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About This Analysis: This is the third iteration of data cleaning that must be done after data_cleaning_2.
Load relevant libraries.
# Load necessary libraries
library(dplyr)Read in the data.
df <- read.csv("data/cleaned_screening_long.csv")Check for missing values.
# 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 column for ethnicity (xhosa or cape_mixed). If participants answered TRUE to ethnicity_african_black, and FALSE to all other ethnicity_ questions, they are designated as xhosa. If participants answered TRUE to ethnicity_coloured and FALSE to all other ethnicity_ questions, they are designated as cape_coloured.
# 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 dataframe
head(df) participant_centre_id interviewer_name today_date age_years date_of_birth
1 VDKH001 Betty 2013-02-11 20 1993-08-07
2 VDKH001 Betty 2013-02-11 20 1993-08-07
3 VDKH001 Betty 2013-02-11 20 1993-08-07
4 VDKH001 Betty 2013-02-11 20 1993-08-07
5 VDKH001 Betty 2013-02-11 20 1993-08-07
6 VDKH001 Betty 2013-02-11 20 1993-08-07
gender ethnicity_coloured ethnicity_white ethnicity_african_black
1 1 FALSE FALSE TRUE
2 1 FALSE FALSE TRUE
3 1 FALSE FALSE TRUE
4 1 FALSE FALSE TRUE
5 1 FALSE FALSE TRUE
6 1 FALSE FALSE TRUE
ethnicity_indian_asian ethnicity_other ethnicity_specify_other ethnicity
1 FALSE FALSE NA xhosa
2 FALSE FALSE NA xhosa
3 FALSE FALSE NA xhosa
4 FALSE FALSE NA xhosa
5 FALSE FALSE NA xhosa
6 FALSE FALSE NA xhosa
refuse_to_answer weight_measure1 weight_measure2 avg_weight height_measure1
1 FALSE 60.6 60.6 60.6 1.728
2 FALSE 60.6 60.6 60.6 1.728
3 FALSE 60.6 60.6 60.6 1.728
4 FALSE 60.6 60.6 60.6 1.728
5 FALSE 60.6 60.6 60.6 1.728
6 FALSE 60.6 60.6 60.6 1.728
height_measure2 avg_height bmi sore_throat_yes sore_throat_no
1 1.728 1.728 20.29482 FALSE TRUE
2 1.728 1.728 20.29482 FALSE TRUE
3 1.728 1.728 20.29482 FALSE TRUE
4 1.728 1.728 20.29482 FALSE TRUE
5 1.728 1.728 20.29482 FALSE TRUE
6 1.728 1.728 20.29482 FALSE TRUE
runny_nose_yes runny_nose_no cough_yes cough_no fever_yes fever_no
1 FALSE TRUE FALSE TRUE FALSE TRUE
2 FALSE TRUE FALSE TRUE FALSE TRUE
3 FALSE TRUE FALSE TRUE FALSE TRUE
4 FALSE TRUE FALSE TRUE FALSE TRUE
5 FALSE TRUE FALSE TRUE FALSE TRUE
6 FALSE TRUE FALSE TRUE FALSE TRUE
night_sweats_yes night_sweats_no unexplained_weight_loss_yes
1 FALSE TRUE FALSE
2 FALSE TRUE FALSE
3 FALSE TRUE FALSE
4 FALSE TRUE FALSE
5 FALSE TRUE FALSE
6 FALSE TRUE FALSE
unexplained_weight_loss_no currently_taking_supplements_yes
1 TRUE FALSE
2 TRUE FALSE
3 TRUE FALSE
4 TRUE FALSE
5 TRUE FALSE
6 TRUE FALSE
currently_taking_supplements_no supplements currently_taking_medications_yes
1 TRUE <NA> FALSE
2 TRUE <NA> FALSE
3 TRUE <NA> FALSE
4 TRUE <NA> FALSE
5 TRUE <NA> FALSE
6 TRUE <NA> FALSE
currently_taking_medications_no medications participant_included_yes
1 TRUE <NA> TRUE
2 TRUE <NA> TRUE
3 TRUE <NA> TRUE
4 TRUE <NA> TRUE
5 TRUE <NA> TRUE
6 TRUE <NA> TRUE
participant_included_no if_no_reason_for_exclusion
1 FALSE <NA>
2 FALSE <NA>
3 FALSE <NA>
4 FALSE <NA>
5 FALSE <NA>
6 FALSE <NA>
smoking_do_you_regularly_smoke_at_least1cigarette5daysa_week smoking_comments
1 FALSE <NA>
2 FALSE <NA>
3 FALSE <NA>
4 FALSE <NA>
5 FALSE <NA>
6 FALSE <NA>
pathcare_sticker_barcode req_num serv_date vit_d_result
1 785293139 785293139 2013-02-11 10:40:00 29.5
2 785293139 785293139 2013-02-11 10:40:00 29.5
3 785293139 785293139 2013-02-11 10:40:00 29.5
4 785293139 785293139 2013-02-11 10:40:00 29.5
5 785293139 785293139 2013-02-11 10:40:00 29.5
6 785293139 785293139 2013-02-11 10:40:00 29.5
res_date_time collection_period continued_in_study
1 2013-02-13 12:13:00 Summer <NA>
2 2013-02-13 12:13:00 Summer <NA>
3 2013-02-13 12:13:00 Summer <NA>
4 2013-02-13 12:13:00 Summer <NA>
5 2013-02-13 12:13:00 Summer <NA>
6 2013-02-13 12:13:00 Summer <NA>
if_not_continued_in_study_reason body_site reflectance_metric
1 <NA> forehead e1
2 <NA> forehead e2
3 <NA> forehead e3
4 <NA> forehead m1
5 <NA> forehead m2
6 <NA> forehead m3
reflectance_value
1 17.01
2 18.73
3 18.61
4 55.99
5 70.57
6 76.28Covert 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.
str(df)'data.frame': 15840 obs. of 55 variables:
$ participant_centre_id : chr "VDKH001" "VDKH001" "VDKH001" "VDKH001" ...
$ interviewer_name : chr "Betty" "Betty" "Betty" "Betty" ...
$ today_date : chr "2013-02-11" "2013-02-11" "2013-02-11" "2013-02-11" ...
$ age_years : int 20 20 20 20 20 20 20 20 20 20 ...
$ date_of_birth : chr "1993-08-07" "1993-08-07" "1993-08-07" "1993-08-07" ...
$ gender : int 1 1 1 1 1 1 1 1 1 1 ...
$ ethnicity_coloured : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ ethnicity_white : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ ethnicity_african_black : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ ethnicity_indian_asian : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ ethnicity_other : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ ethnicity_specify_other : logi NA NA NA NA NA NA ...
$ ethnicity : Factor w/ 2 levels "cape_coloured",..: 2 2 2 2 2 2 2 2 2 2 ...
$ refuse_to_answer : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ weight_measure1 : num 60.6 60.6 60.6 60.6 60.6 ...
$ weight_measure2 : num 60.6 60.6 60.6 60.6 60.6 ...
$ avg_weight : num 60.6 60.6 60.6 60.6 60.6 ...
$ height_measure1 : num 1.73 1.73 1.73 1.73 1.73 ...
$ height_measure2 : num 1.73 1.73 1.73 1.73 1.73 ...
$ avg_height : num 1.73 1.73 1.73 1.73 1.73 ...
$ bmi : num 20.3 20.3 20.3 20.3 20.3 ...
$ sore_throat_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ sore_throat_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ runny_nose_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ runny_nose_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ cough_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ cough_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ fever_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ fever_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ night_sweats_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ night_sweats_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ unexplained_weight_loss_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ unexplained_weight_loss_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ currently_taking_supplements_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ currently_taking_supplements_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ supplements : chr NA NA NA NA ...
$ currently_taking_medications_yes : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ currently_taking_medications_no : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ medications : chr NA NA NA NA ...
$ participant_included_yes : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
$ participant_included_no : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ if_no_reason_for_exclusion : chr NA NA NA NA ...
$ smoking_do_you_regularly_smoke_at_least1cigarette5daysa_week: logi FALSE FALSE FALSE FALSE FALSE FALSE ...
$ smoking_comments : chr NA NA NA NA ...
$ pathcare_sticker_barcode : int 785293139 785293139 785293139 785293139 785293139 785293139 785293139 785293139 785293139 785293139 ...
$ req_num : int 785293139 785293139 785293139 785293139 785293139 785293139 785293139 785293139 785293139 785293139 ...
$ serv_date : chr "2013-02-11 10:40:00" "2013-02-11 10:40:00" "2013-02-11 10:40:00" "2013-02-11 10:40:00" ...
$ vit_d_result : num 29.5 29.5 29.5 29.5 29.5 29.5 29.5 29.5 29.5 29.5 ...
$ res_date_time : chr "2013-02-13 12:13:00" "2013-02-13 12:13:00" "2013-02-13 12:13:00" "2013-02-13 12:13:00" ...
$ collection_period : Factor w/ 3 levels "6Weeks","Summer",..: 2 2 2 2 2 2 2 2 2 2 ...
$ continued_in_study : chr NA NA NA NA ...
$ if_not_continued_in_study_reason : chr NA NA NA NA ...
$ body_site : Factor w/ 3 levels "forehead","left_upper_inner_arm",..: 1 1 1 1 1 1 1 1 1 1 ...
$ reflectance_metric : Factor w/ 24 levels "a_1","a_2","a_3",..: 10 11 12 19 20 21 22 23 24 13 ...
$ reflectance_value : num 17 18.7 18.6 56 70.6 ...Remove columns that are irrelevant to the analysis. Do this through filtering for the columns that are relevant.
df_filtered <- df %>%
select(participant_centre_id, gender, ethnicity,
vit_d_result, collection_period, body_site,
reflectance_metric, reflectance_value)Save the filtered data frame.
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] dplyr_1.1.4
loaded via a namespace (and not attached):
[1] jsonlite_1.8.9 compiler_4.4.1 promises_1.3.0 tidyselect_1.2.1
[5] Rcpp_1.0.13 stringr_1.5.1 git2r_0.33.0 later_1.3.2
[9] jquerylib_0.1.4 yaml_2.3.10 fastmap_1.2.0 R6_2.5.1
[13] generics_0.1.3 workflowr_1.7.1 knitr_1.48 tibble_3.2.1
[17] rprojroot_2.0.4 bslib_0.8.0 pillar_1.9.0 rlang_1.1.4
[21] utf8_1.2.4 cachem_1.1.0 stringi_1.8.4 httpuv_1.6.15
[25] xfun_0.47 fs_1.6.4 sass_0.4.9 cli_3.6.3
[29] withr_3.0.1 magrittr_2.0.3 digest_0.6.37 rstudioapi_0.16.0
[33] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.0 glue_1.7.0
[37] whisker_0.4.1 fansi_1.0.6 rmarkdown_2.28 tools_4.4.1
[41] pkgconfig_2.0.3 htmltools_0.5.8.1