Last updated: 2024-10-03
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 b0ffd83. 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:
Modified: analysis/data_cleaning_1.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_1.Rmd) and
HTML (docs/data_cleaning_1.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 | e8e0e54 | calliquire | 2024-10-03 | data cleaning 2 |
About This Analysis: 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 three long-form data sets (one for each category of data).
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)print(head(screening_long))# A tibble: 6 × 124
participant_centre_id interviewer_name today_date age_years
<chr> <chr> <dttm> <dbl>
1 VDKH001 Betty 2013-02-11 00:00:00 20
2 VDKH002 Betty 2013-02-11 00:00:00 23
3 VDKH003 Betty 2013-02-11 00:00:00 23
4 VDKH004 Betty 2013-02-12 00:00:00 20
5 VDKH005 Betty 2013-02-12 00:00:00 19
6 VDKH006 Betty 2013-02-12 00:00:00 21
# ℹ 120 more variables: date_of_birth <dttm>, gender <dbl>,
# ethnicity_coloured <lgl>, ethnicity_white <lgl>,
# ethnicity_african_black <lgl>, ethnicity_indian_asian <lgl>,
# ethnicity_other <lgl>, ethnicity_specify_other <lgl>, ethnicity <lgl>,
# refuse_to_answer <lgl>, weight_measure1 <dbl>, weight_measure2 <dbl>,
# avg_weight <dbl>, height_measure1 <dbl>, height_measure2 <dbl>,
# avg_height <dbl>, bmi <dbl>, sore_throat_yes <lgl>, sore_throat_no <lgl>, …Repeat the above steps for the sheets in the Food Frequency category and in the Sun Exposure category.
food_freq_summer <- read_excel(file_path, sheet = "FoodFrequencySummer")
food_freq_winter <- read_excel(file_path, sheet = "FoodFrequencyWinter")
food_freq_6weeks <- read_excel(file_path, sheet = "FoodFrequency6Weeks")food_freq_summer <- food_freq_summer %>% clean_names()
food_freq_winter <- food_freq_winter %>% clean_names()
food_freq_6weeks <- food_freq_6weeks %>% clean_names()Step 6 unnecessary.
Add a ‘collection_period’ column to each data frame to indicate when the data was collected.
food_freq_summer <- food_freq_summer %>% mutate(collection_period = "Summer")
food_freq_winter <- food_freq_winter %>% mutate(collection_period = "Winter")
food_freq_6weeks <- food_freq_6weeks %>% mutate(collection_period = "6Weeks")food_freq_long <- bind_rows(food_freq_summer, food_freq_winter, food_freq_6weeks)print(head(food_freq_long))# A tibble: 6 × 201
participant_centre_id interviewer_name today_date sardines_no
<chr> <chr> <dttm> <lgl>
1 VDKH001 Betty 2013-02-11 00:00:00 TRUE
2 VDKH002 Betty 2013-02-11 00:00:00 TRUE
3 VDKH003 Betty 2013-02-11 00:00:00 TRUE
4 VDKH004 Betty 2013-02-12 00:00:00 TRUE
5 VDKH005 Betty 2013-02-12 00:00:00 TRUE
6 VDKH006 Betty 2013-02-12 00:00:00 TRUE
# ℹ 197 more variables: sardines_yes <lgl>, sardines_times_per_week <dbl>,
# sardines_times_per_day <dbl>, sardines_brand_description <chr>,
# sardines_amount_usually_eaten_per_occasion <chr>,
# sardines_amount_eaten_per_day_as_proportion_of100g <dbl>,
# sardines_vit_d_micrograms_per_100g <dbl>,
# sardines_total_daily_vit_d_intake_micro_grams <dbl>, sardines_source <lgl>,
# sardines_notes <lgl>, pilchards_no <lgl>, pilchards_yes <lgl>, …sun_expos_summer <- read_excel(file_path, sheet = "SunExposureSummer")
sun_expos_winter <- read_excel(file_path, sheet = "SunExposureWinter")
sun_expos_6weeks <- read_excel(file_path, sheet = "SunExposure6Weeks")sun_expos_summer <- sun_expos_summer %>% clean_names()
sun_expos_winter <- sun_expos_winter %>% clean_names()
sun_expos_6weeks <- sun_expos_6weeks %>% clean_names()Step 6 unnecessary.
Add a ‘collection_period’ column to each data frame to indicate when the data was collected.
sun_expos_summer <- sun_expos_summer %>% mutate(collection_period = "Summer")
sun_expos_winter <- sun_expos_winter %>% mutate(collection_period = "Winter")
sun_expos_6weeks <- sun_expos_6weeks %>% mutate(collection_period = "6Weeks")sun_expos_long <- bind_rows(sun_expos_summer, sun_expos_winter, sun_expos_6weeks)print(head(sun_expos_long))# A tibble: 6 × 29
participant_centre_id interviewer_name today_date
<chr> <chr> <dttm>
1 VDKH001 Rene 2013-02-11 00:00:00
2 VDKH002 Betty 2013-02-11 00:00:00
3 VDKH003 Rene 2013-02-11 00:00:00
4 VDKH004 Rene 2013-02-12 00:00:00
5 VDKH005 Rene 2013-02-12 00:00:00
6 VDKH006 Rene 2013-02-12 00:00:00
# ℹ 26 more variables: x1time_spent_outdoors_weekday <chr>,
# x2time_of_day_begin_of_exposure_weekday <chr>,
# x3time_spent_outdoors_weekend_day <chr>,
# x4time_of_day_begin_of_exposure_weekend <chr>, x5forehead_exposed <chr>,
# x5cheek_exposed <chr>, x5hand_exposed <chr>, x5shoulder_exposed <chr>,
# x5arm_exposed <chr>, x5back_exposed <chr>, x5chest_exposed <chr>,
# x5thigh_exposed <chr>, x5calf_exposed <chr>, …write.csv(screening_long, "data/screening_long.csv", row.names = FALSE)
write.csv(food_freq_long, "data/food_freq_long.csv", row.names = FALSE)
write.csv(sun_expos_long, "data/sun_expos_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