I have data with a grouping variable ("from") and values ("number"):
from number
1 1
1 1
2 1
2 2
3 2
3 2
I want to subset the data and select groups which have two or more unique values. In my data, only group 2 has more than one distinct 'number', so this is the desired result:
from number
2 1
2 2
Several possibilities, here's my favorite
library(data.table)
setDT(df)[, if(+var(number)) .SD, by = from]
# from number
# 1: 2 1
# 2: 2 2
Basically, per each group we are checking if there is any variance, if TRUE, then return the group values
With base R, I would go with
df[as.logical(with(df, ave(number, from, FUN = var))), ]
# from number
# 3 2 1
# 4 2 2
Edit: for a non numerical data you could try the new uniqueN function for the devel version of data.table (or use length(unique(number)) > 1 instead
setDT(df)[, if(uniqueN(number) > 1) .SD, by = from]
You could try
library(dplyr)
df1 %>%
group_by(from) %>%
filter(n_distinct(number)>1)
# from number
#1 2 1
#2 2 2
Or using base R
indx <- rowSums(!!table(df1))>1
subset(df1, from %in% names(indx)[indx])
# from number
#3 2 1
#4 2 2
Or
df1[with(df1, !ave(number, from, FUN=anyDuplicated)),]
# from number
#3 2 1
#4 2 2
Using concept of variance shared by David but doing it dplyr way:
library(dplyr)
df %>%
group_by(from) %>%
mutate(variance=var(number)) %>%
filter(variance!=0) %>%
select(from,number)
#Source: local data frame [2 x 2]
#Groups: from
#from number
#1 2 1
#2 2 2
Related
I have the data below:
group_id <- c(1,1,1,2,2,2,3,3,3,3,4,4)
member_id <- c(1,2,3,4,4,3,5,6,8,10,12,12)
df <- data.frame(group_id,member_id)
> df
group_id member_id
1 1 1
2 1 2
3 1 3
4 2 4
5 2 4
6 2 3
7 3 5
8 3 6
9 3 8
10 3 10
11 4 12
12 4 12
Each member is paired with a group_id. I would like to extract which member id exists in multiple groups. For the above case, a member_id of 3 exists in both group_id 1 and 2. Is there a way I can use data.table or dplyr to figure out which member_id's exist in more than one group?
You can use n_distinct in dplyr and uniqueN in data.table to get count of unique group_id in each member_id. Select only those member_id which occur more than once.
library(dplyr)
df %>%
group_by(member_id) %>%
filter(n_distinct(group_id) > 1) %>%
pull(member_id) %>% unique
#[1] 3
data.table :
library(data.table)
unique(setDT(df)[, .SD[uniqueN(group_id) > 1], member_id]$member_id)
and for completeion here is base R approach -
unique(subset(df, ave(group_id, member_id, FUN = function(x)
length(unique(x))) > 1, select = member_id))
Using dplyr
library(dplyr)
df %>%
group_by(member_id) %>%
slice(which(n_distinct(group_id) > 1)) %>%
ungroup %>%
distinct(memberid) %>%
pull(memberid)
I have data with a grouping variable ("from") and values ("number"):
from number
1 1
1 1
2 1
2 2
3 2
3 2
I want to subset the data and select groups which have two or more unique values. In my data, only group 2 has more than one distinct 'number', so this is the desired result:
from number
2 1
2 2
Several possibilities, here's my favorite
library(data.table)
setDT(df)[, if(+var(number)) .SD, by = from]
# from number
# 1: 2 1
# 2: 2 2
Basically, per each group we are checking if there is any variance, if TRUE, then return the group values
With base R, I would go with
df[as.logical(with(df, ave(number, from, FUN = var))), ]
# from number
# 3 2 1
# 4 2 2
Edit: for a non numerical data you could try the new uniqueN function for the devel version of data.table (or use length(unique(number)) > 1 instead
setDT(df)[, if(uniqueN(number) > 1) .SD, by = from]
You could try
library(dplyr)
df1 %>%
group_by(from) %>%
filter(n_distinct(number)>1)
# from number
#1 2 1
#2 2 2
Or using base R
indx <- rowSums(!!table(df1))>1
subset(df1, from %in% names(indx)[indx])
# from number
#3 2 1
#4 2 2
Or
df1[with(df1, !ave(number, from, FUN=anyDuplicated)),]
# from number
#3 2 1
#4 2 2
Using concept of variance shared by David but doing it dplyr way:
library(dplyr)
df %>%
group_by(from) %>%
mutate(variance=var(number)) %>%
filter(variance!=0) %>%
select(from,number)
#Source: local data frame [2 x 2]
#Groups: from
#from number
#1 2 1
#2 2 2
I have data with a grouping variable ("from") and values ("number"):
from number
1 1
1 1
2 1
2 2
3 2
3 2
I want to subset the data and select groups which have two or more unique values. In my data, only group 2 has more than one distinct 'number', so this is the desired result:
from number
2 1
2 2
Several possibilities, here's my favorite
library(data.table)
setDT(df)[, if(+var(number)) .SD, by = from]
# from number
# 1: 2 1
# 2: 2 2
Basically, per each group we are checking if there is any variance, if TRUE, then return the group values
With base R, I would go with
df[as.logical(with(df, ave(number, from, FUN = var))), ]
# from number
# 3 2 1
# 4 2 2
Edit: for a non numerical data you could try the new uniqueN function for the devel version of data.table (or use length(unique(number)) > 1 instead
setDT(df)[, if(uniqueN(number) > 1) .SD, by = from]
You could try
library(dplyr)
df1 %>%
group_by(from) %>%
filter(n_distinct(number)>1)
# from number
#1 2 1
#2 2 2
Or using base R
indx <- rowSums(!!table(df1))>1
subset(df1, from %in% names(indx)[indx])
# from number
#3 2 1
#4 2 2
Or
df1[with(df1, !ave(number, from, FUN=anyDuplicated)),]
# from number
#3 2 1
#4 2 2
Using concept of variance shared by David but doing it dplyr way:
library(dplyr)
df %>%
group_by(from) %>%
mutate(variance=var(number)) %>%
filter(variance!=0) %>%
select(from,number)
#Source: local data frame [2 x 2]
#Groups: from
#from number
#1 2 1
#2 2 2
I have two columns in a dataframe advertisementID and Payout, Many advertisementID's have more than one Payout value, but I need to find those advertisementID's which have only one unique Payout value. How to do it in R ?
Example:
advertisementID Payout
1 10
2 3
1 10
2 4
3 5
3 4
So the output should be like this:
advertisementID Payout
1 10
as advertisementID 1 is having payout value unique which is 10
Using R base:
new <- aggregate(Payout ~ advertisementID, dt, unique)
new[lengths(new$Payout)==1, ]
output:
advertisementID Payout
1 1 10
Or in a cleaner way with magrittr:
library(magrittr)
aggregate(Payout ~ advertisementID, dt, unique) %>% subset(lengths(Payout)==1)
A solution from dplyr.
library(dplyr)
dt2 <- dt %>%
group_by(advertisementID) %>%
filter(n_distinct(Payout) == 1) %>%
distinct(advertisementID, Payout) %>%
ungroup()
dt2
# A tibble: 1 x 2
advertisementID Payout
<int> <int>
1 1 10
DATA
dt <- read.table(text = "advertisementID Payout
1 10
2 3
1 10
2 4
3 5
3 4",
header = TRUE)
I have a data set like this:
df <- data.frame(situation1=rnorm(30),
situation2=rnorm(30),
situation3=rnorm(30),
models=c(rep("A",10), rep("B",10), rep("C", 10)))
where I compare three models (A,B,C) in three situations. I have 10 measurements for each model.
I now want to summarise this into ranks, i.e. how often each models wins in each situtation. Win is defined by the highest value.
A final output could be something like this:
model situation1 situtation2 situtation3
A 4 3 3
B 7 1 2
C 1 4 5
In base R:
table(df$models,colnames(df[-4])[max.col(df[-4])])
# situation1 situation2 situation3
# A 2 4 4
# B 4 5 1
# C 2 4 4
Results may change from your OP, since you didn't set a seed.
Here is an option using data.table
library(data.table)
setDT(df)[, lapply(Map(`==`, .SD, list(do.call(pmax, .SD))), sum), models]
Here's a dplyr option:
df %>%
group_by(models) %>%
mutate_all(funs(. == pmax(situation1, situation2, situation3))) %>%
summarise_all(sum)
Or possibly a little more efficient:
df %>%
mutate_at(vars(-models), funs(. == pmax(situation1, situation2, situation3))) %>%
group_by(models) %>%
summarise_all(sum)
## A tibble: 3 × 4
# models situation1 situation2 situation3
# <chr> <int> <int> <int>
#1 A 3 3 3
#2 B 3 5 1
#3 C 6 1 2
If you're looking for the minimum, use pmin instead of pmax. And in case there may be NAs, use the na.rm-argument in pmax/pmin.
Final note: the result doesn't match OP's because the sample data was generated without setting a seed.