I would like to use pivot_longer() from {tidyr} with names_pattern to convert my data to long format while keeping the prefix string from one of the pattern matches in the column names. This seems counter-intuitive, but I want to convert to long format before applying data dictionary cleaning steps, which requires the original column names. Set-up
Fortunately, restructuring your data into the right form is straightforward using the tidyr package and the pivot_longer() function. In this example, I’m going to look at some mocked-up survey data, with six questions stored in variables Q1 through Q6. The original data frame looks like this:
To almost get what I need, I can use the special .value indicator in the names_to argument to pivot_longer and either split the column on the separator with names_sep or use names_pattern and a regular expression with capturing groups to match the strings before and after the underscore. dat %>% pivot_longer (c (heat_mean:cold_se), names_to = c
R tidyverse how to do two pivot_longer after each other instead of a single one. 0. Perform multiples pivot_longer in a data frame. 3. tidyr::pivot_longer to multiple
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pivot_wider Example. Above, we have a data frame with teams from a to t and their corresponding points scored in the league in the years from 2001 to 2020. In order to make this data tidy, we are going to use the pivot_longer () function from the tidyr package, to “lengthen” the data frame. Below, the first argument is the data frame and
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 % pivot_longer (!aid, names_to = c ("id"), names_pattern
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how to use pivot_longer in r
