2021_Laura_RSU

Dataset

df <- read_csv("https://docs.google.com/spreadsheets/d/e/2PACX-1vQx0EAzcq52X0nGCMkBjG7xWj8mvMIFaoVFIJynfeVuH4bYMW4Hua2zQmefpAb1mLT7S5ZP07ILmOx4/pub?gid=594888006&single=true&output=csv")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────
cols(
  SoftTissueGroup = col_double(),
  BIM = col_double(),
  first_PIBM = col_double(),
  first_PIDB = col_double(),
  second_PIBM = col_double(),
  second_PIBD = col_double(),
  Diametrs = col_double(),
  smoking = col_double()
)

Two values corrected

F45: -194???

F145: -196??

Data cleaning

glimpse(df)

Rows: 150
Columns: 8
$ SoftTissueGroup <dbl> 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2,…
$ BIM             <dbl> 2.61, 3.34, 2.07, 2.29, 2.93, 2.54, 2.93, 2.80, 1.42, 2.78, 3.11, 2.01, 1.71, 2.70, 2.…
$ first_PIBM      <dbl> -1.48, 1.68, 2.05, 0.70, 3.25, -1.54, -0.33, -0.80, 0.89, -0.61, 2.38, 1.24, 0.68, -0.…
$ first_PIDB      <dbl> -1.41, 0.00, 1.02, 0.58, -0.60, -1.35, -0.83, 0.00, -0.67, -1.09, 1.65, 0.95, -0.93, -…
$ second_PIBM     <dbl> -0.60, -2.01, -2.29, 0.00, 1.61, -1.12, -2.05, -2.22, -0.80, -2.05, -0.59, -2.70, -1.4…
$ second_PIBD     <dbl> -0.96, -2.04, -2.73, 0.00, -1.20, -1.81, -1.98, 0.00, -0.80, -2.11, -1.45, -2.26, -1.2…
$ Diametrs        <dbl> 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1,…
$ smoking         <dbl> 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2,…

Are NA values?

Changing smoking to factor

Changing soft tissue group to factor

EDA

Calculate the difference for PIBM and PIBD

Graph

Check the BIM

Seems normal or a little skewed to the left

Since no transformation seems necessary, now check if any correlation between BMI and the diff values

Loess regression

Lineal regression

Splines

Also, there is no evidence for a reason to dichotomize the BIM variable