how to assign 0 to na in r

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R – Replace Zero (0) with NA on Dataframe Column

• Post author: Naveen (NNK)
• Post category: R Programming
• Post last modified: February 7, 2023

By using methods from R base, dplyr and tidyr packages we can replace zero (0) with NA value on dataframe columns. In this article, I have covered 10 ways to replace. Also, I have covered replace 0 with NA on a single column, multiple columns and by index position in an R dataframe.

1. Quick Examples of Replace Zero with NA Value

Following are quick examples of how to replace zero with NA values in an R Dataframe.

Let’s create an R dataframe, run these examples and validate the results.

2. Replace 0 with NA in an R Dataframe

As you saw above R provides several ways to replace 0 with NA on dataframe, among all the first approach would be using the directly R base feature. Use df[df==0] to check if the value of a dataframe column is 0, if it is 0 you can assign the value NA . The below example replaces all 0 values on all columns with NA.

Related: Refer to this article, if you wanted to replace NA with 0 before processing your data.

This is the most generic approach where you can use this on vector as well to replace its values.

3. Replace using Negation

Alternatively, you can also achieve using negation and is.na() function.

Yields the same output as above

4. Replace Selected Columns

When you have multiple columns in R dataframe and you would require to select a single column to replace 0 with NA, you can achieve this by following. This updates only column pages .

5. Using R replace() function to update 0 with NA

R has a built-in function called replace() that replaces values in a vector with another value, for example, zeros with NAs.

6. Update 0 with NA using R dplyr::na_if()

All previous examples use the Base R built-in functions that can be used on a smaller dataset but, for bigger data sets, you have to use methods from dplyr package as they perform 30% faster. dplyr package uses C++ code to evaluate.

The  dplyr  is third-party package that is required to install first using  install.packages('dplyr')  and load it using  library("dplyr") . na_if() is a method from dplyr package.

7. Update 0 with NA using dplyr::mutate_all()

mutate_all() is another method in dplyr package to substitute the zero with NA value on all dataframe columns.

8. Replace on All Numeric columns

mutate_if() affects variables selected with a predicate function, here is.numeric is used as a predicate to replace values only on numeric columns. Since we have all numeric columns, it updates all columns with NA for value 0.

Yields the same output as above.

9. Replace Zero with NA Only on Selected Columns

mutate_at() affects variables selected with a character vector or vars() . Here we update values only on pages column.

10. Replace Zero with NA on Selected Column Indexs

If you pass a vector with index position to mutate_at() , it replaces all NA with 0 on selected index position columns in R dataframe. This updates index 2 which is chapters column. Note that in R the index starts from 1.

11. Replacing on tibble

If you have tibble, use the following approach to replace it. Use tibble() to create a tibble.

12. Conclusion

In this article, I have covered 10 ways to replace the zero with NA value in R dataframe. Also, I have covered how to replace on a single column, multiple columns, and columns by index positions using R base function and dplyr package methods.

Related Articles

• How to Replace NA with 0 in Multiple R Dataframe Columns?
• How to Replace NA with Empty String in a R Dataframe?
• R – Replace Empty String with NA
• How to Replace Character in a String
• How to Replace Column Value with Another Column
• R dplyr::mutate() – Replace Column Values
• How to Replace String with Another String or Character
• How to Replace Values Based on Condition
• R – str_replace() to Replace Matched Patterns in a String.
• How to  Replace NA with 0 in Multiple Columns
• https://dplyr.tidyverse.org/reference/mutate_all.html

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R Data Frame – Replace NA with 0

Replace na with 0 in r data frame.

In this tutorial, we will learn how to replace all NA values in a data frame with zero number in R programming.

To replace NA with 0 in an R data frame, use is.na() function and then select all those values with NA and assign them to 0.

The syntax to replace NA values with 0 in R data frame is

• myDataframe is the data frame in which you would like replace all NAs with 0.
• is , na are keywords.

Example 1 – Replace NAs with 0 in R Data Frame

In this example, we will create an R data frame, DF1, with some of the values being NA.

Now use the function is.na() with the data frame DF1 .

In this R Tutorial , we have learnt how to replace all NA values in a data frame with zero number.

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Replace na with zero in r.

Posted on June 28, 2022 by Jim in R bloggers | 0 Comments

The post Replace NA with Zero in R appeared first on Data Science Tutorials

Replace NA with Zero in R, Using the dplyr package in R, you can use the following syntax to replace all NA values with zero in a data frame.

Substitute zero for any NA values.

Create new variables from existing variables in R – Data Science Tutorials

To replace NA values in a particular column of a data frame, use the following syntax:

In column col1, replace NA values with zero.

Additionally, you can substitute a NA value in one of a data frame’s several columns using the following syntax.

Test for Normal Distribution in R-Quick Guide – Data Science Tutorials

in columns col1 and col2, replace NA values with zero

With the help of the following data frame, the following examples demonstrate how to utilize these functions in practice.

Let’s create a data frame

Now we can view the data frame

How to Filter Rows In R? – Data Science Tutorials

Example 1: Replace every NA value across all columns.

Replace all NA values across all columns of a data frame by running the code below.

Yes, now we will replace all NA values with zero

Let’s view the data frame

Example 2: In a Specific Column, Replace NA Values

The code below demonstrates how to change NA values in a particular column of a data frame.

One sample proportion test in R-Complete Guide (datasciencetut.com)

replace NA values with zero in position column only

Example 3: Replace any columns with NA values.

The code that follows demonstrates how to change NA values in one of a data frame’s many columns.

Now we can replace NA values with zero in position and points columns

What Is the Best Way to Filter by Date in R? – Data Science Tutorials

Using the dplyr package in R, you can use the following syntax to replace all NA values with zero in a data frame.

How to make a rounded corner bar plot in R? – Data Science Tutorials

With the help of the following data frame, the following examples demonstrate how to utilize these functions in practice:

Best Data Science YouTube Tutorials Free to Learn – Data Science Tutorials

library(dplyr)

The code below demonstrates how to change NA values in a particular column of a data frame:

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R Replace NA with 0 (10 Examples for Data Frame, Vector & Column)

A common way to treat missing values in R is to replace NA with 0.

You will find a summary of the most popular approaches in the following.

Choose one of these approaches according to your specific needs.

What are you interested in?

R Replace NA with 0 in a Data Frame

Consider the following example data frame in R.

Table 1: Exemplifying Data Frame with Missing Values

I’m creating some duplicates of the data for the following examples.

Data Frame Example 1: The Most Common Way to Replace NA with 0

In this video, I’m applying our is.na() approach of Example 1 to a real data set (and a vector as shown later).

Data Frame Example 2: Replace NA During the Data Export

Data frame example 3: dplyr package, data frame example 4: imputets package, data frame example 5: database with factor variables.

One common issue for replacing NA with 0 in an R database is the class of the variables in your data.

The previous examples work fine, as long as we are dealing with numeric or character variables.

However, if you have factor variables with missing values in your dataset, you have to do an additional step.

Insert Zeros for NA Values in an R Vector (or Column)

As you have seen in the previous examples, R replaces NA with 0 in multiple columns with only one line of code. However, we need to replace only a vector or a single column of our database. Let’s find out how this works.

First, create some example vector with missing values.

Vector Example 1: The Most Common Way to Replace NA in a Vector

Vector example 2: create your own function to replace na’s fun_zero <- function ( vector_with_nas ) { vector_with_nas [ is . na ( vector_with_nas ) ] <- 0 return ( vector_with_nas ) } vec_2 <- fun_zero ( vec_2 )   vector example 3: using the replace() function, vector example 4: using the ifelse() function, vector example 5: exchange na’s with zero in factor vectors.

As you can see, there are many different ways in R to replace NA with 0 – All of them with their own pros and cons.

If you want to investigate even more possibilities for a zero replacement, I can recommend the following thread on stackoverflow .

Is the Replacement of NA’s with 0 Legitimate?

Beside the question how to find and replace NA with 0 in R, the question arises whether such a replacement screws our statistical data analyses.

As most of the time in statistics, the answer is: It depends! If it is meaningful to substitute NA with 0, then go ahead.

For instance, let’s say we have the item “How much did you spend for holidays last year?” and people without any spending for holidays are represented by NA. Then it would be logical to change NA to 0, since these people basically spend zero money for holidays.

However, if we have NA values due to item nonresponse, we should never replace these missing values by a fixed number, i.e. 0.

Consider the following example:

Graphic 1: R Replace NA with 0 – Densities with & without Zero-Replacement

As you can see in the example, the density of a normal distribution would be highly screwed toward zero, if we just substitute all missing values with zero (as indicated by the red density).

Alternatives to the Replacement of Missing Data by 0

The statistical analysis with missing data is a whole domain of statistical research.

The imputation of missing values is one of the most popular approaches nowadays.

When data is imputed, new values are estimated on the basis of imputation models in order to replace missing values by these estimates.

In fact, the replacement of NA’s with zero could also be considered as a very basic data imputation (zero imputation).

Another popular approach is casewise deletion (also called listwise deletion).

In casewise or listwise deletion, all observations with missing values are deleted – an easy task in R .

This approach has its own disadvantages, but it is easy to conduct and the default method in many programming languages such as R.

To change NA to 0 in R can be a good approach in order to get rid of missing values in your data.

The statistical software R (or RStudio) provides many ways for the replacement of NA’s.

However, such a replacement should only be conducted, if there is a logical reasoning for converting NA’s to zero.

Now it’s Your Turn!

I put together 10 different ways how to replace NA’s with 0 in R.

Now I’m interested to hear from you.

Are you handling NA’s with the popular approaches of Data Frame Example 1 and Vector Example 1 ? Or are you using other ways? Do you still have any issues with your NA’s?

Let me know in the comments !

Moritz, S. (2017). Package imputeTS

Wickham, H., Francois, R., Henry, L., Müller, K., and RStudio (2017). Package dplyr

How to create the header graph:

The header graphic of this page shows a correlation plot of two continuous (i.e. numeric) variables, created with the package ggplot2 .

The dark blue dots indicate observed values. The light blue dots indicate NA’s that were replaced by zero.

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12 Comments . Leave new

I simply desired to say thanks once more. I’m not certain the things that I could possibly have used without the entire aspects revealed by you over such subject matter. It seemed to be the alarming dilemma in my opinion, but discovering the very professional fashion you solved it took me to cry for fulfillment. Extremely grateful for this service as well as pray you are aware of a great job that you’re undertaking educating the others through your webblog. Most likely you have never come across all of us.

Thanks for the kind words Ahmad. I’m glad to hear that I could help you!

Thank you so so much!!! It helped

Thanks for your feedback DK, I’m glad to hear that.

This is brilliant! Thank you for taking the time to put together such a well versed set of examples.

Thank you very much Mauricio!

hello, could you provide any assistance with merging data frames. I had a look at your page about it but this particular scenario doesn’t come up. I have 2 dataframes: 1. x y a A1 blue b A2 N/A c A3 yellow 2. x y a A1 N/A b A2 red c A3 N/A

each dataframe fills in the holes in the other (the N/As). I’ve tried rbind, cbind, etc… but they only seem to try and add extra columns or rows. I just want to fill in those blanks by merging the dataframes. I hope there is an easy way to do this… there is in excel, but that’s too slow.

Any guidance appreciated, but no problem if not!

Is the following R code what you are looking for?

Please note that this only works in case both data frames contain exactly the same values in x, and in case both data frames are ordered the same way.

I hope that helps!

Dear Joachim,

Thanks for the explanations. However, in my case, I would like to replace randomly 1000 NA values in a column with 0s. None of your examples provide an exact solution for this task. Could you please suggest one? I think it would be helpful for me and for other people as well.

Hey Filipe,

Thank you for the kind comment.

Below, I have created an example that explains how to do that:

Regards, Joachim

Thank you, excellent explanation

Thanks a lot Marcos, glad you think so!

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I’m Joachim Schork. On this website, I provide statistics tutorials as well as code in Python and R programming.

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Replace NA with 0 (zero) in R | 5 Methods (with code)

• Sep 11, 2023
• 6 Minutes Read
• By Abhisek Ganguly

Missing data is a common issue when working with datasets. In many cases, dealing with missing values is a critical step in data preprocessing, as it can significantly impact the results of your analysis. One approach to handling missing data is to replace NA (Not Available) values with 0s. In this article, we will explore various methods and techniques for replacing NAs with 0s in the R programming language.

Understanding Missing Data in R

Before we dive into the methods for replacing NAs with 0s, let's first understand why missing data is a concern and why choosing the right strategy is crucial.

Missing data can arise for various reasons, such as equipment failures, human errors in data entry, or simply because certain data points are not applicable to some observations. Ignoring missing data or improperly handling it can lead to biased results, reduced statistical power, and incorrect conclusions in your analyses.

R represents missing data with the special value `NA`, which stands for "Not Available." It is essential to address NAs appropriately to ensure the integrity of your data analysis.

Different Methods to Replace NA with 0s

R provides various different methods for us to replace NAs present in the dataset with 0s, all have some different use cases and can be used in different scenarios depending on our needs.

Let us discuss them one by one.

Method 1: Using the is.na() Function

One straightforward method to replace NAs with 0s in R is by using the is.na() function in combination with indexing. Here's how you can do it:

The ` is.na(data) ` function used in this example returns a logical vector containing TRUE for NAs and FALSE for non-missing values inside the vector. This logical vector is then used to index the data vector, and all the NAs are replaced with 0s. 

Method 2: Using the replace() Function

The replace() function is used for replacing NAs with zeros in R. You can employ this function to swap out values in a vector when a specific condition is met. Let's see how we can apply it to address NA replacements:

The `replace()` function takes three arguments: the input vector, the condition (in this case, `is.na(data)` ), and the value to replace with (0).

Method 3: Using the na.replace function from dplyr 

If you are working with data frames, the `dplyr` package provides a convenient function called na.replace() to replace NAs with specific values. Here's how we can use it:

The ` na.replace() ` function from `dplyr` replaces NAs in all columns of the data frame with the specified value (0 in this case)

Method 4: Using the complete.cases() Function

In some cases, you may want to replace NAs only in specific columns or rows. To achieve this, you can use the complete.cases() function to identify rows with missing data and then replace NAs in those rows with 0s. Here's an example:

In this example, we first use the ` complete.cases() ` function to identify rows with missing values in the data frame. Then, we use indexing to replace NAs in specific rows (A and B) with 0s.

Method 5: Using the zoo Package

The zoo package provides advanced tools for handling time series data, including the ability to replace NAs efficiently. Here's how you can use the zoo package to replace NAs with 0s in a vector:

The ` na.locf() ` function from the zoo package replaces NAs with the last non-NA value in the vector, effectively filling in the missing values with 0s. 

Replacing NAs with 0s in R is a common data preprocessing task that ensures missing data does not adversely affect your analyses. In this article, we explored various methods and techniques for accomplishing this task, ranging from basic indexing and logical conditions to more advanced approaches using packages like `dplyr` and `zoo`. Remember that the choice of method depends on your specific dataset and analysis needs.

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3 Ways to Replace NA’s with Zeros in R [Examples]

In this article, we discuss how to replace NA’s (i.e., missing values) with zeros in R.

There are many reasons why a dataset might have missing values, for example, due to missing responses in a survey or NA´s in imported data. Similarly, you can replace missing values with many values, such as the mean, median , or mode . Nevertheless, zeros are probably the most common option.

In R, you replace NA’s with zeros using either basic R code, the COALESCE() function, or the REPLACE_NA() function. All are easy to understand, but the COALESCE() and the REPLACE_NA() function have the advantage that they form part of the tidyverse.

In this article, we discuss all 3 methods using clear examples. We also show how to use the REPLACE_NA() function in combination with the MUTATE_ALL() and MUTATE_AT() functions to replace missing values in those columns that meet a specific condition.

3 Ways to Replace Missing Values with Zeros

1. replace na’s with zeros using r base code.

The classic way to replace NA’s in R is by using the IS.NA() function.

The IS.NA() function takes a vector or data frame as input and returns a logical object that indicates whether a value is missing (TRUE or VALUE). Next, you can use this logical object to create a subset of the missing values and assign them a zero.

For example:

You can also use the IS.NA() function to identify and replace missing values in a data frame.

Although the IS.NA() function is intuitive, it is also a relatively slow function . Moreover, IS.NA() always overwrites the existing vector or data frame. On top of that, you can’t use this function in combination with tidyverse syntax.

2. Replace NA’s with Zeros using the COALESCE() Function

Alternatively, you can use the COALESCE() function to substitute missing values.

The COALESCE() function is part of the dplyr package and returns the first non-missing value of its arguments. Therefore, you can combine the MUTATE_ALL() function with the COALESCE() function, whose second argument is a zero, to replace all NA’s with zeros.

This R code shows an example.

The COALESCE() function has two main benefits, namely, it’s quick and works with the pipe operator.

3. Replace NA’s with Zeros using the REPLACE_NA() Function

The easiest and most versatile way to replace NA’s with zeros in R is by using the REPLACE_NA() function.

The REPLACE_NA() function is part of the tidyr package, takes a vector, column, or data frame as input, and replaces the missing values with a zero. This function has the advantage that it is fast, explicit and part of the tidyverse package.

The next example shows how to apply the REPLACE_NA() function.

Although we’ve used REPLACE_NA() to replace missing values with zeros, its purpose is not limited to this operation. You can use the REPLACE_NA() function also to substitute NA´s with other values.

Replace Missing Values with Zeros in Multiple Columns

Above, we have demonstrated how to replace all missing values in a data frame. However, this might not be also necessary. Instead, you might want to substitute the NA´s in just some columns .

Therefore, in this section, we explain how to use the MUTATE_AT() function and REPLACE_NA() function to replace missing values in those columns that meet a specific condition .

Replace NA’s based on Column Position

First, we discuss how to replace NA´s in columns based on their position. For example, the first, third, or eighth column.

One Position

Replacing the missing values with zeros in one column given its position is easy. You use the REPLACE_AT() function and provide two arguments, namely:

• The position of the column in the dataframe in which you want to replace the NA´s.
• The REPALCE_NA() funcion to replace the NA’s with a zero.

The R code below shows an example to replace all missing values in the first column.

Multiple Positions

Instead, you can use the MUTATE_AT() function also to replace missing values in multiple columns given their positions .

These are the steps.

• First, you create a vector with the positions of the columns with the c() function.
• Then, you use this vector as the first argument of the MUTATE_ALL() function.
• Finally, you use the REPLACE_NA() function to replace the NA´s with zeros.

In the R code below, we substitute the missing values in columns, 1, 3, 4, 5, and 8.

Replace NA’s based on Column Name

Alternatively, you can also replace missing values in a data frame based on the column names .

Below, we show a variety of options to identify columns using their names. But first, we create a vector and assign each column a name.

In general, to replace NA´s with zeros in columns given their names, you need the VARS() function . This function helps R to select columns based on their names.

To replace missing values in one column based on its name, you need to provide the column name to the VARS() function. For example, vars(“AAA”) . Then, you use this as the first argument of the MUTATE_AT() function. Finally, you use the REPLACE_NA() function as the second argument of the MUTATE_AT() function to replace the NA´s with zeros.

Below we show an example.

Multiple Names

Instead, you can provide the VARS() function with multiple column names and replace NA´s in different columns at once . However, it is important to define the names of the columns in the c() function. You can’t write different column names in the VARS() function directly.

For example, below we replace the missing values with zeros in five columns based on their names.

Names Containing Specific Characters

Alternatively, you can also replace NA´s in those columns where the column name meets a specific condition .

For example, you can use the CONTAINS() function to identify all columns that have a specific pattern of characters. In the R code below, we replace the NA´s with zeros only in those columns that contain the characters “AB”.

Note that, the CONTAINS() function is case sensitive . In other words, “AB” and “ab” are not the same.

Names Starting with Specific Characters

Instead of looking for a pattern of characters throughout the entire column name, you can use the STARTS_WITH() function to replace NA´s with zeros in those columns that start with a specific character(s) .

In the example below, we use the STARTS_WITH() function to identify all columns that start with “BB”. Next, we use the VARS() function to obtain the column positions in the data frame. Lastly, we use the REPLACE_NA() function to replace the missing values with zeros.

Names Ending with Specific Characters

Like the STARTS_WITH() function, R also offers the ENDS_WITH() function.

You can use the ENDS_WITH() function to identify column names that end with a specific pattern of characters and replace NA´s only in these columns.

For example, below we substitute missing values with zeros only in the columns whose names end with “BB”.

Replace NA’s based on Column Type

Instead of replacing NA’s in columns based on their position or name, you can also replace missing values in columns of a specific type . For example, integer columns, character columns, or factor columns. In this section, we explain how.

But first, we create a data frame with different types of columns.

In contrast to previous sections, we won’t use the MUTATE_AT() function. Instead, we will use the MUTATE_IF() function.

Numeric Columns

To replace missing values only in the numeric columns, you can use the is.numeric function. This function identifies the numeric columns and returns their positions within a data frame.

You can use the is.numeric function as the first argument of the MUTATE_IF() function. If you use the REPLACE_NA() function as the second argument, then you can replace the NA’s with a zero in all numeric columns.

Note that R distinguishes between numeric and integer data types. However, if you use the is.numeric function, then R returns both numeric and integer columns.

Integer Columns

Instead, if you only want to replace the missing values in integer columns, you can use the is.integer function as the first argument of the MUTATE_IF() function.

The next R code shows how to use the is.integer function to replace missing values.

Character Columns

Like the is.numeric and is.integer , R also provides the is.character function.

The is.character identifies the position of character columns in a data frame and can help you to replace NA’s only in character columns. These are the steps:

• Start the MUTATE_IF() function.
• Use the is.character function as the first argument.
• Use the REPLACE_NA() function as the second argument to replace NA’s with zeros.

Factor Columns

Probably the most difficult is to replace NA’s with zeros in a factor column and consider the 0 as a factor level.

Nevertheless, these are the steps:

• Identify all factor columns in a data frame with the SAPPLY function.
• Convert all factor columns to a character column with the LAPPLY function.
• Replace the NA’s in converted factor columns with zeros using the REPLACE_NA() function.
• Reconvert the original factor columns into factor columns again.

The R code below shows all the steps.

Note that, these steps don’t affect the character column.

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Replace 0 with NA in R DataFrame

In this article, we are going to discuss how to replace 0 with NA values in dataframe in R Programming Language.

NA stands for Null values which can represent Null data / Null elements in a dataframe. The task can be achieved by first defining a dataframe that contains 0 as values. Then we can replace 0 with NA by using index operator [].

dataframe[dataframe== 0] = NA

• dataframe is the input dataframe
• In index we are checking if the value is 0, if it is 0 then we are replacing it as NA

Example: Replacing 0 with NA for integer data

Example: R program to create a dataframe with strings along with 0 and replace 0 with NA

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