Everyone knows that the mean of a dataset is the sum of each row in the dataset divided by the number of rows. But you may have encountered data that doesn’t fit this simple formulation. In this blog post we’ll look at the data structure we use to represent our datasets, and how to compute row means.

If you wanted to compute the means of all the rows in some data frame, you could use the mean() function as follows: mean <- function(df) { sum(row.names(df)[[length(df)]]])/length(df) }

I started by working on a tidyverse package that provided row-wise mean calculation in R. After some initial success, I built upon that idea, and started tackling a more complex problem: computing the row means for a dataset with missing values. I touched upon some ideas that could be useful for non-tidy data, which is when a dataset has a lot of missing data. After working out all the details, I came up with a tidyverse package that provided row-wise mean calculation in R.

This tutorial shows how to perform row operations in R with tidyverse. We will use three main functions, rowwise(), c_across() and rowMeans(), to perform operations on the rowwise data frame. Row-by-row operations in tidyverse: c_across() The rowwise() and c_across() functions are taken from dplyr. The rowwise() function is available in dplyr 1.0.0+ to perform rowwise operations, such as B. by calculating the average value of a row or other summary statistics for each row. With dplyr’s c_aross() function, you can merge values from multiple columns into the data frame. It is designed to work with rowwise() and allows you to perform rowwise operations easily. It is similar to the c() merge function in R basic, but with c_across() we can easily select multiple columns in the data frame using the tidy select frame. And rowMeans() is a function available in R’s basic functions, and we can use it to calculate averages between rows, probably much faster. Let’s start by loading the tidyverse and the dataset needed to calculate the mean values of each numeric column in the data frame. Let’s start by downloading the tidyverse packages. library(tidyverse) We will use a subset of the Gapminder data in raw form and download it directly from the cmdlinetips.com Github page. data_url <- https://raw.githubusercontent.com/cmdlinetips/data/master/gapminder/gapminder_lifeExp_wide.tsv gapminder_wide <- read_tsv(data_url) %>% head() Our toy dataset looks like this. gapminder_wide %>% ## A Tibble : 6 x 14 ## Country Continent lifeExp_1952 lifeExp_1957 lifeExp_1962 lifeExp_1967 ## ## 1 Algeria Africa 43.1 45.7 48.3 51.4 ## 2 Angola Africa 30.0 32.0 34 36.0 ## 3 Benin Africa 38.2 40.4 42.6 44.9 ## 4 Botswana Africa 47.6 49.6 51.5 53.3 ## 5 Burkina… Africa 32.0 34.9 37.8 40.7 ## 6 Burundi Africa 39.0 40.5 42.0 43.5 ## . with 8 other variables : lifeExp_1972 , lifeExp_1977 , ## ## lifeExp_1982 , lifeExp_1987 , lifeExp_1992 , ## lifeExp_1997 , lifeExp_2002 , lifeExp_2007 First, let’s see how to compute rowwise averages on a data frame with numeric columns using rowwise() and the c_across() function in dplyr. Next, we will learn how to calculate different summary statistics for each row. Specifically, we calculate the mean and variance over the series. Finally, we also see an example of using the basic R function rowMeans() within the tidyverse to calculate the average of each row.## Row-by-row operation of a data frame with all numeric columns Calculate the average value of a row with c_across()

Let’s calculate the average values of the rows in the data frame with numeric columns. In this example, we remove all non-numeric columns with select(), then apply rowwise() to complete the rows. To calculate the rows, we use the c_across() function to combine the row elements of all columns. The all() argument to c_across() retrieves all columns. gapminder_wide %>% select(-country,-continent) %>% rowwise() %>% summarise(row_mean=average(c_across(everything())))) We used the summarize() function to calculate the average values of the rows, which produces a table with the average values of the rows. ## Tibble : 6 x 1 ## row_mean ## ## 1 59.0 ## 2 37.9 ## 3 48.8 ## 4 54.6 ## 5 44.7 ## 6 44.8 With rowwise() and c_across(), we can compute multiple operations on each row. In this example, we calculate the mean and variance for each row using summarize() on a data frame with numeric columns. gapminder_wide %>% select(-country,-continent) %>% rowwise() %>% summarize(row_mean=average(c_across(everything()), row_variance=var(c_across(everything())))) We now have two two-page summaries as columns in the results. ## Tibble : 6 x 2 ## row_mean row_variance ## ## 1 59.0 98.0 ## 2 37.9 14.7 ## 3 48.8 34.4 ## 4 54.6 32.2 ## 5 44.7 43.0 ## 6 44.8 9.24 So far, our results only include rule summaries. That’s because we use summarize() to compute string operations. We can use mutate() to store the raw data alongside the row summaries. In this example, we use mutate() with rowwise() and compute two rowwise summaries with c_across(). We also create a row-by-row summary of the results in the first two columns with select(). gapminder_wide %>% select(-country,-continent) %>% rowwise() %>% mutate(row_mean=mean(c_across(everything()), row_variance=var(c_across(everything())))) %>% select(row_mean,row_variance,everything()) ## One Tibble : 6 x 14 ## Row by Row : ## row_mean row_variance lifeExp_1952 lifeExp_1957 lifeExp_1962 lifeExp_1967 ## ## 1 59.0 98.0 43.1 45.7 48.3 51.4 ## 2 37.9 14.7 30.0 32.0 34 36.0 ## 3 48.8 34.4 38.2 40.4 42.6 44.9 ## 4 54.6 32.2 47.6 49.6 51.5 53.3 ## 5 44.7 43.0 32.0 34.9 37.8 40.7 ## 6 44.8 9.24 39.0 40.5 42.0 43.5 ## . With 8 other variables: lifeExp_1972 , lifeExp_1977 , ## lifeExp_1982 , lifeExp_1987 , lifeExp_1992 , ## lifeExp_1997 , lifeExp_2002 , lifeExp_2007

## Row-by-row operation of a data frame with columns of characters and numbers Calculate the average value of a row with c_across()

We can apply rowwise() and calculate row sums, for example. For example, the average value of a row with a data frame of mixed type, i.e., a data frame that contains both numeric and character/factor variables. In c_across(), we use is.numeric to select numeric columns to calculate row sums. In this example, we use summarize() to calculate the mean and variance of the numeric values in each row. gapminder_wide %>% rowwise() %>% summarise(row_mean=mean(c_across(where(is.numeric)))), row_variance=var(c_across(where(is.numeric)))) ## Tibble : 6 x 2 ## row_mean row_variance ## ## 1 59.0 98.0 ## 2 37.9 14.7 ## 3 48.8 34.4 ## 4 54.6 32.2 ## 5 44.7 43.0 ## 6 44.8 9.24 You can also use the c_across() function to select the columns of interest to calculate row totals in different ways. Here we select the columns using the start and end names of the columns to calculate the average values of the rows. gapminder_wide %>% rowwise() %>% summarise(lifeExp_mean=average(c_across(lifeExp_1952:lifeExp_2007)) %>% head() ## Tibble: 6 x ## lifeExp_mean ## ## 1 59.0 ## 2 37.9 ## 3 48.8 ## 4 54.6 ## 5 44.7 ## 6 44.8

**Calculate the row averages with the rowMeans() function in R**

We can also calculate row averages with the rowMeans() function in Base R. Applying Pipe directly to the numeric column data matrix, we get the row average as a vector gapminder_wide %>% select(-country,-continent) %>% rowMeans() We can use dplyr’s summarize function and use rowMeans() to average the rows as a tibble. gapminder_wide %>% summarise(row_mean=rowMeans(across(where(is.numeric)))) ## Tibble : 6 x 1 ## row_mean ## ## 1 59.0 ## 2 37.9 ## 3 48.8 ## 4 54.6 ## 5 44.7 ## 6 44.8 In terms of speed, rowMeans in R is probably much faster than rowwise().This is the third post in the row-wise series, and the first one. If you read the previous post, you are already familiar with the case of the row-wise mean. In fact, you may have even used it. But, what are row-wise means anyway?. Read more about row wise in r and let us know what you think.

### Frequently Asked Questions

#### What is row wise in R?

R is a programming language that is used to analyze data. Before beginning to learn R, you should be familiar with the meaning of rows and columns in R. Row-wise operations refer to computing column means or row means in tidyverse. In this post, I am going to talk about the first part of the tidyverse, known as the ‘tidyverse’. The tidyverse provides several packages that are organized into tidyverse cores, with the possibility of some individual packages being reorganized into new cores. Currently there are three tidyverse cores: ‘tidyverse’, ‘dplyr’, and ‘ggplot2’. The tidyverse provides the core R language package ‘base’ together with most of the tidyverse packages. The tidyverse is designed to allow rapid development of packages that are both useful and dependable.

#### What is row wise mean?

The R function rowwise is designed to generate the means of a matrix column. The rows of the matrix are laid out in a row-wise order (left-to-right), and it is of interest to compute the mean of the column. Rowwise is a library for doing row operations on vectors. The first time I came across this library was when I read the paper, “A Systematic Approach to Rowwise Computations. It was a while ago, so I can’t remember exactly how it was presented.

#### How do you find the mean of a row in R?

The mean function is one of those functions that is really easy to use in R; all you have to do is to say mean(A) , and boom, your output is the mean value of the row. (Most functions like mean have a similar syntax. The only difference is that mean() returns a single value, whereas the other functions return a vector.) I often find myself looking for vectorizable solutions to compute row means in tidyverse, but when I google for them I always end up with a solution that uses a loop. So, I thought I’d make a list of all row-wise operations in tidyverse, grouped by type, so it’s easy to find what you’re looking for.

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