Last updated: 2022-06-16
Checks: 5 2
Knit directory: codemapper_notes/
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library(tidyverse)
library(reactable)
library(readxl)
library(crosstalk)
library(targets)
library(codemapper)
library(flextable)
# read codes that map to opcs4
tar_load(read_codes_that_map_to_opcs4)
# caliber codes
tar_load(caliber_codes)
# self-reported UKB cancer illnesses
coding3 <- tar_read(ukb_codings) %>%
filter(Coding == "3")
# caliber codes - filter for diseases including opcs4 codes, then filter for OPCS4/read codes only, then filter read codes for only those that can map to OPCS4
caliber_codes_cancer <- caliber_codes %>%
# append disease categories and filter for 'Cancers' category only
left_join(codemapper::get_caliber_categories_mapping() %>%
select(disease = phenotype,
caliber_category = category),
by = "disease") %>%
filter(caliber_category == "Cancers")Aim: to assign UKB self-reported cancer illness codes to CALIBER conditions.
Approach:
coding3 %>%
select(-Coding,
data_coding_3 = Value,
ukb_meaning = Meaning) %>%
reactable(filterable = TRUE,
searchable = TRUE,
paginationType = "jump",
showPageSizeOptions = TRUE,
resizable = TRUE,
pageSizeOptions = c(10, 25, 50, 100, 200, 300, 350))caliber_codes_cancer %>%
distinct(disease) %>%
reactable(filterable = TRUE,
paginationType = "jump",
showPageSizeOptions = TRUE,
resizable = TRUE,
pageSizeOptions = c(10, 30))# write to csv
ukb_self_report_cancer_to_caliber_map_unselected <-
caliber_codes_cancer %>%
distinct(disease) %>%
mutate(
description = "",
category = "UKB self-reported",
code_type = "data_coding_3",
code = "",
author = "caliber"
)
ukb_self_report_cancer_to_caliber_map_unselected %>%
write_csv(here::here(file.path("data_small",
"ukb_self_report_cancer_to_caliber_map_raw.csv")))
# NOTE: when manually reviewing, save the csv as a separate file without the '_raw' suffix# test that `ukb_self_report_non_cancer_to_caliber_map.csv` contains the same mappings as `ukb_self_report_non_cancer_to_caliber_map_raw.csv` (apart from the manuall mapping column) - RAISE ERROR AND HALT KNITTING IF NOT
ukb_self_report_cancer_to_caliber_map <- read_csv(here::here(file.path("data_small",
"ukb_self_report_cancer_to_caliber_map.csv")),
col_types = readr::cols(.default = "c"))
assertthat::are_equal(
x = ukb_self_report_cancer_to_caliber_map %>%
distinct(disease) %>%
arrange(),
y = ukb_self_report_cancer_to_caliber_map_unselected %>%
distinct(disease) %>%
arrange(),
msg = "Manually selected mapping csv does not match 'ukb_self_report_cancer_to_caliber_map_raw.csv'. These should contain the same values under column 'disease'. Has the raw version been updated?"
)
# general validation checks
ukb_self_report_cancer_to_caliber_map %>%
filter(selected == "Y") %>%
select(-selected) %>%
ukbwranglr::validate_clinical_codes()
# check self-report codes/descriptions are correct
assertthat::are_equal(
ukb_self_report_cancer_to_caliber_map %>%
filter(selected == "Y") %>%
select(-selected) %>%
inner_join(coding3,
by = c(
"code" = "Value",
"description" = "Meaning"
)) %>%
nrow(),
nrow(
ukb_self_report_cancer_to_caliber_map %>%
filter(selected == "Y")
)
)The UKB self-reported codes for skin cancer include ‘skin cancer’, ‘melanoma’ and ‘non-melanoma skin cancer’. The CALIBER codelist for ‘Primary Malignancy_Other Skin and subcutaneous tissue’ includes codes for BCC.
ukb_self_report_cancer_to_caliber_map %>%
mutate(selected = case_when(is.na(selected) ~ "N",
TRUE ~ selected)) %>%
reactable(filterable = TRUE,
searchable = TRUE,
paginationType = "jump",
showPageSizeOptions = TRUE,
pageSizeOptions = c(10, 25, 50))CALIBER diseases categorised under ‘Cancers’, excluding (i) OPCS4 codes and (ii) Read codes that map to an OPCS4 code.
# filter mapped self-reported op codes for selected CALIBER diseases only
ukb_self_report_cancer_to_caliber_map_selected <-
ukb_self_report_cancer_to_caliber_map %>%
filter(selected == "Y") %>%
select(-selected)
# combine with CALIBER codes, which are filtered for ICD (i.e. not OPCS4) and
# Read codes that do not map to an OPCS4 code
list(
ukb_self_report_cancer_to_caliber_map_selected,
caliber_codes %>%
# filter for opcs4 codes, and read codes that do *not* map to OPCS4
filter((code_type %in% c("icd9", "icd10")) |
((code_type == "read2") &
(!code %in% read_codes_that_map_to_opcs4$read2)
) |
((code_type == "read3") &
(!code %in% read_codes_that_map_to_opcs4$read3)
))
) %>%
bind_rows() %>%
filter(disease %in% ukb_self_report_cancer_to_caliber_map_selected$disease) %>%
arrange(disease,
category,
code_type) %>%
reactable(
filterable = TRUE,
searchable = TRUE,
paginationType = "jump",
showPageSizeOptions = TRUE,
resizable = TRUE,
pageSizeOptions = c(10, 25, 50, 100, 200, 300, 350)
)
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] flextable_0.7.0 codemapper_0.0.0.9001 targets_0.12.0
[4] crosstalk_1.2.0 readxl_1.4.0 reactable_0.3.0
[7] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[10] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[13] tibble_3.1.7 ggplot2_3.3.5 tidyverse_1.3.1
[16] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] fs_1.5.2 bit64_4.0.5 lubridate_1.8.0
[4] httr_1.4.2 rprojroot_2.0.3 tools_4.2.0
[7] backports_1.4.1 bslib_0.3.1 utf8_1.2.2
[10] R6_2.5.1 DBI_1.1.2 colorspace_2.0-3
[13] withr_2.5.0 tidyselect_1.1.2 processx_3.5.3
[16] bit_4.0.4 compiler_4.2.0 git2r_0.30.1
[19] cli_3.3.0 rvest_1.0.2 xml2_1.3.3
[22] officer_0.4.2 sass_0.4.1 scales_1.2.0
[25] callr_3.7.0 systemfonts_1.0.4 digest_0.6.29
[28] rmarkdown_2.14 base64enc_0.1-3 pkgconfig_2.0.3
[31] htmltools_0.5.2 dbplyr_2.2.0 fastmap_1.1.0
[34] highr_0.9 htmlwidgets_1.5.4 rlang_1.0.2
[37] rstudioapi_0.13 shiny_1.7.1 jquerylib_0.1.4
[40] generics_0.1.2 jsonlite_1.8.0 vroom_1.5.7
[43] zip_2.2.0 magrittr_2.0.3 Rcpp_1.0.8.3
[46] munsell_0.5.0 fansi_1.0.3 gdtools_0.2.4
[49] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[52] yaml_2.3.5 grid_4.2.0 parallel_4.2.0
[55] promises_1.2.0.1 crayon_1.5.1 haven_2.5.0
[58] hms_1.1.1 knitr_1.39 ps_1.7.0
[61] pillar_1.7.0 uuid_1.1-0 igraph_1.3.1
[64] base64url_1.4 codetools_0.2-18 reprex_2.0.1
[67] glue_1.6.2 evaluate_0.15 getPass_0.2-2
[70] ukbwranglr_0.0.0.9000 data.table_1.14.2 renv_0.13.2
[73] modelr_0.1.8 vctrs_0.4.1 tzdb_0.3.0
[76] httpuv_1.6.5 cellranger_1.1.0 gtable_0.3.0
[79] reactR_0.4.4 assertthat_0.2.1 xfun_0.30
[82] mime_0.12 xtable_1.8-4 broom_0.8.0
[85] later_1.3.0 ellipsis_0.3.2 here_1.0.1