Dplyr summarize sum values7/22/2023 # expr min lq mean median uq max neval cld check_equal % mutate ( total = A + B + C + D + E + F ) %>% select ( index, total ) }, "gather" =, check = check_equal, times = 10 ) print ( bm, order = 'median', signif = 3 ) # Unit: milliseconds We can measure the running time of every snippet of code using the package microbenchmark. mutate ( df, total = reduce ( select ( df, - index ), `+` )) # A tibble: 1,000,000 x 8 This function lets us take full advantage of R vectorized operation and write the operation very concisely, whether it be 6 or 20 columns. If the output cannot be coerced to the given type an exception will be thrown.įinally, we have the reduce() function from the purrr package (see this chapter from “Advanced R” by Hadley Wickham to learn more). pmap() has variants that let you specifiy the type of the output ( pmap_dbl(), pmap_lgl()) and thus are safer.rowSums() can only be used if we want to perform the sum or the mean ( rowMeans()), but not for other operations.apply() coerces the data frame into a matrix, so care needs to be taken with non-numeric columns.These function perform the same operation but differ in many aspects: mutate ( df, total = rowSums ( select ( df, - index ))) # A tibble: 1,000,000 x 8 Here we can use the functions apply() or rowSums() from base R and pmap() from the purrr package. The next possibility is to iterate over the rows of the original data, summing them up. However, it also may already be in tidy form. the data frame df may not be a tidy dataset, and it is always a good idea to transform those using tidy data principles. Of course, depending on the meaning of the columns “A”, “B”, etc. The downside of this approach is that we have as many groups as rows in the original data frame, and usually grouped operations are not very efficient when the number of groups is very large. The second approach is to use tidy data principles to transform the previous data frame into long form and then perform the operation by group: df %>% gather ( key, value, - index ) %>% group_by ( index ) %>% summarize ( total = sum ( value )) # A tibble: 1,000,000 x 2 The downside is that if we want to sum up say, 20 columns, we have to write down the name of all of them. This is probably going to be very fast, since it takes full advantage of R vectorized operations. Inspired partly by this and this Stackoverflow questions, I wanted to test what is the fastest way to create a new column using dplyr as a combination of others.įirst, let’s create some example data library ( tidyr ) library ( dplyr ) library ( tibble ) library ( stringr ) library ( purrr ) library ( readr ) library ( microbenchmark ) set.seed ( 1234 ) n The database connections essentially remove that limitation in that you can have a database of many 100s GB, conduct queries on it directly and pull back just what you need for analysis in R.Benchmark adding together multiple columns in dplyr This addresses a common problem with R in that all operations are conducted in memory and thus the amount of data you can work with is limited by available memory. The benefits of doing this are that the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of the query returned. An additional feature is the ability to work with data stored directly in an external database. dplyr addresses this by porting much of the computation to C++. The thinking behind it was largely inspired by the package plyr which has been in use for some time but suffered from being slow in some cases. It is built to work directly with data frames. The package dplyr is a fairly new (2014) package that tries to provide easy tools for the most common data manipulation tasks.
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