How to Interpolate Missing Values in R With Example

How to Interpolate Missing Values, In today’s world, data comes from a variety of places, is collected through numerous streams, and is then evaluated using a variety of methodologies.

In this article, we’ve discussed missing values and how to deal with them using the zoo library.

To interpolate missing values in a data frame column in R, use the following basic syntax.

library(dplyr)
library(zoo)
df <- df %>%
        mutate(column_name = na.approx(column_name))

The example below demonstrates how to utilize this syntax in practice.

Interpolate Missing Values in R as an example

Let’s say we have the following data frame in R that shows a store’s total sales for 15 days in a row:

create a data frame

df <- data.frame(day=1:15,
                 sales=c(2, 4, 9, 1, 10, 15, 2, NA, NA, 8, NA, 31, 32, 41, 45))

Now we can view the data frame

df
   day sales
1    1     2
2    2     4
3    3     9
4    4     1
5    5    10
6    6    15
7    7     2
8    8    NA
9    9    NA
10  10     8
11  11    NA
12  12    31
13  13    32
14  14    41

Notice that the data frame is lacking sales numbers for four days.

Here’s what a basic line chart to show sales over time would look like:

To visualize sales, construct a line chart.

plot(df$sales, type='o', pch=16, col='red', xlab='Day', ylab='Sales')

in R, interpolate missing values

We can use the na.approx() function from the zoo package and the modify() method from the dplyr package to fill in the missing values.

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library(dplyr)
library(zoo)

in the sales column, interpolate missing numbers

df <- df %>%
        mutate(sales = na.approx(sales))

Now we can view the updated data frame

df
     day sales
1    1   2.0
2    2   4.0
3    3   9.0
4    4   1.0
5    5  10.0
6    6  15.0
7    7   2.0
8    8   4.0
9    9   6.0
10  10   8.0
11  11  19.5
12  12  31.0
13  13  32.0
14  14  41.0
15  15  45.0

It’s worth noting that each missing value has been updated.

Here’s what it would look like if we made a new line chart to show the updated data frame:

To visualize sales, construct a line chart.

plot(df$sales, type='o', pch=16, col='green', xlab='Day', ylab='Sales')

Notice that the values are chosen by the na.approx() function seem to fit the trend in the data quite well.

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