Publications by Dr Samuel Blay Nguah
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Tables Table 1 df_toothloss %>% select( sex, ageyrs, marital, living_status, educ, income, work_place, house_stat, insurance ) %>% gtsummary::tbl_summary( by = sex, statistic = list( gtsummary::all_categorical()~ "{n} ({p})" ), digits = list( gtsummar...
55 sym 3 tbl
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0.0.1 Summarizing the selected dataset 0.0.2 OUTCOME: Description of balance diet intake 0.0.2.1 Cross Tabulation with all variables by the outcome gtsummary::theme_gtsummary_compact() Setting theme `Compact` df_paper_1 %>% select(-c(h_q27, h_alcohol_smoke)) %>% gtsummary::tbl_summary( by = h_Diet_habit, missing...
1585 sym 16 tbl
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0.0.1 Summarizing the selected dataset 0.0.2 OUTCOME: Description of balance diet intake 0.0.2.1 Cross Tabulation with all variables by the outcome gtsummary::theme_gtsummary_compact() Setting theme `Compact` df_paper_1 %>% select(-c(h_q27, h_alcohol_smoke)) %>% gtsummary::tbl_summary( by = h_Diet_habit, missing...
1479 sym Python (53928 sym/64 pcs) 17 tbl
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0.0.1 Summarizing the selected dataset df_paper_1 %>% summarytools::dfSummary(graph.col = F, labels.col = F) Data Frame Summary df_paper_1 Dimensions: 412 x 25 Duplicates: 17 ---------------------------------------------------------------------------------------------- No Variable Stats / Values Freqs (% o...
1450 sym 17 tbl
Weight Estimation KATH
I first create a couple of functions for picking up outliers fxnBA <- function(x, y){mn <- (x+y)/2 dp <- (x-y)/mn*100 bs <- mean(dp, na.rm = TRUE) uloa <- bs + 1.96*sd(dp, na.rm = T) lloa <- bs - 1.96*sd(dp, na.rm=T) X <- c...
1162 sym Python (34884 sym/30 pcs) 10 img 8 tbl
Merlene MSc
Read in the data patient_raw <- read_csv(here::here("Dataset", "PatientCostAvailabil_DATA_LABELS_DR.MERLENE AGYEKUM 12122022.csv")) %>% janitor::clean_names() %>% filter(!is.na(record_id)) New names: Rows: 906 Columns: 43 ── Column specification ─────────────────...
294 sym Python (48397 sym/40 pcs) 4 img 6 tbl
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0.0.1 Summarizing the selected dataset 0.0.2 OUTCOME: Description of balance diet intake 0.0.2.1 Cross Tabulation with all variables by the outcome gtsummary::theme_gtsummary_compact() Setting theme `Compact` df_paper_1 %>% select(-c(h_q27, h_alcohol_smoke)) %>% gtsummary::tbl_summary( by = h_Diet_habit, missing...
1387 sym 17 tbl
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gtsummary::theme_gtsummary_compact() Setting theme `Compact` df_akua_clean %>% select(sex, eb_class, age_at_clin_diag_days, outcome) %>% gtsummary::tbl_summary(missing_text = "[Missing]") %>% gtsummary::bold_labels() Characteristic N = 181 sex F 13 (72%) M 5 (28%) eb_class Dystrophic 3 (17%) Ju...
19 sym 3 tbl
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Reading in the data df_folate <- readxl::read_xlsx("Folate and stroke_linear regression.xlsx") %>% janitor::clean_names() %>% select(-low_folate_4_logistic, -sex) %>% rename(alcohol_use = which_best_describes_patient_s_history_of_alcohol_use, folate_level = low_folate_4, sex = gender_1) %>% muta...
598 sym 5 tbl
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Read in and manipulate data df_hu <- haven::read_dta("hucdata.dta") %>% select(-c(nameofpatient, foldernumber, visit, age_yrs, age_mths)) %>% mutate( sid = row_number(), rets_p = parse_double(rets_p), sex = factor(sex, levels = c("female", "male"), labels = c("Female", "Male")), weight_kg = if...
592 sym Python (12159 sym/20 pcs) 1 img 3 tbl