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
Related
How does one count the characters based on the order they appear in a single length string. Below is an minimal example:
x <- "abbccdddaab"
First thought was this but it only counts them irrespective of order:
table(unlist(strsplit(x, "\\b")))
a b c d
3 3 2 3
But the desired output is:
a b c d a b
1 2 2 3 2 1
I would imagine the solution would require a for loop?
We can use rle instead of table as rle returns the output as a list of values and lengths based on checking whether the adjacent elements are same or not
out <- rle(strsplit(x, "\\b")[[1]])
setNames(out$lengths, out$values)
# a b c d a b
# 1 2 2 3 2 1
Using data.table::rleid :
x <- "abbccdddaab"
tmp <- strsplit(x, "\\b")[[1]]
table(data.table::rleid(tmp))
#1 2 3 4 5 6
#1 2 2 3 2 1
So, I'm trying to read a excel files. What happens is that some of the rows are empty for some of the columns but not for all of them. I want to skip all the rows that are not complete, i.e., that don't have information in all of the columns. For example:
In this case I would like to skip the lines 1,5,6,7,8 and so on.
There is probably more elegant way of doing it, but a possible solution is to count the number of elements per rows that are not NA and keep only rows with the number of elements equal to the number of columns.
Using this dummy example:
df <- data.frame(A = LETTERS[1:6],
B = c(sample(1:10,5),NA),
C = letters[1:6])
A B C
1 A 5 a
2 B 9 b
3 C 1 c
4 D 3 d
5 E 4 e
6 F NA f
Using apply, you can for each rows count the number of elements without NA:
v <- apply(df,1, function(x) length(na.omit(x)))
[1] 3 3 3 3 3 2
And then, keep only rows with the number of elements equal to the number of columns (which correspond to complete rows):
df1 <- df[v == ncol(df),]
A B C
1 A 5 a
2 B 9 b
3 C 1 c
4 D 3 d
5 E 4 e
Does it answer your question ?
I have a dataframe:
a <- c(rep("A", 3), rep("B", 3), rep("C",2))
b <- c(1,1,2,4,1,1,2,2)
c <- c(1,NA,2,4,NA,1,2,2)
df <-data.frame(a,b,c)
I have a dataframe with some duplicate variables in column 1 but when I use the duplicated function, it randomly chooses the row after de-duping using duplicate(function)
dedup_df = df[!duplicated(df$a), ]
How can I ensure that the output returns me the row that does not contain an NA on column c ?
I tried to use the dplyr package but the output prints only a result
library(dplyr)
options(dplyr.print_max = Inf )
df %>% ## source dataframe
group_by(a) %>% ## grouped by variable
filter(!is.na(c) ) %>% ## filter by Gross value
as.data.frame(dedup_df)
Your use of duplicated function to remove duplicate observations (lines) using a column as key from a data frame is correct.
But it seems that you are worried that it may keep a line that contains NA in another column and drop another line that contains a non NA value.
I'll use you example, but with a slight modification
a <- c(rep("A", 3), rep("B", 3), rep("C",2))
b <- c(1,1,2,4,1,1,2,2)
c <- c(NA,1,2,4,NA,1,2,2)
df <-data.frame(a,b,c)
> df
a b c
1 A 1 NA
2 A 1 1
3 A 2 2
4 B 4 4
5 B 1 NA
6 B 1 1
7 C 2 2
8 C 2 2
In this case, your dedup_df contains an NA for the first value.
> dedup_df = df[!duplicated(df$a), ]
> dedup_df
a b c
1 A 1 NA
4 B 4 4
7 C 2 2
Solution:
Reorder df by column c first and then use the same command. This reordering by column c will send all NAs to the end of the data frame. When the duplicated passes it will see these lines having NA last and will tag them as TRUE if there was a previous one without NA.
df = df[order(df$c),]
dedup_df = df[!duplicated(df$a), ]
> dedup_df
a b c
2 A 1 1
6 B 1 1
7 C 2 2
You can also reorder in descending order
df = df[order(df$c,decreasing = T),]
dedup_df = df[!duplicated(df$a), ]
> dedup_df
a b c
4 B 4 4
3 A 2 2
7 C 2 2
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
I am trying to simulate n times the measuring order and see how measuring order effects my study subject. To do this I am trying to generate integer random numbers to a new column in a dataframe. I have a big dataframe and i would like to add a column into the dataframe that consists a random number according to the number of observations in a block.
Example of data(each row is an observation):
df <- data.frame(A=c(1,1,1,2,2,3,3,3,3),
B=c("x","b","c","g","h","g","g","u","l"),
C=c(1,2,4,1,5,7,1,2,5))
A B C
1 1 x 1
2 1 b 2
3 1 c 4
4 2 g 1
5 2 h 5
6 3 g 7
7 3 g 1
8 3 u 2
9 3 l 5
What I'd like to do is add a D column and generate random integer numbers according to the length of each block. Blocks are defined in column A.
Result should look something like this:
df <- data.frame(A=c(1,1,1,2,2,3,3,3,3),
B=c("x","b","c","g","h","g","g","u","l"),
C=c(1,2,4,1,5,7,1,2,5),
D=c(2,1,3,2,1,4,3,1,2))
> df
A B C D
1 1 x 1 2
2 1 b 2 1
3 1 c 4 3
4 2 g 1 2
5 2 h 5 1
6 3 g 7 4
7 3 g 1 3
8 3 u 2 1
9 3 l 5 2
I have tried to use R:s sample() function to generate random numbers but my problem is splitting the data according to block length and adding the new column. Any help is greatly appreciated.
It can be done easily with ave
df$D <- ave( df$A, df$A, FUN = function(x) sample(length(x)) )
(you could replace length() with max(), or whatever, but length will work even if A is not numbers matching the length of their blocks)
This is really easy with ddply from plyr.
ddply(df, .(A), transform, D = sample(length(A)))
The longer manual version is:
Use split to split the data frame by the first column.
split_df <- split(df, df$A)
Then call sample on each member of the list.
split_df <- lapply(split_df, function(df)
{
df$D <- sample(nrow(df))
df
})
Then recombine with
df <- do.call(rbind, split_df)
One simple way:
df$D = 0
counts = table(df$A)
for (i in 1:length(counts)){
df$D[df$A == names(counts)[i]] = sample(counts[i])
}