如何在数据框中的变量中找到一组的最大值和最小值的差

Question

I have three variables A, B & C in the following format

A         B     C
Cat1      1    NA       
Cat1      2    NA
Cat1      1    NA
Cat1      2    NA
Cat1      NA   4
Cat1      NA   1
Cat1      NA   6
Cat1      NA   4
Cat1      7    NA       
Cat1      9    NA
Cat1      3    NA
Cat1      2    NA
Cat1      NA   2
Cat1      NA   4 
Cat1      NA   5
Cat1      NA   9
.         .    .
.         .    .        
.         .    .
.         .    .

lets say in variable C wherever there are numerical values part from NA, it should be called as one Group and I have to find the difference of maximum & minimum values in that group..Can someone please help

desired Output:

Sure. The desired output be like : A Trips Value

                              Cat 1   Trip1      xx (dif of max & min of that trip)                                                       

Answer 1

From what I understand you could do the following

library(data.table)
dt <- fread(text)
dt[, .(C = diff(range(C))), by = .(grp = rleid(is.na(C)))]
#   grp  C
#1:   1 NA
#2:   2  5
#3:   3 NA
#4:   4  7

For B and C at the same time do

dt[, lapply(.SD, function(x) diff(range(x))), by = .(grp = rleid(is.na(C))), .SDcols = c('B', 'C')]
#   grp  B  C
#1:   1  1 NA
#2:   2 NA  5
#3:   3  7 NA
#4:   4 NA  7

Another option to remove the NA s

cols <- c('B', 'C')
out <- dt[, lapply(.SD, function(x) diff(range(x))), by = rleid(is.na(C)), .SDcols = cols
          ][, lapply(.SD, na.omit), .SDcols = cols
            ][, grp := rleid(B)]
out
#   B C grp
#1: 1 5   1
#2: 7 7   2

Note that the second and third solutions assumes that B is NA when C is not et vice versa.

data

text <- "A         B     C
Cat1      1    NA       
Cat1      2    NA
Cat1      1    NA
Cat1      2    NA
Cat1      NA   4
Cat1      NA   1
Cat1      NA   6
Cat1      NA   4
Cat1      7    NA       
Cat1      9    NA
Cat1      3    NA
Cat1      2    NA
Cat1      NA   2
Cat1      NA   4 
Cat1      NA   5
Cat1      NA   9"

Answer 2

A solution using dplyr and tidyr .

library(dplyr)
library(tidyr)

dat2 <- dat %>%
  mutate(trip = cumsum(is.na(C))) %>%
  drop_na(C) %>%
  mutate(trip = group_indices(., trip)) %>%
  group_by(trip) %>%
  summarize(Diff = max(C) - min(C)) %>%
  ungroup()
dat2

# # A tibble: 2 x 2
#    trip  Diff
#   <int> <dbl>
# 1     1     5
# 2     2     7

Data

dat <- read.table(text = "A         B     C
Cat1      1    NA       
                  Cat1      2    NA
                  Cat1      1    NA
                  Cat1      2    NA
                  Cat1      NA   4
                  Cat1      NA   1
                  Cat1      NA   6
                  Cat1      NA   4
                  Cat1      7    NA       
                  Cat1      9    NA
                  Cat1      3    NA
                  Cat1      2    NA
                  Cat1      NA   2
                  Cat1      NA   4 
                  Cat1      NA   5
                  Cat1      NA   9",
                  header = TRUE, stringsAsFactors = FALSE)