The application of R language in molecular epidemiology

Published on Nov. 28, 2023Total Views: 1877 times Total Downloads: 457 times Download Mobile

Affiliation: School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China

Keywords: R language Molecular epidemiology Graduate students in medical colleges

DOI: 10.12173/j.issn.1004-4337.202309020

Reference: Wu N, Song HL. The application of R language in molecular epidemiology[J]. Journal of Mathematical Medicine, 2023, 36(11): 797-802. DOI: 10.12173/j.issn.1004-4337.202309020 [Article in Chinese]  Copied

Abstract
Full-text
References

Abstract

By comparing the characteristics of R language and SPSS software, this paper focused on the application advantages of R language in molecular epidemiology. As a free and open source programming language, R language has powerful data processing and analysis capabilities, which is suitable for processing large-scale and complex molecular data. Its extensive statistical functions and packages enable graduate students in medical colleges to conduct advanced statistical modeling and bioinformatics analysis to meet the needs of molecular epidemiological research. Through the application examples of R language in molecular epidemiology research, the function of R language in analyzing relevant data was demonstrated. Teachers in medical colleges and universities should train graduate students to master the ability to process and analyze big data using R language according to the requirements of the times and practical needs.

Full-text

Please download the PDF version to read the full text: download

References

1.Schulte Paul A, Perera Frederica P. Molecular epidemiology: principles and practice[M]. Cambridge, Massachusetts: Academic Press, 1993.

2.Rebbeck TR, Ambrosone CB, Shields PG. Molecular epidemiology[M]. Florida: Chemical Rubber Company, 2013.

3.Kilbourne ED. The molecular epidemiology of influenza[J]. J Infect Dis, 1973, 127(4): 478-487. DOI: 10.1093/infdis/127.4.478.

4.金倩莹, 李星明. 流行病学方法在医学研究中的应用概述[J]. 北京医学, 2020, 42(5): 444-451. [Jin QY, Li XM. Overview of the application of epidemiological methods in medical research[J]. Beijing Medical Journal, 2020, 42(5): 444-451.] DOI: 10.15932/j.0253-9713.2020.05.023.

5.Honardoost M, Rajabpour A, Vakili L. Molecular epidemiology; New but impressive[J]. Med J Islam Repub Iran, 2018, 32: 53. DOI: 10.14196/mjiri.32.53.

6.Slattery ML. The science and art of molecular epidemiology[J]. J Epidemiol Community Health, 2002, 56(10): 728-729. DOI: 10.1136/jech.56.10.728.

7.Tümmler B. Molecular epidemiology in current times[J]. Environ Microbiol, 2020, 22(12): 4909-4918. DOI: 10.1111/1462-2920.15238.

8.Chan BKC. Data analysis using R programming[J]. Adv Exp Med Biol, 2018, 1082: 47-122. DOI: 10.1007/978-3-319-93791-5_2.

9.Ihaka R, Gentleman R. R: a language for data analysis and graphics[J]. J Comput Graph Stat, 1996, 5(3): 299-314. DOI: 10.1080/10618600.1996.10474713.

10.Rhys H. Machine learning with R, the tidyverse, and mlr[M]. New York: Simon and Schuster, 2020.

11.Giorgi FM, Ceraolo C, Mercatelli D. The R language: an engine for bioinformatics and data science[J]. Life (Basel), 2022, 12(5): 648. DOI: 10.3390/life12050648.

12.Boehmke B, Greenwell BM. Hands-on machine learning with R[M]. Florida: Chemical Rubber Company, 2019.

13.Wickham H, Bryan J. R packages[M]. California: O'Reilly Media, 2023.

14.Bryan K. Epidemiology and biostatistics[M]. Berlin, Heidelberg: Springer, Cham, 2018.