Get column mean every block on n rows based on condition - r

Question

I have this dataframe
r2 distance
1 33.64 67866
2 8.50 77229
3 15.07 109119
4 24.35 142279
5 7.74 143393
6 8.21 177670
7 12.26 216440
8 12.66 253751
9 26.31 282556
10 39.08 320816
I need to calculate the mean of column r2 for every block of rows where the distance between two values in the column distance is equal or less than 100000.
For this example the desired output would be:
mean_r2 diff_of_distance
1 17.86 75527 ## mean of rows 1 to 5; distance 5 - distance 1
2 13.91 66164 ## mean of rows 2 to 5; distance 5 - distance 2
3 13.84 68551 ## mean of rows 3 to 6; distance 6 - distance 3
4 13.14 74161 ## mean of rows 4 to 7; distance 7 - distance 4
5 9.40 73047 ## mean of rows 5 to 7; distance 7 - distance 5
6 11.04 76081 ## mean of rows 6 to 8; distance 8 - distance 6
and so on.
Edit 1: I have more than 100,000 rows.
Thanks.

Answer 1

Loop through each value of distance, minus this from the values in the distance vector and test if the result is less than 100000. This creates a boolean vector which you sum to identify the index at which the distance is greater than 100000 (i.e. bool becomes FALSE). Use this index to identify your block then take the mean of r2 in each block.
To speed up the code define your vector type and length (to avoid "growing vectors" on each iteration.
means <- vector("numeric", length = nrow(df))
rows <- vector("numeric", length = nrow(df))
distance_diff <- vector("numeric", length = nrow(df))
for (i in seq_along(df$distance)) {
dis_val <- df$distance[i] # the ith distance value
bools <- (df$distance - dis_val) < 100000 # bool indicating if difference between i and every value in vector is less than 100000
block_range <- sum(bools)# taking sum of bools identifies the value at which the distance becomes > 100000
rows[i] <- paste(as.character(i), "-", as.character(block_range))
means[i] <- mean(df$r2[i:block_range]) # take the mean of r2 in the range i to all rows where distance is < 100000
distance_diff[i] <- df$distance[block_range] - dis_val # minus the distance from the value before distance is > 100000 from i
}
data.frame(mean_r2 = means, rows= rows, diff_of_distance=distance_diff)
mean_r2 rows diff_of_distance
1 17.860000 1 - 5 75527
2 13.915000 2 - 5 66164
3 13.842500 3 - 6 68551
4 13.140000 4 - 7 74161
5 9.403333 5 - 7 73047
6 11.043333 6 - 8 76081
7 17.076667 7 - 9 66116
8 26.016667 8 - 10 67065
9 32.695000 9 - 10 38260
10 39.080000 10 - 10 0

Answer 2

You can try:
# your data
d <- read.table(text="r2 distance
1 33.64 67866
2 8.50 77229
3 15.07 109119
4 24.35 142279
5 7.74 143393
6 8.21 177670
7 12.26 216440
8 12.66 253751
9 26.31 282556
10 39.08 320816", header=T)
library(tidyverse) #dplyr_0.7.2
d %>%
mutate(index=1:n()) %>% add row index
group_by(index) %>% # group by this index
# calculate difference and find max row where diff < 100000
mutate(max_row=max(which(.$distance - distance < 100000, arr.ind=T))) %>%
# calculate mean
mutate(mean_r2=mean(.$r2[index:max_row])) %>%
# calculate the difference
mutate(diff_of_distance=.$distance[max_row] - .$distance[index]) %>%
# unite the columns
unite(rows, index, max_row, sep = "-")
# A tibble: 10 x 5
r2 distance rows mean_r2 diff_of_distance
* <dbl> <int> <chr> <dbl> <int>
1 33.64 67866 1-5 17.860000 75527
2 8.50 77229 2-5 13.915000 66164
3 15.07 109119 3-6 13.842500 68551
4 24.35 142279 4-7 13.140000 74161
5 7.74 143393 5-7 9.403333 73047
6 8.21 177670 6-8 11.043333 76081
7 12.26 216440 7-9 17.076667 66116
8 12.66 253751 8-10 26.016667 67065
9 26.31 282556 9-10 32.695000 38260
10 39.08 320816 10-10 39.080000 0
This works because group_by subsets the dataframe, thus you can access within mutate the respective distance value per group and calculate the difference with the complete vector using .$distance as this access the complete column regardless the group_by() function.