colSums, rowSums, colMeans & rowMeans in R | 5 Example Codes + Video

 

In this tutorial, I’ll show you how to use four of the most important R functions for descriptive statistics: colSums, rowSums, colMeans, and rowMeans.

I’ll explain all these functions within the same article, since their usage is very similar. Let’s first check the basic R programming syntax of the four functions:

 

Basic R Syntax:

colSums(data)
rowSums(data)
colMeans(data)
rowMeans(data)

 

  • colSums computes the sum of each column of a numeric data frame, matrix or array.
  • rowSums computes the sum of each row of a numeric data frame, matrix or array.
  • colMeans computes the mean of each column of a numeric data frame, matrix or array.
  • rowMeans computes the mean of each row of a numeric data frame, matrix or array.

 

In the following, I’m going to show you five reproducible examples on how to apply colSums, rowSums, colMeans, and rowMeans in R.

So if you want to know more about the computation of column/row means/sums, keep reading…

 

Example 1: Compute Sum & Mean of Columns & Rows in R

Let’s start with a very simple example. For the example, I’m going to use the following synthetic data set:

set.seed(1234)                                          # Set seed
data <- data.frame(matrix(round(runif(12, 1, 20)),      # Create example data
                          nrow = 3, ncol = 4))
data                                                    # Print data to RStudio console

 

Table 1 Numeric Data Frame

Table 1: Data Frame Containing Numeric Values.

 

Our example data consists of 3 rows and four columns. All values are numeric.

To this data set, we can now apply the four functions. Let’s compute the column sums

colSums(data)                                            # Basic application of colSums
# X1 X2 X3 X4 
# 29 43 20 36

…the row sums…

rowSums(data)                                            # Basic application of rowSums
# 28 49 51

…the column means…

colMeans(data)                                           # Basic application of colMeans
#       X1        X2        X3        X4 
# 9.666667 14.333333  6.666667 12.000000

…and the row means:

rowMeans(data)                                           # Basic application of rowMeans
# 7.00 12.25 12.75

That’s basically how to apply the four functions! However, if you need more explanations you could have a look at the following video of my YouTube channel. In the video, I’m explaining Example 1 in more detail:

 

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Example 2: Add Sums & Means to Data Frame

Typically, we would like to add the computed mean and sum values to our data frame. We can easily column-bind the rowSums and rowMeans with the following code:

data_ext1 <- cbind(data,                                  # Add rowSums & rowMeans to data
                   rowSums = rowSums(data),
                   rowMeans = rowMeans(data))
data_ext1                                                 # Print data to RStudio console

 

Table 2 Numeric Data Frame with sums

Table 2: Data Frame Containing Numeric Values, rowSums & rowMeans.

 

And we can easily row-bind the colSums and colMeans to our data frame with the following code:

data_ext2 <- rbind(data_ext1,                             # Add colSums & colMeans to data
                   c(colSums(data), NA, NA),
                   c(colMeans(data), NA, NA))
data_ext2                                                 # Print data to RStudio console

 

Table 3 Numeric Data Frame with sums & means

Table 3: Data Frame Containing Numeric Values, rowSums, rowMeans, colSums & colMeans.

 

Our final data table contains the values calculated by all of our four functions.

Note: We had to add some NA values at the bottom right, since otherwise these cells of the data would be empty.

Still easy going – But you guessed it, there might occur problems…

 

Example 3: How to Handle NA Values (na.rm)

One of the most common issues of the R colSums, rowSums, colMeans, and rowMeans commands is the existence of NAs (i.e. missing values) in the data. Let’s see what happens, when we apply our functions to data with missing values.

For this example, let’s first add some NAs to our data frame:

data_na <- as.matrix(data)                                 # Create example data with NA
data_na[rbinom(length(data_na), 1, 0.3) == 1] <- NA
data_na <- as.data.frame(data_na)
data_na                                                    # Print data to RStudio console

 

Table 4 Numeric Data Frame with NA

Table 4: Data Frame Containing NA Values.

 

As you can see, our data looks exactly the same as in Example 1, but two of the values were set to NA.

What happens, when we apply our four functions?

colSums(data_na)                                           # colSums with NA output
# X1 X2 X3 X4 
# NA NA 20 36
 
rowSums(data_na)                                           # rowSums with NA output
# NA NA 51
 
colMeans(data_na)                                          # colMeans with NA output
# X1        X2        X3        X4 
# NA        NA  6.666667 12.000000
 
rowMeans(data_na)                                          # rowMeans with NA output
# NA    NA 12.75

All of our results contain NAs… Definitely not what we want.

But no worries, there is an easy solution. We simply have to add na.rm = TRUE within our functions:

colSums(data_na, na.rm = TRUE)                              # Remove NA within colSums
# X1 X2 X3 X4 
# 16 30 20 36
 
rowSums(data_na, na.rm = TRUE)                              # Remove NA within rowSums
# 15 36 51
 
colMeans(data_na, na.rm = TRUE)                             # Remove NA within colMeans
#       X1        X2        X3        X4 
# 8.000000 15.000000  6.666667 12.000000
 
rowMeans(data_na, na.rm = TRUE)                             # Remove NA within rowMeans
# 5.00 12.00 12.75

That’s an easy fix! But please note that the handling of missing values is a research topic by itself. Just ignoring NA values is usually not the best idea. In case you want to learn more about missing values, check out this post.

However, are there other difficulties with colSums, rowSums, colMeans, and rowMeans? Unfortunately, yes…

 

Example 4: Error: X Must be Numeric

The most common error message of colSums, rowSums, colMeans, and rowMeans is the following:

Error in colMeans(x) : ‘x’ must be numeric

Why this error occurs and how to handle it is what I’m going to show you next.

For the example, I’m going to load the iris data set:

data(iris)                                                  # Load iris data
head(iris)                                                  # First 6 rows of iris data

 

nrow function in R - Iris Example Data Frame

Table 5: First 6 Rows of Iris Data Set.

 

The data consists of five columns and 150 rows. So let’s apply our functions as we did before:

colSums(iris)                                               # colSums error
# Error in colSums(iris) : 'x' must be numeric

Error…

rowSums(iris)                                               # rowSums error
# Error in rowSums(iris) : 'x' must be numeric

…error…

colMeans(iris)                                              # colMeans error
# Error in colMeans(iris) : 'x' must be numeric

…another error…

rowMeans(iris)                                              # rowMeans error
# Error in rowMeans(iris) : 'x' must be numeric

…and even more errors. None of the functions worked!

So why did we receive all these errors? The answer is simple: colSums, rowSums, colMeans, and rowMeans can only handle numeric values. Since the fifth column of the iris data set is a factor, the functions return error messages to the RStudio console.

So what is the solution? We need to subset all numeric columns of our data.

Let’s do this!

First, we have to create a logical vector that specifies which of our columns are numeric…

iris_subset <- unlist(lapply(iris, is.numeric))             # Subset containing numeric columns
iris_subset
# Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
#         TRUE         TRUE         TRUE         TRUE        FALSE

…and then we can use this logical vector to exclude all non-numeric columns of our data:

colSums(iris[ , iris_subset])                                # No colSums error anymore
# Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
#        876.5        458.6        563.7        179.9

Works fine…

rowSums(iris[ , iris_subset])                                # No rowSums error anymore
# 10.2  9.5  9.4  9.4 10.2 11.4  9.7 10.1  8.9  9.6...

…very good…

colMeans(iris[ , iris_subset])                               # No colMeans error anymore
# Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
#     5.843333     3.057333     3.758000     1.199333

…nice…

rowMeans(iris[ , iris_subset])                               # No rowMeans error anymore
# 2.550 2.375 2.350 2.350 2.550 2.850 2.425 2.525...

…YAY, no errors anymore!

So, that is basically what I wanted to show you about the R programming functions colSums, rowSums, colMeans, and rowMeans. But stay with me! With just a bit more effort you can learn the usage of even more functions…

 

Example 5: colMedians & rowMedians [robustbase R Package]

So far we have only calculated the sum and mean of our columns and rows. But of cause there are many other statistical descriptive metrics that we might want to compute for our data.

One of them is the median, which is often preferred compared to the arithmetic mean.

Fortunately, the robustbase R package provides functions that are very similar to colMeans and rowMeans.

First, we have to install and load the package:

install.packages("robustbase")                               # Install robustbase package 
library("robustbase")                                        # Load robustbase package

The package contains the functions colMedians and rowMedians. Unfortunately, R returns an error when we apply the functions to our data that we have created in Example 1:

colMedians(data)                                             # Error in colMedians
# Error in colMedians(data) : Argument 'x' must be a matrix
 
rowMedians(data)                                             # Error in rowMedians
# Error in rowMedians(data) : Argument 'x' must be a matrix.

However, there is an easy fix. As you can see, colMedians and rowMedians can only handle matrices:

Error in colMedians(x) : Argument ‘x’ must be a matrix

For that reason, we have to convert our data.frame to the matrix format first:

data_mat <- as.matrix(data)                                  # Convert data.frame to matrix

And then we can apply colMedians…

colMedians(data_mat)                                         # No colMedians error anymore
# X1 X2 X3 X4 
# 13 13  5 11

…and rowMedians without any problems:

rowMedians(data_mat)                                         # No rowMedians error anymore
# 7.0 13.5 13.0

 

Video: How to Sum a Variable by Group in R [dplyr R Package]

Sometimes you might want to calculate row and column sums by group, i.e. not for all values of your data. In the following video tutorial of the thatRnerd YouTube channel, the speaker explains how to sum variables by group in the R programming language.

Instead of the functions that we have learned before, he is using functions of the dplyr package.

Have fun with the video and let me know in the comments, in case you have any further questions or remarks!

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Further Reading

 

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6 Comments. Leave new

  • How do you do sum and find the mean with an existing dataframe? One that you did not create.

    Reply
    • Hey Sarah,

      You can basically apply the same R code to real data as shown in this tutorial. Just replace the data frame name in the R syntax.

      Regards,

      Joachim

      Reply
  • Hallo Joachim, ich weiß, wie ich eine neue Skala erstelle, die aus verschiedenen spalten bestehen soll. Wenn ich einen Mittelwert haben will dann sieht das ganze bei mir so aus.

    Ich erstelle einen Dataframe und bilde dann den Mittelwert indem ich durch die Anzahl der spalten teile.

    Aber was wenn eine Person in einer spalte keine Angabe gemacht hat, also ein NA besteht?

    Dann müsste ich ja in meinen Beispiel nicht mehr durch 4 teilen sondern durch 3, wie schaffe ich das, dass ich das nicht hart reincode, sondern R das automatisch erkennt?

    skalamittelwert <-
    c("spalte1", "spalte2", "spalte3", "spalte4")

    meine_tabelle$skalamittelwert <-
    (meine_tabelle$spalte1 + meine_tabelle$spalte2 + meine_tabelle$spalte1 + meine_tabelle$spalte1) /4

    mit rowSums und means habe ich es versucht ich bekomme es nicht hin, google sagt :

    How to count missing value in R
    sum(is.na 2.7k(tabelle$spalte)

    allerdings weis ich nicht was 2.7k bedeutet soll und auch sonst bekomme ich es so nicht hin.

    Ich wäre super dankbar für deine Hilfe

    Reply
    • Hi Danny,

      hast du dir Beispiel 3 in diesem Tutorial angeschaut?

      Wenn ich deine Frage richtig verstehe, wird sie durch dieses Beispiel beantwortet.

      Viele Grüße

      Joachim

      Reply
      • Lieber Joachim,

        ich habe es mir davor bereits angesehen gehabt. Leider beantwortet es meine Frage nicht ganz oder ich bin nicht in der Lage es umzusetzen. In meinem Beispiel habe ich einen Datensatz, in dem ich viel mehr Variablen zb. 100 und aus den möchte ich nur 10 herausnehmen und in diesen 10 Spalten sind für manche Personen gar keine NAs, bei anderen sind NAs in zwei Spalten und bei wieder anderen nur in einer.

        Ich glaube ich bin leider zu unerfahren um die Zusammenhänge zu verstehen. Es fängt ja schon damit an, dass ich eine Tabelle habe aus der ich erst einige Spalten ziehe um einen Vektor zu erstellen und dann erstelle ich eine Spalte und Teile diese durch die Anzahl.

        Ich versuche es weiter und danke nochmal für die schnelle Antwort.

        Reply
        • Keine Sorge, das ging allen am Anfang so! 🙂

          Ich denke weiterhin, dass das Beispiel 3 deine Frage beantworten sollte (sofern ich dich richtig verstehe).

          Ich würde folgendermaßen vorgehen:

          Schritt 1) Extrahiere die Spalten, die du für deine Analyse verwenden möchtest:

          data_subset <- data[ , c("Spaltenname1", "Spaltenname2", "Spaltenname3")]

          Mehr Informationen findest du hier: https://statisticsglobe.com/extract-certain-columns-of-data-frame-in-r

          Schritt 2) Berechne den Mittelwert für alle Zeilen und schließe hierbei fehlende Werte aus:

          rowSums(data_subset, na.rm = TRUE)

          Weitere Informationen findest du in diesem Tutorial in Beispiel 3.

          Gib Bescheid, ob es geklappt hat bzw. ob du noch Fragen hast!

          Viele Grüße

          Joachim

          Reply

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