We have a data frame as below :
raw<-data.frame(v1=c("A","B","C","D"),v2=c(NA,"B","C","A"),v3=c(NA,"A",NA,"D"),v4=c(NA,"D",NA,NA))
I need a result data frame in the following format :
result<-data.frame(v1=c("A","B","C","D"), v2=c(3,2,2,3))
Used the following code to get the count across one particular column :
count_raw<-sqldf("SELECT DISTINCT(v1) AS V1, COUNT(v1) AS count FROM raw GROUP BY v1")
This would return count of unique values across an individual column.
Any help would be highly appreciated.
Use this
table(unlist(raw))
Output
A B C D
3 2 2 3
For data frame type output wrap this with as.data.frame.table
as.data.frame.table(table(unlist(raw)))
Output
Var1 Freq
1 A 3
2 B 2
3 C 2
4 D 3
If you want a total count,
sapply(unique(raw[!is.na(raw)]), function(i) length(which(raw == i)))
#A B C D
#3 2 2 3
We can use apply with MARGIN = 1
cbind(raw[1], v2=apply(raw, 1, function(x) length(unique(x[!is.na(x)]))))
If it is for each column
sapply(raw, function(x) length(unique(x[!is.na(x)])))
Or if we need the count based on all the columns, convert to matrix and use the table
table(as.matrix(raw))
# A B C D
# 3 2 2 3
If you have only character values in your dataframe as you've provided, you can unlist it and use unique or to count the freq, use count
> library(plyr)
> raw<-data.frame(v1=c("A","B","C","D"),v2=c(NA,"B","C","A"),v3=c(NA,"A",NA,"D"),v4=c(NA,"D",NA,NA))
> unique(unlist(raw))
[1] A B C D <NA>
Levels: A B C D
> count(unlist(raw))
x freq
1 A 3
2 B 2
3 C 2
4 D 3
5 <NA> 6
Related
Not an extremely proficient programmer here so bear with me.
I want to eliminate duplicities in variable 'B' but only within the same values of variable 'A'. That is so that I get only one 'a' value for the group of 1's and I don't eliminate it for the group of 2's.
A <- c(1,1,1,2,2,2)
B <- c('a','b','a','c','a','d')
ab <- cbind(A,B)
AB <- as.data.frame(ab)
Thank you beforehand! Hope it was clear enough.
You may also want to take a look at the duplicated() function. Your example
a <- c(1,1,1,2,2,2)
b <- c('a','b','a','c','a','d')
ab <- cbind(a,b)
ab_df <- as.data.frame(ab)
gives you the following data frame:
> ab_df
a b
1 1 a
2 1 b
3 1 a
4 2 c
5 2 a
6 2 d
Obviously row 3 duplicates row 1. duplicated(ab_df) returns a logical vector indicating duplicated rows:
> duplicated(ab_df)
[1] FALSE FALSE TRUE FALSE FALSE FALSE
This in turn could be used to eliminate the duplicated rows from your original data frame:
> d <- duplicated(ab_df)
> ab_df[!d, ]
a b
1 1 a
2 1 b
4 2 c
5 2 a
6 2 d
You may use unique which removes the duplicated rows of your data frame.
ab <- unique(ab)
ab
# A B
# 1 1 a
# 2 1 b
# 4 2 c
# 5 2 a
# 6 2 d
I have a dataset with several variables, but I want to keep the rows that are the same based on two columns. Here is an example of what I want to do:
a <- c(rep('A',3), rep('B', 3), rep('C',3))
b <- c(1,1,2,4,4,4,5,5,5)
df <- data.frame(a,b)
a b
1 A 1
2 A 1
3 A 2
4 B 4
5 B 4
6 B 4
7 C 5
8 C 5
9 C 5
I know that if I use the duplicated function I can get:
df[!duplicated(df),]
a b
1 A 1
3 A 2
4 B 4
7 C 5
But since the level 'A' on column a does not have a unique value in b, I want to drop both observations to get a new data.frame as this:
a b
4 B 4
7 C 5
I don't mind to have repeated values across b, as long as for every same level on a there is the same value in b.
Is there a way to do this? Thanks!
This one maybe?
ag <- aggregate(b~a, df, unique)
ag[lengths(ag$b)==1,]
# a b
#2 B 4
#3 C 5
Maybe something like this:
> ind <- apply(sapply(with(df, split(b,a)), diff), 2, function(x) all(x==0) )
> out <- df[!duplicated(df),]
> out[out$a %in% names(ind)[ind], ]
a b
4 B 4
7 C 5
Here is another option with data.table
library(data.table)
setDT(df)[, if(uniqueN(b)==1) .SD[1L], by = a]
# a b
#1: B 4
#2: C 5
This is what my dataframe looks like:
a <- c(1,1,4,4,5)
b <- c(1,2,3,3,5)
c <- c(1,4,4,4,5)
d <- c(2,2,4,4,5)
e <- c(1,5,3,3,5)
df <- data.frame(a,b,c,d,e)
I'd like to write something that returns all unique instances of vectors a,b,c,d that have a repeated value in vector e.
For example:
a b c d e
1 1 1 1 2 1
2 1 2 4 2 5
3 4 3 4 4 3
4 4 3 4 4 3
5 5 5 5 5 5
Rows 3 and 4 are exactly the same till vector d (having a combination of 4344) so only one instance of those should be returned, but they have 2 repeated values in vector e. I would want to get a count on those - so the combination of 4344 has 2 repeated values in vector e.
The expected output would me how many times a certain combination such as 4344 had repeated values in vector e. So in this case it would be something like:
a b c d e
4 3 4 4 2
Both R and SQL work, whatever does the job.
Again, see my comments above, but I believe the following gives you a start on your first question. First, create a "key" variable (in this case named key_abcd which uses tidyr::unite to unite columns a, b, c, and d). Then, count up e by this key_abcd variable. The group_by is implicit.
library(tidyr)
library(dplyr)
df <- data.frame(a,b,c,d,e,f,g)
df %>%
unite(key_abcd, a, b, c, d) %>%
count(key_abcd, e)
# key_abcd e n
# (chr) (dbl) (int)
# 1 1_1_1_2 1 1
# 2 1_2_4_2 5 1
# 3 4_3_4_4 3 2
# 4 5_5_5_5 5 1
It appears from how you've worded the question, you are only interested in "more than one" combinations, therefore, you could add %>% filter(n > 1) to the above code.
I have a matrix that contains following:
A B C D
a 1 3 2 5
b 3 2 5 8
a 2 1 0 9
a 4 2 1 3
c 4 3 1 1
b 2 5 1 9
A, B, C, D are column names and
a, b, c, d are row names.
I want to make it look like
A B C D
a 4 3 2 9
b 3 5 5 9
c 4 3 1 1
using R, Which is to
1) order the row in alphabetical order,
2) and then if there are redundant rows (i.e. there are other rows with the same row name), pick a maximum value among the redundant rows for each column and delete the others.
I first used python to do this process, but I was wondering if there is
more convenient way for this job in R.
I would appreciate any help.
You can use data.table
dt_in <- data.table(matrix_in)
dt_in[, name := rownames(matrix_in)]
dt_max <- dt_in[, list(A = max(A), B = max(B), C = max(C), D = max(D)), by = "name"]
as.matrix(data.frame(dt_max))
Here's a one liner using data.table you can keep the rows while converting to data.table and then apply max function over all columns using lapply(.SD,...) by the rn variable (the saved row names)
library(data.table)
data.table(m, keep.rownames = TRUE)[, lapply(.SD, max), by = rn]
# rn A B C D
# 1: a 4 3 2 9
# 2: b 3 5 5 9
# 3: c 4 3 1 1
You can simply use aggregate function:
aggregate(matrix ~ rownames(matrix), matrix, max)
I am trying to convert a dataframe with labels/values to a named numeric vecotr.
For example I have the following dataframe
>df=data.frame(lab=c("A","B","C","D"),values=c(1,2,3,4))
> df
lab values
1 A 1
2 B 2
3 C 3
4 D 4
So what I am trying to do is to iterate or use a function on this data frame to get the following
>v_needed=c("A"=1,"B"=2,"C"=3,"D"=4)
> v_needed
A B C D
1 2 3 4
I tried to convert this to a factor but it didn't give the desired output
>v_failure=factor(df$values,labels=df$lab)
You can use the setNames function
v <- with(df, setNames(values, lab))
v
# A B C D
# 1 2 3 4