{"title":"Working with DNA sequences and other character data.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0284","DOIUrl":"https://doi.org/10.1079/9781789245349.0284","url":null,"abstract":"Abstract\u0000 This chapter describes the use of an internet-based statistical analysis (R) for manipulating nucleotide sequences such as reverse-complementing and complementing, translating DNA to RNA and converting base sequences to amino acids. A few examples that introduce some more useful R functions are also included.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128357556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reading and writing data to and from files.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0031a","DOIUrl":"https://doi.org/10.1079/9781789245349.0031a","url":null,"abstract":"Abstract\u0000 This chapter deals with reading and writing data to and from files, demonstrating the appending of data to an existing file, use of \"read.delim\" with non-tab separator, selection of a file to read interactively, use of excel for data entry, reading of PDF files for data mining, and writing of graphics directly to disc. The \"readxl\" function and tibbles are explained.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133290962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Species abundance, accumulation and diversity data.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0018","DOIUrl":"https://doi.org/10.1079/9781789245349.0018","url":null,"abstract":"Abstract\u0000 Ecologists in particular are often interested in the species richness and diversity of groups of organisms, ranging from studies of small ecosystems to global patterns. In most cases it is not possible to count every individual or to detect every species, and so they use a variety of estimation methods and summary statistics that will be briefly introduce in this chapter. This chapter covers estimating species abundance and species richness by looking at accumulation curves. Analyzing diversity using tests such as the Shannon and Simpson diversity indices are also discussed. Finally, patterns of niche partitioning using the broken stick model are created. An example is shown, using transect surveys of butterflies in Papua New Guinea.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The grammar of graphics family of packages.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0079","DOIUrl":"https://doi.org/10.1079/9781789245349.0079","url":null,"abstract":"Abstract\u0000 This chapter introduces a couple of advanced graphics packages that are becoming more popular because of their customizability (ggplot2, ggpubr, ggplotly). The 'gg' part of the names stands for 'Grammar of Graphics'.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132594874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of covariance (ANCOVA).","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.4135/9780857020123.n16","DOIUrl":"https://doi.org/10.4135/9780857020123.n16","url":null,"abstract":"Abstract\u0000 This chapter deals with analysis of covariance or ANCOVA, a combination of ANOVA and regression. It tests the effects of a mix of continuous and categorical variables on a continuous response variable. Two examples are presented. Example 1 is based on a study investigating the effects of two types of tagging (acrylic paint and subcutaneous microtags) on the growth of the coral reef goby, Coryphopterus glaucofraenum, in the British Virgin Islands and included initial size as a continuous explanatory variable. Example 2 analyses data from a study on the number of pollinaria removed by pollinators from inflorescences of two Sirindhornia orchid species (S. monophylla and S. mirabillis) in relation to the number of flowers in the inflorescence (also count data) and the orchid species (categorical).","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134055164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analysis of variance (ANOVA).","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0013a","DOIUrl":"https://doi.org/10.1079/9781789245349.0013a","url":null,"abstract":"Abstract\u0000 Analysis of variance is used to analyze the differences between group means in a sample, when the response variable is numeric (real numbers) and the explanatory variable(s) are all categorical. Each explanatory variable may have two or more factor levels, but if there is only one explanatory variable and it has only two factor levels, one should use Student's t-test and the result will be identical. Basically an ANOVA fits an intercept and slopes for one or more of the categorical explanatory variables. ANOVA is usually performed using the linear model function lm, or the more specific function aov, but there is a special function oneway.test when there is only a single explanatory variable. For a one-way ANOVA the non-parametric equivalent (if variance assumptions are not met) is the kruskal.test.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114253035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The dataframe concept.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0031","DOIUrl":"https://doi.org/10.1079/9781789245349.0031","url":null,"abstract":"Abstract\u0000 R objects come in a variety of types: dataframes, matrices, vectors, and arrays for example. Dataframes are an important concept in R, allowing vectors of different types to be combined column-wise into a single object. This chapter discusses combining sets of tables for data collected on different dates and converting factors in a dataframe to numeric or character.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114951674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spacing in two dimensions.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0025","DOIUrl":"https://doi.org/10.1079/9781789245349.0025","url":null,"abstract":"Abstract\u0000 There are many interesting questions in biology that revolve around the spacing of individuals, for example in territoriality, or spatial clumping of genotypes. This chapter gives a very brief demonstration from basics of looking at the randomness of spacing of a sedentary, but not immobile animal, a European sea anemone. It will test for spatial structure using nearest neighbour distances.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125787351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Principal components analysis.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0194","DOIUrl":"https://doi.org/10.1079/9781789245349.0194","url":null,"abstract":"Abstract\u0000 This chapter focuses on how to conduct a principal components analysis. To conduct principal components analysis, R has two similar built-in functions prcomp and princomp in the default stats package. Other implementations can be found in various downloadable packages, e.g. the function PCA from the package FactoMineR, the function dudi.pca from the package ade4 and the function acp from the package amap. The functions prcomp and princomp employ different calculation methods but in practice the results they return will be almost identical.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131054874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Phylogenies and trees.","authors":"D. Quicke, B. A. Butcher, R. K. Welton","doi":"10.1079/9781789245349.0275","DOIUrl":"https://doi.org/10.1079/9781789245349.0275","url":null,"abstract":"Abstract\u0000 Several packages have been developed to allow R-users to work with phylogenetic trees, something that most biologists will need to do at some point in their careers. The most basic is the ape package, which stands for Analysis of Phylogenetics and Evolution. This chapter gives some of the basics of handling 'trees' in R and show things that can be calculated with them. Phytools, another package with extra capabilities, are also introduced in this chapter. Insects are given as examples.","PeriodicalId":167700,"journal":{"name":"Practical R for biologists: an introduction","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132564025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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