLView Query params, a list containing type and params key value pairs
`baseurl`	Datim API Base URL

Value

SQLView uid or dataset as data frame

Note

This function should be used to identify Datim SQLView and Extract Data

Examples

## Not run: 
  library(grabr)

  datim_sqlviews(
    username =glamr::datim_user(),
    password =glamr::datim_pwd(),
    view_name = "A list of OUs",
    dataset = TRUE
  )

## End(Not run)

## Not run: 
  library(grabr)

  datim_sqlviews(
    username =glamr::datim_user(),
    password =glamr::datim_pwd(),
    view_name = "A list of OUs",
    dataset = TRUE
  )

## End(Not run)

Generate a API URL

Description

Generate a API URL

Usage

gen_url(
  ou_uid,
  org_lvl,
  org_type = "facility",
  value_type = "results",
  is_hts = FALSE,
  fy_pd = NULL,
  baseurl = "https://final.datim.org/"
)
gen_url(
  ou_uid,
  org_lvl,
  org_type = "facility",
  value_type = "results",
  is_hts = FALSE,
  fy_pd = NULL,
  baseurl = "https://final.datim.org/"
)

Arguments

`ou_uid`	UID for the country, recommend using 'identify_ouuids()'
`org_lvl`	org hierarchy level, eg facility is level 7 in country X, recommend using 'identify_levels()'
`org_type`	organization type, either facility (default) or community
`value_type`	results (default) or targets
`is_hts`	is the API for HTS indicators (HTS_TST or HTS_TST_POS), default = FALSE
`fy_pd`	fiscal year(s) to cover, default will be current FY if not provided
`baseurl`	API base url, default = https://final.datim.org/

Examples

## Not run: 
 #get OU UID
  ouuid <- identify_ouuids() %>% dplyr::filter(ou == "Ghana")
 #get facility level
  faclvl <- identify_levels("facility",
                            username = myuser, password = mypwd()) %>%
                            dplyr::filter(ou == "Ghana")
 #gen url
  myurl <- gen_url(ouuid, faclvl, org_type = facility) 
## End(Not run)
## Not run: 
 #get OU UID
  ouuid <- identify_ouuids() %>% dplyr::filter(ou == "Ghana")
 #get facility level
  faclvl <- identify_levels("facility",
                            username = myuser, password = mypwd()) %>%
                            dplyr::filter(ou == "Ghana")
 #gen url
  myurl <- gen_url(ouuid, faclvl, org_type = facility) 
## End(Not run)

Get base url from a link

Description

Get base url from a link

Usage

get_baseurl(url)
get_baseurl(url)

Arguments

url

DATIM API end points

Value

Base url without trailing slash

DATIM API Call for Targets

Description

DATIM API Call for Targets

Usage

get_datim_data(url, username, password)
get_datim_data(url, username, password)

Arguments

`url`	supply url forAPI call, recommend using 'gen_url()'
`username`	DATIM username
`password`	DATIM password, recommend using 'mypwd()'

Examples

## Not run: 
 myurl <- paste0(baseurl, "api/29/analytics.json?
                 dimension=LxhLO68FcXm:udCop657yzi&
                 dimension=ou:LEVEL-4;HfVjCurKxh2&
                 filter=pe:2018Oct&
                 displayProperty=SHORTNAME&outputIdScheme=CODE")
 myuser <- "UserX"
 df_datim <- get_datim_data(myurl, myuser, mypwd(myuser)) 
## End(Not run)
## Not run: 
 myurl <- paste0(baseurl, "api/29/analytics.json?
                 dimension=LxhLO68FcXm:udCop657yzi&
                 dimension=ou:LEVEL-4;HfVjCurKxh2&
                 filter=pe:2018Oct&
                 displayProperty=SHORTNAME&outputIdScheme=CODE")
 myuser <- "UserX"
 df_datim <- get_datim_data(myurl, myuser, mypwd(myuser)) 
## End(Not run)

DATIM API Call for Targets

Description

DATIM API Call for Targets

Usage

get_datim_targets(url, username, password)
get_datim_targets(url, username, password)

Arguments

`url`	supply url forAPI call, recommend using'gen_url()'
`username`	DATIM username
`password`	DATIM password, recommend using 'mypwd()'

Examples

## Not run: 
 myurl <- paste0(baseurl, "api/29/analytics.json?
                 dimension=LxhLO68FcXm:udCop657yzi&
                 dimension=ou:LEVEL-4;HfVjCurKxh2&
                 filter=pe:2018Oct&
                 displayProperty=SHORTNAME&outputIdScheme=CODE")
 myuser <- "UserX"
 df_targets <- get_datim_targets(myurl, myuser, mypwd(myuser)) 
## End(Not run)
## Not run: 
 myurl <- paste0(baseurl, "api/29/analytics.json?
                 dimension=LxhLO68FcXm:udCop657yzi&
                 dimension=ou:LEVEL-4;HfVjCurKxh2&
                 filter=pe:2018Oct&
                 displayProperty=SHORTNAME&outputIdScheme=CODE")
 myuser <- "UserX"
 df_targets <- get_datim_targets(myurl, myuser, mypwd(myuser)) 
## End(Not run)

Get all orgunits levels in org hierarchy

Description

Get all orgunits levels in org hierarchy

Usage

get_levels(
  username,
  password,
  expand = FALSE,
  reshape = FALSE,
  baseurl = "https://final.datim.org/"
)
get_levels(
  username,
  password,
  expand = FALSE,
  reshape = FALSE,
  baseurl = "https://final.datim.org/"
)

Arguments

`username`	DATIM username, recommed using glamr::datim_user()'
`password`	DATIM password, recommend using glamr::datim_pwd()'
`expand`	Fill in missing snu1 level? default is FALSE
`reshape`	Reshape data as long? default is FALSE
`baseurl`	base API url, default = https://final.datim.org/

Value

Note

Similar to 'grabr::identify_levels()' and 'grabr::get_outable()'

Examples

## Not run: 
  library(grabr)

  # Get PEPFAR Org Levels
  get_levels()
 
## End(Not run)

## Not run: 
  library(grabr)

  # Get PEPFAR Org Levels
  get_levels()
 
## End(Not run)

Get Org UIDS

Description

Get Org UIDS

Usage

get_orguids(
  level = 3,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
get_orguids(
  level = 3,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`level`	Org level
`username`	DATIM Username, recommend using glamr::datim_user()'
`password`	DATIM password, recommend using glamr::datim_pwd()'
`baseurl`	base url for the API, default = https://final.datim.org/

Value

ORG UIDS as tibble

Note

Use with caution. Use 'get_ouorguids()' for levels below 3

Examples

## Not run: 
 library(grabr)

 # All orgunit level 3 uids + names
 orgs <- get_orguids(level = 3)

## End(Not run)

## Not run: 
 library(grabr)

 # All orgunit level 3 uids + names
 orgs <- get_orguids(level = 3)

## End(Not run)

Identify OU/Org Label

Description

Identify OU/Org Label

Usage

get_ouorglabel(
  operatingunit,
  country = NULL,
  org_level = 4,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
get_ouorglabel(
  operatingunit,
  country = NULL,
  org_level = 4,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`operatingunit`	Operating unit
`country`	Country name
`org_level`	OU Org level, default is set to 4, PSNU
`username`	Datim account username
`password`	Datim account password
`baseurl`	Datim base url

Value

Org level label

Examples

## Not run: 
  library(grabr)

  get_ouorglabel(operatingunit = "Zambia", org_level = 5)

## End(Not run)

## Not run: 
  library(grabr)

  get_ouorglabel(operatingunit = "Zambia", org_level = 5)

## End(Not run)

Get OU Org level

Description

Get OU Org level

Usage

get_ouorglevel(
  operatingunit,
  country = NULL,
  org_type = "prioritization",
  username,
  password,
  baseurl = "https://final.datim.org/"
)
get_ouorglevel(
  operatingunit,
  country = NULL,
  org_type = "prioritization",
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`operatingunit`	Operatingunit name
`country`	Country name (default = Operatingunit)
`org_type`	Orgunit type (country_lvl, prioritization, community, facility_lvl)
`username`	Datim Account username
`password`	Datim Account Password
`baseurl`	Datim Base URL

Value

uid

Examples

## Not run: 
 library(grabr)

 cntry <- "Zambia"

 # Get country org level
 get_ouorglevel(cntry)

 # Get community org level
 get_ouorglevel(cntry, org_type = "community")

## End(Not run)

## Not run: 
 library(grabr)

 cntry <- "Zambia"

 # Get country org level
 get_ouorglevel(cntry)

 # Get community org level
 get_ouorglevel(cntry, org_type = "community")

## End(Not run)

Get list of OU Orgs at specific level

Description

Get list of OU Orgs at specific level

Usage

get_ouorgs(
  ouuid,
  level = 4,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
get_ouorgs(
  ouuid,
  level = 4,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`ouuid`	OU uid
`level`	org level
`username`	DATIM Username
`password`	DATIM password, recommend using 'mypwd()'
`baseurl`	base url for the API, default = https://final.datim.org/

Value

ORG UIDS as tibble

Note

Use 'get_orguids()' for levels above 4

Examples

## Not run: 

 library(grabr)

 cntry <- "Zambia"

 uid <- get_ouuid(cntry)

 lvl <- get_ouorglevel(cntry, org_type = "prioritization")

 orgs <- get_ouorgs(ouuid = uid, level = lvl)

## End(Not run)

## Not run: 

 library(grabr)

 cntry <- "Zambia"

 uid <- get_ouuid(cntry)

 lvl <- get_ouorglevel(cntry, org_type = "prioritization")

 orgs <- get_ouorgs(ouuid = uid, level = lvl)

## End(Not run)

Get Orgs uids by level

Description

Get Orgs uids by level

Usage

get_ouorguids(
  ouuid,
  level,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
get_ouorguids(
  ouuid,
  level,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`ouuid`	Operatingunit uid
`level`	Orgunit level
`username`	Datim Account username
`password`	Datim Account Password
`baseurl`	Datim base url

Value

list of uids

Examples

## Not run: 
 library(grabr)

 # Set country of interest
 cntry <- "Zambia"

 # Get OU/Country orgunit uid
 uid <- get_ouuid(cntry)

 # Get org level for psnu
 lvl <- get_ouorglevel(cntry, org_type = "prioritization")

 # Retreived all uids for level 4 (SNU1)
 get_ouorguids(ouuid = uid, level = 4)

## End(Not run)

## Not run: 
 library(grabr)

 # Set country of interest
 cntry <- "Zambia"

 # Get OU/Country orgunit uid
 uid <- get_ouuid(cntry)

 # Get org level for psnu
 lvl <- get_ouorglevel(cntry, org_type = "prioritization")

 # Retreived all uids for level 4 (SNU1)
 get_ouorguids(ouuid = uid, level = 4)

## End(Not run)

Pull Table of OUs/Countries, UIDs, ISO codes and levels

Description

'get_outable' pulls from DATIM to return a dataframe with all PEPFAR Operating Units and countries along with useful information for merging, eg ISO codes, and use in DATIM APIs, eg UIDs and hierarchy levels.

Usage

get_outable(username, password, baseurl = "https://final.datim.org/")
get_outable(username, password, baseurl = "https://final.datim.org/")

Arguments

`username`	DATIM Username, defaults to using glamr::datim_user()' if blank
`password`	DATIM password, defaults to using glamr::datim_pwd()' if blank
`baseurl`	base url for the API, default = https://final.datim.org/

Details

'get_outtable' is a wrapper around 'identify_ouuids' and 'identify_levels' that pulls this information directly from DATIM. The user will need to have a DATIM account to access this data. You can take advantage of storing you credentials locally in a secure way using 'set_datim'.

Value

data frame with all PEPFAR OUs, countries, their UIDs, ISO codes and different levels in the DATIM hierarchy

Examples

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

Get Operatingunit / Country Org UID

Description

Get Operatingunit / Country Org UID

Usage

get_ouuid(
  operatingunit,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
get_ouuid(
  operatingunit,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`operatingunit`	Operatingunit name
`username`	Datim Account username, recommend using glamr::datim_user()'
`password`	Datim Account Password, recommend using glamr::datim_pwd()'
`baseurl`	base url for the API, default = https://final.datim.org/

Value

uid

Examples

## Not run: 
  library(grabr)

  # get orgunit for specific OU/Country: kenya
  get_ouuid(operatingunit = "Kenya")

## End(Not run)

## Not run: 
  library(grabr)

  # get orgunit for specific OU/Country: kenya
  get_ouuid(operatingunit = "Kenya")

## End(Not run)

Get OU Org UIDS

Description

Get OU Org UIDS

Usage

get_ouuids(
  add_details = FALSE,
  username,
  password,
  baseurl = "https://final.datim.org/"
)
get_ouuids(
  add_details = FALSE,
  username,
  password,
  baseurl = "https://final.datim.org/"
)

Arguments

`add_details`	Add countries for regional ou, default is false
`username`	DATIM Username, recommend using glamr::datim_user()'
`password`	DATIM password, recommend using glamr::datim_pwd()'
`baseurl`	base url for the API, default = https://final.datim.org/

Value

OU UIDS as tibble

Examples

## Not run: 
 library(grabr)

 # OU Org UIDs
 ous <- get_ouuids()

## End(Not run)

## Not run: 
 library(grabr)

 # OU Org UIDs
 ous <- get_ouuids()

## End(Not run)

Identify Facility/Community levels in org hierarchy

Description

'identify_levels' pulls from DATIM to return a dataframe with all PEPFAR Operating Units and countries with their ISO codes and hierarhcy levels. This is one of two components that feeds into 'get_outable'.

Usage

identify_levels(username, password, baseurl = "https://final.datim.org/")
identify_levels(username, password, baseurl = "https://final.datim.org/")

Arguments

`username`	DATIM Username, defaults to using glamr::datim_user()' if blank
`password`	DATIM password, defaults to using glamr::datim_pwd()' if blank
`baseurl`	base url for the API, default = https://final.datim.org/

Details

To access the UIDs, the user will need to have a DATIM account. You can take advantage of storing you credentials locally in a secure way using 'set_datim'.

Examples

## Not run: 
 #table for all OUs
  load_secrets()
  identify_levels() 
## End(Not run)
## Not run: 
 #table for all OUs
  load_secrets()
  identify_levels() 
## End(Not run)

Pull OU UIDS

Description

'identify_ouuids' pulls from DATIM to return a dataframe with all PEPFAR Operating Units and countries and their UIDs. This is one of two components that feeds into 'get_outable'.

Usage

identify_ouuids(username, password, baseurl = "https://final.datim.org/")
identify_ouuids(username, password, baseurl = "https://final.datim.org/")

Arguments

`username`	DATIM Username, defaults to using glamr::datim_user()' if blank
`password`	DATIM password, defaults to using glamr::datim_pwd()' if blank
`baseurl`	base url for the API, default = https://final.datim.org/

Details

To access the UIDs, the user will need to have a DATIM account. You can take advantage of storing you credentials locally in a secure way using 'set_datim'.

Value

Datim country names

Examples

## Not run: 
 load_secrets()
 ous <- identify_ouuids() 
## End(Not run)
## Not run: 
 load_secrets()
 ous <- identify_ouuids() 
## End(Not run)

Lazy checking/loading of credentials

Description

This function is useful within another function. It check whether a username or password has been provided by the user and, if not, checks if they were stored via 'glamr' or prompts user to provide credentials through interactive prompt.

Usage

lazy_secrets(service = c("datim", "pano", "pdap", "s3"), username, password)
lazy_secrets(service = c("datim", "pano", "pdap", "s3"), username, password)

Arguments

`service`	account, either datim", "pano", "pdap", or "s3"
`username`	account username or s3 access key
`password`	account password or s3 secret key

Value

returns a list of 2 - username/access and password/secret

Examples

## Not run: 
  accnt <- lazy_secrets("datim", username = username, password = password)
  datim_dimensions(accnt$username, accnt$password)

## End(Not run)
## Not run: 
  accnt <- lazy_secrets("datim", username = username, password = password)
  datim_dimensions(accnt$username, accnt$password)

## End(Not run)

Download file from PEPFAR Panorama

Description

Download file from PEPFAR Panorama

Usage

pano_download(
  item_url,
  username,
  password,
  session = NULL,
  dest_path = NULL,
  uncompress = FALSE
)
pano_download(
  item_url,
  username,
  password,
  session = NULL,
  dest_path = NULL,
  uncompress = FALSE
)

Arguments

`item_url`	URL for the item to be downlaoded
`username`	Username for PEPFAR Panorama Account. Recommend using 'pano_user()'
`password`	Password for PEPFAR Panorama Account. Recommend using 'pano_pwd()'
`session`	Login session, only used within other 'pano_extract_*()'
`dest_path`	Location and name of the destination file
`uncompress`	If yes, the downloaded zip file will be decompressed. Default is FALSE

Value

file content as binary

Examples

## Not run: 
  library(tidyverse)
  library(grabr)
  library(glamr)

  url <- "https://pepfar-panorama.org/forms/downloads"

  elts <- pano_items(page_html = url,
                     username = pano_user(), password = pano_pwd())

  f_url <- elts %>% filter(type == "file zipfile") %>% pull(path) %>% first()

  pano_download(item_url = url, session = s, dest = "Data") 
## End(Not run)

## Not run: 
  library(tidyverse)
  library(grabr)
  library(glamr)

  url <- "https://pepfar-panorama.org/forms/downloads"

  elts <- pano_items(page_html = url,
                     username = pano_user(), password = pano_pwd())

  f_url <- elts %>% filter(type == "file zipfile") %>% pull(path) %>% first()

  pano_download(item_url = url, session = s, dest = "Data") 
## End(Not run)

Extract data outputs from Panorama

Description

Extract data outputs from Panorama

Usage

pano_extract(
  item = "mer",
  version,
  fiscal_year,
  quarter,
  unpack = FALSE,
  username,
  password,
  session = NULL,
  baseurl = "https://pepfar-panorama.org"
)
pano_extract(
  item = "mer",
  version,
  fiscal_year,
  quarter,
  unpack = FALSE,
  username,
  password,
  session = NULL,
  baseurl = "https://pepfar-panorama.org"
)

Arguments

`item`	Panorama data type. Eg: mer, financial, sims, narratives
`version`	Data release version: Initial or Clean, defaults to current version if blank
`fiscal_year`	Reporting Fiscal year, defaults to current FY if blank
`quarter`	Reporting Quarter, defaults to current quarter if blank
`unpack`	If TRUE, unpack nested directories
`username`	Panorama username, recommend using 'glamr::pano_user()'
`password`	Panorama password, recommend using 'glamr::pano_pwd()'
`session`	Login session, only used within other 'pano_extract_*()'
`baseurl`	Panorama base url

Value

list of output files as data frame

Note

This function combines 'pano_session()', 'pano_content()', 'pano_elements()', and in some cases 'pano_unpack()'

Examples

## Not run: 
  library(tidyverse)
  library(grabr)

  pano_extract(item = "mer")

## End(Not run)

## Not run: 
  library(tidyverse)
  library(grabr)

  pano_extract(item = "mer")

## End(Not run)

Downloads Country Specific MSDs

Description

Downloads Country Specific MSDs

Usage

pano_extract_msd(
  operatingunit = NULL,
  version,
  fiscal_year,
  quarter,
  level = c("psnu", "ou", "site", "nat"),
  dest_path,
  username,
  password,
  baseurl = "https://pepfar-panorama.org"
)
pano_extract_msd(
  operatingunit = NULL,
  version,
  fiscal_year,
  quarter,
  level = c("psnu", "ou", "site", "nat"),
  dest_path,
  username,
  password,
  baseurl = "https://pepfar-panorama.org"
)

Arguments

`operatingunit`	PEPFAR Operating Unit. Default is set to NULL for to return global datasets
`version`	Data release version: "initial" or "clean", defaults to current version
`fiscal_year`	Reporting Fiscal year, defaults to current version
`quarter`	Reporting Quarter: Single digit quarters, defaults to current version
`level`	Org level, options are psnu" (default), "ou", "site", or "nat"
`dest_path`	Directory path to download file. Default set to 'si_path()'
`username`	Panorama username, recommend using 'glamr::pano_user()', which is the default if left blank
`password`	Panorama password, recommend using 'glamr::pano_pwd()', which is the default if left blank
`baseurl`	Pano base url

Examples

## Not run: 
 pano_extract_msd(operatingunit = "Zambia", level = "site")

## End(Not run)

## Not run: 
 pano_extract_msd(operatingunit = "Zambia", level = "site")

## End(Not run)

Downloads All Global + OU Specific MSDs

Description

Downloads All Global + OU Specific MSDs

Usage

pano_extract_msds(
  operatingunit,
  add_global = TRUE,
  items = "mer",
  archive = FALSE,
  dest_path,
  username,
  password,
  baseurl = "https://pepfar-panorama.org"
)
pano_extract_msds(
  operatingunit,
  add_global = TRUE,
  items = "mer",
  archive = FALSE,
  dest_path,
  username,
  password,
  baseurl = "https://pepfar-panorama.org"
)

Arguments

`operatingunit`	PEPFAR Operating Unit. Default is set to NULL for global datasets
`add_global`	Add global datasets in this extract? Default is TRUE
`items`	Panorama data set, default option is 'mer'
`archive`	Logical, should the old files be archived? default=FALSE
`dest_path`	Directory path to download file. Default set to 'glamr::si_path()'
`username`	Panorama username, recommend using 'glamr::pano_user()'
`password`	Panorama password, recommend using 'glamr::pano_pwd()'
`baseurl`	Panorama base url, default="https://pepfar-panorama.org"

Examples

## Not run: 
 dir_mer <- si_path()

 pano_extract_msds(operatingunit = "Zambia",
                   archive = TRUE,
                   dest_path = dir_mer)

## End(Not run)
## Not run: 
 dir_mer <- si_path()

 pano_extract_msds(operatingunit = "Zambia",
                   archive = TRUE,
                   dest_path = dir_mer)

## End(Not run)

Extract data items from url

Description

Extract data items from url

Usage

pano_items(page_url, username, password, session = NULL)
pano_items(page_url, username, password, session = NULL)

Arguments

`page_url`	Current html page url
`username`	Username for PEPFAR Panorama Account. Recommend using 'pano_user()'
`password`	Password for PEPFAR Panorama Account. Recommend using 'pano_pwd()'
`session`	Login session, only used within other 'pano_extract_*()'

Value

data items as data frame

Examples

## Not run: 
  library(grabr)

  s <- pano_session("<my-pano-user>", "<my-password>")
  url <- "https://pepfar-panorama.org/forms/downloads"

  items <- pano_items(page_url = url, session = s)

## End(Not run)

## Not run: 
  library(grabr)

  s <- pano_session("<my-pano-user>", "<my-password>")
  url <- "https://pepfar-panorama.org/forms/downloads"

  items <- pano_items(page_url = url, session = s)

## End(Not run)

Create an active session for PEPFAR Panorama

Description

Create an active session for PEPFAR Panorama

Usage

pano_session(username, password, baseurl = "https://pepfar-panorama.org")
pano_session(username, password, baseurl = "https://pepfar-panorama.org")

Arguments

`username`	Username for PEPFAR Panorama Account. Recommend using 'pano_user()'
`password`	Password for PEPFAR Panorama Account. Recommend using 'pano_pwd()'
`baseurl`	PEPFAR Panorama base url

Value

Examples

## Not run: 
  library(grabr)

  s <- pano_session("<my-pano-user>", "<my-password>")

## End(Not run)

## Not run: 
  library(grabr)

  s <- pano_session("<my-pano-user>", "<my-password>")

## End(Not run)

Pull Partner/Mechanism Info from DATIM

Description

Pull Partner/Mechanism Info from DATIM

Usage

pull_mech(usaid_only = TRUE, ou_sel = NULL, folderpath_output = NULL)
pull_mech(usaid_only = TRUE, ou_sel = NULL, folderpath_output = NULL)

Arguments

`usaid_only`	specify if only USAID mechansism should be returned, default = TRUE
`ou_sel`	option to specify an operating unit, default = NULL
`folderpath_output`	provide the full path to the folder for saving

Examples

## Not run: 
#pull mechanism/partner information
df <- pull_mech() 
## End(Not run)
## Not run: 
#pull mechanism/partner information
df <- pull_mech() 
## End(Not run)

Extract DATIM Results and Targets (DATIM API Call)

Description

Extract DATIM Results and Targets (DATIM API Call)

Usage

pull_mer(
  ou_name = NULL,
  username,
  password,
  baseurl = "https://final.datim.org/",
  fy_pd = NULL,
  quarters_complete = NULL,
  folderpath_output = NULL
)
pull_mer(
  ou_name = NULL,
  username,
  password,
  baseurl = "https://final.datim.org/",
  fy_pd = NULL,
  quarters_complete = NULL,
  folderpath_output = NULL
)

Arguments

`ou_name`	Operating Unit name, if mechanism is not specified
`username`	DATIM username
`password`	DATIM password, recommend using 'mypwd()'
`baseurl`	API base url, default = https://final.datim.org/
`fy_pd`	fiscal year(s) to cover, default will be current FY if not provided
`quarters_complete`	no. of quarters completed through FY to determine weeks left in year
`folderpath_output`	folder path to store DATIM output, default = NULL

Examples

## Not run: 
 #ou mer data
 myuser <- "UserX"
 mech_x_dta <- pull_mer(ou_name = "Namibia", username = myuser, password = mypwd(myuser))
 
## End(Not run)
## Not run: 
 #ou mer data
 myuser <- "UserX"
 mech_x_dta <- pull_mer(ou_name = "Namibia", username = myuser, password = mypwd(myuser))
 
## End(Not run)

Get S3 Buckets list

Description

Get S3 Buckets list

Usage

s3_buckets(access_key, secret_key, ...)
s3_buckets(access_key, secret_key, ...)

Arguments

`access_key`	S3 Access Key ID
`secret_key`	S3 Secret Access Key
`...`	Additional arguments passed to s3HTTP

Value

S3 Backets list as tibble

Examples

## Not run: 
s3_buckets() 
## End(Not run)

## Not run: 
s3_buckets() 
## End(Not run)

Download S3 Objects

Description

Download S3 Objects

Usage

s3_download(bucket, object, filepath = NULL, access_key, secret_key, ...)
s3_download(bucket, object, filepath = NULL, access_key, secret_key, ...)

Arguments

`bucket`	S3 Bucket name
`object`	S3 Object key (id)
`filepath`	Full path of destination file
`access_key`	S3 Access key id
`secret_key`	S3 Secret Access key
`...`	Additional aws.S3::save_object() options

Value

file name

Examples

## Not run: 
s3_objects("sample-bucket") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_download()
## End(Not run)

## Not run: 
s3_objects("sample-bucket") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_download()
## End(Not run)

Read sheets from S3 Objects / Excel

Description

Read sheets from S3 Objects / Excel

Usage

s3_excel_sheets(bucket, object_key, access_key, secret_key)
s3_excel_sheets(bucket, object_key, access_key, secret_key)

Arguments

`bucket`	S3 Bucket
`object_key`	S3 Object Key
`access_key`	S3 Access Key
`secret_key`	S3 Secret Key

Value

Excel sheets as data frame

Examples

## Not run: 
s3_objects(
    bucket = "sample-bucket",
    prefix = "ddc/xyz/ABC") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_excel_sheets(bucket = "<sample-bucket>", object_key = .)
## End(Not run)

## Not run: 
s3_objects(
    bucket = "sample-bucket",
    prefix = "ddc/xyz/ABC") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_excel_sheets(bucket = "<sample-bucket>", object_key = .)
## End(Not run)

Idenfity S3 Object type

Description

Idenfity S3 Object type

Usage

s3_object_type(object)
s3_object_type(object)

Arguments

object

S3 Object key

Value

file type: text, csv, excel, json, python, shell

Examples

## Not run: 
s3_objects("sample-bucket") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_object_type()
## End(Not run)

## Not run: 
s3_objects("sample-bucket") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_object_type()
## End(Not run)

Get S3 Bucket objects list

Description

Get S3 Bucket objects list

Usage

s3_objects(
  bucket,
  prefix = NULL,
  n = 1000,
  unpack_keys = FALSE,
  access_key,
  secret_key,
  ...
)
s3_objects(
  bucket,
  prefix = NULL,
  n = 1000,
  unpack_keys = FALSE,
  access_key,
  secret_key,
  ...
)

Arguments

`bucket`	S3 Bucket name
`prefix`	Limits response by key. Default set to NULL
`n`	Max number of record, default = 1000
`unpack_keys`	Separate key column, default is false
`access_key`	S3 Access Key ID
`secret_key`	S3 Secret Access Key
`...`	Additional aws.s3::get_bucket_df() options

Value

S3 Objects list as tibble

Examples

## Not run: 
s3_objects("sample-bucket") 
## End(Not run)

## Not run: 
s3_objects("sample-bucket") 
## End(Not run)

Read content of S3 Objects

Description

Read content of S3 Objects

Usage

s3_read_object(bucket, object, sheet = NULL, access_key, secret_key, ...)
s3_read_object(bucket, object, sheet = NULL, access_key, secret_key, ...)

Arguments

`bucket`	S3 Bucket name
`object`	S3 Object key (id)
`sheet`	S3 Excel object sheet name / index
`access_key`	S3 Access key id
`secret_key`	S3 Secret Access key
`...`	Additional arguments passed to s3HTTP

Value

Examples

## Not run: 
s3_objects("sample-bucket") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_read_object(bucket = "sample-bucket", object_key = .)
## End(Not run)

## Not run: 
s3_objects("sample-bucket") %>%
  filter(str_detect(key, "^HFR")) %>%
  pull(key) %>%
  first() %>%
  s3_read_object(bucket = "sample-bucket", object_key = .)
## End(Not run)

Remove objects from S3 bucket

Description

Remove objects from S3 bucket

Usage

s3_remove(objects, bucket, access_key, secret_key, ...)
s3_remove(objects, bucket, access_key, secret_key, ...)

Arguments

`objects`	S3 object keys (full path)
`bucket`	S3 backet name
`access_key`	S3 Access Key
`secret_key`	S3 Secret Key
`...`	Additional aws.S3::delete_object() options

Value

boolean

Examples

## Not run: 
df_objects %>%
  pull(key) %>%
  first() %>%
  s3_remove(objects = .,
            bucket = "test-bkt")
## End(Not run)

## Not run: 
df_objects %>%
  pull(key) %>%
  first() %>%
  s3_remove(objects = .,
            bucket = "test-bkt")
## End(Not run)

Unpack Objects Key

Description

Unpack Objects Key

Usage

s3_unpack_keys(df_objects, rmv_sysfiles = TRUE, rmv_hidden = TRUE)
s3_unpack_keys(df_objects, rmv_sysfiles = TRUE, rmv_hidden = TRUE)

Arguments

`df_objects`	S3 objects as df
`rmv_sysfiles`	Remove System object (logs, git folders, etc.)?
`rmv_hidden`	Remove Hidden objects (folders/files starting with dot) ?

Value

S3 Cleaned Objects list as tibble

Examples

## Not run: 
s3_objects("sample-bucket") %>% s3_unpack_keys()
## End(Not run)

## Not run: 
s3_objects("sample-bucket") %>% s3_unpack_keys()
## End(Not run)

Upload file to S3 Bucket

Description

Upload file to S3 Bucket

Usage

s3_upload(
  filepath,
  bucket,
  prefix = NULL,
  object = NULL,
  access_key,
  secret_key,
  ...
)
s3_upload(
  filepath,
  bucket,
  prefix = NULL,
  object = NULL,
  access_key,
  secret_key,
  ...
)

Arguments

`filepath`	Source file path
`bucket`	S3 backet name
`prefix`	S3 Prefix (folder structure). Default set to NULL
`object`	Destination S3 object name (with file extention)
`access_key`	S3 Access Key
`secret_key`	S3 Secret Key
`...`	Additional aws.S3::put_object() options

Value

boolean

Examples

## Not run: 
filepath %>% s3_upload(bucket = "test-bkt")
## End(Not run)

## Not run: 
filepath %>% s3_upload(bucket = "test-bkt")
## End(Not run)

Check if variable exist

Description

Check if variable exist

Usage

var_exists(df, var)
var_exists(df, var)

Arguments

`df`	data frame to check against
`var`	quoted variable of interest

Examples

## Not run: 
var_exists(df, "val") 
## End(Not run)
## Not run: 
var_exists(df, "val") 
## End(Not run)

Pull Genie Data from PDAP Wave

Description

This function provides streamlined API access to PDAP Wave data, the successor to DATIM Genie. PDAP Wave API simplifies the requests that previously needed to be made with DATIM and returns a dataset back that matches the MSD structure. Further documentation can be found at https://wave.test.pdap.pepfar.net/api/docs#/.

Usage

wave_process_query(
  request_body,
  folderpath_dwnld = "Data",
  psd_type = c("psnu_im", "ou_im", "site_im"),
  request_type = c("POST", "GET"),
  username,
  password
)
wave_process_query(
  request_body,
  folderpath_dwnld = "Data",
  psd_type = c("psnu_im", "ou_im", "site_im"),
  request_type = c("POST", "GET"),
  username,
  password
)

Arguments

`request_body`	elements to pass into the PDAP Wave POST API
`folderpath_dwnld`	where to download, default = "Data"
`psd_type`	Type of PEPFAR Structured dataset: "psnu_im" (default), "ou_im", or "site_im"
`request_type`	API request type: "POST" (default) or "GET
`username`	DATIM username, if blank looks for stored credentials (`glamr::set_datim()`) and then prompts for credentials if not found
`password`	DATIM password, if blank looks for stored credentials (`glamr::set_datim()`) and then prompts for credentials if not found

Details

Users must pass their query filter in a list form into request_body, which matches what you would manually do in Genie previously. You can proceed with either POST or GET requests to access PSNUxIM, OUxIM, and SitexIM data.

This function was adapted from code developed and shared by Derek Wood (GHSD/PRIME).

Value

list of request and stored data in zip

Examples

## Not run: 
 library(tidyverse)
 library(glamr)

 #get country uid for API
cntry_uid <- pepfar_country_list %>%
 filter(country == "Tanzania") %>%
 pull(country_uid)

 #establish parameters to pass into POST API
 post_body <- list(
   daily_frozen='daily',
   fiscal_year=list(2023, 2024),
   funding_agency = list("USAID"),
   indicator=list("TX_CURR","TX_ML","TX_CURR_LAG2", "TX_NET_NEW","TX_NEW",
                  "TX_RTT","PMTCT_STAT", "PMTCT_STAT_POS", "PMTCT_ART"),
   uid_hierarchy_list=list(str_glue('-|-|{cntry_uid}')))

 #run POST API
 wave_process_query(post_body)

 #load data
 df_wave <- return_latest("Data") %>%
    read_psd()
    
## End(Not run)
## Not run: 
 library(tidyverse)
 library(glamr)

 #get country uid for API
cntry_uid <- pepfar_country_list %>%
 filter(country == "Tanzania") %>%
 pull(country_uid)

 #establish parameters to pass into POST API
 post_body <- list(
   daily_frozen='daily',
   fiscal_year=list(2023, 2024),
   funding_agency = list("USAID"),
   indicator=list("TX_CURR","TX_ML","TX_CURR_LAG2", "TX_NET_NEW","TX_NEW",
                  "TX_RTT","PMTCT_STAT", "PMTCT_STAT_POS", "PMTCT_ART"),
   uid_hierarchy_list=list(str_glue('-|-|{cntry_uid}')))

 #run POST API
 wave_process_query(post_body)

 #load data
 df_wave <- return_latest("Data") %>%
    read_psd()
    
## End(Not run)

Package 'grabr'

Help Index

Get dimension / item id

Description

Usage

Arguments

Value

Examples

Get PEPFAR/DATIM Dimension Items

Description

Usage

Arguments

Value

Examples

Build PEPFAR/DATIM Query dimension

Description

Usage

Arguments

Value

Examples

Get PEPFAR/DATIM Dimension ID

Description

Usage

Arguments

Value

Examples

Get PEPFAR/DATIM dimensions

Description

Usage

Arguments

Value

Examples

Execute Datim Query

Description

Usage

Arguments

Value

Examples

Extract Mechanisms infos from Datim

Description

Usage

Arguments

Value

Examples

Pull Orgunits SQLView

Description

Usage

Arguments

Value

Examples

Extract PLHIV and General POP Estimates from datim

Description

Usage

Arguments

Value

Examples

Process Datim Query results

Description

Usage

Arguments

Value

Examples

Extract PEPFAR Org Hierarchy

Description

Usage

Arguments

Note

Examples

Query PEPFAR/DATIM targets/results data

Description

Usage

Arguments

Value

Examples

Query Datim SQLViews

Description

Usage

Arguments

Value

Note