Reorder a subset of an R data.frame modifying the row names as well - r

Given a data.frame:
foo <- data.frame(ID=1:10, x=1:10)
rownames(foo) <- LETTERS[1:10]
I would like to reorder a subset of rows, defined by their row names. However, I would like to swap the row names of foo as well. I can do
sel <- c("D", "H") # rows to reorder
foo[sel,] <- foo[rev(sel),]
sel.wh <- match(sel, rownames(foo))
rownames(foo)[sel.wh] <- rownames(foo)[rev(sel.wh)]
but that is long and complicated. Is there a simpler way?


We can replace the sel values in rownames with the reverse of sel.
x <- rownames(foo)
foo[replace(x, x %in% sel, rev(sel)), ]
# ID x
#A 1 1
#B 2 2
#C 3 3
#H 8 8
#E 5 5
#F 6 6
#G 7 7
#D 4 4
#I 9 9
#J 10 10


Not as concise as ronak-shah's answer, but you could also use order.
# extract row names
temp <- row.names(foo)
# reset of vector
temp[which(temp %in% sel)] <- temp[rev(which(temp %in% sel))]
# reset order of data.frame
foo[order(temp),]
ID x
A 1 1
B 2 2
C 3 3
H 8 8
E 5 5
F 6 6
G 7 7
D 4 4
I 9 9
J 10 10
As noted in the comments, this relies on the row names following a lexicographical order. In instances where this is not true, we can use match.
# set up
set.seed(1234)
foo <- data.frame(ID=1:10, x=1:10)
row.names(foo) <- sample(LETTERS[1:10])
sel <- c("D", "H")
Now, the rownames are
# initial data.frame
foo
ID x
B 1 1
F 2 2
E 3 3
H 4 4
I 5 5
D 6 6
A 7 7
G 8 8
J 9 9
C 10 10
# grab row names
temp <- row.names(foo)
# reorder vector containing row names
temp[which(temp %in% sel)] <- temp[rev(which(temp %in% sel))]
Using, match along with order
foo[order(match(row.names(foo), temp)),]
ID x
B 1 1
F 2 2
E 3 3
D 6 6
I 5 5
H 4 4
A 7 7
G 8 8
J 9 9
C 10 10


your data frame is small so you can duplicate it then change the value of each raw:
footmp<-data.frame(foo)
foo[4,]<-footemp[8,]
foot{8,]<-footemp[4,]
Bob

Related

Create a matrix symmetric from long table in r

This is my data
df <- data.frame (Var1 <- c("a", "b", "c","d","e","b","c","d","e","c","d","e","d","e","e"),
Var2 <- c("a","a","a","a","a","b","b","b","b","c","c","c","d","d","e")
pre <- c(1,2,3,4,5,1,6,7,8,1,9,10,1,11,1) )
I would like to build a symmetric matrix with Var1 and Var2 function as rownames and colnames, and the matrix values are the Corresponding number in "pre" in r, like this:
a b c d e
a 1 2 3 4 5
b 2 1 6 7 8
c 3 6 1 9 10
d 4 7 9 1 11
e 5 8 10 11 1
This seems to be an easy problem, but I have googled a lot of posts, but it has not been solved, so I come here to ask, thank you!
Mengying

You can get the data in wide format first.
library(dplyr)
library(tidyr)
mat <- df %>%
pivot_wider(names_from = Var2, values_from = pre, values_fill = 0) %>%
column_to_rownames('Var1') %>%
as.matrix()
mat
# a b c d e
#a 1 0 0 0 0
#b 2 1 0 0 0
#c 3 6 1 0 0
#d 4 7 9 1 0
#e 5 8 10 11 1
Since you have a symmetric matrix you can copy the lower triangular matrix to upper triangle.
mat[upper.tri(mat)] <- t(mat)[upper.tri(mat)]
mat
# a b c d e
#a 1 2 3 4 5
#b 2 1 6 7 8
#c 3 6 1 9 10
#d 4 7 9 1 11
#e 5 8 10 11 1
data
df <- data.frame (Var1 = c("a", "b", "c","d","e","b","c","d","e","c","d","e","d","e","e"),
Var2 = c("a","a","a","a","a","b","b","b","b","c","c","c","d","d","e"),
pre = c(1,2,3,4,5,1,6,7,8,1,9,10,1,11,1) )

Here is an option with igraph package
g <- graph_from_data_frame(df,directed = FALSE)
E(g)$pre <- df$pre
get.adjacency(g,attr = "pre")
which gives
a b c d e
a 1 2 3 4 5
b 2 1 6 7 8
c 3 6 1 9 10
d 4 7 9 1 11
e 5 8 10 11 1

Base R solution (using data provided by Ronak):
# Crosstab:
mdat <- as.data.frame.matrix(xtabs(pre ~ Var1 + Var2, df))
# Reflect on the diag (thanks #Ronak Shah):
mdat[upper.tri(mdat)] <- t(mdat)[upper.tri(mdat)]
As #ThomasIsCoding points as well we can use this one-liner:
xtabs(pre ~ ., unique(rbind(df, cbind(setNames(rev(df[-3]), names(df)[-3]), df[3] ))))
As #thelatemail points out we can also:
xtabs(pre ~ ., unique(data.frame(Map(c, df, df[c(2,1,3)]))))

Here's a base R version:
df <- data.frame (Var1 = c("a", "b", "c","d","e","b","c","d","e","c","d","e","d","e","e"),
Var2 = c("a","a","a","a","a","b","b","b","b","c","c","c","d","d","e"),
pre = c(1,2,3,4,5,1,6,7,8,1,9,10,1,11,1))
# Generate new matrix/data frame
mat2 <- matrix(0, length(unique(df$Var1)), length(unique(df$Var2)))
# Name the columns and rows so we can access values
rownames(mat2) <- unique(df$Var1)
colnames(mat2) <- unique(df$Var2)
# Save values into appropriate places into data frame
mat2[as.matrix(df[, 1:2])] <- as.matrix(df[, 3])
# Using upper triangle trick from #Ronak Shah's answer
mat2[upper.tri(mat)] <- t(mat2)[upper.tri(mat2)]
# See results
mat2
# a b c d e
# a 1 2 3 4 5
# b 2 1 6 7 8
# c 3 6 1 9 10
# d 4 7 9 1 11
# e 5 8 10 11 1

Combine Strings with Missing Value

This is my sample data.
index <- c(1,2,3,4,5,6,7,8,9,10)
a <- c('a','b','c',NA,'D','e',NA,'g','h','i')
data <- data.frame(index,a)
What I would like to is create a new column name where only 'a' and 'b' stay. All others like 'c','d','e'...will be tagged as others, while NA stays as NA.
data$name = ifelse(!grepl('(a|b)',data$a),'others',data$name)
I tried to use the grepl function and it seems it is not working with data with missing values

In base R:
data$res <- as.character(data$a)
data$res[! data$a %in% c("a","b") & !is.na(data$a)] <- "Other"
data
# index a res
# 1 1 a a
# 2 2 b b
# 3 3 c Other
# 4 4 <NA> <NA>
# 5 5 D Other
# 6 6 e Other
# 7 7 <NA> <NA>
# 8 8 g Other
# 9 9 h Other
# 10 10 i Other
Note that the new column is of type character here.

Using dplyr and its recode function, you could do
data %>% mutate(name=recode(a, a="a", b="b", .default="other"))
# index a name
# 1 1 a a
# 2 2 b b
# 3 3 c other
# 4 4 <NA> <NA>
# 5 5 D other
# 6 6 e other
# 7 7 <NA> <NA>
# 8 8 g other
# 9 9 h other
# 10 10 i other
With a more complicated match, you migth use case_when instead
data %>% mutate(name=case_when(
is.na(a) ~ NA_character_,
a %in% c("a","b") ~ as.character(a),
TRUE ~ "other"))

R - split list every x items

I have data to analyse that is presented in the form of a list (just one row and MANY columns).
A B C D E F G H I
1 2 3 4 5 6 7 8 9
Is there a way to tell R to split this list every x items and get something as seen below (the columns C D E F G H I are virtually the same as A B)?
A B
1 2
3 4
5 6
7 8
9

If the number of columns is a multiple of 'x', then we unlist the dataset, and use matrix to create the expected output.
as.data.frame(matrix(unlist(df1), ncol=2, dimnames=list(NULL, c("A", "B")) , byrow=TRUE))
If the number of columns is not a multiple of 'x', then
x <- 2
gr <- as.numeric(gl(ncol(df1), x, ncol(df1)))
lst <- split(unlist(df1), gr)
do.call(rbind, lapply(lst, `length<-`, max(lengths(lst))))
# A B
# 1 1 2
# 2 3 4
# 3 5 6
# 4 7 8
# 5 9 NA

add column to existing column in r

How do I convert 2 columns from a data.frame onto 2 different columns?
I.E:
Data
A B C D
1 3 5 7
2 4 6 8
to
Data
A B
1 3
2 4
5 7
6 8

You can use rbind
rbind(df[,1:2], data.frame(A = df$C, B = df$D))

You can use a fast version of rbind, rbindlist from data.table:
library(data.table)
rbindlist(lapply(seq(1, ncol(df), 2), function(i) df[,i:(i+1)]))

Here is my solution but it requires to change names of the columns.
names(dat) <- c("A", "B", "A", "B")
merge(dat[1:2], dat[3:4], all = T)
A B
1 1 3
2 2 4
3 5 7
4 6 8
And here is another solution more easy.
dat[3:4, ] <- dat[ ,3:4]
dat <- dat[1:2]
dat
A B
1 1 3
2 2 4
3 5 7
4 6 8

For scalability, a solution that will halve any even size data frame and append the rows:
half <- function(df) {m <- as.matrix(df)
dim(m) <- c(nrow(df)*2,ncol(df)/2)
nd <- as.data.frame(m)
names(nd) <- names(df[(1:dim(nd)[2])]);nd}
half(Data)
A B
1 1 5
2 2 6
3 3 7
4 4 8

Convert a matrix with dimnames into a long format data.frame

Hoping there's a simple answer here but I can't find it anywhere.
I have a numeric matrix with row names and column names:
# 1 2 3 4
# a 6 7 8 9
# b 8 7 5 7
# c 8 5 4 1
# d 1 6 3 2
I want to melt the matrix to a long format, with the values in one column and matrix row and column names in one column each. The result could be a data.table or data.frame like this:
# col row value
# 1 a 6
# 1 b 8
# 1 c 8
# 1 d 1
# 2 a 7
# 2 c 5
# 2 d 6
...
Any tips appreciated.

Use melt from reshape2:
library(reshape2)
#Fake data
x <- matrix(1:12, ncol = 3)
colnames(x) <- letters[1:3]
rownames(x) <- 1:4
x.m <- melt(x)
x.m
Var1 Var2 value
1 1 a 1
2 2 a 2
3 3 a 3
4 4 a 4
...

The as.table and as.data.frame functions together will do this:
> m <- matrix( sample(1:12), nrow=4 )
> dimnames(m) <- list( One=letters[1:4], Two=LETTERS[1:3] )
> as.data.frame( as.table(m) )
One Two Freq
1 a A 7
2 b A 2
3 c A 1
4 d A 5
5 a B 9
6 b B 6
7 c B 8
8 d B 10
9 a C 11
10 b C 12
11 c C 3
12 d C 4

Assuming 'm' is your matrix...
data.frame(col = rep(colnames(m), each = nrow(m)),
row = rep(rownames(m), ncol(m)),
value = as.vector(m))
This executes extremely fast on a large matrix and also shows you a bit about how a matrix is made, how to access things in it, and how to construct your own vectors.

A modification that doesn't require you to know anything about the storage structure, and that easily extends to high dimensional arrays if you use the dimnames, and slice.index functions:
data.frame(row=rownames(m)[as.vector(row(m))],
col=colnames(m)[as.vector(col(m))],
value=as.vector(m))

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