# 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.

data <- data.frame(x1 = c(3, 7, 2, 5, 5), x2 = c(NA, 8, 6, NA, 5), x3 = c(3, NA, 5, 1, 9)) data |

data <- data.frame(x1 = c(3, 7, 2, 5, 5), x2 = c(NA, 8, 6, NA, 5), x3 = c(3, NA, 5, 1, 9)) data

**Table 1: Exemplifying Data Frame with Missing Values**

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

data_1 <- data data_2 <- data data_3 <- data data_4 <- data data_5 <- data # Example for data frame with factor variable data_5$x3 <- as.factor(data_5$x3) |

data_1 <- data data_2 <- data data_3 <- data data_4 <- data data_5 <- data # Example for data frame with factor variable data_5$x3 <- as.factor(data_5$x3)

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

data_1[is.na(data_1)] <- 0 |

data_1[is.na(data_1)] <- 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

setwd("Insert your path here") write.csv(data_2, "data_2.csv", na = "0") |

setwd("Insert your path here") write.csv(data_2, "data_2.csv", na = "0")

### Data Frame Example 3: dplyr Package

library("dplyr") data_3 <- data_3 %>% mutate(x2 = coalesce(x2, 0), x3 = coalesce(x3, 0)) |

library("dplyr") data_3 <- data_3 %>% mutate(x2 = coalesce(x2, 0), x3 = coalesce(x3, 0))

### Data Frame Example 4: imputeTS Package

library("imputeTS") data_4 <- na.replace(data_4, 0) |

library("imputeTS") data_4 <- na.replace(data_4, 0)

### 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.

i <- sapply(data_5, is.factor) # Identify all factor variables in your data data_5[i] <- lapply(data_5[i], as.character) # Convert factors to character variables data_5[is.na(data_5)] <- 0 # Replace NA with 0, as shown in Example 1 data_5[i] <- lapply(data_5[i], as.factor) # Convert character columns back to factors |

i <- sapply(data_5, is.factor) # Identify all factor variables in your data data_5[i] <- lapply(data_5[i], as.character) # Convert factors to character variables data_5[is.na(data_5)] <- 0 # Replace NA with 0, as shown in Example 1 data_5[i] <- lapply(data_5[i], as.factor) # Convert character columns back to factors

## 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.

vec <- c(1, 9, NA, 5, 3, NA, 8, 9) vec # Duplicate vector for later examples vec_1 <- vec vec_2 <- vec vec_3 <- vec vec_4 <- vec vec_5 <- as.factor(vec) # Example for factor vector |

vec <- c(1, 9, NA, 5, 3, NA, 8, 9) vec # Duplicate vector for later examples vec_1 <- vec vec_2 <- vec vec_3 <- vec vec_4 <- vec vec_5 <- as.factor(vec) # Example for factor vector

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

vec_1[is.na(vec_1)] <- 0 |

vec_1[is.na(vec_1)] <- 0

### 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)

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)

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) |

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

vec_3 <- replace(vec_3, is.na(vec_3), 0) |

vec_3 <- replace(vec_3, is.na(vec_3), 0)

### Vector Example 4: Using the ifelse() Function

vec_4 <- ifelse(is.na(vec_4), 0, vec_4) |

vec_4 <- ifelse(is.na(vec_4), 0, vec_4)

### Vector Example 5: Exchange NA’s with Zero in Factor Vectors

vec_5 <- as.numeric(as.character(vec_5)) # Note: Transform vec_5 as.character first, # otherwise you might lose the levels of your vector vec_5[is.na(vec_5)] <- 0 # Similar to Example 1 vec_5 <- as.factor(vec_5) |

vec_5 <- as.numeric(as.character(vec_5)) # Note: Transform vec_5 as.character first, # otherwise you might lose the levels of your vector vec_5[is.na(vec_5)] <- 0 # Similar to Example 1 vec_5 <- as.factor(vec_5)

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:

set.seed(765) # Set seed to make the example reproducible example_vector <- rnorm(10000) # Example vector: Normal distribution with 10000 observations example_vector[1:1000] <- NA # Insert missing values for the first 1000 observations plot(density(example_vector, na.rm = TRUE), ylim = c(0, 0.7), xlab = "Example Vector", main = "With & without replacement of NA with 0") example_vector[is.na(example_vector)] <- 0 # As in Example 1 in R: Replace NA with 0 lines(density(example_vector, na.rm = TRUE), col = "red") # Plot density of the example vector # after replacing NA's with 0 |

set.seed(765) # Set seed to make the example reproducible example_vector <- rnorm(10000) # Example vector: Normal distribution with 10000 observations example_vector[1:1000] <- NA # Insert missing values for the first 1000 observations plot(density(example_vector, na.rm = TRUE), ylim = c(0, 0.7), xlab = "Example Vector", main = "With & without replacement of NA with 0") example_vector[is.na(example_vector)] <- 0 # As in Example 1 in R: Replace NA with 0 lines(density(example_vector, na.rm = TRUE), col = "red") # Plot density of the example vector # after replacing NA's with 0

**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.

## Conclusion

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!

## References

Moritz, S. (2017). Package imputeTS

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

## Appendix

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.

library("ggplot2") # Load R package ggplot2 set.seed(9876543) # Set seed to ensure reproducibility x1 <- rnorm(2000) # Random normally distributed x1 x2 <- 2 * x1 + rnorm(2000) # Generate x2 correlated with x1 x2[1801:2000] <- 0 # Set some values of x2 to zero data_ggp <- data.frame(x1, x2) # Store x1 and x2 in a data frame colours <- c(rep(1, 1800), rep(2, 200)) # Set colours ggp <- ggplot(data_ggp, aes(x = x1, y = x2)) + # Create ggplot geom_point(aes(col = colours , size = 1.1)) + theme(legend.position = "none") ggp |

library("ggplot2") # Load R package ggplot2 set.seed(9876543) # Set seed to ensure reproducibility x1 <- rnorm(2000) # Random normally distributed x1 x2 <- 2 * x1 + rnorm(2000) # Generate x2 correlated with x1 x2[1801:2000] <- 0 # Set some values of x2 to zero data_ggp <- data.frame(x1, x2) # Store x1 and x2 in a data frame colours <- c(rep(1, 1800), rep(2, 200)) # Set colours ggp <- ggplot(data_ggp, aes(x = x1, y = x2)) + # Create ggplot geom_point(aes(col = colours , size = 1.1)) + theme(legend.position = "none") ggp

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