# Install release version from CRAN
install.packages("chmsflow")
# Install the most recent version from GitHub
devtools::install_github("Big-Life-Lab/chmsflow")1. Load packages
2. Recode medication variables for individual cycles
Medication data object always has to be called “cyclex_meds” in order for recoding to work properly, with rest of the data being separated and called “cyclex” as stated in recoding-variables.qmd.
Note that row headers for medication data of cycles 1, 4, and 6 must be put to lower case prior to recoding.
# Load recodeflow and dplyr
library(recodeflow)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# Recoding basic variables from cycles 1 and 3
cycle2_medication_data <- rec_with_table(cycle2_meds, c("clinicid", "atc_101a", "mhr_101b"), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
head(cycle2_medication_data) atc_101a clinicid mhr_101b
1 C07AA05 1 2
2 C09AA06 2 3
3 C08CA01 3 1
4 A10BC02 4 3
cycle3_medication_data <- rec_with_table(cycle3_meds, c("clinicid", "meucatc", "npi_25b"), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
head(cycle3_medication_data) clinicid meucatc npi_25b
1 1 C07AA05 2
2 1 C09AA06 3
3 1 C08CA01 1
4 1 A10BC02 3
5 1 C07AA05 2
6 1 C09AA06 3
# Recoding derived variable acemed for cycle2. Select vars by role used to avoid writing all med variables in one line
cycle2_ace_medication_data <- rec_with_table(cycle2_meds, c("clinicid", "acemed", recodeflow:::select_vars_by_role("Drugs", variables)), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recodedINFO [2025-09-23 04:21:11] Adding variable 'ACEmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ACEmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ACEmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ACEmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'diabetes_drug' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'diabetes_drug' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'diabetes_drug' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'diabetes_drug' to the data frame. clinicid acemed
1 1 1
2 2 1
3 3 1
4 4 0
# Recoding derived variable acemed for cycle3. Much simpler function used here.
cycle3_ace_medication_data <- rec_with_table(cycle3_meds, c("clinicid", "meucatc", "npi_25b", "acemed"), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
head(cycle3_ace_medication_data) clinicid meucatc npi_25b acemed
1 1 C07AA05 2 0
2 1 C09AA06 3 1
3 1 C08CA01 1 0
4 1 A10BC02 3 0
5 1 C07AA05 2 0
6 1 C09AA06 3 13. Merge recoded medication data from different cycles
# Aggregating recoded cycle3 data by clinicid
cycle3_ace_medication_data <- cycle3_ace_medication_data %>%
group_by(clinicid) %>%
summarize(
meucatc = paste(unique(meucatc), collapse = ", "), # Concatenate unique values of meucatc
npi_25b = paste(unique(npi_25b), collapse = ", "), # Concatenate unique values of npi_25b
acemed = max(as.numeric(as.character(acemed))) # Find maximum of acemed
)
head(cycle3_ace_medication_data)# A tibble: 4 × 4
clinicid meucatc npi_25b acemed
<dbl> <chr> <chr> <dbl>
1 1 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 1
2 2 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 1
3 3 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 1
4 4 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 1
# De-factorizing acemed variable in recoded cycle2 data
cycle2_ace_medication_data$acemed <- as.numeric(as.character(cycle2_ace_medication_data$acemed))
# Merging recoded cycle2 and cycle3 data, and ensuring one acemed column in the end
cycles2and3_ace_medication_data <- merge(cycle2_ace_medication_data, cycle3_ace_medication_data, by = "clinicid")
cycles2and3_ace_medication_data <- cycles2and3_ace_medication_data %>%
mutate(acemed = pmax(acemed.x, acemed.y)) %>%
select(-c(acemed.x, acemed.y))
head(cycles2and3_ace_medication_data) clinicid atc_101a atc_102a atc_103a atc_104a atc_105a atc_106a atc_107a
1 1 C07AA05 A01AB05 C02CC07 C03CA01 C09AA04 C09AA04 C09AA04
2 2 C09AA06 C09AA02 C08CA06 C03CA01 C07AB02 C07AB02 C07AB02
3 3 C08CA01 C07AB02 C07AB07 C09AA04 C08CA02 C08CA02 C08CA02
4 4 A10BC02 C03AA03 C07AB03 C08CA02 A10BD05 A10BD05 A10BD05
atc_108a atc_109a atc_110a atc_111a atc_112a atc_113a atc_114a atc_115a
1 C09AA04 C09AA04 C09AA04 C09AA04 C09AA04 C09AA04 C09AA04 C09AA04
2 C07AB02 C07AB02 C07AB02 C07AB02 C07AB02 C07AB02 C07AB02 C07AB02
3 C08CA02 C08CA02 C08CA02 C08CA02 C08CA02 C08CA02 C08CA02 C08CA02
4 A10BD05 A10BD05 A10BD05 A10BD05 A10BD05 A10BD05 A10BD05 A10BD05
atc_131a atc_132a atc_133a atc_134a atc_135a atc_201a atc_202a atc_203a
1 C07AA05 C03BA08 C03BC02 C02KX01 A01AB05 C03BA08 C07AA05 C03BA08
2 C09AA06 C07AA07 C08CA01 C02AA05 C09AA02 C07AA07 C09AA06 C07AA07
3 C08CA01 C07AA12 C07AA05 C07AG02 C07AB02 C07AA12 C08CA01 C07AA12
4 A10BC02 M01AG02 C07AA06 C07AA06 C03AA03 M01AG02 A10BC02 M01AG02
atc_204a atc_205a atc_206a atc_207a atc_208a atc_209a atc_210a atc_211a
1 C03BC02 C02KX01 A01AB05 C02CC07 C03CA01 C09AA04 C09AA04 C09AA04
2 C08CA01 C02AA05 C09AA02 C08CA06 C03CA01 C07AB02 C07AB02 C07AB02
3 C07AA05 C07AG02 C07AB02 C07AB07 C09AA04 C08CA02 C08CA02 C08CA02
4 C07AA06 C07AA06 C03AA03 C07AB03 C08CA02 A10BD05 A10BD05 A10BD05
atc_212a atc_213a atc_214a atc_215a atc_231a atc_232a atc_233a atc_234a
1 C09AA04 C09AA04 C09AA04 C09AA04 C02CC07 C03CA01 C09AA04 C09AA04
2 C07AB02 C07AB02 C07AB02 C07AB02 C08CA06 C03CA01 C07AB02 C07AB02
3 C08CA02 C08CA02 C08CA02 C08CA02 C07AB07 C09AA04 C08CA02 C08CA02
4 A10BD05 A10BD05 A10BD05 A10BD05 C07AB03 C08CA02 A10BD05 A10BD05
atc_235a mhr_101b mhr_102b mhr_103b mhr_104b mhr_105b mhr_106b mhr_107b
1 C09AA04 2 1 3 1 3 3 3
2 C07AB02 3 4 2 2 2 2 2
3 C08CA02 1 3 1 4 1 1 1
4 A10BD05 3 2 4 3 4 4 4
mhr_108b mhr_109b mhr_110b mhr_111b mhr_112b mhr_113b mhr_114b mhr_115b
1 3 3 3 3 3 3 3 3
2 2 2 2 2 2 2 2 2
3 1 1 1 1 1 1 1 1
4 4 4 4 4 4 4 4 4
mhr_131b mhr_132b mhr_133b mhr_134b mhr_135b mhr_201b mhr_202b mhr_203b
1 2 3 3 1 1 3 2 3
2 3 2 1 2 4 2 3 2
3 1 2 5 3 3 2 1 2
4 3 3 1 4 2 3 3 3
mhr_204b mhr_205b mhr_206b mhr_207b mhr_208b mhr_209b mhr_210b mhr_211b
1 3 1 1 3 1 3 3 3
2 1 2 4 2 2 2 2 2
3 5 3 3 1 4 1 1 1
4 1 4 2 4 3 4 4 4
mhr_212b mhr_213b mhr_214b mhr_215b mhr_231b mhr_232b mhr_233b mhr_234b
1 3 3 3 3 3 1 3 3
2 2 2 2 2 2 2 2 2
3 1 1 1 1 1 4 1 1
4 4 4 4 4 4 3 4 4
mhr_235b anymed diab_drug meucatc npi_25b acemed
1 3 1 0 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 1
2 2 1 0 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 1
3 1 1 0 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 1
4 4 1 1 C07AA05, C09AA06, C08CA01, A10BC02 2, 3, 1 14. Example: Determine hypertension status by recoding medications
If you need medication variables in your analysis (especially to derive other variables), always recode them before recoding other variables.
# Cycles 1-2
# Recode medication variables first - anymed for hypertension and diab_drug for diabetes which is involved in determining hypertension status
cycle2_htn_medication_data <- rec_with_table(cycle2_meds, c("clinicid", recodeflow:::select_vars_by_role("Drugs", variables)), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recodedINFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:11] Adding variable 'ANYmed' to the data frame.
INFO [2025-09-23 04:21:12] Adding variable 'diabetes_drug' to the data frame.
INFO [2025-09-23 04:21:12] Adding variable 'diabetes_drug' to the data frame.
INFO [2025-09-23 04:21:12] Adding variable 'diabetes_drug' to the data frame.
INFO [2025-09-23 04:21:12] Adding variable 'diabetes_drug' to the data frame.
# Create dummy medication variable by duplicating recoded medication variable
cycle2_htn_medication_data$anymed2 <- as.numeric(as.character(cycle2_htn_medication_data$anymed))
cycle2_htn_medication_data$diab_drug2 <- as.numeric(as.character(cycle2_htn_medication_data$diab_drug))
cycle2_htn_medication_data <- select(cycle2_htn_medication_data, clinicid, anymed2, diab_drug2)
# Merge medication data with rest of data
cycle2 <- merge(cycle2, cycle2_htn_medication_data, by = "clinicid")
# Recode hypertension (and diabetes) status using recoded, dummy medication variables
cycle2_htn_data <- rec_with_table(cycle2, c(recodeflow:::select_vars_by_role("Test", variables)), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recodedNOTE for pgdcgt: Respondents who respond as indigenous to previous question are identified as 'not applicable' in this question. Recode to "other", as per OCAP.
head(cycle2_htn_data) anymed2 bpmdpbpd bpmdpbps ccc_32 ccc_51 ccc_61 ccc_63 ccc_81 clc_age clc_sex
1 1 95 140 2 1 2 2 1 20 1
2 1 85 130 1 2 2 2 2 30 2
3 1 75 120 1 2 2 2 2 40 1
4 1 65 110 1 2 2 2 1 50 2
diab_drug2 diab_m lab_bcre lab_hba1 pgdcgt cardiov gfr ckd dbp_adj diabx
1 0 1 20 0.07 1 1 529.3541 2 94.45 1
2 0 2 30 0.06 1 2 226.5658 2 86.15 2
3 0 2 40 0.05 1 2 206.6563 2 77.85 2
4 1 NA(b) 50 NA 2 1 137.0664 2 69.55 1
sbp_adj highbp14090_adj
1 141.6 1
2 132.3 1
3 123.0 1
4 113.7 1
# Cycles 3-6
# Recode medication variables first - - anymed for hypertension and diab_drug for diabetes which is involved in determining hypertension status
cycle3_htn_medication_data <- rec_with_table(cycle3_meds, c("clinicid", "meucatc", "npi_25b", "anymed", "diab_drug"), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
# Aggregating recoded cycle3 data by clinicid
cycle3_htn_medication_data <- cycle3_htn_medication_data %>%
group_by(clinicid) %>%
summarize(
meucatc = paste(unique(meucatc), collapse = ", "), # Concatenate unique values of meucatc
npi_25b = paste(unique(npi_25b), collapse = ", "), # Concatenate unique values of npi_25b
anymed = max(as.numeric(as.character(anymed))), # Find maximum of anymed
diab_drug = max(as.numeric(as.character(diab_drug))) # Find maximum of diab_drug
)
# Create dummy medication variable by duplicating recoded medication variable
cycle3_htn_medication_data$anymed2 <- as.numeric(as.character(cycle3_htn_medication_data$anymed))
cycle3_htn_medication_data$diab_drug2 <- as.numeric(as.character(cycle3_htn_medication_data$diab_drug))
cycle3_htn_medication_data <- select(cycle3_htn_medication_data, clinicid, anymed2, diab_drug2)
# Merge medication data with original data
# Call overall data "cyclex" in order to recode other variables afterwards
cycle3 <- merge(cycle3, cycle3_htn_medication_data, by = "clinicid")
# Recode hypertension (and diabetes) status using recoded, dummy medication variables
cycle3_htn_data <- rec_with_table(cycle3, c(recodeflow:::select_vars_by_role("Test", variables)), variable_details = variable_details)Using the passed data variable name as database_nameWarning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recodedNOTE for pgdcgt: Respondents who respond as indigenous to previous question are identified as 'not applicable' in this question. Recode to "other", as per OCAP.
head(cycle3_htn_data) anymed2 bpmdpbpd bpmdpbps ccc_32 ccc_51 ccc_61 ccc_63 ccc_81 clc_age clc_sex
1 1 95 140 2 1 2 2 1 20 1
2 1 85 130 1 2 2 2 2 30 2
3 1 75 120 1 2 2 2 2 40 1
4 1 65 110 1 2 2 2 1 50 2
diab_drug2 diab_m lab_bcre lab_hba1 pgdcgt cardiov gfr ckd dbp_adj diabx
1 1 1 20 0.07 1 1 529.3541 2 94.45 1
2 1 2 30 0.06 1 2 226.5658 2 86.15 1
3 1 2 40 0.05 1 2 206.6563 2 77.85 1
4 1 NA(b) 50 NA 2 1 137.0664 2 69.55 1
sbp_adj highbp14090_adj
1 141.6 1
2 132.3 1
3 123.0 1
4 113.7 1