In my data below, I wonder how to delete all rows with a given value of outcome (say "A") from n (say 1) randomly selected studyies?
The only condition is that we want to select only from studies that have used more than one value of outcome (e.g., study==1 and study==2 each of which has both outcome == "A" and outcome == "B").
For example, below let's say the given value of outcome is "A". Then, for a given n (say n = 1), we delete all rows with with outcome == "A" from n = 1 randomly selected study from study==1 or study==2.
Is this possible in R?
m =
"
study group outcome
1 1 1 A
2 1 1 B
3 1 2 A
4 1 2 B
5 2 1 A
6 2 1 B
7 2 2 A
8 2 2 B
9 3 1 B
10 4 1 B
"
data <- read.table(text=m,h=T)
library(dplyr)
n = 1
studies_to_remove = sample(unique(data$study), size = n)
outcome_to_remove = "A"
data %>%
filter(
!(
study %in% studies_to_remove &
outcome %in% outcome_to_remove
)
)
# study group outcome
# 2 1 1 B
# 4 1 2 B
# 5 2 1 A
# 6 2 1 B
# 7 2 2 A
# 8 2 2 B
# 9 3 1 B
# 10 4 1 B
Related
My title might not be very informative but this is an example which exposes my problem :
I have this dataframe :
df=data.frame(cond1=c(1,1,1,2,2,2,3,3,3,1,1,1,2,2,2,3,3,3),
group=c("F","V","M","F","V","M","F","V","M","F","V","M","F","V","M","F","V","M"),
gene=c("A","A","A","A","A","A","A","A","A","B","B","B","B","B","B","B","B","B"),
value=c(1,2,3,4,5,6,7,8,9,1,3,2,4,3,2,2,3,4))
df
cond1 group gene value
1 1 F A 1
2 1 V A 2
3 1 M A 3
4 2 F A 4
5 2 V A 5
6 2 M A 6
7 3 F A 7
8 3 V A 8
9 3 M A 9
10 1 F B 1
11 1 V B 3
12 1 M B 2
13 2 F B 4
14 2 V B 3
15 2 M B 2
16 3 F B 2
17 3 V B 3
18 3 M B 4
What I would like to obtain is for each gene, the sum of how many different cond1 have their value corresponding with F group smaller than their value corresponding with V their value corresponding with M.
In the 3 first lines, we are in gene A for the cond1. value correspoding to group F=1, V=2, M=3. So F<V<M for the A gene for the cond1=1 group.
My expected output for the gene A is 3 as all cond1 groups meet F<V<M for value.
My expected output for the gene B is 1 as only cond1=3 group meet F<V<M for value.
My desired output would be ideally a dataframe with gene and the sum of cond1 than meet my criterion :
gene count
1 A 3
2 B 1
I would be very grateful if you could provide me any tips on how should I proceed
Check if all the data is in increasing order and count how many such values exist for each gene.
library(dplyr)
df %>%
#If the data is not ordered, order it using arrange
#arrange(gene, cond1, match(group, c('F', 'V', 'M'))) %>%
group_by(gene, cond1) %>%
summarise(cond = all(diff(value) > 0)) %>%
summarise(count = sum(cond))
# gene count
# <chr> <int>
#1 A 3
#2 B 1
Using data.table
library(data.table)
setDT(df)[, .(cond = all(diff(value) > 0)), .(gene, cond1)][, .(count = sum(cond)), gene]
gene count
1: A 3
2: B 1
I have a dataset with a unique ID for groups of patients called match_no and i want to count how many patients got sick in two different years by running a loop function to count the occurrences in a large dataset
for (i in db$match_no){(with(db, sum(db$TBHist16 == 1 & db$match_no == i))}
This is my attempt. I need i to cycle through each of the match numbers and count how many TB occurrences there was.
Can anyone correct my formula please.
Example here
df1 <- data.frame(Match_no = c(1, 1,1,1,1,2,2,2,2,2, 3,3,3,3,3, 4,4,4,4,4, 5,5,5,5,5),
var1 = c(1,1,1,0,0,1,1,1,0,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0,1))
I want to count how many 1 values there are in each match number.
Thank you
Some ideas:
Simple summary of all Match_no values:
xtabs(~var1 + Match_no, data = df1)
# Match_no
# var1 1 2 3 4 5
# 0 2 2 1 3 1
# 1 3 3 4 2 4
Same as 1, but with a subset:
xtabs(~ Match_no, data = subset(df1, var1 == 1))
# Match_no
# 1 2 3 4 5
# 3 3 4 2 4
Results in a frame:
aggregate(var1 ~ Match_no, data = subset(df1, var1 == 1), FUN = length)
# Match_no var1
# 1 1 3
# 2 2 3
# 3 3 4
# 4 4 2
# 5 5 4
In base R you can use aggregate and sum:
aggregate(var1 ~ Match_no, data = df1, FUN = sum)
Match_no var1
1 1 3
2 2 3
3 3 4
4 4 2
5 5 4
Let's say I got a data.frame like the following:
u <- as.numeric(rep(rep(1:5,3)))
w <- as.factor(c(rep("a",5), rep("b",5), rep("c",5)))
q <- data.frame(w,u)
q
w u
1 a 1
2 a 2
3 a 3
4 a 4
5 a 5
6 b 1
7 b 2
8 b 3
9 b 4
10 b 5
11 c 1
12 c 2
13 c 3
14 c 4
15 c 5
and the vector:
v <- c(2,3,1)
Now I want to find the first row in the respective group [i] where the value [i] from vector "v" is bigger than the value in column "u".
The result should look like this:
1 a 3
2 b 4
3 c 2
I tried:
fun <- function (m) {
first(which(m[,2]>v))
}
ddply(q, .(w), summarise, fun(q))
and got as a result:
w fun(q)
1 a 3
2 b 3
3 c 3
Thus it seems like, ddply is only taking the first value from the vector "v".
Does anyone know how to solve this?
We can join the vector by creating a data.frame with 'w' as the unique values from 'w' column of 'q', then do a group_by 'w' and get the first row index where u is greater than the corresponding 'vector' column value
library(dplyr)
q %>%
left_join(data.frame(w = unique(q$w), new = v)) %>%
group_by(w) %>%
summarise(n = which(u > new)[1])
# // or use findInterval
#summarise(n = findInterval(new[1], u)+1)
-output
# A tibble: 3 x 2
# w n
#* <fct> <int>
#1 a 3
#2 b 4
#3 c 2
or use Map after splitting the data by 'w' column
Map(function(x, y) which(x$u > y)[1], split(q,q$w), v)
#$a
#[1] 3
#$b
#[1] 4
#$c
#[1] 2
OP mentioned that comparison starts from the beginning and it is not correct because we have a group_by operation. If we create a column of sequence, it resets at each group
q %>%
left_join(data.frame(w = unique(q$w), new = v)) %>%
group_by(w) %>%
mutate(rn = row_number())
Joining, by = "w"
# A tibble: 15 x 4
# Groups: w [3]
w u new rn
<fct> <dbl> <dbl> <int>
1 a 1 2 1
2 a 2 2 2
3 a 3 2 3
4 a 4 2 4
5 a 5 2 5
6 b 1 3 1
7 b 2 3 2
8 b 3 3 3
9 b 4 3 4
10 b 5 3 5
11 c 1 1 1
12 c 2 1 2
13 c 3 1 3
14 c 4 1 4
15 c 5 1 5
Using data.table: for each 'w' (by = w), subset 'v' with the group index .GRP. Compare the value with 'u' (v[.GRP] < u). Get the index for the first TRUE (which.max):
library(data.table)
setDT(q)[ , which.max(v[.GRP] < u), by = w]
# w V1
# 1: a 3
# 2: b 4
# 3: c 2
I would like to break a dataset into two frames - one for which the original dataset has duplicate observations based on a condition and one for which the original dataset does not have duplicate observations based on a condition. In the following example, I would like to break the frame into one for which there is only one coder for an observation and one for which there are two coders::
frame <- data.frame(id = c(1,1,1,2,2,3), coder = c("A", "A", "B", "A", "B", "A"), y = c(4,5,4,1,1,2))
frame
For this, I would like to produce, such that:
frame1:
id coder y
1 1 A 4
2 1 A 5
3 1 B 4
4 2 A 1
5 2 B 1
frame2:
6 3 A 2
You can use aggregate to determine the ids you want in each data frame:
cts <- aggregate(coder~id, frame, function(x) length(unique(x)))
cts
# id coder
# 1 1 2
# 2 2 2
# 3 3 1
Then you can subset as appropriate based on this:
subset(frame, id %in% cts$id[cts$coder >= 2])
# id coder y
# 1 1 A 4
# 2 1 A 5
# 3 1 B 4
# 4 2 A 1
# 5 2 B 1
subset(frame, id %in% cts$id[cts$coder < 2])
# id coder y
# 6 3 A 2
You may also try:
indx <- !colSums(!table(frame$coder, frame$id))
frame[frame$id %in% names(indx)[indx],]
# id coder y
#1 1 A 4
#2 1 A 5
#3 1 B 4
#4 2 A 1
#5 2 B 1
frame[frame$id %in% names(indx)[!indx],]
# id coder y
#6 3 A 2
Explanation
table(frame$coder, frame$id)
# 1 2 3
# A 2 1 1
# B 1 1 0 #Here for id 3, B==0
If we Negate that, the result would be a logical index
!table(frame$coder, frame$id).
Do the colSums of the above, which results
# 1 2 3
# 0 0 1
Negate again and get the index for ids and subset those ids which are TRUE
From this you can subset by matching with the names of the ids
In a dataframe I'd like to replace values in a series where they exceed a given threshold.
For example, within a group ('ID') in a series designated by 'time', if 'value' ever exceeds 3, I'd like to make all following entries also equal 3.
ID <- as.factor(c(rep("A", 3), rep("B",3), rep("C",3)))
time <- rep(1:3, 3)
value <- c(c(1,1,2), c(2,3,2), c(3,3,2))
dat <- cbind.data.frame(ID, time, value)
dat
ID time value
A 1 1
A 2 1
A 3 2
B 1 2
B 2 3
B 3 2
C 1 3
C 2 3
C 3 2
I'd like it to be:
ID time value
A 1 1
A 2 1
A 3 2
B 1 2
B 2 3
B 3 3
C 1 3
C 2 3
C 3 3
This should be easy, but I can't figure it out. Thanks!
The ave function makes this very easy by allowing you to apply a function to each of the groupings. In this case, we will adapth the cummax (cumulative maximum) to see if we've seen a 3 yet.
dat$value2<-with(dat, ave(value, ID, FUN=
function(x) ifelse(cummax(x)>=3, 3, x)))
dat;
# ID time value value2
# 1 A 1 1 1
# 2 A 2 1 1
# 3 A 3 2 2
# 4 B 1 2 2
# 5 B 2 3 3
# 6 B 3 2 3
# 7 C 1 3 3
# 8 C 2 3 3
# 9 C 3 2 3
You could also just use FUN=cummax if you want never-decreasing values. I wasn't sure about the sequence c(1,2,1) if you wanted to keep that unchanged or not.
If you can assume your data are sorted by group, then this should be fast, essentially relying on findInterval() behind the scenes:
library(IRanges)
id <- Rle(ID)
three <- which(value>=3L)
ir <- reduce(IRanges(three, end(id)[findRun(three, id)])))
dat$value[as.integer(ir)] <- 3L
This avoids looping over the groups.