I have a dataframe and the row values are first ordered from smallest to largest. I compute row value differences between adjacent rows, combine rows with similar differences (e.g., smaller than 1), and return averaged values of combined rows. I could check each row differences with a for loop, but seems a very inefficient way. Any better ideas? Thanks.
library(dplyr)
DF <- data.frame(ID=letters[1:12],
Values=c(1, 2.2, 3, 5, 6.2, 6.8, 7, 8.5, 10, 12.2, 13, 14))
DF <- DF %>%
mutate(Diff=c(0, diff(Values)))
The expected output of DF would be
ID Values
a 1.0
b/c 2.6 # (2.2+3.0)/2
d 5.0
e/f/g 6.67 # (6.2+6.8+7.0)/3
h 8.5
i 10.0
j/k 12.6 # (12.2+13.0)/2
i 14.0
Calculate difference between Values of every row and check if those are >= 1. Cumulative sum of that >=1 will provide you distinct group on which one can summarize to get desired result.
library(dplyr)
DF %>% arrange(Values) %>%
group_by(Diff = cumsum(c(1,diff(Values)) >= 1) ) %>%
summarise(ID = paste0(ID, collapse = "/"), Values = mean(Values)) %>%
ungroup() %>% select(-Diff)
# # A tibble: 8 x 2
# ID Values
# <chr> <dbl>
# 1 a 1.00
# 2 b/c 2.60
# 3 d 5.00
# 4 e/f/g 6.67
# 5 h 8.50
# 6 i 10.0
# 7 j/k 12.6
# 8 l 14.0
Here is an option with data.table
library(data.table)
setDT(DF)[, .(ID = toString(ID), Values = round(mean(Values), 2)),
by = .(Diff = cumsum(c(TRUE, diff(Values)>=1)))][, -1, with = FALSE]
# ID Values
#1: a 1.00
#2: b, c 2.60
#3: d 5.00
#4: e, f, g 6.67
#5: h 8.50
#6: i 10.00
#7: j, k 12.60
#8: l 14.00
library(magrittr)
df <- DF[order(DF$Values),]
df$Values %>%
#Find groups for each row
outer(., ., function(x, y) x >= y & x < y + 1) %>%
# Remove sub-groups
`[<-`(apply(., 1, cumsum) > 1, F) %>%
# Remove sub-group columns
.[, colSums(.) > 0] %>%
# select these groups from data
apply(2, function(x) data.frame(ID = paste(df$ID[x], collapse = '/')
, Values = mean(df$Values[x]))) %>%
# bind results by row
do.call(what = rbind)
# ID Values
# 1 a 1.000000
# 2 b/c 2.600000
# 4 d 5.000000
# 5 e/f/g 6.666667
# 8 h 8.500000
# 9 i 10.000000
# 10 j/k 12.600000
# 12 l 14.000000
Note:
This method is different from those using diff because it groups rows together only if all Values are within < 1 of each other.
Example:
Change the dataset so that Value is 7.3 at ID g.
Above method: The IDs e, f, and g are no longer grouped together because the value at ID e is 6.2 and 7.2 - 6.2 > 1.
Diff Method: IDs e, f, and g are still grouped together because the diff of IDs at e and f is < 1 and the diff of IDs F and G is < 1
