The EDA process for the Plant Ecosystem Analysis project involved a comprehensive examination of the "PlantData.txt" dataset using R. This dataset contains 137 observations of 9 variables related to various geographical and environmental gradients impacting plant ecosystems. The primary objective was to explore the relationship between Native plant species Richness (NR) and ecological/environmental factors such as Area, Latitude, Elevation, Distance to significant landmarks (Dist), Soil types, Age of ecosystems (Years), Time since last deglaciation (Deglac), and adjacent Human population densities (Human.pop).

• Data Import: The dataset was loaded into R using the read.csv function, specifying the path to the "PlantData.txt" file.
• Initial Peeking: Utilized the head() function to examine the first few rows of the dataset, ensuring proper loading and to get an initial sense of the data structure.

• Structure Inspection: The str() function revealed the dataset as a data frame with 137 observations and 9 variables, with all variables being numerical.
• Summary Statistics: Employed summary() to obtain a descriptive summary of each variable, including measures of central tendency and dispersion.

• Checked for missing values using colSums(is.na(Data)) and confirmed the dataset had no missing values.
• Calculated unique value counts for each column to determine the diversity of data and assess if any categorical variables were present.

• Used the GGally package to generate pairwise relationship plots with ggpairs(), aiding in visualizing correlations and potential relationships between variables.
• Constructed a correlation matrix to quantify the strength of relationships between variables, identifying particularly strong correlations worth further investigation.

• Visualized the distribution of each numerical variable using histograms, noting the distribution shape, skewness, and potential outliers.
• Generated boxplots for each variable to further assess data spread and identify outliers.

• Created a correlation plot using the corrplot package, visually representing the correlation matrix and highlighting significant correlations.

• Applied transformations to certain variables (e.g., log transformation to Human.pop) to address skewness and improve model fitting.
• Conducted diagnostics using plots of residuals, Q-Q plots, and other checks to evaluate model assumptions and fit.

• Explored multiple linear regression models, starting with a full model including all predictors and progressively refining the model based on statistical significance and model diagnostics.
• Utilized backward selection, AIC, BIC, and adjusted R-squared values to identify the most parsimonious and informative model.

• The selected final model incorporated log-transformed predictors and demonstrated the best fit based on adjusted R-squared and residual diagnostics.
• Key predictors identified as significantly impacting Native plant species Richness included Area, Elevation, and Soil types.

This detailed EDA process laid a solid foundation for subsequent analysis and modeling, providing valuable insights into the complex relationships within the plant ecosystem data.