To pivot the data frame in R from long to wide format, I will apply the function pivot_wider from the tidyr. This function is relatively easy to use. I think it is one of to most popular choices to do this task. Here is my data frame that contains randomly generated values. Here is more about that.
or with tidyverse, the transpose can be done in two steps, 1), reshape to long format with pivot_longer, 2) reshape back to wide with a different column with pivot_wider. library (dplyr) library (tidyr) df1 %>% pivot_longer (cols = -people, names_to = 'People') %>% pivot_wider (names_from = people, values_from = value) # A tibble: 5 x 5
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I would like to create a dataframe with a more long format, which puts the age categories as values in one column and the population and number of events as well, like this: 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. The pivot_longer() function comes from the tidyr package and is used to format the output to make it easier to read. This particular syntax calculates the following summary statistics for each numeric variable in a data frame:
I am trying to find a simple way to pivot_longer a dataframe that has multiple columns containing different data for each case. Using multiple names in names_to doesn't seem to solve the problem. Here is a worked example:
Solution. There are two sets of methods that are explained below: gather () and spread () from the tidyr package. This is a newer interface to the reshape2 package. melt () and dcast () from the reshape2 package. There are a number of other methods which aren’t covered here, since they are not as easy to use: The reshape () function, which is Finally, widen the tibble using pivot_wider(). Use surveys_spread_genera and pivot_longer() to create a data frame where each row is a unique plot_id by year combination. The surveys data set has two measurement columns: hindfoot_length and weight. This makes it difficult to do things like look at the relationship between mean values of each From wide to long. To go from wide to long with tidyr, use the pivot_longer() function. It has the following syntax: pivot_longer(mydata, cols, # columns that should pivot from wide to long I thought about this conceptually using pivot long, but I am unsure how to do this as I am almost pivoting within column values. Also tried splitting the columns into multiple, but I run into the problem that there are different orders and amounts of event per id. Thanks for the help! 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 Sorted by: 3. With tidyverse, we can pivot on the two sets of columns that starts with belief and norm. We can then use regex to split into groups according to the first underscore (since some column names have multiple underscores). Essentially, we are saying to put belief or norm (the first group in the column name) into their own columns (i 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.
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Pivot_longer on integer and factor. I have a dataset that looks like the following. # A tibble: 1 x 4 hhm1q001 hhm2q001 hhm1q002 hhm2q002 1 blue red 30 50. I have been trying to transform it to long using tidyr::pivot_longer my expected output looks like this: HHS_long
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    • how to use pivot_longer in r