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Source: vignettes/get_started.qmd

1. Load packages 

# Install release version from CRAN
install.packages("chmsflow")

# Install the most recent version from GitHub
devtools::install_github("Big-Life-Lab/chmsflow")

2. Transform variables into harmonized versions

Use rec_with_table() of recodeflow to transform the variables of a CHMS dataset. Cycle data object always has to be called “cyclex” in order for recoding to work properly.

At the RDC, each cycle is split into multiple components (e.g., household data, clinic data, laboratory data, etc.), so it is the analyst’s responsibility to merge their required components in one database named “cyclex”. However, keep medication data separate from the rest of the cycle data (see here). Note that row headers for cycle 6 must be put to lower case prior to recoding.

# Load recodeflow
library(recodeflow)

# Recoding basic variables
cycle4_ages <- rec_with_table(cycle4, "clc_age", variable_details = variable_details, log = TRUE)
Using the passed data variable name as database_name
Warning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
The variable clc_age was recoded into clc_age for the database cycle4 the following recodes were made: 
  value_to       From rows_recoded
1     copy    [3, 80]         1000
2    NA::a        996            0
3    NA::b [997, 999]            0
4     <NA>       else            0
head(cycle4_ages)
  clc_age
1      50
2      34
3      70
4      33
5      22
6      61
cycle4_sexes <- rec_with_table(cycle4, "clc_sex", variable_details = variable_details, log = TRUE)
Using the passed data variable name as database_name
Warning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
The variable clc_sex was recoded into clc_sex for the database cycle4 the following recodes were made: 
  value_to   From rows_recoded
1        1      1          516
2        2      2          484
3    NA::a      6            0
4    NA::b [7, 9]            0
5    NA(b)   else            0
head(cycle4_sexes)
  clc_sex
1       2
2       2
3       1
4       1
5       1
6       2
# Recoding a transformed variable (cont to cat)
cycle4_categorical_ages <- rec_with_table(cycle4, "agegroup4", variable_details = variable_details, log = TRUE)
Using the passed data variable name as database_name
Warning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
The variable clc_age was recoded into agegroup4 for the database cycle4 the following recodes were made: 
  value_to       From rows_recoded
1        1   [20, 39]          337
2        2   [40, 59]          332
3        3   [60, 69]          161
4        4   [70, 79]          170
5    NA::a        996            0
6    NA::b [997, 999]            0
7    NA(b)       else            0
head(cycle4_categorical_ages)
  agegroup4
1         2
2         1
3         4
4         1
5         1
6         3
# Recoding a derived variable
cycle4_adjusted_SBPs <- rec_with_table(cycle4, c("bpmdpbps", "sbp_adj"), variable_details = variable_details, log = TRUE)
Using the passed data variable name as database_name
Warning in recode_call(variables = variables, data = data, database_name =
database_name, : is missing from variable details therefore cannot be recoded
The variable bpmdpbps was recoded into bpmdpbps for the database cycle4 the following recodes were made: 
  value_to       From rows_recoded
1     copy  [73, 216]          977
2    NA::a        996            1
3    NA::b [997, 999]           22
4     <NA>       else            0
head(cycle4_adjusted_SBPs)
  bpmdpbps sbp_adj
1      118  121.14
2      123  125.79
3      104  108.12
4      201  198.33
5       82   87.66
6      141  142.53