You can use the **lag()** function from the dplyr package in R to calculated lagged values.

This function uses the following basic syntax:

**lag(x, n=1, …)**

where:

**x**: vector of values**n**: number of positions to lag by

The following example shows how to use this function to calculated lagged values in practice.

**Example: Calculating Lagged Values in R**

Suppose we have the following data frame in R that shows the number of sales made by some store on 10 consecutive days:

**#create data frame
df <- data.frame(day=1:10,
sales=c(18, 10, 14, 13, 19, 24, 25, 29, 15, 18))
#view data frame
df
day sales
1 1 18
2 2 10
3 3 14
4 4 13
5 5 19
6 6 24
7 7 25
8 8 29
9 9 15
10 10 18**

We can use the **lag()** function from the dplyr package to create a lag column that displays the sales for the previous day for each row:

**library(dplyr)
#add new column that shows sales for previous day
df$previous_day_sales <- dplyr::lag(df$sales, n=1)
#view updated data frame
df
day sales previous_day_sales
1 1 18 NA
2 2 10 18
3 3 14 10
4 4 13 14
5 5 19 13
6 6 24 19
7 7 25 24
8 8 29 25
9 9 15 29
10 10 18 15**

Here’s how to interpret the output:

- The first value in the lag column is
**NA**since there is no prior value in the sales column. - The second value in the lag column is
**18**since this is the prior value in the sales column. - The third value in the lag column is
**10**since this is the prior value in the sales column.

And so on.

We can also modify the value for the **n** argument in the **lag()** function to calculate a lagged value for a different number of previous positions:

**library(dplyr)
#add new column that shows sales for two days prior
df$previous_day_sales <- dplyr::lag(df$sales, n=2)
#view updated data frame
df
day sales previous_day_sales
1 1 18 NA
2 2 10 NA
3 3 14 18
4 4 13 10
5 5 19 14
6 6 24 13
7 7 25 19
8 8 29 24
9 9 15 25
10 10 18 29
**

**Note**: To create a lead column, use the **lead()** function from the dplyr package instead of the **lag()** function.

**Additional Resources**

The following tutorials explain how to use other common functions in R:

How to Use the n() Function in dplyr

How to Use the across() Function in dplyr

How to Use the relocate() Function in dplyr

How to Use the slice() Function in dplyr