database_week_4
Yagna Praseeda Atmuri
2023-09-18
R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr)
library(readr)path='C:/Users/prase/OneDrive/Documents/signal_metrics.csv'data = read.csv('C:/Users/prase/OneDrive/Documents/signal_metrics.csv')str(data)## 'data.frame': 12621 obs. of 11 variables:
## $ Timestamp : chr "51:30.7" "23:56.4" "24:39.7" "02:26.4" ...
## $ Locality : chr "Danapur" "Bankipore" "Ashok Rajpath" "Rajendra Nagar" ...
## $ Latitude : num 25.4 25.6 25.5 25.5 25.6 ...
## $ Longitude : num 85.1 85.3 85.1 85.2 85.1 ...
## $ SignalStrength: num -76.7 -77.5 -78.6 -78.8 -77.3 ...
## $ DataThroughput: num 1.11 2.48 1.03 1.46 1.79 ...
## $ Latency : num 139 138 165 102 177 ...
## $ NetworkType : chr "LTE" "LTE" "LTE" "LTE" ...
## $ BB60C : num -72.5 -73.5 -73.9 -74 -74.1 ...
## $ srsRAN : num -85 -84.8 -84.8 -87.3 -85.9 ...
## $ BladeRFxA9 : num -75.1 -77.9 -77.2 -77.9 -75.6 ...total_rows <- nrow(data)
sample_size <- round(0.5 * total_rows)
set.seed(1)
random_sample_1<-sample(1:total_rows,sample_size,replace=T)
set.seed(3)
random_sample_2<-sample(1:total_rows,sample_size,replace=T)
set.seed(5)
random_sample_3<-sample(1:total_rows,sample_size,replace=T)
set.seed(7)
random_sample_4<-sample(1:total_rows,sample_size,replace=T)
set.seed(9)
random_sample_5<-sample(1:total_rows,sample_size,replace=T)df_1<- data.frame(data[random_sample_1,])
df_2<- data.frame(data[random_sample_2,])
df_3<- data.frame(data[random_sample_3,])
df_4<- data.frame(data[random_sample_4,])
df_5<- data.frame(data[random_sample_5,])view(df_1)view(df_2)view(df_3)view(df_4)view(df_5)summary_df_1 <- df_1 |>
summarise(min_signal_strength=min(SignalStrength),
max_signal_strength=max(SignalStrength),
mean_signal_strength=mean(SignalStrength),
median_signal_strength=median(SignalStrength),
sd_signal_strength=sd(SignalStrength),
IQR_signal_strength=IQR(SignalStrength),
min_data_throughput=min(DataThroughput),
max_data_throughput=max(DataThroughput),
mean_data_throughput=mean(DataThroughput),
median_data_throughput=median(DataThroughput),
sd_data_throughput=sd(DataThroughput),
IQR_data_throughput=IQR(DataThroughput),
min_latency=min(Latency),
max_latency=max(Latency),
mean_latency=mean(Latency),
median_latency=median(Latency),
sd_latency=sd(Latency),
IQR_latency=IQR(Latency),
min_BB60C=min(BB60C),
max_BB60C=max(BB60C),
mean_BB60C=mean(BB60C),
median_BB60C=median(BB60C),
sd_BB60C=sd(BB60C),
IQR_BB60C=IQR(BB60C),
min_srsRAN=min(srsRAN),
max_srsRAN=max(srsRAN),
mean_srsRAN=mean(srsRAN),
median_srsRAN=median(srsRAN),
sd_srsRAN=sd(srsRAN),
IQR_srsRAN=IQR(srsRAN))summary_df_2 <- df_2 |>
summarise(min_signal_strength=min(SignalStrength),
max_signal_strength=max(SignalStrength),
mean_signal_strength=mean(SignalStrength),
median_signal_strength=median(SignalStrength),
sd_signal_strength=sd(SignalStrength),
IQR_signal_strength=IQR(SignalStrength),
min_data_throughput=min(DataThroughput),
max_data_throughput=max(DataThroughput),
mean_data_throughput=mean(DataThroughput),
median_data_throughput=median(DataThroughput),
sd_data_throughput=sd(DataThroughput),
IQR_data_throughput=IQR(DataThroughput),
min_latency=min(Latency),
max_latency=max(Latency),
mean_latency=mean(Latency),
median_latency=median(Latency),
sd_latency=sd(Latency),
IQR_latency=IQR(Latency),
min_BB60C=min(BB60C),
max_BB60C=max(BB60C),
mean_BB60C=mean(BB60C),
median_BB60C=median(BB60C),
sd_BB60C=sd(BB60C),
IQR_BB60C=IQR(BB60C),
min_srsRAN=min(srsRAN),
max_srsRAN=max(srsRAN),
mean_srsRAN=mean(srsRAN),
median_srsRAN=median(srsRAN),
sd_srsRAN=sd(srsRAN),
IQR_srsRAN=IQR(srsRAN))summary_df_3 <- df_3 |>
summarise(min_signal_strength=min(SignalStrength),
max_signal_strength=max(SignalStrength),
mean_signal_strength=mean(SignalStrength),
median_signal_strength=median(SignalStrength),
sd_signal_strength=sd(SignalStrength),
IQR_signal_strength=IQR(SignalStrength),
min_data_throughput=min(DataThroughput),
max_data_throughput=max(DataThroughput),
mean_data_throughput=mean(DataThroughput),
median_data_throughput=median(DataThroughput),
sd_data_throughput=sd(DataThroughput),
IQR_data_throughput=IQR(DataThroughput),
min_latency=min(Latency),
max_latency=max(Latency),
mean_latency=mean(Latency),
median_latency=median(Latency),
sd_latency=sd(Latency),
IQR_latency=IQR(Latency),
min_BB60C=min(BB60C),
max_BB60C=max(BB60C),
mean_BB60C=mean(BB60C),
median_BB60C=median(BB60C),
sd_BB60C=sd(BB60C),
IQR_BB60C=IQR(BB60C),
min_srsRAN=min(srsRAN),
max_srsRAN=max(srsRAN),
mean_srsRAN=mean(srsRAN),
median_srsRAN=median(srsRAN),
sd_srsRAN=sd(srsRAN),
IQR_srsRAN=IQR(srsRAN))summary_df_4 <- df_4 |>
summarise(min_signal_strength=min(SignalStrength),
max_signal_strength=max(SignalStrength),
mean_signal_strength=mean(SignalStrength),
median_signal_strength=median(SignalStrength),
sd_signal_strength=sd(SignalStrength),
IQR_signal_strength=IQR(SignalStrength),
min_data_throughput=min(DataThroughput),
max_data_throughput=max(DataThroughput),
mean_data_throughput=mean(DataThroughput),
median_data_throughput=median(DataThroughput),
sd_data_throughput=sd(DataThroughput),
IQR_data_throughput=IQR(DataThroughput),
min_latency=min(Latency),
max_latency=max(Latency),
mean_latency=mean(Latency),
median_latency=median(Latency),
sd_latency=sd(Latency),
IQR_latency=IQR(Latency),
min_BB60C=min(BB60C),
max_BB60C=max(BB60C),
mean_BB60C=mean(BB60C),
median_BB60C=median(BB60C),
sd_BB60C=sd(BB60C),
IQR_BB60C=IQR(BB60C),
min_srsRAN=min(srsRAN),
max_srsRAN=max(srsRAN),
mean_srsRAN=mean(srsRAN),
median_srsRAN=median(srsRAN),
sd_srsRAN=sd(srsRAN),
IQR_srsRAN=IQR(srsRAN))summary_df_5 <- df_5 |>
summarise(min_signal_strength=min(SignalStrength),
max_signal_strength=max(SignalStrength),
mean_signal_strength=mean(SignalStrength),
median_signal_strength=median(SignalStrength),
sd_signal_strength=sd(SignalStrength),
IQR_signal_strength=IQR(SignalStrength),
min_data_throughput=min(DataThroughput),
max_data_throughput=max(DataThroughput),
mean_data_throughput=mean(DataThroughput),
median_data_throughput=median(DataThroughput),
sd_data_throughput=sd(DataThroughput),
IQR_data_throughput=IQR(DataThroughput),
min_latency=min(Latency),
max_latency=max(Latency),
mean_latency=mean(Latency),
median_latency=median(Latency),
sd_latency=sd(Latency),
IQR_latency=IQR(Latency),
min_BB60C=min(BB60C),
max_BB60C=max(BB60C),
mean_BB60C=mean(BB60C),
median_BB60C=median(BB60C),
sd_BB60C=sd(BB60C),
IQR_BB60C=IQR(BB60C),
min_srsRAN=min(srsRAN),
max_srsRAN=max(srsRAN),
mean_srsRAN=mean(srsRAN),
median_srsRAN=median(srsRAN),
sd_srsRAN=sd(srsRAN),
IQR_srsRAN=IQR(srsRAN))summaries_df <- rbind(summary_df_1, summary_df_2, summary_df_3, summary_df_4, summary_df_5)
view(summaries_df)boxplot(df_1$SignalStrength,df_2$SignalStrength,df_3$SignalStrength,df_4$SignalStrength,df_5$SignalStrength,
names = c("Subsample 1", "Subsample 2", "Subsample 3", "Subsample 4","Subsample 5"),
main = "Comparison of signal_strength Across Subsamples",
ylab = "signal strength (dBm)",
col=c("skyblue","pink","brown","gold","magenta"),
border="black"
)
boxplot(df_1$DataThroughput,df_2$DataThroughput,df_3$DataThroughput,df_4$DataThroughput,df_5$DataThroughput,
names = c("Subsample 1", "Subsample 2", "Subsample 3", "Subsample 4","Subsample 5"),
main = "Comparison of DataThroughput Across Subsamples",
ylab = "Data Throughput (Mbps)",
col=c("red","blue","green","purple","orange"),
border="black"
)
par(mfrow = c(2, 3))
hist(df_1$Latency, main = " Subsample 1", xlab = "Latency (Latency)", col = "red")
hist(df_2$Latency, main = " Subsample 2", xlab = "Latency (Latency)", col = "blue")
hist(df_3$Latency, main = " Subsample 3", xlab = "Latency (Latency)", col = "green")
hist(df_4$Latency, main = " Subsample 4", xlab = "Latency (Latency)", col = "purple")
hist(df_5$Latency, main = " Subsample 5", xlab = "Latency (Latency)", col = "orange")
par(mfrow = c(2, 3))
hist(df_1$BB60C, main = " Subsample 1", xlab = "BB60C (humidity_max)", col = "skyblue")
hist(df_2$BB60C, main = " Subsample 2", xlab = "BB60C (humidity_max)", col = "pink")
hist(df_3$BB60C, main = " Subsample 3", xlab = "BB60C (humidity_max)", col = "brown")
hist(df_4$BB60C, main = " Subsample 4", xlab = "BB60C (humidity_max)", col = "gold")
hist(df_5$BB60C, main = " Subsample 5", xlab = "BB60C (humidity_max)", col = "magenta")
par(mfrow = c(2, 3))
plot(density(df_1$srsRAN), main = "srsRAN (Subsample 1)", col = "red")
plot(density(df_2$srsRAN), main = "srsRAN (Subsample 2)", col = "skyblue")
plot(density(df_3$srsRAN), main = "srsRAN (Subsample 3)", col = "pink")
plot(density(df_4$srsRAN), main = "srsRAN (Subsample 4)", col = "purple")
plot(density(df_5$srsRAN), main = "srsRAN (Subsample 5)", col = "gold")
Q1 <- quantile(df_1$SignalStrength, 0.25)
Q3 <- quantile(df_1$SignalStrength, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_1_anomalies_SignalStrength <-df_1$SignalStrength[df_1$SignalStrength < lower_bound |df_1$SignalStrength > upper_bound]
Q1 <- quantile(df_2$SignalStrength, 0.25)
Q3 <- quantile(df_2$SignalStrength, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_2_anomalies_SignalStrength <-df_2$SignalStrength[df_2$SignalStrength < lower_bound |df_2$SignalStrength > upper_bound]
Q1 <- quantile(df_3$SignalStrength, 0.25)
Q3 <- quantile(df_3$SignalStrength, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_3_anomalies_SignalStrength<-df_3$SignalStrength[df_3$SignalStrength < lower_bound |df_3$SignalStrength > upper_bound]
Q1 <- quantile(df_4$SignalStrength, 0.25)
Q3 <- quantile(df_4$SignalStrength, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_4_anomalies_SignalStrength <-df_4$SignalStrength[df_4$SignalStrength < lower_bound |df_4$SignalStrength > upper_bound]
Q1 <- quantile(df_5$SignalStrength, 0.25)
Q3 <- quantile(df_5$SignalStrength, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_5_anomalies_SignalStrength <-df_5$SignalStrength[df_5$SignalStrength < lower_bound |df_5$SignalStrength > upper_bound]print(df_1_anomalies_SignalStrength)## [1] -106.41505 -105.86679 -105.33513 -106.33030 -108.70213 -108.70213
## [7] -107.36125 -108.45138 -105.02128 -104.61850 -106.75781 -77.15572
## [13] -78.31335 -105.99143 -109.82467 -105.68522 -106.41505 -105.62223
## [19] -107.73059 -104.71875 -105.99143 -105.02128 -105.43014 -104.85476
## [25] -105.62028 -104.87385 -108.73752 -105.74592 -105.68522 -106.33451
## [31] -107.73059 -106.25338 -104.59389 -105.34397 -105.62028 -105.49735
## [37] -106.39594 -108.25428 -108.29521 -105.28288 -111.89905 -78.31335
## [43] -107.80894 -105.09909 -108.59011 -104.98812 -76.37918 -106.33030
## [49] -107.49820 -106.59373 -109.85812 -108.41540 -105.86122 -105.40214
## [55] -105.02128 -105.33936 -105.14534 -108.59011 -107.67168 -108.77854
## [61] -104.98812 -108.73752 -78.31335 -108.41540 -108.70213 -107.73059
## [67] -106.39594 -108.73752 -104.69932 -77.15572 -105.67107 -109.59066print(df_2_anomalies_SignalStrength)## [1] -105.86122 -105.09909 -76.37918 -105.49735 -106.51225 -108.50729
## [7] -110.12709 -108.45138 -106.41505 -105.68047 -109.63650 -108.41278
## [13] -105.07332 -104.87385 -108.25428 -108.50729 -111.89905 -105.33513
## [19] -108.41278 -106.39594 -104.85965 -77.77420 -106.17990 -106.00434
## [25] -106.28676 -106.84027 -107.73059 -109.25017 -78.02876 -105.85491
## [31] -106.91367 -78.31335 -109.25017 -107.36125 -105.43014 -108.15081
## [37] -105.22582 -106.51225 -77.80179 -109.82467 -105.67429 -109.25017
## [43] -76.37918 -109.63650 -108.29521 -106.41505 -107.15926 -106.84027
## [49] -106.33030 -104.78422 -107.67849 -106.20114 -106.93783 -113.08282
## [55] -105.40214 -105.43014 -77.27129 -107.15926 -105.68047 -105.49735
## [61] -106.87605 -116.94227print(df_3_anomalies_SignalStrength)## [1] -104.90820 -104.69152 -111.89905 -105.02128 -107.70361 -108.76535
## [7] -75.67285 -104.69932 -106.28676 -106.72407 -106.17990 -77.27129
## [13] -106.59373 -105.02128 -106.28676 -107.60508 -78.02876 -106.17990
## [19] -109.25017 -111.89905 -76.72446 -105.56778 -106.72407 -107.60508
## [25] -104.90820 -105.68522 -104.90820 -106.93783 -107.67849 -107.51544
## [31] -105.34397 -105.74592 -108.76535 -105.07332 -105.49735 -106.00434
## [37] -104.91591 -108.41430 -105.02128 -106.51225 -76.50138 -109.63650
## [43] -111.89905 -107.36125 -112.90796 -106.87605 -108.15081 -77.15572
## [49] -105.62028 -105.40214 -108.59011 -105.67429 -108.15081 -107.67168
## [55] -105.67429 -105.22582 -104.70793 -113.08282 -78.31335 -108.76535
## [61] -105.67429 -108.41540 -76.72446 -104.66646 -106.59373 -74.64485
## [67] -108.59011 -104.78422 -108.77854print(df_4_anomalies_SignalStrength)## [1] -106.28676 -104.69152 -104.74551 -106.25338 -105.59243 -105.49020
## [7] -104.89704 -106.59373 -105.22582 -106.87605 -106.33030 -105.14593
## [13] -78.02876 -105.34397 -105.60443 -106.72407 -105.14593 -106.28676
## [19] -107.67168 -105.56778 -111.89905 -104.78422 -78.54834 -107.60508
## [25] -106.41505 -105.62028 -107.49820 -105.52877 -78.51341 -107.24013
## [31] -106.75781 -108.04954 -105.07332 -105.86122 -77.77420 -109.25017
## [37] -77.77420 -108.04954 -109.25017 -106.74836 -106.93783 -105.08875
## [43] -105.94904 -104.91591 -106.41505 -107.24013 -105.08875 -112.90796
## [49] -77.52335 -104.66646 -105.86122 -105.09909 -105.09909 -105.94904
## [55] -106.74836 -105.22582 -106.20114 -104.78422 -105.64214 -107.70361
## [61] -104.80447 -106.41505 -107.91700 -104.85965 -106.51225 -105.08875
## [67] -106.33030 -105.67107 -105.85615 -112.90796 -105.74592 -105.07335
## [73] -105.08875 -105.33936 -105.64214 -105.62223 -106.33030 -108.45138print(df_5_anomalies_SignalStrength)## [1] -105.07335 -105.09909 -106.74836 -108.41540 -104.71875 -108.25428
## [7] -106.91367 -104.70793 -104.66646 -104.70793 -105.09909 -105.99143
## [13] -108.04954 -105.44083 -106.83516 -104.64497 -77.52335 -107.38674
## [19] -109.63650 -107.49820 -104.96866 -105.43014 -104.69932 -104.70793
## [25] -104.85965 -106.87605 -78.54834 -107.91700 -106.83516 -105.60443
## [31] -78.31335 -107.73059 -78.55790 -106.61160 -77.27129 -76.50138
## [37] -104.66646 -105.14593 -107.38674 -105.14534 -104.80447 -105.85615
## [43] -77.15572 -104.90820 -106.28676 -105.68047 -105.44083 -106.41505
## [49] -106.93783 -105.08875 -107.24013 -106.61160 -104.69932 -104.57595
## [55] -105.44083 -108.59011 -104.64497 -105.92936 -106.17990 -107.91700
## [61] -105.52877 -107.80894 -105.07335 -106.74836 -108.70213 -106.74836
## [67] -78.02876 -104.74551 -104.85476 -104.61850 -78.54834 -78.54243
## [73] -108.29521 -106.83516 -104.80447 -105.68522 -104.59389 -106.93783
## [79] -104.59389 -107.91700 -108.59011 -105.86122Q1 <- quantile(df_1$DataThroughput, 0.25)
Q3 <- quantile(df_1$DataThroughput, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_1_anomalies_DataThroughput <-df_1$DataThroughput[df_1$DataThroughput < lower_bound |df_1$DataThroughput > upper_bound]
Q1 <- quantile(df_2$DataThroughput, 0.25)
Q3 <- quantile(df_2$DataThroughput, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_2_anomalies_DataThroughput <-df_2$DataThroughput[df_2$DataThroughput < lower_bound |df_2$DataThroughput > upper_bound]
Q1 <- quantile(df_3$DataThroughput, 0.25)
Q3 <- quantile(df_3$DataThroughput, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_3_anomalies_DataThroughput<-df_3$DataThroughput[df_3$DataThroughput < lower_bound |df_3$DataThroughput > upper_bound]
Q1 <- quantile(df_4$DataThroughput, 0.25)
Q3 <- quantile(df_4$DataThroughput, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_4_anomalies_DataThroughput <-df_4$DataThroughput[df_4$DataThroughput < lower_bound |df_4$DataThroughput > upper_bound]
Q1 <- quantile(df_5$DataThroughput, 0.25)
Q3 <- quantile(df_5$DataThroughput, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_5_anomalies_DataThroughput <-df_5$DataThroughput[df_5$DataThroughput < lower_bound |df_5$DataThroughput > upper_bound]print(df_1_anomalies_DataThroughput)## [1] 90.61174 97.89590 98.61393 92.65997 86.06315 83.37910 91.15234 95.11390
## [9] 87.45289 99.26797 98.29446 83.23080 94.29124 94.37811 87.79150 94.30469
## [17] 92.67161 82.66611 83.31528 82.79897 97.73888 86.27256 87.29776 84.23508
## [25] 95.82849 94.35477 96.76009 87.24436 94.89475 87.24436 86.62887 95.04924
## [33] 99.61928 91.74116 90.62396 96.26053 86.10562 96.22811 92.05035 93.53242
## [41] 86.36950 89.91279 83.55501 85.38742 84.96729 84.38086 89.68946 97.87232
## [49] 89.38414 88.86050 95.16853 98.74311 93.09228 84.05168 95.73361 99.05200
## [57] 92.12179 85.83458 98.23138 84.96729 84.66259 97.57716 85.15238 94.97444
## [65] 95.01685 94.37811 83.62988 87.79150 91.79239 84.46633 84.54124 92.83712
## [73] 89.61148 85.05505 86.88376 93.21892 91.94976 98.29876 96.48614 89.37637
## [81] 83.91946 95.54273 91.80360 88.80113 88.64084 93.75343 84.96729 89.45747
## [89] 91.66753 87.29776 86.00183 92.97869 95.53832 91.01431 94.92932 91.10584
## [97] 88.55148 95.89699 96.39683 88.80113 94.28979 84.58001 96.11171 90.31873
## [105] 98.77123 92.60613 84.58001 84.19761 89.81783 99.15827 91.16774 98.17962
## [113] 95.41049 84.68725 89.01371 95.06933 96.04885 96.76009 87.52430 82.93516
## [121] 95.18109 88.90892 94.92932 87.06759 95.15742 88.78276 90.63386 86.08290
## [129] 84.11737 95.11516 98.42567 87.37335 83.83781 83.23080 84.50752 95.49644
## [137] 88.35980 86.13116 83.91946 85.42789 87.47136 88.81492 95.43391 94.89475
## [145] 84.05168 85.05505 99.94855 89.89646 84.93693 94.47281 96.68431 84.10069
## [153] 89.88680 85.07051 98.74311 87.99583 95.24471 94.77569 92.79528 84.43374
## [161] 93.18774 88.06284 94.52360 87.82799 91.16774 95.94160 87.34217 88.86050
## [169] 98.92700 89.01371 83.15263 85.89085 92.04843 98.61393 91.74116 83.89710
## [177] 86.39984 85.97381 92.99322 85.10431 90.77607 96.60377 92.04158 97.73565
## [185] 90.99140 83.90950 85.68888 83.91473 99.30461 87.99583 88.85517 87.99980
## [193] 92.29272 94.44596 91.83194 84.99531 86.27256 86.10562 85.11482 91.72314
## [201] 91.92583 85.97381 98.98802 94.37811 82.71299 83.17909 97.59521 96.48614
## [209] 83.18721 88.33870 84.99531 89.37637 86.86775 97.87232 88.80113 97.73565
## [217] 92.12179 91.65266 91.44703 88.81492 85.70684 90.04551 90.18088 97.29322
## [225] 86.39520 85.48496 82.79635 91.70364 89.01491 91.31663 98.79023 89.60626
## [233] 83.89710 82.93516 95.09618 85.38742 99.10080 87.01218 93.32272 95.32876
## [241] 92.43833 93.20449 98.06785 89.90850 82.79680 95.94160 83.04131 90.95654
## [249] 95.97127 92.14167 87.02971 87.54801 88.18271 97.17045 85.63116 88.56783
## [257] 92.93600 90.24038 93.44353 85.77566 99.13975 91.47473 95.27087 83.91946
## [265] 94.60244 85.15172 95.93698 97.17045 90.21913 99.41446 98.67865 86.44761
## [273] 91.62480 95.70103 94.68926 99.85128 84.73094 92.58788 94.40052 89.45747
## [281] 84.54124 83.69720 95.69026 91.18791 92.06887 83.02984 84.66259 92.60613
## [289] 92.41987 96.38053 88.34765 86.47205 95.24471 99.03661 93.70789 96.29753
## [297] 91.72314 87.71699 87.02593 96.20505 83.22614 91.79239 89.04326 96.40144
## [305] 94.35477 97.70630 92.26615 86.10562 90.00310 96.68431 87.89096 83.83808
## [313] 91.65266 96.23928 95.30184 88.80774 82.62713 99.61928 89.95465 94.49170
## [321] 85.44463 87.06759 93.55678 98.25317 89.30681 99.41446 95.11062 86.49896
## [329] 95.32876 91.09621 89.83598 99.53098 86.47205 89.61824 93.22610 83.40103
## [337] 87.82799 96.12375 89.81783 94.56251 97.22266 93.11094 85.69196 93.71047
## [345] 92.76366 91.91776 96.87838 85.63116 98.02220 85.97381 99.26797 88.63640
## [353] 95.15742 94.49170 85.70857 82.79635 99.62275 83.12104 95.53832 86.54349
## [361] 97.22008 84.68725 87.73009 95.43391 99.92178 86.55945 98.80579 83.25407
## [369] 91.66753 92.80444 91.08178 92.65997 86.03380 89.91279 87.47136 84.37947
## [377] 83.25407 84.32351 94.60244 88.81492 97.78347 87.93514 98.64456 91.85220
## [385] 99.58166 92.06887 93.34144print(df_2_anomalies_DataThroughput)## [1] 98.00639 95.44987 88.82313 83.54749 92.82660 78.97512 85.83458 76.61826
## [9] 78.65089 79.22672 79.85776 91.18235 75.65693 85.45232 95.69026 87.45289
## [17] 87.82799 94.76019 95.21516 83.03328 90.18088 85.76379 88.82313 89.19643
## [25] 98.50334 83.04615 84.65314 92.53010 93.81437 87.01218 92.65997 79.68585
## [33] 85.80548 78.10597 77.88118 91.94976 84.28713 95.11390 90.75899 96.12375
## [41] 97.84705 76.87926 95.41781 98.63382 85.42789 80.63760 78.48387 92.77601
## [49] 93.52974 76.48117 97.70630 83.49896 85.15238 80.01185 93.44353 82.93516
## [57] 93.91184 95.69026 81.89828 94.73210 85.70857 85.97381 92.67161 80.07721
## [65] 92.38497 85.40699 91.70364 98.63382 83.93883 88.46040 90.57597 76.98232
## [73] 87.71699 79.92475 76.39108 78.14312 86.84719 94.36617 92.29272 94.77569
## [81] 89.32226 95.93698 81.89828 76.33494 92.14167 92.05035 81.23794 87.24436
## [89] 79.66087 94.18079 87.99980 86.58756 76.33494 77.25203 82.87460 93.70789
## [97] 91.37208 76.18835 98.76558 82.99651 94.31506 99.13975 94.52360 86.09475
## [105] 77.23532 98.47046 81.19417 76.75823 78.32753 97.17045 75.57226 77.39440
## [113] 82.34059 87.19890 75.73673 98.50334 91.08178 90.74417 76.43839 97.68971
## [121] 79.92475 89.38414 80.33550 85.53821 99.48658 85.44463 78.80066 94.86418
## [129] 99.54886 86.09475 94.85497 77.25839 84.79624 86.71788 92.90365 96.42935
## [137] 83.83781 98.06785 79.31053 97.15939 89.08048 83.67339 82.34059 85.98145
## [145] 91.92583 83.17909 93.83956 87.19890 83.93883 98.67311 93.18774 96.45015
## [153] 87.73009 77.36079 89.13565 80.75418 82.94465 94.30277 88.57898 95.24471
## [161] 82.26859 98.63382 78.01884 90.61174 83.93995 92.05035 81.38048 84.65314
## [169] 91.22082 83.37771 95.87631 78.32917 78.74185 93.44353 86.96855 90.62396
## [177] 94.87815 81.21603 84.28713 85.22764 87.32574 94.23248 94.31793 91.62480
## [185] 94.35477 84.99531 93.57888 85.00367 76.12001 96.87838 85.68888 89.13565
## [193] 93.71047 84.93322 97.17535 94.83156 85.88951 88.57580 78.37305 85.40699
## [201] 89.38414 90.58647 98.43211 76.58219 93.44353 84.72118 85.87068 92.80444
## [209] 95.06933 91.79983 84.58001 92.12179 78.91948 75.39903 75.99806 86.04803
## [217] 87.99583 95.23283 78.68350 91.07075 99.33129 88.92929 97.50165 84.58001
## [225] 96.60078 90.26029 79.04911 88.66123 88.62971 84.37947 95.27087 85.15172
## [233] 87.56057 96.45015 86.84719 95.69026 93.74075 78.87676 98.07916 77.31859
## [241] 98.00639 79.09689 99.72390 92.65997 95.09618 95.18109 77.52977 95.21516
## [249] 97.95027 81.61609 87.64460 76.61826 92.77601 89.17816 84.05601 88.74322
## [257] 83.52943 78.39535 99.94855 98.59164 86.52955 96.97897 75.65693 78.46118
## [265] 80.79042 92.04843 86.97073 94.77569 94.85497 92.12256 95.65135 91.94468
## [273] 82.97465 94.35477 79.69731 91.31663 95.65135 91.09621 79.12373 95.86414
## [281] 80.29541 88.57580 93.74075 86.06315 83.62988 94.30277 94.30469 96.23411
## [289] 81.36034 90.85126 82.58740 90.65705 82.79897 82.17649 87.02593 83.12930
## [297] 85.76379 86.90922 78.32917 76.87288 94.72583 93.76229 79.35642 98.62982
## [305] 87.34217 94.49765 78.63081 92.06887 91.90978 84.46476 95.16853 90.16359
## [313] 79.53693 86.33394 84.88901 77.58437 87.02630 84.93322 83.92952 83.37910
## [321] 83.79526 83.12930 76.84331 98.74311 84.79518 94.67481 93.86179 92.16158
## [329] 86.27256 76.61434 80.55750 78.89635 98.60942 99.72390 86.00181 87.29776
## [337] 90.32622 89.49931 80.14603 90.61075 85.11482 94.28979 99.13371 83.16300
## [345] 95.70103 96.42312 88.81371 81.37948 83.04615 85.84168 94.28979 90.05759
## [353] 77.36317 91.54074 88.82313 83.93115 90.03458 80.69700 89.15238 76.87926
## [361] 95.21516 85.38742 89.83598 83.03328 76.87105 82.34059 98.77123 83.12104
## [369] 87.34217 77.39440 89.19643 87.17561 80.14288 91.90978 80.78160 86.45171
## [377] 95.41049 94.35477 88.33870 93.73786 84.35328 95.16853 95.33565 99.89014
## [385] 91.10960 78.31873 87.89096 83.76234 86.13116 77.49883 76.87926 94.52360
## [393] 98.63382 90.85126 91.93954 92.76864 91.08178 97.26569 94.36617 90.03458
## [401] 80.55750 89.84205 79.97310 85.86388 78.27357 76.19623 87.78665 99.40710
## [409] 94.44596 75.98800 91.93954 86.03380 99.05367 98.56606 88.66791 95.89699
## [417] 76.52572 82.17272 83.15263 87.79150 84.71826 80.07721 83.52943 99.13371
## [425] 81.19417 91.64784 96.11171 98.43141 96.82964 92.82660 79.09689 76.94907
## [433] 80.79042 96.42312 88.46440 97.22266 95.15498 93.34144 89.17464 77.49284
## [441] 81.70643 78.13288 82.37561 80.90516 92.79528 98.00639 79.93865 85.15238
## [449] 93.73786 84.05419 93.32272 92.06887 83.54749 78.57847 87.02630 78.84344
## [457] 84.21374 78.49513 88.57580 89.02327 91.70364 95.47809 85.10431 83.83808
## [465] 92.15216 87.34217 84.68725 88.16433 96.26053 96.53603 84.66259 88.86050
## [473] 86.41019 77.23532 78.10597 83.22614 84.79624 99.72390 91.62480 88.06284
## [481] 90.31873 86.45171 88.53329 78.16710 80.15283 78.54868 92.85935 86.08290
## [489] 81.80540 93.26288 95.86414 91.74116 94.30469 87.10713 94.67481 99.98583
## [497] 89.33887 86.37270 78.48387 98.50334 90.85126 78.04639 87.82799 88.92270
## [505] 79.68624 85.38742 78.12639 82.34059 87.78665 81.67966 96.87838 79.68585
## [513] 80.69700 88.89843 95.01242 87.79150 79.22654 77.22031 87.54801 84.32351
## [521] 85.68852 89.45636 80.73081 87.99980 91.79983 92.51908 98.18654 93.20449
## [529] 96.27162 89.37637 99.15827 81.37948 80.29113 94.09549 83.32599 87.97456
## [537] 85.48650 81.83250 91.37208 96.11171 87.73009print(df_3_anomalies_DataThroughput)## [1] 85.77566 94.88311 78.32753 99.54367 88.16433 89.17464 93.11094 88.90035
## [9] 83.69720 83.25407 84.93322 94.60244 79.53693 88.78276 90.02682 88.34765
## [17] 84.93322 82.17272 84.79624 99.40710 80.01185 76.19623 84.70466 86.76839
## [25] 90.69137 85.23671 93.81437 94.89475 79.87717 91.97358 81.67966 81.41722
## [33] 82.37561 94.31793 92.38497 91.92583 96.42935 94.43699 89.37637 83.92952
## [41] 80.45751 82.98270 86.06315 77.75635 84.93693 78.48726 95.14714 92.82660
## [49] 78.80505 95.53832 89.32226 91.94976 99.45177 76.48117 99.94181 85.76379
## [57] 92.77601 97.39107 92.90365 77.50200 97.70630 93.35401 76.62425 80.41922
## [65] 86.39520 93.22610 83.79526 94.52360 90.61075 92.79528 96.62285 77.88118
## [73] 90.00310 85.89085 92.45138 96.23411 83.31528 79.53693 76.84331 75.76111
## [81] 86.52992 88.53329 90.21913 94.76019 93.55678 84.75221 77.39440 89.68946
## [89] 79.18109 95.62686 86.86775 99.82493 90.04149 95.14714 85.53821 98.63551
## [97] 84.38086 88.64084 80.58208 81.70643 91.10584 86.09475 90.97252 86.88376
## [105] 94.12820 78.46118 77.79182 91.01431 76.51302 76.25659 82.97465 79.87717
## [113] 89.29917 88.35980 85.83458 85.48650 95.11390 97.94698 86.49896 80.30517
## [121] 87.01218 76.18835 93.18774 94.43699 99.19993 93.04738 78.74185 95.62686
## [129] 94.52360 91.99664 93.84893 76.90917 89.50389 93.44540 90.05759 82.94465
## [137] 83.91946 97.15939 91.63338 84.32351 89.33887 82.36986 96.22811 91.92583
## [145] 89.46968 86.47205 90.75899 76.39108 88.86050 98.91438 92.93600 79.87717
## [153] 75.72653 98.62982 95.06933 87.78665 76.12001 92.16158 87.45441 75.74026
## [161] 82.05363 88.90035 99.19993 96.20505 76.94907 82.89480 98.06785 83.17909
## [169] 87.78665 78.68350 82.71299 94.89475 91.62480 82.26859 81.37171 97.40766
## [177] 77.36742 78.48726 75.75440 84.70466 93.24525 96.68431 88.57580 89.13565
## [185] 98.56606 99.81717 86.08290 77.49883 85.97381 91.47473 99.05200 91.83194
## [193] 79.93865 95.66432 79.49700 83.32599 88.78276 83.96655 82.79635 98.81000
## [201] 88.58434 75.74026 76.18835 80.29113 81.83046 78.24184 87.34217 91.14992
## [209] 87.99583 92.26615 84.93322 84.71826 93.84893 94.30469 78.79322 90.54696
## [217] 90.67170 91.18235 94.22542 92.99322 76.81471 98.59164 90.20200 82.17649
## [225] 94.49170 89.15238 89.95465 89.45636 84.23508 98.59334 88.63640 78.61023
## [233] 85.00367 95.49644 79.75012 77.75635 83.79526 76.52572 85.70684 82.79635
## [241] 82.19756 88.73264 81.57735 89.68946 98.07916 83.91946 97.50165 97.29322
## [249] 87.02049 98.03181 75.92493 85.63116 82.34719 99.15827 82.36986 84.35328
## [257] 95.23283 83.40103 95.81734 80.01185 90.75899 94.24493 91.90978 92.51908
## [265] 95.11062 97.91384 77.39440 96.54543 93.73352 87.52430 85.88447 92.02218
## [273] 85.63116 93.91184 85.19650 86.95614 88.64084 96.20505 79.29871 83.03328
## [281] 94.40052 85.70684 95.99052 89.08048 78.65089 98.60942 76.09340 90.61075
## [289] 89.60626 97.62430 89.17816 76.19623 75.98393 94.36617 95.65135 91.92583
## [297] 95.84948 83.62988 92.82660 93.44353 88.56783 96.97897 83.67339 97.73888
## [305] 90.72881 90.21913 88.66791 83.92952 80.38475 87.71699 98.59164 83.93115
## [313] 82.19756 93.73406 76.53712 76.57291 79.49700 97.87232 93.70789 88.74322
## [321] 90.26029 79.35642 83.67339 89.60626 85.11482 84.75221 80.14288 99.58166
## [329] 78.54868 93.39820 75.68063 96.29753 84.55068 90.57597 86.49896 83.84081
## [337] 77.58437 97.62236 89.01371 76.12001 92.05035 80.90516 87.07554 90.20169
## [345] 99.05367 96.43934 87.01218 85.22764 92.86504 80.01185 91.44703 81.35810
## [353] 75.99806 94.52360 87.99980 78.48726 86.24807 84.75388 81.09158 82.79680
## [361] 90.63386 99.41446 81.95726 90.65705 82.37561 76.45669 97.73888 87.79150
## [369] 91.65266 92.92769 76.19623 80.27074 95.01685 96.88023 81.38048 87.37335
## [377] 87.73448 88.85517 85.19612 86.55945 84.35328 86.52992 93.75343 87.73448
## [385] 77.49883 83.67312 99.04489 80.78160 89.60626 91.94468 80.78160 93.91010
## [393] 89.49931 91.18791 93.90947 83.04131 83.67312 91.91776 86.55945 95.27087
## [401] 84.79624 79.62371 82.94465 92.41987 88.35980 80.65117 76.12518 94.28979
## [409] 87.10713 77.84593 77.39528 90.24038 85.70857 97.26569 78.79896 78.63081
## [417] 88.56389 87.64460 85.96861 98.17962 80.78160 95.97127 98.63382 80.73081
## [425] 96.68431 93.44353 94.80157 97.94698 75.99806 98.50334 99.72390 87.45289
## [433] 82.34719 76.90917 91.63338 79.22672 93.09228 92.45138 83.57726 99.54886
## [441] 79.35744 83.23080 97.94698 85.80548 92.51908 89.49931 78.27357 88.06284
## [449] 76.25659 89.17464 95.11516 96.11171 96.87838 79.23075 82.94465 99.05200
## [457] 86.52955 79.93865 97.21511 94.22542 94.56251 95.65135 82.93516 81.80540
## [465] 80.53823 84.05460 82.84769 94.80157 93.44540 87.89789 84.93693 85.97381
## [473] 95.49644 85.87068 81.09158 90.87809 79.22672 91.65266 78.87676 86.88376
## [481] 89.83598 82.97662 91.72314 87.32574 95.15742 80.53823 88.46040 84.46794
## [489] 79.93865 80.12492 94.88311 90.31873 81.83250 92.92769 92.29272 91.07075
## [497] 84.79518 76.95345 85.48496 86.88376 79.35744 97.95754 91.97358 98.61393
## [505] 81.12550 80.45751 90.02411 99.05367 79.40419 95.99052 83.48177 78.54951
## [513] 95.06933 76.42903 86.00183 93.44353 88.80113 91.91776 84.58001 81.97056
## [521] 87.79150 83.15263 83.67339print(df_4_anomalies_DataThroughput)## [1] 85.45232 86.13116 99.94181 85.68888 96.30718 78.71003 84.99017 84.32351
## [9] 86.90922 89.13565 84.71826 84.38086 80.65117 97.59265 78.21318 91.85220
## [17] 85.77566 85.48496 87.82799 87.99980 82.84769 78.84344 97.87462 90.21911
## [25] 86.04803 83.10132 84.75221 89.51829 84.79624 98.63382 77.36317 88.76917
## [33] 99.07377 87.89789 76.79997 92.60613 83.37910 95.36655 95.01242 78.16061
## [41] 94.40052 86.41019 97.30048 87.37335 82.20716 98.91438 77.44493 94.86418
## [49] 96.05196 90.97252 94.97444 93.76229 80.36772 76.25659 85.89085 87.52430
## [57] 80.45751 81.70643 77.26172 83.52943 79.18109 92.85703 77.38776 82.26859
## [65] 78.46118 78.37305 85.45232 85.42789 78.54868 89.58484 90.77607 93.91184
## [73] 81.37171 96.86252 88.18271 83.16707 99.05200 92.83712 86.41019 88.81371
## [81] 90.95654 93.44540 94.40052 76.26732 78.32917 79.09689 94.53070 77.25203
## [89] 82.89480 96.30718 97.68971 77.25839 83.17909 81.47237 98.59984 83.52943
## [97] 98.56606 93.73406 90.26029 91.37208 86.04803 79.40419 83.92952 91.16229
## [105] 85.23671 95.14714 83.48177 76.98232 79.75012 91.92583 92.29272 97.30048
## [113] 90.65705 95.35195 83.07781 77.53115 84.54124 76.48203 78.80505 95.41049
## [121] 91.74116 87.78665 80.04942 97.57716 92.86504 83.57726 99.88732 83.15263
## [129] 86.49896 97.73565 76.26732 82.15217 77.84593 99.30461 90.24038 99.19993
## [137] 86.84719 86.91750 89.01491 99.13371 79.92475 82.99651 97.62430 86.71208
## [145] 95.82849 97.91384 95.21143 85.48275 99.45177 89.01371 88.46440 80.41922
## [153] 99.04489 80.07721 88.64323 99.58166 78.10597 87.73448 76.45185 80.09430
## [161] 89.48588 89.04326 88.81371 79.55774 81.87213 78.12639 95.25009 90.03458
## [169] 85.19650 95.32876 91.07438 78.86987 95.41781 82.17272 98.00639 99.95467
## [177] 91.08178 90.99140 89.02327 99.05625 89.37637 79.13060 90.02411 96.64798
## [185] 96.12028 94.47281 85.87068 90.97252 86.52992 91.14992 79.13060 99.62976
## [193] 99.95467 89.29917 91.47473 94.73210 78.16061 76.39108 76.53712 94.22542
## [201] 99.62976 77.71711 78.27357 90.16359 85.22764 92.83712 78.13445 82.91799
## [209] 85.76379 85.06139 85.88447 99.05367 93.24525 84.70466 98.74311 97.49211
## [217] 96.56962 76.81471 95.27087 83.93995 88.62971 83.79526 85.88951 89.19643
## [225] 79.05183 94.77569 88.81492 82.54259 86.88376 92.29695 76.45669 89.81783
## [233] 97.84705 93.53242 85.23296 86.06315 84.37947 99.15827 78.91948 98.81503
## [241] 77.50200 97.15939 92.85471 98.17962 96.60377 85.33534 77.83439 82.15217
## [249] 82.93516 77.32975 93.91184 89.38414 89.95465 93.74075 92.04843 92.86504
## [257] 94.47281 77.25203 85.23296 78.14312 96.62285 86.72849 76.90917 83.54749
## [265] 82.17649 87.64460 93.91010 96.60078 81.83250 94.43699 80.55831 84.21374
## [273] 77.37833 86.08290 97.14452 95.82546 93.26288 88.06284 82.62713 92.77601
## [281] 95.16853 82.03446 98.79023 98.18654 85.83458 92.43833 95.97127 79.35744
## [289] 89.33887 89.61148 86.39520 95.11516 98.06783 80.41514 78.49513 85.76379
## [297] 95.31970 86.33394 78.13288 93.22610 79.69731 84.05601 83.91232 95.82546
## [305] 95.94160 91.83378 95.15742 99.45177 77.32975 78.46118 93.52974 78.38176
## [313] 84.50752 87.54865 76.94426 91.07438 80.90516 80.38475 81.41722 85.69196
## [321] 98.54584 87.97456 99.54886 99.13975 96.68431 78.12639 95.87631 93.75343
## [329] 79.29871 87.45441 88.12796 87.99583 96.12028 84.23508 96.48614 90.24038
## [337] 91.16774 90.21913 99.62976 81.83250 89.88680 99.95467 99.15827 85.15172
## [345] 93.26288 85.06139 85.69196 77.58437 99.50054 78.39837 83.04615 96.12375
## [353] 89.95465 82.87460 84.99531 88.12796 95.86414 92.85935 92.16158 95.97127
## [361] 82.79897 93.90947 83.18721 92.51908 77.44493 78.54868 79.66087 92.26615
## [369] 90.03458 94.89475 96.07838 83.49896 88.81371 96.45015 99.48658 92.35352
## [377] 99.92178 78.53131 99.85128 86.58756 79.64546 97.73565 93.52974 86.00183
## [385] 96.38053 89.18036 81.83250 91.73983 90.31873 90.61174 80.75418 95.36655
## [393] 88.66791 86.49896 76.87288 97.57716 82.54259 98.00639 80.53823 86.71788
## [401] 96.27162 80.29113 98.91438 82.86528 78.32753 89.60626 97.85684 95.04924
## [409] 88.05666 82.26859 76.39108 78.97512 88.22188 89.01491 86.44761 95.31970
## [417] 81.49974 92.83712 95.11516 98.60942 92.77601 90.95654 81.08401 87.19890
## [425] 93.21892 82.45448 77.22031 94.30277 82.29882 97.40766 84.93693 87.17561
## [433] 93.75343 77.25839 76.48203 92.64096 83.03328 84.42283 91.99664 90.02682
## [441] 94.09549 89.01491 99.85128 88.87737 94.83156 77.32975 90.02411 81.25560
## [449] 77.79182 95.24898 76.90917 94.14834 78.68350 92.06887 90.24038 95.65135
## [457] 81.08401 86.45171 92.99322 78.39837 95.21143 85.98145 91.49074 79.40419
## [465] 96.54543 94.38045 76.94907 76.36380 79.35744 79.94800 80.38475 99.72390
## [473] 91.09621 76.87213 94.43699 94.87815 97.22335 78.37305 94.80595 98.62982
## [481] 90.85126 96.38053 79.64546 86.27256 79.97310 77.39440 92.06887 82.20716
## [489] 88.73543 81.12550 84.46633 92.79528 91.35040 92.02218 95.11062 98.09609
## [497] 88.05666 91.47473 77.32975 77.29974 78.74185 80.63760 80.41922 78.53131
## [505] 86.62887 81.80540 80.01185 91.72314 97.87232 94.16719 78.89635 80.29113
## [513] 78.39535 86.55945 96.68431 96.88023 86.71208 99.94181 91.10960 89.08152
## [521] 85.33534 95.43391 91.94976 87.99980 80.55831 85.19612 91.66753 81.36034
## [529] 96.17719 92.67161 79.55369 97.60088 84.93322 95.87631print(df_5_anomalies_DataThroughput)## [1] 93.84893 88.80419 98.29446 77.31859 84.93322 84.75221 78.10597 96.07838
## [9] 78.13288 81.23794 92.35352 86.84719 75.08408 88.56389 79.64546 76.42903
## [17] 75.92493 74.32133 84.10069 84.72118 93.70789 86.47205 85.06139 93.82259
## [25] 87.45289 91.37208 88.66791 92.02417 87.71699 95.82849 82.40945 83.57726
## [33] 80.24757 82.93516 75.40003 85.15238 85.84168 83.18721 74.55149 88.35901
## [41] 89.61148 98.67311 77.83439 74.62996 76.46553 75.82996 94.28979 75.70614
## [49] 90.69137 79.31053 96.42935 96.87838 96.27162 76.12001 88.86050 94.49765
## [57] 78.37535 80.36056 80.69700 83.12930 76.95345 85.53821 99.54886 76.95345
## [65] 78.32753 78.13288 82.99651 94.87815 94.56251 98.67311 95.73361 74.63657
## [73] 98.74311 97.89717 92.85471 93.74075 94.60244 81.36034 76.06342 97.73565
## [81] 96.53603 83.79526 90.03458 98.59164 93.22610 90.62396 88.86050 99.61928
## [89] 94.78101 76.33494 79.33531 96.38053 91.47473 85.84168 75.98393 80.30517
## [97] 89.18036 92.58788 97.22008 92.51908 93.35401 80.34586 92.90365 93.35401
## [105] 90.26029 94.86418 93.73406 80.79042 92.86504 92.29272 83.76905 94.36617
## [113] 78.54951 91.10584 98.98802 99.41446 93.09228 95.36655 77.88118 85.05505
## [121] 80.12492 98.54584 77.75635 93.26288 77.17511 90.69137 82.97662 97.70630
## [129] 93.74075 88.56783 80.69700 86.52955 82.98270 85.23671 95.97127 81.47237
## [137] 76.45185 99.82493 79.05994 84.24040 94.40052 79.85776 79.18109 93.83956
## [145] 91.16774 96.86252 77.25839 75.97198 98.02220 75.48259 88.74322 84.68725
## [153] 98.00639 87.93514 88.85517 88.12796 80.58208 97.73888 90.20200 80.33550
## [161] 76.84331 97.49211 95.47809 84.11737 93.91184 99.30461 86.33394 82.37561
## [169] 95.68379 87.54865 85.22764 78.21318 98.59334 86.45171 98.06785 75.46427
## [177] 91.10584 88.85517 86.52955 94.85497 87.40082 94.80595 92.53010 98.47046
## [185] 97.59265 78.10597 95.31970 77.36742 99.53229 98.62982 99.94855 75.24753
## [193] 97.57716 87.32574 86.03380 87.20918 90.87809 78.63081 78.61023 91.71215
## [201] 83.15263 99.13371 77.23532 96.40144 80.15283 76.33494 91.16774 98.23138
## [209] 95.11516 97.69866 80.63760 82.58740 85.88951 99.05367 86.64322 88.48105
## [217] 84.37947 97.22266 98.02220 84.42283 79.92475 79.92475 89.30122 81.57735
## [225] 95.35195 95.64445 91.49120 92.16158 78.27357 86.39984 87.99980 91.16229
## [233] 79.68585 83.18721 99.54886 76.46553 78.10597 94.28979 74.52730 91.31663
## [241] 87.82799 94.30277 78.57847 99.82493 92.76366 96.23411 94.14834 79.65627
## [249] 78.32753 83.79526 79.05994 74.90025 75.65693 80.41922 90.54696 96.62285
## [257] 91.10584 91.94468 88.82526 81.38048 93.18774 83.69720 74.79413 77.97505
## [265] 75.07641 87.73448 97.39107 93.75343 99.89014 92.41987 85.38742 80.45751
## [273] 85.19650 95.36655 77.31904 89.60626 75.10294 85.88447 95.11516 79.09689
## [281] 81.47275 78.16710 80.79042 91.18235 74.52730 79.92475 77.17511 75.68063
## [289] 96.76009 81.67966 92.12179 74.31033 96.88023 99.89014 91.94976 95.89699
## [297] 83.90950 77.44493 85.15172 93.99018 88.78276 76.51431 99.94181 90.26029
## [305] 77.79182 95.14714 99.42711 76.62425 76.26732 87.02049 97.95754 99.58166
## [313] 79.33531 96.37687 83.96655 94.41929 84.13630 78.80505 91.48403 92.35352
## [321] 85.19650 94.30469 93.91010 78.48726 91.63338 92.74873 94.78101 99.45177
## [329] 88.62971 76.95345 96.29753 83.84081 91.47473 80.41922 83.91946 84.54124
## [337] 86.49896 80.09430 79.87717 95.24471 82.79635 91.14031 88.05666 81.35810
## [345] 86.39984 97.60088 88.63640 75.97198 88.46040 99.72390 74.91965 96.82964
## [353] 86.71788 77.32975 88.46040 82.54259 94.67481 94.30469 85.40699 76.53712
## [361] 76.87288 74.31153 91.83194 94.88311 84.73094 74.69304 80.55831 85.70684
## [369] 84.68725 86.71788 91.99664 78.87676 94.68926 93.22610 97.22008 98.23138
## [377] 78.79896 75.85293 90.85126 84.83667 80.36056 74.34519 94.78101 91.65266
## [385] 97.29322 86.84719 80.04942 98.59164 80.60148 91.35040 87.36775 94.72583
## [393] 96.12375 85.45232 88.78276 92.26615 77.52977 83.52943 88.81371 78.39837
## [401] 86.54349 83.54309 86.96855 95.21516 78.16061 82.45448 94.35477 88.64323
## [409] 85.19612 76.72293 80.14288 88.56389 97.87462 77.31904 77.50200 83.67312
## [417] 92.93600 81.80540 87.89096 95.36655 88.97514 77.75635 82.79680 98.42567
## [425] 79.55369 83.89690 94.88311 84.72118 83.17909 99.10080 87.16638 97.69866
## [433] 88.80419 86.49896 99.53229 90.54696 85.77566 97.85684 94.60244 92.41987
## [441] 99.88732 85.11482 94.73210 81.36034 90.21913 98.62982 76.58219 91.79983
## [449] 91.18235 75.74203 83.55501 88.46440 84.93322 96.26053 97.17535 97.85684
## [457] 91.79239 88.35901 83.87850 96.26053 75.39903 97.22008 84.19761 74.51859
## [465] 96.12375 75.72653 74.76056 90.75899 76.71664 98.06785 96.86252 81.08401
## [473] 92.82046 86.72849 85.44463 76.48117 77.44493 89.68946 87.82799 95.68379
## [481] 94.60244 99.94181 84.65314 94.29124 88.57898 95.25009 83.04615 84.75388
## [489] 92.53010 92.12179 84.99017 97.49211 94.49170 98.18654 75.98393 78.71003
## [497] 88.34765 96.60377 95.24898 81.24328 91.91776 98.80579 82.10044 94.72583
## [505] 99.53098 86.44761 87.54865 84.73094 83.91232 97.87462 93.35797 88.82526
## [513] 83.89690 94.80157 81.70643 86.94218 99.72390 85.70857 93.04738 95.64445
## [521] 74.27987 91.65266 93.55678 86.13116 94.37811 81.21603 75.75440 82.28729
## [529] 85.77566Q1 <- quantile(df_1$Latency, 0.25)
Q3 <- quantile(df_1$Latency, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_1_anomalies_Latency <-df_1$Latency[df_1$Latency < lower_bound |df_1$Latency > upper_bound]
Q1 <- quantile(df_2$Latency, 0.25)
Q3 <- quantile(df_2$Latency, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_2_anomalies_Latency <-df_2$Latency[df_2$Latency < lower_bound |df_2$Latency > upper_bound]
Q1 <- quantile(df_3$Latency, 0.25)
Q3 <- quantile(df_3$Latency, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_3_anomalies_Latency<-df_3$Latency[df_3$Latency < lower_bound |df_3$Latency > upper_bound]
Q1 <- quantile(df_4$Latency, 0.25)
Q3 <- quantile(df_4$Latency, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_4_anomalies_Latency <-df_4$Latency[df_4$Latency < lower_bound |df_4$Latency > upper_bound]
Q1 <- quantile(df_5$ Latency, 0.25)
Q3 <- quantile(df_5$ Latency, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_5_anomalies_Latency <-df_5$Latency[df_5$Latency < lower_bound |df_5$Latency > upper_bound]print(df_1_anomalies_Latency)## numeric(0)print(df_2_anomalies_Latency)## numeric(0)print(df_3_anomalies_Latency)## numeric(0)print(df_4_anomalies_Latency)## numeric(0)print(df_5_anomalies_Latency)## numeric(0)Q1 <- quantile(df_1$BB60C, 0.25)
Q3 <- quantile(df_1$BB60C, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_1_anomalies_BB60C <-df_1$BB60C[df_1$BB60C < lower_bound |df_1$BB60C > upper_bound]
Q1 <- quantile(df_2$BB60C, 0.25)
Q3 <- quantile(df_2$BB60C, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_2_anomalies_BB60C <-df_2$BB60C[df_2$BB60C < lower_bound |df_2$BB60C > upper_bound]
Q1 <- quantile(df_3$BB60C, 0.25)
Q3 <- quantile(df_3$BB60C, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_3_anomalies_BB60C<-df_3$BB60C[df_3$BB60C < lower_bound |df_3$BB60C > upper_bound]
Q1 <- quantile(df_4$BB60C, 0.25)
Q3 <- quantile(df_4$BB60C, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_4_anomalies_BB60C <-df_4$BB60C[df_4$BB60C < lower_bound |df_4$BB60C > upper_bound]
Q1 <- quantile(df_5$ BB60C, 0.25)
Q3 <- quantile(df_5$ BB60C, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_5_anomalies_BB60C <-df_5$BB60C[df_5$BB60C < lower_bound |df_5$BB60C > upper_bound]print(df_1_anomalies_BB60C)## [1] -109.42090 -112.51374 -112.51374 -107.19473 -108.53221 -108.90478
## [7] -108.82135 -109.87340 -108.04518 -109.24158 -109.24158 -110.95394
## [13] -107.43767 -108.82135 -107.37011 -107.35165 -108.53221 -108.66336
## [19] -74.87395 -107.37011 -107.41043 -109.16153 -107.29518 -110.95394
## [25] -107.19500 -107.64086 -111.02842 -110.82453 -107.81257 -75.70932
## [31] -112.67853 -108.95830 -107.55081 -108.53221 -75.77120 -108.90478
## [37] -75.79857 -110.82453 -109.55900 -107.81257 -108.95830 -112.51374
## [43] -110.95394 -109.55014 -107.35392 -108.82239 -74.87395 -107.37614print(df_2_anomalies_BB60C)## [1] -75.20550 -75.70932 -108.23714 -76.01485 -108.80335 -75.20550
## [7] -110.12311 -107.91664 -109.16153 -107.64086 -108.80335 -111.02842
## [13] -109.42090 -107.41043 -108.90478 -107.19500 -76.13552 -75.77120
## [19] -110.00125 -108.48066 -108.04136 -110.95394 -108.13989 -108.90478
## [25] -76.18193 -108.32923 -75.57373 -108.23714 -75.77120 -108.13989
## [31] -107.19473 -76.01485 -109.38893 -108.04518 -109.87340 -107.41043
## [37] -108.90478 -108.13989 -75.70932 -108.83908 -107.91664 -107.19500
## [43] -107.97551 -108.99764 -108.57643 -111.18802 -108.08766 -74.08004
## [49] -107.75814 -109.40649 -114.61026print(df_3_anomalies_BB60C)## [1] -76.63643 -108.83908 -111.02842 -75.79857 -108.53221 -110.48844
## [7] -106.80744 -74.87395 -106.98045 -74.66450 -109.55014 -108.48066
## [13] -106.70706 -108.83132 -74.08004 -106.98587 -75.24133 -108.53221
## [19] -108.48066 -109.11784 -106.70706 -106.94572 -108.13989 -111.02842
## [25] -106.72458 -72.50342 -108.21363 -108.83132 -109.11784 -108.04518
## [31] -111.18802 -108.99764 -76.63643 -107.29518 -110.00125 -107.35165
## [37] -108.53221 -107.91664 -111.02842 -107.19473 -115.66751 -109.40649
## [43] -106.73683 -107.97551 -75.24133 -110.82453 -109.55900 -109.38893
## [49] -75.95761 -108.08766 -108.95830 -72.50342 -106.98587 -75.38481
## [55] -110.82453 -106.77505 -106.81144print(df_4_anomalies_BB60C)## [1] -108.48066 -108.83908 -108.04136 -109.38893 -109.40649 -108.83132
## [7] -108.48066 -109.55900 -108.21363 -111.02842 -75.58124 -109.11784
## [13] -75.24133 -108.32920 -112.14518 -75.87241 -108.13989 -112.14518
## [19] -108.13989 -75.20550 -108.03741 -107.19824 -111.18802 -108.97984
## [25] -115.66751 -73.45848 -107.75814 -108.97984 -108.03741 -75.77120
## [31] -109.38893 -108.57643 -110.40344 -110.48844 -109.60192 -110.73846
## [37] -75.20550 -75.95761 -108.82239 -115.66751 -107.29518 -109.24158
## [43] -108.64735 -110.40344 -107.37011print(df_5_anomalies_BB60C)## [1] -108.64735 -108.03741 -108.95830 -107.64086 -108.82135 -112.14518
## [7] -107.55559 -107.58669 -73.45848 -111.06559 -107.91664 -109.24158
## [13] -109.55014 -109.40649 -75.58124 -110.73846 -107.55559 -110.95394
## [19] -73.88210 -74.08004 -75.20550 -107.35392 -107.37011 -75.95761
## [25] -111.06559 -109.60192 -108.48066 -111.18802 -109.55014 -110.82453
## [31] -110.73846 -108.90478 -108.64735 -108.03741 -112.51374 -108.03741
## [37] -108.04136 -108.66336 -75.58124 -75.57373 -107.55559 -109.60192
## [43] -75.24133 -107.19500 -111.18802 -107.19500 -75.77120 -110.73846
## [49] -110.82453Q1 <- quantile(df_1$srsRAN, 0.25)
Q3 <- quantile(df_1$srsRAN, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_1_anomalies_srsRAN <-df_1$srsRAN[df_1$srsRAN < lower_bound |df_1$srsRAN > upper_bound]
Q1 <- quantile(df_2$srsRAN, 0.25)
Q3 <- quantile(df_2$srsRAN, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_2_anomalies_srsRAN <-df_2$srsRAN[df_2$srsRAN < lower_bound |df_2$srsRAN > upper_bound]
Q1 <- quantile(df_3$srsRAN, 0.25)
Q3 <- quantile(df_3$srsRAN, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_3_anomalies_srsRAN <-df_3$srsRAN[df_3$srsRAN < lower_bound |df_3$srsRAN > upper_bound]
Q1 <- quantile(df_4$srsRAN, 0.25)
Q3 <- quantile(df_4$srsRAN, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_4_anomalies_srsRAN <-df_4$srsRAN[df_4$srsRAN < lower_bound |df_4$srsRAN > upper_bound]
Q1 <- quantile(df_5$ srsRAN, 0.25)
Q3 <- quantile(df_5$ srsRAN, 0.75)
IQR_value <- Q3 - Q1
lower_bound <- Q1 - 1.5 * IQR_value
upper_bound <- Q3 + 1.5 * IQR_value
df_5_anomalies_srsRAN <-df_5$srsRAN[df_5$srsRAN < lower_bound |df_5$srsRAN > upper_bound]print(df_1_anomalies_srsRAN)## [1] -113.58399 -114.51600 -117.14268 -117.14268 -116.15423 -113.14431
## [7] -83.43214 -83.42197 -118.06257 -113.58399 -113.19050 -114.85031
## [13] -84.10311 -114.47656 -113.80703 -113.57573 -113.12137 -116.74820
## [19] -113.17433 -113.69946 -114.85031 -113.59532 -114.96243 -113.34410
## [25] -113.57573 -114.03134 -114.46765 -113.81421 -115.67799 -117.42540
## [31] -83.42197 -113.53125 -114.42859 -81.98091 -114.51600 -116.10891
## [37] -116.05711 -118.41288 -117.25471 -84.70266 -114.77714 -114.42859
## [43] -114.15060 -113.82530 -115.47954 -116.74820 -83.42197 -117.25471
## [49] -84.89450 -117.14268 -114.85031 -114.46765 -116.74820 -83.43214
## [55] -115.47331 -118.58093print(df_2_anomalies_srsRAN)## [1] -113.19050 -84.81174 -81.98091 -114.03134 -117.21316 -84.81174
## [7] -117.04424 -116.15423 -113.58399 -114.03720 -113.75678 -118.19819
## [13] -84.90492 -118.27524 -113.75678 -113.81421 -117.21316 -117.42540
## [19] -113.12137 -118.27524 -114.46765 -114.34544 -113.14431 -84.70266
## [25] -115.97689 -113.91490 -116.45188 -114.85031 -118.54014 -113.14431
## [31] -84.19625 -84.70266 -83.42197 -118.54014 -114.47656 -113.20995
## [37] -84.79184 -118.06257 -115.17427 -113.12137 -118.54014 -81.98091
## [43] -118.19819 -115.67799 -113.58399 -114.07816 -84.89450 -113.82530
## [49] -116.45188 -114.51600 -114.85282 -114.12328 -115.81834 -121.18181
## [55] -114.47656 -114.07816 -114.03720 -114.03134 -115.72961 -124.65205print(df_3_anomalies_srsRAN)## [1] -114.82739 -117.42540 -84.89450 -115.79228 -114.06545 -85.16332
## [7] -84.90492 -114.20488 -84.89450 -114.34544 -116.05711 -116.71812
## [13] -113.19050 -112.92924 -114.34544 -118.54014 -117.42540 -84.97208
## [19] -114.20488 -116.71812 -114.82739 -114.82739 -115.81834 -114.85282
## [25] -114.25503 -113.19050 -114.96243 -113.23207 -113.17433 -114.06545
## [31] -113.23207 -84.10311 -114.03134 -115.97689 -113.69638 -113.23207
## [37] -114.95762 -83.11343 -118.19819 -117.42540 -112.71651 -84.10311
## [43] -121.59876 -115.72961 -113.20995 -83.43214 -112.94118 -112.72142
## [49] -113.57573 -84.31198 -112.80328 -114.42859 -115.17427 -113.20995
## [55] -114.15060 -115.17427 -112.92924 -121.18181 -83.42197 -114.06545
## [61] -115.17427 -117.25471 -84.97208 -116.05711 -112.92924 -81.32009
## [67] -84.90492 -114.42859 -113.82530 -115.47954print(df_4_anomalies_srsRAN)## [1] -115.06872 -114.40072 -116.05711 -115.72961 -114.51600 -113.03303
## [7] -114.96243 -113.82530 -113.03576 -114.20488 -114.15060 -117.42540
## [13] -116.71812 -113.75678 -113.58399 -112.91187 -113.57573 -116.10891
## [19] -113.52073 -115.34901 -114.06222 -113.16767 -113.59532 -85.25143
## [25] -118.54014 -85.25143 -113.16767 -118.54014 -84.81174 -116.22532
## [31] -115.81834 -113.69638 -113.58399 -115.34901 -121.59876 -84.77590
## [37] -116.22532 -84.70266 -114.12328 -115.07333 -115.79228 -113.58399
## [43] -116.09668 -84.81174 -114.51600 -115.47331 -115.80789 -121.59876
## [49] -113.17433 -113.89508 -114.77714 -115.07333 -114.51600 -116.15423print(df_5_anomalies_srsRAN)## [1] -113.89508 -116.22532 -117.25471 -113.81421 -113.16767 -112.84385
## [7] -113.72294 -84.77590 -116.20058 -118.19819 -116.10891 -114.47656
## [13] -115.72961 -116.09668 -113.72294 -113.03576 -83.42197 -114.85031
## [19] -84.76128 -113.69946 -84.81174 -113.75678 -113.75678 -83.11343
## [25] -116.20058 -112.94429 -115.80789 -113.75678 -83.43214 -114.82739
## [31] -114.03720 -112.84385 -113.58399 -115.81834 -115.34901 -112.84385
## [37] -114.42859 -114.23214 -114.34544 -116.09668 -113.52073 -113.53125
## [43] -113.89508 -116.22532 -117.14268 -116.22532 -113.59532 -85.50319
## [49] -113.80703 -113.14431 -84.19625 -115.67799 -113.72294 -84.31198
## [55] -115.81834 -84.70266 -116.09668 -114.42859SignalStrength_df<-list(df_1_anomalies_SignalStrength,df_2_anomalies_SignalStrength,df_3_anomalies_SignalStrength,
df_4_anomalies_SignalStrength,df_5_anomalies_SignalStrength)
anomalies_SignalStrength <- Reduce(setdiff,SignalStrength_df)
print(anomalies_SignalStrength)## [1] -105.8668 -108.7375 -106.3345 -105.2829 -104.9881 -109.8581 -109.5907DataThroughput_df<-list(df_1_anomalies_DataThroughput,df_2_anomalies_DataThroughput,df_3_anomalies_DataThroughput,
df_4_anomalies_DataThroughput,df_5_anomalies_DataThroughput)
anomalies_DataThroughput <- Reduce(setdiff,DataThroughput_df)
print(anomalies_DataThroughput)## [1] 97.89590 91.15234 99.26797 82.66611 86.10562 86.36950 89.91279 84.96729
## [9] 84.05168 98.29876 95.54273 91.80360 89.45747 92.97869 94.92932 88.55148
## [17] 96.39683 96.04885 88.90892 87.06759 87.47136 89.89646 85.07051 84.43374
## [25] 98.92700 83.89710 92.04158 83.91473 97.59521 90.04551 89.90850 87.02971
## [33] 98.67865 83.02984 99.03661 96.23928 95.30184 88.80774 98.25317 89.30681
## [41] 89.61824 99.62275 97.78347 98.64456Latency_df<-list(df_1_anomalies_Latency,df_2_anomalies_Latency,df_3_anomalies_Latency,
df_4_anomalies_Latency,df_5_anomalies_Latency)
anomalies_Latency <- Reduce(setdiff,Latency_df)
print(anomalies_Latency)## numeric(0)BB60C_df<-list(df_1_anomalies_BB60C,df_2_anomalies_BB60C,df_3_anomalies_BB60C,
df_4_anomalies_BB60C,df_5_anomalies_BB60C)
anomalies_BB60C <- Reduce(setdiff,BB60C_df)
print(anomalies_BB60C)## [1] -107.4377 -107.8126 -112.6785 -107.5508 -107.3761srsRAN_df<-list(df_1_anomalies_srsRAN,df_2_anomalies_srsRAN,df_3_anomalies_srsRAN,
df_4_anomalies_srsRAN,df_5_anomalies_srsRAN)
anomalies_srsRAN <- Reduce(setdiff,srsRAN_df)
print(anomalies_srsRAN)## [1] -116.7482 -113.3441 -118.4129 -118.5809