What is the performance difference between mutate_at and mutate?
I'm spending a bit of time improving performance of some tidyverse-based dataset analysis code which works on a dataset of between 100-500 columns and 250,000 rows of data (the number of columns you need depends upon the task you are performing).
The code reads data from a CSV file and then does some early data import tidying from CSV - for example fixing booleans which are recorded as "Y", "N", "" to be boolean rather than factors.
Originally we imported all the columns regardless of task - the work I've been doing has moved us to selective column import - skipping a load of work which may not be needed for certain tasks, and resulting in a factor-of-3 performance gain. Of course this makes data tidying a little more complex.
I've resorted to using mutate with a list of expressions to do the data tidy.
However I'm wondering at what point it is beneficial to group identical expressions together to perform a mutate_at (eg. do all the boolean columns which use "Y", "N", "NA" together), and then perform a subsequent mutate of the remaining columns; versus having a function per row to perform a mutate? Is there a performance difference between the two such that it's worth complicating the code?
Appreciating that there will be use-case variation here - just wondered whether anyone had a feeling about some general rules before I either attempt it or spend time building some test examples to check whether it's worthwhile.
r dplyr
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
add a comment |
I'm spending a bit of time improving performance of some tidyverse-based dataset analysis code which works on a dataset of between 100-500 columns and 250,000 rows of data (the number of columns you need depends upon the task you are performing).
The code reads data from a CSV file and then does some early data import tidying from CSV - for example fixing booleans which are recorded as "Y", "N", "" to be boolean rather than factors.
Originally we imported all the columns regardless of task - the work I've been doing has moved us to selective column import - skipping a load of work which may not be needed for certain tasks, and resulting in a factor-of-3 performance gain. Of course this makes data tidying a little more complex.
I've resorted to using mutate with a list of expressions to do the data tidy.
However I'm wondering at what point it is beneficial to group identical expressions together to perform a mutate_at (eg. do all the boolean columns which use "Y", "N", "NA" together), and then perform a subsequent mutate of the remaining columns; versus having a function per row to perform a mutate? Is there a performance difference between the two such that it's worth complicating the code?
Appreciating that there will be use-case variation here - just wondered whether anyone had a feeling about some general rules before I either attempt it or spend time building some test examples to check whether it's worthwhile.
r dplyr
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
6
You can check it usingmicrobenchmarkorsystem.time
– akrun
Jan 2 at 9:28
add a comment |
I'm spending a bit of time improving performance of some tidyverse-based dataset analysis code which works on a dataset of between 100-500 columns and 250,000 rows of data (the number of columns you need depends upon the task you are performing).
The code reads data from a CSV file and then does some early data import tidying from CSV - for example fixing booleans which are recorded as "Y", "N", "" to be boolean rather than factors.
Originally we imported all the columns regardless of task - the work I've been doing has moved us to selective column import - skipping a load of work which may not be needed for certain tasks, and resulting in a factor-of-3 performance gain. Of course this makes data tidying a little more complex.
I've resorted to using mutate with a list of expressions to do the data tidy.
However I'm wondering at what point it is beneficial to group identical expressions together to perform a mutate_at (eg. do all the boolean columns which use "Y", "N", "NA" together), and then perform a subsequent mutate of the remaining columns; versus having a function per row to perform a mutate? Is there a performance difference between the two such that it's worth complicating the code?
Appreciating that there will be use-case variation here - just wondered whether anyone had a feeling about some general rules before I either attempt it or spend time building some test examples to check whether it's worthwhile.
r dplyr
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
I'm spending a bit of time improving performance of some tidyverse-based dataset analysis code which works on a dataset of between 100-500 columns and 250,000 rows of data (the number of columns you need depends upon the task you are performing).
The code reads data from a CSV file and then does some early data import tidying from CSV - for example fixing booleans which are recorded as "Y", "N", "" to be boolean rather than factors.
Originally we imported all the columns regardless of task - the work I've been doing has moved us to selective column import - skipping a load of work which may not be needed for certain tasks, and resulting in a factor-of-3 performance gain. Of course this makes data tidying a little more complex.
I've resorted to using mutate with a list of expressions to do the data tidy.
However I'm wondering at what point it is beneficial to group identical expressions together to perform a mutate_at (eg. do all the boolean columns which use "Y", "N", "NA" together), and then perform a subsequent mutate of the remaining columns; versus having a function per row to perform a mutate? Is there a performance difference between the two such that it's worth complicating the code?
Appreciating that there will be use-case variation here - just wondered whether anyone had a feeling about some general rules before I either attempt it or spend time building some test examples to check whether it's worthwhile.
r dplyr
r dplyr
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
asked Jan 2 at 9:28
Andrew HillAndrew Hill
8219
8219
6
You can check it usingmicrobenchmarkorsystem.time
– akrun
Jan 2 at 9:28
add a comment |
6
You can check it usingmicrobenchmarkorsystem.time
– akrun
Jan 2 at 9:28
6
6
You can check it using
microbenchmark or system.time– akrun
Jan 2 at 9:28
You can check it using
microbenchmark or system.time– akrun
Jan 2 at 9:28
add a comment |
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6
You can check it using
microbenchmarkorsystem.time– akrun
Jan 2 at 9:28