I have some data in long form that looks like this:
dat1 = data.frame(
id = rep(LETTERS[1:2], each=4),
value = 1:8
)
In table form:
id value
A 1
A 2
A 3
A 4
B 5
B 6
B 7
B 8
And I want it to be in short form and look like this:
dat1 = data.frame(A = 1:4, B = 5:8)
In table form:
A B
1 5
2 6
3 7
4 8
Now I could solve this by looping with cbind() and stuff, but I want to use some kind of reshape/melt function as these are the best way to do this kind of thing I think.
However, from spending >30 minutes trying to get melt() and reshape() to work, reading answers on SO, it seems that these functions requires the id.var to be set. Now, it is plainly redundant for this kind of thing, so how do I do what I want to do without having to resort to some kind of looping?
I'm pretty sure this has been answered before. Anyway, unstack is convenient in this particular case with equal group size:
unstack(dat1, form = value ~ id)
# A B
# 1 1 5
# 2 2 6
# 3 3 7
# 4 4 8
Solution below works when there are different numbers of As and Bs. For equal counts, unstack works great and with less code (Henrik's answer).
# create more general data (unbalanced 'id')
each <- c(4,2,3)
dat1 = data.frame(
id = unlist(mapply(rep, x = LETTERS[1:length(each)], each = each)),
value = 1:sum(each),
row.names = 1:sum(each) # to reproduce original row.names
)
tab <- table(dat1$id)
dat1$timevar <- unlist(sapply(tab, seq))
library(reshape2)
dcast(dat1, timevar ~ id )[-1]
initial data:
id value
1 A 1
2 A 2
3 A 3
4 A 4
5 B 5
6 B 6
7 C 7
8 C 8
9 C 9
result:
A B C
1 1 5 7
2 2 6 8
3 3 NA 9
4 4 NA NA
Here's a base R approach to consider. It uses the lengths function, which I believe was introduced in R 3.2.
x <- split(dat1$value, dat1$id)
as.data.frame(lapply(x, function(y) `length<-`(y, max(lengths(x)))))
# A B C
# 1 1 5 7
# 2 2 6 8
# 3 3 NA 9
# 4 4 NA NA
Related
I have a data frame. Let's say it looks like this:
Input data set
I have simulated some values and put them into a vector c(4,5,8,8). I want to add these simulated values to columns a, b and c.
I have tried rbind or inserting the vector into the existing data frame, but that replaced the existing values with the simulated ones, instead of adding the simulated values below the existing ones.
x <- data.frame("a" = c(2,3,1), "b" = c(5,1,2), "c" = c(6,4,7))
y <- c(4,5,8,8)
This is the output I expect to see:
Output
Help would be greatly appreciated. Thank you.
Can do:
as.data.frame(sapply(x,
function(z)
append(z,y)))
a b c
1 2 5 6
2 3 1 4
3 1 2 7
4 4 4 4
5 5 5 5
6 8 8 8
7 8 8 8
An option is assignment
n <- nrow(x)
x[n + seq_along(y), ] <- y
x
# a b c
#1 2 5 6
#2 3 1 4
#3 1 2 7
#4 4 4 4
#5 5 5 5
#6 8 8 8
#7 8 8 8
Another option is replicate the 'y' and rbind
rbind(x, `colnames<-`(replicate(ncol(x), y), names(x)))
x[(nrow(x)+1):(nrow(x)+length(y)),] <- y
I have a long data list similar to the following one:
set.seed(9)
part_number<-sample(1:5,5,replace=TRUE)
Type<-sample( c("A","B","C"),5, replace=TRUE)
rank<-sample(1:20,5,replace=TRUE)
data<-data.frame(cbind(part_number,Type,rank))
data
part_number Type rank
1 2 A 3
2 1 B 1
3 2 B 18
4 2 C 7
5 3 C 10
I want to rearrange the data in the following way:
part_number A B C
1 1
2 3 18 7
3 10
I think I need to use the reshape library. But I am not sure.
libary(tidyr)
data %>% spread(Type,rank)
# part_number A B C
# 1 1 <NA> 1 <NA>
# 2 2 3 18 7
# 3 3 <NA> <NA> 10
You would go about doing the following:
data <- reshape(data, idvar = "part_number", timevar = "Type", direction = "wide")
data
To format it exactly as you asked, I would add in,
library(tidyverse)
data %>%
arrange(part_number) %>%
dplyr::select(part_number, A = rank.A, B = rank.B, C = rank.C)
If you however had a lot more columns to rename, I would use the gsub function to rename by pattern. In addition, since now the row names are messy,
rownames(data) <- c()
Let me know if this doesn't work or this wasn't what you had in mind.
I have an automated script that produces a standard formula (i.e., y~x1+x2) and I would like to screen my data out based on those variables.
So far I have gotten this far, but I hit a sticking point where I can't quite figure it out:
#Example data
df <- data.frame(x=1:5, y=2:6, z=3:7, u=4:8)
df
x y z u
1 1 2 3 4
2 2 3 4 5
3 3 4 5 6
4 4 5 6 7
5 5 6 7 8
#Example formula
ex_form = "x~y+u"
#Delete the ~ and add a + sign to be consistent
step1 = gsub("~","+", ex_form)
#Remove + signs
step2 = strsplit(step1, "\\+")
#Final list of variables
step3 = unlist(step2)
Most solutions I've seen is something along the lines of:
#Create list of variables
mylist = c("x", "y", "u")
#Cut data
temp = df[ ,mylist]
temp
x y u
1 1 2 4
2 2 3 5
3 3 4 6
4 4 5 7
5 5 6 8
But this solution doesn't quite fit into the automation...so I need to jump from what I have to that outcome. Any thoughts?
Note: Tags are my guesses.
If you don't put your formula between " " it will be recognized as such, and can use all.vars() to extract variables from it.
ex_form = x~y+u #Without quotes it is a formula, check str(ex_form)
df[, all.vars(ex_form)]
# x y u
#1 1 2 4
#2 2 3 5
#3 3 4 6
#4 4 5 7
#5 5 6 8
Am I missing something or does simply doing temp <- df[,step3] return exactly what you say you want?
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))
I just have a simple question, I really appreciate everyones input, you have been a great help to my project. I have an additional question about data frames in R.
I have data frame that looks similar to something like this:
C <- c("","","","","","","","A","B","D","A","B","D","A","B","D")
D <- c(NA,NA,NA,2,NA,NA,1,1,4,2,2,5,2,1,4,2)
G <- list(C=C,D=D)
T <- as.data.frame(G)
T
C D
1 NA
2 NA
3 NA
4 2
5 NA
6 NA
7 1
8 A 1
9 B 4
10 D 2
11 A 2
12 B 5
13 D 2
14 A 1
15 B 4
16 D 2
I would like to be able to condense all the repeat characters into one, and look similar to this:
J B C E
1 2 1
2 A 1 2 1
3 B 4 5 4
4 D 2 2 2
So of course, the data is all the same, it is just that it is condensed and new columns are formed to hold the data. I am sure there is an easy way to do it, but from the books I have looked through, I haven't seen anything for this!
EDIT I edited the example because it wasn't working with the answers so far. I wonder if the NA's, blanks, and unevenness from the blanks are contributing??
here´s a reshape solution:
require(reshape)
cast(T, C ~ ., function(x) x)
Changed T to df to avoid a bad habit. Returns a list, which my not be what you want but you can convert from there.
C <- c("A","B","D","A","B","D","A","B","D")
D <- c(1,4,2,2,5,2,1,4,2)
my.df <- data.frame(id=C,val=D)
ret <- function(x) x
by.df <- by(my.df$val,INDICES=my.df$id,ret)
This seems to get the results you are looking for. I'm assuming it's OK to remove the NA values since that matches the desired output you show.
T <- na.omit(T)
T$ind <- ave(1:nrow(T), T$C, FUN = seq_along)
reshape(T, direction = "wide", idvar = "C", timevar = "ind")
# C D.1 D.2 D.3
# 4 2 1 NA
# 8 A 1 2 1
# 9 B 4 5 4
# 10 D 2 2 2
library(reshape2)
dcast(T, C ~ ind, value.var = "D", fill = "")
# C 1 2 3
# 1 2 1
# 2 A 1 2 1
# 3 B 4 5 4
# 4 D 2 2 2