The goal of {tidyndr} is to provide a specialized, simple and easy to
use functions that wrap around existing functions in R for
manipulation of the NDR patient
line-list file allowing the user to focus on the tasks to be completed
rather than the code/formula details.
The functions presented are similar to the PEPFAR MER indicators and are currently grouped into four categories:
-
The
read_ndrfunction for reading the patient-level line-list downloaded from the front-end of the NDR in ‘csv’ format. -
The PEPFAR treatment group of indicators that can be performed on the NDR line-list.
-
The ‘Viral Load’ indicators (
tx_vl_eligible(),tx_pvls_den()tx_pvls_num()andtx_vl_unsuppressed()). -
The summary functions (
summarise_ndr()anddisaggregrate()) provides a tabular summary for the tasks that have been completed using any of the functions above.
You can install the released version of tidyndr from CRAN with:
install.packages("tidyndr")Or the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("stephenbalogun/tidyndr",
build_vignette = TRUE)library(tidyndr)
#> Attaching package: 'tidyndr'
#> A package for analysis of the front-end patient-level data from the Nigeria National Data Repository.read_ndr() reads the downloaded “.csv” file into
R using
vroom::vroom() behind the scene and
passing appropriate column types to the col_types argument. It also
formats the variable names using the
snakecase style.
## read from a local file path (not run)
# file_path <- system.file("extdata", "ndr_example.csv", package = "tidyndr")
# read_ndr(file_path, time_stamp = "2021-02-15")
### read line-list available on the internet
path <- "https://raw.githubusercontent.com/stephenbalogun/example_files/main/ndr_example.csv"
ndr_example <- read_ndr(path, time_stamp = "2021-02-20")
#>
#> Three new variables created:
#> [1] `date_lost`
#> [2] `appointment_date
#> [2] `current_statusThe functions included in this group are:
-
tx_new() -
tx_curr() -
tx_ml()andtx_ml_outcomes() -
tx_rtt() -
Other supporting functions are:
tx_mmd(),tx_regimen()andtx_appointment()
## Subset "TX_NEW"
tx_new(ndr_example)
#> Warning: One or more parsing issues, see `problems()` for details
#> # A tibble: 5,239 x 52
#> ip state lga facility datim_code sex patient_identif~ hospital_number
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 1004 0003
#> 2 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 3008 0003
#> 3 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 2008 0003
#> 4 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30012 0003
#> 5 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20011 0005
#> 6 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30025 0008
#> 7 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 30026 0007
#> 8 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30037 00013
#> 9 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30039 00014
#> 10 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30047 00019
#> # ... with 5,229 more rows, and 44 more variables: date_of_birth <date>,
#> # age_at_art_initiation <dbl>, current_age <dbl>, art_start_date <date>,
#> # art_start_date_source <fct>, last_drug_pickup_date <date>,
#> # last_drug_pickup_date_q1 <date>, last_drug_pickup_date_q2 <date>,
#> # last_drug_pickup_date_q3 <date>, last_drug_pickup_date_q4 <date>,
#> # last_regimen <fct>, last_clinic_visit_date <date>,
#> # days_of_arv_refill <dbl>, pregnancy_status <fct>, current_viral_load <dbl>,
#> # date_of_current_viral_load <date>, current_viral_load_q1 <dbl>,
#> # date_of_current_viral_load_q1 <date>, current_viral_load_q2 <dbl>,
#> # date_of_current_viral_load_q2 <date>, current_viral_load_q3 <dbl>,
#> # date_of_current_viral_load_q3 <date>, current_viral_load_q4 <dbl>,
#> # date_of_current_viral_load_q4 <date>, current_status_28_days <fct>,
#> # current_status_90_days <fct>, current_status_q1_28_days <fct>,
#> # current_status_q1_90_days <fct>, current_status_q2_28_days <fct>,
#> # current_status_q2_90_days <fct>, current_status_q3_28_days <fct>,
#> # current_status_q3_90_days <fct>, current_status_q4_28_days <fct>,
#> # current_status_q4_90_days <fct>, patient_has_died <lgl>,
#> # patient_deceased_date <date>, patient_transferred_out <lgl>,
#> # transferred_out_date <date>, patient_transferred_in <lgl>,
#> # transferred_in_date <date>, x49 <chr>, date_lost <date>,
#> # appointment_date <date>, current_status <chr>
## Subset "TX_CURR" for a state
ndr_example %>%
tx_curr(states = "State 1")
#> # A tibble: 3,726 x 52
#> ip state lga facility datim_code sex patient_identif~ hospital_number
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 1001 0001
#> 2 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 1002 0001
#> 3 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 1004 0003
#> 4 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 1005 0004
#> 5 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 1009 0001
#> 6 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10010 0001
#> 7 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10011 0002
#> 8 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10014 0005
#> 9 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10022 0007
#> 10 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10027 00013
#> # ... with 3,716 more rows, and 44 more variables: date_of_birth <date>,
#> # age_at_art_initiation <dbl>, current_age <dbl>, art_start_date <date>,
#> # art_start_date_source <fct>, last_drug_pickup_date <date>,
#> # last_drug_pickup_date_q1 <date>, last_drug_pickup_date_q2 <date>,
#> # last_drug_pickup_date_q3 <date>, last_drug_pickup_date_q4 <date>,
#> # last_regimen <fct>, last_clinic_visit_date <date>,
#> # days_of_arv_refill <dbl>, pregnancy_status <fct>, current_viral_load <dbl>,
#> # date_of_current_viral_load <date>, current_viral_load_q1 <dbl>,
#> # date_of_current_viral_load_q1 <date>, current_viral_load_q2 <dbl>,
#> # date_of_current_viral_load_q2 <date>, current_viral_load_q3 <dbl>,
#> # date_of_current_viral_load_q3 <date>, current_viral_load_q4 <dbl>,
#> # date_of_current_viral_load_q4 <date>, current_status_28_days <fct>,
#> # current_status_90_days <fct>, current_status_q1_28_days <fct>,
#> # current_status_q1_90_days <fct>, current_status_q2_28_days <fct>,
#> # current_status_q2_90_days <fct>, current_status_q3_28_days <fct>,
#> # current_status_q3_90_days <fct>, current_status_q4_28_days <fct>,
#> # current_status_q4_90_days <fct>, patient_has_died <lgl>,
#> # patient_deceased_date <date>, patient_transferred_out <lgl>,
#> # transferred_out_date <date>, patient_transferred_in <lgl>,
#> # transferred_in_date <date>, x49 <chr>, date_lost <date>,
#> # appointment_date <date>, current_status <chr>
## Generate line-list of clients with medication refill in January 2021 for a facility (Facility 1)
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-01-31",
facilities = "Facility 1")
#> # A tibble: 413 x 52
#> ip state lga facility datim_code sex patient_identif~ hospital_number
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 2003 0002
#> 2 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 3008 0003
#> 3 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20016 0004
#> 4 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 30026 0007
#> 5 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10016 0003
#> 6 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20064 00010
#> 7 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 200122 00020
#> 8 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 200131 00021
#> 9 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 100131 00019
#> 10 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 200256 00045
#> # ... with 403 more rows, and 44 more variables: date_of_birth <date>,
#> # age_at_art_initiation <dbl>, current_age <dbl>, art_start_date <date>,
#> # art_start_date_source <fct>, last_drug_pickup_date <date>,
#> # last_drug_pickup_date_q1 <date>, last_drug_pickup_date_q2 <date>,
#> # last_drug_pickup_date_q3 <date>, last_drug_pickup_date_q4 <date>,
#> # last_regimen <fct>, last_clinic_visit_date <date>,
#> # days_of_arv_refill <dbl>, pregnancy_status <fct>, current_viral_load <dbl>,
#> # date_of_current_viral_load <date>, current_viral_load_q1 <dbl>,
#> # date_of_current_viral_load_q1 <date>, current_viral_load_q2 <dbl>,
#> # date_of_current_viral_load_q2 <date>, current_viral_load_q3 <dbl>,
#> # date_of_current_viral_load_q3 <date>, current_viral_load_q4 <dbl>,
#> # date_of_current_viral_load_q4 <date>, current_status_28_days <fct>,
#> # current_status_90_days <fct>, current_status_q1_28_days <fct>,
#> # current_status_q1_90_days <fct>, current_status_q2_28_days <fct>,
#> # current_status_q2_90_days <fct>, current_status_q3_28_days <fct>,
#> # current_status_q3_90_days <fct>, current_status_q4_28_days <fct>,
#> # current_status_q4_90_days <fct>, patient_has_died <lgl>,
#> # patient_deceased_date <date>, patient_transferred_out <lgl>,
#> # transferred_out_date <date>, patient_transferred_in <lgl>,
#> # transferred_in_date <date>, x49 <chr>, date_lost <date>,
#> # appointment_date <date>, current_status <chr>
## Generate list of clients who were active at the beginning of FY21 but became inactive at the end of Q1 of FY21.
tx_ml(new_data = ndr_example,
from = "2020-10-01",
to = "2020-12-31")
#> # A tibble: 2,593 x 52
#> ip state lga facility datim_code sex patient_identif~ hospital_number
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 1007 0003
#> 2 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30025 0008
#> 3 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20041 0002
#> 4 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20050 00020
#> 5 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 300107 00040
#> 6 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20090 00025
#> 7 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 300111 00042
#> 8 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 10065 00011
#> 9 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 200118 00047
#> 10 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 10073 00023
#> # ... with 2,583 more rows, and 44 more variables: date_of_birth <date>,
#> # age_at_art_initiation <dbl>, current_age <dbl>, art_start_date <date>,
#> # art_start_date_source <fct>, last_drug_pickup_date <date>,
#> # last_drug_pickup_date_q1 <date>, last_drug_pickup_date_q2 <date>,
#> # last_drug_pickup_date_q3 <date>, last_drug_pickup_date_q4 <date>,
#> # last_regimen <fct>, last_clinic_visit_date <date>,
#> # days_of_arv_refill <dbl>, pregnancy_status <fct>, current_viral_load <dbl>,
#> # date_of_current_viral_load <date>, current_viral_load_q1 <dbl>,
#> # date_of_current_viral_load_q1 <date>, current_viral_load_q2 <dbl>,
#> # date_of_current_viral_load_q2 <date>, current_viral_load_q3 <dbl>,
#> # date_of_current_viral_load_q3 <date>, current_viral_load_q4 <dbl>,
#> # date_of_current_viral_load_q4 <date>, current_status_28_days <fct>,
#> # current_status_90_days <fct>, current_status_q1_28_days <fct>,
#> # current_status_q1_90_days <fct>, current_status_q2_28_days <fct>,
#> # current_status_q2_90_days <fct>, current_status_q3_28_days <fct>,
#> # current_status_q3_90_days <fct>, current_status_q4_28_days <fct>,
#> # current_status_q4_90_days <fct>, patient_has_died <lgl>,
#> # patient_deceased_date <date>, patient_transferred_out <lgl>,
#> # transferred_out_date <date>, patient_transferred_in <lgl>,
#> # transferred_in_date <date>, x49 <chr>, date_lost <date>,
#> # appointment_date <date>, current_status <chr>The tx_vl_eligible(), tx_pvls_den() and the tx_pvls_num()
functions come in handy when you need to generate the line-list of
clients who are eligible for viral load test at a given point for a
given facility/state, those who have a valid viral load result (not more
than 1 year for people aged 20 years and above and not more than 6
months for paediatrics and adolescents less or equal to 19 years), and
those who are virally suppressed (out of those with valid viral load
results). When the sample = TRUE attribute is supplied to the
tx_vl_eligible() function, it generates the line-list of only those
who are due for a viral load test out of all those who are eligible.
## Generate list of clients who are eligible for VL (i.e. expected to have a documented VL result)
ndr_example %>%
tx_vl_eligible()
#> # A tibble: 14,160 x 52
#> ip state lga facility datim_code sex patient_identif~ hospital_number
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 1001 0001
#> 2 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 2001 0001
#> 3 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 3001 0001
#> 4 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 1002 0001
#> 5 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 3005 0001
#> 6 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 2005 0001
#> 7 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 3006 0002
#> 8 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 1005 0004
#> 9 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 3009 0004
#> 10 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 2009 0001
#> # ... with 14,150 more rows, and 44 more variables: date_of_birth <date>,
#> # age_at_art_initiation <dbl>, current_age <dbl>, art_start_date <date>,
#> # art_start_date_source <fct>, last_drug_pickup_date <date>,
#> # last_drug_pickup_date_q1 <date>, last_drug_pickup_date_q2 <date>,
#> # last_drug_pickup_date_q3 <date>, last_drug_pickup_date_q4 <date>,
#> # last_regimen <fct>, last_clinic_visit_date <date>,
#> # days_of_arv_refill <dbl>, pregnancy_status <fct>, current_viral_load <dbl>,
#> # date_of_current_viral_load <date>, current_viral_load_q1 <dbl>,
#> # date_of_current_viral_load_q1 <date>, current_viral_load_q2 <dbl>,
#> # date_of_current_viral_load_q2 <date>, current_viral_load_q3 <dbl>,
#> # date_of_current_viral_load_q3 <date>, current_viral_load_q4 <dbl>,
#> # date_of_current_viral_load_q4 <date>, current_status_28_days <fct>,
#> # current_status_90_days <fct>, current_status_q1_28_days <fct>,
#> # current_status_q1_90_days <fct>, current_status_q2_28_days <fct>,
#> # current_status_q2_90_days <fct>, current_status_q3_28_days <fct>,
#> # current_status_q3_90_days <fct>, current_status_q4_28_days <fct>,
#> # current_status_q4_90_days <fct>, patient_has_died <lgl>,
#> # patient_deceased_date <date>, patient_transferred_out <lgl>,
#> # transferred_out_date <date>, patient_transferred_in <lgl>,
#> # transferred_in_date <date>, x49 <chr>, date_lost <date>,
#> # appointment_date <date>, current_status <chr>
## Generate list of clients that will be expected to have a viral load test done in Q2 of FY21 for "State 2"
ndr_example %>%
tx_vl_eligible("2021-03-31",
states = "State 2",
sample = TRUE)
#> # A tibble: 2,360 x 52
#> ip state lga facility datim_code sex patient_identif~ hospital_number
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20012 0001
#> 2 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20016 0004
#> 3 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20018 0004
#> 4 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20021 0008
#> 5 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20025 0009
#> 6 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20027 0008
#> 7 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 20033 00011
#> 8 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20034 00012
#> 9 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20035 00013
#> 10 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 20037 00015
#> # ... with 2,350 more rows, and 44 more variables: date_of_birth <date>,
#> # age_at_art_initiation <dbl>, current_age <dbl>, art_start_date <date>,
#> # art_start_date_source <fct>, last_drug_pickup_date <date>,
#> # last_drug_pickup_date_q1 <date>, last_drug_pickup_date_q2 <date>,
#> # last_drug_pickup_date_q3 <date>, last_drug_pickup_date_q4 <date>,
#> # last_regimen <fct>, last_clinic_visit_date <date>,
#> # days_of_arv_refill <dbl>, pregnancy_status <fct>, current_viral_load <dbl>,
#> # date_of_current_viral_load <date>, current_viral_load_q1 <dbl>,
#> # date_of_current_viral_load_q1 <date>, current_viral_load_q2 <dbl>,
#> # date_of_current_viral_load_q2 <date>, current_viral_load_q3 <dbl>,
#> # date_of_current_viral_load_q3 <date>, current_viral_load_q4 <dbl>,
#> # date_of_current_viral_load_q4 <date>, current_status_28_days <fct>,
#> # current_status_90_days <fct>, current_status_q1_28_days <fct>,
#> # current_status_q1_90_days <fct>, current_status_q2_28_days <fct>,
#> # current_status_q2_90_days <fct>, current_status_q3_28_days <fct>,
#> # current_status_q3_90_days <fct>, current_status_q4_28_days <fct>,
#> # current_status_q4_90_days <fct>, patient_has_died <lgl>,
#> # patient_deceased_date <date>, patient_transferred_out <lgl>,
#> # transferred_out_date <date>, patient_transferred_in <lgl>,
#> # transferred_in_date <date>, x49 <chr>, date_lost <date>,
#> # appointment_date <date>, current_status <chr>
### Calculate the Viral Load Coverage for State 3
no_of_vl_results <- tx_pvls_den(ndr_example,
states = "State 3") %>%
nrow()
no_of_vl_eligible <- tx_vl_eligible(ndr_example,
states = "State 3") %>%
nrow()
vl_coverage <- scales::percent(no_of_vl_results / no_of_vl_eligible)
print(vl_coverage)
#> [1] "56%"For all the ‘Treatment’ and ‘Viral Suppression’ indicators (except
tx_ml_outcomes(), which should be use with tx_ml()), you have
control over the level of action (state or facility) by supplying to the
states and/or facilities arguments the values of interest . For more
than one state or facility, combine the values with the c() e.g.
## subset clients that have medication appointment in Q2 of FY21 for "State 1" and "State 3" and are also due for viral load
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-03-31",
states = c("State 1", "State 3")) %>%
tx_vl_eligible(sample = TRUE)
#> # A tibble: 2,643 x 52
#> ip state lga facility datim_code sex patient_identif~ hospital_number
#> <fct> <fct> <fct> <fct> <fct> <fct> <chr> <chr>
#> 1 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ M State 1001 0001
#> 2 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 3006 0002
#> 3 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30027 0009
#> 4 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10011 0002
#> 5 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30031 00010
#> 6 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30034 00011
#> 7 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30044 0002
#> 8 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30045 00018
#> 9 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 30049 00021
#> 10 IP_na~ Stat~ LGA0~ Facilit~ datim_cod~ F State 10022 0007
#> # ... with 2,633 more rows, and 44 more variables: date_of_birth <date>,
#> # age_at_art_initiation <dbl>, current_age <dbl>, art_start_date <date>,
#> # art_start_date_source <fct>, last_drug_pickup_date <date>,
#> # last_drug_pickup_date_q1 <date>, last_drug_pickup_date_q2 <date>,
#> # last_drug_pickup_date_q3 <date>, last_drug_pickup_date_q4 <date>,
#> # last_regimen <fct>, last_clinic_visit_date <date>,
#> # days_of_arv_refill <dbl>, pregnancy_status <fct>, current_viral_load <dbl>,
#> # date_of_current_viral_load <date>, current_viral_load_q1 <dbl>,
#> # date_of_current_viral_load_q1 <date>, current_viral_load_q2 <dbl>,
#> # date_of_current_viral_load_q2 <date>, current_viral_load_q3 <dbl>,
#> # date_of_current_viral_load_q3 <date>, current_viral_load_q4 <dbl>,
#> # date_of_current_viral_load_q4 <date>, current_status_28_days <fct>,
#> # current_status_90_days <fct>, current_status_q1_28_days <fct>,
#> # current_status_q1_90_days <fct>, current_status_q2_28_days <fct>,
#> # current_status_q2_90_days <fct>, current_status_q3_28_days <fct>,
#> # current_status_q3_90_days <fct>, current_status_q4_28_days <fct>,
#> # current_status_q4_90_days <fct>, patient_has_died <lgl>,
#> # patient_deceased_date <date>, patient_transferred_out <lgl>,
#> # transferred_out_date <date>, patient_transferred_in <lgl>,
#> # transferred_in_date <date>, x49 <chr>, date_lost <date>,
#> # appointment_date <date>, current_status <chr>You might want to generate a summary table of all the indicators you
have pulled out. The summarise_ndr() (or summarize_ndr()) allows you
to do this with ease. It accepts all the line-lists you are interested
in creating a summary table for, the level at which you want the summary
to be created (country/ip, state or facility), and the names you want to
give to each of your summary column.
new <- tx_new(ndr_example) ## generates line-list of TX_NEW for the FY
curr <- tx_curr(ndr_example) ## generates line-list of currently active clients
ml <- tx_ml(new_data = ndr_example) ## generates line-list of clients who were active at the beginning of the FY but currently inactive
summarise_ndr(new, curr, ml,
level = "state",
names = c("tx_new", "tx_curr", "tx_ml"))
#> # A tibble: 4 x 5
#> ip state tx_new tx_curr tx_ml
#> <chr> <chr> <int> <int> <int>
#> 1 IP_name State 1 662 3726 3081
#> 2 IP_name State 2 1239 5058 4910
#> 3 IP_name State 3 3338 7912 4005
#> 4 Total - 5239 16696 11997The disaggregate() allows you to summarise an indicator of interest
into finer details based on “age”, “sex” or “pregnancy status”. These
are supplied to the by parameter of the function. The default
disaggregates the variable of interest at the level of “states” but can
also do this at “country/ip”, “lga” or “facility” level when any of this
is supplied to the level parameter.
new_clients <- tx_new(ndr_example) ## generates line-list of TX_NEW for the FY
disaggregate(new_clients,
by = "current_age")
#> # A tibble: 4 x 14
#> ip state `1-4` `5-9` `10-14` `15-19` `20-24` `25-29` `30-34` `35-39`
#> <chr> <chr> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 IP_name State 1 4 2 1 19 78 167 169 94
#> 2 IP_name State 2 3 3 5 10 145 317 349 174
#> 3 IP_name State 3 10 12 10 131 436 702 703 474
#> 4 Total - 17 17 16 160 659 1186 1221 742
#> # ... with 4 more variables: 40-44 <int>, 45-49 <int>, 50+ <int>, Total <dbl>
## disaggregate 'TX_CURR' by sex
ndr_example %>%
tx_curr() %>%
disaggregate(by = "sex")
#> # A tibble: 4 x 6
#> ip state Male Female unknown Total
#> <chr> <chr> <int> <int> <int> <dbl>
#> 1 IP_name State 1 1102 2624 0 3726
#> 2 IP_name State 2 1606 3451 1 5058
#> 3 IP_name State 3 3386 4526 0 7912
#> 4 Total - 6094 10601 1 16696Please note that the tidyndr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.