An option would be to replace the duplicated elements by ‘Letter’ to NA and then in the reshaped data, remove the columns that are all NA library(data.table) out <- dcast(setDT(sample_df)[, lapply(.SD, function(x) replace(x, duplicated(x), NA)), Letter], Letter ~ rowid(Letter), value.var = c(“Number”, “Fruit”)) nm1 <- out[, names(which(!colSums(!is.na(.SD))))] out[, (nm1) := NULL][] # Letter Number_1 Number_2 … Read more
I still can’t believe I beat Andrie with an answer. 🙂 > library(reshape) > my.df <- read.table(text = “Country 2001 2002 2003 + Nigeria 1 2 3 + UK 2 NA 1”, header = TRUE) > my.result <- melt(my.df, id = c(“Country”)) > my.result[order(my.result$Country),] Country variable value 1 Nigeria X2001 1 3 Nigeria X2002 2 … Read more
Your data are already in a long format that can be used easily by “reshape2”, like this: library(reshape) dcast(df, bdate ~ sex + age + diag, value.var = “admissions”) # bdate Female_35-64_card Female_35-64_cere Female_65-74_card Female_65-74_cere # 1 1987-01-01 1 6 1 6 # 2 1987-01-02 4 4 0 6 # 3 1987-01-03 2 6 4 … Read more
You can use the ggplot2 package from Hadley Wickham for that. R> library(ggplot2) An example data set : R> d <- data.frame(t=rep(0:23,each=4),var=rep(LETTERS[1:4],4),val=round(runif(4*24,0,50))) R> head(d,10) t var val 1 0 A 1 2 0 B 45 3 0 C 6 4 0 D 14 5 1 A 35 6 1 B 21 7 1 C 13 … Read more
reshape is of course the proper solution, as stated by @gnovice. A nice feature of reshape is that it allows this: A = 1:12; B = reshape(A,4,[]); B = 1 5 9 2 6 10 3 7 11 4 8 12 So if you don’t know how many columns there will be, reshape will compute … Read more
This is an aggregation problem, not a reshaping problem as the question originally suggested — we wish to aggregate each column into a mean and standard deviation by ID. There are many packages that handle such problems. In the base of R it can be done using aggregate like this (assuming DF is the input … Read more
Wrap the condition in which: df[which(df$number1 < df$number2), ] How it works: It returns the row numbers where the condition matches (where the condition is TRUE) and subsets the data frame on those rows accordingly. Say that: which(df$number1 < df$number2) returns row numbers 1, 2, 3, 4 and 5. As such, writing: df[which(df$number1 < df$number2), … Read more
EDIT: Many years later For a pure ggplot2 + utils::stack() solution, see the answer by @markus! A somewhat verbose tidyverse solution, with all non-base packages explicitly stated so that you know where each function comes from: library(magrittr) # needed for %>% if dplyr is not attached “http://pastebin.com/raw.php?i=L8cEKcxS” %>% utils::read.csv(sep = “,”) %>% tidyr::pivot_longer(cols = c(Food, … Read more
The aggregation parameter in the dcast function of the reshape2-package defaults to length (= count). In the data.table-package an improved version of the dcastfunction is implemented. So in your case this would be: library(‘reshape2’) # or library(‘data.table’) newdf <- dcast(sample.data, Case ~ Decision) or with using the parameters explicitly: newdf <- dcast(sample.data, Case ~ Decision, … Read more
a = np.arange(18).reshape(9,2) b = a.reshape(3,3,2).swapaxes(0,2) # a: array([[ 0, 1], [ 2, 3], [ 4, 5], [ 6, 7], [ 8, 9], [10, 11], [12, 13], [14, 15], [16, 17]]) # b: array([[[ 0, 6, 12], [ 2, 8, 14], [ 4, 10, 16]], [[ 1, 7, 13], [ 3, 9, 15], [ 5, … Read more