title: "mtp_p8105_jl6458" output: github_document --- # Problem 1 - Data *Import* and *clean* the data. *Format* the data to use appropriate variable names; *fill in missing values* with data where appropriate (as indicated in the header information); *create character* and *ordered factors for categorical variables*. Briefly *describe the data cleaning process* and *the resulting dataset*, *identifying key variables* based on your understanding of the original scientific report. How many *participants* are included? What is the *age and gender distribution* (a human-readable table may help here)? Note (*but don't correct*) issues in the available data - in particular, whether categorical variables in the dataset correctly implement the definitions based on underlying continuous variables. *Use tables, figures, or specific examples (i.e. data for particular subjects) as needed to illustrate these issues.* ```{r} # Load necessary libraries library(tidyverse) library(readxl) library(janitor) library(skimr) ``` *Import* and *clean* the data. ```{r} posture_df = read_excel("p8105_mtp_data.xlsx", range = "A9:I1230") %>% janitor::clean_names() # Get an overview of the raw data skimr::skim(posture_df) ```