Here is my small dataset.
Indvidual <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
Parent1 <- c(NA, NA, "A", "A", "C", "C", "C", "E", "A", NA)
Parent2 <- c(NA, NA, "B", "C", "D", "D", "D", NA, "D", NA)
mydf <- data.frame (Indvidual, Parent1, Parent2)
Indvidual Parent1 Parent2
1 A <NA> <NA>
2 B <NA> <NA>
3 C A B
4 D A C
5 E C D
6 F C D
7 G C D
8 H E <NA>
9 I A D
10 J <NA> <NA>
Just consider people who has two or one known parents. I need to compare and derieve score by calculating scores that their parents have.
The rules is that either one of parent (names in parent1 or parent2 column) is known (not NA), will get 1 one additional score plus score their parents have. If there are two parents known, the highest scorer will be taken into consideration.
Here is an example:
Individual "A", has both parents unknown so will get score 0
Indiviudal "C", has both parents known (i.e. A, B)
will get 0 score (maximum of their parents)
plus 1 (as it has either one of parents known)
Thus expected output from above dataframe (with explanation) is:
Indvidual Parent1 Parent2 Scores Explanation
1 A <NA> <NA> 0 0 (Max of parent Scores NA) + 0 (neither parent knwon)
2 B <NA> <NA> 0 0 (Max of parent Scores NA) + 0 (neither parent knwon)
3 C A B 1 0 (Max of parent Scores) + 1 (either parent knwon)
4 D A C 2 1 (Max of parent scores) + 1 (either parent knwon)
5 E C D 3 2 (Max of parent scores) + 1 (either parent knwon)
6 F C D 3 2 (Max of parent scores) + 1 (either parent knwon)
7 G C D 3 2 (Max of parent scores) + 1 (either parent knwon)
8 H E <NA> 4 3 (Max of parent scores) + 1 (either parent knwon)
9 I A D 3 2 (Max of parent scores) + 1 (either parent knwon)
10 J <NA> <NA> 0 0 (Max of parent scores NA) + 0 (neither parent knwon)
Explanation: As loop goes on, it takes into account on the Scores already calculated.
Max of parent scores
Edits: based on chase's question
For example:
Individual C has two parents A and B, each of which has Scores calculated as 0 and 0
(in row 1 and 2 and column Scores), means that max (c(0,0)) will be 0
Individual E has parents C and D, whose scores in Scores column is (in row 3 and 4),
1 and 2, respectively. So maximum of max(c(1,2)) will be 2.
Example using plyr and a recursive argument
library(plyr)
Indvidual <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
Parent1 <- c(NA, NA, "A", "A", "C", "C", "C", "E", "A", NA)
Parent2 <- c(NA, NA, "B", "C", "D", "D", "D", NA, "D", NA)
mydf <- data.frame (Indvidual, Parent1, Parent2)
scor.fun<-function(x,mydf){
Explanation<-0
P1<-as.character(x$Parent1)
P2<-as.character(x$Parent2)
score<-as.numeric(!(is.na(P1)||is.na(P1)))
if(!(is.na(P1)||is.na(P2))){
Explanation<-max(scor.fun(subset(mydf,Indvidual==P1),mydf)[1],scor.fun(subset(mydf,Indvidual==P2),mydf)[1])
score<-score+Explanation
}else{
Explanation<-ifelse(is.na(P1),0,scor.fun(subset(mydf,Indvidual==P1),mydf)[1])
Explanation<-max(Explanation,ifelse(is.na(P2),0,scor.fun(subset(mydf,Indvidual==P2),mydf)[1]))
score<-score+Explanation
}
c(score,Explanation)
}
adply(mydf,1,scor.fun,mydf)
Probably not the best idea with the recursion on a big dataframe.
Individual <- c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J")
Parent1 <- c(NA, NA, "A", "A", "C", "C", "C", "E", "A", NA)
Parent2 <- c(NA, NA, "B", "C", "D", "D", "D", NA, "D", NA)
mydf <- data.frame (Individual, Parent1, Parent2, stringsAsFactors = FALSE)
mydf$Scores <- NA
mydf$Scores[rowSums(is.na(mydf[, c("Parent1", "Parent2")])) == 2] <- 0
while(any(is.na(mydf$Scores))){
KnownScores <- mydf[!is.na(mydf$Scores), c(1, 4)]
ToCalculate <- mydf[
mydf$Parent1 %in% c(KnownScores$Individual, NA) &
mydf$Parent2 %in% c(KnownScores$Individual, NA) &
is.na(mydf$Scores),
-4]
ToCalculate$Score <- apply(
merge(
merge(
ToCalculate,
KnownScores,
by.x = "Parent1",
by.y = "Individual",
all.x = TRUE
),
KnownScores,
by.x = "Parent2",
by.y = "Individual",
all.x = TRUE
)[, 4:5],
1,
max,
na.rm = TRUE) + 1
mydf <- merge(mydf, ToCalculate[, c(1, 4)], all.x = TRUE)
mydf$Scores[!is.na(mydf$Score)] <- mydf$Score[!is.na(mydf$Score)]
mydf$Score <- NULL
}
Related
I have a dataset that looks like this:
data <- data.frame(Name1 = c("A", "B", "D", "E", "H"),
Name2 = c("B", "C", "E", "G", "I"))
I would like to add an ID column to help me trace groups of names, i.e. who references who? So with the example data, the groups would be:
Name1 Name2 GroupID
A B 1
B C 1
D E 2
E G 2
H I 3
Please note that my original data is not ordered as this example is. Thanks in advance for any help!
You can use the igraph package to make a network from your data set and determine clusters:
data <- data.frame(Name1 = c("A", "B", "D", "E", "H"),
Name2 = c("B", "C", "E", "G", "I"))
library(igraph)
graph <- graph_from_data_frame(data, directed = FALSE)
clusters <- components(graph)
#data$GroupId <- sapply(data$Name1, function(x) clusters$membership[which(names(clusters$membership) == x)])
# Simpler version
data$GroupId <- clusters$membership[data$Name1]
That gives:
> data
Name1 Name2 GroupId
1 A B 1
2 B C 1
3 D E 2
4 E G 2
5 H I 3
I have a dataset which is of the following form:-
a <- data.frame(X1=c("A", "B", "C", "A", "B", "C"),
X2=c("B", "C", "C", "A", "A", "B"),
X3=c("B", "E", "A", "A", "A", "B"),
X4=c("E", "C", "A", "A", "A", "C"),
X5=c("A", "C", "C", "A", "B", "B")
)
And I have another set of the following form:-
b <- data.frame(col_1=c("ASD", "ASD", "BSD", "BSD"),
col_2=c(1, 1, 1, 1),
col_3=c(12, 12, 31, 21),
col_4=("A", "B", "B", "A")
)
What I want to do is to take the column col_4 from set b and match row wise in set a, so that it tell me which row has how many elements from col_4 in a new column. The name of the new column does not matters.
For ex:- The first and fifth row in set a has all the elements of col_4 from set b.
Also, duplicates shouldn't be found. For ex. sixth row in set a has 3 "B"s. But since col_4 from set b has only two "B"s, it should tell me 2 and not 3.
Expected output is of the form:-
c <- data.frame(X1=c("A", "B", "C", "A", "B", "C"),
X2=c("B", "C", "C", "A", "A", "B"),
X3=c("B", "E", "A", "A", "A", "B"),
X4=c("E", "C", "A", "A", "A", "C"),
X5=c("A", "C", "C", "A", "B", "B"),
found=c(4, 1, 2, 2, 4, 2)
)
We can use vecsets::vintersect which takes care of duplicates.
Using apply row-wise we can count how many common values are there between b$col4 and each row in a.
apply(a, 1, function(x) length(vecsets::vintersect(b$col_4, x)))
#[1] 4 1 2 2 4 2
An option using data.table:
library(data.table)
#convert a into a long format
m <- melt(setDT(a)[, rn:=.I], id.vars="rn", value.name="col_4")
#order by row number and create an index for identical occurrences in col_4
setorder(m, rn, col_4)[, vidx := rowid(col_4), rn]
#create a similar index for b
setDT(b, key="col_4")[, vidx := rowid(col_4)]
#count occurrences and lookup this count into original data
a[b[m, on=.(col_4, vidx), nomatch=0L][, .N, rn], on=.(rn), found := N]
output:
X1 X2 X3 X4 X5 rn found
1: A B B E A 1 4
2: B C E C C 2 1
3: C C A A C 3 2
4: A A A A A 4 2
5: B A A A B 5 4
6: C B B C B 6 2
Another idea to operate on sets efficiently is to count and compare the element occurences of b$col_4 in each row of a:
b1 = c(table(b$col_4))
#b1
#A B
#2 2
a1 = table(factor(as.matrix(a), names(b1)), row(a))
#a1
#
# 1 2 3 4 5 6
# A 2 0 2 5 3 0
# B 2 1 0 0 2 3
Finally, identify the least amount of occurences per element (for each row) and sum:
colSums(pmin(a1, b1))
#1 2 3 4 5 6
#4 1 2 2 4 2
In case of a larger dimension a "data.frame" and more elements, Matrix::sparseMatrix offers an appropriate alternative:
library(Matrix)
a.fac = factor(as.matrix(a), names(b1))
.i = as.integer(a.fac)
.j = c(row(a))
noNA = !is.na(.i) ## need to remove NAs manually
.i = .i[noNA]
.j = .j[noNA]
a1 = sparseMatrix(i = .i, j = .j, x = 1L, dimnames = list(names(b1), 1:nrow(a)))
a1
#2 x 6 sparse Matrix of class "dgCMatrix"
# 1 2 3 4 5 6
#A 2 . 2 5 3 .
#B 2 1 . . 2 3
colSums(pmin(a1, b1))
#1 2 3 4 5 6
#4 1 2 2 4 2
So, if I have two lists, one being a "master list" without repeats, and the other being a subset with possible repeats, I would like to be able to check how many of each element are in the secondary subset list.
So if I have these lists:
a <- (a, b, c, d, e, f, g)
b <- (a, d, c, d, a, f, f, g, c, c)
I'd like to determine how many times each element from list a appear in list b and the frequency of each. My ideal output would be an r table that looks like:
c <- a b c d e f g
2 0 3 1 0 2 1
I've been trying to think through it with %in% and table()
You can use table and match - but first make the vectors factors so levels not present are included in the output:
a <- factor(c("a", "b", "c", "d", "e", "f", "g"))
b <- factor(c("a", "d", "c", "d", "a", "f", "f", "g", "c", "c"))
table(a[match(b, a)])
a b c d e f g
2 0 3 2 0 2 1
If for some reason you want a tidyverse solution. This method preserves the original data type in the lists.
library(tidyverse)
a <- c("a", "b", "c", "d", "e", "f", "g")
b <- c("a", "d", "c", "d", "a", "f", "f", "g", "c", "c")
tibble(letters = a, count = unlist(map(a, function(x) sum(b %in% x))))
# A tibble: 7 x 2
letters count
<chr> <int>
1 a 2
2 b 0
3 c 3
4 d 2
5 e 0
6 f 2
7 g 1
The following code:
df <- data.frame(
"letter" = c("a", "b", "c", "d", "e", "f"),
"score" = seq(1,6)
)
Results in the following dataframe:
letter score
1 a 1
2 b 2
3 c 3
4 d 4
5 e 5
6 f 6
I want to get the scores for a sequence of letters, for example the scores of c("f", "a", "d", "e"). It should result in c(6, 1, 4, 5).
What's more, I want to get the scores for c("c", "o", "f", "f", "e", "e"). Now the o is not in the letter column so it should return NA, resulting in c(3, NA, 6, 6, 5, 5).
What is the best way to achieve this? Can I use dplyr for this?
We can use match to create an index and extract the corresponding 'score' If there is no match, then by default it gives NA
df$score[match(v1, df$letter)]
#[1] 3 NA 6 6 5 5
df$score[match(v2, df$letter)]
#[1] 6 1 4 5
data
v1 <- c("c", "o", "f", "f", "e", "e")
v2 <- c("f", "a", "d", "e")
If you want to use dplyr I would use a join:
df <- data.frame(
"letter" = c("a", "b", "c", "d", "e", "f"),
"score" = seq(1:6)
)
library(dplyr)
df2 <- data.frame(letter = c("c", "o", "f", "f", "e", "e"))
left_join(df2, df, by = "letter")
letter score
1 c 3
2 o NA
3 f 6
4 f 6
5 e 5
6 e 5
I am quite new to R programming, and am having some difficulty with ANOTHER step of my project. I am not even sure at this point if I am asking the question correctly. I have a dataframe of actual and predicted values:
actual predicted.1 predicted.2 predicted.3 predicted.4
a a a a a
a a a b b
b b a b b
b a b b c
c c c c c
c d c c d
d d d c d
d d d d a
The issue that I am having is that I need to create a vector of mismatches between the actual value and each of the four predicted values. This should result in a single vector: c(2,1,2,4)
I am trying to use a boolean mask to sum over the TRUE values...but something is not working right. I need to do this sum for each of the four predicted values to actual value comparisons.
discordant_sums(df[,seq(1,ncol(df),2)]!=,df[,seq(2,ncol(df),2)])
Any suggestions would be greatly appreciated.
You can use apply to compare values in 1st column with values in each of all other columns.
apply(df[-1], 2, function(x)sum(df[1]!=x))
# predicted.1 predicted.2 predicted.3 predicted.4
# 2 1 2 4
Data:
df <- read.table(text =
"actual predicted.1 predicted.2 predicted.3 predicted.4
a a a a a
a a a b b
b b a b b
b a b b c
c c c c c
c d c c d
d d d c d
d d d d a",
header = TRUE, stringsAsFactors = FALSE)
We can replicate the first column to make the lengths equal between the comparison objects and do the colSums
as.vector(colSums(df[,1][row(df[-1])] != df[-1]))
#[1] 2 1 2 4
data
df <- structure(list(actual = c("a", "a", "b", "b", "c", "c", "d",
"d"), predicted.1 = c("a", "a", "b", "a", "c", "d", "d", "d"),
predicted.2 = c("a", "a", "a", "b", "c", "c", "d", "d"),
predicted.3 = c("a", "b", "b", "b", "c", "c", "c", "d"),
predicted.4 = c("a", "b", "b", "c", "c", "d", "d", "a")),
.Names = c("actual",
"predicted.1", "predicted.2", "predicted.3", "predicted.4"),
class = "data.frame", row.names = c(NA,
-8L))