ISO Year Use of Electricity Population ARB 2009 0.5 na ARB 2010 0.5 na ARB 2011 0.4 na CSS 2009 na 2.5 CSS 2010 na 3.5 CSS 2011 na 0.5 I started of with a "gather" function to create the Year column and then I transformed Year to numeric and created a result column for the values. reshape to long format with pivot_longer - taking into account the pattern in names with either names_sep or names_pattern, specify the names_to as a vector of c (".value", "trait") in the same order we want the column values and the suffix value to be stored as separate columns. Step 1 Identify the column whose values will supply the column names. pivot_wider () turns the values from one column and turns them into column names. In our example, we want the unique values from year to become column names. pivot_wider () ’s names_from argument controls which column is pivoted into column names. I chose to use mutate in my original solution in the comments to split this column into two columns with equivalent info, a loop_number column and a question_number column. spread can then be used to transform the long form data, which are key value pairs (question_number, value) to wide form data. Change it to a single class by converting to character in values_transform. According to ?pivot_longer. names_ptypes, values_ptypes - A list of column name-prototype pairs. A prototype (or ptype for short) is a zero-length vector (like integer () or numeric ()) that defines the type, class, and attributes of a vector. First, transform the dataframe into one with only three columns id, nameyear, and value; concurrently separate the second column nameyear into name and year. Then, just pivot the two columns name and value wider. Output. # A tibble: 14 x 4 id year emp marstat 1 1 1 ft married 2 1 2 ft divorced 3 2 1 ft married 4 2 2 ft kpN4PZE. I am trying to practice the "tidy" transformation of the data by using pivot_long. Using the movie dataset from the ggplot2movies package. I am curious to know how I can pivot the binary columns into a wide table by creating a new column "genre": something like Here is my code for the first step, I filter all Action and Romance movie out: Steps: Make the data long. Change the positions to numbers (if you wanted to sort by position, it would give you it in alphabetical order - it's better to convert it to a factor, and best to convert it to a number! :-) Sort by the column 'name', as in the desired output. Remove the 'name' column. Remove duplicate rows. One option is the pivot_longer function from pyjanitor that could help in abstracting the reshaping from wide to long form: # pip install pyjanitor import pandas as pd import janitor as jn df.pivot_longer(index = None, names_to = ('Manufacturer', 'Device', '.value'), names_pattern = r"(.+)\s\|(.+)\s\|(.+)\s\|.+") Manufacturer Device Model Quantity Max-quant 0 Sony TV A222 5 10 1 Sony TV A234 5 df %>% pivot_longer(-c(ID, Date_First_test, Date_second_test, Date_third_test), names_to = "test", values_to = "hemoglobin") Whilst this part works, I want one date for each test in one column. However, I want to be even more sophisticated and rather than use the date itself, use the relative admission day. How to use Pivot_longer to reshape from wide-type data to long-type data with multiple variables (1 answer) Closed 3 years ago . I have a dataset of adolescents over 3 waves.

how to use pivot_longer in r