You can use the following syntax to calculate lagged values by group in R using the dplyr package:

df %>% group_by(var1) %>% mutate(lag1_value = lag(var2, n=1, order_by=var1))

**Note**: The mutate() function adds a new variable to the data frame that contains the lagged values.

The following example shows how to use this syntax in practice.

**Example: Calculate Lagged Values by Group Using dplyr**

Suppose we have the following data frame in R that shows the sales made by two different stores during various days:

#create data frame df <- data.frame(store=c('A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'), sales=c(7, 12, 10, 9, 9, 11, 18, 23)) #view data frame df store sales 1 A 7 2 B 12 3 A 10 4 B 9 5 A 9 6 B 11 7 A 18 8 B 23

We can use the following code to create a new column that shows the lagged values of sales for each store:

library(dplyr) #calculate lagged sales by group df %>% group_by(store) %>% mutate(lag1_sales = lag(sales, n=1, order_by=store)) # A tibble: 8 x 3 # Groups: store [2] store sales lag1_sales 1 A 7 NA 2 B 12 NA 3 A 10 7 4 B 9 12 5 A 9 10 6 B 11 9 7 A 18 9 8 B 23 11

Here’s how to interpret the output:

- The first value of
**lag1_sales**is**NA**because there is no previous value for sales for store A. - The second value of
**lag1_sales**is**NA**because there is no previous value for sales for store B. - The third value of
**lag1_sales**is**7**because this is the previous value for sales for store A. - The fourth value of
**lag1_sales**is**12**because this is the previous value for sales for store B.

And so on.

Note that you can also change the number of lags used by modifying the value for **n** in the **lag()** function.

**Additional Resources**

The following tutorials explain how to perform other common calculations in R:

How to Calculate a Cumulative Sum Using dplyr

How to Calculate the Sum by Group in R

How to Calculate the Mean by Group in R