I have some large data frames that are big enough to push the limits of R on my machine; e.g., the one on which I'm currently working is 2 columns by 70 million rows. The contents aren't important, but just in case, column 1 is a string and column 2 is an integer.
What I would like to do is split that data frame into n parts (say, 20, but preferably something that could change on a case-by-case basis) so that I can work on each of the smaller data frames one at a time. That means that (a) the result has to produce things that are named (e.g., "newdf_1", "newdf_2", ... "newdf_20" or something), and (b) each line in the original data frame needs to be in one (and only one) of the new "sub" data frames. The order does not matter, but doing it sequentially by rows makes sense to me.
Once I do the work, I will start to recombine them (using rbind()) one pair at a time.
I've looked at split(), but from what I can tell, it is designed to work with factors (which I don't have).
Any ideas?
You can create a new column and split the data frame based on that column. The column does not need to be a factor, but need to be a data type that can be converted to a factor by the split function.
# Number of groups
N <- 20
dat$group <- 1:nrow(dat) %% N
# Add 1 to group
dat$group <- dat$group + 1
# Split the dat by group
dat_list <- split(dat, f = ~group)
# Set the name of the list
names(dat_list) <- paste0("newdf_", 1:N)
Data
set.seed(123)
# Create example data frame
dat <- data.frame(
A = sample(letters, size = 70000000, replace = TRUE),
B = rpois(70000000, lambda = 1)
)
Here's a tidyverse based solution. Try using read_csv_chunked().
# practice data
tibble(string = sample(letters, 1e6, replace = TRUE),
value = rnorm(1e6) %>%
write_csv("test.csv")
# here's the solution
partial_data <- read_csv_chunked("test.csv",
DataFrameCallback$new(function(x, pos) filter(x, string == "a")),
chunk_size = 1000)
You can wrap the call to read_csv_chunked in a function where you change the string that you subset on.
This is more or less a repeat of this question:
How to read only lines that fulfil a condition from a csv into R?
I am quite new to R and have quite a challenging Question. I have a large dataframe consisting of 110,000 rows representing high-Resolution data from a Sediment core. I would like to select multiple rows based on Depth (which is recorded in mm to 3 decimal points). Of Course, I have not the time to go through the entire dataframe and pick the rows that I Need. I would like to be able to select the rows I would like based on the decimal Point part of the number and not the first Digit. I.e. I would like to be able to subset to a dataframe where all the .035 values would be returned. I have so far tried using the which() function but had no luck
newdata <- Linescan_EN18218[which(Linescan_EN18218$Position.mm.== .035),]
Can anyone offer any hints/suggestions how I can solve this Problem. Link to the first part of the dataframe csv
Welcome to stack overflow
Can you please further describe what you mean with had no luck. Did you get an error message or an empty data.frame?
In principle, your method should work. I have replicated it with simulated data.
n = 100
test <- data.frame(
a = 1:n,
b = rnorm(n = n),
c = sample(c(0.1,0.035, 0.0001), size = n, replace =T)
)
newdata <- test[which(test$c == 0.035),]
I'm trying since more than an hour to split randomly my data frame into two frame based on a given percentage, however, I can't make it work i don't know why.
I saw those posts :
How to split data into training/testing sets using sample function in R program
R: How to split a data frame into training, validation, and test sets?
How can divide a dataset based on percentage?
What I want is basically to take as input a data frame df, and a real number α ∈ (0, 1) and returns a list consisting of two data frames df1 and df2. df1 is finally (a * 100)% of df, and df2 the rest of df, the unselected rows.
For example, if df has 100 rows, and α = 0.4, then df1 will consist of 40 randomly selected rows of df, and df2 will consist of the other 60 rows.
I could do it with a big function and loops etc, make my algorithm, but I'm pretty sure, another way to do it should exists and I would like to share this solution with the community !
Thank for your help !
Here is a function that splits the data into two data.frames using sample:
splitTable <- function(df, prob) {
variant <- sample(seq(1, 0), size = nrow(df), replace = TRUE, prob = c(prob, 1 - prob))
res <- split(df, variant)
return(res)
}
res <- splitTable(iris, 0.4)
I'm trying to figure out how remove duplicates based on three variables (id, key, and num). I would like to remove the duplicate with the least amount of columns filled. If an equal number are filled, either can be removed.
For example,
Original <- data.frame(id= c(1,2,2,3,3,4,5,5),
key=c(1,2,2,3,3,4,5,5),
num=c(1,1,1,1,1,1,1,1),
v4= c(1,NA,5,5,NA,5,NA,7),
v5=c(1,NA,5,5,NA,5,NA,7))
The output would be the following:
Finished <- data.frame(id= c(1,2,3,4,5),
key=c(1,2,3,4,5),
num=c(1,1,1,1,1),
v4= c(1,5,5,5,7),
v5=c(1,5,5,5,7))
My real dataset is bigger and a mix of mostly numerical, but some character variables, but I couldn't determine the best way to go about doing this. I've previously used a program that would do something similar within the duplicates command called check.all.
So far, my thoughts have been to use grepl and determine where "anything" is present
Present <- apply(Original, 2, function(x) grepl("[[:alnum:]]", x))
Then, using the resultant dataframe I ask for rowSums and Cbind it to the original.
CompleteNess <- rowSums(Present)
cbind(Original, CompleteNess)
This is the point where I'm unsure of my next steps... I have a variable which tells me how many columns are filled in each row (CompleteNess); however, I'm unsure of how to implement duplicates.
Simply, I'm looking for When id, key, and num are duplicated - keep the row with the highest value of CompleteNess.
If anybody can think of a better way to do this or get me through the last little bit I would greatly appreciate it. Thanks All!
Here is a solution. It is not very pretty but it should work for your application:
#Order by the degree of completeness
Original<-Original[order(CompleteNess),]
#Starting from the bottom select the not duplicated rows
#based on the first 3 columns
Original[!duplicated(Original[,1:3], fromLast = TRUE),]
This does rearrange your original data frame so beware if there is additional processing later on.
You can aggregate your data and select the row with max score:
Original <- data.frame(id= c(1,2,2,3,3,4,5,5),
key=c(1,2,2,3,3,4,5,5),
num=c(1,1,1,1,1,1,1,1),
v4= c(1,NA,5,5,NA,5,NA,7),
v5=c(1,NA,5,5,NA,5,NA,7))
Present <- apply(Original, 2, function(x) grepl("[[:alnum:]]", x))
#get the score
Original$present <- rowSums(Present)
#create a column to aggregate on
Original$id.key.num <- paste(Original$id, Original$key, Original$num, sep = "-")
library("plyr")
#aggregate here
Final <- ddply(Original,.(id.key.num),summarize,
Max = max(present))
And if you want to keep the other columns, just do this:
Final <- ddply(Original,.(id.key.num),summarize,
Max = max(present),
v4 = v4[which.max(present)],
v5 = v5[which.max(present)]
)
I have a data frame of 15 columns where the first column is an integer and others are numeric. I have to generate a one-liner summary of the sum of all columns except the last one. I need to generate mean of the last column. So, I am doing something as below:
summary <- c(sum(df$col1), ... mean(df$col15))
The summary then appears with values up to two decimal places even for the integer column (first one). I have been trying the round function to fix this. I can understand, when different types are added, e.g. 1 + 1.0. But, in this case, shouldn't the summation maintain the data-type?
Please let me know what am I missing?
If you are looking for a one-line summary:
lst <- c(lapply(df[-ncol(df)], function(x) sum(x)), mean=mean(df[,ncol(df)]))
as.data.frame(lst)
# int num1 mean
#1 10 6 2.5
The output is a data frame that preserves the classes of each vector. If you would like the output to be added to the original data frame you can replace as.data.frame(lst) with:
names(lst) <- names(df)
rbind(df, lst)
If you are trying to get the sum of all integer columns and the mean of numeric columns, go with #Frank's answer.
Data
df <- data.frame(int=1:4, num1=seq(1,2,length.out=4), num2=seq(2,3,length.out=4))
Perhaps an adaptation of this?
apply(iris[,1:4], 2, sum) / c(rep(1,3), nrow(iris))