library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(purrr)
data_path <- "C:/Users/shanata/Downloads/smoking_driking_dataset_Ver01.csv"
data <- read.csv(data_path)

Columns/ Values that are unclear until documentation is read:

1) SMK_stat_type_cd (Smoking Status Type Code):

This column likely encodes information about smoking status, but the specific values and their meanings are unclear until we refer to the documentation. It’s crucial to understand what each code represents, such as “1” or “4,” and how they correspond to different smoking statuses.

2) DRK_YN (Alcohol Consumption Indicator):

This column encodes whether an individual drinks alcohol or not, but the exact meaning of “Y” and “N” may not be clear without documentation. We need to determine if “Y” means “Yes” for alcohol consumption and “N” means “No.”

3) sight_left and sight_right:

These columns appear to represent eye sight measurements, but the unit of measurement (e.g., diopters) and the reference range are unclear without documentation.

Reason for encoding it in this way:

  1. Encoding data using codes or abbreviations is common in health datasets to save space and maintain privacy. For example, using “Y” and “N” instead of “Yes” and “No” for binary indicators.

  2. Numeric encoding for categorical variables allows for efficient storage and analysis.

  3. Encoding might follow industry or medical standards to ensure consistency across datasets.

Without documentation:

  1. I could have misinterpreted the data. I have no prior knowledge of health data, so I could have analyzed it wrongly.

  2. I would have not been able to fully understand the data.

One element that is unclear even after the documentation:

1)The documentation doesn’t provide details on the reference ranges or units for sight_left and sight_right. Knowing the unit and reference range is essential for interpreting these values accurately.

Visual Representation of smoking status:

A bar plot to visualize the distribution of smoking status types

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ readr     2.1.4     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggplot2)
ggplot(data, aes(x = factor(SMK_stat_type_cd))) +
  geom_bar(fill = "lightblue") +
  labs(
    title = "Distribution of Smoking Status Types",
    x = "Smoking Status Type",
    y = "Count"
  ) +
  theme_minimal()

Issue identified:

  1. The issue over here is we don’t understand the meaning of the numerical codes on the x-axis, it is difficlt to interpret without the documentation.

Visual Represenation of hearing capacity:

ggplot(data, aes(x = hear_left, y = hear_right)) +
  geom_point() +
  labs(
    title = "Scatter Plot of hear_left vs. hear_right",
    x = "hear_left",
    y = "hear_right"
  ) +
  theme_minimal()

Issue identified:

  1. The issue over here is we don’t understand the meaning of the numerical codes on the x-axis, it is difficlt to interpret without the documentation. It is necessary to get the unit and the reference range used to measure the hearing capacity of a person.

Significant Risks :

Misinterpretation:

The significant risk is misinterpreting the encoded data due to lack of documentation. To mitigate this risk, it’s essential to contact the data source or seek additional documentation that explains the codes and units used.

Errors in Analysis:

Using unclear data in analyses can lead to errors in research or healthcare decisions. Clarifying the encoding and units is crucial to ensure accurate analysis.

Ways ro reduce the risk:

  1. We can establish open communication with the data providers who was responsible for documenting the data. Through this way we can seek clarification regarding the data encoding

Conclusion:

For appropriate analysis and interpretation, it is essential to comprehend the data units and encoding. Data exploration and thorough documentation are crucial since unclear data can cause misinterpretation and errors.