Let's say we have a DF like this:
col1 col2
A 1
A 5
A 3
A 16
B 5
B 4
B 3
C 7
C 2
I'm trying to order col2 but only for same values in col1. Better said, I want it to look like this:
col1 col2
A 1
A 3
A 5
A 16
B 3
B 4
B 5
C 2
C 7
So order col2 only for A, B and C values, not order the entire col2 column
x <- function() {
values<- unique(DF[, 1])
for (i in values) {
currentData <- which(DF$col1== i)
## what to do here ?
data[order(data[, 2]), ]
}
}
so in CurrentData I have indexes for col2 values for only As, Bs etc. But how do I order only those items in my entire DF data frame ? Is it somehow possible to tell the order function to do order only on certain row indexes of data frame ?
ave will group the data by the first element, and apply the named function to the second element for each group. Here is an application of ave sorting within groups:
DF$col2 <- ave(DF$col2, DF$col1, FUN=sort)
DF
## col1 col2
## 1 A 1
## 2 A 3
## 3 A 5
## 4 A 16
## 5 B 3
## 6 B 4
## 7 B 5
## 8 C 2
## 9 C 7
This will work even if the values in col1 are not consecutive, leaving them in their original positions.
If that is not an important consideration, there are better ways to do this, such as the answer by #user314046.
It seems that
my_df[with(my_df, order(col1, col2)), ]
will do what you want - this just sorts the dataframe by col1 and col2. If you don't want to order by col1 a method is provided in the other answer.
Related
I have a data set:
col1 col2
A 3
A 3
B 2
C 1
B 2
A 3
D 5
B 2
D 5
B 2
F 0
F 0
A 3
C 1
C 1
How can I subset it so as to "leave" the top 2 col1 values. So my output is this:
col1 col2
A 3
A 3
A 3
D 5
A 3
I have viewed this question, but it didn't answer my question.
Try this, but not sure why you only have one D:
#Code
newdf <- df[df$col2 %in% sort(unique(df$col2),decreasing = T)[1:2],]
I assume that your data is in a data.frame.
First of all, you need to get the top 2 values of col2. Therefore you can take the unique values of it, sort them in decreasing order, and take the first two elements:
col2Values <- unique(df$col2)
top2Elements <- sort(col2Values,decreasing = TRUE)[c(1,2)]
Now you know the top2 values, so you just need to check where these values appear in col2. This can be done via:
df[df$col2 %in% top2Elements,]
Update: Now it should work, I had some typos in there.
In R, I want to match and merge two matrices.
For example,
> A
ID a b c d e f g
1 ex 3 8 7 6 9 8 4
2 am 7 5 3 0 1 8 3
3 ple 8 5 7 9 2 3 1
> B
col1
1 a
2 c
3 e
4 f
Then, I want to match header of matrix A and 1st column of matrix B.
The final result should be a matrix like below.
> C
ID a c e f
1 ex 3 7 9 8
2 am 7 3 1 8
3 ple 8 7 2 3
*(My original data has more than 500 columns and more than 20,000 rows.)
Are there any tips for that? Would really appreciate your help.
*In advance, if the matrix B is like below,
> B
col1 col2 col3 col4
1 a c e f
How to make the matrix C in this case?
You want:
A[, c('ID', B[, 1])]
For the second case, you want to use row number 1 of the second matrix, instead of its first column.
A[, c('ID', B[1, ])]
If B is a data.frame instead of a matrix, the syntax changes somewhat — you can use B$col1 instead of B[, 1], and to select by row, you need to transform the result to a vector, because the result of selecting a row in a data.frame is again a data.frame, i.e. you need to do unlist(B[1, ]).
You can use a subset:
cbind(A$ID, A[names(A) %in% B$col1])
Consider the following code
a = "col1"
b = "col2"
d = data.frame(a=c(1,2,3),b=c(4,5,6))
This code produces the following data frame
a b
1 1 4
2 2 5
3 3 6
However the desired data frame is
col1 col2
1 1 4
2 2 5
3 3 6
Further, I'd like to be able to do something like d$a which would then grab d$col1 since a = "col1"
How can I tell R that "a" is a variable and not a name of a column?
After creating your data frame, you need to use ?colnames. For example, you would have:
d = data.frame(a=c(1,2,3), b=c(4,5,6))
colnames(d) <- c("col1", "col2")
You can also name your variables when you create the data frame. For example:
d = data.frame(col1=c(1,2,3), col2=c(4,5,6))
Further, if you have the names of columns stored in variables, as in
a <- "col1"
you can't use $ to select a column via d$a. R will look for a column whose name is a. Instead, you can do either d[[a]] or d[,a].
You can do it this way
a = "col1"
b = "col2"
d = data.frame(a=c(1,2,3),b=c(4,5,6))
>d
a b
1 1 4
2 2 5
3 3 6
#Renaming the columns
names(d) <- c(a,b)
> d
col1 col2
1 1 4
2 2 5
3 3 6
#Calling by names
d[,a]
I'm stuck with a quite complex problem. I have a data frame with three rows: id, info and rownum. The data looks like this:
id info row
1 a 1
1 b 2
1 c 3
2 a 4
3 b 5
3 a 6
4 b 7
4 c 8
What I want to do now is to delete all other rows of one id if one of the rows contains the info a. This would mean for example that row 2 and 3 should be removed as row 1's coloumn info contains the value a. Please note that the info values are not ordered (id 3/row 5 & 6) and cannot be ordered due to other data limitations.
I solved the case using a for loop:
# select all id containing an "a"-value
a_val <- data$id[grep("a", data$info)]
# check for every id containing an "a"-value
for(i in a_val) {
temp_data <- data[which(data$id == i),]
# only go on if the given id contains more than one row
if (nrow(temp_data) > 1) {
for (ii in nrow(temp_data)) {
if (temp_data$info[ii] != "a") {
temp <- temp_data$row[ii]
if (!exists("delete_rows")) {
delete_rows <- temp
} else {
delete_rows <- c(delete_rows, temp)
}
}
}
}
}
My solution works quite well. Nevertheless, it is very, very, very slow as the original data contains more than 700k rows and more that 150k rows with an "a"-value.
I could use a foreach loop with 4 cores to speed it up, but maybe someone could give me a hint for a better solution.
Best regards,
Arne
[UPDATE]
The outcome should be:
id info row
1 a 1
2 a 4
3 a 6
4 b 7
4 c 8
Here is one possible solution.
First find ids where info contains "a":
ids <- with(data, unique(id[info == "a"]))
Subset the data:
subset(data, (id %in% ids & info == "a") | !id %in% ids)
Output:
id info row
1 1 a 1
4 2 a 4
6 3 a 6
7 4 b 7
8 4 c 8
An alternative solution (maybe harder to decipher):
subset(data, info == "a" | !rep.int(tapply(info, id, function(x) any(x == "a")),
table(id)))
Note. #BenBarnes found out that this solution only works if the data frame is ordered according to id.
You might want to investigate the data.table package:
EDIT: If the row variable is not a sequential numbering of each row in your data (as I assumed it was), you could create such a variable to obtain the original row order:
library(data.table)
# Create data.table of your data
dt <- as.data.table(data)
# Create index to maintain row order
dt[, idx := seq_len(nrow(dt))]
# Set a key on id and info
setkeyv(dt, c("id", "info"))
# Determine unique ids
uid <- dt[, unique(id)]
# subset your data to select rows with "a"
dt2 <- dt[J(uid, "a"), nomatch = 0]
# identify rows of dataset where the id doesn't have an "a"
dt3 <- dt[J(dt2[, setdiff(uid, id)])]
# rbind those two data.tables together
(dt4 <- rbind(dt2, dt3))
# id info row idx
# 1: 1 a 1 1
# 2: 2 a 4 4
# 3: 3 a 6 6
# 4: 4 b 7 7
# 5: 4 c 8 8
# And if you need the original ordering of rows,
dt5 <- dt4[order(idx)]
Note that setting a key for the data.table will order the rows according to the key columns. The last step (creating dt5) sets the row order back to the original.
Here is a way using ddply:
df <- read.table(text="id info row
1 a 1
1 b 2
1 c 3
2 a 4
3 b 5
3 a 6
4 b 7
4 c 8",header=TRUE)
library("plyr")
ddply(df,.(id),subset,rep(!'a'%in%info,length(info))|info=='a')
Returns:
id info row
1 1 a 1
2 2 a 4
3 3 a 6
4 4 b 7
5 4 c 8
if df is this (RE Sacha above) use match which just finds the index of the first occurrence:
df <- read.table(text="id info row
1 a 1
1 b 2
1 c 3
2 a 4
3 b 5
3 a 6
4 b 7
4 c 8",header=TRUE)
# the first info row matching 'a' and all other rows that are not 'a'
with(df, df[c(match('a',info), which(info != 'a')),])
id info row
1 1 a 1
2 1 b 2
3 1 c 3
5 3 b 5
7 4 b 7
8 4 c 8
try to take a look at subset, it's quite easy to use and it will solve your problem.
you just need to specify the value of the column that you want to subset based on, alternatively you can choose more columns.
http://stat.ethz.ch/R-manual/R-devel/library/base/html/subset.html
http://www.statmethods.net/management/subset.html
I have two data frames: A and B of the same number of columns names and content. Data frame B is the subset of A. I want to get A without B. I have tried different functions like setdiff, duplicated, which and others. None of them worked for me, perhaps I didn't use them correctly. Any help is appreciated.
You could use merge e.g.:
df1 <- data.frame(col1=c('A','B','C','D','E'),col2=1:5,col3=11:15)
subset <- df1[c(2,4),]
subset$EXTRACOL <- 1 # use a column name that is not present among
# the original data.frame columns
merged <- merge(df1,subset,all=TRUE)
dfdifference <- merged[is.na(merged$EXTRACOL),]
dfdifference$EXTRACOL <- NULL
-----------------------------------------
> df1:
col1 col2 col3
1 A 1 11
2 B 2 12
3 C 3 13
4 D 4 14
5 E 5 15
> subset:
col1 col2 col3
2 B 2 12
4 D 4 14
> dfdifference:
col1 col2 col3
1 A 1 11
3 C 3 13
5 E 5 15