dplyr broadcasting single value per group in mutate

Question

I am trying to do something very similar to Scale relative to a value in each group (via dplyr) (however this solution seems to crash R for me). I would like to replicate a single value for each group and add a new column with this value repeated. As an example I have

library(dplyr)

data = expand.grid(
  category = LETTERS[1:2],
  year = 2000:2003)
data$value = runif(nrow(data))

data

  category year     value
1        A 2000 0.6278798
2        B 2000 0.6112281
3        A 2001 0.2170495
4        B 2001 0.6454874
5        A 2002 0.9234604
6        B 2002 0.9311204
7        A 2003 0.5387899
8        B 2003 0.5573527

And I would like a dataframe like

data

  category year     value    value2
1        A 2000 0.6278798 0.6278798
2        B 2000 0.6112281 0.6112281
3        A 2001 0.2170495 0.6278798
4        B 2001 0.6454874 0.6112281
5        A 2002 0.9234604 0.6278798
6        B 2002 0.9311204 0.6112281
7        A 2003 0.5387899 0.6278798
8        B 2003 0.5573527 0.6112281

i.e. the value for each category is the value from year 2000. I was trying to think of a general solution extensible to a given filtering criteria, i.e. something like

data %>% group_by(category) %>% mutate(value = filter(data, year==2002))

however this does not work because of incorrect length in the assignment.

Answer 1

Do this:

data %>% group_by(category) %>%
  mutate(value2 = value[year == 2000])

You could also do it this way:

data %>% group_by(category) %>%
  arrange(year) %>%
  mutate(value2 = value[1])

or

data %>% group_by(category) %>%
  arrange(year) %>%
  mutate(value2 = first(value))

or

data %>% group_by(category) %>%
  mutate(value2 = nth(value, n = 1, order_by = "year"))

or probably several other ways.

Your attempt with mutate(value = filter(data, year==2002)) doesn't make sense for a few reasons.

1. When you explicitly pass in data again, it's not part of the chain that got grouped earlier, so it doesn't know about the grouping.

2. All dplyr verbs take a data frame as first argument and return a data frame, including filter. When you do value = filter(...) you're trying to assign a full data frame to the single column value.