I would like to create a table or a new data frame that displays, for each row in the original data frame, how many times a specific value precedes another specific value. For example, if I have the following data frame:
x <- data.frame("Red" = c("a", "b", "a", "a", "c", "d"), "Blue" = c("b", "a", "b", "a", "b", "a"), "Green" = c("a", "a", "b", "a", "b", "a"))
and I want to know, for each color (Red, Blue, and Green) how many times the sequence "b", "a" occurs (i.e., how many times b precedes a in the sequence).
The correct answer would look something like this:
Color ba
1 Red 1
2 Blue 3
3 Green 2
here is one solution using stringr
library(stringr)
count_pair <- function(x, pattern) {
p <- paste(pattern, collapse = "")
s <- paste(x, collapse = "")
str_count(s, pattern = p)
}
z <- apply(x, 2, count_pair, pattern = c("b", "a"))
# Red Blue Green
# 1 3 2
# if you want the output in form of a data.frame you could run:
df <- as.data.frame(as.table(z))
# Var1 Freq
# 1 Red 1
# 2 Blue 3
# 3 Green 2
Related
Let's say I have two or more vectors with to or more elements (single factor) each, e.g.
v1 = c("a", "a", "a")
v2 = c("b", "b")
What I want to do is to merge all vectors and distribute the elements for each group as equally as possible.
For the simple example above there would be a single solution:
c("a", "b", "a", "b", "a")
If v1 = c("a", "a", "a", "a") any of these
c("a", "b", "a", "b", "a", "a")
c("a", "b", "a", "a", "b", "a")
c("a", "a", "b", "a", "b", "a")
would be the best solution. Is there a built-in function that can do this? Any ideas how to implement it?
This would work for two vectors.
v1 = c("a", "a", "a")
v2 = c("b", "b")
distribute_equally <- function(v1, v2) {
v3 <- c(v1, v2)
tab <- sort(table(v3))
c(rep(names(tab), min(tab)), rep(names(tab)[2], diff(range(tab))))
}
distribute_equally(v1, v2)
#[1] "b" "a" "b" "a" "a"
distribute_equally(c('a', 'a'), c('b', 'b'))
#[1] "a" "b" "a" "b"
Thinking of the problem in terms of experimental design optimization, we can get a general solution using the MaxProQQ function in the MaxPro package.
Each position in the merged vector can be thought of as coming from a discrete quantitative factor, and the factors from your v1, v2, etc. can be thought of as qualitative factors. Here's some example code (MaxProQQ takes integer factors instead of characters, but you can convert it afterward):
library(MaxPro)
set.seed(1)
v1 <- rep(1, sample.int(10, 1))
v2 <- rep(2, sample.int(10, 1))
v3 <- rep(3, sample.int(10, 1))
v4 <- rep(4, sample.int(10, 1))
vComb <- c(v1, v2, v3, v4)
vMerge1234 <- MaxProQQ(cbind(1:length(vComb), sample(vComb, length(vComb))), p_nom = 1)$Design
vMerge1234 <- vMerge1234[order(vMerge1234[,1]),][,2]
> vMerge1234
[1] 4 3 4 2 4 3 4 1 2 4 3 4 2 4 3 1 4 3 2 4 1 3 4
Generate 100 samples, say, without replacement from c(v1, v2) giving m which is 5x100 with one column per sample. Then find the column for which the sum of the variances of the frequencies over each group is minimized. If there are more than two vectors just concatenate them in the line marked ## and the rest of the code stays the same.
set.seed(123)
v1 = c("a", "a", "a")
v2 = c("b", "b")
v <- c(v1, v2) ##
m <- replicate(100, sample(v))
varsum <- apply(m, 2, function(x) {
f <- factor(x, levels = unique(v))
sum(tapply(f, v, function(x) var(table(x))))
})
m[, which.min(varsum)]
## [1] "a" "a" "b" "b" "a"
This question already has an answer here:
Using anti_join() from the dplyr on two tables from two different databases
(1 answer)
Closed 2 years ago.
I'm looking for a way to join two tibbles in a a way to leave rows only unique to the first first tibble or unique in both tibbles - simply those one that do not have any matched key.
Let's see example:
A <- tibble( A = c("a", "b", "c", "d", "e"))
B <- tibble( A = c("a", "b", "c"))
With common dplyr::join I am not able to get this:
A
1 d
2 e
Is there some way within dplyr to overcome it or in general in tidyverse to overcome it?
Use setdiff() function from dplyr library
A <- tibble( A = c("a", "b", "c", "d", "e"))
B <- tibble( A = c("a", "b", "c"))
C <- setdiff(A,B)
Just to add.
Setdiff(A,B) gives out those elements present in A but not in B.
dplyr::anti_join will keep only the rows that are unique to the tibble/data.frame of the first argument.
A <- tibble( A = c("a", "b", "c", "d", "e"))
B <- tibble( A = c("a", "b", "c"))
dplyr::anti_join(A, B, by = "A")
# A
# <chr>
# 1 d
# 2 e
A base R possibility (well except the tibble):
A[!A$A %in% B$A,]
returns
# A tibble: 2 x 1
A
<chr>
1 d
2 e
SEE UPDATE BELOW:
Given a data frame with two columns (x1, x2) representing pairs of objects, I would like to generate groups where all members of each group are paired with all other members in that group. Thus far, I have been able to generate groups by showing all items in x2 that are paired with each item in x1, but this leaves me with groups where a couple of members are only paired with one other group member. I'm having a hard time getting off the ground with this one... Thanks in advance for any help you may have. Please let me know if I should edit this post as I am new to Stack Overflow and new to R coding.
x1 <- c("A", "B", "B", "B", "C", "C", "D", "D", "D", "E", "E")
x2 <- c("A", "B", "C", "D", "B", "C", "B", "D", "E", "D", "E")
df <- data.frame(x1, x2)
I would like to go from this df, to an output that looks like df2.
group1 <- c("A")
group2 <- c("B", "C")
group3 <- c("B", "D")
group4 <- c("D", "E")
df2 <- data.frame(cbind.fill(group1, group2, group3, group4, fill = "NULL"))
UPDATE:
Given the following dataset....
x1 <- c("A", "B", "B", "B", "C", "C", "D", "D", "D", "E", "E", "B", "C", "F")
x2 <- c("A", "B", "C", "D", "B", "C", "B", "D", "E", "D", "E", "F", "F", "F")
df <- data.frame(x1, x2)
.... I would like to identify groups of x1/x2 where all objects within said group are connected to all other objects of that group.
This is what I have thus far (I'm sure this is riddled with best-practice errors, feel free to call them out. I'm eager to learn)...
n <- nrow(as.data.frame(unique(df$x1)))
RosterGuide <- as.data.frame(matrix(nrow = n , ncol = 1))
RosterGuide$V1 <- seq.int(nrow(RosterGuide))
RosterGuide$Object <- (unique(df$x1))
colnames(RosterGuide) <- c("V1","Object")
groups_frame <- matrix(, ncol= length(n), nrow = length(n))
for (loopItem in 1:nrow(RosterGuide)) {
object <- subset(RosterGuide$Object, RosterGuide$V1 == loopItem)
group <- as.data.frame(subset(df$x2, df$x1 == object))
groups_frame <- cbind.fill(group, groups_frame, fill = "NULL")
}
Groups <- as.data.frame(groups_frame)
Groups <- subset(Groups, select = - c(object))
colnames(Groups) <- RosterGuide$V1
This yields the data frame 'Groups'....
1 2 3 4 5 6
1 F D B B B A
2 NULL E D C C NULL
3 NULL NULL E F D NULL
4 NULL NULL NULL NULL F NULL
... which is exactly what I am looking for, except that if you look at the original df, objects F and D are never paired, rendering group 5 invalid. Also, objects B and E are never paired, rendering group 3 invalid. A valid output should look like this...
1 2 3 4 5
1 D B B B A
2 E D C C NULL
3 NULL NULL NULL F NULL
Question: is there some way that I can relate the groups listed above in the 'Groups' data frame to the original df to remove groups with invalid relationships? This really has me stumped.
For context: What I am really trying to do is group items based on pairwise connections derived from a network of nodes where not all nodes are connected.
Here is one way doing it in base R using apply and unique
df <- data.frame(x1, x2, stringsAsFactors = F)
df <- df[df$x1 != df$x2, ]
unique(t(apply(df, 1, sort)))
[,1] [,2]
3 "B" "C"
4 "B" "D"
9 "D" "E"
dplyr
df %>%
dplyr::filter(x1 != x2) %>%
dplyr::filter(!duplicated(paste(pmin(x1,x2), pmax(x1,x2), sep = "-")))
x1 x2
1 B C
2 B D
3 D E
data.table (there might be another better way)
library(data.table)
as.data.table(df)[, .SD[x1 != x2]][, .GRP, by = .(x1 = pmin(x1,x2), x2 = pmax(x1,x2))]
x1 x2 GRP
1: B C 1
2: B D 2
3: D E 3
I have two vectors of factor data with equal length. Just for examples sake:
observed=c("a", "b", "c", "a", "b", "c", "a")
predicted=c("a", "a", "b", "b", "b", "c", "c")
Ultimately, I am trying to generate a classification matrix showing the number of times each factor is correctly predicted. This would look like the following for the example:
name T F
a 1 2
b 1 1
c 1 1
Note that the tables() command doesn't work here because I have 11 different factors, and the output would be 11x11 instead of 11x2. My plan is to create three vectors, and combine them into a data frame.
First, a vector of the unique factor values in the existing vectors. This is simple enough,
names=unique(df$observed)
Next, a vector of values showing the number of correct predictions. This is where I am running into trouble. I can get the number of correct predictions for an individual factor like so:
correct.a=sum(predicted[which(observed == "a")] == "a")
But this is cumbersome to repeat time and time again, and then combine into a vector like
correct=c("correct.a", "correct.b", correct.c")
Is there a way to use a loop (or other strategy that you can think of) to improve this process?
Also note that the final vector I would create would be something like this:
incorrect.a=sum(observed == "a")-correct.a
t(sapply(split(predicted == observed, observed), table))
# FALSE TRUE
#a 2 1
#b 1 1
#c 1 1
I would suggest you use data.table for explicit clean way to define your results:
library(data.table)
observed=c("a", "b", "c", "a", "b", "c", "a")
predicted=c("a", "a", "b", "b", "b", "c", "c")
dt <- data.table(observed, predicted)
res <- dt[, .(
T = sum(observed == predicted),
F = sum(observed != predicted)),
observed
]
res
# observed T F
# 1: a 1 2
# 2: b 1 1
# 3: c 1 1
I am a beginner in R. I have a dataframe in which there are two factor columns. One column is a company column, second is a product column. There are several missing values in product column and so I want to count the number of values in product column for each company (or each level of the company variable). I tried table, and count function in plyr package but they only seem to work with numeric variables. Please help!
Lets say the data frame looks like this:
df <- data.frame(company= c("A", "B", "C", "D", "A", "B", "C", "C", "D", "D"), product = c(1, 1, 2, 3, 4, 3, 3, NA, NA, NA))
So the output I am looking for is -
A 2
B 2
C 3
D 2
Thanks in advance!!
A dplyr solution.
df %>%
filter(!is.na(product)) %>%
group_by(company) %>%
count()
# A tibble: 4 × 2
comp n
<fctr> <int>
1 A 2
2 B 2
3 C 3
4 D 1
We can use rowsum from base R
with(df, rowsum(+!is.na(prod), comp))
Assuming your df is :
CASE 1) As give in question
Data for df:
options(stringsAsFactors = F)
comp <- c("A", "B", "C", "D", "A", "B", "C", "C", "D","D" )
prod <- c(1,1,2,3,4,3,3,1,NA,NA)
df <- data.frame(comp=comp,prod=prod)
Program:
df$prodflag <- !is.na(df$prod)
tapply(df$prodflag , df$comp,sum)
Output:
> tapply(df$prodflag , df$comp,sum)
A B C D
2 2 3 1
#########################################################################
CASE 2) In case stringsAsFactors is on and prod is in characters, even NAs are quoted as characters and marked as factors then you can do:
Data:
comp <- c("A", "B", "C", "D", "A", "B", "C", "C", "D","D" )
prod <- c("a","a","b","c","d","c","c","a","NA","NA")
df <- data.frame(comp=comp,prod=prod,stringsAsFactors = T)
Solution:
df$prodflag <- as.numeric(!as.character(df$prod)=="NA")
tapply(df$prodflag , df$comp,sum)
#########################################################################
CASE 3) In case the prod is a character and stringsAsFactors is on but NAs are not quoted then you can do:
Data:
comp <- c("A", "B", "C", "D", "A", "B", "C", "C", "D","D" )
prod <- c("a","a","b","c","d","c","c","a",NA,NA)
df <- data.frame(comp=comp,prod=prod,stringsAsFactors = T)
Solution:
df$prodflag <- as.numeric(!is.na(df$prod))
tapply(df$prodflag , df$comp,sum)
Moral of the story, we should understand our data and then we can the logic which best suits our need.