start on #170 and ms3_common.r script

scripts/r/ms3_common.r

@@ -0,0 +1,37 @@
+source("scripts/r/common.r")
+
+memaslap_summary <- function(df) {
+  DROP_TIMES_BEFORE = 0 #2 * 60 # How many seconds in the beginning we want to drop
+  DROP_TIMES_AFTER = max((df %>% filter(type=="t"))$time) # - 2 * 60
+
+  df2 <- df %>%
+    mutate(min=min/1000, max=max/1000, avg=avg/1000, std=std/1000) %>%
+    filter(type=="t" & time > DROP_TIMES_BEFORE & time <= DROP_TIMES_AFTER)
+
+  means <- df2 %>%
+    group_by(request_type) %>%
+    summarise(mean_response_time=sum(ops*avg)/sum(ops))
+
+  res <- list()
+  tps_summed <- df2 %>% group_by(time, repetition) %>%
+    summarise(tps=sum(tps))
+  tps_values <- tps_summed$tps
+  res$tps_mean <- mean(tps_values)
+  res$mean_response_time_get <- (means %>% filter(request_type=="GET"))$mean_response_time[1]
+  res$mean_response_time_set <- (means %>% filter(request_type=="SET"))$mean_response_time[1]
+  res$mean_response_time <- (res$mean_response_time_get * sum((df2 %>% filter(request_type=="GET"))$ops) +
+                               res$mean_response_time_set * sum((df2 %>% filter(request_type=="SET"))$ops)) / sum(df2$ops)
+  res$std_response_time <- sqrt(sum(df2$ops * df2$std * df2$std) / sum(df2$ops))
+
+  return(as.data.frame(res))
+}
+
+file_to_df <- function(file_path, sep=";") {
+  if(file.exists(file_path)) {
+    df <- read.csv(file_path, header=TRUE, sep=sep)
+    result <- df
+  } else {
+    result <- data.frame()
+  }
+  return(result)
+}

scripts/r/part1_mmm.r

@@ -0,0 +1,6 @@
+source("scripts/r/common.r")
+source("scripts/r/ms3_common.r")
+
+# ---- Directories ----
+output_dir <- "results/analysis/part1_mmm"
+

scripts/r/part5_irtl.r

@@ -1,50 +1,10 @@
 source("scripts/r/common.r")
-
+source("scripts/r/ms3_common.r")
 
 # ---- Directories ----
 output_dir <- "results/analysis/part5_irtl"
 result_dir_base <- "results/replication"
 
-# ---------------------------
-# ---- HELPER FUNCTIONS -----
-# ---------------------------
-
-memaslap_summary <- function(df) {
-  DROP_TIMES_BEFORE = 0 #2 * 60 # How many seconds in the beginning we want to drop
-  DROP_TIMES_AFTER = max((df %>% filter(type=="t"))$time) # - 2 * 60
-
-  df2 <- df %>%
-    mutate(min=min/1000, max=max/1000, avg=avg/1000, std=std/1000) %>%
-    filter(type=="t" & time > DROP_TIMES_BEFORE & time <= DROP_TIMES_AFTER)
-
-  means <- df2 %>%
-    group_by(request_type) %>%
-    summarise(mean_response_time=sum(ops*avg)/sum(ops))
-
-  res <- list()
-  tps_summed <- df2 %>% group_by(time, repetition) %>%
-    summarise(tps=sum(tps))
-  tps_values <- tps_summed$tps
-  res$tps_mean <- mean(tps_values)
-  res$mean_response_time_get <- (means %>% filter(request_type=="GET"))$mean_response_time[1]
-  res$mean_response_time_set <- (means %>% filter(request_type=="SET"))$mean_response_time[1]
-  res$mean_response_time <- (res$mean_response_time_get * sum((df2 %>% filter(request_type=="GET"))$ops) +
-                               res$mean_response_time_set * sum((df2 %>% filter(request_type=="SET"))$ops)) / sum(df2$ops)
-  res$std_response_time <- sqrt(sum(df2$ops * df2$std * df2$std) / sum(df2$ops))
-
-  return(as.data.frame(res))
-}
-
-file_to_df <- function(file_path, sep=";") {
-  if(file.exists(file_path)) {
-    df <- read.csv(file_path, header=TRUE, sep=sep)
-    result <- df
-  } else {
-    result <- data.frame()
-  }
-  return(result)
-}
-
 # ------------------
 # ---- FILE IO -----
 # ------------------