Assignment5

library(tidyverse)

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library(ggthemes)
library(ggrepel)

Question 1 :-

Columns/Values Unclear Until Reading Documentation:

conservation Column:

The values in the conservation column, such as “lc,” “nt,” “vu,” and “en,” are unclear without the documentation. These codes likely represent conservation statuses, but their specific meanings are unclear.

sleep_rem Column:

The sleep_rem column contains values like “NA” and numbers. It’s unclear what “NA” represents, and the documentation should clarify whether it means missing data or has a specific meaning.

bodywt Column:

The bodywt column contains numerical values. However, without documentation, it’s unclear what units of measurement are used for body weight (e.g., kilograms, grams, pounds).

Why Data Encoding Choices:

Compactness:

Using codes like “lc,” “nt,” and “vu” for conservation statuses saves space and will be easier to handle in data storage and transmission.

Privacy:

Some data may be encoded to protect privacy or to anonymize sensitive information.

Consistency:

Encoding values as “NA” for missing data or “0” for specific cases can ensure uniformity in data processing.

Without reading the documentation:

There would be confusion about the meanings of the conservation codes, making it challenging to understand the conservation statuses of the animals. It would be unclear whether “NA” in the sleep_rem column signifies missing data, zero values, or a specific condition. Interpreting the body weight without units would lead to incorrect analyses and conclusions.

Question 2 :-

After reviewing the dataset and its documentation, one element that remains unclear even after reading the documentation is the meaning of the “NA” values in various columns.

In the dataset, the following columns contain “NA” values:

sleep_rem sleep_cycle brainwt

The documentation does not explicitly explain the significance or interpretation of these “NA” values. It is common in data analysis to use “NA” to represent missing or unknown data, but the documentation should specify whether “NA” indeed denotes missing data in these columns and if so, why the data is missing.

Without clarification, it is unclear whether “NA” in these columns means missing data, unavailable data, or some other specific condition. Understanding the nature of these “NA” values is essential for accurate data analysis and interpretation.

To resolve this ambiguity, the documentation should provide information about the presence of missing data in these columns and any potential reasons for their absence. This clarification would help users correctly handle and interpret the data when performing analyses.

Question 3 :-

To build a visualization that highlights the issue of “NA” values in the dataset and to explain why it might be unclear, we can create a bar chart using the “sleep_rem” column, which contains “NA” values. We will use color to indicate missing data and add an annotation to clarify the issue:

msleep <- read.csv("C:/Users/ABHIRAM/Downloads/msleep.csv")

# Sample data
data <- data.frame(
  name = c('Cheetah', 'Owl monkey', 'Mountain beaver', 'Greater short-tailed shrew', 'Cow'),
  sleep_rem = c(NA, 1.8, 2.4, 2.3, NA)  # Sample "sleep_rem" data with "NA" values
)

# Create a bar chart
bar_colors <- ifelse(is.na(data$sleep_rem), 'red', 'blue')

barplot(data$sleep_rem, names.arg = data$name, col = bar_colors, 
        xlab = 'Animal Name', ylab = 'Sleep REM (hours)', 
        main = 'Sleep REM Duration for Different Animals', 
        border = 'black', ylim = c(0, max(data$sleep_rem, na.rm = TRUE) + 1))

# Add annotations for "NA" values
for (i in 1:nrow(data)) {
  if (is.na(data$sleep_rem[i])) {
    text(i, 1.5, 'NA', col = 'red', font = 2, cex = 1.2)
  }
}

Explanation of the visualization and the issue:

In the bar chart, each bar represents the sleep REM (Rapid Eye Movement) duration for a different animal. The bars are colored differently: blue bars represent animals with known sleep REM values, while red bars represent animals with “NA” values (missing data) in the “sleep_rem” column. The annotation “NA” is added to the red bars to explicitly indicate the missing data. The issue with “NA” values in this context is that it’s unclear why some animals have missing data for sleep REM duration. The visualization highlights this issue by marking the missing data in red. However, the reasons for missing data are not explained in the dataset or documentation. Without understanding why data is missing, it may lead to incorrect interpretations or biased analyses.

Significant Risks:

One significant risk is that analysts or researchers may unintentionally exclude or mishandle records with missing data, leading to biased results or incomplete analyses. Another risk is misinterpretation of the “NA” values, as they could be mistaken for zero values or other meaningful values if not properly documented.

To reduce negative consequences:

The dataset documentation should provide information on why data is missing in these columns. It could be due to data collection limitations, specific conditions, or other factors. Providing context for missing data is essential for accurate analysis. Data analysts should handle missing data appropriately, either by imputing values or considering the missing data as a separate category, depending on the nature of the data and research goals. Transparency in reporting the handling of missing data should be maintained in any research or analysis based on this dataset.