README.md
In ColinFay/dockerstats: A Wrapper Around 'docker stats'
dockerstats
{dockerstats} is a small wrapper around docker stats that returns
the output of this command as an R data.frame.
Note that this package calls system("docker stats") so you should be
able to do this from your R command line. I’ll probably refactor that at
some point.
Installation
You can install the released version of {dockerstats} from GitHub
with:
remotes::install_github("ColinFay/dockerstats")
How to
library(dockerstats)
dockerstats()
By default, dockerstats() returns the stats for running containers.
dockerstats()
#> Container Name ID CPUPerc MemUsage MemLimit MemPerc
#> 1 144087c5fbaf couchdb 144087c5fbaf 6.85 55.75MiB 12.69GiB 0.43
#> 2 5017ccedf5d5 strange_maxwell 5017ccedf5d5 0.10 12.09MiB 12.69GiB 0.09
#> 3 ca323ae39e42 proxy ca323ae39e42 0.00 21.75MiB 12.69GiB 0.17
#> 4 1c32d35562fb rocketchat 1c32d35562fb 0.16 477.8MiB 12.69GiB 3.68
#> 5 9e9605a015d4 mongo 9e9605a015d4 2.23 42.73MiB 12.69GiB 0.33
#> NetI NetO BlockI BlockO PIDs record_time extra
#> 1 150kB 203kB 0B 0B 30 2020-04-24 11:59:21
#> 2 2.68MB 3.36MB 0B 0B 6 2020-04-24 11:59:21
#> 3 9.73kB 1.84kB 0B 0B 11 2020-04-24 11:59:21
#> 4 107MB 86.8MB 0B 0B 12 2020-04-24 11:59:21
#> 5 86.6MB 107MB 0B 0B 55 2020-04-24 11:59:21
You can return stats for all containers (not just running)
dockerstats(all = TRUE)
#> Container Name ID CPUPerc MemUsage MemLimit
#> 1 144087c5fbaf couchdb 144087c5fbaf 1.61 55.75MiB 12.69GiB
#> 2 5017ccedf5d5 strange_maxwell 5017ccedf5d5 0.14 12.09MiB 12.69GiB
#> 3 a25f4ee0fb8d elastic_khayyam a25f4ee0fb8d 0.00 0B 0B
#> 4 04e001a4b1db sad_proskuriakova 04e001a4b1db 0.00 0B 0B
#> 5 3aa1aea8186e elegant_dewdney 3aa1aea8186e 0.00 0B 0B
#> 6 40d8f43add75 dreamy_gould 40d8f43add75 0.00 0B 0B
#> 7 b30725ee216a hardcore_ptolemy b30725ee216a 0.00 0B 0B
#> 8 579eb8936c44 zen_swanson 579eb8936c44 0.00 0B 0B
#> 9 3925b39043f4 hardcore_murdock 3925b39043f4 0.00 0B 0B
#> 10 ea30ec3c5424 affectionate_hypatia ea30ec3c5424 0.00 0B 0B
#> 11 0366f78dc8e9 wonderful_taussig 0366f78dc8e9 0.00 0B 0B
#> 12 baf592f3d6a1 suspicious_lovelace baf592f3d6a1 0.00 0B 0B
#> 13 deb4e2cc9664 loving_hypatia deb4e2cc9664 0.00 0B 0B
#> 14 2492225605f5 inspiring_bose 2492225605f5 0.00 0B 0B
#> 15 2cb8e121f57e funny_lumiere 2cb8e121f57e 0.00 0B 0B
#> 16 840acbf622a7 recursing_robinson 840acbf622a7 0.00 0B 0B
#> 17 3ed5a6b4b8d0 clever_volhard 3ed5a6b4b8d0 0.00 0B 0B
#> 18 dbcf81982870 mystifying_hofstadter dbcf81982870 0.00 0B 0B
#> 19 841be05ca06f nice_einstein 841be05ca06f 0.00 0B 0B
#> 20 bdac8ae662bd rocketchating bdac8ae662bd 0.00 0B 0B
#> 21 ad640c7420ea compassionate_ellis ad640c7420ea 0.00 0B 0B
#> 22 ea890803de3c sleepy_colden ea890803de3c 0.00 0B 0B
#> 23 5cef479fec0e boring_swartz 5cef479fec0e 0.00 0B 0B
#> 24 658ed62c82ed elegant_pare 658ed62c82ed 0.00 0B 0B
#> 25 d74bd9004627 exciting_yalow d74bd9004627 0.00 0B 0B
#> 26 f5cb6430e103 gallant_lichterman f5cb6430e103 0.00 0B 0B
#> 27 f8c3f4b47931 heuristic_proskuriakova f8c3f4b47931 0.00 0B 0B
#> 28 1a8588105357 pedantic_booth 1a8588105357 0.00 0B 0B
#> 29 7c16ac79e629 focused_chaum 7c16ac79e629 0.00 0B 0B
#> 30 e2f0dc1dab14 boring_liskov e2f0dc1dab14 0.00 0B 0B
#> 31 7f3b0a08a7aa stupefied_ishizaka 7f3b0a08a7aa 0.00 0B 0B
#> 32 194f087c356e boring_mcclintock 194f087c356e 0.00 0B 0B
#> 33 7f575179ad72 flamboyant_sammet 7f575179ad72 0.00 0B 0B
#> 34 82b75a1bdbe8 jack 82b75a1bdbe8 0.00 0B 0B
#> 35 5b1197c61bfb login 5b1197c61bfb 0.00 0B 0B
#> 36 ca323ae39e42 proxy ca323ae39e42 0.00 21.75MiB 12.69GiB
#> 37 1c32d35562fb rocketchat 1c32d35562fb 0.28 477MiB 12.69GiB
#> 38 9e9605a015d4 mongo 9e9605a015d4 1.67 42.73MiB 12.69GiB
#> MemPerc NetI NetO BlockI BlockO PIDs record_time extra
#> 1 0.43 150kB 203kB 0B 0B 30 2020-04-24 11:59:23
#> 2 0.09 2.68MB 3.36MB 0B 0B 6 2020-04-24 11:59:23
#> 3 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 4 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 5 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 6 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 7 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 8 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 9 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 10 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 11 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 12 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 13 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 14 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 15 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 16 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 17 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 18 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 19 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 20 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 21 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 22 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 23 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 24 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 25 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 26 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 27 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 28 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 29 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 30 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 31 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 32 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 33 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 34 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 35 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 36 0.17 9.73kB 1.84kB 0B 0B 11 2020-04-24 11:59:23
#> 37 3.67 107MB 86.8MB 0B 0B 12 2020-04-24 11:59:23
#> 38 0.33 86.6MB 107MB 0B 0B 55 2020-04-24 11:59:23
Or from a subset of containers:
dockerstats("mongo", "proxy")
#> Container Name ID CPUPerc MemUsage MemLimit MemPerc NetI NetO
#> 1 mongo mongo 9e9605a015d4 1.74 42.83MiB 12.69GiB 0.33 86.6MB 107MB
#> 2 proxy proxy ca323ae39e42 0.00 21.75MiB 12.69GiB 0.17 9.73kB 1.84kB
#> BlockI BlockO PIDs record_time extra
#> 1 0B 0B 55 2020-04-24 11:59:26
#> 2 0B 0B 11 2020-04-24 11:59:26
The extra param is used to add extra information to the recording,
which can be usefull is you want to tag the specific recording.
For example, here we mimic a connection on the hexmake container
system("docker run --name hexmake --rm -p 2811:80 colinfay/hexmake", wait = FALSE)
library(crrri)
chrome <- Chrome$new(
bin = pagedown::find_chrome(),
debug_port = httpuv::randomPort()
)
client <- chrome$connect(callback = function(client) {
Page <- client$Page
Page$navigate(url = "http://localhost:2811")
print({
dockerstats::dockerstats("hexmake", extra = "Connection via chrome")
})
})
chrome$close()
dockerstats_recurse
dockerstats_recurse() is a wrapper around dockerstats() that runs
every every seconds, calling the callback function everytime a loop
is completed.
By default, the callback is print, but you can define your own. For
example, this function will run the dockerstats() fun every 2 seconds
and save it to a file.
dockerstats_recurse(
"hexmake",
callback = function(res){
print(
paste("Mem usage: ", res$MemUsage)
)
write.table(
res,
"dockerstats.csv",
append = TRUE,
col.names = FALSE,
row.names = FALSE,
sep = ","
)
}
)
As this is a pretty common use-case, a wrapper for this is implemented:
dockerstats_recurse(
"hexmake",
callback = append_csv("dockerstats.csv", print = TRUE)
)
Kill the container once done
system("docker kill hexmake")
Byte conversions
To handle the MemUsage columns, expressed as byte, {dockerstats}
provides a series of functions to convert to byte, kib, mib or gib.
The unit is kept in attr("units") of the result
dock_stats <- dockerstats()
dock_stats$MemUsag
#> [1] "55.75MiB" "12.09MiB" "21.75MiB" "477.6MiB" "42.73MiB"
# Convert to kib
to_kib(dock_stats$MemUsag)
#> [1] 57088.00 12380.16 22272.00 489062.40 43755.52
#> attr(,"unit")
#> [1] "kib"
How columns are transformed
-
CPUPerc and MemPerc are stripped from the trailing %, and are
turned into numeric
-
MEM USAGE / LIMIT is splitted into two columns, MemUsage and
`MemLimit
-
NET I/O and BLOCK I/O are splitted into two columns,
respectively NetI & NetO, and BlockI & BlockO
Manipulate columns expressed in bite size
You can call as_fs_byte() from the {fs} package to manipulate the
columns which are expressed in bytes.
dock_stats <- dockerstats()
dock_stats$MemUsage <- to_kib(dock_stats$MemUsag)
library(ggplot2)
ggplot(
dock_stats,
aes(
reorder(Name, MemUsage),
MemUsage
)
) +
geom_col() +
scale_y_continuous(labels = scales::label_bytes(units = "kiB")) +
coord_flip() +
labs(
title = "MemUsage of running containers",
y = "MemUsage in kiB",
x = "Containers"
)
ColinFay/dockerstats documentation built on May 18, 2020, 11:54 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
dockerstats
{dockerstats} is a small wrapper around docker stats that returns
the output of this command as an R data.frame.
Note that this package calls system("docker stats") so you should be
able to do this from your R command line. I’ll probably refactor that at
some point.
Installation
You can install the released version of {dockerstats} from GitHub
with:
remotes::install_github("ColinFay/dockerstats")
How to
library(dockerstats)
dockerstats()
By default, dockerstats() returns the stats for running containers.
dockerstats()
#> Container Name ID CPUPerc MemUsage MemLimit MemPerc
#> 1 144087c5fbaf couchdb 144087c5fbaf 6.85 55.75MiB 12.69GiB 0.43
#> 2 5017ccedf5d5 strange_maxwell 5017ccedf5d5 0.10 12.09MiB 12.69GiB 0.09
#> 3 ca323ae39e42 proxy ca323ae39e42 0.00 21.75MiB 12.69GiB 0.17
#> 4 1c32d35562fb rocketchat 1c32d35562fb 0.16 477.8MiB 12.69GiB 3.68
#> 5 9e9605a015d4 mongo 9e9605a015d4 2.23 42.73MiB 12.69GiB 0.33
#> NetI NetO BlockI BlockO PIDs record_time extra
#> 1 150kB 203kB 0B 0B 30 2020-04-24 11:59:21
#> 2 2.68MB 3.36MB 0B 0B 6 2020-04-24 11:59:21
#> 3 9.73kB 1.84kB 0B 0B 11 2020-04-24 11:59:21
#> 4 107MB 86.8MB 0B 0B 12 2020-04-24 11:59:21
#> 5 86.6MB 107MB 0B 0B 55 2020-04-24 11:59:21
You can return stats for all containers (not just running)
dockerstats(all = TRUE)
#> Container Name ID CPUPerc MemUsage MemLimit
#> 1 144087c5fbaf couchdb 144087c5fbaf 1.61 55.75MiB 12.69GiB
#> 2 5017ccedf5d5 strange_maxwell 5017ccedf5d5 0.14 12.09MiB 12.69GiB
#> 3 a25f4ee0fb8d elastic_khayyam a25f4ee0fb8d 0.00 0B 0B
#> 4 04e001a4b1db sad_proskuriakova 04e001a4b1db 0.00 0B 0B
#> 5 3aa1aea8186e elegant_dewdney 3aa1aea8186e 0.00 0B 0B
#> 6 40d8f43add75 dreamy_gould 40d8f43add75 0.00 0B 0B
#> 7 b30725ee216a hardcore_ptolemy b30725ee216a 0.00 0B 0B
#> 8 579eb8936c44 zen_swanson 579eb8936c44 0.00 0B 0B
#> 9 3925b39043f4 hardcore_murdock 3925b39043f4 0.00 0B 0B
#> 10 ea30ec3c5424 affectionate_hypatia ea30ec3c5424 0.00 0B 0B
#> 11 0366f78dc8e9 wonderful_taussig 0366f78dc8e9 0.00 0B 0B
#> 12 baf592f3d6a1 suspicious_lovelace baf592f3d6a1 0.00 0B 0B
#> 13 deb4e2cc9664 loving_hypatia deb4e2cc9664 0.00 0B 0B
#> 14 2492225605f5 inspiring_bose 2492225605f5 0.00 0B 0B
#> 15 2cb8e121f57e funny_lumiere 2cb8e121f57e 0.00 0B 0B
#> 16 840acbf622a7 recursing_robinson 840acbf622a7 0.00 0B 0B
#> 17 3ed5a6b4b8d0 clever_volhard 3ed5a6b4b8d0 0.00 0B 0B
#> 18 dbcf81982870 mystifying_hofstadter dbcf81982870 0.00 0B 0B
#> 19 841be05ca06f nice_einstein 841be05ca06f 0.00 0B 0B
#> 20 bdac8ae662bd rocketchating bdac8ae662bd 0.00 0B 0B
#> 21 ad640c7420ea compassionate_ellis ad640c7420ea 0.00 0B 0B
#> 22 ea890803de3c sleepy_colden ea890803de3c 0.00 0B 0B
#> 23 5cef479fec0e boring_swartz 5cef479fec0e 0.00 0B 0B
#> 24 658ed62c82ed elegant_pare 658ed62c82ed 0.00 0B 0B
#> 25 d74bd9004627 exciting_yalow d74bd9004627 0.00 0B 0B
#> 26 f5cb6430e103 gallant_lichterman f5cb6430e103 0.00 0B 0B
#> 27 f8c3f4b47931 heuristic_proskuriakova f8c3f4b47931 0.00 0B 0B
#> 28 1a8588105357 pedantic_booth 1a8588105357 0.00 0B 0B
#> 29 7c16ac79e629 focused_chaum 7c16ac79e629 0.00 0B 0B
#> 30 e2f0dc1dab14 boring_liskov e2f0dc1dab14 0.00 0B 0B
#> 31 7f3b0a08a7aa stupefied_ishizaka 7f3b0a08a7aa 0.00 0B 0B
#> 32 194f087c356e boring_mcclintock 194f087c356e 0.00 0B 0B
#> 33 7f575179ad72 flamboyant_sammet 7f575179ad72 0.00 0B 0B
#> 34 82b75a1bdbe8 jack 82b75a1bdbe8 0.00 0B 0B
#> 35 5b1197c61bfb login 5b1197c61bfb 0.00 0B 0B
#> 36 ca323ae39e42 proxy ca323ae39e42 0.00 21.75MiB 12.69GiB
#> 37 1c32d35562fb rocketchat 1c32d35562fb 0.28 477MiB 12.69GiB
#> 38 9e9605a015d4 mongo 9e9605a015d4 1.67 42.73MiB 12.69GiB
#> MemPerc NetI NetO BlockI BlockO PIDs record_time extra
#> 1 0.43 150kB 203kB 0B 0B 30 2020-04-24 11:59:23
#> 2 0.09 2.68MB 3.36MB 0B 0B 6 2020-04-24 11:59:23
#> 3 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 4 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 5 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 6 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 7 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 8 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 9 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 10 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 11 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 12 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 13 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 14 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 15 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 16 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 17 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 18 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 19 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 20 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 21 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 22 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 23 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 24 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 25 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 26 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 27 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 28 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 29 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 30 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 31 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 32 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 33 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 34 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 35 0.00 0B 0B 0B 0B 0 2020-04-24 11:59:23
#> 36 0.17 9.73kB 1.84kB 0B 0B 11 2020-04-24 11:59:23
#> 37 3.67 107MB 86.8MB 0B 0B 12 2020-04-24 11:59:23
#> 38 0.33 86.6MB 107MB 0B 0B 55 2020-04-24 11:59:23
Or from a subset of containers:
dockerstats("mongo", "proxy")
#> Container Name ID CPUPerc MemUsage MemLimit MemPerc NetI NetO
#> 1 mongo mongo 9e9605a015d4 1.74 42.83MiB 12.69GiB 0.33 86.6MB 107MB
#> 2 proxy proxy ca323ae39e42 0.00 21.75MiB 12.69GiB 0.17 9.73kB 1.84kB
#> BlockI BlockO PIDs record_time extra
#> 1 0B 0B 55 2020-04-24 11:59:26
#> 2 0B 0B 11 2020-04-24 11:59:26
The extra param is used to add extra information to the recording,
which can be usefull is you want to tag the specific recording.
For example, here we mimic a connection on the hexmake container
system("docker run --name hexmake --rm -p 2811:80 colinfay/hexmake", wait = FALSE)
library(crrri)
chrome <- Chrome$new(
bin = pagedown::find_chrome(),
debug_port = httpuv::randomPort()
)
client <- chrome$connect(callback = function(client) {
Page <- client$Page
Page$navigate(url = "http://localhost:2811")
print({
dockerstats::dockerstats("hexmake", extra = "Connection via chrome")
})
})
chrome$close()
dockerstats_recurse
dockerstats_recurse() is a wrapper around dockerstats() that runs
every every seconds, calling the callback function everytime a loop
is completed.
By default, the callback is print, but you can define your own. For
example, this function will run the dockerstats() fun every 2 seconds
and save it to a file.
dockerstats_recurse(
"hexmake",
callback = function(res){
print(
paste("Mem usage: ", res$MemUsage)
)
write.table(
res,
"dockerstats.csv",
append = TRUE,
col.names = FALSE,
row.names = FALSE,
sep = ","
)
}
)
As this is a pretty common use-case, a wrapper for this is implemented:
dockerstats_recurse(
"hexmake",
callback = append_csv("dockerstats.csv", print = TRUE)
)
Kill the container once done
system("docker kill hexmake")
Byte conversions
To handle the MemUsage columns, expressed as byte, {dockerstats}
provides a series of functions to convert to byte, kib, mib or gib.
The unit is kept in attr("units") of the result
dock_stats <- dockerstats()
dock_stats$MemUsag
#> [1] "55.75MiB" "12.09MiB" "21.75MiB" "477.6MiB" "42.73MiB"
# Convert to kib
to_kib(dock_stats$MemUsag)
#> [1] 57088.00 12380.16 22272.00 489062.40 43755.52
#> attr(,"unit")
#> [1] "kib"
How columns are transformed
-
CPUPercandMemPercare stripped from the trailing%, and are turned into numeric -
MEM USAGE / LIMITis splitted into two columns,MemUsageand `MemLimit -
NET I/OandBLOCK I/Oare splitted into two columns, respectivelyNetI&NetO, andBlockI&BlockO
Manipulate columns expressed in bite size
You can call as_fs_byte() from the {fs} package to manipulate the
columns which are expressed in bytes.
dock_stats <- dockerstats()
dock_stats$MemUsage <- to_kib(dock_stats$MemUsag)
library(ggplot2)
ggplot(
dock_stats,
aes(
reorder(Name, MemUsage),
MemUsage
)
) +
geom_col() +
scale_y_continuous(labels = scales::label_bytes(units = "kiB")) +
coord_flip() +
labs(
title = "MemUsage of running containers",
y = "MemUsage in kiB",
x = "Containers"
)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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