Performing operations with lag on a dataframe to calculate a new value in R [duplicate]

Question

I have the following code, I am calculating the percent growth in the data points and then I calculate the change in the percent growth, what I am looking for is to be able to add a column where I count the number of readings where the percent growth change is negative

df <- data.frame(id = c(1,2,3,4,5,6,7,8,9,10,11,12), data = c(19, 19, 27, 27, 38, 42, 47, 48, 49, 50, 51, 53))
df <- mutate(df, pct_growth = (data - lag(data))/lag(data))
df <- mutate(df, pct_growth_change = pct_growth - lag(pct_growth))
df$pct_growth_streak <- 0
df <- dplyr::mutate(df, pct_growth_streak = ifelse(pct_growth_change <=0, lag(pct_growth_streak)+1,0) )

What I am getting as the output is the following

   id data pct_growth pct_growth_change pct_growth_streak
1   1   19         NA                NA                NA
2   2   19 0.00000000                NA                NA
3   3   27 0.42105263      0.4210526316                 0
4   4   27 0.00000000     -0.4210526316                 1
5   5   38 0.40740741      0.4074074074                 0
6   6   42 0.10526316     -0.3021442495                 1
7   7   47 0.11904762      0.0137844612                 0
8   8   48 0.02127660     -0.0977710233                 1
9   9   49 0.02083333     -0.0004432624                 1
10 10   50 0.02040816     -0.0004251701                 1
11 11   51 0.02000000     -0.0004081633                 1
12 12   53 0.03921569      0.0192156863                 0

And what I need is

   id data pct_growth pct_growth_change pct_growth_streak
1   1   19         NA                NA                NA
2   2   19 0.00000000                NA                NA
3   3   27 0.42105263      0.4210526316                 0
4   4   27 0.00000000     -0.4210526316                 1
5   5   38 0.40740741      0.4074074074                 0
6   6   42 0.10526316     -0.3021442495                 1
7   7   47 0.11904762      0.0137844612                 0
8   8   48 0.02127660     -0.0977710233                 1
9   9   49 0.02083333     -0.0004432624                 2
10 10   50 0.02040816     -0.0004251701                 3
11 11   51 0.02000000     -0.0004081633                 4
12 12   53 0.03921569      0.0192156863                 0

Answer 1

We can use rleid to create groups of consecutive streaks and calculate cumsum over it.

library(data.table)

setDT(df)[, pct_growth_streak := cumsum(pct_growth_streak), 
            rleid(pct_growth_streak)]

df
#    id data pct_growth pct_growth_change pct_growth_streak
# 1:  1   19         NA                NA                NA
# 2:  2   19 0.00000000                NA                NA
# 3:  3   27 0.42105263      0.4210526316                 0
# 4:  4   27 0.00000000     -0.4210526316                 1
# 5:  5   38 0.40740741      0.4074074074                 0
# 6:  6   42 0.10526316     -0.3021442495                 1
# 7:  7   47 0.11904762      0.0137844612                 0
# 8:  8   48 0.02127660     -0.0977710233                 1
# 9:  9   49 0.02083333     -0.0004432624                 2
#10: 10   50 0.02040816     -0.0004251701                 3
#11: 11   51 0.02000000     -0.0004081633                 4
#12: 12   53 0.03921569      0.0192156863                 0

We can use it dplyr too :

library(dplyr)

df %>%
   group_by(grp = rleid(pct_growth_streak)) %>%
   mutate(pct_growth_streak = cumsum(pct_growth_streak))

Or with ave :

with(df, ave(pct_growth_streak, rleid(pct_growth_streak), FUN = cumsum))

Answer 2

One approach: first define a grouping variable sgrp that increments with each sign change of pct_growth_change:

df %<>% mutate(sgrp = cumsum(if_else(sign(pct_growth_change) == 
                               sign(lag(pct_growth_change, 1)), 0, 1, 1)))

Then group by sgrp and set pct_growth_streak as the row number within the group if pct_growth_change is negative.

df %>% 
group_by(sgrp) %>% 
mutate(pct_growth_streak = 
    (pct_growth_change < 0) * row_number()
) %>%
ungroup() %>%
select(-sgrp);