Last updated: 2024-10-07
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Knit directory: SAPPHIRE/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 7edaefa | calliquire | 2024-10-07 | stupid mistake 3 |
| html | 7edaefa | calliquire | 2024-10-07 | stupid mistake 3 |
| Rmd | c17b81a | calliquire | 2024-10-07 | stupid mistake 2 |
| html | c17b81a | calliquire | 2024-10-07 | stupid mistake 2 |
| Rmd | 9bb72be | calliquire | 2024-10-07 | found stupid mistake |
| html | 9bb72be | calliquire | 2024-10-07 | found stupid mistake |
| Rmd | f6f08f1 | calliquire | 2024-10-07 | editing data modeling |
| html | f6f08f1 | calliquire | 2024-10-07 | editing data modeling |
| Rmd | ed111ae | calliquire | 2024-10-07 | stopped being a file hoarder and put all of the data-cleaning steps into one page |
| html | ed111ae | calliquire | 2024-10-07 | stopped being a file hoarder and put all of the data-cleaning steps into one page |
| 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_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/screening_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$collection_period,
levels = c("Summer", "Winter", "6Weeks"))
df$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, ethnicity, collection_period, body_site,
reflectance_metric, reflectance_value)write.csv(df_filtered, "data/screening_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