reshape2

问题 I am trying to run multiple simple linear regressions based on data from a nested data frame and store the regression fit coefficients in a dataframe using tidy(). My code block is as follows library(tidyverse) library(broom) library(reshape2) library(dplyr) Factors <- as.factor(c("A","B","C","D")) set.seed(5) DF <- data.frame(Factors, X = rnorm(4), Y = rnorm(4), Z= rnorm(4)) MDF <- melt(DF, id.vars=c("Factors","X")) DFF <- MDF %>% nest(-Factors) If it is a single dataframe with many columns,

问题 I am trying to run multiple simple linear regressions based on data from a nested data frame and store the regression fit coefficients in a dataframe using tidy(). My code block is as follows library(tidyverse) library(broom) library(reshape2) library(dplyr) Factors <- as.factor(c("A","B","C","D")) set.seed(5) DF <- data.frame(Factors, X = rnorm(4), Y = rnorm(4), Z= rnorm(4)) MDF <- melt(DF, id.vars=c("Factors","X")) DFF <- MDF %>% nest(-Factors) If it is a single dataframe with many columns,

问题 I'm melting some data and don't want to provide an id.var parameter to melt. The data melts fine, but I get the "No id variables; using all as measure variables" Is there a way to prevent that message from coming up, or a way to say id.var=default or something like that? An iris example using dplyr: > dt <- iris %>% summarize_at(c("Sepal.Length","Sepal.Width"), funs(mean)) > dt Sepal.Length Sepal.Width 1 5.843333 3.057333 > melt(dt, value.name="Mean") No id variables; using all as measure

来源： https://stackoverflow.com/questions/57012546/r-reshaping-multiple-columns-from-long-to-wide

来源： https://stackoverflow.com/questions/61570710/how-to-use-pivot-longer-to-reshape-from-wide-type-data-to-long-type-data-with-mu

来源： https://stackoverflow.com/questions/61570710/how-to-use-pivot-longer-to-reshape-from-wide-type-data-to-long-type-data-with-mu

问题 I have searched and found a number of examples, so far I have not been able to solve a problem in transforming my data from wide to long. Below is an example of the data: set.seed(12345) id = 1:100 age = sample(1:100, 100, replace=TRUE) group = sample(1:2, 100, replace=TRUE) t0_var1 = sample(1:300, 100, replace=TRUE) t2_var1 = sample(1:300, 100, replace=TRUE) t0_var2 = sample(1:600, 100, replace=TRUE) t2_var2 = sample(1:600, 100, replace=TRUE) t0_var3 = sample(1:700, 100, replace=TRUE) t2

问题 This question already has answers here : How to reshape data from long to wide format (11 answers) Closed 2 years ago . I have the following table: perid date rating 10001 2005 RD 10001 2006 GN 10001 2007 GD 10002 2008 GD 10002 2009 YW 10002 2010 GN 10002 2011 GN 10003 2005 GD 10003 2006 GN 10003 2007 YW how can I turn this table to the following format: perid 2005 2006 2007 2008 2009 2010 2011 10001 RD GN GN N/A N/A N/A N/A 10002 N/A N/A N/A GD YW GN GN 10003 GD GN YW N/A N/A N/A N/A Or if I

问题 I have a dataset of events, grouped by let like so: set.seed(3) events <- data.frame( let = rep(LETTERS[1:2], each=3), age = c(0,sample(1:20, size=2), 0,sample(1:20, size=2)), value = sample(1:100, size=6)) let age value 1 A 0 61 2 A 4 60 3 A 16 13 4 B 0 29 5 B 8 56 6 B 7 99 How can I cast the data frame so that age is multiple columns grouped into weeks? So for each column, take the value of the largest age that is less than or equal to 0, 7, 14, 21 days. events.cast <- data.frame( let =

问题 I have the following DF: DF = structure(list(ID = c(21785L, 21785L, 21785L), V1 = c(0.828273303, 6.404590021, 0.775568448), V2 = c(2L, 3L, 2L), V3 = c(NA, 1.122899914, 0.850113234), V4 = c(NA, 4L, 3L), V5 = c(NA, 0.866757168, 0.868943246 ), V6 = c(NA, 5L, 4L), V7 = c(NA, 0.563804788, 0.728656328), V8 = c(NA, 6L, 5L), V9 = c(NA, 0.888109208, 0.823803733), V10 = c(NA, 7L, 6L), V11 = c(NA, 0.578834113, 0.863467391 ), V12 = c(NA, 1L, 7L), V13 = c(NA, NA, 0.939920869)), class = "data.frame", row