This is the sample data with 'y' being the new variable created.
x
A
B
C
y
A
1
4
7
B
5
6
7
C
3
5
3
If the value of column x ="A", I would like the value of col.A to be displayed in column y. And similarly for the "B" & "C" values in column x.
Final result should be something like this.
x
A
B
C
y
A
1
4
7
1
B
5
6
7
6
C
3
5
3
3
A proposition :
df <- read.table(header=TRUE, text="
x A B C
A 1 4 7
B 5 6 7
C 3 5 3
"
)
df$y <- paste0("df$",df$x,"[df$x=='",df$x,"']")
df
#> x A B C y
#> 1 A 1 4 7 df$A[df$x=='A']
#> 2 B 5 6 7 df$B[df$x=='B']
#> 3 C 3 5 3 df$C[df$x=='C']
df$y <- eval(ivmte:::unstring(df$y))
df
#> x A B C y
#> 1 A 1 4 7 1
#> 2 B 5 6 7 6
#> 3 C 3 5 3 3
# Created on 2021-01-30 by the reprex package (v0.3.0.9001)
Regards,
Try this:
create_column<-function(){
y<-numeric(nrow(your_dataframe))
for (i in 1:nrow(your_dataframe)){
y[i]<-your_dataframe[i, which(names(your_dataframe)==your_dataframe$x[i])]
}
cbind(your_dataframe, y)
}
create_column()
x A B C y
1 A 1 4 7 1
2 B 5 6 7 6
3 C 3 5 3 3
>
another option with apply:
cbind(your_dataframe, y=apply(your_dataframe, 1, function(x){
x[which(names(x)==x['x'])]
}))
> your_dataframe
x A B C y
1 A 1 4 7 1
2 B 5 6 7 6
3 C 3 5 3 3
Try this
df$y <- df[-1][cbind(seq(nrow(df)),match(df$x,names(df)[-1]))]
Related
I have run across similar questions, but have not been able to find an answer for my specific needs.
I have a data set with a nested group design and I need to include a unique non-repeating ID to nested groups that can have identical values. While I regularly conduct this type of data wrangling, both the structure of this data set as well as the required outcome are beyond my skillset at this time.
Below I have provided an example data set (df) and what the results should look like.
I used the below code in my actual data set, but realized that it fails under certain circumstances...which are exaggerated in the example data set provided here. I prefer the ID to be sequentially numbered.
df$ID = cumsum(c(TRUE, diff(df$LENGTH) != 0))
I am open to all options (e.g., library(data.table), library(boot), etc) as it would be great if others find this post useful. However, I prefer solutions that do not require the installation and loading of additional packages.
Thanks in advance for you help.
Take care.
df <- read.table(text = "GROUP REGION TIME LENGTH
a x 1 3
a x 2 3
a x 3 3
a y 4 3
a y 5 3
a y 6 3
a z 7 2
a z 8 2
b z 1 2
b z 2 2
b x 3 2
b x 4 2
c x 1 2
c x 2 2
c y 3 2
c y 4 2
c x 5 2
c x 6 2
c z 7 1", header = TRUE)
result <- read.table(text = "GROUP REGION TIME LENGTH ID
a x 1 3 1
a x 2 3 1
a x 3 3 1
a y 4 3 2
a y 5 3 2
a y 6 3 2
a z 7 2 3
a z 8 2 3
b z 1 2 4
b z 2 2 4
b x 3 2 5
b x 4 2 5
c x 1 2 6
c x 2 2 6
c y 3 2 7
c y 4 2 7
c x 5 2 8
c x 6 2 8
c z 7 1 9", header = TRUE)
Paste GROUP and REGION columns and use rle to create a sequential ID column.
transform(df,ID = with(rle(paste(GROUP, REGION)),rep(seq_along(values),lengths)))
In data.table we can use rleid.
library(data.table)
setDT(df)[, ID := rleid(GROUP, REGION)]
# GROUP REGION TIME LENGTH ID
# 1: a x 1 3 1
# 2: a x 2 3 1
# 3: a x 3 3 1
# 4: a y 4 3 2
# 5: a y 5 3 2
# 6: a y 6 3 2
# 7: a z 7 2 3
# 8: a z 8 2 3
# 9: b z 1 2 4
#10: b z 2 2 4
#11: b x 3 2 5
#12: b x 4 2 5
#13: c x 1 2 6
#14: c x 2 2 6
#15: c y 3 2 7
#16: c y 4 2 7
#17: c x 5 2 8
#18: c x 6 2 8
#19: c z 7 1 9
Another base R option, but without rle
transform(
df,
ID = cumsum(c(1, (s <- paste0(GROUP, REGION))[-1] != head(s, -1)))
)
gives
GROUP REGION TIME LENGTH ID
1 a x 1 3 1
2 a x 2 3 1
3 a x 3 3 1
4 a y 4 3 2
5 a y 5 3 2
6 a y 6 3 2
7 a z 7 2 3
8 a z 8 2 3
9 b z 1 2 4
10 b z 2 2 4
11 b x 3 2 5
12 b x 4 2 5
13 c x 1 2 6
14 c x 2 2 6
15 c y 3 2 7
16 c y 4 2 7
17 c x 5 2 8
18 c x 6 2 8
19 c z 7 1 9
With dplyr
library(dplyr)
library(data.table)
df %>%
mutate(ID = rleid(GROUP, REGION))
I have two lists. Each of them with many vectors (around 500) of different lengths and I would like to get a tibble data frame with three columns.
My reproducible example is the following:
> a
[[1]]
[1] 1 3 6
[[2]]
[1] 5 4
> b
[[1]]
[1] 3 4
[[2]]
[1] 5 6 7
I would like to get the following tibble data frame:
name index value
a 1 1
a 1 3
a 1 6
a 2 5
a 2 4
b 1 3
b 1 4
b 2 5
b 2 6
b 2 7
I would be grateful if someone could help me with this issue
using Base R:
transform(stack(c(a=a,b=b)),name=substr(ind,1,1),ind=substr(ind,2,2))
values ind name
1 1 1 a
2 2 1 a
3 3 1 a
4 5 2 a
5 6 2 a
6 3 1 b
7 4 1 b
8 5 2 b
9 6 2 b
10 7 2 b
using tidyverse:
library(tidyverse)
list(a=a,b=b)%>%map(~stack(setNames(.x,1:length(.x))))%>%bind_rows(.id = "name")
name values ind
1 a 1 1
2 a 2 1
3 a 3 1
4 a 5 2
5 a 6 2
6 b 3 1
7 b 4 1
8 b 5 2
9 b 6 2
10 b 7 2
Here is one option with tidyverse
library(tidyverse)
list(a= a, b = b) %>%
map_df(enframe, name = "index", .id = 'name') %>%
unnest
# A tibble: 10 x 3
# name index value
# <chr> <int> <dbl>
# 1 a 1 1
# 2 a 1 3
# 3 a 1 6
# 4 a 2 5
# 5 a 2 4
# 6 b 1 3
# 7 b 1 4
# 8 b 2 5
# 9 b 2 6
#10 b 2 7
data
a <- list(c(1, 3, 6), c(5, 4))
b <- list(c(3, 4), c(5, 6, 7))
I have dataframe like below :-
x<-c(3,2,1,8,7,11,10,9,7,5,4)
y<-c("a","a","a", "b","b","c","c","c","c","c","c")
z<-c(2,2,2,1,1,3,3,3,3,3,3)
df<-data.frame(x,y,z)
df
x y z
1 3 a 2
2 2 a 2
3 1 a 2
4 8 b 1
5 7 b 1
6 11 c 3
7 10 c 3
8 9 c 3
9 7 c 3
10 5 c 3
11 4 c 3
I want to select top n row for each group by column y where n is provided in column z.
So the output should be like :
output:
x y z
1 3 a 2
2 2 a 2
3 8 b 1
4 11 c 3
5 10 c 3
6 9 c 3
A solution with base R:
# df is split according to y, then we keep only the top "z" value (after ordering x)
# and rbind everything back together:
do.call(rbind,
lapply(split(df, df$y),
function(df1) df1[order(df1$x, decreasing=TRUE), ][1:unique(df1$z), ]))
# x y z
#a.1 3 a 2
#a.2 2 a 2
#b 8 b 1
#c.6 11 c 3
#c.7 10 c 3
#c.8 9 c 3
EDIT:
A much more direct way (still in base R) provided in comment by #mt1022:
df[ave(1:nrow(df), df$y, FUN = seq_along) <= df$z, ]
# x y z
#1 3 a 2
#2 2 a 2
#4 8 b 1
#6 11 c 3
#7 10 c 3
#8 9 c 3
One approach with data.table:
library(data.table)
setDT(df)
df[,.(inc=seq_len(.N)<=z,x,z),by=.(y)][inc==T ,-2]
# y x z
#1: a 3 2
#2: a 2 2
#3: b 8 1
#4: c 11 3
#5: c 10 3
#6: c 9 3
A solution with dplyr that uses do:
df %>%
group_by(y) %>%
do(head(.,as.numeric(unique(.$z))))
I'm posting the solution I was looking for using dplyr. It is based on #HNSKD:
library(dplyr)
x<-c(3,2,1,8,7,11,10,9,7,5,4)
y<-c("a","a","a", "b","b","c","c","c","c","c","c")
z<-c(2,2,2,1,1,3,3,3,3,3,3)
df<-data.frame(x,y,z)
df %>% group_by(y) %>% slice(1:2)
Which returns the first two elements for each y:
# A tibble: 6 x 3
# Groups: y [3]
x y z
<dbl> <fct> <dbl>
1 3 a 2
2 2 a 2
3 8 b 1
4 7 b 1
5 11 c 3
6 10 c 3
I have a data frame,I want to create a variable z,count duplicate of "y variable", if y have 1,1 set z = 2,2, if y have 3,3,3, set z = 3,3,3.
x = c("a","b","c","d","e","a","b","c","d","e","a","b","c")
y = c(1,1,2,2,2,3,3,4,4,4,5,5,5)
data <- data.frame(x,y)
data
x y z
1 a 1 2
2 b 1 2
3 c 2 3
4 d 2 3
5 e 2 3
6 a 3 2
7 b 3 2
8 c 4 3
9 d 4 3
10 e 4 3
11 a 5 3
12 b 5 3
13 c 5 3
Thanks for your help.
You can try the rle:
data$z <- with(data, unlist(mapply(rep, rle(y)$lengths, rle(y)$lengths)))
data
x y z
1 a 1 2
2 b 1 2
3 c 2 3
4 d 2 3
5 e 2 3
6 a 3 2
7 b 3 2
8 c 4 3
9 d 4 3
10 e 4 3
11 a 5 3
12 b 5 3
13 c 5 3
If your your variable y is sorted as an increasing sequence as you say, then the following solution will work:
# calculate counts of each level
counts <- table(data$y)
# fill in z
data$z <- counts[match(data$y, names(counts))]
Note, however, that this method will fail if y is not ordered and, since you want to restart the count when a different level occurs. For these purposes, #psidom's solution is more robust to mis-ordered data as rle will reset the count.
This method calculates the total occurrences of a level and then feeds these total counts to the proper location using match.
Here is a quick method using dplyr, and its rather intuitive syntax:
library(dplyr)
left_join(data, data %>%
group_by(y) %>%
summarize(z = n()),
by = "y")
x y z
1 a 1 2
2 b 1 2
3 c 2 3
4 d 2 3
5 e 2 3
6 a 3 2
7 b 3 2
8 c 4 3
9 d 4 3
10 e 4 3
11 a 5 3
12 b 5 3
13 c 5 3
We can do this easily with data.table
library(data.table)
setDT(data)[, z := .N , rleid(y)]
data
# x y z
# 1: a 1 2
# 2: b 1 2
# 3: c 2 3
# 4: d 2 3
# 5: e 2 3
# 6: a 3 2
# 7: b 3 2
# 8: c 4 3
# 9: d 4 3
#10: e 4 3
#11: a 5 3
#12: b 5 3
#13: c 5 3
Or using rle from base R without any loops
inverse.rle(within.list(rle(data$y), values <- lengths))
#[1] 2 2 3 3 3 2 2 3 3 3 3 3 3
Or another base R method with ave
with(data, ave(y, cumsum(c(TRUE, y[-1]!= y[-length(y)])), FUN=length))
#[1] 2 2 3 3 3 2 2 3 3 3 3 3 3
Here's a short version of my large dataframe
>k
a b c d e f
1 3 4 5 7 8
2 1 7 9 0 3
3 2 2 5 6 9
I want to split in a way so that I can make separate dataframes of a,b,& c and d,e,& f like this
>k
$`1`
a b c
1 3 4
2 1 7
3 2 2
$`2`
d e f
5 7 8
9 0 3
5 6 9
I tried something like this -
range = seq(3,6,3)
k<-split(k, cut(colnames(k), range))
But it doesn't work since colnames(k) has to be numeric. Any other simple idea?
Something like this?
group <- rep(1:2, each=3)
lapply(unique(group), FUN=function(n) k[group==n])
# [[1]]
# a b c
# 1 1 3 4
# 2 2 1 7
# 3 3 2 2
#
# [[2]]
# d e f
# 1 5 7 8
# 2 9 0 3
# 3 5 6 9