jebyrnes - 7 months ago 46

R Question

So, while

`lag`

`lead`

`tdf <- data.frame(time=1:5, pop=50)`

for(i in 2:5){

tdf$pop[i] = 1.1*tdf$pop[i-1]

}

which produces

`time pop`

1 1 50.000

2 2 55.000

3 3 60.500

4 4 66.550

5 5 73.205

I feel like there has to be a

`dplyr`

`tidyverse`

But, something like

`tdf <- data.frame(time=1:5, pop=50) %>%`

mutate(pop = 1.1*lag(pop))

which would have been my first guess just produces

`time pop`

1 1 NA

2 2 55

3 3 55

4 4 55

5 5 55

I feel like I'm missing something obvious.... what is it?

Note - this is a trivial example - my real examples use multiple parameters, many of which are time-varying (I'm simulating forecasts under different GCM scenarios), so, the tidyverse is proving to be a powerful tool in bringing my simulations together.

Answer

`Reduce`

(or its purrr variants, if you like) is what you want for cumulative functions that don't already have a `cum*`

version written:

```
data.frame(time = 1:5, pop = 50) %>%
mutate(pop = Reduce(function(x, y){x * 1.1}, pop, accumulate = TRUE))
## time pop
## 1 1 50.000
## 2 2 55.000
## 3 3 60.500
## 4 4 66.550
## 5 5 73.205
```

or with purrr,

```
data.frame(time = 1:5, pop = 50) %>%
mutate(pop = accumulate(pop, ~.x * 1.1))
## time pop
## 1 1 50.000
## 2 2 55.000
## 3 3 60.500
## 4 4 66.550
## 5 5 73.205
```