Is there an R function for comparing rows in data.frame?

I think the issue was by ths following , I have a data frame that I'm working with in which I'd like to compare a data point Genotype with two references S288C and SK1. This comparison will be done across many rows (100+) of the data frame. Here are the first few lines of my data frame: , A nested ifelse should do it (take a look at help(ifelse) for usage):

ifelse(dat$Genotype==dat$S288C,1,ifelse(dat$Genotype==dat$SK1,0,NA))

> dat
     Genotype S288C SK1
[1,] "G"      "A"   "G"
[2,] "G"      "A"   "G"
[3,] "C"      "T"   "C"
[4,] "G"      "A"   "G"
[5,] "G"      "G"   "T"
[6,] "G"      "A"   "A"

> ifelse(dat$Genotype==dat$S288C,1,ifelse(dat$Genotype==dat$SK1,0,NA))
[1]  0  0  0  0  1 NA

will be helpful for those in need I have two data frames. One is considered a reference and has every value, the other may or may not be missing values. I want to compare both data frames, then delete the values from the reference data frame that have NA in the other. However, each row of the data frame that can have missing values needs to be treated as a single comparison so you are developing a unique reference for every single row. For example the reference dataframe(1): , Try:

> ref<-data.frame(var1=c('a','q','z'),var2=c('b','w','x'),var3=c('c','e','n'))
> new<-data.frame(var1=c('p','u',NA,'l'),var2=c('o','y','e','k'),var3=c('i','t','w',NA))
> apply(new,1,function(x) ref[,which(!is.na(x))] )
[[1]]
  var1 var2 var3
1    a    b    c
2    q    w    e
3    z    x    n

[[2]]
  var1 var2 var3
1    a    b    c
2    q    w    e
3    z    x    n

[[3]]
  var2 var3
1    b    c
2    w    e
3    x    n

[[4]]
  var1 var2
1    a    b
2    q    w
3    z    x

is.odd <- function(x) x %% 2 == 1
apply(new, 1, function(x) {
    toremove <-which(is.na(x))
    toremove1<-sapply(toremove,function(x) ifelse(is.odd(x),x+1,x-1) )
    ref[,!(1:ncol(ref) %in% c(toremove,toremove1)),drop=F]
})

it fixes the issue One option would be to create an | condition to get those rows having NA for 'x'

subset(my.df, x != y | is.na(x))

subset(my.df, x != y | is.na(x)|is.na(y))

subset(my.df, (x != y | is.na(x)|is.na(y)) & !(is.na(x) & is.na(y)))

I hope this helps . Scenario: , Here's an answer with dplyr:

library(dplyr)

df1 <- tribble(
     ~CHR, ~POS,
     1,  2000,                  
     1,  3000,
     2,  1500,
     3,  3000
)

df2 <- tribble(
     ~CHR, ~POS_START, ~POS_END,
     1, 1500, 2500,                  
     1, 3200, 4000,
     2, 1200, 1600,
     2, 2000, 2200,
     3, 5000, 5500,
     4, 1000, 1200
)

df1 %>% 
     left_join(df2, by = 'CHR') %>% 
     mutate(IN_RANGE = POS >= POS_START & POS <= POS_END) %>% 
     group_by(CHR, POS) %>% 
     summarize(IN_RANGE = sum(IN_RANGE) > 0)

wish helps you Here is an option using base R by making use of table and crossprod. Set the lower triangular values of the matrix output of crossprod to NA, convert it to 'long' format by converting to data.frame and then subset the rows that are non-NA for 'Freq' column

out <- with(df, crossprod(table(paste(category, value), ID)))
out[lower.tri(out, diag = TRUE)] <- NA
subset(as.data.frame.table(out), !is.na(Freq))
#    ID ID.1 Freq
#4 ID1  ID2    2
#7 ID1  ID3    1
#8 ID2  ID3    2

df <- structure(list(ID = c("ID1", "ID1", "ID1", "ID2", "ID2", "ID2", 
"ID3", "ID3", "ID3"), category = c("length", "type", "color", 
 "length", "type", "color", "length", "type", "color"), 
 value = c("100", 
 "L", "Blue", "100", "M", "Blue", "150", "M", "Blue")), 
 class = "data.frame", row.names = c(NA, -9L))