This question already has answers here:
Extract row corresponding to minimum value of a variable by group
(9 answers)
Closed 5 years ago.
I have a table which contains multiple rows of the different data for a key of multiple columns.
Table looks like this:
A B C
1 1 1 2
2 1 1 3
3 2 1 4
4 1 2 4
5 2 2 3
6 2 3 1
7 2 3 2
8 2 3 2
I also discovered how to remove all of the duplicate elements using unique command for multiple colums, so the data duplication is not a problem.
I would like to know how to for every key(columns A and B in example) in the table to find only the minimum value in third column(C column in table)
At the end table should look like this
A B C
1 1 1 2
3 2 1 4
4 1 2 4
5 2 2 3
6 2 3 1
Thanks for any help. It is really appreciated
In any question, feel free to ask
con <- textConnection(" A B C
1 1 1 2
2 1 1 3
3 2 1 4
4 1 2 4
5 2 2 3
6 2 3 1
7 2 3 2
8 2 3 2")
df <- read.table(con, header = T)
df[with(df, order(A, B, C)), ]
df[!duplicated(df[1:2]),]
# A B C
# 1 1 1 2
# 3 2 1 4
# 4 1 2 4
# 5 2 2 3
# 6 2 3 1
Related
I have the following dataframes:
A<-data.frame(AgentNo=c(1,2,3,4,5,6),
N=c(2,5,6,1,9,0),
Rarity=c(1,2,1,1,2,2))
AgentNo N Rarity
1 1 2 1
2 2 5 2
3 3 6 1
4 4 1 1
5 5 9 2
6 6 0 2
B<-data.frame(Rank=c(1,5),
AgentNo.x=c(2,5),
AgentNo.y=c(1,4),
N=c(3,1),
Rarity=c(1,2))
Rank AgentNo.x AgentNo.y N Rarity
1 1 2 1 3 1
2 5 5 4 1 2
I would like to left join B onto A by columns "AgentNo"="AgentNo.y" and "N"="N" but rather than add new columns to A from B I want the same columns from A but where joined values have been updated and taken from B.
For any joined rows I want A.AgentNo to now be B.AgentNo.x, A.N to be B.N and A.Rarity to be B.Rarity. I would like to drop B.Rank and B.Agent.y completely.
The result should be:
Result<-data.frame(AgentNo=c(2,2,3,5,5,6), N=c(2,5,6,1,9,0), Rarity=c(1,2,1,1,2,2))
AgentNo N Rarity
1 2 3 1
2 2 5 2
3 3 6 1
4 5 1 2
5 5 9 2
6 6 0 2
After some data wrangling, you can use rows_update to update the rows of A by the values of B:
library(dplyr)
A <- A %>%
mutate(AgentNo.y = AgentNo)
B <- select(B, AgentNo = AgentNo.x, AgentNo.y, N, Rarity)
rows_update(A, B, by = "AgentNo.y") %>%
select(-AgentNo.y)
output
AgentNo N Rarity
1 2 3 1
2 2 5 2
3 3 6 1
4 5 1 1
5 5 9 2
6 6 0 2
This question already has answers here:
Fill missing dates by group
(3 answers)
Fastest way to add rows for missing time steps?
(4 answers)
Closed 3 years ago.
I have a data frame of ids with number column
df <- read.table(text="
id nr
1 1
2 1
1 2
3 1
1 3
", header=TRUE)
I´d like to create new dataframe from it, where each id will have unique nr from df dataframe. As you may notice, id 3 have only nr 1, but no 2 and 3. So result should be.
result <- read.table(text="
id nr
1 1
1 2
1 3
2 1
2 2
2 3
3 1
3 2
3 3
", header=TRUE)
You can use expand.grid as:
library(dplyr)
result <- expand.grid(id = unique(df$id), nr = unique(df$nr)) %>%
arrange(id)
result
id nr
1 1 1
2 1 2
3 1 3
4 2 1
5 2 2
6 2 3
7 3 1
8 3 2
9 3 3
We can do:
tidyr::expand(df,id,nr)
# A tibble: 9 x 2
id nr
<int> <int>
1 1 1
2 1 2
3 1 3
4 2 1
5 2 2
6 2 3
7 3 1
8 3 2
9 3 3
This question already has answers here:
How to create a consecutive group number
(13 answers)
How to convert three columns into single one
(2 answers)
Assign unique ID per multiple columns of data table
(2 answers)
Closed 4 years ago.
Hi I'm using R and I have a data like this:
1 2 3 4 5
1 2 1 2 2
3 4 1 2 3
1 2 3 4 5
3 4 1 2 3
I want to number the identical lines together with the same number, for the above ex
1 2 3 4 5 --> 1
1 2 1 2 2 --> 2
3 4 1 2 3 --> 3
1 2 3 4 5 --> 1
3 4 1 2 3 --> 3
Does any know how to do this in R (for both numeric case and character case)?
Your help is really appreciated!
This is your data:
df <- data.frame(a=c(1,1,3,1,3),
b=c(2,2,4,2,4),
c=c(3,1,1,3,1),
d=c(4,2,2,4,2),
e=c(5,2,3,5,3))
Approach 1:
You would need the data.table package to perform the below approach:
library(data.table)
i <- interaction(data.table(df), drop=TRUE)
df.out <- cbind(df, id=factor(i,labels=length(unique(i)):1))
This would give you the following:
# a b c d e id
#1 1 2 3 4 5 1
#2 1 2 1 2 2 3
#3 3 4 1 2 3 2
#4 1 2 3 4 5 1
#5 3 4 1 2 3 2
Approach 2:
Another approach is by using the plyr package, as follows:
library(plyr)
.id <- 0
df.out <- ddply(df, colnames(df), transform, id=(.id<<-.id+1))
This will give you the following output:
# a b c d e id
#1 1 2 1 2 2 1
#2 1 2 3 4 5 2
#3 1 2 3 4 5 2
#4 3 4 1 2 3 3
#5 3 4 1 2 3 3
Hope it helps.
This question already has answers here:
Delete duplicate rows in two columns simultaneously [duplicate]
(2 answers)
Closed 6 years ago.
I have got the following data.frame:
df = read.table(text = 'a b c d
1 12 2 1
1 13 2 1
1 3 3 1
2 12 6 2
2 11 2 2
2 14 2 2
1 12 1 2
1 13 2 2
2 11 4 3, header = TRUE')
I need to remove the rows which have the same observations based on columns a and b, so that the results would be:
a b c d
1 12 2 1
1 13 2 1
1 3 3 1
2 12 6 2
2 11 2 2
2 13 2 2
Thank you for any help
We can use duplicated
df[!duplicated(df[1:2]),]
I want repeat a sequence for specific length:
Sequence is 1:4 and I want to repeat the sequence till number of rows in a data frame.
Lets say length of the data frame is 24
I tried following:
test <- rep(1:4, each=24/4)
1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4
Lengthwise this is fine but i want to retain the sequence
1 2 3 4 1 2 3 4 1 2 3 4.....
You need to use times instead of each
rep(1:4, times=24/4)
[1] 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
We can just pass it without any argument and it takes the times by default
rep(1:4, 24/4)
#[1] 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4