summer-winter

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Last updated: 2025-03-09

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Knit directory: SAPPHIRE/

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Unstaged changes:
    Modified:   analysis/pigmentationdata.Rmd
    Modified:   analysis/summer-winter.Rmd
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_B.png
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_CIE_L.png
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_CIE_a.png
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_CIE_b.png
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_E.png
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_G.png
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_M.png
    Modified:   output/coeff_plots_summer-winter/coeff_Forehead_R.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_B.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_CIE_L.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_CIE_a.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_CIE_b.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_E.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_G.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_M.png
    Modified:   output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_R.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_B.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_CIE_L.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_CIE_a.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_CIE_b.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_E.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_G.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_M.png
    Modified:   output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_R.png
    Modified:   output/withinstats_filtered/Forehead_B.png
    Modified:   output/withinstats_filtered/Forehead_CIE_L.png
    Modified:   output/withinstats_filtered/Forehead_CIE_a.png
    Modified:   output/withinstats_filtered/Forehead_CIE_b.png
    Modified:   output/withinstats_filtered/Forehead_E.png
    Modified:   output/withinstats_filtered/Forehead_G.png
    Modified:   output/withinstats_filtered/Forehead_M.png
    Modified:   output/withinstats_filtered/Forehead_R.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_B.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_CIE_L.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_CIE_a.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_CIE_b.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_E.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_G.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_M.png
    Modified:   output/withinstats_filtered/LeftUpperInnerArm_R.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_B.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_CIE_L.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_CIE_a.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_CIE_b.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_E.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_G.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_M.png
    Modified:   output/withinstats_filtered/RightUpperInnerArm_R.png

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/summer-winter.Rmd) and HTML (docs/summer-winter.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	9217a86	Tina Lasisi	2025-03-07	Cleaned data and redid analyses

---

Introduction

library(ggstatsplot)

You can cite this package as:
     Patil, I. (2021). Visualizations with statistical details: The 'ggstatsplot' approach.
     Journal of Open Source Software, 6(61), 3167, doi:10.21105/joss.03167

median data

## ---- ensure-both-seasons-and-then-ggwithinstats ----
## This code explicitly filters your data so that each participant has BOTH Summer and Winter 
## for each (body_site, measurement_type). We then run grouped_ggwithinstats on that subset.
## This ensures a truly paired analysis with no partial data.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(ggstatsplot)

# 1) Read your final medians data, with columns like:
#    ParticipantCentreID, Season, Ethnicity, final_median, body_site, measurement_type
df <- read_csv("data/ScreeningDataCollection_medians.csv") %>%
  filter(Season %in% c("Summer","Winter")) %>%
  mutate(Season = factor(Season, levels = c("Summer","Winter")))

Rows: 5280 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): ParticipantCentreID, Ethnicity, Season, body_site, measurement_type
dbl (3): Gender, final_median, n_valid

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.

# 2) Keep only those participants who have BOTH Summer and Winter for each site+metric.
#    i.e., for each (ParticipantCentreID, body_site, metric), n_distinct(Season) == 2
df_valid <- df %>%
  group_by(ParticipantCentreID, body_site, measurement_type) %>%
  filter(n_distinct(Season) == 2) %>%
  ungroup()

# 3) Now for each (body_site, measurement_type) combo, we do a grouped_ggwithinstats
#    that splits the data by Ethnicity, and does a repeated-measures (paired) test
#    comparing Summer vs. Winter. 
combos <- df_valid %>%
  distinct(body_site, measurement_type)

# 4) We'll loop over combos and produce a plot for each
#    (If you want a single big combined plot, you'd do a different approach.)
plots <- combos %>%
  mutate(
    plotobj = map2(body_site, measurement_type, ~ {
      
      sub_data <- df_valid %>%
        filter(body_site == .x, measurement_type == .y)
      
      # If we have no data, skip
      if (nrow(sub_data) < 2) return(NULL)
      
      grouped_ggwithinstats(
        data         = sub_data,
        x            = Season,             # 2-level repeated measure
        y            = final_median,       # numeric outcome
        subject.id   = ParticipantCentreID,# repeated ID column
        grouping.var = Ethnicity,          # one subplot per Ethnicity
        paired       = TRUE,              # do a paired test
        type         = "parametric",       # or "nonparametric", etc.
        title.text   = paste("Body site:", .x, "| Metric:", .y),
        caption.text = "Paired test (Summer vs. Winter) within each Ethnicity",
        plotgrid.args= list(ncol = 2)      # if you have 2 Ethnicities, side-by-side
      )
    })
  )

# 5) Save each figure
dir.create("output/withinstats_filtered", showWarnings = FALSE, recursive = TRUE)

plots %>%
  rowwise() %>%
  mutate(
    outfile = paste0("output/withinstats_filtered/", body_site, "_", measurement_type, ".png"),
    saved = {
      if (!is.null(plotobj)) {
        ggsave(outfile, plotobj, width=10, height=6)
        TRUE
      } else FALSE
    }
  )

# A tibble: 24 × 5
# Rowwise: 
   body_site         measurement_type plotobj    outfile                   saved
   <chr>             <chr>            <list>     <chr>                     <lgl>
 1 Forehead          B                <patchwrk> output/withinstats_filte… TRUE 
 2 Forehead          CIE_L            <patchwrk> output/withinstats_filte… TRUE 
 3 Forehead          CIE_a            <patchwrk> output/withinstats_filte… TRUE 
 4 Forehead          CIE_b            <patchwrk> output/withinstats_filte… TRUE 
 5 Forehead          E                <patchwrk> output/withinstats_filte… TRUE 
 6 Forehead          G                <patchwrk> output/withinstats_filte… TRUE 
 7 Forehead          M                <patchwrk> output/withinstats_filte… TRUE 
 8 Forehead          R                <patchwrk> output/withinstats_filte… TRUE 
 9 LeftUpperInnerArm B                <patchwrk> output/withinstats_filte… TRUE 
10 LeftUpperInnerArm CIE_L            <patchwrk> output/withinstats_filte… TRUE 
# ℹ 14 more rows

replicate data

stage2_data <- read_csv("data/ScreeningDataCollection_stage2.csv") %>%
  group_by(ParticipantCentreID, body_site, measurement_type, Season) %>%
  mutate(
    # Subtract however many NA values are in 'value' from n_valid
    n_valid = n_valid - sum(is.na(value))
  ) %>%
  ungroup() %>%
  # Then remove rows with NA in 'value'
  filter(!is.na(value))

Rows: 15840 Columns: 24
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (7): ParticipantCentreID, Season, body_site, measurement_type, Ethnicit...
dbl (16): Gender, value, replicate, mean_val, sd_val, n_valid, cv, n, min_va...
lgl  (1): is_outlier_allSites

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.

write.csv(stage2_data, "data/ScreeningDataCollection_stage2_clean.csv", row.names = FALSE)

stage2_data <- stage2_data %>%
  filter(Season %in% c("Summer","Winter")) %>%
  mutate(Season = factor(Season, levels=c("Summer","Winter")))

One model per metric per body site

# Suppose your replicate-level data is in 'stage2_data' with columns:
#   ParticipantCentreID, body_site, metric, Ethnicity, Season, value, etc.
#   We have 2 levels in Season: "Summer","Winter".

library(lmerTest)

Loading required package: lme4

Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:tidyr':

    expand, pack, unpack

Attaching package: 'lmerTest'

The following object is masked from 'package:lme4':

    lmer

The following object is masked from 'package:stats':

    step

library(tidyverse)


# 2) Identify combos of body_site, metric, and Ethnicity
combos <- stage2_data %>%
  distinct(body_site, measurement_type, Ethnicity)

results_list <- list()

# 3) Loop over each combination
for (i in seq_len(nrow(combos))) {
  bs <- combos$body_site[i]
  mt <- combos$measurement_type[i]
  eth <- combos$Ethnicity[i]
  
  # subset
  sub_data <- stage2_data %>%
    filter(body_site == bs, measurement_type == mt, Ethnicity == eth)
  
  # skip if not enough data
  if (nrow(sub_data) < 4) {
    next
  }
  
  # 4) Fit random intercept model
  #   value ~ Season + (1|ParticipantCentreID)
  mod <- tryCatch({
    lmer(
      value ~ Season + (1|ParticipantCentreID),
      data = sub_data
    )
  }, error=function(e) NULL)
  
  if (!is.null(mod)) {
    # store summary or any results
    sum_mod <- summary(mod)
    results_list[[paste(bs,mt,eth,sep="_")]] <- sum_mod
  }
}

# Then you can examine 'results_list', each containing the model summary for
# that site–metric–ethnicity combination. You see if 'Season' is significant
# in each subset.

## ---- coeff-plots-summer-winter ----
## This script creates a single side-by-side (ncol=2) coefficient plot
## for each (body_site, measurement_type), comparing the two ethnic groups
## (e.g., "CapeMixed" vs. "Xhosa"). Each subplot shows a random-intercept
## model's coefficients for value ~ Season + (1|ParticipantCentreID).

library(lmerTest)
library(tidyverse)
library(ggstatsplot)

# 1) Load and clean your stage2_data
stage2_data <- read_csv("data/ScreeningDataCollection_stage2.csv") %>%
  group_by(ParticipantCentreID, body_site, measurement_type, Season) %>%
  mutate(
    # Subtract however many NA values are in 'value' from n_valid
    n_valid = n_valid - sum(is.na(value))
  ) %>%
  ungroup() %>%
  # Then remove rows with NA in 'value'
  filter(!is.na(value)) %>%
  # Keep only Summer/Winter
  filter(Season %in% c("Summer","Winter")) %>%
  mutate(
    Season             = factor(Season, levels=c("Summer","Winter")),
    body_site          = factor(body_site),
    measurement_type   = factor(measurement_type),
    Ethnicity          = factor(Ethnicity),
    ParticipantCentreID= factor(ParticipantCentreID)
  )

Rows: 15840 Columns: 24
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (7): ParticipantCentreID, Season, body_site, measurement_type, Ethnicit...
dbl (16): Gender, value, replicate, mean_val, sd_val, n_valid, cv, n, min_va...
lgl  (1): is_outlier_allSites

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.

# 2) Identify combos of body_site + measurement_type
combos <- stage2_data %>%
  distinct(body_site, measurement_type) %>%
  arrange(body_site, measurement_type)

# 3) Create a directory "output/coeff_plots_summer-winter"
dir.create("output/coeff_plots_summer-winter", showWarnings = FALSE, recursive = TRUE)

# 4) We'll handle two ethnic groups side-by-side: "CapeMixed" & "Xhosa"
ethnic_groups <- c("CapeMixed", "Xhosa")

for (i in seq_len(nrow(combos))) {
  bs <- combos$body_site[i]
  mt <- combos$measurement_type[i]
  
  # We'll store the two coefficient plots in a list
  plot_list <- list()
  valid_plots <- 0
  
  for (eth in ethnic_groups) {
    # Subset data
    sub_data <- stage2_data %>%
      filter(body_site == bs, measurement_type == mt, Ethnicity == eth)
    
    if (nrow(sub_data) < 4) {
      message("Skipping: ", bs, " - ", mt, " - ", eth, " => not enough data.")
      plot_list[[eth]] <- NULL
      next
    }
    
    # Fit a random intercept model
    mod <- tryCatch({
      lmer(value ~ Season + (1|ParticipantCentreID), data=sub_data)
    }, error = function(e) NULL)
    
    if (is.null(mod)) {
      message("Model failed for: ", bs, " - ", mt, " - ", eth)
      plot_list[[eth]] <- NULL
      next
    }
    
    # Create a coefficient plot
    plot_title <- paste0(bs, " - ", as.character(mt), " - ", eth)
    p <- ggcoefstats(
      x = mod,
      title = plot_title,
      xlab  = "Coefficient (beta)",
      ylab  = "Fixed Effects"
    )
    
    plot_list[[eth]] <- p
    valid_plots <- valid_plots + 1
  }
  
  # If neither ethnicity had enough data, skip saving
  if (valid_plots == 0) {
    next
  }
  
  # 5) Combine the two coefficient plots side by side (ncol=2)
  #    If one ethnicity was missing data, combine_plots can handle a NULL
  final_plot <- combine_plots(
    plotlist       = plot_list,
    plotgrid.args  = list(ncol = 2),
    annotation.args= list(
      title    = paste0("Coefficients: ", bs, " - ", mt),
      caption  = "Random intercept model: value ~ Season + (1|ParticipantCentreID)"
    )
  )
  
  # 6) Save
  outfile <- paste0("output/coeff_plots_summer-winter/coeff_", bs, "_", mt, ".png")
  ggsave(outfile, final_plot, width=10, height=6)
  message("Saved: ", outfile)
}

Saved: output/coeff_plots_summer-winter/coeff_Forehead_B.png
Saved: output/coeff_plots_summer-winter/coeff_Forehead_CIE_a.png
Saved: output/coeff_plots_summer-winter/coeff_Forehead_CIE_b.png
Saved: output/coeff_plots_summer-winter/coeff_Forehead_CIE_L.png
Saved: output/coeff_plots_summer-winter/coeff_Forehead_E.png
Saved: output/coeff_plots_summer-winter/coeff_Forehead_G.png
Saved: output/coeff_plots_summer-winter/coeff_Forehead_M.png
Saved: output/coeff_plots_summer-winter/coeff_Forehead_R.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_B.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_CIE_a.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_CIE_b.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_CIE_L.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_E.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_G.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_M.png
Saved: output/coeff_plots_summer-winter/coeff_LeftUpperInnerArm_R.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_B.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_CIE_a.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_CIE_b.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_CIE_L.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_E.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_G.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_M.png
Saved: output/coeff_plots_summer-winter/coeff_RightUpperInnerArm_R.png

Session information

sessionInfo()

R version 4.4.3 (2025-02-28)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.3.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

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/Detroit
tzcode source: internal

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

other attached packages:
 [1] lmerTest_3.1-3     lme4_1.1-36        Matrix_1.7-2       lubridate_1.9.4   
 [5] forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4        purrr_1.0.4       
 [9] readr_2.1.5        tidyr_1.3.1        tibble_3.2.1       ggplot2_3.5.1     
[13] tidyverse_2.0.0    ggstatsplot_0.13.0 workflowr_1.7.1   

loaded via a namespace (and not attached):
 [1] Rdpack_2.6.2           pbapply_1.7-2          rematch2_2.1.2        
 [4] rlang_1.1.5            magrittr_2.0.3         git2r_0.35.0          
 [7] compiler_4.4.3         statsExpressions_1.6.2 getPass_0.2-4         
[10] systemfonts_1.2.1      callr_3.7.6            vctrs_0.6.5           
[13] pkgconfig_2.0.3        crayon_1.5.3           fastmap_1.2.0         
[16] labeling_0.4.3         effectsize_1.0.0       utf8_1.2.4            
[19] promises_1.3.2         rmarkdown_2.29         tzdb_0.4.0            
[22] ps_1.8.1               nloptr_2.1.1           ragg_1.3.3            
[25] MatrixModels_0.5-3     bit_4.5.0.1            xfun_0.50             
[28] cachem_1.1.0           jsonlite_1.8.9         later_1.4.1           
[31] parallel_4.4.3         R6_2.5.1               bslib_0.9.0           
[34] stringi_1.8.4          boot_1.3-31            numDeriv_2016.8-1.1   
[37] jquerylib_0.1.4        estimability_1.5.1     Rcpp_1.0.14           
[40] knitr_1.49             parameters_0.24.2      correlation_0.8.7     
[43] httpuv_1.6.15          splines_4.4.3          timechange_0.3.0      
[46] tidyselect_1.2.1       rstudioapi_0.17.1      yaml_2.3.10           
[49] processx_3.8.5         lattice_0.22-6         withr_3.0.2           
[52] bayestestR_0.15.2      coda_0.19-4.1          evaluate_1.0.3        
[55] RcppParallel_5.1.10    pillar_1.10.1          whisker_0.4.1         
[58] reformulas_0.4.0       insight_1.1.0          generics_0.1.3        
[61] vroom_1.6.5            rprojroot_2.0.4        paletteer_1.6.0       
[64] hms_1.1.3              rstantools_2.4.0       munsell_0.5.1         
[67] scales_1.3.0           minqa_1.2.8            glue_1.8.0            
[70] emmeans_1.10.7         tools_4.4.3            fs_1.6.5              
[73] mvtnorm_1.3-3          grid_4.4.3             rbibutils_2.3         
[76] datawizard_1.0.1       colorspace_2.1-1       nlme_3.1-167          
[79] patchwork_1.3.0        performance_0.13.0     cli_3.6.3             
[82] textshaping_1.0.0      gtable_0.3.6           zeallot_0.1.0         
[85] sass_0.4.9             digest_0.6.37          prismatic_1.1.2       
[88] ggrepel_0.9.6          farver_2.1.2           htmltools_0.5.8.1     
[91] lifecycle_1.0.4        httr_1.4.7             bit64_4.6.0-1         
[94] BayesFactor_0.9.12-4.7 MASS_7.3-64