I have a data set with observations that are both grouped and ordered (by rank). I'd like to add a third variable that is a count of the number of observations for each grouping variable. I'm aware of ways to group and count variables but I can't find a way to re-insert these counts back into the original data set, which has more rows. I'd like to get the variable C in the example table below.
A B C
1 1 3
1 2 3
1 3 3
2 1 4
2 2 4
2 3 4
2 4 4
Here's one way using ave:
DF <- within(DF, {C <- ave(A, A, FUN=length)})
# A B C
# 1 1 1 3
# 2 1 2 3
# 3 1 3 3
# 4 2 1 4
# 5 2 2 4
# 6 2 3 4
# 7 2 4 4
Here is one approach using data.table that makes use of .N, which is described in the help file to "data.table" as .N is an integer, length 1, containing the number of rows in the group.
> library(data.table)
> DT <- data.table(A = rep(c(1, 2), times = c(3, 4)), B = c(1:3, 1:4))
> DT
A B
1: 1 1
2: 1 2
3: 1 3
4: 2 1
5: 2 2
6: 2 3
7: 2 4
> DT[, C := .N, by = "A"]
> DT
A B C
1: 1 1 3
2: 1 2 3
3: 1 3 3
4: 2 1 4
5: 2 2 4
6: 2 3 4
7: 2 4 4
Related
I would like to create an adjacency list from a dataset like the following:
id group
1 1
2 1
3 1
4 2
5 2
The connected id are those who are in the same group. Therefore, I would like to get the following adjacency list:
id id2
1 2
1 3
2 1
2 3
3 1
3 2
4 5
5 4
I am struggling in figuring out how to do it. In particular, I have found a solution where order does not matter (split and expand.grid by group on large data set). In my case, it does, so I would not like to have those observations dropped.
Maybe something like this, using data.table:
require(data.table)
dt <- fread('id group
1 1
2 1
3 1
4 2
5 2')
dt[, expand.grid(id, id), by = group][Var1 != Var2][, -1]
# Var1 Var2
# 1: 2 1
# 2: 3 1
# 3: 1 2
# 4: 3 2
# 5: 1 3
# 6: 2 3
# 7: 5 4
# 8: 4 5
This question already has answers here:
Create counter with multiple variables [duplicate]
(6 answers)
Closed 9 years ago.
I am trying to obtain a sequence within category.
My data are:
A B
1 1
1 2
1 2
1 3
1 3
1 3
1 4
1 4
and I want to get variable "c" such as my data look like:
A B C
1 1 1
1 2 1
1 2 2
1 3 1
1 3 2
1 3 3
1 4 1
1 4 2
Use ave with seq_along:
> mydf$C <- with(mydf, ave(A, A, B, FUN = seq_along))
> mydf
A B C
1 1 1 1
2 1 2 1
3 1 2 2
4 1 3 1
5 1 3 2
6 1 3 3
7 1 4 1
8 1 4 2
If your data are already ordered (as they are in this case), you can also use sequence with rle (mydf$C <- sequence(rle(do.call(paste, mydf))$lengths)), but you don't have that limitation with ave.
If you're a data.table fan, you can make use of .N as follows:
library(data.table)
DT <- data.table(mydf)
DT[, C := sequence(.N), by = c("A", "B")]
DT
# A B C
# 1: 1 1 1
# 2: 1 2 1
# 3: 1 2 2
# 4: 1 3 1
# 5: 1 3 2
# 6: 1 3 3
# 7: 1 4 1
# 8: 1 4 2
I have a problem with moving the rows to one upper row. When the rows become completely NA I would like to flush those rows (see the pic below). My current approach for this solution however still keeping the second rows.
Here is my approach
data <- data.frame(gr=c(rep(1:3,each=2)),A=c(1,NA,2,NA,4,NA), B=c(NA,1,NA,3,NA,7),C=c(1,NA,4,NA,5,NA))
> data
gr A B C
1 1 1 NA 1
2 1 NA 1 NA
3 2 2 NA 4
4 2 NA 3 NA
5 3 4 NA 5
6 3 NA 7 NA
so using this approach
data.frame(apply(data,2,function(x){x[complete.cases(x)]}))
gr A B C
1 1 1 1 1
2 1 2 3 4
3 2 4 7 5
4 2 1 1 1
5 3 2 3 4
6 3 4 7 5
As we can see still I am having the second rows in each group!
The expected output
> data
gr A B C
1 1 1 1 1
2 2 2 3 4
3 3 4 7 5
thanks!
If there's at most one valid value per gr, you can use na.omit then take the first value from it:
data %>% group_by(gr) %>% summarise_all(~ na.omit(.)[1])
# [1] is optional depending on your actual data
# A tibble: 3 x 4
# gr A B C
# <int> <dbl> <dbl> <dbl>
#1 1 1 1 1
#2 2 2 3 4
#3 3 4 7 5
You can do it with dplyr like this:
data$ind <- rep(c(1,2), replace=TRUE)
data %>% fill(A,B,C) %>% filter(ind == 2) %>% mutate(ind=NULL)
gr A B C
1 1 1 1 1
2 2 2 3 4
3 3 4 7 5
Depending on how consistent your full data is, this may need to be adjusted.
One more solution using data.table:-
data <- data.frame(gr=c(rep(1:3,each=2)),A=c(1,NA,2,NA,4,NA), B=c(NA,1,NA,3,NA,7),C=c(1,NA,4,NA,5,NA))
library(data.table)
library(zoo)
setDT(data)
data[, A := na.locf(A), by = gr]
data[, B := na.locf(B), by = gr]
data[, C := na.locf(C), by = gr]
data <- unique(data)
data
gr A B C
1: 1 1 1 1
2: 2 2 3 4
3: 3 4 7 5
Take this data frame for example:
DT <- data.table(A = rep(1:3, each=4),
B = rep(c(NA,1,2,4), each=3),
C = rep(1:2, 6))
I want to append a column that assign index to unique combinations of A and B, but ignore C. I also want another column that count the number of duplicates, that looks like this:
A B C Index Count
1: 1 NA 1 1 3
2: 1 NA 2 1 3
3: 1 NA 1 1 3
4: 1 1 2 2 1
5: 2 1 1 3 2
6: 2 1 2 3 2
7: 2 2 1 4 2
8: 2 2 2 4 2
9: 3 2 1 5 1
10: 3 4 2 6 3
11: 3 4 1 6 3
12: 3 4 2 6 3
I don't want to trim the data frame and (preferably)I don't want to reorder the rows.
I tried setDT, such as
setDT(DT)[,.(.I, .N), by = names(DT[,1:2])]
But the I column is not the index I want, and Column C is gone.
Thanks in advance!
I have a table similar this, with more columns. What I am trying to do is creating a new table that shows, for each ID, the number of Counts of each Type, the Value of each Type.
df
ID Type Counts Value
1 A 1 5
1 B 2 4
2 A 2 1
2 A 3 4
2 B 1 3
2 B 2 3
I am able to do it for one single column by using
dcast(df[,j=list(sum(Counts,na.rm = TRUE)),by = c("ID","Type")],ID ~ paste(Type,"Counts",sep="_"))
However, I want to use a loop through each column within the data table. but there is no success, it will always add up all the rows. I have try to use
sum(df[[i]],na.rm = TRUE)
sum(names(df)[[i]] == "",na.rm = TRUE)
sum(df[[names(df)[i]]],na.rm = TRUE)
j = list(apply(df[,c(3:4),with=FALSE],2,function(x) sum(x,na.rm = TRUE)
I want to have a new table similar like
ID A_Counts B_Counts A_Value B_Value
1 1 2 5 4
2 5 3 5 6
My own table have more columns, but the idea is the same. Do I over-complicated it or is there a easy trick I am not aware of? Please help me. Thank you!
You have to melt your data first, and then dcast it:
library(reshape2)
df2 <- melt(df,id.vars = c("ID","Type"))
# ID Type variable value
# 1 1 A Counts 1
# 2 1 B Counts 2
# 3 2 A Counts 2
# 4 2 A Counts 3
# 5 2 B Counts 1
# 6 2 B Counts 2
# 7 1 A Value 5
# 8 1 B Value 4
# 9 2 A Value 1
# 10 2 A Value 4
# 11 2 B Value 3
# 12 2 B Value 3
dcast(df2,ID ~ Type + variable,fun.aggregate=sum)
# ID A_Counts A_Value B_Counts B_Value
# 1 1 1 5 2 4
# 2 2 5 5 3 6
Another solution with base functions only:
df3 <- aggregate(cbind(Counts,Value) ~ ID + Type,df,sum)
# ID Type Counts Value
# 1 1 A 1 5
# 2 2 A 5 5
# 3 1 B 2 4
# 4 2 B 3 6
reshape(df3, idvar='ID', timevar='Type',direction="wide")
# ID Counts.A Value.A Counts.B Value.B
# 1 1 1 5 2 4
# 2 2 5 5 3 6
Data
df <- read.table(text ="ID Type Counts Value
1 A 1 5
1 B 2 4
2 A 2 1
2 A 3 4
2 B 1 3
2 B 2 3",stringsAsFactors=FALSE,header=TRUE)