Split mixed tables, i.e. tables that hold both long data (N values per patient) and short data (one value per patient, duplicated on N lines), into one long table and one short table.
Usage
split_mixed_datasets(
datasets = get_datasets(),
id = get_subjid_cols(),
...,
ignore_cols = get_meta_cols(0.95),
output_code = FALSE,
verbose = TRUE
)Arguments
- datasets
a dataframe or a list of dataframes to split. Default to all the datasets from
.lookup.- id
the patient identifier, probably "SUBJID". Should be shared by all datasets. Case-insensitive.
- ...
not used
- ignore_cols
columns to ignore when considering a table as long. Default to
getOption("edc_cols_crfname", "CRFNAME"). Case-insensitive.- output_code
whether to print the code to explicitly write. Can also be a file path.
- verbose
whether to print informations about the process.
Value
a list of the new long and short tables. Use load_list() to load them into the global environment.
Examples
#tm = read_trialmaster("filename.zip", pw="xx")
tm = edc_example_mixed()
#> Warning: Option "edc_lookup" has been overwritten.
names(tm)
#> [1] "short" "long_pure" "long_mixed"
#> [4] "date_extraction" "datetime_extraction" ".lookup"
#load_list(tm)
print(tm$long_mixed) #`val1` and `val2` are long but `val3` is short
#> # A tibble: 200 × 5
#> SUBJID crfname val1b val2b val3b
#> <int> <chr> <dbl> <dbl> <chr>
#> 1 1 long_mixed 0.689 12.3 B
#> 2 1 long_mixed 0.725 10.5 B
#> 3 2 long_mixed 0.217 11.0 C
#> 4 2 long_mixed -0.202 10.4 C
#> 5 3 long_mixed -1.37 9.00 D
#> 6 3 long_mixed -0.309 9.40 D
#> 7 4 long_mixed -0.453 10.2 E
#> 8 4 long_mixed 0.663 7.07 E
#> 9 5 long_mixed 1.31 9.15 F
#> 10 5 long_mixed 0.501 10.8 F
#> # ℹ 190 more rows
mixed_data = split_mixed_datasets(tm, id="subjid", verbose=TRUE)
#> ✔ There was 1 short table:
#> "short"
#> ✔ There was 1 pure long table:
#> "long_pure"
#> ✔ There was 1 mixed (short+long) table:
#> "long_mixed"
#> → Use `EDCimport::load_list()` on the result to get separated long and short
#> data.
load_list(mixed_data)
print(long_mixed_short)
#> # A tibble: 100 × 3
#> SUBJID crfname val3b
#> <int> <chr> <chr>
#> 1 1 long_mixed B
#> 2 2 long_mixed C
#> 3 3 long_mixed D
#> 4 4 long_mixed E
#> 5 5 long_mixed F
#> 6 6 long_mixed G
#> 7 7 long_mixed H
#> 8 8 long_mixed I
#> 9 9 long_mixed J
#> 10 10 long_mixed K
#> # ℹ 90 more rows
print(long_mixed_long)
#> # A tibble: 200 × 3
#> SUBJID val1b val2b
#> <int> <dbl> <dbl>
#> 1 1 0.689 12.3
#> 2 1 0.725 10.5
#> 3 2 0.217 11.0
#> 4 2 -0.202 10.4
#> 5 3 -1.37 9.00
#> 6 3 -0.309 9.40
#> 7 4 -0.453 10.2
#> 8 4 0.663 7.07
#> 9 5 1.31 9.15
#> 10 5 0.501 10.8
#> # ℹ 190 more rows
#alternatively, get the code and only use the datasets you need
split_mixed_datasets(tm, id="SUBJID", output_code=TRUE)
#> ✔ There was 1 short table:
#> "short"
#> ✔ There was 1 pure long table:
#> "long_pure"
#> ✔ There was 1 mixed (short+long) table:
#> "long_mixed"
#> → Copy the following code in your script to separate long and short data:
#> ## `long_mixed` (dim=200x5) ----
#>
#> long_mixed_short = long_mixed %>%
#> select(SUBJID, crfname, val3b) %>%
#> group_by(SUBJID) %>%
#> summarise(across(everything(), unify)) #dim=100x3
#>
#> long_mixed_long = long_mixed %>%
#> select(SUBJID, val1b, val2b) #dim=200x3
filename = tempfile("mixed_code", fileext=".R")
split_mixed_datasets(tm, id="SUBJID", output_code=filename)
#> ✔ There was 1 short table:
#> "short"
#> ✔ There was 1 pure long table:
#> "long_pure"
#> ✔ There was 1 mixed (short+long) table:
#> "long_mixed"
#> → Copy the code from /tmp/RtmpE1byhS/mixed_code17fe1e01efa4.R in your script to
#> separate long and short data:
#> `utils::browseURL(/tmp/RtmpE1byhS/mixed_code17fe1e01efa4.R)`
readLines(filename)
#> [1] "## `long_mixed` (dim=200x5) ---- "
#> [2] ""
#> [3] "long_mixed_short = long_mixed %>% "
#> [4] " select(SUBJID, crfname, val3b) %>% "
#> [5] " group_by(SUBJID) %>% "
#> [6] " summarise(across(everything(), unify)) #dim=100x3 "
#> [7] ""
#> [8] " long_mixed_long = long_mixed %>% "
#> [9] " select(SUBJID, val1b, val2b) #dim=200x3 "