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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.
Summary of the agreed changes is below (email correspondance between Celine and Enrique on 13/02/2020 and translation):
Je fais un petit résumé de ce dont on a convenu :After this update and manual revisions, new data was provided on 16/03/2020: data/raw/phenotype_data/PHENO_GWAS_160320_noaccent.csv
pheno_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(Date = as.Date(Date, format = '%d.%m.%Y')) %>% arrange(Date) %>%
mutate(AP1 = gsub(" ", "_", AP1)) %>% mutate_at("AP1", as.factor) %>%
mutate(AP1 = gsub("_.*$", "", AP1)) %>%
mutate(AP1 = na_if(AP1, "")) %>% ## merge retard/depot with original
group_by(GEN) %>%
mutate(sex = check_sex(Sexe)) %>%
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
mutate(BMI = Poids / (PatientsTaille / 100) ^ 2) %>%
group_by(GEN, PatientsRecNum) %>% mutate(drug_instance = row_number()) %>%
mutate(date_difference = as.numeric(difftime(lag(Date), Date, units = "days"))) %>%
mutate(
follow_up = case_when(
Mois > 12 ~ "greater_12months",
date_difference > 0 ~ "month_descrepency",
Mois == 0 ~ "new_regimen",
abs(date_difference) >= (Mois - lag(Mois)) *
30 - leeway_time &
abs(date_difference) <= (Mois - lag(Mois)) * 30 + leeway_time ~ "sensible",
is.na(date_difference) ~ "NA",
### these are follow-ups that didn't start at month 0
TRUE ~ "leeway_exceeds"
)
) %>%
mutate(AP1_mod = paste0(AP1, "_round", rename_meds(follow_up))) %>%
mutate(problems = case_when(Mois < lag(Mois) |
date_difference >= 0 ~ "problem",
TRUE ~ "fine")) %>%
mutate(drug_match = check_drug(PatientsRecNum, AP1))
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")filter (grouped): removed 10,000 rows (>99%), 19 rows remainingmutate (grouped): new variable 'problem_category' with one unique value and 0% NA## missing sex
na_sex <- t %>% filter(is.na(sex)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "sex_problem")filter (grouped): removed 9,780 rows (98%), 239 rows remaining
mutate (grouped): new variable 'problem_category' with one unique value and 0% NA## missing AP1
missing_AP1 <- t %>% filter(is.na(AP1)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_AP1")filter (grouped): removed 9,987 rows (>99%), 32 rows remaining
mutate (grouped): new variable 'problem_category' with one unique value and 0% NA## missing PatientsRecNum (none)
missing_patrec <- t %>% filter(is.na(PatientsRecNum)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_PatientsRecNum")filter (grouped): removed all rows (100%)mutate (grouped): new variable 'problem_category' with 0 unique values and 100% NA## month mismatch
leeway_exceeds <- t %>% filter(follow_up == "leeway_exceeds") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_mismatch")filter (grouped): removed 9,813 rows (98%), 206 rows remainingmutate (grouped): new variable 'problem_category' with one unique value and 0% NAhead(leeway_exceeds)# A tibble: 6 x 4
# Groups: GEN, PatientsRecNum [6]
GEN Date PatientsRecNum problem_category
<chr> <date> <dbl> <chr>
1 YSFHMSHX 7-07-18 56 month_mismatch
2 QFIDUFIG 8-08-24 169 month_mismatch
3 BSBZPQOV 8-09-03 28 month_mismatch
4 QCXAFXPK 8-09-16 190 month_mismatch
5 LHORDBHE 8-09-21 3 month_mismatch
6 SHHQLBQL 8-12-08 150 month_mismatch t %>% filter(GEN=="YSFHMSHX") %>% dplyr::select(follow_up, Date, date_difference, Mois)filter (grouped): removed 10,003 rows (>99%), 16 rows remainingAdding missing grouping variables: `GEN`, `PatientsRecNum`# A tibble: 16 x 6
# Groups: GEN, PatientsRecNum [3]
GEN PatientsRecNum follow_up Date date_difference Mois
<chr> <dbl> <chr> <date> <dbl> <dbl>
1 YSFHMSHX 56 new_regimen 7-01-14 NA 0
2 YSFHMSHX 56 leeway_exceeds 7-07-18 -185 3
3 YSFHMSHX 56 sensible 7-10-28 -102 6
4 YSFHMSHX 56 sensible 8-01-28 -92 9
5 YSFHMSHX 56 sensible 8-04-27 -90 12
6 YSFHMSHX 57 new_regimen 8-10-30 NA 0
7 YSFHMSHX 57 sensible 8-11-26 -27 1
8 YSFHMSHX 57 sensible 8-12-28 -32 3
9 YSFHMSHX 58 new_regimen 9-02-19 NA 0
10 YSFHMSHX 58 sensible 9-03-18 -27 1
11 YSFHMSHX 58 sensible 9-04-19 -32 2
12 YSFHMSHX 58 sensible 9-05-25 -36 3
13 YSFHMSHX 58 sensible 9-08-31 -98 6
14 YSFHMSHX 58 sensible 9-12-07 -98 9
15 YSFHMSHX 58 sensible 10-03-15 -98 12
16 YSFHMSHX 58 greater_12months 17-02-22 -2536 1204## month discrepency
problem_ids <- t %>% filter(problems == "problem") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_discrepency")filter (grouped): removed 9,980 rows (>99%), 39 rows remainingmutate (grouped): new variable 'problem_category' with one unique value and 0% NAhead(problem_ids)# A tibble: 6 x 4
# Groups: GEN, PatientsRecNum [6]
GEN Date PatientsRecNum problem_category
<chr> <date> <dbl> <chr>
1 JWWJQJGS 10-03-01 2762 month_discrepency
2 YTYDZYJH 10-03-14 6880 month_discrepency
3 CZAFDOTO 10-10-07 476 month_discrepency
4 PBAIFEMQ 13-06-11 1425 month_discrepency
5 UGCKMMCC 14-03-26 1663 month_discrepency
6 TIEQMSVB 15-02-04 2592 month_discrepencyt %>% filter(GEN=="JWWJQJGS") %>% dplyr::select(follow_up, Date, date_difference, Mois, problems)filter (grouped): removed 10,010 rows (>99%), 9 rows remainingAdding missing grouping variables: `GEN`, `PatientsRecNum`# A tibble: 9 x 7
# Groups: GEN, PatientsRecNum [2]
GEN PatientsRecNum follow_up Date date_difference Mois problems
<chr> <dbl> <chr> <date> <dbl> <dbl> <chr>
1 JWWJQJGS 2762 NA 10-01-24 NA 12 fine
2 JWWJQJGS 2762 leeway_exce… 10-03-01 -36 2 problem
3 JWWJQJGS 493 new_regimen 10-03-02 NA 0 fine
4 JWWJQJGS 493 sensible 10-03-21 -19 1 fine
5 JWWJQJGS 493 sensible 10-06-17 -88 3 fine
6 JWWJQJGS 2762 sensible 10-06-17 -108 6 fine
7 JWWJQJGS 493 sensible 10-09-21 -96 6 fine
8 JWWJQJGS 2762 sensible 10-09-21 -96 9 fine
9 JWWJQJGS 493 sensible 11-01-24 -125 12 fine # drug mismatch
problem_drugs <- t %>% filter(drug_match=="non-match") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "drug_mismatch")filter (grouped): removed 9,979 rows (>99%), 40 rows remainingmutate (grouped): new variable 'problem_category' with one unique value and 0% NAflagged_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 missing_AP1 missing_Date month_discrepency
40 32 19 39
month_mismatch sex_problem
206 239 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")filter (grouped): removed 9,864 rows (>99%), 10 rows remainingmutate (grouped): new variable 'problem_category' with one unique value and 0% NA## missing sex
na_sex <- t %>% filter(is.na(sex)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "sex_problem")filter (grouped): removed 9,872 rows (>99%), 2 rows remaining
mutate (grouped): new variable 'problem_category' with one unique value and 0% NA## missing AP1
missing_AP1 <- t %>% filter(is.na(AP1)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_AP1")filter (grouped): removed 9,867 rows (>99%), 7 rows remaining
mutate (grouped): new variable 'problem_category' with one unique value and 0% NA## missing PatientsRecNum (none)
missing_patrec <- t %>% filter(is.na(PatientsRecNum)) %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "missing_PatientsRecNum")filter (grouped): removed all rows (100%)mutate (grouped): new variable 'problem_category' with 0 unique values and 100% NA## month mismatch
leeway_exceeds <- t %>% filter(follow_up == "leeway_exceeds") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_mismatch")filter (grouped): removed 9,687 rows (98%), 187 rows remainingmutate (grouped): new variable 'problem_category' with one unique value and 0% NA## month discrepency
problem_ids <- t %>% filter(problems == "problem") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "month_discrepency")filter (grouped): removed 9,851 rows (>99%), 23 rows remaining
mutate (grouped): new variable 'problem_category' with one unique value and 0% NA# drug mismatch
problem_drugs <- t %>% filter(drug_match=="non-match") %>% dplyr::select(GEN, Date, PatientsRecNum) %>%
mutate(problem_category = "drug_mismatch")filter (grouped): removed 9,857 rows (>99%), 17 rows remaining
mutate (grouped): new variable 'problem_category' with one unique value and 0% NAflagged_rows2 <- rbind(missing_date, na_sex, missing_AP1, leeway_exceeds, problem_ids, problem_drugs)
table(flagged_rows2$problem_category)
drug_mismatch missing_AP1 missing_Date month_discrepency
17 7 10 23
month_mismatch sex_problem
187 2 t %>% filter(drug_match=="non-match") %>% dplyr::select(AP1, drug_match) %>% unique %>% arrange(GEN)filter (grouped): removed 9,857 rows (>99%), 17 rows remainingAdding missing grouping variables: `GEN`, `PatientsRecNum`# A tibble: 8 x 4
# Groups: GEN, PatientsRecNum [4]
GEN PatientsRecNum AP1 drug_match
<chr> <dbl> <chr> <chr>
1 BPOCXXYD 18 Aripiprazole non-match
2 BPOCXXYD 18 Amisulpride non-match
3 JTEXKBJN 5374 Mirtazapine non-match
4 JTEXKBJN 5374 Lithium non-match
5 KOPRATFS 3418 Mirtazapine non-match
6 KOPRATFS 3418 Quetiapine non-match
7 VGWWZXDK 311 Amisulpride non-match
8 VGWWZXDK 311 Risperidone non-match Date 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")filter (grouped): removed all rows (100%)mutate (grouped): new variable 'problem_category' with 0 unique values and 100% NAt %>% filter(drug_match=="non-match") %>% dplyr::select(AP1, drug_match) %>% unique %>% arrange(GEN)filter (grouped): removed all rows (100%)Adding missing grouping variables: `GEN`, `PatientsRecNum`# A tibble: 0 x 4
# Groups: GEN, PatientsRecNum [0]
# … with 4 variables: GEN <chr>, PatientsRecNum <dbl>, AP1 <chr>,
# drug_match <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] tidylog_1.0.1 OpenImageR_1.1.6 fuzzyjoin_0.1.5
[4] kableExtra_1.1.0 R.utils_2.9.2 R.oo_1.23.0
[7] R.methodsS3_1.7.1 TwoSampleMR_0.4.25 reader_1.0.6
[10] NCmisc_1.1.6 optparse_1.6.4 readxl_1.3.1
[13] ggthemes_4.2.0 tryCatchLog_1.1.6 futile.logger_1.4.3
[16] DataExplorer_0.8.0 taRifx_1.0.6.1 qqman_0.1.4
[19] MASS_7.3-51.5 bit64_0.9-7 bit_1.1-14
[22] rslurm_0.5.0 rmeta_3.0 devtools_2.2.1
[25] usethis_1.5.1 data.table_1.12.8 clustermq_0.8.8.1
[28] future.batchtools_0.8.1 future_1.15.1 rlang_0.4.5
[31] knitr_1.26 drake_7.12.0.9000 forcats_0.4.0
[34] stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[37] readr_1.3.1 tidyr_1.0.3 tibble_2.1.3
[40] ggplot2_3.2.1 tidyverse_1.3.0 pacman_0.5.1
[43] processx_3.4.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] backports_1.1.6 plyr_1.8.5 igraph_1.2.5
[4] lazyeval_0.2.2 storr_1.2.1 listenv_0.8.0
[7] digest_0.6.25 htmltools_0.4.0 tiff_0.1-5
[10] fansi_0.4.1 magrittr_1.5 checkmate_1.9.4
[13] memoise_1.1.0 base64url_1.4 remotes_2.1.0
[16] globals_0.12.5 modelr_0.1.5 prettyunits_1.1.0
[19] jpeg_0.1-8.1 colorspace_1.4-1 rvest_0.3.5
[22] rappdirs_0.3.1 haven_2.2.0 xfun_0.11
[25] callr_3.4.0 crayon_1.3.4 jsonlite_1.6
[28] brew_1.0-6 glue_1.4.0 gtable_0.3.0
[31] webshot_0.5.2 pkgbuild_1.0.6 scales_1.1.0
[34] futile.options_1.0.1 DBI_1.1.0 Rcpp_1.0.3
[37] xtable_1.8-4 viridisLite_0.3.0 progress_1.2.2
[40] txtq_0.2.0 clisymbols_1.2.0 htmlwidgets_1.5.1
[43] httr_1.4.1 getopt_1.20.3 calibrate_1.7.5
[46] ellipsis_0.3.0 pkgconfig_2.0.3 dbplyr_1.4.2
[49] utf8_1.1.4 tidyselect_0.2.5 reshape2_1.4.3
[52] later_1.0.0 munsell_0.5.0 cellranger_1.1.0
[55] tools_3.5.3 cli_2.0.1 generics_0.0.2
[58] broom_0.5.3 fastmap_1.0.1 evaluate_0.14
[61] yaml_2.2.0 fs_1.3.1 packrat_0.5.0
[64] nlme_3.1-143 mime_0.8 whisker_0.4
[67] formatR_1.7 proftools_0.99-2 xml2_1.2.2
[70] compiler_3.5.3 rstudioapi_0.10 png_0.1-7
[73] filelock_1.0.2 testthat_2.3.1 reprex_0.3.0
[76] stringi_1.4.5 ps_1.3.0 desc_1.2.0
[79] lattice_0.20-38 vctrs_0.2.4 pillar_1.4.3
[82] lifecycle_0.1.0 networkD3_0.4 httpuv_1.5.2
[85] R6_2.4.1 promises_1.1.0 gridExtra_2.3
[88] sessioninfo_1.1.1 codetools_0.2-16 lambda.r_1.2.4
[91] assertthat_0.2.1 pkgload_1.0.2 rprojroot_1.3-2
[94] withr_2.1.2 batchtools_0.9.12 parallel_3.5.3
[97] hms_0.5.3 grid_3.5.3 rmarkdown_1.18
[100] git2r_0.26.1 shiny_1.4.0 lubridate_1.7.4