Scale relative to a value in each group (via dplyr)

Question

I have a set of time series, and I want to scale each of them relative to their value in a specific interval. That way, each series will be at 1.0 at that time and change pr

Answer 1

This solution is very similar to @thelatemail, but I think it's sufficiently different enough to merit its own answer because it chooses the index based on a condition:

data %>%
    group_by(category) %>%
    mutate(value = value/value[year == baseYear])

#   category  year      value
#...     ...   ...       ...
#7         A  2002 1.00000000
#8         B  2002 1.00000000
#9         C  2002 1.00000000
#10        A  2003 0.86462789
#11        B  2003 1.07217943
#12        C  2003 0.82209897

(Data output has been truncated. To replicate these results, set.seed(123) when creating data.)

Answer 2

Something like this:

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

Result:

#   category year     value
#1         A 2000 1.0000000
#2         A 2001 0.2882984
#3         A 2002 1.5224308
#4         A 2003 0.8369343
#5         A 2004 2.0868684
#6         A 2005 0.2196814
#7         B 2000 1.0000000
#8         B 2001 0.5952027

Answer 3

Use first in dplyr, ensuring you use order_by

data %>% 
  group_by(category) %>% 
  mutate(value = value / first(value, order_by = year))