\n
A useful package for this may be dplyr. I’ll use a couple of bogus variables from the included iris dataset to give an indication of how this would work. In your case, replace iris with your dataset, and change the various calculations to what you need.<\/p>\n
require(dplyr)\n\niris %>% \n mutate(# Calculate required variables at the level of your raw data\n Sepal.Area = Sepal.Length * Sepal.Width,\n Petal.Area = Petal.Length * Petal.Width\n ) %>%\n group_by(# Choose variable to group by\n Species\n ) %>%\n summarize(# Perform some grouping calculations\n Mean.Sepal.Area = mean(Sepal.Area),\n Mean.Petal.Area = mean(Petal.Area),\n count = n()\n ) %>%\n mutate(# Calculate required variables at the level of your summarized data\n Sepal.Times.Petal = Mean.Sepal.Area * Mean.Petal.Area) ->\n output\n<\/code><\/pre>\n<\/p><\/div>\n<\/div>\n<\/div>\n
3<\/span> <\/p><\/div>\n<\/div>\n[ad_2]<\/p>\n
solved How do I break up a dataset in R so that particular values of a categorical variable are together, and I can then perform analysis of those values? [closed] <\/p>\n","protected":false},"excerpt":{"rendered":"
[ad_1] A useful package for this may be dplyr. I’ll use a couple of bogus variables from the included iris dataset to give an indication of how this would work. In your case, replace iris with your dataset, and change the various calculations to what you need. require(dplyr) iris %>% mutate(# Calculate required variables at … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[321],"class_list":["post-15829","post","type-post","status-publish","format-standard","hentry","category-solved","tag-r"],"yoast_head":"\n[Solved] How do I break up a dataset in R so that particular values of a categorical variable are together, and I can then perform analysis of those values? 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