I have data that looks like:
player event diff
A x NA
A y 2
A z 240
A w 3
A x 9
B x NA
B y 3
B z 120
C x NA
C x 8
What I did to get this was to group by the player column and take the difference between time events, hence the NA's for diff column whenever a new player has an event.
What I want to do is to partition the data into player specific interactions that are within a few minutes of each other (say a cutoff of diff = 20). What I want in the end is to have:
player event diff interaction
A x NA 1
A y 2 1
A z 240 2
A w 3 2
A x 9 2
B x NA 1
B y 3 1
B z 120 2
C x NA 1
C x 8 1
So basically the interactions are grouped based on having the same player and the difference being less than 20, otherwise a new interaction is started. A new interaction is also started if an NA is present. I'm not really sure how to do this in a fast/efficient way as I've got a large data set with many players. My preference is for a dplyr solution
You can replace NA with 0(or other number that is below your threshold) using coalesce in the diff column and do a cumsum on the diff >= 20 condition, which will give a distinct id whenever diff exceed some threshold:
library(dplyr)
df %>% group_by(player) %>%
mutate(interaction = cumsum(coalesce(diff, 0L) >= 20) + 1)
# Source: local data frame [10 x 4]
# Groups: player [3]
# player event diff interaction
# <fctr> <fctr> <int> <dbl>
# 1 A x NA 1
# 2 A y 2 1
# 3 A z 240 2
# 4 A w 3 2
# 5 A x 9 2
# 6 B x NA 1
# 7 B y 3 1
# 8 B z 120 2
# 9 C x NA 1
# 10 C x 8 1
We can also use base R to get the expected output
df1$interaction <- with(df1, ave(diff, player, FUN = function(x)
cumsum(x > 20 & !is.na(x))+1))
df1$interaction
#[1] 1 1 2 2 2 1 1 2 1 1
Related
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 have a list of observations grouped by samples. I want to find the samples that share the most identical observations. An identical observation is where the start and end number are both matching between two samples. I'd like to use R and preferably dplyr to do this if possible.
I've been getting used to using dplyr for simpler data handling but this task is beyond what I am currently able to do. I've been thinking the solution would involve grouping the start and end into a single variable: group_by(start,end) but I also need to keep the information about which sample each observation belongs to and compare between samples.
example:
sample start end
a 2 4
a 3 6
a 4 8
b 2 4
b 3 6
b 10 12
c 10 12
c 0 4
c 2 4
Here samples a, b and c share 1 observation (2, 4)
sample a and b share 2 observations (2 4, 3 6)
sample b and c share 2 observations (2 4, 10 12)
sample a and c share 1 observation (2 4)
I'd like an output like:
abc 1
ab 2
bc 2
ac 1
and also to see what the shared observations are if possible:
abc 2 4
ab 2 4
ab 3 6
etc
Thanks in advance
Here's something that should get you going:
df %>%
group_by(start, end) %>%
summarise(
samples = paste(unique(sample), collapse = ""),
n = length(unique(sample)))
# Source: local data frame [5 x 4]
# Groups: start [?]
#
# start end samples n
# <int> <int> <chr> <int>
# 1 0 4 c 1
# 2 2 4 abc 3
# 3 3 6 ab 2
# 4 4 8 a 1
# 5 10 12 bc 2
Here is an idea via base R,
final_d <- data.frame(count1 = sapply(Filter(nrow, split(df, list(df$start, df$end))), nrow),
pairs1 = sapply(Filter(nrow, split(df, list(df$start, df$end))), function(i) paste(i[[1]], collapse = '')))
# count1 pairs1
#0.4 1 c
#2.4 3 abc
#3.6 2 ab
#4.8 1 a
#10.12 2 bc
I am trying to calculate a variable that depends on the value of multiple other columns, but in other rows.
Here's the sample data:
set.seed(2)
df1 <- data.frame(Participant=c(rep(1,5),rep(2,7),rep(3,10)),
Action=sample(c(rep("Play",9),rep("Other",13))),
time = c(sort(runif(5,1,100)),sort(runif(7,1,100)),sort(runif(10,1,100))))
df1$Action[2] ="Play" # edited to provide important test case
What I am trying to achieve is a column that tests whether the last "play" event is at most 10s ago (time column). If there is no "Play" event in the last 10s, the value of StillPlaying should be "n", regardless of current action. Here's a sample of what I would like to have:
Part Action time StillPlaying
1 1 Play 15.77544 n
2 1 Play 15.89964 y
3 1 Other 35.37995 n
4 1 Play 49.38855 n
5 1 Other 83.85203 n
6 2 Other 2.031038 n
7 2 Play 14.10483 n
8 2 Other 17.29958 y
9 2 Play 36.3492 n
10 2 Play 81.20902 n
11 2 Other 87.01724 y
12 2 Other 96.30176 n
It seems like you want to group by participant and flag any row with action "Other" and where the last "Play" was within 10 seconds. You can do this using group_by in dplyr, using cummax to determine the last time a "Play" action occurred:
library(dplyr)
df1 %>%
group_by(Participant) %>%
mutate(StillPlaying=ifelse(time - c(-100, head(cummax(ifelse(Action == "Play", time, -100)), -1)) <= 10, "y", "n"))
# Participant Action time StillPlaying
# (dbl) (fctr) (dbl) (chr)
# 1 1 Play 15.775439 n
# 2 1 Play 15.899643 y
# 3 1 Other 35.379953 n
# 4 1 Play 49.388550 n
# 5 1 Other 83.852029 n
# 6 2 Other 2.031038 n
# 7 2 Play 14.104828 n
# 8 2 Other 17.299582 y
# 9 2 Play 36.349196 n
# 10 2 Play 81.209022 n
# .. ... ... ... ...
If you want to keep this in base R, you could do split-apply-combine with the same basic commands using:
do.call(rbind, lapply(split(df1, df1$Participant), function(x) {
x$StillPlaying <- ifelse(x$time - c(-100, head(cummax(ifelse(x$Action == "Play", x$time, -100)), -1)) <= 10, "y", "n")
x
}))
# Participant Action time StillPlaying
# 1.1 1 Play 15.775439 n
# 1.2 1 Play 15.899643 y
# 1.3 1 Other 35.379953 n
# 1.4 1 Play 49.388550 n
# 1.5 1 Other 83.852029 n
# 2.6 2 Other 2.031038 n
# 2.7 2 Play 14.104828 n
# 2.8 2 Other 17.299582 y
# 2.9 2 Play 36.349196 n
# 2.10 2 Play 81.209022 n
# 2.11 2 Other 87.017243 y
# 2.12 2 Other 96.301761 n
# ...
Suppose i have a data which looks like this
ID A B C
1 X 1 10
1 X 2 10
1 Z 3 15
1 Y 4 12
2 Y 1 15
2 X 2 13
2 X 3 13
2 Y 4 13
3 Y 1 16
3 Y 2 18
3 Y 3 19
3 Y 4 10
I Wanted to compare these values with each other so if an ID has changed its value of A variable over a period of B variable(which is from 1 to 4) it goes into data frame K and if it hasn't then it goes to data frame L.
so in this data set K will look like
ID A B C
1 X 1 10
1 X 2 10
1 Z 3 15
1 Y 4 12
2 Y 1 15
2 X 2 13
2 X 3 13
2 Y 4 13
and L will look like
ID A B C
3 Y 1 16
3 Y 2 18
3 Y 3 19
3 Y 4 10
In terms of nested loops and if then else statement it can be solved like following
for ( i in 1:length(ID)){
m=0
for (j in 1: length(B)){
ifelse( A[j] == A[j+1],m,m=m+1)
}
ifelse(m=0, L=c[,df[i]], K=c[,df[i]])
}
I have read in some posts that in R nested loops can be replaced by apply and outer function. if someone can help me understand how it can be used in such circumstances.
So basically you don't need a loop with conditions here, all you need to do is to check if there's a variance (and then converting it to a logical using !) in A during each cycle of B (IDs) by converting A to a numeric value (I'm assuming its a factor in your real data set, if its not a factor, you can use FUN = function(x) length(unique(x)) within ave instead ) and then split accordingly. With base R we can use ave for such task, for example
indx <- !with(df, ave(as.numeric(A), ID , FUN = var))
Or (if A is a character rather a factor)
indx <- with(df, ave(A, ID , FUN = function(x) length(unique(x)))) == 1L
Then simply run split
split(df, indx)
# $`FALSE`
# ID A B C
# 1 1 X 1 10
# 2 1 X 2 10
# 3 1 Z 3 15
# 4 1 Y 4 12
# 5 2 Y 1 15
# 6 2 X 2 13
# 7 2 X 3 13
# 8 2 Y 4 13
#
# $`TRUE`
# ID A B C
# 9 3 Y 1 16
# 10 3 Y 2 18
# 11 3 Y 3 19
# 12 3 Y 4 10
This will return a list with two data frames.
Similarly with data.table
library(data.table)
setDT(df)[, indx := !var(A), by = ID]
split(df, df$indx)
Or dplyr
library(dplyr)
df %>%
group_by(ID) %>%
mutate(indx = !var(A)) %>%
split(., indx)
Since you want to understand apply rather than simply getting it done, you can consider tapply. As a demonstration:
> tapply(df$A, df$ID, function(x) ifelse(length(unique(x))>1, "K", "L"))
1 2 3
"K" "K" "L"
In a bit plainer English: go through all df$A grouped by df$ID, and apply the function on df$A within each groupings (i.e. the x in the embedded function): if the number of unique values is more than 1, it's "K", otherwise it's "L".
We can do this using data.table. We convert the 'data.frame' to 'data.table' (setDT(df1)). Grouped by 'ID', we check the length of unique elements in 'A' (uniqueN(A)) is greater than 1 or not, create a column 'ind' based on that. We can then split the dataset based on that
'ind' column.
library(data.table)
setDT(df1)[, ind:= uniqueN(A)>1, by = ID]
setDF(df1)
split(df1[-5], df1$ind)
#$`FALSE`
# ID A B C
#9 3 Y 1 16
#10 3 Y 2 18
#11 3 Y 3 19
#12 3 Y 4 10
#$`TRUE`
# ID A B C
#1 1 X 1 10
#2 1 X 2 10
#3 1 Z 3 15
#4 1 Y 4 12
#5 2 Y 1 15
#6 2 X 2 13
#7 2 X 3 13
#8 2 Y 4 13
Or similarly using dplyr, we can use n_distinct to create a logical column and then split by that column.
library(dplyr)
df2 <- df1 %>%
group_by(ID) %>%
mutate(ind= n_distinct(A)>1)
split(df2, df2$ind)
Or a base R option with table. We get the table of the first two columns of 'df1' i.e. the 'ID' and 'A'. By double negating (!!) the output, we can get the '0' values convert to 'TRUE' and all other frequency as 'FALSE'. Get the rowSums ('indx'). We match the ID column in 'df1' with the names of the 'indx', use that to replace the 'ID' with TRUE/FALSE, and split the dataset with that.
indx <- rowSums(!!table(df1[1:2]))>1
lst <- split(df1, indx[match(df1$ID, names(indx))])
lst
#$`FALSE`
# ID A B C
#9 3 Y 1 16
#10 3 Y 2 18
#11 3 Y 3 19
#12 3 Y 4 10
#$`TRUE`
# ID A B C
#1 1 X 1 10
#2 1 X 2 10
#3 1 Z 3 15
#4 1 Y 4 12
#5 2 Y 1 15
#6 2 X 2 13
#7 2 X 3 13
#8 2 Y 4 13
If we need to get individual datasets on the global environment, change the names of the list elements to the object names we wanted and use list2env (not recommended though)
list2env(setNames(lst, c('L', 'K')), envir=.GlobalEnv)
I took a random sample from a data frame. But I don't know how to get the remaining data frame.
df <- data.frame(x=rep(1:3,each=2),y=6:1,z=letters[1:6])
#select 3 random rows
df[sample(nrow(df),3)]
What I want is to get the remaining data frame with the other 3 rows.
sample sets a random seed each time you run it, thus if you want to reproduce its results you will either need to set.seed or save its results in a variable.
Addressing your question, you simply need to add - before your index in order to get the rest of the data set.
Also, don't forget to add a comma after the indx if you want to select rows (unlike in your question)
set.seed(1)
indx <- sample(nrow(df), 3)
Your subset
df[indx, ]
# x y z
# 2 1 5 b
# 6 3 1 f
# 3 2 4 c
Remaining data set
df[-indx, ]
# x y z
# 1 1 6 a
# 4 2 3 d
# 5 3 2 e
Try:
> df
x y z
1 1 6 a
2 1 5 b
3 2 4 c
4 2 3 d
5 3 2 e
6 3 1 f
>
> df2 = df[sample(nrow(df),3),]
> df2
x y z
5 3 2 e
3 2 4 c
1 1 6 a
> df[!rownames(df) %in% rownames(df2),]
x y z
1 1 6 a
2 1 5 b
5 3 2 e