I have an issue of expanding rows of my data frame. I tried expand from tidyr inside of a dplyr chain. The point is that it seems that this function is expanding the data but by changing the order of expand element which is not desired. I want to keep order of sp column after expand.
Here is my attempt
df <- data.frame(label1=letters[1:6],label2=letters[7:12])
sp <- c(-1,0,seq(0.1,0.5,0.1),seq(-2,-2.5,-0.1),seq(0.1,0.5,0.1))
sp
# [1] -1.0 0.0 0.1 0.2 0.3 0.4 0.5 -2.0 -2.1 -2.2 -2.3 -2.4 -2.5 0.1 0.2 0.3 0.4 0.5
library(dplyr)
library(tidyr)
expanded <- df%>%
expand(df,sp)
> head(expanded)
label1 label2 sp
1 a g -2.5
2 a g -2.4
3 a g -2.3
4 a g -2.2
5 a g -2.1
6 a g -2.0
I want to expand df based on sp order. how can we do that?
expected output
label1 label2 sp
1 a g -1.0
2 a g 0.0
3 a g 0.1
4 a g 0.2
5 a g 0.3
6 a g 0.4
7 a g 0.5
8 a g -2
9 a g -2.1
10 a g -2.2
11 a g -2.3
12 a g -2.4
13 a g -2.5
14 b h -1.0
15 b h 0.0
16 b h 0.1
and so on
We can match the column 'sp' with the vector sp in the global environment to do the ordering
r1 <- df %>%
expand(df, sp) %>%
arrange(label1, label2, match(sp, unique(.GlobalEnv$sp)))
dim(r1)
#[1] 78 3
identical(unique(r1$sp), unique(sp))
#[1] TRUE
Update
If there are duplicates in the 'sp' vector and we want to expand on all the values, one option is to do the expansion on the sequence of the vector and later change the values
r2 <- df %>%
expand(df, sp=seq_along(sp)) %>%
mutate(sp = .GlobalEnv$sp[sp])
dim(r2)
#[1] 108 3
head(r2, length(sp))
# label1 label2 sp
# 1 a g -1.0
# 2 a g 0.0
# 3 a g 0.1
# 4 a g 0.2
# 5 a g 0.3
# 6 a g 0.4
# 7 a g 0.5
# 8 a g -2.0
# 9 a g -2.1
# 10 a g -2.2
# 11 a g -2.3
# 12 a g -2.4
# 13 a g -2.5
# 14 a g 0.1
# 15 a g 0.2
# 16 a g 0.3
# 17 a g 0.4
# 18 a g 0.5
Related
Looking for a way to select the top 3 AND bottom 3 rows by value. I have tried using slice_max() in conjunction with slice_min() with no success.
id value
a 0.9
b 0.2
c -0.4
d -0.9
e 0.6
f 0.8
g -0.3
h 0.1
i 0.2
j 0.5
k -0.2
# Desired output: <br>
a 0.9
f 0.8
e 0.6
d -0.9
c -0.4
g -0.3
dplyr
dat %>%
filter(!between(dense_rank(value), 4, n() - 4))
# id value
# 1 a 0.9
# 2 c -0.4
# 3 d -0.9
# 4 e 0.6
# 5 f 0.8
# 6 g -0.3
or
dat %>%
arrange(value) %>%
slice( unique(c(1:3, n() - 0:2)) )
I have two data base, df and cf. I want to multiply each value of A in df by each coefficient in cf depending on the value of B and C in table df.
For example
row 2 in df A= 20 B= 4 and C= 2 so the correct coefficient is 0.3,
the result is 20*0.3 = 6
There is a simple way to do that in R!?
Thanks in advance!!
df
A B C
20 4 2
30 4 5
35 2 2
24 3 3
43 2 1
cf
C
B/C 1 2 3 4 5
1 0.2 0.3 0.5 0.6 0.7
2 0.1 0.5 0.3 0.3 0.4
3 0.9 0.1 0.6 0.6 0.8
4 0.7 0.3 0.7 0.4 0.6
One solution with apply:
#iterate over df's rows
apply(df, 1, function(x) {
x[1] * cf[x[2], x[3]]
})
#[1] 6.0 18.0 17.5 14.4 4.3
Try this vectorized:
df[,1] * cf[as.matrix(df[,2:3])]
#[1] 6.0 18.0 17.5 14.4 4.3
A solution using dplyr and a vectorised function:
df = read.table(text = "
A B C
20 4 2
30 4 5
35 2 2
24 3 3
43 2 1
", header=T, stringsAsFactors=F)
cf = read.table(text = "
0.2 0.3 0.5 0.6 0.7
0.1 0.5 0.3 0.3 0.4
0.9 0.1 0.6 0.6 0.8
0.7 0.3 0.7 0.4 0.6
")
library(dplyr)
# function to get the correct element of cf
# vectorised version
f = function(x,y) cf[x,y]
f = Vectorize(f)
df %>%
mutate(val = f(B,C),
result = val * A)
# A B C val result
# 1 20 4 2 0.3 6.0
# 2 30 4 5 0.6 18.0
# 3 35 2 2 0.5 17.5
# 4 24 3 3 0.6 14.4
# 5 43 2 1 0.1 4.3
The final dataset has both result and val in order to check which value from cf was used each time.
Here is a sample data set:
sample1 <- data.frame(Names=letters[1:10], Values=sample(seq(0.1,1,0.1)))
When I'm reordering the data set, I'm losing the row names order
sample1[order(sample1$Values), ]
Names Values
7 g 0.1
4 d 0.2
3 c 0.3
9 i 0.4
10 j 0.5
5 e 0.6
8 h 0.7
6 f 0.8
1 a 0.9
2 b 1.0
Desired output:
Names Values
1 g 0.1
2 d 0.2
3 c 0.3
4 i 0.4
5 j 0.5
6 e 0.6
7 h 0.7
8 f 0.8
9 a 0.9
10 b 1.0
Try
rownames(Ordersample2) <- 1:10
or more generally
rownames(Ordersample2) <- NULL
I had a dplyr usecase:
df %>% as.data.frame(row.names = 1:nrow(.))
Suppose I have a data.frame where if I take multiple columns together (say a, b, and c), then I have an identifier that is unique to two different rows (that differ on column name, and a bunch of value columns x, y, and z).
I'd like to take the difference on the value columns, preserve the key columns, and give the name column a new value like diff.
So for example, suppose I have the following data:
a b c x y z name
1 1 M J 0.0 1.0 2.0 alpha
2 1 M K 0.1 0.9 2.0 alpha
3 1 O J 0.2 0.8 2.0 alpha
4 1 O K 0.3 0.7 2.0 alpha
5 2 M J 0.4 0.6 2.0 alpha
6 2 M K 0.5 0.5 2.0 alpha
7 2 O J 0.6 0.4 2.0 alpha
8 2 O K 0.7 0.3 2.0 alpha
9 1 M J 0.0 2.0 1.0 beta
10 1 M K 0.1 1.9 3.0 beta
11 1 O J 0.2 1.8 1.0 beta
12 1 O K 0.3 1.7 3.0 beta
13 2 M J 0.4 1.6 1.0 beta
14 2 M K 0.5 1.5 3.0 beta
15 2 O J 0.6 1.4 1.0 beta
16 2 O K 0.7 1.3 3.0 beta
Then I want the new data frame to be:
a b c x y z name
1 1 M J 0.0 1.0 2.0 alpha
2 1 M K 0.1 0.9 2.0 alpha
3 1 O J 0.2 0.8 2.0 alpha
4 1 O K 0.3 0.7 2.0 alpha
5 2 M J 0.4 0.6 2.0 alpha
6 2 M K 0.5 0.5 2.0 alpha
7 2 O J 0.6 0.4 2.0 alpha
8 2 O K 0.7 0.3 2.0 alpha
9 1 M J 0.0 2.0 1.0 beta
10 1 M K 0.1 1.9 3.0 beta
11 1 O J 0.2 1.8 1.0 beta
12 1 O K 0.3 1.7 3.0 beta
13 2 M J 0.4 1.6 1.0 beta
14 2 M K 0.5 1.5 3.0 beta
15 2 O J 0.6 1.4 1.0 beta
16 2 O K 0.7 1.3 3.0 beta
17 1 M J 0.0 -1.0 1.0 diff
18 1 M K 0.0 -1.0 -1.0 diff
19 1 O J 0.0 -1.0 1.0 diff
20 1 O K 0.0 -1.0 -1.0 diff
21 2 M J 0.0 -1.0 1.0 diff
22 2 M K 0.0 -1.0 -1.0 diff
23 2 O J 0.0 -1.0 1.0 diff
24 2 O K 0.0 -1.0 -1.0 diff
What's the easiest way to accomplish this?
You could make each column individually:
colx = ave(df$x, paste(df$a, df$b, df$c), FUN=function(x) x[1]-x[2])
coly = ave(df$y, paste(df$a, df$b, df$c), FUN=function(x) x[1]-x[2])
colz = ave(df$z, paste(df$a, df$b, df$c), FUN=function(x) x[1]-x[2])
And then put them together:
df2 = subset(df, name=="alpha")
df2$name = "diff"
df2$x = colx[1:(length(colx)/2)]
df2$y = coly[1:(length(coly)/2)]
df2$z = colz[1:(length(colz)/2)]
Now join to original
df = rbind(df, df2)
That gives:
a b c x y z name
1 1 m j 0.0 1.0 2 a
2 1 m k 0.1 0.9 2 a
3 1 o j 0.2 0.8 2 a
4 1 o k 0.3 0.7 2 a
5 2 m j 0.4 0.6 2 a
6 2 m k 0.5 0.5 2 a
7 2 o j 0.6 0.4 2 a
8 2 o k 0.7 0.3 2 a
9 1 m j 0.0 2.0 1 b
10 1 m k 0.1 1.9 3 b
11 1 o j 0.2 1.8 1 b
12 1 o k 0.3 1.7 3 b
13 2 m j 0.4 1.6 1 b
14 2 m k 0.5 1.5 3 b
15 2 o j 0.6 1.4 1 b
16 2 o k 0.7 1.3 3 b
17 1 m j 0.0 -1.0 1 diff
18 1 m k 0.0 -1.0 -1 diff
19 1 o j 0.0 -1.0 1 diff
20 1 o k 0.0 -1.0 -1 diff
21 2 m j 0.0 -1.0 1 diff
22 2 m k 0.0 -1.0 -1 diff
23 2 o j 0.0 -1.0 1 diff
24 2 o k 0.0 -1.0 -1 diff
If your matrix is always sorted and ballanced. Then this should work
half<-1:(nrow(df)/2)
rbind(
df,
cbind(
df[half, 1:3],
df[half, 4:6] - df[half+half[length(half)], 4:6],
name="diff"
)
)
I have this data frame:
df <- data.frame(A=c("a","b","c","d","e","f","g","h","i"),
B=c("1","1","1","2","2","2","3","3","3"),
C=c(0.1,0.2,0.4,0.1,0.5,0.7,0.1,0.2,0.5))
> df
A B C
1 a 1 0.1
2 b 1 0.2
3 c 1 0.4
4 d 2 0.1
5 e 2 0.5
6 f 2 0.7
7 g 3 0.1
8 h 3 0.2
9 i 3 0.5
I would like to add 1000 further columns and fill this columns with the values generated by :
transform(df, D=ave(C, B, FUN=function(b) sample(b, replace=TRUE)))
I've tried with a for loop but it does not work:
for (i in 4:1000){
df[, 4:1000] <- NA
df[,i] = transform(df, D=ave(C, B, FUN=function(b) sample(b, replace=TRUE)))
}
For efficiency reasons, I suggest running sample only once for each group. This can be achieved with this:
sample2 <- function(x, size)
{
if(length(x)==1) rep(x, size) else sample(x, size, replace=TRUE)
}
new_df <- do.call(rbind, by(df, df$B,
function(d) cbind(d, matrix(sample2(d$C, length(d$C)*1000),
ncol=1000))))
Notes:
I've created sample2 in case there is a group with only one C value. Check ?sample to see what I mean.
The names of the columns will be numbers, from 1 to 1000. This can be changed as in the answer by #agstudy.
The row names are also changed. "Fixing" them is similar, just use row.names instead of col.names.
Using replicate for example:
cbind(df,replicate(1000,ave(df$C, df$B,
FUN=function(b) sample(b, replace=TRUE))))
To add 4 columns for example:
cbind(df,replicate(4,ave(df$C, df$B,
FUN=function(b) sample(b, replace=TRUE))))
A B C 1 2 3 4
1 a 1 0.1 0.2 0.2 0.1 0.2
2 b 1 0.2 0.4 0.2 0.4 0.4
3 c 1 0.4 0.1 0.1 0.1 0.1
4 d 2 0.1 0.1 0.5 0.5 0.1
5 e 2 0.5 0.7 0.1 0.5 0.1
6 f 2 0.7 0.1 0.7 0.7 0.7
7 g 3 0.1 0.2 0.5 0.2 0.2
8 h 3 0.2 0.2 0.1 0.2 0.1
9 i 3 0.5 0.5 0.5 0.1 0.5
Maybe you need to rename columns by something like :
gsub('([0-9]+)','D\\1',colnames(res))
1] "A" "B" "C" "D1" "D2" "D3" "D4"