First, I'm brand new to R and am making the switch from SAS. I have a dataset that is 1000 rows by 24 columns, where the columns are different treatments. I want to count the number of times an observation meets a criteria across rows of my dataset listed below.
Gene A B C D
1 AARS_3 NA NA 4.168365 NA
2 AASDHPPT_21936 NA NA NA -3.221287
3 AATF_26432 NA NA NA NA
4 ABCC2_22 4.501518 3.17992 NA NA
5 ABCC2_26620 NA NA NA NA
I was trying to create column vectors that counted
1) Number of NAs
2) Number of columns <0
3) Number of columns >0
I would then use cbind to add these to my large dataset
I solved the first one with :
NA.Count <- (apply(b01,MARGIN=1,FUN=function(x) length(x[is.na(x)])))
I tried to modify this to count evaluate the !is.na and then count the number of times the value was less than zero with this:
lt0 <- (apply(b01,MARGIN=1,FUN=function(x) ifelse(x[!is.na(x)],count(x[x<0]))))
which didn't work at all.
I tried a dozen ways to get dplyr mutate to work with this and did not succeed.
What I want are the last two columns below; and if you had a cleaner version of the NA.Count I did, that would also be greatly appreciated.
Gene A B C D NA.Count lt0 gt0
1 AARS_3 NA NA 4.168365 NA 3 0 1
2 AASDHPPT_21936 NA NA NA -3.221287 3 1 0
3 AATF_26432 NA NA NA NA 4 0 0
4 ABCC2_22 4.501518 3.17992 NA NA 2 0 2
5 ABCC2_26620 NA NA NA NA 4 0 0
Here is one way to do it taking advantage of the fact that TRUE equals 1 in R.
# test data frame
lil_df <- data.frame(Gene = c("AAR3", "ABCDE"),
A = c(NA, 3),
B = c(2, NA),
C = c(-1, -2),
D = c(NA, NA))
# is.na
NA.count <- rowSums(is.na(lil_df[,-1]))
# less than zero
lt0 <- rowSums(lil_df[,-1]<0, na.rm = TRUE)
# more that zero
mt0 <- rowSums(lil_df[,-1]>0, na.rm = TRUE)
# cbind to data frame
larger_df <- cbind(lil_df, NA.count, lt0, mt0 )
larger_df
Gene A B C D NA.count lt0 mt0
1 AAR3 NA 2 -1 NA 2 1 1
2 ABCDE 3 NA -2 NA 2 1 1
Related
lets say data is 'ab':
a <- c(1,2,3,NA,5,NA)
b <- c(5,NA,4,NA,NA,6)
ab <-c(a,b)
I would like to have new variable which is sum of the two but keeping NA's as follows:
desired output:
ab$c <-(6,2,7,NA,5,6)
so addition of number + NA should equal number
I tried following but does not work as desired:
ab$c <- a+b
gives me : 6 NA 7 NA NA NA
Also don't know how to include "na.rm=TRUE", something I was trying.
I would also like to create third variable as categorical based on cutoff <=4 then event 1, otherwise 0:
desired output:
ab$d <-(1,1,1,NA,0,0)
I tried:
ab$d =ifelse(ab$a<=4|ab$b<=4,1,0)
print(ab$d)
gives me logical(0)
Thanks!
a <- c(1,2,3,NA,5,NA)
b <- c(5,NA,4,NA,NA,6)
dfd <- data.frame(a,b)
dfd$c <- rowSums(dfd, na.rm = TRUE)
dfd$c <- ifelse(is.na(dfd$a) & is.na(dfd$b), NA_integer_, dfd$c)
dfd$d <- ifelse(dfd$c >= 4, 1, 0)
dfd
a b c d
1 1 5 6 1
2 2 NA 2 0
3 3 4 7 1
4 NA NA NA NA
5 5 NA 5 1
6 NA 6 6 1
I would like to ask the R community for help with finding a solution for my data, where any consecutive row with numerous NA values is combined and put into a new column.
For example:
df <- data.frame(A= c(1,2,3,4,5,6), B=c(2, "NA", "NA", 5, "NA","NA"), C=c(1,2,"NA",4,5,"NA"), D=c(3,"NA",5,"NA","NA","NA"))
A B C D
1 1 2 1 3
2 2 NA 2 NA
3 3 NA NA 5
4 4 5 4 NA
5 5 NA 5 NA
6 6 NA NA NA
Must be transformed to this:
A B C D E
1 1 2 1 3 2 NA 2 NA 3 NA NA 5
2 4 5 4 NA 5 NA 5 NA 6 NA NA NA
I would like to do the following:
Identify consecutive rows that have more than 1 NA value -> combine entries from those consecutive rows into a single combined entiry
Place the above combined entry in new column "E" on the prior row
This is quite complex (for me!) and I am wondering if anyone can offer any help with this. I have searched for some similar problems, but have been unable to find one that produces a similar desired output.
Thank you very much for your thoughts--
Using tidyr and dplyr:
Concatenate values for each row.
Keep the concatenated values only for rows with more than one NA.
Group each “good” row with all following “bad” rows.
Use a grouped summarize() to concatenate “bad” row values to a single string.
df %>%
unite("E", everything(), remove = FALSE, sep = " ") %>%
mutate(
E = if_else(
rowSums(across(!E, is.na)) > 1,
E,
""
),
new_row = cumsum(E == "")
) %>%
group_by(new_row) %>%
summarize(
across(A:D, first),
E = trimws(paste(E, collapse = " "))
) %>%
select(!new_row)
# A tibble: 2 × 5
A B C D E
<dbl> <dbl> <dbl> <dbl> <chr>
1 1 2 1 3 2 NA 2 NA 3 NA NA 5
2 4 5 4 NA 5 NA 5 NA 6 NA NA NA
How can I drop all the elements with missing values but instead of deleting entire columns, create columns with just the populated cells? For example getting from this
A B C D
1 NA 2 NA
NA 3 NA 4
NA 5 6 NA
(data1) in order to create a data-set containing only the populated cells, as this
AB BB
1 2
3 4
5 6
Below I have created a small working example to test a solution.
># Create example dataset (data1)
>data1 <- data.frame(matrix(c(1,NA,2,NA,NA,3,NA,4,NA,5,6,NA),nrow = 3, byrow = T))
>colnames(data1) <- c("A","B","C","D")
>print(data1)
A B C D
1 NA 2 NA
NA 3 NA 4
NA 5 6 NA
> # Create new dataset?
Here is a potential solution using akrun's/Valentin's answer from this question.
Let's say the data is
data1 <- data.frame(matrix(c(1,NA,2,NA,NA,3,NA,4,NA,5,NA,NA),nrow = 3, byrow = T))
> data1
X1 X2 X3 X4
1 1 NA 2 NA
2 NA 3 NA 4
3 NA 5 NA NA
Then use
df1 <- t(sapply(apply(data1, 1, function(x) x[!is.na(x)]), "length<-", max(lengths(lapply(data1, function(x) x[!is.na(x)])))))
to arrive at
> df1
X1 X3
[1,] 1 2
[2,] 3 4
[3,] 5 NA
I have ranked rows in a data frame based on values in each column.Ranking 1-10. not every column in picture
I have code that replaces values to NA or 1. But I can't figure out how to replace range of numbers, e.g. 3-6 with 1 and then replace the rest (1-2 and 7-10) with NA.
lag.rank <- as.matrix(lag.rank)
lag.rank[lag.rank > n] <- NA
lag.rank[lag.rank <= n] <- 1
At the moment it only replaces numbers above or under n. Any suggestions? I figure it should be fairly simple?
Is this what your are trying to accomplish?
> x <- sample(1:10,20, TRUE)
> x
[1] 1 2 8 2 6 4 9 1 4 8 6 1 2 5 8 6 9 4 7 6
> x <- ifelse(x %in% c(3:6), 1, NA)
> x
[1] NA NA NA NA 1 1 NA NA 1 NA 1 NA NA 1 NA 1 NA 1 NA 1
If your data aren't integers but numeric you can use between from the dplyr package:
x <- ifelse(between(x,3,6), 1, NA)
This question already has answers here:
ifelse matching vectors in r
(2 answers)
Closed 9 years ago.
I have a dataframe that looks like this:
> df<-data.frame(A=c(NA,1,2,3,4),B=c(NA,5,NA,3,4),C=c(NA,NA,NA,NA,4))
> df
A B C
1 NA NA NA
2 1 5 NA
3 2 NA NA
4 3 3 NA
5 4 4 4
I am trying to create a "D" column based on the row values in df, where D gets an NA if the values in the row are different (i.e. row 2) or all NAs (i.e. row 1), and the value in the row if the values in that row are the same, excluding NAs (i.e. rows 3, 4, 5). This would produce a vector and dataframe that looks like this:
> df$D<-c(NA,NA,2,3,4)
> df
A B C D
1 NA NA NA NA
2 1 5 NA NA
3 2 NA NA 2
4 3 3 NA 3
5 4 4 4 4
Thank you in advance for your suggestions.
You can use apply() to do calculation for each row and then use unique() and !is.na(). With !is.na() you select values that are not NA. With unique() you get unique values and then with length() get number of unique values. If number is 1 then use first non NA value, if not then NA.
df$D<-apply(df,1,function(x)
ifelse(length(unique(x[!is.na(x)]))==1,x[!is.na(x)][1],NA))
Here is one possible approach:
FUN <- function(x) {
no.na <- x[!is.na(x)]
len <- length(no.na)
if (len == 0) return(NA)
if (len == 1) return(no.na)
runs <- rle(no.na)[[2]]
if(length(runs) > 1) return(NA)
runs
}
df$D <- apply(df, 1, FUN)
## > df
## A B C D
## 1 NA NA NA NA
## 2 1 5 NA NA
## 3 2 NA NA 2
## 4 3 3 NA 3
## 5 4 4 4 4