For a single dataframe, summarise the memory usage in each column. If two dataframes are supplied, compare memory usage for columns appearing in both dataframes. For grouped dataframes, summarise the memory usage separately for each group.
inspect_mem(df1, df2 = NULL)A data frame.
An optional second data frame with which to comparing memory usage.
Defaults to NULL.
A tibble summarising and comparing the columnwise memory usage for one or a pair of data frames.
For a single dataframe, the tibble returned contains the columns:
col_name, a character vector containing column names of df1.
bytes, integer vector containing the number of bytes in each column of df1.
size, a character vector containing display-friendly memory usage of each column.
pcnt, the percentage of the dataframe's total memory footprint
used by each column.
For a pair of dataframes, the tibble returned contains the columns:
col_name, a character vector containing column names of df1
and df2.
size_1, size_2, a character vector containing memory usage of each column in
each of df1 and df2.
pcnt_1, pcnt_2, the percentage of total memory usage of each column within
each of df1 and df2.
For a grouped dataframe, the tibble returned is as for a single dataframe, 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.
# Load dplyr for starwars data & pipe
library(dplyr)
# Single dataframe summary
inspect_mem(starwars)
#> # A tibble: 14 × 4
#> col_name bytes size pcnt
#> <chr> <int> <chr> <dbl>
#> 1 films 20008 19.54 Kb 35.9
#> 2 starships 7448 7.27 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 6.48
#> 6 species 2952 2.88 Kb 5.30
#> 7 skin_color 2656 2.59 Kb 4.77
#> 8 eye_color 1608 1.57 Kb 2.89
#> 9 hair_color 1440 1.41 Kb 2.59
#> 10 sex 976 976 bytes 1.75
#> 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 0.718
# Paired dataframe 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 35.9 40.5
#> 2 starships 7.27 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 6.48 4.46
#> 6 species 2.88 Kb 552 bytes 5.30 3.02
#> 7 skin_color 2.59 Kb 808 bytes 4.77 4.41
#> 8 eye_color 1.57 Kb 664 bytes 2.89 3.63
#> 9 hair_color 1.41 Kb 736 bytes 2.59 4.02
#> 10 sex 976 bytes 440 bytes 1.75 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 0.718 0.962
# Grouped dataframe 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 feminine films 3816 3.73 Kb 33.0
#> 2 feminine starships 1256 1.23 Kb 10.9
#> 3 feminine name 1248 1.22 Kb 10.8
#> 4 feminine vehicles 1176 1.15 Kb 10.2
#> 5 feminine homeworld 776 776 bytes 6.71
#> 6 feminine skin_color 744 744 bytes 6.43
#> 7 feminine species 664 664 bytes 5.74
#> 8 feminine eye_color 528 528 bytes 4.56
#> 9 feminine hair_color 520 520 bytes 4.50
#> 10 feminine sex 296 296 bytes 2.56
#> # … with 29 more rows
#> # ℹ Use `print(n = ...)` to see more rows