This question uses a data.frame which contains list-columns (nested). It had me wondering why/if there's an advantage to working this way. I assumed you would want to minimize the amount of memory each table uses...But when I checked I was surprised:
Compare table sizes for nested vs. tidy format:
1. Generate nested/tidy versions of a 2-col and 5-col data.frame:
library(pryr)
library(dplyr)
library(tidyr)
library(ggvis)
n <- 1:1E6
df <- data_frame(id = n, vars = lapply(n, function(x) x <- sample(letters,sample(1:26,1))))
dfu <- df %>% unnest(vars)
df_morecols <- data_frame(id = n, other1 = n, other2 = n, other3 = n,
vars = lapply(n, function(x) x <- sample(letters,sample(1:26,1))))
dfu_morecols <- df_morecols %>% unnest(vars)
they look like:
head(df)
#> Source: local data frame [6 x 2]
#> id vars
#> 1 1 <chr[16]>
#> 2 2 <chr[4]>
#> 3 3 <chr[26]>
#> 4 4 <chr[9]>
#> 5 5 <chr[11]>
#> 6 6 <chr[18]>
head(dfu)
#> Source: local data frame [6 x 2]
#> id vars
#> 1 1 k
#> 2 1 d
#> 3 1 s
#> 4 1 j
#> 5 1 m
#> 6 1 t
head(df_morecols)
#> Source: local data frame [6 x 5]
#> id other1 other2 other3 vars
#> 1 1 1 1 1 <chr[4]>
#> 2 2 2 2 2 <chr[22]>
#> 3 3 3 3 3 <chr[24]>
#> 4 4 4 4 4 <chr[6]>
#> 5 5 5 5 5 <chr[15]>
#> 6 6 6 6 6 <chr[11]>
head(dfu_morecols)
#> Source: local data frame [6 x 5]
#> id other1 other2 other3 vars
#> 1 1 1 1 1 r
#> 2 1 1 1 1 p
#> 3 1 1 1 1 s
#> 4 1 1 1 1 w
#> 5 2 2 2 2 l
#> 6 2 2 2 2 j
2. Calculate object sizes and col sizes
from: lapply(list(df,dfu,df_morecols,dfu_morecols),object_size)
170 MB vs. 162 MB for nested vs. tidy 2-col df
170 MB vs. 324 MB for nested vs. tidy 5-col df
col_sizes <- sapply(c(df,dfu,df_morecols,dfu_morecols),object_size)
col_names <- names(col_sizes)
parent_obj <- c(rep(c('df','dfu'),each = 2),
rep(c('df_morecols','dfu_morecols'),each = 5))
res <- data_frame(parent_obj,col_names,col_sizes) %>%
unite(elementof, parent_obj,col_names, remove = F)
3. Plot columns sizes coloured by parent object:
res %>%
ggvis(y = ~elementof, x = ~0, x2 = ~col_sizes, fill = ~parent_obj) %>%
layer_rects(height = band())
Questions:
- What explains the smaller footprint of the tidy 2-col df compared to the nested one?
- Why doesn't this effect change for to the 5-col df?
