Week 5 Data Dive - Documentation

Load in obesity dataset

library (tidyverse)

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obesity <- read.csv(file.choose())

1. A list of at least three columns that were unclear until after reading the documentation

Column 1: FAVC

• I don’t have an idea as to why they decided to encode this variable the way they did. This is a binary variable describing whether or not the individual eats high caloric food frequently. I would assume that the F stands for frequency, and maybe the C stands for calories.

• If I didn’t read the documentation, I would have zero idea what this variable stood for, and likely would not have used it in my analysis.

Column 2: FAF

• I also do not understand why this variable is encoded the way it is, as this variable represents a continuous variable of how often the individual does physical activity. One of the F’s might stand for “Feature”, as the metadata categorizes this variable as a feature, and the A might stand for activity as well.

• As with the last variable, I likely would have had zero idea what this variable stood for and would not have used it in my analysis.

Column 3: CALC

• This variable is a categorical variable that represents how often the individual drinks alcohol. I think the variable was encoded like this for the C to represent categorical, and the ALC to reference alcohol.

• I think that this one would be easy to figure out if there was a standard naming schema across the board, but the C could stand for categorical or continuous, and CALC together at first made me think of a calculator or some kind of calculation, not consumption of alcohol.

2. At least one element that is still unclear after reading the documentation

• One element that is still unclear to me after reading the documentation is how individuals are placed in their categories for the NObeyesdad column, which represents the obesity level. I tried doing some additional research, but there are varying boundaries for each level of obesity, so I don’t know what these categories are based on.

3. A visualization of the column brought up in question 2

obesity |>
  ggplot()+
  geom_bar(mapping = aes(x = NObeyesdad, fill = NObeyesdad)) +
  theme_minimal() +
  scale_fill_brewer(palette = 'RdPu') +
  labs(title='Obesity Level', x = 'Obesity Level', y = 'Count')+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))  

This visualization looks at the one column that is still not entirely clear to me, even after reading the documentation, NObeyesdad (Obesity level). I chose to represent this in a bar graph because it is categorical data. However, what was unclear to me is still unclear after visualizing it - what are the requirements for each obesity type, and how are they calculating them? Is there a standard, or is it their own categorization based on their sample? They could be using BMI to categorize people, or just weight, or another categorization technique entirely. As I mentioned previously, there is not a standard way to put people in these categories across the board, so documentation on how they are placed is necessary.