Split up a dataframe by number of rows

Make your own grouping variable.

d <- split(my_data_frame,rep(1:400,each=1000))

You should also consider the ddply function from the plyr package, or the group_by() function from dplyr.

edited for brevity, after Hadley's comments.

If you don't know how many rows are in the data frame, or if the data frame might be an unequal length of your desired chunk size, you can do

chunk <- 1000
n <- nrow(my_data_frame)
r  <- rep(1:ceiling(n/chunk),each=chunk)[1:n]
d <- split(my_data_frame,r)

You could also use

r <- ggplot2::cut_width(1:n,chunk,boundary=0)

For future readers, methods based on the dplyr and data.table packages will probably be (much) faster for doing group-wise operations on data frames, e.g. something like

(my_data_frame 
   %>% mutate(index=rep(1:ngrps,each=full_number)[seq(.data)])
   %>% group_by(index)
   %>% [mutate, summarise, do()] ...
)

There are also many answers here

I had a similar question and used this:

library(tidyverse)
n = 100 #number of groups
split <- df %>% group_by(row_number() %/% n) %>% group_map(~ .x)

from left to right:

• you assign your result to split
• you start with df as your input dataframe
• then you group your data by dividing the row_number by n (number of groups) using modular division.
• then you just pass that group through the group_map function which returns a list.

So in the end your split is a list with in each element a group of your dataset. On the other hand, you could also immediately write your data by replacing the group_map call by e.g. group_walk(~ write_csv(.x, paste0("file_", .y, ".csv"))).

You can find more info on these powerful tools on: Cheat sheet of dplyr explaining group_by and also below for: group_map, group_walk follow up functions