Last updated: 2021-03-10
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 941b66d | Jenny Sjaarda | 2021-03-02 | add new Rmd files and respective html files |
| Rmd | 85955b3 | Jenny Sjaarda | 2020-06-23 | Update site |
| html | 85955b3 | Jenny Sjaarda | 2020-06-23 | Update site |
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The following document outlines and summarizes the phenotypic quality control and processing procedure that was followed to create a clean dataset.
Je fais un petit résumé de ce dont on a convenu : 1. Modifier l’entrée d’un nouveau mois, pour que ce soit uniquement possible de choisir une entrée proposée (0-1-2-3-6-…) et non taper un autre chiffre 2. Ajouter un champ pour l’identité de la personne qui entre ou modifie un bilan et un champ pour la date à laquelle a lieu cette modification (emplacement proposé : en dessous de « contrôle » ?) 3. Eliminer l’option de reprendre les données d’un mois précédent et à la place : vider tous les champs SAUF ceux entrés dans les fenêtres « Antipsychotiques » et « Co-Médication » 4. Ajouter une validation de la date d’évaluation au moment de sauver les données d’un bilan entré
A short summary of what we agreed: 1. Modify the entry for a new month, so that it is only possible to choose a proposed entry (0-1-2-3-6 -…) and not enter another number 2. Add a field for the identity of the person entering or modifying a balance sheet and a field for the date on which this modification takes place (proposed location: below “control”?) 3. Eliminate the option to resume data from a previous month and instead: empty all fields EXCEPT those entered in the “Antipsychotics” and “Co-Medication” windows 4. Add validation of the evaluation date when saving the data of an entered balance sheet
data/raw/phenotype_data/PHENO_GWAS_160320_noaccent.csvpheno_file <- "data/raw/phenotype_data/PHENO_GWAS_160320_noaccent.csv"
pheno_raw <- readr::read_delim(pheno_file, col_types = cols(.default = col_character()), delim = ",") %>% type_convert(col_types = cols())
process_pheno_raw <- function(pheno_raw) {
output <- pheno_raw %>%
mutate_all( ~ replace(., . == 999, NA)) %>% filter(!is.na(PatientsTaille) &
!is.na(Poids)) %>%
mutate(Date = as.Date(Date, format = '%d.%m.%y')) %>%
filter(!is.na(Date)) %>% arrange(PatientsRecNum, Date) %>%
mutate(AP1 = gsub(" ", "_", AP1)) %>% mutate_at("AP1", as.factor) %>% mutate(AP1 = gsub("_.*$", "", AP1)) %>% mutate(AP1 = na_if(AP1, "")) %>% ## merge retard/depot with original
filter(!is.na(AP1)) %>%
group_by(GEN) %>% mutate(sex = check_sex(Sexe)) %>% filter(!is.na(Sexe)) %>% ## if any sex is missing take sex from other entries
ungroup() %>%
filter(remove_outliers(PatientsTaille)) %>% # this removes patients with the following heights (cm): 106 106 106 106 106 106 106 106 116 96 96 90
#filter(remove_outliers(Poids)) %>%
group_by(GEN) %>%
mutate_at("PatientsTaille", as.numeric) %>% mutate(height = check_height(PatientsTaille)) %>% ### take average of all heights
mutate_at(vars(Quetiapine:Doxepine), list(ever_drug = ever_drug)) %>% ungroup() %>% ### create ever on any drug
rename(weight = Poids) %>%
mutate(BMI = weight / (height / 100) ^ 2) %>% filter(!is.na(BMI)) %>% ## create BMI
group_by(GEN, PatientsRecNum) %>% mutate(drug_instance = row_number()) %>%
mutate(date_difference = as.numeric(difftime(lag(Date), Date, units = "days"))) %>%
mutate(
AP1 = case_when(
AP1 == "Risperdal" ~ "Risperidone",
AP1 == "Paliperidone" ~ "Risperidone",
TRUE ~ AP1
)
) %>%
mutate(
follow_up = case_when(
abs(date_difference) >= (Mois - lag(Mois)) * 30 - leeway_time &
abs(date_difference) <= (Mois - lag(Mois)) * 30 + leeway_time ~ "sensible",
is.na(date_difference) ~ "NA",
Mois == 0 ~ "new_regimen",
date_difference < 0 ~ "leeway_exceeds",
TRUE ~ "dupliate"
)
) %>%
mutate(
month_descrepency = case_when(
Mois < lag(Mois) | date_difference >= 0 ~ "month_discrepency",
TRUE ~ "sensible"
)
) %>%
mutate(drug_match = check_drug(PatientsRecNum, AP1)) %>%
mutate(date_difference_first = as.numeric(difftime(Date, first(Date)), units = "days")) %>%
ungroup() %>%
group_by(GEN) %>%
mutate(AP1_mod = rename_meds(AP1, PatientsRecNum, Date)) %>%
ungroup()
return(output)
}
options("tidylog.display" = list()) # turn off
t <- process_pheno_raw(pheno_raw)
options("tidylog.display" = NULL) # turn on
## missing date
missing_date <- t %>% filter(is.na(Date)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_Date")
## missing sex
na_sex <- t %>% filter(is.na(sex)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "sex_problem")
## missing AP1
missing_AP1 <- t %>% filter(is.na(AP1)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_AP1")
## missing PatientsRecNum (none)
missing_patrec <- t %>% filter(is.na(PatientsRecNum)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_PatientsRecNum")
## month mismatch
leeway_exceeds <- t %>% filter(follow_up == "leeway_exceeds") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_mismatch")
head(leeway_exceeds)# A tibble: 6 x 4
GEN Date PatientsRecNum problem_category
<chr> <date> <dbl> <chr>
1 LHORDBHE 2008-09-21 3 month_mismatch
2 LHORDBHE 2010-04-20 5 month_mismatch
3 PGLTWVVK 2011-10-11 17 month_mismatch
4 BPOCXXYD 2011-04-13 18 month_mismatch
5 BPOCXXYD 2018-08-23 18 month_mismatch
6 NALFXWBN 2010-04-11 19 month_mismatch t %>% filter(GEN=="YSFHMSHX") %>% dplyr::select(follow_up, Date, date_difference, Mois)# A tibble: 15 x 4
follow_up Date date_difference Mois
<chr> <date> <dbl> <dbl>
1 NA 2007-01-14 NA 0
2 leeway_exceeds 2007-07-18 -185 3
3 sensible 2007-10-28 -102 6
4 sensible 2008-04-27 -182 12
5 NA 2008-10-30 NA 0
6 sensible 2008-11-26 -27 1
7 sensible 2008-12-28 -32 3
8 NA 2009-02-19 NA 0
9 sensible 2009-03-18 -27 1
10 sensible 2009-04-19 -32 2
11 sensible 2009-05-25 -36 3
12 sensible 2009-08-31 -98 6
13 sensible 2009-12-07 -98 9
14 sensible 2010-03-15 -98 12
15 leeway_exceeds 2017-02-22 -2536 1204## month discrepency
problem_ids <- t %>% filter(month_descrepency == "month_discrepency") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_discrepency")
head(problem_ids)# A tibble: 6 x 4
GEN Date PatientsRecNum problem_category
<chr> <date> <dbl> <chr>
1 CZAFDOTO 2010-10-07 476 month_discrepency
2 JWRLCQCT 2017-06-20 741 month_discrepency
3 AGROMGBJ 2018-09-10 852 month_discrepency
4 PBAIFEMQ 2013-06-11 1425 month_discrepency
5 QWJBVPKW 2017-04-06 1558 month_discrepency
6 UGCKMMCC 2014-03-26 1663 month_discrepencyt %>% filter(GEN=="JWWJQJGS") %>% dplyr::select(follow_up, Date, date_difference, Mois, month_descrepency)# A tibble: 9 x 5
follow_up Date date_difference Mois month_descrepency
<chr> <date> <dbl> <dbl> <chr>
1 NA 2010-03-02 NA 0 sensible
2 sensible 2010-03-21 -19 1 sensible
3 sensible 2010-06-17 -88 3 sensible
4 sensible 2010-09-21 -96 6 sensible
5 sensible 2011-01-24 -125 12 sensible
6 NA 2010-01-24 NA 12 sensible
7 leeway_exceeds 2010-03-01 -36 2 month_discrepency
8 sensible 2010-06-17 -108 6 sensible
9 sensible 2010-09-21 -96 9 sensible # drug mismatch
problem_drugs <- t %>% filter(drug_match=="non-match") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "drug_mismatch")
flagged_rows <- rbind(missing_date, na_sex, missing_AP1, leeway_exceeds, problem_ids, problem_drugs)
write.table(flagged_rows, "data/raw/phenotype_data/PHENO_GWAS_160320_flagged_rows.txt", row.names = F, col.names = T, quote = T)
table(flagged_rows$problem_category)
drug_mismatch month_discrepency month_mismatch sex_problem
34 27 700 98 Mois column is less than the previous row (for e.g. month 3 occurs on January 1/2010, but month 0 occurs on March 1/2010 for the same PatientsRecNum), 38 individuals/39 rows, e.g. JWWJQJGS on 01-03-2010 indicates month 2 which occurs after month 12 on 01-24-2010 at the same PatientsRecNum of 2762.Date and Mois column, these may not be as big of a problem - but I have still flagged them (for e.g. say participant has the following entries: month 0 on January 1, and month 3 on September 1. The number of days between those two dates is 244 which is greater than 3 - the month follow-up it was supposed to be based on the Mois column - times 30 + 90 days, in this case 3*30 + 90 = 180. Since 244 is greater than 180, this follow-up for at the month 3 mark would be flagged). Note that I chose 90 days as an arbitrary cutoff. There are 162 individuals in this category/206 rows, e.g. YSFHMSHX on 07-07-2018.GEN and PatientsRecNum, 25 individuals, not sure the number of rows as it’s unclear which ones are correct, e.g. BPOCXXYD has both aripiprazole and amisulpride listed for PatientsRecNum 18.data/raw/phenotype_data/PHENO_GWAS_160320_flagged_rows.txt to Celine for revision (along with help of Claire, Marianna and Nermine).PHENO_GWAS_160420.xlsx (processed according to description at Data Sources page):pheno_file2 <- "data/raw/phenotype_data/PHENO_GWAS_160420_noaccent.csv"
pheno_raw2 <- readr::read_delim(pheno_file2, col_types = cols(.default = col_character()), delim = ",") %>% type_convert(col_types = cols())
options("tidylog.display" = list()) # turn off
t <- process_pheno_raw(pheno_raw2)
options("tidylog.display" = NULL) # turn on
## repeat above procedure to see if all problems are fixed
## missing date
missing_date <- t %>% filter(is.na(Date)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_Date")
## missing sex
na_sex <- t %>% filter(is.na(sex)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "sex_problem")
## missing AP1
missing_AP1 <- t %>% filter(is.na(AP1)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_AP1")
## missing PatientsRecNum (none)
missing_patrec <- t %>% filter(is.na(PatientsRecNum)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_PatientsRecNum")
## month mismatch
leeway_exceeds <- t %>% filter(follow_up == "leeway_exceeds") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_mismatch")
## month discrepency
problem_ids <- t %>% filter(month_descrepency == "month_descrepency") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_discrepency")
# drug mismatch
problem_drugs <- t %>% filter(drug_match=="non-match") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "drug_mismatch")
flagged_rows2 <- rbind(missing_date, na_sex, missing_AP1, leeway_exceeds, problem_ids, problem_drugs)
table(flagged_rows2$problem_category)
drug_mismatch month_mismatch
15 683 t %>% filter(drug_match=="non-match") %>% dplyr::select(AP1, drug_match, GEN) %>% unique %>% arrange(GEN)# A tibble: 6 x 3
AP1 drug_match GEN
<chr> <chr> <chr>
1 Aripiprazole non-match BPOCXXYD
2 Amisulpride non-match BPOCXXYD
3 Mirtazapine non-match KOPRATFS
4 Quetiapine non-match KOPRATFS
5 Amisulpride non-match VGWWZXDK
6 Risperidone non-match VGWWZXDKDate column), but the Mois column suggests this visit occured before a previous visit have been checked and the Mois column should be ignored. Celine’s explanation for why these were not all corrected: …we can not correct the month entry without deleting and reentering all data from one visit… thus we only checked that the dates were correct! It means that the mistakes you still have with month mismatchs, you should use dates without considering the mois column.Mois column should be ignored.PatientsRecNum, these were sent to Celine and corrected in subsequent data extraction (see table above for list of particpants corrected).PHENO_GWAS_160420_corr.xlsx (processed according to description at Data Sources page).pheno_file3 <- "data/raw/phenotype_data/PHENO_GWAS_160420_corr_noaccent.csv"
pheno_raw3 <- readr::read_delim(pheno_file3, col_types = cols(.default = col_character()), delim = ",") %>% type_convert(col_types = cols())
options("tidylog.display" = list()) # turn off
t <- process_pheno_raw(pheno_raw3)
options("tidylog.display" = NULL) # turn on
# drug mismatch
problem_drugs <- t %>% filter(drug_match=="non-match") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "drug_mismatch")
t %>% filter(drug_match=="non-match") %>% dplyr::select(AP1, drug_match, GEN) %>% unique %>% arrange(GEN)# A tibble: 0 x 3
# … with 3 variables: AP1 <chr>, drug_match <chr>, GEN <chr># empty table
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /data/sgg2/jenny/bin/R-3.5.3/lib64/R/lib/libRblas.so
LAPACK: /data/sgg2/jenny/bin/R-3.5.3/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rbgen_0.1 ukbtools_0.11.3 hrbrthemes_0.8.0
[4] OpenImageR_1.1.6 fuzzyjoin_0.1.5 kableExtra_1.1.0
[7] R.utils_2.9.2 R.oo_1.23.0 R.methodsS3_1.7.1
[10] TwoSampleMR_0.4.25 reader_1.0.6 NCmisc_1.1.6
[13] optparse_1.6.4 readxl_1.3.1 ggthemes_4.2.0
[16] tryCatchLog_1.1.6 futile.logger_1.4.3 DataExplorer_0.8.0
[19] taRifx_1.0.6.1 qqman_0.1.4 MASS_7.3-51.5
[22] bit64_0.9-7 bit_1.1-14 rslurm_0.5.0
[25] rmeta_3.0 devtools_2.2.1 usethis_1.5.1
[28] data.table_1.12.8 clustermq_0.8.8.1 future.batchtools_0.8.1
[31] future_1.15.1 rlang_0.4.5 knitr_1.26
[34] drake_7.12.0.9000 forcats_0.4.0 stringr_1.4.0
[37] dplyr_0.8.3 purrr_0.3.3 readr_1.3.1
[40] tidyr_1.0.3 tibble_2.1.3 ggplot2_3.3.2
[43] tidyverse_1.3.0 pacman_0.5.1 processx_3.4.1
[46] workflowr_1.6.0
loaded via a namespace (and not attached):
[1] backports_1.1.6 systemfonts_0.2.3 plyr_1.8.5
[4] igraph_1.2.5 storr_1.2.1 listenv_0.8.0
[7] digest_0.6.25 foreach_1.4.7 htmltools_0.4.0
[10] tiff_0.1-5 fansi_0.4.1 magrittr_1.5
[13] checkmate_1.9.4 memoise_1.1.0 base64url_1.4
[16] doParallel_1.0.15 remotes_2.1.0 globals_0.12.5
[19] extrafont_0.17 modelr_0.1.5 extrafontdb_1.0
[22] prettyunits_1.1.0 jpeg_0.1-8.1 colorspace_1.4-1
[25] rvest_0.3.5 rappdirs_0.3.1 haven_2.2.0
[28] xfun_0.11 callr_3.4.0 crayon_1.3.4
[31] jsonlite_1.6 iterators_1.0.12 brew_1.0-6
[34] glue_1.4.0 gtable_0.3.0 webshot_0.5.2
[37] pkgbuild_1.0.6 Rttf2pt1_1.3.8 scales_1.1.0
[40] futile.options_1.0.1 DBI_1.1.0 Rcpp_1.0.3
[43] xtable_1.8-4 viridisLite_0.3.0 progress_1.2.2
[46] txtq_0.2.0 htmlwidgets_1.5.1 httr_1.4.1
[49] getopt_1.20.3 calibrate_1.7.5 ellipsis_0.3.0
[52] XML_3.98-1.20 pkgconfig_2.0.3 dbplyr_1.4.2
[55] utf8_1.1.4 tidyselect_0.2.5 reshape2_1.4.3
[58] later_1.0.0 munsell_0.5.0 cellranger_1.1.0
[61] tools_3.5.3 cli_2.0.1 generics_0.0.2
[64] broom_0.5.3 fastmap_1.0.1 evaluate_0.14
[67] yaml_2.2.0 fs_1.3.1 packrat_0.5.0
[70] nlme_3.1-143 mime_0.8 whisker_0.4
[73] formatR_1.7 proftools_0.99-2 xml2_1.2.2
[76] compiler_3.5.3 rstudioapi_0.10 png_0.1-7
[79] filelock_1.0.2 testthat_2.3.1 reprex_0.3.0
[82] stringi_1.4.5 ps_1.3.0 desc_1.2.0
[85] gdtools_0.2.2 lattice_0.20-38 vctrs_0.2.4
[88] pillar_1.4.3 lifecycle_0.1.0 networkD3_0.4
[91] httpuv_1.5.2 R6_2.4.1 promises_1.1.0
[94] gridExtra_2.3 sessioninfo_1.1.1 codetools_0.2-16
[97] lambda.r_1.2.4 assertthat_0.2.1 pkgload_1.0.2
[100] rprojroot_1.3-2 withr_2.1.2 batchtools_0.9.12
[103] parallel_3.5.3 hms_0.5.3 grid_3.5.3
[106] rmarkdown_1.18 git2r_0.26.1 shiny_1.4.0
[109] lubridate_1.7.4