How to Calculate Lag by Group in R?, The dplyr package in R can be used to calculate lagged values by group using the following syntax.
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df %>% Â group_by(var1) %>% Â mutate(lag1_value = lag(var2, n=1, order_by=var1))
The data frame containing the lagged values gains a new variable as a result of the mutate() procedure.
The usage of this syntax in practice is demonstrated by the example that follows.
How to Calculate Lag by Group in R?
Assume we have the following R data frame, which displays the sales generated by two separate stores on various days.
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Let’s create a data frame
df <- data.frame(store=c('Store1', 'Store2', 'Store1', 'Store2', 'Store1', 'Store2', Store1', 'Store2'),sales=c(1057, 1212, 1560, 459, 1259, 4511, 28718, 789523))
Now we can view the data frame
df
store sales 1 Store1  1057 2 Store2  1212 3 Store1  1560 4 Store2   459 5 Store1  1259 6 Store2  4511 7 Store1 28718 8 Store2 789523
The new column that displays the lagged values of sales for each retailer may be made using the code below:
library(dplyr)
Let’s calculate the lagged sales by group
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df %>% Â group_by(store) %>% Â mutate(lag1_sales = lag(sales, n=1, order_by=store))
store  sales lag1_sales  <chr>  <dbl>     <dbl> 1 Store1  1057        NA 2 Store2  1212        NA 3 Store1  1560      1057 4 Store2   459      1212 5 Store1  1259      1560 6 Store2  4511       459 7 Store1 28718      1259 8 Store2 789523      4511
How to interpret the result is as follows:
Due to the absence of a prior sales value for the store Store1A, the first value of lag1 sales is NA.
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Due to the absence of a previous sales value for store 2, the second value of lag1 sales is NA.
Because 1057 was store 1’s prior sales figure, it is the third value of lag1 sales.
Due to store 2’s prior sales value of 1212, the fourth value of lag1 sales is 1212.
so forth.
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Keep in mind that by altering the value for n in the lag() method, you can also adjust the number of lags that are used.