For a single data frame, summarise the memory usage in each column. If two data frames are supplied, compare memory usage for columns appearing in both data frames. For grouped data frames, summarise the memory usage separately for each group.
Value
A tibble summarising and comparing the columnwise memory usage for one or a pair of data frames.
Details
For a single data frame, the tibble returned contains the columns:
col_name, a character vector containing column names ofdf1.bytes, integer vector containing the number of bytes in each column ofdf1.size, a character vector containing display-friendly memory usage of each column.pcnt, the percentage of the data frame's total memory footprint used by each column.
For a pair of data frames, the tibble returned contains the columns:
col_name, a character vector containing column names ofdf1anddf2.size_1,size_2, a character vector containing memory usage of each column in each ofdf1anddf2.pcnt_1,pcnt_2, the percentage of total memory usage of each column within each ofdf1anddf2.
For a grouped data frame, the tibble returned is as for a single data frame, but where
the first k columns are the grouping columns. There will be as many rows in the result
as there are unique combinations of the grouping variables.
Examples
# Load dplyr for starwars data & pipe
library(dplyr)
# Single data frame summary
inspect_mem(starwars)
#> # A tibble: 14 × 4
#> col_name bytes size pcnt
#> <chr> <int> <chr> <dbl>
#> 1 films 20008 19.54 Kb 36.0
#> 2 starships 7424 7.25 Kb 13.4
#> 3 name 6280 6.13 Kb 11.3
#> 4 vehicles 5944 5.8 Kb 10.7
#> 5 homeworld 3608 3.52 Kb 6.49
#> 6 species 2952 2.88 Kb 5.31
#> 7 skin_color 2656 2.59 Kb 4.78
#> 8 eye_color 1608 1.57 Kb 2.89
#> 9 hair_color 1384 1.35 Kb 2.49
#> 10 sex 976 976 bytes 1.76
#> 11 gender 872 872 bytes 1.57
#> 12 mass 744 744 bytes 1.34
#> 13 birth_year 744 744 bytes 1.34
#> 14 height 400 400 bytes 0.719
# Paired data frame comparison
inspect_mem(starwars, starwars[1:20, ])
#> # A tibble: 14 × 5
#> col_name size_1 size_2 pcnt_1 pcnt_2
#> <chr> <chr> <chr> <dbl> <dbl>
#> 1 films 19.54 Kb 7.23 Kb 36.0 40.5
#> 2 starships 7.25 Kb 2.58 Kb 13.4 14.4
#> 3 name 6.13 Kb 1.49 Kb 11.3 8.35
#> 4 vehicles 5.8 Kb 1.74 Kb 10.7 9.75
#> 5 homeworld 3.52 Kb 816 bytes 6.49 4.46
#> 6 species 2.88 Kb 552 bytes 5.31 3.02
#> 7 skin_color 2.59 Kb 808 bytes 4.78 4.41
#> 8 eye_color 1.57 Kb 664 bytes 2.89 3.63
#> 9 hair_color 1.35 Kb 736 bytes 2.49 4.02
#> 10 sex 976 bytes 440 bytes 1.76 2.40
#> 11 gender 872 bytes 336 bytes 1.57 1.84
#> 12 mass 744 bytes 208 bytes 1.34 1.14
#> 13 birth_year 744 bytes 208 bytes 1.34 1.14
#> 14 height 400 bytes 176 bytes 0.719 0.962
# Grouped data frame summary
starwars %>% group_by(gender) %>% inspect_mem()
#> # A tibble: 39 × 5
#> # Groups: gender [3]
#> gender col_name bytes size pcnt
#> <chr> <chr> <int> <chr> <dbl>
#> 1 masculine films 15592 15.23 Kb 36.0
#> 2 masculine starships 5856 5.72 Kb 13.5
#> 3 masculine name 4776 4.66 Kb 11.0
#> 4 masculine vehicles 4592 4.48 Kb 10.6
#> 5 masculine homeworld 3088 3.02 Kb 7.13
#> 6 masculine species 2536 2.48 Kb 5.86
#> 7 masculine skin_color 2208 2.16 Kb 5.10
#> 8 masculine eye_color 1320 1.29 Kb 3.05
#> 9 masculine hair_color 1104 1.08 Kb 2.55
#> 10 masculine sex 752 752 bytes 1.74
#> # ℹ 29 more rows