I've been trying to randomly subsample my seurat object.
I'm interested in subsampling based on 2 columns: condition and cell type. I have 5 conditions and 5 cell types. Main goal is to have 1000 cells for each cell type in each condition.
I've tried this so far:
First thing is subsetting my seurat object:
my.list <- list(hipo.c1.neurons = hipo %>%
subset(., condition %in% "c1" & group %in% "Neurons"),
hipo.c1.oligo = hipo %>%
subset(., condition %in% "c1" & group %in% "Oligod")...etc...)
And then subsample it using sample function:
set.seed(0)
my.list.sampled <- lapply(X = my.list, FUN = function(x) {
x <- x[,sample(ncol(x), 1000, replace = FALSE)]
})
And I get this error since there are some objects with less than 1000 cells: error in evaluating the argument 'j' in selecting a method for function '[': cannot take a sample larger than the population when 'replace = FALSE'
Then I've tried with this function:
lapply_with_error <- function(X,FUN,...){
lapply(X, function(x, ...) tryCatch(FUN(x, ...),
error = function(e)NULL))
}
But then it gives me 0 in those objects that have less than 1000 cells. What would be the way to skip those objects that have less than 1000 cells and leave it like they are (not sample those ones)?
Is there a simpler way to do this, so I don't have to subset all of my objects separately?
I can't say for certain without seeing your data, but could you just add an if statement in the function? It looks like you're sampling column-wise, so check the number of columns. Just return x if the number of columns is less than the number you'd like to sample.
set.seed(0)
my.list.sampled <- lapply(X = my.list, FUN = function(x) {
if(ncol(x) > 1000){
x <- x[,sample(ncol(x), 1000, replace = FALSE)]
} else {
x
}
})
You could make it more flexible if you want to sample something other than 1000.
set.seed(0)
my.list.sampled <- lapply(X = my.list, B = 1000, FUN = function(x, B) {
if(ncol(x) > B){
x <- x[,sample(ncol(x), B, replace = FALSE)]
} else {
x
}
})
I wrote some code to performed oversampling, meaning that I replicate my observations in a data.frame and add noise to the replicates, so they are not exactly the same anymore. I'm quite happy that it works now as intended, but...it is too slow. I'm just learning dplyr and have no clue about data.table, but I hope there is a way to improve my function. I'm running this code in a function for 100s of data.frames which may contain about 10,000 columns and 400 rows.
This is some toy data:
library(tidyverse)
train_set1 <- rep(0, 300)
train_set2 <- rep("Factor1", 300)
train_set3 <- data.frame(replicate(1000, sample(0:1, 300, rep = TRUE)))
train_set <- cbind(train_set1, train_set2, train_set3)
row.names(train_set) <- c(paste("Sample", c(1:nrow(train_set)), sep = "_"))
This is the code to replicate each row a given number of times and a function to determine whether the added noise later will be positive or negative:
# replicate each row twice, added row.names contain a "."
train_oversampled <- train_set[rep(seq_len(nrow(train_set)), each = 3), ]
# create a flip function
flip <- function() {
sample(c(-1,1), 1)
}
In the relevant "too slow" piece of code, I'm subsetting the row.names for the added "." to filter for the replicates. Than I select only the numeric columns. I go through those columns row by row and leave the values untouched if they are 0. If not, a certain amount is added (here +- 1 %). Later on, I combine this data set with the original data set and have my oversampled data.frame.
# add percentage of noise to non-zero values in numerical columns
noised_copies <- train_oversampled %>%
rownames_to_column(var = "rowname") %>%
filter(grepl("\\.", row.names(train_oversampled))) %>%
rowwise() %>%
mutate_if(~ is.numeric(.), ~ if_else(. == 0, 0,. + (. * flip() * 0.01 ))) %>%
ungroup() %>%
column_to_rownames(var = "rowname")
# combine original and oversampled, noised data set
train_noised <- rbind(noised_copies, train_set)
I assume there are faster ways using e.g. data.table, but it was already tough work to get this code running and I have no idea how to improve its performance.
EDIT:
The solution is working perfectly fine with fixed values, but called within a for loop I receive "Error in paste(Sample, n, sep = ".") : object 'Sample' not found"
Code to replicate:
library(data.table)
train_set <- data.frame(
x = c(rep(0, 10)),
y = c(0:9),
z = c(rep("Factor1", 10)))
# changing the row name to avoid confusion with "Sample"
row.names(train_set) <- c(paste("Observation", c(1:nrow(train_set)), sep = "_"))
train_list <- list(aa = train_set, bb = train_set, cc = train_set)
for(current_table in train_list) {
setDT(current_table, keep.rownames="Sample")
cols <- names(current_table)[sapply(current_table, is.numeric)]
noised_copies <- lapply(c(1,2), function(n) {
copy(current_table)[,
c("Sample", cols) := c(.(paste(Sample, n, sep=".")),
.SD * sample(c(-1.01, 1.01), .N*ncol(.SD), TRUE)),
.SDcols=cols]
})
train_noised <- rbindlist(c(noised_copies, list(train_set)), use.names=FALSE)
# As this is an example, I did not write anything to actually
# store the results, so I have to remove the object
rm(train_noised)
}
Any ideas why the column Sample can't be found now?
Here is a more vectorized approach using data.table:
library(data.table)
setDT(train_set, keep.rownames="Sample")
cols <- names(train_set)[sapply(train_set, is.numeric)]
noised_copies <- lapply(c(1,2), function(n) {
copy(train_set)[,
c("Sample", cols) := c(.(paste(Sample, n, sep=".")),
.SD * sample(c(-1.01, 1.01), .N*ncol(.SD), TRUE)),
.SDcols=cols]
})
train_noised <- rbindlist(c(noised_copies, list(train_set)), use.names=FALSE)
With data.table version >= 1.12.9, you can pass is.numeric directly to .SDcols argument and maybe a shorter way (e.g. (.SD) or names(.SD)) to pass to the left hand side of :=
address OP's updated post:
The issue is that although each data.frame within the list is converted to a data.table, the train_list is not updated. You can update the list with a left bind before the for loop:
library(data.table)
train_set <- data.frame(
x = c(rep(0, 10)),
y = c(0:9),
z = c(rep("Factor1", 10)))
# changing the row name to avoid confusion with "Sample"
row.names(train_set) <- c(paste("Observation", c(1:nrow(train_set)), sep = "_"))
train_list <- list(aa = train_set, bb = copy(train_set), cc = copy(train_set))
train_list <- lapply(train_list, setDT, keep.rownames="Sample")
for(current_table in train_list) {
cols <- names(current_table)[sapply(current_table, is.numeric)]
noised_copies <- lapply(c(1,2), function(n) {
copy(current_table)[,
c("Sample", cols) := c(.(paste(Sample, n, sep=".")),
.SD * sample(c(-1.01, 1.01), .N*ncol(.SD), TRUE)),
.SDcols=cols]
})
train_noised <- rbindlist(c(noised_copies, train_list), use.names=FALSE)
# As this is an example, I did not write anything to actually
# store the results, so I have to remove the object
rm(train_noised)
}
Good evening,
I asked a question earlier and found it hard to implement the solution so I am gonna reask it in a more clear way.
I have the problem, that I want to add a column to a dataframe of daily returns of a stock. Lets say its normally distributed and I would like to add a column that contains the value at risk (hist) whose function I wrote myself.
The restriction is that each observation should be assigned to my function and take the last 249 observations as well.
So when the next observation is calculated it should also take only the last 249 observations of the das before. So the input values should move as the time goes on. In other words I want values from 251 days ago to be excluded. Hopefully I explained myself well enough. If not maybe the code speaks for me:
df<- data.frame(Date=seq(ISOdate(2000,1,1), by = "days", length.out = 500), Returns=rnorm(500))
#function
VaR.hist<- function(x, n=250, hd=20, q=0.05){
width<-nrow(x)
NA.x<-na.omit(x)
quantil<-quantile(NA.x[(width-249):width],probs=q)
VaR<- quantil*sqrt(hd)%>%
return()
}
# Run the function on the dataframe
df$VaR<- df$Returns%>%VaR.hist()
Error in (width - 249):width : argument of length 0
This is the Error code that I get and not my new Variable...
Thanks !!
As wibom wrote in the comment nrow(x) does not work for vectors. What you need is length() instead. Also you do not need return() in the last line as R automatically returns the last line of a function if there is no early return() before.
library(dplyr)
df<- data.frame(Date=seq(ISOdate(2000,1,1), by = "days", length.out = 500), Returns=rnorm(500))
#function
VaR.hist <- function(x, n=250, hd=20, q=0.05){
width <- length(x) # here you need length as x is a vector, nrow only works for data.frames/matrixes
NA.x <- na.omit(x)
quantil <- quantile(NA.x[(width-249):width], probs = q)
quantil*sqrt(hd)
}
# Run the function on the dataframe
df$VaR <- df$Returns %>% VaR.hist()
It's a bit hard to understand what you want to do exactly.
My understanding is that you wish to compute a new variable VarR, calculated based on the current and previous 249 observations of df$Returns, right?
Is this about what you wish to do?:
library(tidyverse)
set.seed(42)
df <- tibble(
Date = seq(ISOdate(2000, 1, 1), by = "days", length.out = 500),
Returns=rnorm(500)
)
the_function <- function(i, mydata, hd = 20, q = .05) {
r <-
mydata %>%
filter(ridx <= i, ridx > i - 249) %>%
pull(Returns)
quantil <- quantile(r, probs = q)
VaR <- quantil*sqrt(hd)
}
df <-
df %>%
mutate(ridx = row_number()) %>%
mutate(VaR = map_dbl(ridx, the_function, mydata = .))
If you are looking for a base-R solution:
set.seed(42)
df <- data.frame(
Date = seq(ISOdate(2000, 1, 1), by = "days", length.out = 500),
Returns = rnorm(500)
)
a_function <- function(i, mydata, hd = 20, q = .05) {
r <- mydata$Returns[mydata$ridx <= i & mydata$ridx > (i - 249)]
quantil <- quantile(r, probs = q)
VaR <- quantil*sqrt(hd)
}
df$ridx <- 1:nrow(df) # add index
df$VaR <- sapply(df$ridx, a_function, mydata = df)
I wrote a special "impute' function that replaces the column values that have missing (NA) values with either mean() or mode() based on the specific column name.
The input dataframe is 400,000+ rows and its vert slow , how can i speed up the imputation part using lapply() or apply().
Here is the function , mark section I want optimized with START OPTIMIZE & END OPTIMIZE:
specialImpute <- function(inputDF)
{
discoveredDf <- data.frame(STUDYID_SUBJID=character(), stringsAsFactors=FALSE)
dfList <- list()
counter = 1;
Whilecounter = nrow(inputDF)
#for testing just do 10 iterations,i = 10;
while (Whilecounter >0)
{
studyid_subjid=inputDF[Whilecounter,"STUDYID_SUBJID"]
vect = which(discoveredDf$STUDYID_SUBJID == studyid_subjid)
#was discovered and subset before
if (!is.null(vect))
{
#not subset before
if (length(vect)<1)
{
#subset the dataframe base on regex inputDF$STUDYID_SUBJID
df <- subset(inputDF, regexpr(studyid_subjid, inputDF$STUDYID_SUBJID) > 0)
#START OPTIMIZE
for (i in nrow(df))
{
#impute , add column mean & add to list
#apply(df[,c("y1","y2","y3","etc..")],2,function(x){x[is.na(x)] =mean(x, na.rm=TRUE)})
if (is.na(df[i,"y1"])) {df[i,"y1"] = mean(df[,"y1"], na.rm = TRUE)}
if (is.na(df[i,"y2"])) {df[i,"y2"] =mean(df[,"y2"], na.rm = TRUE)}
if (is.na(df[i,"y3"])) {df[i,"y3"] =mean(df[,"y3"], na.rm = TRUE)}
#impute using mean for CONTINUOUS variables
if (is.na(df[i,"COVAR_CONTINUOUS_2"])) {df[i,"COVAR_CONTINUOUS_2"] =mean(df[,"COVAR_CONTINUOUS_2"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_3"])) {df[i,"COVAR_CONTINUOUS_3"] =mean(df[,"COVAR_CONTINUOUS_3"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_4"])) {df[i,"COVAR_CONTINUOUS_4"] =mean(df[,"COVAR_CONTINUOUS_4"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_5"])) {df[i,"COVAR_CONTINUOUS_5"] =mean(df[,"COVAR_CONTINUOUS_5"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_6"])) {df[i,"COVAR_CONTINUOUS_6"] =mean(df[,"COVAR_CONTINUOUS_6"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_7"])) {df[i,"COVAR_CONTINUOUS_7"] =mean(df[,"COVAR_CONTINUOUS_7"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_10"])) {df[i,"COVAR_CONTINUOUS_10"] =mean(df[,"COVAR_CONTINUOUS_10"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_14"])) {df[i,"COVAR_CONTINUOUS_14"] =mean(df[,"COVAR_CONTINUOUS_14"], na.rm = TRUE)}
if (is.na(df[i,"COVAR_CONTINUOUS_30"])) {df[i,"COVAR_CONTINUOUS_30"] =mean(df[,"COVAR_CONTINUOUS_30"], na.rm = TRUE)}
#impute using mode ordinal & nominal values
if (is.na(df[i,"COVAR_ORDINAL_1"])) {df[i,"COVAR_ORDINAL_1"] =Mode(df[,"COVAR_ORDINAL_1"])}
if (is.na(df[i,"COVAR_ORDINAL_2"])) {df[i,"COVAR_ORDINAL_2"] =Mode(df[,"COVAR_ORDINAL_2"])}
if (is.na(df[i,"COVAR_ORDINAL_3"])) {df[i,"COVAR_ORDINAL_3"] =Mode(df[,"COVAR_ORDINAL_3"])}
if (is.na(df[i,"COVAR_ORDINAL_4"])) {df[i,"COVAR_ORDINAL_4"] =Mode(df[,"COVAR_ORDINAL_4"])}
#nominal
if (is.na(df[i,"COVAR_NOMINAL_1"])) {df[i,"COVAR_NOMINAL_1"] =Mode(df[,"COVAR_NOMINAL_1"])}
if (is.na(df[i,"COVAR_NOMINAL_2"])) {df[i,"COVAR_NOMINAL_2"] =Mode(df[,"COVAR_NOMINAL_2"])}
if (is.na(df[i,"COVAR_NOMINAL_3"])) {df[i,"COVAR_NOMINAL_3"] =Mode(df[,"COVAR_NOMINAL_3"])}
if (is.na(df[i,"COVAR_NOMINAL_4"])) {df[i,"COVAR_NOMINAL_4"] =Mode(df[,"COVAR_NOMINAL_4"])}
if (is.na(df[i,"COVAR_NOMINAL_5"])) {df[i,"COVAR_NOMINAL_5"] =Mode(df[,"COVAR_NOMINAL_5"])}
if (is.na(df[i,"COVAR_NOMINAL_6"])) {df[i,"COVAR_NOMINAL_6"] =Mode(df[,"COVAR_NOMINAL_6"])}
if (is.na(df[i,"COVAR_NOMINAL_7"])) {df[i,"COVAR_NOMINAL_7"] =Mode(df[,"COVAR_NOMINAL_7"])}
if (is.na(df[i,"COVAR_NOMINAL_8"])) {df[i,"COVAR_NOMINAL_8"] =Mode(df[,"COVAR_NOMINAL_8"])}
}#for
#END OPTIMIZE
dfList[[counter]] <- df
#add to discoveredDf since already substed
discoveredDf[nrow(discoveredDf)+1,]<- c(studyid_subjid)
counter = counter +1;
#for debugging to check progress
if (counter %% 100 == 0)
{
print(counter)
}
}
}
Whilecounter = Whilecounter -1;
}#end while
return (dfList)
}
Thanks
It's likely that performance can be improved in many ways, so long as you use a vectorized function on each column. Currently, you're iterating through each row, and then handling each column separately, which really slows you down. Another improvement is to generalize the code so you don't have to keep typing a new line for each variable. In the examples I'll give below, this is handled because continuous variables are numeric, and categorical are factors.
To get straight to an answer, you can replace your code to be optimized with the following (though fixing variable names) provided that your numeric variables are numeric and ordinal/categorical are not (e.g., factors):
impute <- function(x) {
if (is.numeric(x)) { # If numeric, impute with mean
x[is.na(x)] <- mean(x, na.rm = TRUE)
} else { # mode otherwise
x[is.na(x)] <- names(which.max(table(x)))
}
x
}
# Correct cols_to_impute with names of your variables to be imputed
# e.g., c("COVAR_CONTINUOUS_2", "COVAR_NOMINAL_3", ...)
cols_to_impute <- names(df) %in% c("names", "of", "columns")
library(purrr)
df[, cols_to_impute] <- dmap(df[, cols_to_impute], impute)
Below is a detailed comparison of five approaches:
Your original approach using for to iterate on rows; each column then handled separately.
Using a for loop.
Using lapply().
Using sapply().
Using dmap() from the purrr package.
The new approaches all iterate on the data frame by column and make use of a vectorized function called impute, which imputes missing values in a vector with the mean (if numeric) or the mode (otherwise). Otherwise, their differences are relatively minor (except sapply() as you'll see), but interesting to check.
Here are the utility functions we'll use:
# Function to simulate a data frame of numeric and factor variables with
# missing values and `n` rows
create_dat <- function(n) {
set.seed(13)
data.frame(
con_1 = sample(c(10:20, NA), n, replace = TRUE), # continuous w/ missing
con_2 = sample(c(20:30, NA), n, replace = TRUE), # continuous w/ missing
ord_1 = sample(c(letters, NA), n, replace = TRUE), # ordinal w/ missing
ord_2 = sample(c(letters, NA), n, replace = TRUE) # ordinal w/ missing
)
}
# Function that imputes missing values in a vector with mean (if numeric) or
# mode (otherwise)
impute <- function(x) {
if (is.numeric(x)) { # If numeric, impute with mean
x[is.na(x)] <- mean(x, na.rm = TRUE)
} else { # mode otherwise
x[is.na(x)] <- names(which.max(table(x)))
}
x
}
Now, wrapper functions for each approach:
# Original approach
func0 <- function(d) {
for (i in 1:nrow(d)) {
if (is.na(d[i, "con_1"])) d[i,"con_1"] <- mean(d[,"con_1"], na.rm = TRUE)
if (is.na(d[i, "con_2"])) d[i,"con_2"] <- mean(d[,"con_2"], na.rm = TRUE)
if (is.na(d[i,"ord_1"])) d[i,"ord_1"] <- names(which.max(table(d[,"ord_1"])))
if (is.na(d[i,"ord_2"])) d[i,"ord_2"] <- names(which.max(table(d[,"ord_2"])))
}
return(d)
}
# for loop operates directly on d
func1 <- function(d) {
for(i in seq_along(d)) {
d[[i]] <- impute(d[[i]])
}
return(d)
}
# Use lapply()
func2 <- function(d) {
lapply(d, function(col) {
impute(col)
})
}
# Use sapply()
func3 <- function(d) {
sapply(d, function(col) {
impute(col)
})
}
# Use purrr::dmap()
func4 <- function(d) {
purrr::dmap(d, impute)
}
Now, we'll compare the performance of these approaches with n ranging from 10 to 100 (VERY small):
library(microbenchmark)
ns <- seq(10, 100, by = 10)
times <- sapply(ns, function(n) {
dat <- create_dat(n)
op <- microbenchmark(
ORIGINAL = func0(dat),
FOR_LOOP = func1(dat),
LAPPLY = func2(dat),
SAPPLY = func3(dat),
DMAP = func4(dat)
)
by(op$time, op$expr, function(t) mean(t) / 1000)
})
times <- t(times)
times <- as.data.frame(cbind(times, n = ns))
# Plot the results
library(tidyr)
library(ggplot2)
times <- gather(times, -n, key = "fun", value = "time")
pd <- position_dodge(width = 0.2)
ggplot(times, aes(x = n, y = time, group = fun, color = fun)) +
geom_point(position = pd) +
geom_line(position = pd) +
theme_bw()
It's pretty clear that the original approach is much slower than the new approaches that use the vectorized function impute on each column. What about differences between the new ones? Let's bump up our sample size to check:
ns <- seq(5000, 50000, by = 5000)
times <- sapply(ns, function(n) {
dat <- create_dat(n)
op <- microbenchmark(
FOR_LOOP = func1(dat),
LAPPLY = func2(dat),
SAPPLY = func3(dat),
DMAP = func4(dat)
)
by(op$time, op$expr, function(t) mean(t) / 1000)
})
times <- t(times)
times <- as.data.frame(cbind(times, n = ns))
times <- gather(times, -n, key = "fun", value = "time")
pd <- position_dodge(width = 0.2)
ggplot(times, aes(x = n, y = time, group = fun, color = fun)) +
geom_point(position = pd) +
geom_line(position = pd) +
theme_bw()
Looks like sapply() is not great (as #Martin pointed out). This is because sapply() is doing extra work to get our data into a matrix shape (which we don't need). If you run this yourself without sapply(), you'll see that the remaining approaches are all pretty comparable.
So the major performance improvement is to use a vectorized function on each column. I suggested using dmap at the beginning because I'm a fan of the function style and the purrr package generally, but you can comfortably substitute for whichever approach you prefer.
Aside, many thanks to #Martin for the very useful comment that got me to improve this answer!
If you are going to be working with what looks like a matrix, then use a matrix instead of a dataframe, since indexing into a dataframe, like it was a matrix, is very costly. You might want to extract the numerical values to a matrix for part of your calculations. This can provide a significant increase in speed.
Here is a really simple and fast solution using data.table.
library(data.table)
# name of columns
cols <- c("a", "c")
# impute date
setDT(dt)[, (cols) := lapply(.SD, function(x) ifelse( is.na(x) & is.numeric(x), mean(x, na.rm = T),
ifelse( is.na(x) & is.character(x), names(which.max(table(x))), x))) , .SDcols = cols ]
I haven't compared the performance of this solution to the one provided by #Simon Jackson, but this should be pretty fast.
data from reproducible example
set.seed(25)
dt <- data.table(a=c(1:5,NA,NA,1,1),
b=sample(1:15, 9, replace=TRUE),
c=LETTERS[c(1:6,NA,NA,1)])