You can use the lag() function from the 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.