Creating Groups by Matching Values of Different Columns - r

I would like to create groups from a base by matching values.
I have the following data table:
now<-c(1,2,3,4,24,25,26,5,6,21,22,23)
before<-c(0,1,2,3,23,24,25,4,5,0,21,22)
after<-c(2,3,4,5,25,26,0,6,0,22,23,24)
df<-as.data.frame(cbind(now,before,after))
which reproduces the following data:
now before after
1 1 0 2
2 2 1 3
3 3 2 4
4 4 3 5
5 24 23 25
6 25 24 26
7 26 25 0
8 5 4 6
9 6 5 0
10 21 0 22
11 22 21 23
12 23 22 24
I would like to get:
now before after group
1 1 0 2 A
2 2 1 3 A
3 3 2 4 A
4 4 3 5 A
5 5 4 6 A
6 6 5 0 A
7 21 0 22 B
8 22 21 23 B
9 23 22 24 B
10 24 23 25 B
11 25 24 26 B
12 26 25 0 B
I would like to reach the answer to this without using a "for" loop becouse the real data is too large.
Any you could provide will be appreciated.

Here is one way. It is hard to avoid a for-loop as this is quite a tricky algorithm. The objection to them is often on the grounds of elegance rather than speed, but sometimes they are entirely appropriate.
df$group <- seq_len(nrow(df)) #assign each row to its own group
stop <- FALSE #indicates convergence
while(!stop){
pre <- df$group #group column at start of loop
for(i in seq_len(nrow(df))){
matched <- which(df$before==df$now[i] | df$after==df$now[i]) #check matches in before and after columns
group <- min(df$group[i], df$group[matched]) #identify smallest group no of matching rows
df$group[i] <- group #set to smallest group
df$group[matched] <- group #set to smallest group
}
if(identical(df$group, pre)) stop <- TRUE #stop if no change
}
df$group <- LETTERS[match(df$group, sort(unique(df$group)))] #convert groups to letters
#(just use match(...) to keep them as integers - e.g. if you have more than 26 groups)
df <- df[order(df$group, df$now),] #reorder as required
df
now before after group
1 1 0 2 A
2 2 1 3 A
3 3 2 4 A
4 4 3 5 A
8 5 4 6 A
9 6 5 0 A
10 21 0 22 B
11 22 21 23 B
12 23 22 24 B
5 24 23 25 B
6 25 24 26 B
7 26 25 0 B

Related

R:How to apply a sliding conditional branch to consecutive values in the sequential data

I want to use conditional statement to consecutive values in the sliding manner.
For example, I have dataset like this;
data <- data.frame(ID = rep.int(c("A","B"), times = c(24, 12)),
+ time = c(1:24,1:12),
+ visit = as.integer(runif(36, min = 0, max = 20)))
and I got table below;
> data
ID time visit
1 A 1 7
2 A 2 0
3 A 3 6
4 A 4 6
5 A 5 3
6 A 6 8
7 A 7 4
8 A 8 10
9 A 9 18
10 A 10 6
11 A 11 1
12 A 12 13
13 A 13 7
14 A 14 1
15 A 15 6
16 A 16 1
17 A 17 11
18 A 18 8
19 A 19 16
20 A 20 14
21 A 21 15
22 A 22 19
23 A 23 5
24 A 24 13
25 B 1 6
26 B 2 6
27 B 3 16
28 B 4 4
29 B 5 19
30 B 6 5
31 B 7 17
32 B 8 6
33 B 9 10
34 B 10 1
35 B 11 13
36 B 12 15
I want to flag each ID by continuous values of "visit".
If the number of "visit" continued less than 10 for 6 times consecutively, I'd attach "empty", and "busy" otherwise.
In the data above, "A" is continuously below 10 from rows 1 to 6, then "empty". On the other hand, "B" doesn't have 6 consecutive one digit, then "busy".
I want to apply the condition to next segment of 6 values if the condition weren't fulfilled in the previous segment.
I'd like achieve this using R. Any advice will be appreciated.

How to randomly split a data frame into halves that are balanced on subject and item

The following randomly splits a data frame into halves.
df <- read.csv("https://raw.githubusercontent.com/HirokiYamamoto2531/data/master/data.csv")
head(df, 3)
# dv iv subject item
#1 562 -0.5 1 7
#2 790 0.5 1 21
#3 NA -0.5 1 19
r <- seq_len(nrow(df))
first <- sample(r, 240)
second <- r[!r %in% first]
df_1 <- df[first, ]
df_2 <- df[second, ]
However, in this way, each data frame (df_1 and df_2) is not balanced on subject and item: e.g.,
table(df_1$subject)
# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
# 7 8 3 5 5 3 8 1 5 7 7 6 7 7 9 8 8 9 6 7 8 5 4 4 5 2 7 6 9
# 30 31 32 33 34 35 36 37 38 39 40
# 7 5 7 7 7 3 5 7 5 3 8
table(df_1$item)
# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
# 12 11 12 12 9 11 11 8 11 12 10 8 14 7 14 10 8 7 9 9 7 11 9 8
# There are 40 subjects and 24 items, and each subject is assigned to 12 items and each item to 20 subjects.
I would like to know how to split the data frame into halves that are balanced on subject and item (i.e., exactly 6 data points from each subject and 10 data points from each item).
You can use the createDataPartition function from the caret package to create a balanced partition of one variable.
The code below creates a balanced partition of the dataset according to the variable subject:
df <- read.csv("https://raw.githubusercontent.com/HirokiYamamoto2531/data/master/data.csv")
partition <- caret::createDataPartition(df$subject, p = 0.5, list = FALSE)
first.half <- df[partition, ]
second.half <- df[-partition, ]
table(first.half$subject)
table(second.half$subject)
I'm not sure whether it's possible to balance two variables at once. You can try balancing for one variable and checking if you're happy with the partition of the second variable.

Cumulative function for a specific range of values

I have a table with a column "Age" that has a values from 1 to 10, and a column "Population" that has values specified for each of the "age" values. I want to generate a cumulative function for population such that resultant values start from ages at least 1 and above, 2 and above, and so on. I mean, the resultant array should be (203,180..and so on). Any help would be appreciated!
Age Population Withdrawn
1 23 3
2 12 2
3 32 2
4 33 3
5 15 4
6 10 1
7 19 2
8 18 3
9 19 1
10 22 5
You can use cumsum and rev:
df$sum_above <- rev(cumsum(rev(df$Population)))
The result:
> df
Age Population sum_above
1 1 23 203
2 2 12 180
3 3 32 168
4 4 33 136
5 5 15 103
6 6 10 88
7 7 19 78
8 8 18 59
9 9 19 41
10 10 22 22

r - aggregate / substract two variables, rows

I'm using the aggregate function for calculating the difference for every observation of two variables,so somehow like this (and the I want to save the result as a new variable) :
data1
Group Points_Attempt1 Points_Attempt2
1 1 10 5
2 1 34 23
3 1 50 5
4 1 10 12
5 2 11 21
6 2 23 23
7 2 32 10
8 2 12 10
I'm able to do something like this:
aggregate(data1[c("Points_Attempt1","Points_Attempt2")],list(data1$group),diff)
But I want it for every single observations and I just do not now to select the observations, so somehow the row numbers (here from 1-8).
So I'm searching for the following fourth column (Difference), which I then would like to safe as a new variable:
Group Points_Attempt1 Points_Attempt2 Difference
1 1 10 5 5
2 1 34 23 11
3 1 50 5 45
4 1 10 12 -2
5 2 11 21 -10
6 2 23 23 0
7 2 32 10 22
8 2 12 10 2
I would be highly thankful, if someone could help me with this.
We can use mutate_each
library(dplyr)
data1 %>%
group_by(Group) %>%
mutate_each(funs(c(NA, diff(.))), 2:3)
Or if we need to subtract between the variables,
data1 %>%
mutate(Difference = Points_Attemp1 - Points_Attemp2)

Counting rows based on column values in R

I have a dataframe df
Reads Counts
aaaa 10
bbbb 20
cccc 25
and so on.
I want to calculate the number of reads which exceed a certain value of counts and plot that. Example I want a data frame that looks like
Counts>= #reads with Counts>=
1 3
2 3
3 3
11 2
20 2
21 1
and so on. Can you suggest how I can get such a dataframe and plot it.
Given the levels you want to plot at...
cutoffs <- 1:30
... you could do something like:
data.frame(cutoff=cutoffs, num.above=Reduce("+", lapply(dat$Counts, ">=", cutoffs)))
# cutoff num.above
# 1 1 3
# 2 2 3
# 3 3 3
# 4 4 3
# 5 5 3
# 6 6 3
# 7 7 3
# 8 8 3
# 9 9 3
# 10 10 3
# 11 11 2
# 12 12 2
# 13 13 2
# 14 14 2
# 15 15 2
# 16 16 2
# 17 17 2
# 18 18 2
# 19 19 2
# 20 20 2
# 21 21 1
# 22 22 1
# 23 23 1
# 24 24 1
# 25 25 1
# 26 26 0
# 27 27 0
# 28 28 0
# 29 29 0
# 30 30 0
Basically for each value in the original data frame you compute a vector of whether it's greater than or equal to each cutoff (using lapply with >=). Then you add them up (using Reduce with +), getting the total number greater than or equal to each cutoff.
Another option would be using outer/colSums
cutoff <- 1:30
data.frame(cutoff=cutoffs, num.above=colSums(outer(df$Counts, cutoffs, ">=")))

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