Nothing
## ---- echo=FALSE, warning=FALSE, message=FALSE---------------------------
security_logs <- anomalyDetection::security_logs
security_logs <- tibble::as_tibble(security_logs)
## ---- collapse=TRUE, message=FALSE, warning=FALSE------------------------
library(dplyr) # common data manipulations
library(tidyr) # common data manipulations
library(tibble) # turning output into convenient tibble
library(ggplot2) # visualizations
library(anomalyDetection)
security_logs
## ---- collapse=TRUE------------------------------------------------------
tabulate_state_vector(security_logs, 10)
## ---- collapse=TRUE------------------------------------------------------
(state_vec <- security_logs %>%
tabulate_state_vector(10) %>%
mc_adjust())
## ---- collapse=TRUE------------------------------------------------------
state_vec %>%
mahalanobis_distance("both", normalize = TRUE) %>%
as_tibble
## ---- fig.align='center', fig.width=9, fig.height=6----------------------
state_vec %>%
mahalanobis_distance("both", normalize = TRUE) %>%
as_tibble %>%
mutate(Block = 1:n()) %>%
gather(Variable, BD, -c(MD, Block)) %>%
ggplot(aes(factor(Block), Variable, color = MD, size = BD)) +
geom_point()
## ---- collapse=TRUE------------------------------------------------------
state_vec %>%
mahalanobis_distance("bd", normalize = TRUE) %>%
bd_row(17, 10)
## ---- collapse=TRUE------------------------------------------------------
horns_curve(state_vec)
## ---- collapse=TRUE------------------------------------------------------
state_vec %>%
horns_curve() %>%
factor_analysis(state_vec, hc_points = .) %>%
str
## ---- collapse=TRUE------------------------------------------------------
state_vec %>%
horns_curve() %>%
factor_analysis(state_vec, hc_points = .) %>%
factor_analysis_results(4) %>%
as_tibble
## ---- collapse=TRUE------------------------------------------------------
state_vec %>%
horns_curve() %>%
factor_analysis(data = state_vec, hc_points = .) %>%
factor_analysis_results(fa_loadings_rotated) %>%
kaisers_index()
## ---- fig.align='center', fig.height=7, fig.width=7----------------------
fa_loadings <- state_vec %>%
horns_curve() %>%
factor_analysis(state_vec, hc_points = .) %>%
factor_analysis_results(fa_loadings_rotated)
row.names(fa_loadings) <- colnames(state_vec)
gplots::heatmap.2(fa_loadings, dendrogram = 'both', trace = 'none',
density.info = 'none', breaks = seq(-1, 1, by = .25),
col = RColorBrewer::brewer.pal(8, 'RdBu'))
## ---- fig.align='center', fig.height=7, fig.width=7----------------------
state_vec %>%
horns_curve() %>%
factor_analysis(state_vec, hc_points = .) %>%
factor_analysis_results(fa_scores_rotated) %>%
as_tibble() %>%
mutate(Block = 1:n()) %>%
gather(Factor, Score, -Block) %>%
mutate(Absolute_Score = abs(Score)) %>%
ggplot(aes(Factor, Absolute_Score, label = Block)) +
geom_text() +
geom_boxplot(outlier.shape = NA)
## ---- collapse=TRUE------------------------------------------------------
principal_components(state_vec) %>% str
## ---- collapse=TRUE------------------------------------------------------
state_vec %>%
principal_components() %>%
principal_components_result(pca_rotated) %>%
as_tibble
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