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Cancer causes immense suffering globally, and data constitute the cornerstone of cancer research. Analyzing data is pivotal, but manual analysis of vast datasets within constrained time frames is challenging and error-prone. Even minor inaccuracies can lead to false interpretations, affecting lives. This review explores the free, open-source, and widely acclaimed R software. Our goal was to facilitate data analysis and visualization in the scientific writing of clinical projects. R offers a wide range of features and packages for tasks like data manipulation, cleaning, analysis, and creating informative graphs, including traditional statistics, hypothesis testing, regression, time series, survival analysis, machine learning, and medical image analysis. These capabilities aid in accurate data analysis, facilitating a deeper understanding of cancer mechanisms and predicting outcomes. To prepare this review, we performed an online literature search in Scopus, PubMed, and Google for articles and books related to R software published between March 2012 and January 2024, using specific keywords such as “medical data analysis,” “RStudio,” “statistical software,” “clinical data management,” “R programming,” and “research tools.” Articles, books, and online sources lacking full-text options in English or complete information were excluded. A total of 66 articles and book chapters were retrieved, 22 were excluded, and 44 were included in this review. Through this article, our goal was to provide a user-friendly guide to employing R software for fundamental analysis with dummy data, making it accessible even to non-programmers. This will empower individuals to perform statistical analyses independently, contributing to cancer research with flexibility and accuracy.
Cancer causes immense suffering globally, and data constitute the cornerstone of cancer research. Analyzing data is pivotal, but manual analysis of vast datasets within constrained time frames is challenging and error-prone. Even minor inaccuracies can lead to false interpretations, affecting lives. This review explores the free, open-source, and widely acclaimed R software. Our goal was to facilitate data analysis and visualization in the scientific writing of clinical projects. R offers a wide range of features and packages for tasks like data manipulation, cleaning, analysis, and creating informative graphs, including traditional statistics, hypothesis testing, regression, time series, survival analysis, machine learning, and medical image analysis. These capabilities aid in accurate data analysis, facilitating a deeper understanding of cancer mechanisms and predicting outcomes. To prepare this review, we performed an online literature search in Scopus, PubMed, and Google for articles and books related to R software published between March 2012 and January 2024, using specific keywords such as “medical data analysis,” “RStudio,” “statistical software,” “clinical data management,” “R programming,” and “research tools.” Articles, books, and online sources lacking full-text options in English or complete information were excluded. A total of 66 articles and book chapters were retrieved, 22 were excluded, and 44 were included in this review. Through this article, our goal was to provide a user-friendly guide to employing R software for fundamental analysis with dummy data, making it accessible even to non-programmers. This will empower individuals to perform statistical analyses independently, contributing to cancer research with flexibility and accuracy.
The cultivation of cashew crops carries numerous economic advantages, and countries worldwide that produce this crop face a high demand. The effects of wind speed and wind direction on crop yield prediction using proficient deep learning algorithms are less emphasized or researched. We propose a combination of advanced deep learning techniques, specifically focusing on long short-term memory (LSTM) and random forest models. We intend to enhance this ensemble model using dynamic time warping (DTW) to assess the spatiotemporal data (wind speed and wind direction) similarities within Jaman North, Jaman South, and Wenchi with their respective production yield. In the Bono region of Ghana, these three areas are crucial for cashew production. The LSTM-DTW-RF model with wind speed and wind direction achieved an R2 score of 0.847 and the LSTM-RF model without these two key features R2 score of (0.74). Both models were evaluated using the augmented Dickey-Fuller (ADF) test, which is commonly used in time series analysis to assess stationarity, where the LSTM-DTW-RF achieved a 90% level of confidence, while LSTM-RF attained an 87.99% level. Among the three municipalities, Jaman South had the highest evaluation scores for the model, with an RMSE of 0.883, an R2 of 0.835, and an MBE of 0.212 when comparing actual and predicted values for Wenchi. In terms of the annual average wind direction, Jaman North recorded (270.5 SW°), Jaman South recorded (274.8 SW°), and Wenchi recorded (272.6 SW°). The DTW similarity distance for the annual average wind speed across these regions fell within specific ranges: Jaman North (±25.72), Jaman South (±25.89), and Wenchi (±26.04). Following the DTW similarity evaluation, Jaman North demonstrated superior performance in wind speed, while Wenchi excelled in wind direction. This underscores the potential efficiency of DTW when incorporated into the analysis of environmental factors affecting crop yields, given its invariant nature. The results obtained can guide further exploration of DTW variations in combination with other machine learning models to predict higher cashew yields. Additionally, these findings emphasize the significance of wind speed and direction in vertical farming, contributing to informed decisions for sustainable agricultural growth and development.
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