Loading the Dataset

# Load the UCI Bike Sharing Dataset
bike_sharing_data <- read.csv("C:/Statistics for Data Science/Week 2/bike+sharing+dataset/hour.csv")

# Display the first few rows of the dataset
knitr::kable(head(bike_sharing_data))
instant dteday season yr mnth hr holiday weekday workingday weathersit temp atemp hum windspeed casual registered cnt
1 2011-01-01 1 0 1 0 0 6 0 1 0.24 0.2879 0.81 0.0000 3 13 16
2 2011-01-01 1 0 1 1 0 6 0 1 0.22 0.2727 0.80 0.0000 8 32 40
3 2011-01-01 1 0 1 2 0 6 0 1 0.22 0.2727 0.80 0.0000 5 27 32
4 2011-01-01 1 0 1 3 0 6 0 1 0.24 0.2879 0.75 0.0000 3 10 13
5 2011-01-01 1 0 1 4 0 6 0 1 0.24 0.2879 0.75 0.0000 0 1 1
6 2011-01-01 1 0 1 5 0 6 0 2 0.24 0.2576 0.75 0.0896 0 1 1

Unclear Columns from the Data

Upon analyzing the UCI Bike Sharing Dataset and its documentation, the following columns are identified as unclear without proper documentation:

  1. temp: Represents normalized temperature values. Without documentation, it is unclear what the normalization range is (e.g., 0 to 1) and the original temperature scale (Celsius or Fahrenheit).

  2. weathersit: Encoded as integers (1 to 4) corresponding to different weather situations. Without documentation, the specific weather conditions each integer represents are ambiguous.

  3. atemp: Stands for “feels like” temperature, also normalized. It’s unclear how this differs from the temp column and what factors contribute to this perceived temperature.

Why the Data Was Encoded This Way

  1. temp and atemp: Normalizing temperature values facilitates easier integration with machine learning models by scaling features to a similar range. However, without knowing the normalization parameters, interpreting these values becomes challenging.

  2. weathersit: Encoding categorical weather conditions as integers saves storage space and simplifies analysis. Nonetheless, without clear labels, the encoded integers lose their descriptive meaning.

Potential Issues if Documentation is Not Read

  1. Misinterpretation of Temperature Values: Assuming incorrect normalization ranges or temperature scales can lead to faulty analyses, such as underestimating the impact of temperature on bike rentals.

  2. Ambiguity in Weather Conditions: Misunderstanding the weathersit encoding can result in incorrect associations between weather and rental patterns, skewing the analysis.

  3. Confusion Between temp and atemp: Without distinguishing factors, it’s unclear how “feels like” temperature influences bike usage compared to actual temperature.

Unclear Element Even After Reading Documentation

Despite thorough review, the cnt column remains partially unclear. While it is documented as the total count of bike rentals, it’s not explicitly stated whether this includes both casual and registered users, or how overlapping categories are handled. Additionally, the documentation does not clarify how data anomalies (e.g., extreme weather events) are treated in the cnt counts.

Visualization Highlighting the Unclear Element

To explore the ambiguity surrounding the cnt column, especially its composition from casual and registered users, the following visualization examines the relationship between registered users and total rentals (cnt).

# Scatter plot of Registered Users vs Total Count
ggplot(bike_sharing_data, aes(x = registered, y = cnt)) +
  geom_point(alpha = 0.6, color = 'steelblue') +
  geom_smooth(method = 'lm', color = 'darkred', se = FALSE) +
  labs(title = "Relationship Between Registered Users and Total Rentals",
       x = "Number of Registered Users",
       y = "Total Bike Rentals (cnt)") +
  annotate("text", x = max(bike_sharing_data$registered)*0.6, 
           y = max(bike_sharing_data$cnt)*0.9, 
           label = "Unclear if 'cnt' includes only registered users or both \nregistered and casual users",
           color = "darkred",
           size = 4,
           hjust = 0) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

Explanation of the visualization

The scatter plot illustrates the relationship between the number of registered users and the cnt (total bike rentals). An evident linear trend suggests that as the number of registered users increases, the total rentals also rise. However, the annotation highlights the uncertainty regarding whether cnt exclusively counts registered users or aggregates both registered and casual users. This ambiguity can affect the interpretation of how different user types contribute to overall bike rentals.

Potential Risks

Several risks emerge from the unclear aspects of the cnt column:

  1. Modeling Inaccuracy: If cnt includes both casual and registered users without differentiation, models may misattribute the influence of user types on bike rentals, leading to biased predictions.

  2. Misguided Business Decisions: Misunderstanding the composition of cnt can result in ineffective strategies, such as overemphasizing one user type over another based on incorrect assumptions.

  3. Data Integrity Issues: Without clarity on how anomalies are handled, outliers may skew the data analysis, compromising the reliability of insights drawn from the dataset.

Risk Mitigation Strategies

To mitigate these risks, the following actions are recommended:

  1. Data Decomposition: Separate the cnt column into casual and registered components to analyze their individual contributions to total rentals.

  2. Anomaly Analysis: Investigate instances of extreme cnt values to understand how anomalies are treated and whether they represent genuine spikes or data recording issues.

  3. Seek Clarification: If documentation remains insufficient, contact the data provider or refer to supplementary resources to gain a clearer understanding of the cnt column’s composition.

  4. Robust Modeling Techniques: Employ models that can handle potential data ambiguities, such as ensemble methods or models with built-in mechanisms for uncertainty estimation.

Conclusion

This data dive underscored the critical role of comprehensive documentation in data analysis and model building. By identifying unclear columns such as temp, weathersit, and atemp, and exploring the ambiguities surrounding the cnt column,I highlighted potential pitfalls in data interpretation. The visualization emphasized the uncertainty in cnt’s composition, while the discussion on risks and mitigation strategies provided actionable insights to enhance data reliability and model accuracy. Moving forward, ensuring clarity in data documentation will be paramount to deriving meaningful and accurate analytical outcomes.

Further Questions

  1. Composition of cnt: Does cnt exclusively represent the sum of casual and registered users, or are there additional factors involved?

  2. Handling of Anomalies: How are extreme values or anomalies in bike rentals addressed in the dataset? Are they excluded, adjusted, or treated as outliers?

  3. Normalization Parameters: What specific normalization techniques were applied to temp and atemp, and what are their original scales?

  4. Impact of External Factors: How do external factors like public events or city-wide initiatives influence bike rental patterns beyond what’s captured in the dataset?