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Is it possible to use spread on multiple columns in tidyr similar to dcast? [duplicate]

One option would be to create a new ‘Prod_Count’ by joining the ‘Product’ and ‘Country’ columns by paste, remove those columns with the select and reshape from ‘long’ to ‘wide’ using spread from tidyr. library(dplyr) library(tidyr) sdt %>% mutate(Prod_Count=paste(Product, Country, sep=”_”)) %>% select(-Product, -Country)%>% spread(Prod_Count, value)%>% head(2) # Year A_AI B_EI #1 1990 0.7878674 0.2486044 … Read more

melt / reshape in excel using VBA?

I’ve got two posts, with usable code and downloadable workbook, on doing this in Excel/VBA on my blog: http://yoursumbuddy.com/data-normalizer http://yoursumbuddy.com/data-normalizer-the-sql/ Here’s the code: ‘Arguments ‘List: The range to be normalized. ‘RepeatingColsCount: The number of columns, starting with the leftmost, ‘ whose headings remain the same. ‘NormalizedColHeader: The column header for the rolled-up category. ‘DataColHeader: The … Read more

Compute mean and standard deviation by group for multiple variables in a data.frame

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

Grouped bar plot in ggplot

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

Reshape multiple values at once

In “reshape2”, you can use recast (though in my experience, this isn’t a widely known function). library(reshape2) recast(mydf, id ~ variable + type, id.var = c(“id”, “type”)) # id transactions_expense transactions_income amount_expense amount_income # 1 20 25 20 95 100 # 2 30 45 50 250 300 You can also use base R’s reshape: reshape(mydf, … Read more

Reshaping wide to long with multiple values columns [duplicate]

reshape does this with the appropriate arguments. varying lists the columns which exist in the wide format, but are split into multiple rows in the long format. v.names is the long format equivalents. Between the two, a mapping is created. From ?reshape: Also, guessing is not attempted if v.names is given explicitly. Notice that the … Read more

dcast warning: ‘Aggregation function missing: defaulting to length’

The reason why you are getting this warning is in the description of fun.aggregate (see ?dcast): aggregation function needed if variables do not identify a single observation for each output cell. Defaults to length (with a message) if needed but not specified So, an aggregation function is needed when there is more than one value … Read more

How to use Pivot_longer to reshape from wide-type data to long-type data with multiple variables

You can try : tidyr::pivot_longer(df, cols = -ID_IE, names_to = c(‘.value’, ‘grade’), names_pattern = ‘(.*)(\\d+)’) # A tibble: 8 x 4 # ID_IE grade BLS_tchrG ELS_tchrG # <dbl> <chr> <dbl> <dbl> #1 2135 2 1 1 #2 2135 7 1 1 #3 2101 2 0 0 #4 2101 7 2 0 #5 2103 2 0 … Read more

Faster ways to calculate frequencies and cast from long to wide

You could just use the table command: table(data$id,data$week) 1 2 3 1 2 1 1 2 0 0 1 If “id” and “week” are the only columns in your data frame, you can simply use: table(data) # week # id 1 2 3 # 1 2 1 1 # 2 0 0 1

Reshaping multiple sets of measurement columns (wide format) into single columns (long format)

reshape(dat, idvar=”ID”, direction=”long”, varying=list(Start=c(2,5,8), End=c(3,6,9), Value=c(4,7,10)), v.names = c(“DateRangeStart”, “DateRangeEnd”, “Value”) ) #————- ID time DateRangeStart DateRangeEnd Value 1.1 1 1 1/1/90 3/1/90 4.4 1.2 1 2 4/5/91 6/7/91 6.2 1.3 1 3 5/5/95 6/6/96 3.3 (Added the v.names per Josh’s suggestion.)