split dataframe in R by row - r

I have a long dataframe like this:
Row Conc group
1 2.5 A
2 3.0 A
3 4.6 B
4 5.0 B
5 3.2 C
6 4.2 C
7 5.3 D
8 3.4 D
...
The actual data have hundreds of row. I would like to split A to C, and D. I looked up the web and found several solutions but not applicable to my case.
How to split a data frame?
For example:
Case 1:
x = data.frame(num = 1:26, let = letters, LET = LETTERS)
set.seed(10)
split(x, sample(rep(1:2, 13)))
I don't want to split by arbitrary number
Case 2: Split by level/factor
data2 <- data[data$sum_points == 2500, ]
I don't want to split by a single factor either. Sometimes I want to combine many levels together.
Case 3: select by row number
newdf <- mydf[1:3,]
The actual data have hundreds of rows. I don't know the row number. I just know the level I would like to split at.

It sounds like you want two data frames, where one has (A,B,C) in it and one has just D. In that case you could do
Data1 <- subset(Data, group %in% c("A","B","C"))
Data2 <- subset(Data, group=="D")
Correct me if you were asking something different

For those who end up here through internet search engines time after time, the answer to the question in the title is:
x <- data.frame(num = 1:26, let = letters, LET = LETTERS)
split(x, sort(as.numeric(rownames(x))))
Assuming that your data frame has numerically ordered row names. Also split(x, rownames(x)) works, but the result is rearranged.

You may consider using the recode() function from the "car" package.
# Load the library and make up some sample data
library(car)
set.seed(1)
dat <- data.frame(Row = 1:100,
Conc = runif(100, 0, 10),
group = sample(LETTERS[1:10], 100, replace = TRUE))
Currently, dat$group contains the upper case letters A to J. Imagine we wanted the following four groups:
"one" = A, B, C
"two" = D, E, J
"three" = F, I
"four" = G, H
Now, use recode() (note the semicolon and the nested quotes).
recodes <- recode(dat$group,
'c("A", "B", "C") = "one";
c("D", "E", "J") = "two";
c("F", "I") = "three";
c("G", "H") = "four"')
split(dat, recodes)

With base R, we can input the factor that we want to split on.
split(df, df$group == "D")
Output
$`FALSE`
Row Conc group
1 1 2.5 A
2 2 3.0 A
3 3 4.6 B
4 4 5.0 B
5 5 3.2 C
6 6 4.2 C
$`TRUE`
Row Conc group
7 7 5.3 D
8 8 3.4 D
If you wanted to split on multiple letters, then we could:
split(df, df$group %in% c("A", "D"))
Another option is to use group_split from dplyr, but will need to make a grouping variable first for the split.
library(dplyr)
df %>%
mutate(spl = ifelse(group == "D", 1, 0)) %>%
group_split(spl, .keep = FALSE)

Related

Problem in merging a gff file and a csv file in R [duplicate]

I want to merge two data frames keeping the original row order of one of them (df.2 in the example below).
Here are some sample data (all values from class column are defined in both data frames):
df.1 <- data.frame(class = c(1, 2, 3), prob = c(0.5, 0.7, 0.3))
df.2 <- data.frame(object = c('A', 'B', 'D', 'F', 'C'), class = c(2, 1, 2, 3, 1))
If I do:
merge(df.2, df.1)
Output is:
class object prob
1 1 B 0.5
2 1 C 0.5
3 2 A 0.7
4 2 D 0.7
5 3 F 0.3
If I add sort = FALSE:
merge(df.2, df.1, sort = F)
Result is:
class object prob
1 2 A 0.7
2 2 D 0.7
3 1 B 0.5
4 1 C 0.5
5 3 F 0.3
But what I would like is:
class object prob
1 2 A 0.7
2 1 B 0.5
3 2 D 0.7
4 3 F 0.3
5 1 C 0.5
You just need to create a variable which gives the row number in df.2. Then, once you have merged your data, you sort the new data set according to this variable. Here is an example :
df.1<-data.frame(class=c(1,2,3), prob=c(0.5,0.7,0.3))
df.2<-data.frame(object=c('A','B','D','F','C'), class=c(2,1,2,3,1))
df.2$id <- 1:nrow(df.2)
out <- merge(df.2,df.1, by = "class")
out[order(out$id), ]
Check out the join function in the plyr package. It's like merge, but it allows you to keep the row order of one of the data sets. Overall, it's more flexible than merge.
Using your example data, we would use join like this:
> join(df.2,df.1)
Joining by: class
object class prob
1 A 2 0.7
2 B 1 0.5
3 D 2 0.7
4 F 3 0.3
5 C 1 0.5
Here are a couple of links describing fixes to the merge function for keeping the row order:
http://www.r-statistics.com/2012/01/merging-two-data-frame-objects-while-preserving-the-rows-order/
http://r.789695.n4.nabble.com/patching-merge-to-allow-the-user-to-keep-the-order-of-one-of-the-two-data-frame-objects-merged-td4296561.html
You can also check out the inner_join function in Hadley's dplyr package (next iteration of plyr). It preserves the row order of the first data set. The minor difference to your desired solution is that it also preserves the original column order of the first data set. So it does not necessarily put the column we used for merging at the first position.
Using your example above, the inner_join result looks like this:
inner_join(df.2,df.1)
Joining by: "class"
object class prob
1 A 2 0.7
2 B 1 0.5
3 D 2 0.7
4 F 3 0.3
5 C 1 0.5
From data.table v1.9.5+, you can do:
require(data.table) # v1.9.5+
setDT(df.1)[df.2, on="class"]
The performs a join on column class by finding out matching rows in df.1 for each row in df.2 and extracting corresponding columns.
For the sake of completeness, updating in a join preserves the original row order as well. This might be an alternative to Arun's data.table answer if there are only a few columns to append:
library(data.table)
setDT(df.2)[df.1, on = "class", prob := i.prob][]
object class prob
1: A 2 0.7
2: B 1 0.5
3: D 2 0.7
4: F 3 0.3
5: C 1 0.5
Here, df.2 is right joined to df.1 and gains a new column prob which is copied from the matching rows of df.1.
The accepted answer proposes a manual way to keep order when using merge, which works most of the times but requires unnecessary manual work. This solution comes on the back of How to ddply() without sorting?, which deals with the issue of keeping order but in a split-apply-combine context:
This came up on the plyr mailing list a while back (raised by #kohske no less) and this is a solution offered by Peter Meilstrup for limited cases:
#Peter's version used a function gensym to
# create the col name, but I couldn't track down
# what package it was in.
keeping.order <- function(data, fn, ...) {
col <- ".sortColumn"
data[,col] <- 1:nrow(data)
out <- fn(data, ...)
if (!col %in% colnames(out)) stop("Ordering column not preserved by function")
out <- out[order(out[,col]),]
out[,col] <- NULL
out
}
So now you can use this generic keeping.order function to keep the original row order of a merge call:
df.1<-data.frame(class=c(1,2,3), prob=c(0.5,0.7,0.3))
df.2<-data.frame(object=c('A','B','D','F','C'), class=c(2,1,2,3,1))
keeping.order(df.2, merge, y=df.1, by = "class")
Which will yield, as requested:
> keeping.order(df.2, merge, y=df.1, by = "class")
class object id prob
3 2 A 1 0.7
1 1 B 2 0.5
4 2 D 3 0.7
5 3 F 4 0.3
2 1 C 5 0.5
So keeping.order effectively automates the approach in the accepted answer.
Thanks to #PAC , I came up with something like this:
merge_sameord = function(x, y, ...) {
UseMethod('merge_sameord')
}
merge_sameord.data.frame = function(x, y, ...) {
rstr = paste(sample(c(0:9, letters, LETTERS), 12, replace=TRUE), collapse='')
x[, rstr] = 1:nrow(x)
res = merge(x, y, all.x=TRUE, sort=FALSE, ...)
res = res[order(res[, rstr]), ]
res[, rstr] = NULL
res
}
This assumes that you want to preserve the order the first data frame, and the merged data frame will have the same number of rows as the first data frame. It will give you the clean data frame without extra columns.
In this specific case you could us factor for a compact base solution:
df.2$prob = factor(df.2$class,labels=df.1$prob)
df.2
# object class prob
# 1 A 2 0.7
# 2 B 1 0.5
# 3 D 2 0.7
# 4 F 3 0.3
# 5 C 1 0.5
Not a general solution however, it works if:
You have a lookup table containing unique values
You want to update a table, not create a new one
the lookup table is sorted by the merging column
The lookup table doesn't have extra levels
You want a left_join
If you're fine with factors
1 is not negotiable, for the rest we can do:
df.3 <- df.2 # deal with 2.
df.1b <- df.1[order(df.1$class),] # deal with 3
df.1b <- df.1b[df.1$class %in% df.2$class,] # deal with 4.
df.3$prob = factor(df.3$class,labels=df.1b$prob)
df.3 <- df3[!is.na(df.3$prob),] # deal with 5. if you want an `inner join`
df.3$prob <- as.numeric(as.character(df.3$prob)) # deal with 6.
For package developers
As a package developer, you want to be dependent on as few other packages as possible. Especially tidyverse functions, that change way too often for package developers IMHO.
To be able to make use of the join functions of the dplyr package without importing dplyr, below is a quick implementation. It keeps the original sorting (as requested by OP) and does not move the joining column to the front (which is another annoying thing of merge()).
left_join <- function(x, y, ...) {
merge_exec(x = x, y = y, all.x = TRUE, ...)
}
right_join <- function(x, y, ...) {
merge_exec(x = x, y = y, all.y = TRUE, ...)
}
inner_join <- function(x, y, ...) {
merge_exec(x = x, y = y, all = TRUE, ...)
}
full_join <- function(x, y, ...) {
merge_exec(x = x, y = y, ...)
}
# workhorse:
merge_exec <- function(x, y, ...) {
# set index
x$join_id_ <- 1:nrow(x)
# do the join
joined <- merge(x = x, y = y, sort = FALSE, ...)
# get suffices (yes, I prefer this over suffixes)
if ("suffixes" %in% names(list(...))) {
suffixes <- list(...)$suffixes
} else {
suffixes <- c("", "")
}
# get columns names in right order, so the 'by' column won't be forced first
cols <- unique(c(colnames(x),
paste0(colnames(x), suffixes[1]),
colnames(y),
paste0(colnames(y), suffixes[2])))
# get the original row and column index
joined[order(joined$join_id),
cols[cols %in% colnames(joined) & cols != "join_id_"]]
}
The highest rated answer does not produce what the Original Poster would like, i.e., "class" in column 1. If OP would allow switching column order in df.2, then here is a possible base R non-merge one-line answer:
df.1 <- data.frame(class = c(1, 2, 3), prob = c(0.5, 0.7, 0.3))
df.2 <- data.frame(class = c(2, 1, 2, 3, 1), object = c('A', 'B', 'D', 'F', 'C'))
cbind(df.2, df.1[match(df.2$class, df.1$class), -1, drop = FALSE])
I happen to like the information portrayed in the row.names. A complete one-liner that exactly duplicates the OP's desired outcome is
data.frame(cbind(df.2, df.1[match(df.2$class, df.1$class), -1, drop = FALSE]),
row.names = NULL)
I agree with https://stackoverflow.com/users/4575331/ms-berends that the fewer dependencies of a package developer on another package (or "verse") the better because development paths frequently diverge over time.
Note: The one-liner above does not work when there are duplicates in df.1$class. This can be overcome sans merge with 'outer' and a loop, or more generally with Ms Berend's clever post-merge rescrambling code.
There are several uses cases in which a simple subset will do:
# Use the key variable as row.names
row.names(df.1) = df.1$key
# Sort df.1 so that it's rows match df.2
df.3 = df.1[df.2$key, ]
# Create a data.frame with cariables from df.1 and (the sorted) df.2
df.4 = cbind(df.1, df.3)
This code will preserve df.2 and it's order and add only matching data from df.1
If only one variable is to be added, the cbind() ist not required:
row.names(df.1) = df.1$key
df.2$data = df.1[df.2$key, "data"]
I had the same problem with it but I simply used a dummy vector c(1:5) applied to a new column 'num'
df.2 <- data.frame(object = c('A', 'B', 'D', 'F', 'C'), class = c(2, 1, 2, 3, 1))
df.2$num <- c(1:5) # This range you can order in the last step.
dfm <- merge(df.2, df.1) # merged
dfm <- dfm[order(dfm$num),] # ascending order
There may be a more efficient way in base. This would be fairly simple to make into a function.
varorder <- names(mydata) # --- Merge
mydata <- merge(mydata, otherData, by="commonVar")
restOfvars <- names(mydata[!(names(mydata) %in% varorder)])
mydata[c(varorder,restOfvars)]

Using mutate with a stored list of columns and procedures

I would like to iterate through a stored list of columns and procedures to create n new columns based on this list. In the example below, we start with 3 columns, a, b, c and two simple functions func1, func1.
The data frame col_mod contains two sets of modifications that should be applied to the data frame. Each of these modifications should be an addition to the data frame, rather than replacements of the specified columns.
In col_mod row 1, we see that column a should be modified using func1, and in row 2, we see that column c should be modified using func2. The new names of these columns should be a_new and c_new, respectively.
At the bottom of the reprex below, I obtain my desired result, but I would like to do so without hard coding each modification individually . Is there any way to use maybe something from purrr:map or anything similiar?
library(tidyverse)
## fake data
dat <- data.frame(a = 1:5,
b = 6:10,
c = 11:15)
## functions
func1 <- function(x) {x + 2}
func2 <- function(x) {x - 4}
## modification list
col_mod <- data.frame("col" = c("a", "c"),
"func" = c("func1", "func2"),
stringsAsFactors = FALSE)
## desired end result
dat %>%
mutate("a_new" = func1(a),
"c_new" = func2(c))
edit: if it is easier to store the modifications in a list, as shown below, a solution using that would be fine as well, as I am able to store the modifications in either a data frame or list.
col_mod <- list("set1" = list("a", "func1"),
"set2" = list("c", "func2"))
We can do this with the help of Map, use match.fun to apply the function
dat[paste0(col_mod$col, '_new')] <- Map(function(x, y) match.fun(y)(x),
dat[col_mod$col], col_mod$func)
dat
# a b c a_new c_new
#1 1 6 11 3 7
#2 2 7 12 4 8
#3 3 8 13 5 9
#4 4 9 14 6 10
#5 5 10 15 7 11
Using col_mod as dataframe.
col_mod <- data.frame("col" = c("a", "c"),"func" = c("func1", "func2"))
We can use the tidyverse approach to do this
library(dplyr)
library(purrr)
library(stringr)
library(tibble)
imap_dfc(deframe(col_mod), ~ dat %>%
transmute(!! str_c(.y, "_new") := match.fun(.x)(!! rlang::sym(.y)))) %>%
bind_cols(dat, .)

Is there a way to replace rows in one dataframe with another in R?

I'm trying to figure out how to replace rows in one dataframe with another by matching the values of one of the columns. Both dataframes have the same column names.
Ex:
df1 <- data.frame(x = c(1,2,3,4), y = c("a", "b", "c", "d"))
df2 <- data.frame(x = c(1,2), y = c("f", "g"))
Is there a way to replace the rows of df1 with the same row in df2 where they share the same x variable? It would look like this.
data.frame(x = c(1,2,3,4), y = c("f","g","c","d")
I've been working on this for a while and this is the closest I've gotten -
df1[which(df1$x %in% df2$x),]$y <- df2[which(df1$x %in% df2$x),]$y
But it just replaces the values with NA.
Does anyone know how to do this?
We can use match. :
inds <- match(df1$x, df2$x)
df1$y[!is.na(inds)] <- df2$y[na.omit(inds)]
df1
# x y
#1 1 f
#2 2 g
#3 3 c
#4 4 d
First off, well done in producing a nice reproducible example that's directly copy-pastable. That always helps, specially with an example of expected output. Nice one!
You have several options, but lets look at why your solution doesn't quite work:
First of all, I tried copy-pasting your last line into a new session and got the dreaded factor-error:
Warning message:
In `[<-.factor`(`*tmp*`, iseq, value = 1:2) :
invalid factor level, NA generated
If we look at your data frames df1 and df2 with the str function, you will see that they do not contain text but factors. These are not text - in short they represent categorical data (male vs. female, scores A, B, C, D, and F, etc.) and are really integers that have a text as label. So that could be your issue.
Running your code gives a warning because you are trying to import new factors (labels) into df1 that don't exist. And R doesn't know what to do with them, so it just inserts NA-values.
As r2evens answered, he used the stringsAsFactors to disable using strings as Factors - you can even go as far as disabling it on a session-wide basis using options(stringsAsFactors=FALSE) (and I've heard it will be disabled as default in forthcoming R4.0 - yay!).
After disabling stringsAsFactors, your code works - or does it? Try this on for size:
df2 <- df2[c(2,1),]
df1[which(df1$x %in% df2$x),]$y <- df2[which(df1$x %in% df2$x),]$y
What's in df1 now? Not quite right anymore.
In the first line, I swapped the two rows in df2 and lo and behold, the replaced values in df1 were swapped. Why is that?
Let's deconstruct your statement df2[which(df1$x %in% df2$x),]$y
Call df1$x %in% df2$x returns a logical vector (boolean) of which elements in df1$x are found ind df2 - i.e. the first two and not the second two. But it doesn't relate which positions in the first vector corresponds to which in the second.
Calling which(df1$x %in% df2$x) then reduces the logical vector to which indices were TRUE. Again, we do not now which elements correspond to which.
For solutions, I would recommend r2evans, as it doesn't rely on extra packages (although data.table or dplyr are two powerful packages to get to know).
In his solution, he uses merge to perform a "full join" which matches rows based on the value, rather than - well, what you did. With transform, he assigns new variables within the context of the data.frame returned from the merge function called in the first argument.
I think what you need here is a "merge" or "join" operation.
(I add stringsAsFactors=FALSE to the frames so that the merging and later work is without any issue, as factors can be disruptive sometimes.)
Base R:
df1 <- data.frame(x = c(1,2,3,4), y = c("a", "b", "c", "d"), stringsAsFactors = FALSE)
# df2 <- data.frame(x = c(1,2), y = c("f", "g"), stringsAsFactors = FALSE)
merge(df1, df2, by = "x", all = TRUE)
# x y.x y.y
# 1 1 a f
# 2 2 b g
# 3 3 c <NA>
# 4 4 d <NA>
transform(merge(df1, df2, by = "x", all = TRUE), y = ifelse(is.na(y.y), y.x, y.y))
# x y.x y.y y
# 1 1 a f f
# 2 2 b g g
# 3 3 c <NA> c
# 4 4 d <NA> d
transform(merge(df1, df2, by = "x", all = TRUE), y = ifelse(is.na(y.y), y.x, y.y), y.x = NULL, y.y = NULL)
# x y
# 1 1 f
# 2 2 g
# 3 3 c
# 4 4 d
Dplyr:
library(dplyr)
full_join(df1, df2, by = "x") %>%
mutate(y = coalesce(y.y, y.x)) %>%
select(-y.x, -y.y)
# x y
# 1 1 f
# 2 2 g
# 3 3 c
# 4 4 d
A join option with data.table where we join on the 'x' column, assign the values of 'y' in second dataset (i.y) to the first one with :=
library(data.table)
setDT(df1)[df2, y := i.y, on = .(x)]
NOTE: It is better to use stringsAsFactors = FALSE (in R 4.0.0 - it is by default though) or else we need to have all the levels common in both datasets

Combining values Boolean columns to one with Priority in R

Gone through below links but it solved my problem partially.
merge multiple TRUE/FALSE columns into one
Combining a matrix of TRUE/FALSE into one
R: Converting multiple boolean columns to single factor column
I have a dataframe which looks like:
dat <- data.frame(Id = c(1,2,3,4,5,6,7,8),
A = c('Y','N','N','N','N','N','N','N'),
B = c('N','Y','N','N','N','N','Y','N'),
C = c('N','N','Y','N','N','Y','N','N'),
D = c('N','N','N','Y','N','Y','N','N'),
E = c('N','N','N','N','Y','N','Y','N')
)
I want to make a reshape my df with one column but it has to give priorities when there are 2 "Y" in a row.
THE priority is A>B>C>D>E which means if their is "Y" in A then the resultant value should be A. Similarly, in above example df both C and D has "Y" but there should be "C" in the resultant df.
Hence output should look like:
resultant_dat <- data.frame(Id = c(1,2,3,4,5,6,7,8),
Result = c('A','B','C','D','E','C','B','NA')
)
I have tried this:
library(reshape2)
new_df <- melt(dat, "Id", variable.name = "Result")
new_df <-new_df[new_df$value == "Y", c("Id", "Result")]
But the problem is doesn't handle the priority thing, it creates 2 rows for the same Id.
tmp = data.frame(ID = dat[,1],
Result = col_order[apply(
X = dat[col_order],
MARGIN = 1,
FUN = function(x) which(x == "Y")[1])],
stringsAsFactors = FALSE)
tmp$Result[is.na(tmp$Result)] = "Not Present"
tmp
# ID Result
#1 1 A
#2 2 B
#3 3 C
#4 4 D
#5 5 E
#6 6 C
#7 7 B
#8 8 Not Present

R - Specifying a desired row order for the output data.frame of aggregate()

I aggregate() the value column sums per site levels of the R data.frame given below:
set.seed(2013)
df <- data.frame(site = sample(c("A","B","C"), 10, replace = TRUE),
currency = sample(c("USD", "EUR", "GBP", "CNY", "CHF"),10, replace=TRUE, prob=c(10,6,5,6,0.5)),
value = sample(seq(1:10)/10,10,replace=FALSE))
df.site.sums <- aggregate(value ~ site, data=df, FUN=sum)
df.site.sums
# site value
#1 A 0.2
#2 B 0.6
#3 C 4.7
However, I would like to be able to specify the row order of the resulting df.site.sums. For instance like:
reorder <- c("C","B","A")
?special_sort(df, BY=site, ORDER=reorder) # imaginary function
# site value
#1 C 4.7
#2 B 0.6
#3 A 0.2
How can I do this using base R? Just to be clear, this is essentially a data frame row ordering question where the context is the aggregate() function (which may or may not matter).
This is relevant but does not directly address my issue, or I am missing the crux of the solution.
UPDATE
For future reference, I found a solution to ordering a data.frame's rows with respect to a target vector on this link. I guess it can be applied as a post-processing step.
df.site.sums[match(reorder,df.site.sums$site),]
This may be a possibility: convert 'site' to a factor and specify the order in levels.
df$site2 <- factor(df$site, levels = c("C", "B", "A"))
aggregate(value ~ site2, data = df, FUN = sum)
# site2 value
# 1 C 4.7
# 2 B 0.6
# 3 A 0.2
Update following #Ananda Mahto's comment (thanks!). You can use the 'non-formula' approach of aggregate:
reorder <- c("C", "B", "A")
with(df, aggregate(x = list(value = value),
by = list(site = factor(site, levels = reorder)),
FUN = sum))
# site value
# 1 C 4.7
# 2 B 0.6
# 3 A 0.2
Or, converting to factor within the formula interface, and rename the converted site column:
df2 <- aggregate(value ~ factor(site, levels = c("C", "B", "A")),
data = df, FUN = sum)
df2
names(df2) <- c("site", "value")
df2

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