Last updated: 2024-10-07
Checks: 6 1
Knit directory: SAPPHIRE/
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.
The R Markdown file has unstaged changes. To know which version of
the R Markdown file created these results, you’ll want to first commit
it to the Git repo. If you’re still working on the analysis, you can
ignore this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
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.
The command set.seed(20240923) 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.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
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 5f88ad5. 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: .Rapp.history
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/.DS_Store
Ignored: data/.DS_Store
Unstaged changes:
Deleted: analysis/data_cleaning_1.Rmd
Deleted: analysis/data_cleaning_2.Rmd
Deleted: analysis/data_cleaning_3.Rmd
Modified: analysis/data_cleaning_composite.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/data_cleaning_composite.Rmd) and HTML
(docs/data_cleaning_composite.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 |
|---|---|---|---|---|
| html | 5f88ad5 | calliquire | 2024-10-07 | Build site. |
| Rmd | 0acb3db | calliquire | 2024-10-07 | wflow_publish("analysis/data_cleaning_composite.Rmd") |
About This Analysis: This analysis is the composite data-cleaning process that includes all three data_cleaning_ pages in one.
The goal of this analysis is to initiate the wrangling of the original, raw data through manual editing in Google Sheets and a R pipeline to leave us with a long-form data set.
library(readxl)
library(dplyr)
library(janitor)
library(tidyverse)file_path <- "data/serum_vit_D_study_with_lab_results.xlsx"screening_summer <- read_excel(file_path, sheet = "ScreeningDataCollectionSummer")
screening_winter <- read_excel(file_path, sheet = "ScreeningDataCollectionWinter")
screening_6weeks <- read_excel(file_path, sheet = "ScreeningDataCollection6Weeks")screening_summer <- screening_summer %>% clean_names()
screening_winter <- screening_winter %>% clean_names()
screening_6weeks <- screening_6weeks %>% clean_names()screening_summer <- screening_summer %>% mutate(age_years = as.numeric(age_years))
screening_winter <- screening_winter %>% mutate(age_years = as.numeric(age_years))
screening_6weeks <- screening_6weeks %>% mutate(age_years = as.numeric(age_years))screening_summer <- screening_summer %>% mutate(collection_period = "Summer")
screening_winter <- screening_winter %>% mutate(collection_period = "Winter")
screening_6weeks <- screening_6weeks %>% mutate(collection_period = "6Weeks")screening_long <- bind_rows(screening_summer, screening_winter, screening_6weeks)write.csv(screening_long, "data/screening_long.csv", row.names = FALSE)The goal of this analysis is to clean the screening_long.csv long-form dataset to have columns for body_site and reflectance_type (as well as collection_period, which was done in Part 1).
library(tidyr)
library(dplyr)data <- read.csv("data/screening_long.csv")data_long <- pivot_longer(data,
cols = starts_with("skin_reflectance"),
names_to = "measurement",
values_to = "reflectance_value")data_long <- data_long %>%
extract(col = "measurement",
into = c("body_site", "reflectance_metric"),
regex = "skin_reflectance_([a-zA-Z_]+?)(l[123]|l_[123]|a[123]|a_[123]|b[123]|b_[123]|g[123]|r[123]|m[123]|e[123])",
remove = FALSE) %>%
mutate(body_site = gsub("_$", "", body_site)) # Remove any trailing underscore from body_sitedata_long <- data_long %>%
select(-measurement)write.csv(data_long, "data/cleaned_screening_long.csv", row.names = FALSE)The goal of this analysis is to:
- Handle missing values
- Convert categorical values to factors
- Reshape and filter data again
- Remove irrelevant data columns
# Load necessary libraries
library(dplyr)df <- read.csv("data/cleaned_screening_long.csv")# Check for missing values
missing_summary <- sapply(df, function(x) sum(is.na(x)))
print(missing_summary) participant_centre_id
0
interviewer_name
0
today_date
72
age_years
72
date_of_birth
0
gender
0
ethnicity_coloured
0
ethnicity_white
0
ethnicity_african_black
0
ethnicity_indian_asian
0
ethnicity_other
0
ethnicity_specify_other
15840
ethnicity
15840
refuse_to_answer
0
weight_measure1
0
weight_measure2
0
avg_weight
0
height_measure1
0
height_measure2
72
avg_height
72
bmi
72
sore_throat_yes
0
sore_throat_no
0
runny_nose_yes
0
runny_nose_no
0
cough_yes
0
cough_no
0
fever_yes
0
fever_no
0
night_sweats_yes
0
night_sweats_no
0
unexplained_weight_loss_yes
0
unexplained_weight_loss_no
0
currently_taking_supplements_yes
0
currently_taking_supplements_no
0
supplements
15120
currently_taking_medications_yes
0
currently_taking_medications_no
0
medications
15264
participant_included_yes
0
participant_included_no
0
if_no_reason_for_exclusion
15768
smoking_do_you_regularly_smoke_at_least1cigarette5daysa_week
0
smoking_comments
11304
pathcare_sticker_barcode
288
req_num
288
serv_date
216
vit_d_result
216
res_date_time
288
collection_period
0
continued_in_study
9864
if_not_continued_in_study_reason
15624
body_site
0
reflectance_metric
0
reflectance_value
319 # Create the ethnicity column based on conditions
df <- df %>%
mutate(ethnicity = case_when(
ethnicity_african_black == TRUE &
ethnicity_coloured == FALSE &
ethnicity_white == FALSE &
ethnicity_indian_asian == FALSE &
ethnicity_other == FALSE ~ "xhosa",
ethnicity_coloured == TRUE &
ethnicity_african_black == FALSE &
ethnicity_white == FALSE &
ethnicity_indian_asian == FALSE &
ethnicity_other == FALSE ~ "cape_coloured",
TRUE ~ NA_character_ # Assign NA for all other cases
))
# Fill NA values with 'Other' if needed
df$ethnicity[is.na(df$ethnicity)] <- "Other"Check the updated data frame.
Covert categorical variables into factors.
df$body_site <- factor(df$body_site)
df$collection_period <- factor(df$collection_period)
df$reflectance_metric <- factor(df$reflectance_metric)
df$ethnicity <- factor(df$ethnicity)Check structure of data frame.
Remove columns that are irrelevant to the analysis. Do this through filtering for the columns/variables that are relevant.
df_filtered <- df %>%
select(participant_centre_id, gender, ethnicity,
vit_d_result, collection_period, body_site,
reflectance_metric, reflectance_value)write.csv(df_filtered, "data/filtered_screening_long.csv", row.names = FALSE)
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.6.1
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] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 purrr_1.0.2
[5] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1
[9] tidyverse_2.0.0 janitor_2.2.0 dplyr_1.1.4 readxl_1.4.3
loaded via a namespace (and not attached):
[1] sass_0.4.9 utf8_1.2.4 generics_0.1.3 stringi_1.8.4
[5] hms_1.1.3 digest_0.6.37 magrittr_2.0.3 evaluate_1.0.0
[9] grid_4.4.1 timechange_0.3.0 fastmap_1.2.0 cellranger_1.1.0
[13] rprojroot_2.0.4 workflowr_1.7.1 jsonlite_1.8.9 whisker_0.4.1
[17] promises_1.3.0 fansi_1.0.6 scales_1.3.0 jquerylib_0.1.4
[21] cli_3.6.3 rlang_1.1.4 munsell_0.5.1 withr_3.0.1
[25] cachem_1.1.0 yaml_2.3.10 tools_4.4.1 tzdb_0.4.0
[29] colorspace_2.1-1 httpuv_1.6.15 vctrs_0.6.5 R6_2.5.1
[33] lifecycle_1.0.4 git2r_0.33.0 snakecase_0.11.1 fs_1.6.4
[37] pkgconfig_2.0.3 pillar_1.9.0 bslib_0.8.0 later_1.3.2
[41] gtable_0.3.5 glue_1.7.0 Rcpp_1.0.13 xfun_0.47
[45] tidyselect_1.2.1 rstudioapi_0.16.0 knitr_1.48 htmltools_0.5.8.1
[49] rmarkdown_2.28 compiler_4.4.1