How to do this calculation in R? -

i have dataset looks this:

groups <- c(1:20) values1 <- c(1,3,2,4,2,5,1,6,2,7,3,5,2,6,3,5,1,5,3,4) values2 <- c(3,2,4,1,5,2,4,1,3,2,6,1,4,2,5,3,7,1,4,2) sample.data <- data.frame(groups,values1,values2) head(sample.data)    groups values1 values2 1      1       1       3 2      2       3       2 3      3       2       4 4      4       4       1 5      5       2       5 6      6       5       2 

description: dataframe has 20 sets of values (values1 , values2) of 20 different groups. numbers follow these patterns: (1) numbers alternatively highs , lows (2) if number in values1 column higher 2 adjacent numbers, corresponding number in values2 column lower 2 adjacent numbers (as can seen in sample)

what want calculate these numbers called "delta value" (dv) - calculated differences between high-value number , mean of 2 low-value numbers adjacent it. example, first 3-number set of values1 column (1,3,2), first dv 3-((2+1)/2)=1.5; take last number of set first number of next set, same calculation, second dv 4-((2+2)/2)=2; , on. record of these dv values values1 column in new column called dv1. same thing values2 column , record dv values in new dv2 column. note in values2 column, first number higher second one, ignore , start calculation next 3-number set (2,4,1) , repeat calculation above.

so how execute calculation in r?

thank in advance!

p/s: sorry, long question think it's got required information calculation.

because want use dv values plotting, might easiest twice, once each column

library(dplyr) sample.data1 <- sample.data %>%    mutate(alt = row_number()%%2,          mean_adj_1 = (lag(values1) + lead(values1))/2,          dv1 = (values1 - mean_adj_1) * (1-alt)) %>%    filter(alt == 0 & !is.na(dv1)) %>%    select(-c(alt:mean_adj_1))  sample.data.2 <- sample.data %>%    mutate(alt = row_number()%%2,          mean_adj_2 = (lag(values2) + lead(values2))/2,          dv2 = (values2 - mean_adj_2) * alt) %>%    filter(alt == 1 & !is.na(dv2)) %>%    select(-c(alt:mean_adj_2)) 

explanation: done in fewer steps way can see what's going on. mean_adj columns mean of row before , after, , alt column lets put zeroes in rows don't want dv calculated for. the dv column value - mean_adj.

result:

 > sample.data.1   groups values1 values2 dv1 1      2       3       2 1.5 2      4       4       1 2.0 3      6       5       2 3.5 4      8       6       1 4.5 5     10       7       2 4.5 6     12       5       1 2.5 7     14       6       2 3.5 8     16       5       3 3.0 9     18       5       1 3.0 > sample.data.2   groups values1 values2 dv2 1      3       2       4 2.5 2      5       2       5 3.5 3      7       1       4 2.5 4      9       2       3 1.5 5     11       3       6 4.5 6     13       2       4 2.5 7     15       3       5 2.5 8     17       1       7 5.0 9     19       3       4 2.5