Open echaritos opened 1 month ago
wibeasley
commented
1 month ago Thanks for the details, @echaritos. What version of REDCap? These may be fields that were added a year or two ago. Did this function work before?
If this is happening on a recent-ish version of REDCap, I'd like to know more about the scenario so I can modify the code and add regression tests.
echaritos
commented
1 month ago We are on 14.6.10
echaritos
commented
1 month ago Yes the functions has been working without problems for 2 years now until today.
echaritos
commented
1 month ago My suspicion is that the bug might be introduced when tibble is created internally from redcap_read()
wibeasley
commented
1 month ago Bummer. It looks like a problem with these two lines in the non-public function REDCapR:::redcap_metadata_internal().
Can you try these calls (to test projects on our server)? If that works, can you replace your uri & token with the project you're having trouble with?
uri <- "https://bbmc.ouhsc.edu/redcap/api/"
# A simple project (pid 153)
REDCapR:::redcap_metadata_internal(uri, "9A81268476645C4E5F03428B8AC3AA7B")$d_variable
# A longitudinal project (pid 212)
REDCapR:::redcap_metadata_internal(uri, "0434F0E9CF53ED0587847AB6E51DE762")$d_variable
# A repeating measures (pid 3181)
REDCapR:::redcap_metadata_internal(uri, "22C3FF1C8B08899FB6F86D91D874A159")$d_variableI had to downgrade to 1.1.0 because this was on our production server. The problem almost went away. I have the suspicion that it is not purely a problem of REDCapR but an interaction with it and tibble::. I'll try to test tomorrow but my test capability is very limited. Thanks for your help.
echaritos
commented
1 month ago
Restarting R session...
> #New
> library(REDCapR, lib.loc = "/home/r_studio_rc/redcap_files/libs")
> packageVersion("REDCapR")
[1] ‘1.2.0’
> print(R.version.string)
[1] "R version 4.2.3 (2023-03-15)"
> packageVersion("tidyverse")
[1] ‘2.0.0’
> packageVersion("tibble")
[1] ‘3.2.1’
> packageVersion("dplyr")
[1] ‘1.1.4’
>
>
> uri <- "https://bbmc.ouhsc.edu/redcap/api/"
>
> # A simple project (pid 153)
> REDCapR:::redcap_metadata_internal(uri, "9A81268476645C4E5F03428B8AC3AA7B")$d_variable
# A tibble: 25 × 9
field_name form_name field_type validation_type autonumber readr_col_type aligned field_name_base plumbing
<chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <lgl>
1 record_id demographi… text NA TRUE col_character… " rec… record_id TRUE
2 name_first demographi… text NA FALSE col_character… " nam… name_first FALSE
3 name_last demographi… text NA FALSE col_character… " nam… name_last FALSE
4 address demographi… notes NA FALSE col_character… " add… address FALSE
5 telephone demographi… text phone FALSE col_character… " tel… telephone FALSE
6 email demographi… text email FALSE col_character… " ema… email FALSE
7 dob demographi… text date_ymd FALSE col_date() " dob… dob FALSE
8 age demographi… text NA FALSE col_character… " age… age FALSE
9 sex demographi… radio NA FALSE col_character… " sex… sex FALSE
10 demographics_complete demographi… complete NA FALSE col_integer() " dem… demographics_c… FALSE
# ℹ 15 more rows
# ℹ Use `print(n = ...)` to see more rows
> REDCapR:::redcap_read(redcap_uri = uri, token = "9A81268476645C4E5F03428B8AC3AA7B")
24 variable metadata records were read from REDCap in 0.3 seconds. The http status code was 200.
The data dictionary describing 17 fields was read from REDCap in 0.3 seconds. The http status code was 200.
3 instrument metadata records were read from REDCap in 0.3 seconds. The http status code was 200.
1 rows were read from REDCap in 0.3 seconds. The http status code was 200.
2 data access groups were read from REDCap in 0.3 seconds. The http status code was 200.
5 records and 1 columns were read from REDCap in 0.4 seconds. The http status code was 200.
Starting to read 5 records at 2024-10-02 09:44:36.
Reading batch 1 of 1, with subjects 1 through 5 (ie, 5 unique subject records).
5 records and 25 columns were read from REDCap in 0.4 seconds. The http status code was 200.
$data
# A tibble: 5 × 25
record_id name_first name_last address telephone email dob age sex demographics_complete height weight bmi
<dbl> <chr> <chr> <chr> <chr> <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Nutmeg Nutmouse "14 Ros… (405) 32… nutt… 2003-08-30 11 0 2 7 1 204.
2 2 Tumtum Nutmouse "14 Ros… (405) 32… tumm… 2003-03-10 11 1 2 6 1 278.
3 3 Marcus Wood "243 Hi… (405) 32… mw@m… 1934-04-09 80 1 2 180 80 24.7
4 4 Trudy DAG "342 El… (405) 32… pero… 1952-11-02 61 0 2 165 54 19.8
5 5 John Lee Walker "Hotel … (405) 32… left… 1955-04-15 59 1 2 193. 104 27.9
# ℹ 12 more variables: comments <chr>, mugshot <chr>, health_complete <dbl>, race___1 <dbl>, race___2 <dbl>,
# race___3 <dbl>, race___4 <dbl>, race___5 <dbl>, race___6 <dbl>, ethnicity <dbl>, interpreter_needed <dbl>,
# race_and_ethnicity_complete <dbl>
$success
[1] TRUE
$status_codes
[1] "200"
$outcome_messages
[1] "5 records and 25 columns were read from REDCap in 0.4 seconds. The http status code was 200."
$records_collapsed
[1] ""
$fields_collapsed
[1] ""
$forms_collapsed
[1] ""
$events_collapsed
[1] ""
$filter_logic
[1] ""
$datetime_range_begin
[1] NA
$datetime_range_end
[1] NA
$elapsed_seconds
[1] 2.845126
>
> # A longitudinal project (pid 212)
> REDCapR:::redcap_metadata_internal(uri, "0434F0E9CF53ED0587847AB6E51DE762")$d_variable
# A tibble: 125 × 9
field_name form_name field_type validation_type autonumber readr_col_type aligned field_name_base plumbing
<chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <lgl>
1 study_id demographics text NA FALSE col_character… " stu… study_id TRUE
2 redcap_event_name longitudinal/r… event_name NA FALSE col_character… " red… redcap_event_n… TRUE
3 date_enrolled demographics text date_ymd FALSE col_date() " dat… date_enrolled FALSE
4 patient_document demographics file NA FALSE col_character… " pat… patient_docume… FALSE
5 first_name demographics text NA FALSE col_character… " fir… first_name FALSE
6 last_name demographics text NA FALSE col_character… " las… last_name FALSE
7 telephone_1 demographics text phone FALSE col_character… " tel… telephone_1 FALSE
8 email demographics text email FALSE col_character… " ema… email FALSE
9 dob demographics text date_ymd FALSE col_date() " dob… dob FALSE
10 age demographics calc NA FALSE col_character… " age… age FALSE
# ℹ 115 more rows
# ℹ Use `print(n = ...)` to see more rows
>
> # A repeating measures (pid 3181)
> REDCapR:::redcap_metadata_internal(uri, "22C3FF1C8B08899FB6F86D91D874A159")$d_variable
# A tibble: 15 × 9
field_name form_name field_type validation_type autonumber readr_col_type aligned field_name_base plumbing
<chr> <chr> <chr> <chr> <lgl> <chr> <chr> <chr> <lgl>
1 record_id intake text NA TRUE col_integer() " rec… record_id TRUE
2 redcap_repeat_instrume… longitud… repeat_in… NA FALSE col_character… " red… redcap_repeat_… TRUE
3 redcap_repeat_instance longitud… repeat_in… NA FALSE col_integer() " red… redcap_repeat_… TRUE
4 height intake text number_1dp FALSE col_double() " hei… height FALSE
5 weight intake text number_1dp FALSE col_double() " wei… weight FALSE
6 bmi intake text number_1dp FALSE col_double() " bmi… bmi FALSE
7 intake_complete intake complete NA FALSE col_integer() " int… intake_complete FALSE
8 sbp blood_pr… text number FALSE col_double() " sbp… sbp FALSE
9 dbp blood_pr… text number FALSE col_double() " dbp… dbp FALSE
10 blood_pressure_complete blood_pr… complete NA FALSE col_integer() " blo… blood_pressure… FALSE
11 lab laborato… text NA FALSE col_character… " lab… lab FALSE
12 conc laborato… text NA FALSE col_character… " con… conc FALSE
13 laboratory_complete laborato… complete NA FALSE col_integer() " lab… laboratory_com… FALSE
14 image_profile image file NA FALSE col_character… " ima… image_profile FALSE
15 image_complete image complete NA FALSE col_integer() " ima… image_complete FALSE Ok here are some more diagnostics. The issues seems to be with an interaction between dplyr and REDCapR 1.2.0
packageVersion("REDCapR")
[1] ‘1.2.0’
> print(R.version.string)
[1] "R version 4.2.3 (2023-03-15)"
> packageVersion("tidyverse")
[1] ‘2.0.0’
> packageVersion("tibble")
[1] ‘3.2.1’
> packageVersion("dplyr")
[1] ‘1.1.4’
> data<-redcap_read(batch_size=300, redcap_uri="http://192.168.28.111/redcap/api/", token=rstudio.HC.DB.token,records = i )$data
598 variable metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
The data dictionary describing 396 fields was read from REDCap in 0.1 seconds. The http status code was 200.
19 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
0 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
Error in `dplyr::select()`:
! Can't select columns that don't exist.
✖ Column `export_field_name` doesn't exist.
Run `rlang::last_trace()` to see where the error occurred.
Warning messages:
1: The following named parsers don't match the column names: NA
2: Unknown or uninitialised column: `original_field_name`.
> data<-redcap_read_oneshot(redcap_uri="http://192.168.28.111/redcap/api/", token=rstudio.HC.DB.token,records = i )$data
1 records and 605 columns were read from REDCap in 0.1 seconds. The http status code was 200.
> detach("package:REDCapR", unload = TRUE)> #Old
> library(REDCapR)
> packageVersion("REDCapR")
[1] ‘1.1.0’
> print(R.version.string)
[1] "R version 4.2.3 (2023-03-15)"
> packageVersion("tidyverse")
[1] ‘2.0.0’
> packageVersion("tibble")
[1] ‘3.2.1’
> packageVersion("dplyr")
[1] ‘1.1.4’
> data<-redcap_read(batch_size=300, redcap_uri="http://192.168.28.111/redcap/api/", token=rstudio.HC.DB.token,records = i )$data
The data dictionary describing 396 fields was read from REDCap in 0.1 seconds. The http status code was 200.
1 records and 1 columns were read from REDCap in 0.1 seconds. The http status code was 200.
Starting to read 1 records at 2024-10-02 09:51:20.
Reading batch 1 of 1, with subjects 2833 through 2833 (ie, 1 unique subject records).
1 records and 605 columns were read from REDCap in 0.1 seconds. The http status code was 200.
── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────
cols(
...
# ... with 276 more columns
)
ℹ Use `spec()` for the full column specifications.
> data<-redcap_read_oneshot(redcap_uri="http://192.168.28.111/redcap/api/", token=rstudio.HC.DB.token,records = i )$data
1 records and 605 columns were read from REDCap in 0.1 seconds. The http status code was 200. The error backtrace remains the same
rlang::last_trace()
<error/vctrs_error_subscript_oob>
Error in `dplyr::select()`:
! Can't select columns that don't exist.
✖ Column `export_field_name` doesn't exist.
---
Backtrace:
▆
1. ├─REDCapR::redcap_read(...)
2. │ └─REDCapR:::redcap_metadata_internal(...)
3. │ └─d_var %>% ...
4. ├─dplyr::select(., field_name = "export_field_name", field_name_base = "original_field_name")
5. └─dplyr:::select.data.frame(., field_name = "export_field_name", field_name_base = "original_field_name")
Run rlang::last_trace(drop = FALSE) to see 17 hidden frames.
> rlang::last_trace(drop = FALSE)
<error/vctrs_error_subscript_oob>
Error in `dplyr::select()`:
! Can't select columns that don't exist.
✖ Column `export_field_name` doesn't exist.
---
Backtrace:
▆
1. ├─REDCapR::redcap_read(...)
2. │ └─REDCapR:::redcap_metadata_internal(...)
3. │ └─d_var %>% ...
4. ├─dplyr::select(., field_name = "export_field_name", field_name_base = "original_field_name")
5. ├─dplyr:::select.data.frame(., field_name = "export_field_name", field_name_base = "original_field_name")
6. │ └─tidyselect::eval_select(expr(c(...)), data = .data, error_call = error_call)
7. │ └─tidyselect:::eval_select_impl(...)
8. │ ├─tidyselect:::with_subscript_errors(...)
9. │ │ └─base::withCallingHandlers(...)
10. │ └─tidyselect:::vars_select_eval(...)
11. │ └─tidyselect:::walk_data_tree(expr, data_mask, context_mask)
12. │ └─tidyselect:::eval_c(expr, data_mask, context_mask)
13. │ └─tidyselect:::reduce_sels(node, data_mask, context_mask, init = init)
14. │ └─tidyselect:::walk_data_tree(new, data_mask, context_mask)
15. │ └─tidyselect:::as_indices_sel_impl(...)
16. │ └─tidyselect:::as_indices_impl(...)
17. │ └─tidyselect:::chr_as_locations(x, vars, call = call, arg = arg)
18. │ └─vctrs::vec_as_location(...)
19. └─vctrs (local) `<fn>`()
20. └─vctrs:::stop_subscript_oob(...)
21. └─vctrs:::stop_subscript(...)
22. └─rlang::abort(...)in the old version i have deleted the output of column specifications.
echaritos
commented
1 month ago I am on REDCap 14.6.10
wibeasley
commented
1 month ago weird. I have the same package versions (with REDCapR 1.2.0) and don't see the error. The only difference is I'm running the current version of R (R 4.4.1). It sounds like you're locked into the version of R from 18 months ago (ie, 4.2.3)? I don't see why that would matter, but that's the only difference I see right now. I should have time tomorrow to debug things with you. If possible, can you try R 4.4.1 from another computer?
echaritos
commented
1 month ago It does not look like an R problem. Below the output from a current R version
> #Download the DB
> #New
> library(REDCapR)
> packageVersion("REDCapR")
[1] ‘1.2.0’
> print(R.version.string)
[1] "R version 4.4.1 (2024-06-14)"
> packageVersion("tidyverse")
[1] ‘2.0.0’
> packageVersion("tibble")
[1] ‘3.2.1’
> packageVersion("dplyr")
[1] ‘1.1.4’
> data<-redcap_read(batch_size=300, redcap_uri="http://192.168.28.111/redcap/api/", token=rstudio.HC.DB.token )$data
598 variable metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
The data dictionary describing 396 fields was read from REDCap in 0.1 seconds. The http status code was 200.
19 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
0 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
Error in `dplyr::select()`:
! Can't select columns that don't exist.
✖ Column `export_field_name` doesn't exist.
Run `rlang::last_trace()` to see where the error occurred.
Warning messages:
1: The following named parsers don't match the column names: NA
2: Unknown or uninitialised column: `original_field_name`.
> data<-redcap_read_oneshot(redcap_uri="http://192.168.28.111/redcap/api/", token=rstudio.HC.DB.token )$data
> data<-redcap_read_oneshot(redcap_uri="http://192.168.28.111/redcap/api/", token=rstudio.HC.DB.token )$data
7,873 records and 605 columns were read from REDCap in 1.4 seconds. The http status code was 200.
> detach("package:REDCapR", unload = TRUE)weird. I have the same package versions (with REDCapR 1.2.0) and don't see the error. The only difference is I'm running the current version of R (R 4.4.1). It sounds like you're locked into the version of R from 18 months ago (ie, 4.2.3)? I don't see why that would matter, but that's the only difference I see right now. I should have time tomorrow to debug things with you. If possible, can you try R 4.4.1 from another computer?
redcap_read() on 1.1.0 works fine. It is 1.2.0 that returns the error
wibeasley
commented
1 month ago one last thing to try. If this fails, let's get on a call and debug it together (if you'd like).
Can you add all permissions to this user? That's the only thing I can think of that might be different from (a) you calling my server's project and (b) you calling your server's project.
echaritos
commented
1 month ago The user permissions is pretty much maxed out. This is certainly a new issue and behaviour. It seems to be an issue with dplyr::select() when called from redcap_read() but redcap_read_oneshot() is not affected. I am on GMT+2 time
echaritos
commented
1 month ago export_field_name is missing from metadata that REDCap sends
> redcap_uri <- "http://192.168.28.111/redcap/api/"
> token <- rstudio.HC.DB.token
>
>
> library(httr)
> library(readr)
>
> # Set your REDCap API URL and token
>
> # Export metadata
> response <- httr::POST(
+ url = redcap_uri,
+ body = list(
+ token = token,
+ content = 'metadata',
+ format = 'csv'
+ ),
+ encode = 'form'
+ )
>
> # Read metadata into a data frame
> metadata <- read_csv(httr::content(response, as = "text"))
Rows: 396 Columns: 18
── Column specification ────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (14): field_name, form_name, section_header, field_type, field_label, select_choices_or_calculations, field_note, text_validatio...
dbl (1): text_validation_max
lgl (3): question_number, matrix_group_name, matrix_ranking
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
>
> # Check if 'export_field_name' exists
> if (!"export_field_name" %in% names(metadata)) {
+ print("export_field_name is missing from metadata.")
+ } else {
+ print("export_field_name is present in metadata.")
+ }
[1] "export_field_name is missing from metadata."Here are some modifications that work for me as a temporary solution:
# Load required packages
library(REDCapR)
library(dplyr)
library(tidyr)
library(tibble)
library(readr)
library(stringr)
# Replicate 'sanitize_token' function
sanitize_token <- function(token) {
token <- stringr::str_trim(token)
if (stringr::str_detect(token, "\\s")) {
stop("The token (length ", nchar(token), " characters) should not have any spaces.")
} else if (nchar(token) != 32L) {
stop("The token (length ", nchar(token), " characters) should be exactly 32 characters.")
}
return(token)
}
# Replicate 'verbose_prepare' function
verbose_prepare <- function(verbose) {
if (is.null(verbose) || is.na(verbose)) {
verbose <- FALSE
}
return(verbose)
}
# Modified 'redcap_metadata_internal' function with NA handling
redcap_metadata_internal_modified <- function(
redcap_uri,
token,
http_response_encoding = "UTF-8",
locale = readr::default_locale(),
verbose = FALSE,
config_options = NULL,
handle_httr = NULL
) {
# Ensure the token is sanitized
token <- sanitize_token(token)
# Prepare verbosity settings
verbose <- verbose_prepare(verbose)
# Retrieve project metadata
d_meta <- REDCapR::redcap_metadata_read(
redcap_uri = redcap_uri,
token = token,
verbose = verbose,
handle_httr = handle_httr
)$data
# Retrieve instrument metadata
d_inst <- REDCapR::redcap_instruments(
redcap_uri = redcap_uri,
token = token,
verbose = verbose,
handle_httr = handle_httr
)$data
# Retrieve project information
d_proj <- REDCapR::redcap_project_info_read(
redcap_uri = redcap_uri,
token = token,
verbose = verbose,
handle_httr = handle_httr
)$data
# Print d_proj to inspect its contents
print("Contents of d_proj:")
print(d_proj)
# Retrieve data access groups (DAGs)
d_dags <- REDCapR::redcap_dag_read(
redcap_uri = redcap_uri,
token = token,
verbose = verbose,
handle_httr = handle_httr
)
# Determine record ID field from metadata
if ("field_name" %in% names(d_meta)) {
.record_field <- d_meta$field_name[1]
} else {
stop("Cannot determine record ID field; 'field_name' not found in metadata.")
}
# Determine if DAGs are used
.dags <- (nrow(d_dags$data) >= 1) || (grepl("do not have permission", d_dags$raw_text))
# Possible plumbing variables
.plumbing_possibles <- c(.record_field, "redcap_event_name", "redcap_repeat_instrument", "redcap_repeat_instance")
# Check decimal mark in locale
decimal_period <- (locale$decimal_mark == ".")
decimal_comma <- (locale$decimal_mark == ",")
# Prepare instrument data
d_inst <- d_inst %>%
dplyr::rename(form_name = "instrument_name") %>%
dplyr::mutate(form_order = dplyr::row_number())
# Prepare completion status fields
d_complete <- d_inst %>%
dplyr::mutate(
field_name = paste0(form_name, "_complete"),
field_name_base = field_name,
field_type = "complete",
vt = NA_character_
) %>%
dplyr::select(field_name, field_name_base, form_name, field_type, vt)
# Initialize additional variables
d_again <- tibble::tibble(
field_name = character(0),
field_name_base = character(0),
form_name = character(0),
field_type = character(0),
vt = character(0)
)
# Handle longitudinal and repeating instruments
is_longitudinal <- d_proj$is_longitudinal[1]
if (is.na(is_longitudinal) || !is.logical(is_longitudinal)) {
is_longitudinal <- FALSE
}
if (is_longitudinal) {
d_again <- d_again %>%
dplyr::add_row(
field_name = "redcap_event_name",
field_name_base = "redcap_event_name",
form_name = "longitudinal/repeating",
field_type = "event_name",
vt = NA_character_
)
}
has_repeating <- d_proj$has_repeating_instruments_or_events[1]
if (is.na(has_repeating) || !is.logical(has_repeating)) {
has_repeating <- FALSE
}
if (has_repeating) {
d_again <- d_again %>%
dplyr::add_row(
field_name = c("redcap_repeat_instrument", "redcap_repeat_instance"),
field_name_base = c("redcap_repeat_instrument", "redcap_repeat_instance"),
form_name = "longitudinal/repeating",
field_type = c("repeat_instrument", "repeat_instance"),
vt = NA_character_
)
}
# Prepare metadata
d_meta <- d_meta %>%
dplyr::rename(
field_name_base = "field_name",
vt = "text_validation_type_or_show_slider_number"
) %>%
dplyr::filter(field_type != "descriptive") %>%
dplyr::mutate(
field_name = field_name_base
) %>%
dplyr::select(
field_name,
field_name_base,
form_name,
field_type,
vt
) %>%
dplyr::bind_rows(d_complete) %>%
dplyr::left_join(d_inst, by = "form_name") %>%
dplyr::group_by(form_name) %>%
dplyr::mutate(field_order_within_form = dplyr::row_number()) %>%
dplyr::ungroup() %>%
dplyr::arrange(form_order, field_order_within_form) %>%
dplyr::select(-form_order, -field_order_within_form) %>%
dplyr::bind_rows(d_again) %>%
dplyr::mutate(plumbing = field_name %in% .plumbing_possibles)
# Prepare final variable data
d <- d_meta %>%
dplyr::mutate(
dags = .dags & (field_name == .record_field),
autonumber = d_proj$record_autonumbering_enabled[1] & (field_name == .record_field)
) %>%
# Determine readr column types based on field types and validation
dplyr::mutate(
readr_col_type = dplyr::case_when(
dags ~ "col_character()",
autonumber & !dags ~ "col_integer()",
field_type == "event_name" ~ "col_character()",
field_type == "repeat_instrument" ~ "col_character()",
field_type == "repeat_instance" ~ "col_integer()",
field_type == "complete" ~ "col_integer()",
field_type == "truefalse" ~ "col_logical()",
field_type == "yesno" ~ "col_logical()",
field_type == "checkbox" ~ "col_logical()",
field_type == "radio" ~ "col_character()",
field_type == "dropdown" ~ "col_character()",
field_type == "file" ~ "col_character()",
field_type == "notes" ~ "col_character()",
field_type == "slider" ~ "col_integer()",
field_type == "calc" ~ "col_character()",
field_type == "sql" ~ "col_character()",
field_type == "text" & is.na(vt) ~ "col_character()",
field_type == "text" & vt == "" ~ "col_character()",
vt == "integer" ~ "col_integer()",
vt == "number" & decimal_period ~ "col_double()",
vt == "number" & !decimal_period ~ "col_character()",
vt %in% c("date_ymd", "date_mdy", "date_dmy") ~ "col_date()",
vt %in% c("datetime_ymd", "datetime_mdy", "datetime_dmy") ~ "col_datetime()",
vt %in% c("datetime_seconds_ymd", "datetime_seconds_mdy", "datetime_seconds_dmy") ~ "col_datetime()",
TRUE ~ "col_character()"
)
) %>%
dplyr::select(
field_name,
form_name,
field_type,
validation_type = vt,
autonumber,
readr_col_type,
field_name_base,
plumbing
)
# Determine plumbing variables
.plumbing_variables <- intersect(d$field_name, .plumbing_possibles)
# Return the list with variable data and project info
list(
d_variable = d,
success = TRUE,
longitudinal = is_longitudinal,
repeating = has_repeating,
record_id_name = .record_field,
plumbing_variables = .plumbing_variables
)
}
# Overwrite the original function in the REDCapR package namespace
assignInNamespace(
"redcap_metadata_internal",
redcap_metadata_internal_modified,
ns = "REDCapR"
)
like
result <- redcap_read(batch_size = 1500,
+ redcap_uri = redcap_uri,
+ token = token
+ )
The data dictionary describing 396 fields was read from REDCap in 0.1 seconds. The http status code was 200.
19 instrument metadata records were read from REDCap in 0.1 seconds. The http status code was 200.
1 rows were read from REDCap in 0.1 seconds. The http status code was 200.
[1] "Contents of d_proj:"
# A tibble: 1 × 27
project_id;project_title…¹ project_id project_title creation_time production_time in_production project_language purpose purpose_other
<chr> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl> <lgl>
1 "13;\"Statistik\";\"20… NA NA NA NA NA NA NA NA
# ℹ abbreviated name:
# ¹`project_id;project_title;creation_time;production_time;in_production;project_language;purpose;purpose_other;project_notes;custom_record_label;secondary_unique_field;is_longitudinal;has_repeating_instruments_or_events;surveys_enabled;scheduling_enabled;record_autonumbering_enabled;randomization_enabled;ddp_enabled;project_irb_number;project_grant_number;project_pi_firstname;project_pi_lastname;display_today_now_button;missing_data_codes;external_modules;bypass_branching_erase_field_prompt`
# ℹ 18 more variables: project_notes <lgl>, custom_record_label <lgl>, secondary_unique_field <lgl>, is_longitudinal <lgl>,
# has_repeating_instruments_or_events <lgl>, surveys_enabled <lgl>, scheduling_enabled <lgl>, record_autonumbering_enabled <lgl>,
# randomization_enabled <lgl>, ddp_enabled <lgl>, project_irb_number <lgl>, project_grant_number <lgl>, project_pi_firstname <lgl>,
# project_pi_lastname <lgl>, display_today_now_button <lgl>, missing_data_codes <lgl>, external_modules <lgl>,
# bypass_branching_erase_field_prompt <lgl>
0 data access groups were read from REDCap in 0.1 seconds. The http status code was 200.
7,875 records and 1 columns were read from REDCap in 0.4 seconds. The http status code was 200.
Starting to read 7,875 records at 2024-10-04 11:48:03.500117.
Reading batch 1 of 6, with subjects 9 through 1531 (ie, 1500 unique subject records).
1,500 records and 605 columns were read from REDCap in 0.9 seconds. The http status code was 200.
Reading batch 2 of 6, with subjects 1532 through 3037 (ie, 1500 unique subject records).
1,500 records and 605 columns were read from REDCap in 1.3 seconds. The http status code was 200.
Reading batch 3 of 6, with subjects 3038 through 4549 (ie, 1500 unique subject records).
1,500 records and 605 columns were read from REDCap in 1.2 seconds. The http status code was 200.
Reading batch 4 of 6, with subjects 4550 through 6051 (ie, 1500 unique subject records).
1,500 records and 605 columns were read from REDCap in 1.2 seconds. The http status code was 200.
Reading batch 5 of 6, with subjects 6052 through 7563 (ie, 1500 unique subject records).
1,500 records and 605 columns were read from REDCap in 1.1 seconds. The http status code was 200.
Reading batch 6 of 6, with subjects 7564 through 7938 (ie, 375 unique subject records).
375 records and 605 columns were read from REDCap in 0.3 seconds. The http status code was 200.
Warning message:
The following named parsers don't match the column names: NA
I have
but
It seems that the internal call of redcap_read() throws an error. I am able to read the projects metadata