Pivot data from wide to long. Source: R/pivot-long.R. pivot_longer () "lengthens" data, increasing the number of rows and decreasing the number of columns. The inverse transformation is pivot_wider () Learn more in vignette ("pivot"). @syntheso you can use the cols argument to select which columns you want to pivot into longer format. Check my edit. Check my edit. You can try different functions to choose columns you want to pivot. We could reshape to 'long' with pivot_longer, specifying the names_pattern to capture substring from column names (()) that matches with the same order of names_to - i.e.. wave column will get the digits (\\d+) after the 'W', where as the .value (value of the columns) correspond to the substring after the first _ in column names. That means, in this example x and y have 2 columns each. But for a another data set it may be different. If there are more columns, it would be difficult to type everything under varying parameter. In order to avoid specifying the columns when pivoting, I tried this code: data1 <- data%>% pivot_longer (!aid, names_to = c ("id"), names_pattern Use mutate() with across() to create the TRUE / FALSE versions of the x variables. Note that across() requires dplyr 1.0.0, which is currently in development but due for production release the week of May 25th. Use pivot_longer() to allow us to summarise() multiple measures without a lot of complicated code. How adjust pivot_longer in a R code for no specific column name. 3. pivot_longer with multiple columns in R. 2. Use dplyr pivot_longer to transpose dataset. 1. Apr 13 at 1:09. Right -- in conjunction with the argument names_to = c ("Status", ".value"), this tells R to apply the pattern to the select column names, pivot all unique results of the first capture group to separate rows (with the matched pattern as values in a column named Status ), but to retain all unique results of the second capture Or first reshape to 'long' format and then do a group by summarise. Data %>% pivot_longer (cols = Tiger_Shark:White_shark) %>% group_by (Country, Year) %>% summarise (value = mean (value)) The names_to should be a single element here as these columns seems to belong to a single group. Share. To reshape this data into a long format, one needs to specify columns to pivot, along with a new name for the gathered column and the values it will hold. Here is a schematic outline of applying pivot_longer: Source Data Frame: Define the initial data frame with defined columns per planet. Long Data Transformation: AaTpBm.

how to use pivot_longer in r