Last updated: 2025-09-11

Checks: 7 0

Knit directory: genomics_ancest_disease_dispar/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20220216)

The command set.seed(20220216) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: e692d81

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version e692d81. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rproj.user/
    Ignored:    analysis/figure/
    Ignored:    data/.DS_Store
    Ignored:    data/gwas_catalog/
    Ignored:    output/gwas_cat/
    Ignored:    output/gwas_study_info_cohort_corrected.csv
    Ignored:    output/gwas_study_info_trait_corrected.csv
    Ignored:    output/gwas_study_info_trait_ontology_info.csv
    Ignored:    output/gwas_study_info_trait_ontology_info_l1.csv
    Ignored:    output/gwas_study_info_trait_ontology_info_l2.csv
    Ignored:    output/trait_ontology/
    Ignored:    renv/

Untracked files:
    Untracked:  data/gbd/
    Untracked:  data/who/

Unstaged changes:
    Modified:   analysis/index.Rmd
    Deleted:    analysis/level_1_disease_group.Rmd
    Deleted:    analysis/non_ontology_trait_collapse.Rmd
    Deleted:    analysis/trait_ontology_collapse.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/gbd_data_plots.Rmd) and HTML (docs/gbd_data_plots.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd e692d81 IJbeasley 2025-09-11 Initial investigation into gbd paf 
library(data.table)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:data.table':

    between, first, last
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(ggplot2)
library(stringr)
gbd_data <- data.table::fread(here::here("data/gbd/IHME-GBD_2021_DATA-aa22a7fd-1.csv"))

Global disease burden - top 15 non-communicable diseases by DALYs (2019)

gbd_data |>
slice_max(n = 15, order_by = val) |>
ggplot(aes(x = reorder(cause, val), y = val)) +
  geom_col(fill = "steelblue") +
  geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.3) +
  coord_flip() +
  labs(
    x = "Disease",
    y = "DALYs (rate per 100,000)",
    title = "Non-communicable diseases with the greatest global disease durden (DALYs - 2019)"
  ) +
  theme_minimal(base_size = 14)

## Distribution of DALYs (rate per 100,000) for non-communicable diseases (2019)

gbd_data |>
  ggplot(aes(x = val)) +
  geom_histogram(bins = 30, fill = "steelblue", color = "black") +
  labs(
    x = "DALYs (rate per 100,000)",
    y = "# non-communicable diseases",
    title = "Distribution of DALYs (rate per 100,000) for non-communicable diseases (2019)"
  ) +
  theme_minimal(base_size = 14)

top_non_comm_diseases <- gbd_data |>
  slice_max(n = 15, order_by = val) 

top_non_comm_diseases_daly = top_non_comm_diseases |> pull(val)
top_non_comm_diseases = top_non_comm_diseases |> pull(cause)

Global disease burden - Population Attributable Fraction (PAF) for top 15 non-communicable diseases by DALYs (2019)

Load + basic processing of GBD PAF data

gbd_paf_dir <- "data/gbd/IHME_GBD_2021_RISK_1990_2021_RESULTS_APPENDIX_TABLES"

paf_file <- paste0(gbd_paf_dir,
                     "/IHME_GBD_2021_RISK_1990_2021_RESULTS_APPENDIX_TABLE_S1_GLOBAL_Y2024M05D14.XLSX")

gbd_paf_data <- readxl::read_xlsx(here::here(paf_file),
                                  skip = 1
                                  )

# Clean column names
names(gbd_paf_data) <- names(gbd_paf_data) |>
  stringr::str_replace_all("\\r|\\n", " ") |>   # remove carriage returns/newlines
  stringr::str_squish() |>                     # trim extra spaces
  stringr::str_replace_all(" ", "_") |># replace spaces with underscores
  stringr::str_replace("^([0-9]{4})_(.*)$", "\\2_\\1")

# Clean all character cells
gbd_paf_data <- gbd_paf_data |>
  dplyr::mutate(across(where(is.character),
                ~ .x |> stringr::str_replace_all("\\r|\\n", " ") |> stringr::str_squish()))

head(gbd_paf_data)
# A tibble: 6 × 41
  Risk_Name      Deaths_PAF_1990 Deaths_PAF_2000 Deaths_PAF_2010 Deaths_PAF_2021
  <chr>          <chr>           <chr>           <chr>           <chr>          
1 All risk fact… 59·6 (57·1 – 6… 59·9 (57·4 – 6… 59·7 (56·8 – 6… 50·2 (46·9 – 5…
2 Environmental… 27·3 (24·3 – 3… 25·6 (22·8 – 2… 24·5 (21·8 – 2… 18·9 (16·3 – 2…
3 Unsafe water,… 6·84 (4·83 – 8… 4·91 (3·30 – 6… 3·64 (2·33 – 4… 1·79 (1·07 – 2…
4 Unsafe water … 4·71 (2·76 – 6… 3·34 (1·86 – 4… 2·46 (1·23 – 3… 1·18 (0·547 – …
5 Diarrhoeal di… 74·2 (43·8 – 9… 73·7 (43·0 – 9… 72·1 (40·7 – 9… 68·8 (36·6 – 8…
6 Unsafe sanita… 4·05 (3·13 – 5… 2·81 (2·15 – 3… 1·99 (1·50 – 2… 0·877 (0·624 –…
# ℹ 36 more variables: `Deaths_(in_thousands)_1990` <chr>,
#   `Deaths_(in_thousands)_2000` <chr>, `Deaths_(in_thousands)_2010` <chr>,
#   `Deaths_(in_thousands)_2021` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_1990` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_2000` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_2010` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_2021` <chr>, …

Grouping and filtering of GBD PAF data

# remove risk factor rows 
gbd_paf_data = gbd_paf_data |>
  rowwise() |>
  mutate(causal_group = ifelse(grepl("All causes", Risk_Name), Risk_Name, NA)) |>
  ungroup() |>
  tidyr::fill(causal_group, .direction = "down") |>
  relocate(causal_group, .before = Risk_Name)


gbd_paf_data = gbd_paf_data |>
  filter(!grepl("All causes", Risk_Name)) |>
  mutate(causal_group = stringr::str_remove_all(causal_group, ": All causes")) 


head(gbd_paf_data)
# A tibble: 6 × 42
  causal_group         Risk_Name Deaths_PAF_1990 Deaths_PAF_2000 Deaths_PAF_2010
  <chr>                <chr>     <chr>           <chr>           <chr>          
1 Unsafe water source  Diarrhoe… 74·2 (43·8 – 9… 73·7 (43·0 – 9… 72·1 (40·7 – 9…
2 Unsafe sanitation    Diarrhoe… 63·6 (57·9 – 6… 62·1 (56·4 – 6… 58·5 (52·6 – 6…
3 No access to handwa… Diarrhoe… 23·6 (3·36 – 4… 23·8 (3·38 – 4… 22·7 (3·23 – 4…
4 No access to handwa… Lower re… 14·7 (-10·7 – … 14·0 (-10·3 – … 12·6 (-9·07 – …
5 Ambient particulate… Diarrhoe… 0·256 (0·157 –… 0·216 (0·135 –… 0·176 (0·105 –…
6 Ambient particulate… Lower re… 9·26 (1·83 – 1… 10·0 (2·02 – 1… 10·9 (2·02 – 1…
# ℹ 37 more variables: Deaths_PAF_2021 <chr>,
#   `Deaths_(in_thousands)_1990` <chr>, `Deaths_(in_thousands)_2000` <chr>,
#   `Deaths_(in_thousands)_2010` <chr>, `Deaths_(in_thousands)_2021` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_1990` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_2000` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_2010` <chr>,
#   `all-age_Deaths_rate_(per_100,000)_2021` <chr>, …
gbd_paf_data |>
  select(causal_group, Risk_Name, DALYs_PAF_2021, 'all-age_DALYs_rate_(per_100,000)_2021') |>
  distinct() |>
  arrange(Risk_Name)
# A tibble: 707 × 4
   causal_group                  Risk_Name DALYs_PAF_2021 all-age_DALYs_rate_(…¹
   <chr>                         <chr>     <chr>          <chr>                 
 1 Drug use                      Acute he… 1·04 (0·630 –… 0·3 (0·1 – 0·4)       
 2 Drug use                      Acute he… 28·1 (19·3 – … 0·9 (0·4 – 1·7)       
 3 Occupational exposure to ben… Acute ly… 0·728 (0·212 … 0·3 (0·1 – 0·6)       
 4 Occupational exposure to for… Acute ly… 0·274 (0·222 … 0·1 (0·1 – 0·2)       
 5 Smoking                       Acute ly… 2·70 (0·988 –… 1·3 (0·4 – 2·2)       
 6 High body-mass index          Acute ly… 4·02 (2·99 – … 1·9 (1·2 – 2·5)       
 7 Occupational exposure to ben… Acute my… 0·829 (0·243 … 0·4 (0·1 – 0·7)       
 8 Occupational exposure to for… Acute my… 0·264 (0·223 … 0·1 (0·1 – 0·2)       
 9 Smoking                       Acute my… 7·47 (2·76 – … 3·9 (1·4 – 6·7)       
10 High body-mass index          Acute my… 7·76 (5·79 – … 4·1 (3·0 – 5·3)       
# ℹ 697 more rows
# ℹ abbreviated name: ¹​`all-age_DALYs_rate_(per_100,000)_2021`

Looking into 2021 PAF data for top 15 non-communicable diseases by DALYs (2019)

gbd_paf_data <- gbd_paf_data |>
  # Work just on the DALYs_PAF_2021 column
  mutate(DALYs_PAF_2021 = str_replace_all(DALYs_PAF_2021, "·", ".")) |>  # replace middle dot with .
  tidyr::extract(
    DALYs_PAF_2021,
    into = c("val", "lower", "upper"),
    regex = "([0-9.]+) \\(([-0-9.]+) – ([-0-9.]+)\\)",
    convert = TRUE
  ) 


gbd_paf_data |>
  select(val, lower, upper, Risk_Name)|>
  filter(Risk_Name == "Chronic kidney disease")
# A tibble: 0 × 4
# ℹ 4 variables: val <dbl>, lower <dbl>, upper <dbl>, Risk_Name <chr>
gbd_paf_plot_data =
gbd_paf_data |>
  select(val, lower, upper, Risk_Name) |>
  filter(grepl(paste0(top_non_comm_diseases, collapse = "|"), Risk_Name)) |> 
  mutate(Risk_Name = 
        case_when(grepl("Chronic kidney disease due to", Risk_Name) ~ "Chronic kidney disease",
                  TRUE ~ Risk_Name
         )) |>
  group_by(Risk_Name) |>
  summarise(val = sum(val, na.rm = TRUE),
            lower = sum(lower, na.rm = TRUE),
            upper = sum(upper, na.rm = TRUE)
            ) 

left_join(gbd_paf_plot_data,
          data.frame(Risk_Name = top_non_comm_diseases,
                     daly = top_non_comm_diseases),
          by = "Risk_Name") |>
  mutate(Risk_Name = factor(Risk_Name, levels = unique(Risk_Name))) |>
  ggplot(aes(x = reorder(Risk_Name, val), y = val)) +
  geom_col(fill = "steelblue") +
  geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.3) +
  coord_flip() +
  labs(
    x = "Risk factor",
    y = "Population Attributable Fraction (%)",
    title = "Risk factors with the greatest global disease burden (PAF - 2021)"
  ) +
  theme_minimal(base_size = 14)


sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
[1] stringr_1.5.1     ggplot2_3.5.2     dplyr_1.1.4       data.table_1.17.8
[5] workflowr_1.7.1  

loaded via a namespace (and not attached):
 [1] sass_0.4.10        utf8_1.2.6         generics_0.1.4     tidyr_1.3.1       
 [5] renv_1.0.3         stringi_1.8.7      digest_0.6.37      magrittr_2.0.3    
 [9] evaluate_1.0.4     grid_4.3.1         RColorBrewer_1.1-3 fastmap_1.2.0     
[13] cellranger_1.1.0   rprojroot_2.1.0    jsonlite_2.0.0     processx_3.8.6    
[17] whisker_0.4.1      ps_1.9.1           promises_1.3.3     httr_1.4.7        
[21] purrr_1.1.0        scales_1.4.0       jquerylib_0.1.4    cli_3.6.5         
[25] rlang_1.1.6        withr_3.0.2        cachem_1.1.0       yaml_2.3.10       
[29] tools_4.3.1        httpuv_1.6.16      here_1.0.1         vctrs_0.6.5       
[33] R6_2.6.1           lifecycle_1.0.4    git2r_0.36.2       fs_1.6.6          
[37] pkgconfig_2.0.3    callr_3.7.6        pillar_1.11.0      bslib_0.9.0       
[41] later_1.4.2        gtable_0.3.6       glue_1.8.0         Rcpp_1.1.0        
[45] xfun_0.52          tibble_3.3.0       tidyselect_1.2.1   rstudioapi_0.17.1 
[49] knitr_1.50         farver_2.1.2       htmltools_0.5.8.1  rmarkdown_2.29    
[53] labeling_0.4.3     compiler_4.3.1     getPass_0.2-4      readxl_1.4.5