rex009x
commented
4 months ago Hi Yaning, I would like to first start off by thanking you for spending the time to read over our paper. While your feedback does not seem to properly reflect our work or refer to any points in our project at all, we appreciate your insights.
Please feel free to revisit the sections you reviewed as we do in fact have many fruitful findings throughout.
To begin with, there are several problems in your submission that you should be concerned with.
For LLM Usage Statement:
No explicit statement similar to the provided example is found. Consider adding a section acknowledging the use of language models for assistance.
For Simulation:
There are no simulation in your paper. Any simulation procedures should be clearly documented in the code. To simulate all variables in your paper, you'll need to write R code that generates data based on the desired characteristics of each variable. Here's a general approach. Identify Variable Characteristics: Determine the type of each variable (e.g., numeric, categorical) and their distributions (e.g., normal, binomial).Use R Functions for Simulation: Use R functions like rnorm() for normal distributions, runif() for uniform, rbinom() for binomial, etc., to simulate the data.Set a Seed for Reproducibility: Use set.seed() to ensure your simulated data can be reproduced. Combine Variables into a DataFrame: Once all variables are simulated, combine them into a single data frame.Here's a simplified example assuming you want to simulate two variables, one normally distributed and one binomially distributed:
Meaningful README and Paper Title
Revise both titles to directly reflect the core findings or value proposition of the paper. For example, "Analyzing the Impact of Socioeconomic Factors on US Birth Rate Decline: A Comprehensive Study."
Abstract Conciseness
Ensure the abstract directly states the main findings and their implications, avoiding vague language. For example, "This study finds a significant correlation between [specific factor] and the decline in US birth rates, indicating [specific implication]."
Data Section Expansion
Provide a more detailed narrative around the data, including its significance, limitations, and the rationale behind its use.
Measurement Clarification
Explain how the data were measured and any implications this has on the analysis. Detail the methodology behind data collection and any relevant metrics.
Test
no test in your paper. Writing test data in R can be crucial for validating the accuracy and robustness of your data analysis scripts, especially when developing packages or conducting complex data analyses. For example:
Library Loading
Utilizes tidyverse for data manipulation and visualization, dplyr and ggplot2 for specific tasks within these domains, here for path management, and haven for reading Stata files.
Data Import and Transformation
Reads CSV and DTA files containing data on US birth rates, education levels, and other demographic information. Employs pivot_longer to reshape data frames, making them suitable for analysis by converting wide data into a long format.
Merging and Mutating
Combines datasets using merge to align data by state and year. Creates new variables to reflect specific rates (e.g., birth rates by parity) and performs calculations to derive these rates.
Cohort Data Processing
Aggregates and transforms cohort data to examine birth rates across different mother's age groups and birth cohorts. Joins population data with birth data to calculate birth rates and cumulative birth rates for specific cohorts. This code is structured to facilitate detailed analysis of birth rates in the US, focusing on various demographic aspects. It exemplifies good practices in data management, including clear variable selection, data transformation for analytical purposes, and thoughtful preparation for visual representation.
Data analysis
Your paper lacks data analysis, there are no graphs and tables, once you have graphs and changes you can do data analysis. To conduct data analysis based on a chart, follow these steps: Understand the Chart: Determine what type of chart it is (e.g., bar, line, pie) and what variables are represented. Identify Key Trends: Look for patterns, trends, or outliers in the data. Gather Data: If the chart is a summary, you may need to gather the underlying data for detailed analysis. Choose Analysis Methods: Based on your research question and data type, select appropriate statistical methods or models. Validate Findings: Ensure your analysis is robust by checking assumptions, using cross-validation, or comparing with known benchmarks. Communicate Results: Present your findings with clear visualizations, highlighting key insights derived from the original chart.