[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-573838-105":53,"doc-detail-573838-en":125},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":118,"head_meta":120,"extra_data":122,"updated_unix":124},105,"en","r-syntax-comparison-cheat-sheet","R Syntax Comparison - CHEAT SHEET","","Compares three major R syntax styles—dollar sign (base R), formula (e.g., for modeling and certain graphics), and tidyverse/piping (dplyr and related workflows)—and shows how each handles the same tasks. Summarizes example commands for summary statistics and plotting, including histograms, boxplots, bargraphs, scatterplots, and mosaic plots, along with wrangling patterns like subsetting and creating new variables. Includes guidance for recognizing unknown R code and searching 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are the three most prevalent R syntaxes covered in the cheat sheet?","Question",{"text":107,"@type":108},"The cheat sheet covers dollar sign syntax (dataset$variable), formula syntax using tilde (~) for modeling and graphics, and tidyverse syntax that works with the pipe (%>%) and expects data as the first argument.","Answer",{"name":110,"@type":105,"acceptedAnswer":111},"How does the cheat sheet compare approaches for summary statistics and plotting across syntaxes?",{"text":112,"@type":108},"It provides equivalent examples for the same analysis goals—such as means, tables, and different plot types—showing different syntactic forms that produce the same kind of result.",{"name":114,"@type":105,"acceptedAnswer":115},"What does the Internet research tip recommend when you encounter unfamiliar R code?",{"text":116,"@type":108},"It advises checking whether the code matches one of the three common syntaxes on the cheat sheet, then re-searching using a relevant syntax or package keyword such as “tidyverse” or “mosaic”.","https://schema.org",{"og:url":78,"og:type":119,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":121,"canonical":78},"index,follow",{"doc_id":123,"site_id":56},573838,1792014120,{"code":4,"msg":5,"data":126},{"doc_id":123,"user_id":127,"nickname":88,"user_avatar":128,"doc_module":9,"category_id":8,"category_name":11,"doc_title":59,"doc_description":61,"doc_content":129,"file_id":130,"file_url":131,"file_type":132,"file_size":133,"view_count":9,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":73,"language":134,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":135,"faqs":136,"seo_title":137,"seo_description":61,"update_tm":138,"read_time":9},137455076865,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","R Syntax Comparison : : CHEAT SHEET  \n| \u003Cbr>Dollar sign syntax |\n| --- |\n| goal(data$x, data$y) |\n\nFormula syntax  \ngoal(y~x| z, data=data, group=w)  \nTidyverse syntax  \ndata %>% goal(x)  \nSUMMARY STATISTICS:  \none continuous variable: mean(mtcars$mpg)  \none categorical variable:  \ntable(mtcars$cyl)  \ntwo categorical variables:  \ntable(mtcars$cyl, mtcars$am)  \none continuous, one categorical:  \nmean(mtcars$mpg[mtcars$cyl==4])  \nmean(mtcars$mpg[mtcars$cyl==6])  \nmean(mtcars$mpg[mtcars$cyl==8])  \nPLOTTING:  \none continuous variable:  \nhist(mtcars$disp)  \nboxplot(mtcars$disp)  \none categorical variable:  \nbarplot(table(mtcars$cyl))  \ntwo continuous variables:  \nplot(mtcars$disp, mtcars$mpg)  \ntwo categorical variables:  \nmosaicplot(table(mtcars$am, mtcars$cyl))  \none continuous, one categorical:  \nhistogram(mtcars$disp[mtcars$cyl==4])  \nhistogram(mtcars$disp[mtcars$cyl==6])  \nhistogram(mtcars$disp[mtcars$cyl==8])  \nSUMMARY STATISTICS:  \none continuous variable:  \nmosaic::mean(~mpg, data=mtcars)  \none categorical variable:  \nmosaic:: tally(~cyl, data=mtcars)  \ntwo categorical variables:  \nmosaic:: tally(cyl~am, data=mtcars)  \none continuous, one categorical:  \nmosaic::mean(mpg~cyl, data=mtcars)  \ntilde  \nPLOTTING:  \none continuous variable:  \nlattice:: histogram(~disp, data=mtcars)  \nlattice:: bwplot(~disp, data=mtcars)  \none categorical variable:  \nmosaic:: bargraph(~cyl, data=mtcars)  \ntwo continuous variables:  \nlattice::xyplot(mpg~disp, data=mtcars)  \ntwo categorical variables:  \nmosaic:: bargraph(~am, data=mtcars, group=cyl)  \none continuous, one categorical:  \nlattice:: histogram(~disp| cyl, data=mtcars)  \nlattice:: bwplot(cyl~disp, data=mtcars)  \nSUMMARY STATISTICS:  \none continuous variable:  \nmtcars %>% dplyr::summarize(mean(mpg))  \none categorical variable:  \nmtcars %>% dplyr::group_by(cyl) %>% dplyr::summarize(n())  \ntwo categorical variables:  \nmtcars %>% dplyr::group_by(cyl, am) %>% dplyr::summarize(n())  \nthe pipe  \none continuous, one categorical:  \nmtcars %>% dplyr::group_by(cyl) %>% dplyr::summarize(mean(mpg))  \nPLOTTING:  \none continuous variable:  \nggplot2:: qplot(x=mpg, data=mtcars, geom = \"histogram\") ggplot2:: qplot(y=disp, x=1, data=mtcars, geom=\"boxplot\")  \none categorical variable:  \nggplot2:: qplot(x=cyl, data=mtcars, geom=\"bar\")  \ntwo continuous variables:  \nggplot2:: qplot(x=disp, y=mpg, data=mtcars, geom=\"point\")  \ntwo categorical variables:  \nggplot2:: qplot(x=factor(cyl), data=mtcars, geom=\"bar\") + facet_grid( .~am)  \none continuous, one categorical:  \nggplot2:: qplot(x=disp, data=mtcars, geom = \"histogram\") + facet_grid( .~cyl)  \nboxplot(mtcars$disp[mtcars$cyl==4])  \nboxplot(mtcars$disp[mtcars$cyl==6])  \nboxplot(mtcars$disp[mtcars$cyl==8])  \nWRANGLING:  \nsubsetting:  \nmtcars[mtcars$mpg>30, ]  \nmaking a new variable:  \nmtcars$efficient[mtcars$mpg>30] \u003C -TRUE  \nmtcars$efficient[mtcars$mpg\u003C30] \u003C -FALSE  \nThe variety of R syntaxes give you many ways to “say” the same thing  \nread across the cheatsheet to see how different syntaxes approach the same problem  \nggplot2:: qplot(y=disp, x=factor(cyl), data=mtcars,  \ngeom=\"boxplot\")  \nWRANGLING:  \nsubsetting:  \nmtcars %>% dplyr::filter(mpg>30)  \nmaking a new variable:  \nmtcars \u003C -mtcars %>%  \ndplyr::mutate(efficient = if else(mpg>30, TRUE, FALSE))  \n_  \nRStudio® is a trademark of RStudio, Inc. CC BY Amelia McNamara • [amcnamara@smith.edu](amcnamara@smith.edu) • @AmeliaMN • [science.smith.edu/](science.smith.edu/)~amcnamara/ • Updated: 2018-01  \nR Syntax Comparison : : CHEAT SHEET  \nSyntax is the set of rules that govern what code works and doesn’t work in a programming language. Most programming languages offer one standardized syntax, but R allows package developers to specify their own syntax. As a result, there is a large variety of (equally valid) R syntaxes.  \nThe three most prevalent R syntaxes are:  \n1. The dollar sign syntax, sometimes called base R syntax, expected by most base R functions. It is characterized by the use of dataset$variablename, and is","cbCaipsq1hkRWLQY","https://ap.wps.com/l/cbCaipsq1hkRWLQY","pdf",518084,"English","# Syntax Overview\n## Dollar Sign Syntax\n## Formula Syntax\n## Tidyverse Syntax\n# Summary Statistics\n## Continuous vs Categorical Variables\n# Plotting\n## Common Plot Types by Variable Type\n# Wrangling\n## Subsetting and Creating Variables\n# How to Identify Unknown R Code","[{\"question\":\"What are the three most prevalent R syntaxes covered in the cheat sheet?\",\"answer\":\"The cheat sheet covers dollar sign syntax (dataset$variable), formula syntax using tilde (~) for modeling and graphics, and tidyverse syntax that works with the pipe (%\\u003e%) and expects data as the first argument.\"},{\"question\":\"How does the cheat sheet compare approaches for summary statistics and plotting across syntaxes?\",\"answer\":\"It provides equivalent examples for the same analysis goals—such as means, tables, and different plot types—showing different syntactic forms that produce the same kind of result.\"},{\"question\":\"What does the Internet research tip recommend when you encounter unfamiliar R code?\",\"answer\":\"It advises checking whether the code matches one of the three common syntaxes on the cheat sheet, then re-searching using a relevant syntax or package keyword such as “tidyverse” or “mosaic”.\"}]","R Syntax Comparison - CHEAT SHEET | PDF",1791948131]