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Quentin 2019-09-17 15:59:38 +02:00
parent b969ae7077
commit 5b1c0c077b
1 changed files with 42 additions and 8 deletions
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50
r/thunder_configure.R
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@ -148,7 +148,7 @@ v8 <- ggplot(data=latency_evol, aes(x=ident,y=lat_ms)) +
theme_classic()
thunder_drop <- read.csv("thunder_configure_16_drop.csv")
thunder_drop_2 <- sqldf("select run, packet_range, 1.0*count / 990 as packet_ratio, row_number() OVER (partition by packet_range order by run) sorting from thunder_drop where run LIKE '%-24' ")
thunder_drop_2 <- sqldf("select run, packet_range, 1.0*count / 990 as packet_ratio, row_number() OVER (partition by packet_range order by run) sorting from thunder_drop where run LIKE '%-26' ")
#cats <- c("0-989","990-1979","1980-2969","2970-3959","3960-4949","4950-5939","5940-6929","6930-7919","7920-8909","8910-9899")
thunder_drop_2$packet_range <- as.factor(thunder_drop_2$packet_range)
thunder_drop_2$sorting <- as.factor(thunder_drop_2$sorting)
@ -171,28 +171,62 @@ v9 <- ggplot(data = thunder_drop_2, aes(x=packet_range, y=packet_ratio,fill=sort
thunder_drop_burst <- read.csv("thunder_configure_16_drop_burst.csv")
tdb_ag <- sqldf("select run,count,COUNT(count) as oc from thunder_drop_burst where run LIKE '%-24' group by run,count")
tdb_ag_2 <- sqldf("select run,count,oc,row_number() OVER (partition by count order by run) as sorting from tdb_ag")
tdb_ag_2 <- sqldf(
"
select
td.run as r,
count,
oc,
total,
1.0 * oc / total as oc_ratio,
row_number() OVER (partition by count order by td.run) as sorting
from
tdb_ag as td,
(select run,SUM(oc) as total from tdb_ag group by run) as ag
where
td.run = ag.run
")
tdb_ag_2$sorting <- as.factor(tdb_ag_2$sorting)
tdb_ag_2$count <- as.factor(tdb_ag_2$count)
v10 <- ggplot(data = tdb_ag_2, aes(x=count, y=oc)) +
v10 <- ggplot(data = tdb_ag_2, aes(x=count, y=oc_ratio)) +
#geom_bar(stat="summary",position = "dodge") +
#scale_y_log10() +
geom_violin(scale='width') +
geom_boxplot(width=0.1, outlier.shape=NA) +
scale_y_continuous() +
ylab("Occurence") +
xlab("Packets dropped in a row") +
scale_y_continuous(labels = scales::percent) +
ylab("% observed drops") +
xlab("Packets lost during the drop") +
scale_fill_grey() +
theme_classic()
thunder_red <- read.csv("thunder_configure_16_red.csv")
tred <- sqldf("select run,delivered_at_once,occur,row_number() OVER (partition by delivered_at_once order by run) as sorting from thunder_red WHERE run LIKE '%-24'")
tred <- sqldf(
"
select
tr.run as r,
delivered_at_once,
1.0 * occur / total as occur_ratio,
occur,
total,
row_number() OVER (partition by delivered_at_once order by tr.run) as sorting
from
thunder_red tr,
(select run,SUM(occur) as total from thunder_red group by run) as ag
WHERE
tr.run LIKE '%-26'
and tr.run = ag.run
")
tred$sorting <- as.factor(tred$sorting)
tred$delivered_at_once <- as.factor(tred$delivered_at_once)
v11 <- ggplot(data = tred, aes(x=delivered_at_once, y=occur)) +
v11 <- ggplot(data = tred, aes(x=delivered_at_once, y=occur_ratio)) +
#geom_bar(stat="summary",position = "dodge") +
geom_violin(scale='width') +
xlab('Fresh packets per cell') +
ylab('% of received cells') +
scale_y_continuous(labels = scales::percent) +
geom_boxplot(width=0.1, outlier.shape=NA) +
theme_classic()