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library(statsr)
library(tidyverse)
library(readxl)
library(visNetwork)
library(igraph)
library(tidygraph)
library(ggraph)
library(formattable)# Let's load up the Authors data from excel file
benefit_list <- read_excel("Data.xlsx", sheet = "Benefits")
article_list <- read_excel("Data.xlsx", sheet = "Articles")# Display Conditions by Article Count
benefit_list$StudyDesign <- factor(benefit_list$StudyDesign, levels = c("Randomised controlled trial" , "Non-randomised controlled trial", "Before and after study", "Descriptive cross-sectional studies", "Case series", "Case report", "Animal", "Cell", "Chemical"))
conditions_artcount <- benefit_list %>%
select(ArticleID, StudyDesign, Condition ) %>% distinct_all() %>%
group_by(Condition, StudyDesign) %>% summarise(N = n()) %>%
mutate(n_Chemical = ifelse(as.numeric(StudyDesign) == 9, N, 0))%>%
mutate(n_Cell = ifelse(as.numeric(StudyDesign) == 8, N, 0))%>%
mutate(n_Animal = ifelse(as.numeric(StudyDesign) == 7, N, 0))%>%
mutate(n_Observational = ifelse(as.numeric(StudyDesign) > 3 & as.numeric(StudyDesign) < 7, N, 0))%>%
mutate(n_Interventional = ifelse(as.numeric(StudyDesign) <= 3, N, 0))%>%
select(-c("StudyDesign")) %>%
arrange(desc(N))## `summarise()` has grouped output by 'Condition'. You can override using the
## `.groups` argument.#conditions_artcount %>% arrange(Condition)
conditions_artcount <- conditions_artcount %>%
group_by(Condition) %>%
summarise(Total = sum(N),Chem = sum(n_Chemical), Cell = sum(n_Cell), Animal = sum(n_Animal), H_O = sum(n_Observational), H_I = sum(n_Interventional)) %>%
arrange(desc(Total), desc(H_I),desc(H_O), desc(Animal), desc(Cell), desc(Chem) )
#conditions_artcount %>% arrange(Condition)N_art = nrow(article_list)
conditions_artcount <- conditions_artcount %>%
mutate(Total_p = percent(Total /N_art) ) %>%
mutate(Chem_p = percent(Chem /Total) ) %>%
mutate(Cell_p = percent(Cell /Total) ) %>%
mutate(Animal_p = percent(Animal /Total) ) %>%
mutate(H_O_p = percent(H_O /Total) ) %>%
mutate(H_I_p = percent(H_I /Total) ) %>%
select(c(1,2,8,3,9,4,10,5,11,6,12,7,13))
conditions_artcount## # A tibble: 17 × 13
## Condi…¹ Total Total_p Chem Chem_p Cell Cell_p Animal Anima…² H_O H_O_p
## <chr> <int> <formt> <dbl> <formt> <dbl> <form> <dbl> <formt> <dbl> <formt>
## 1 Cancer 45 45.92% 0 0.00% 8 17.78% 12 26.67% 12 26.67%
## 2 Health… 31 31.63% 1 3.23% 20 64.52% 7 22.58% 0 0.00%
## 3 Hepati… 9 9.18% 0 0.00% 0 0.00% 7 77.78% 0 0.00%
## 4 Geriat… 6 6.12% 0 0.00% 1 16.67% 1 16.67% 0 0.00%
## 5 HIV 4 4.08% 0 0.00% 1 25.00% 0 0.00% 0 0.00%
## 6 Allergy 4 4.08% 0 0.00% 0 0.00% 4 100.00% 0 0.00%
## 7 Chroni… 3 3.06% 0 0.00% 0 0.00% 0 0.00% 0 0.00%
## 8 Gastro… 3 3.06% 0 0.00% 0 0.00% 2 66.67% 0 0.00%
## 9 Cold /… 2 2.04% 0 0.00% 0 0.00% 0 0.00% 0 0.00%
## 10 Diabet… 2 2.04% 0 0.00% 0 0.00% 2 100.00% 0 0.00%
## 11 Endoto… 2 2.04% 0 0.00% 0 0.00% 2 100.00% 0 0.00%
## 12 Chemic… 1 1.02% 0 0.00% 0 0.00% 0 0.00% 0 0.00%
## 13 Irrita… 1 1.02% 0 0.00% 0 0.00% 0 0.00% 0 0.00%
## 14 Rheuma… 1 1.02% 0 0.00% 0 0.00% 0 0.00% 1 100.00%
## 15 Alzhei… 1 1.02% 0 0.00% 0 0.00% 1 100.00% 0 0.00%
## 16 Bacter… 1 1.02% 0 0.00% 0 0.00% 1 100.00% 0 0.00%
## 17 Oxidat… 1 1.02% 1 100.00% 0 0.00% 0 0.00% 0 0.00%
## # … with 2 more variables: H_I <dbl>, H_I_p <formttbl>, and abbreviated
## # variable names ¹Condition, ²Animal_p# Condense the table
conditions_artcount <- conditions_artcount %>% mutate(precl = Chem+Cell+Animal,
precl_p = Chem_p+Cell_p+Animal_p) %>%
select(c(1,2,3, 14,15, 10,11,12,13))
conditions_artcount## # A tibble: 17 × 9
## Condition Total Total_p precl precl_p H_O H_O_p H_I H_I_p
## <chr> <int> <formt> <dbl> <formt> <dbl> <formt> <dbl> <formt>
## 1 Cancer 45 45.92% 20 44.44% 12 26.67% 13 28.89%
## 2 Healthy / Nonspecific 31 31.63% 28 90.32% 0 0.00% 3 9.68%
## 3 Hepatitis / Liver Di… 9 9.18% 7 77.78% 0 0.00% 2 22.22%
## 4 Geriatric 6 6.12% 2 33.33% 0 0.00% 4 66.67%
## 5 HIV 4 4.08% 1 25.00% 0 0.00% 3 75.00%
## 6 Allergy 4 4.08% 4 100.00% 0 0.00% 0 0.00%
## 7 Chronic fatigue synd… 3 3.06% 0 0.00% 0 0.00% 3 100.00%
## 8 Gastroenteritis 3 3.06% 2 66.67% 0 0.00% 1 33.33%
## 9 Cold / Flu 2 2.04% 0 0.00% 0 0.00% 2 100.00%
## 10 Diabetes mellitus 2 2.04% 2 100.00% 0 0.00% 0 0.00%
## 11 Endotoxemia 2 2.04% 2 100.00% 0 0.00% 0 0.00%
## 12 Chemical exposure 1 1.02% 0 0.00% 0 0.00% 1 100.00%
## 13 Irritable bowel synd… 1 1.02% 0 0.00% 0 0.00% 1 100.00%
## 14 Rheumatism 1 1.02% 0 0.00% 1 100.00% 0 0.00%
## 15 Alzheimer's disease 1 1.02% 1 100.00% 0 0.00% 0 0.00%
## 16 Bacterial infection 1 1.02% 1 100.00% 0 0.00% 0 0.00%
## 17 Oxidative stress 1 1.02% 1 100.00% 0 0.00% 0 0.00%# Display Beneficial Effects by Article Count
benefits_artcount <- benefit_list %>%
select(ArticleID, StudyDesign, BeneficialEffect ) %>% distinct_all() %>%
group_by(BeneficialEffect, StudyDesign) %>% summarise(N = n()) %>%
mutate(n_Chemical = ifelse(as.numeric(StudyDesign) == 9, N, 0))%>%
mutate(n_Cell = ifelse(as.numeric(StudyDesign) == 8, N, 0))%>%
mutate(n_Animal = ifelse(as.numeric(StudyDesign) == 7, N, 0))%>%
mutate(n_Observational = ifelse(as.numeric(StudyDesign) > 3 & as.numeric(StudyDesign) < 7, N, 0))%>%
mutate(n_Interventional = ifelse(as.numeric(StudyDesign) <= 3, N, 0))%>%
select(-c("StudyDesign")) %>%
arrange(desc(N))## `summarise()` has grouped output by 'BeneficialEffect'. You can override using
## the `.groups` argument.benefits_artcount <- benefits_artcount %>%
group_by(BeneficialEffect) %>%
summarise(Total = sum(N),Chem = sum(n_Chemical), Cell = sum(n_Cell), Animal = sum(n_Animal), H_O = sum(n_Observational), H_I = sum(n_Interventional)) %>%
arrange(desc(Total), desc(H_I),desc(H_O), desc(Animal), desc(Cell), desc(Chem) )
benefits_artcount <- benefits_artcount %>%
mutate(Total_p = percent(Total /N_art) ) %>%
mutate(Chem_p = percent(Chem /Total) ) %>%
mutate(Cell_p = percent(Cell /Total) ) %>%
mutate(Animal_p = percent(Animal /Total) ) %>%
mutate(H_O_p = percent(H_O /Total) ) %>%
mutate(H_I_p = percent(H_I /Total) ) %>%
select(c(1,2,8,3,9,4,10,5,11,6,12,7,13))# Condense the table
benefits_artcount <- benefits_artcount %>% mutate(precl = Chem+Cell+Animal,
precl_p = Chem_p+Cell_p+Animal_p) %>%
select(c(1,2,3, 14,15, 10,11,12,13))
benefits_artcount %>% filter(Total>1)## # A tibble: 16 × 9
## BeneficialEffect Total Total_p precl precl_p H_O H_O_p H_I H_I_p
## <chr> <int> <formt> <dbl> <formt> <dbl> <form> <dbl> <formt>
## 1 Immunomodulation 36 36.73% 21 58.33% 1 2.78% 14 38.89%
## 2 Synergistic anticance… 19 19.39% 7 36.84% 10 52.63% 2 10.53%
## 3 Hepatoprotection 13 13.27% 10 76.92% 1 7.69% 2 15.38%
## 4 Anticancer 10 10.20% 9 90.00% 1 10.00% 0 0.00%
## 5 Psychoneuroimmuno-mod… 8 8.16% 0 0.00% 2 25.00% 6 75.00%
## 6 Antiinflammation 7 7.14% 6 85.71% 1 14.29% 0 0.00%
## 7 Antioxidant 7 7.14% 7 100.00% 0 0.00% 0 0.00%
## 8 Antiallergy 5 5.10% 5 100.00% 0 0.00% 0 0.00%
## 9 Radioprotection 3 3.06% 2 66.67% 0 0.00% 1 33.33%
## 10 Chemoprevention 3 3.06% 3 100.00% 0 0.00% 0 0.00%
## 11 Antibacterial 3 3.06% 3 100.00% 0 0.00% 0 0.00%
## 12 Antifatigue 2 2.04% 0 0.00% 0 0.00% 2 100.00%
## 13 Antiflu 2 2.04% 0 0.00% 0 0.00% 2 100.00%
## 14 No significant effect 2 2.04% 0 0.00% 0 0.00% 2 100.00%
## 15 Gastroprotection 2 2.04% 1 50.00% 0 0.00% 1 50.00%
## 16 Antihyperlipidemic ef… 2 2.04% 2 100.00% 0 0.00% 0 0.00%benefits_artcount %>% filter(Total > 1) ## # A tibble: 16 × 9
## BeneficialEffect Total Total_p precl precl_p H_O H_O_p H_I H_I_p
## <chr> <int> <formt> <dbl> <formt> <dbl> <form> <dbl> <formt>
## 1 Immunomodulation 36 36.73% 21 58.33% 1 2.78% 14 38.89%
## 2 Synergistic anticance… 19 19.39% 7 36.84% 10 52.63% 2 10.53%
## 3 Hepatoprotection 13 13.27% 10 76.92% 1 7.69% 2 15.38%
## 4 Anticancer 10 10.20% 9 90.00% 1 10.00% 0 0.00%
## 5 Psychoneuroimmuno-mod… 8 8.16% 0 0.00% 2 25.00% 6 75.00%
## 6 Antiinflammation 7 7.14% 6 85.71% 1 14.29% 0 0.00%
## 7 Antioxidant 7 7.14% 7 100.00% 0 0.00% 0 0.00%
## 8 Antiallergy 5 5.10% 5 100.00% 0 0.00% 0 0.00%
## 9 Radioprotection 3 3.06% 2 66.67% 0 0.00% 1 33.33%
## 10 Chemoprevention 3 3.06% 3 100.00% 0 0.00% 0 0.00%
## 11 Antibacterial 3 3.06% 3 100.00% 0 0.00% 0 0.00%
## 12 Antifatigue 2 2.04% 0 0.00% 0 0.00% 2 100.00%
## 13 Antiflu 2 2.04% 0 0.00% 0 0.00% 2 100.00%
## 14 No significant effect 2 2.04% 0 0.00% 0 0.00% 2 100.00%
## 15 Gastroprotection 2 2.04% 1 50.00% 0 0.00% 1 50.00%
## 16 Antihyperlipidemic ef… 2 2.04% 2 100.00% 0 0.00% 0 0.00%benefits_artcount %>% filter(Total == 1) %>% select(BeneficialEffect) %>% arrange(BeneficialEffect)## # A tibble: 13 × 1
## BeneficialEffect
## <chr>
## 1 Antiangiogenesis
## 2 Antiasthma
## 3 Antihyperglycemic effect
## 4 Antimetastatic effect
## 5 Antiretroviral
## 6 Antirheumatic effect
## 7 Antiviral
## 8 Antiwasting
## 9 Chemoprotection
## 10 Endothelial improvement
## 11 Memory enhancer
## 12 Noncytotoxic
## 13 Taste influencer#Create network nodes from the conditions and benefits
cnodes <- conditions_artcount %>% select(Condition, Total) %>%
rename(label = Condition, value = Total) %>%
mutate(group = "Condition")
bnodes <- benefits_artcount %>% select(BeneficialEffect, Total) %>%
rename(label = BeneficialEffect, value = Total) %>%
mutate(group = "Effect")
nodes <- rbind(cnodes, bnodes) %>%
mutate(group = factor(group, levels=c("Condition", "Effect", "Outcome"))) %>%
mutate(level = as.numeric(group)) %>%
rowid_to_column("id")
nodes## # A tibble: 46 × 5
## id label value group level
## <int> <chr> <int> <fct> <dbl>
## 1 1 Cancer 45 Condition 1
## 2 2 Healthy / Nonspecific 31 Condition 1
## 3 3 Hepatitis / Liver Disease 9 Condition 1
## 4 4 Geriatric 6 Condition 1
## 5 5 HIV 4 Condition 1
## 6 6 Allergy 4 Condition 1
## 7 7 Chronic fatigue syndrome 3 Condition 1
## 8 8 Gastroenteritis 3 Condition 1
## 9 9 Cold / Flu 2 Condition 1
## 10 10 Diabetes mellitus 2 Condition 1
## # … with 36 more rows# Edges are defined as the condition->effect within the same article with Study Design as the category
edges <- benefit_list %>% select(Condition, BeneficialEffect, StudyDesign) %>%
rename(fromA = Condition, toA = BeneficialEffect, category = StudyDesign) %>%
left_join(nodes, by = c("fromA"= "label")) %>% rename(from=id) %>%
left_join(nodes, by = c("toA"= "label")) %>% rename(to=id) %>%
select(from, to, category)
edges## # A tibble: 138 × 3
## from to category
## <int> <int> <fct>
## 1 2 18 Randomised controlled trial
## 2 1 21 Animal
## 3 6 25 Animal
## 4 2 46 Cell
## 5 2 24 Chemical
## 6 1 21 Animal
## 7 1 27 Animal
## 8 1 19 Animal
## 9 1 27 Animal
## 10 1 20 Animal
## # … with 128 more rows# Display the raw network diagram
visNetwork(nodes, edges) #collapse the edges by creating a value to each edge based on count of co-occurance
edges1 <- edges %>% filter(from != to) %>% group_by(from, to, category) %>% summarise(value = n())## `summarise()` has grouped output by 'from', 'to'. You can override using the
## `.groups` argument.#cat_color_list <- c("#FF4040", "#FF1493", "#FFA07A", "#EEAD0E", "#00FF00", "#CAFF70", "#C1FFC1","#7FFFD4", "#98F5FF")
cat_color_list <- c("#FF0000", "#FF0000", "#FF0000", "#00FF00", "#00FF00", "#00FF00", "#00EEFF","#00EEFF", "#00EEFF")
cat_color_list <- data.frame(color = cat_color_list) %>% rowid_to_column("id")
cat_color_list## id color
## 1 1 #FF0000
## 2 2 #FF0000
## 3 3 #FF0000
## 4 4 #00FF00
## 5 5 #00FF00
## 6 6 #00FF00
## 7 7 #00EEFF
## 8 8 #00EEFF
## 9 9 #00EEFFedges1 <- edges1 %>% mutate(cat = as.numeric(category)) %>%
left_join(cat_color_list, by = c("cat"= "id"))
edges1## # A tibble: 75 × 6
## # Groups: from, to [54]
## from to category value cat color
## <int> <int> <fct> <int> <dbl> <chr>
## 1 1 18 Randomised controlled trial 2 1 #FF0000
## 2 1 18 Before and after study 4 3 #FF0000
## 3 1 18 Case series 1 5 #00FF00
## 4 1 18 Animal 1 7 #00EEFF
## 5 1 19 Randomised controlled trial 2 1 #FF0000
## 6 1 19 Case series 3 5 #00FF00
## 7 1 19 Case report 7 6 #00FF00
## 8 1 19 Animal 2 7 #00EEFF
## 9 1 19 Cell 5 8 #00EEFF
## 10 1 20 Case series 1 5 #00FF00
## # … with 65 more rows# Define node size based on the value
nodes <- nodes %>% mutate(font.size = 20+(value)/2)# Plot the network
visNetwork(nodes, edges1, width="100%", height="800") %>%
visNodes(scaling = list(min = 5, max = 35), borderWidth=0) %>%
visEdges(arrows = "to", scaling = list(min = 1, max = 10)) %>%
visPhysics(stabilization= FALSE, solver = "forceAtlas2Based")# visIgraphLayout()visNetwork(nodes, edges1) %>%
visEdges(arrows = "to", smooth = TRUE, color=edges1$category) %>%
visHierarchicalLayout(direction = "LR")%>%
visNodes(scaling = list(min = 10, max = 20), borderWidth=0, shape = "dot")# Types of cancer investigated
cancer_p <- benefit_list %>% filter(Condition == "Cancer" & !is.na(PrimarySite)) %>%
select(PrimarySite, StudyDesign) %>% rename(site = PrimarySite)
cancer_s <- benefit_list %>% filter(Condition == "Cancer" & !is.na(SecondarySite)) %>%
select(SecondarySite, StudyDesign) %>% rename(site = SecondarySite)
Cancer_sites <- rbind(cancer_p, cancer_s) %>%
group_by(site, StudyDesign) %>% summarise(N=n()) %>% arrange(desc(N))## `summarise()` has grouped output by 'site'. You can override using the
## `.groups` argument.Cancer_sites## # A tibble: 31 × 3
## # Groups: site [16]
## site StudyDesign N
## <chr> <fct> <int>
## 1 Breast Cell 4
## 2 Liver Animal 4
## 3 Various Before and after study 4
## 4 Breast Case series 3
## 5 Head & Neck Randomised controlled trial 3
## 6 Liver Case series 3
## 7 Various Case series 3
## 8 Blood Cell 2
## 9 Lung Case report 2
## 10 Ovary Case report 2
## # … with 21 more rows# Display Cancer Site by Article Count
cancer_p <- benefit_list %>% filter(Condition == "Cancer" & !is.na(PrimarySite)) %>%
select(ArticleID, PrimarySite, StudyDesign) %>% rename(Site = PrimarySite)
cancer_s <- benefit_list %>% filter(Condition == "Cancer" & !is.na(SecondarySite)) %>%
select(ArticleID, SecondarySite, StudyDesign) %>% rename(Site = SecondarySite)
Cancer_sites <- rbind(cancer_p, cancer_s) %>% distinct_all()
Cancer_sites_artcount <- Cancer_sites %>%
group_by(Site, StudyDesign) %>% summarise(N = n()) %>%
mutate(n_Chemical = ifelse(as.numeric(StudyDesign) == 9, N, 0))%>%
mutate(n_Cell = ifelse(as.numeric(StudyDesign) == 8, N, 0))%>%
mutate(n_Animal = ifelse(as.numeric(StudyDesign) == 7, N, 0))%>%
mutate(n_Observational = ifelse(as.numeric(StudyDesign) > 3 & as.numeric(StudyDesign) < 7, N, 0))%>%
mutate(n_Interventional = ifelse(as.numeric(StudyDesign) <= 3, N, 0))%>%
select(-c("StudyDesign")) %>%
arrange(desc(N))## `summarise()` has grouped output by 'Site'. You can override using the
## `.groups` argument.Cancer_sites_artcount <- Cancer_sites_artcount %>%
group_by(Site) %>%
summarise(Total = sum(N),Chem = sum(n_Chemical), Cell = sum(n_Cell), Animal = sum(n_Animal), H_O = sum(n_Observational), H_I = sum(n_Interventional)) %>%
arrange(desc(Total), desc(H_I),desc(H_O), desc(Animal), desc(Cell), desc(Chem) )
cancer_art_list <- benefit_list %>% filter(Condition == "Cancer") %>% select(ArticleID) %>% distinct()
N_cancer_art = nrow(cancer_art_list)
Cancer_sites_artcount <- Cancer_sites_artcount %>%
mutate(Total_p = percent(Total /N_cancer_art) ) %>%
mutate(Chem_p = percent(Chem /Total) ) %>%
mutate(Cell_p = percent(Cell /Total) ) %>%
mutate(Animal_p = percent(Animal /Total) ) %>%
mutate(H_O_p = percent(H_O /Total) ) %>%
mutate(H_I_p = percent(H_I /Total) ) %>%
select(c(1,2,8,3,9,4,10,5,11,6,12,7,13))
Cancer_sites_artcount## # A tibble: 16 × 13
## Site Total Total_p Chem Chem_p Cell Cell_p Animal Anima…¹ H_O H_O_p
## <chr> <int> <formt> <dbl> <form> <dbl> <formt> <dbl> <formt> <dbl> <formt>
## 1 Various 9 20.00% 0 0.00% 0 0.00% 0 0.00% 2 22.22%
## 2 Breast 8 17.78% 0 0.00% 4 50.00% 0 0.00% 3 37.50%
## 3 Liver 5 11.11% 0 0.00% 0 0.00% 2 40.00% 2 40.00%
## 4 Blood 4 8.89% 0 0.00% 2 50.00% 0 0.00% 0 0.00%
## 5 Colore… 2 4.44% 0 0.00% 0 0.00% 0 0.00% 2 100.00%
## 6 Lung 2 4.44% 0 0.00% 0 0.00% 0 0.00% 2 100.00%
## 7 Ovary 2 4.44% 0 0.00% 0 0.00% 0 0.00% 2 100.00%
## 8 Stomach 2 4.44% 0 0.00% 0 0.00% 1 50.00% 1 50.00%
## 9 Skin 2 4.44% 0 0.00% 1 50.00% 0 0.00% 1 50.00%
## 10 Cervic… 1 2.22% 0 0.00% 0 0.00% 0 0.00% 0 0.00%
## 11 Head &… 1 2.22% 0 0.00% 0 0.00% 0 0.00% 0 0.00%
## 12 Bile d… 1 2.22% 0 0.00% 0 0.00% 0 0.00% 1 100.00%
## 13 Pancre… 1 2.22% 0 0.00% 0 0.00% 0 0.00% 1 100.00%
## 14 Umbili… 1 2.22% 0 0.00% 0 0.00% 0 0.00% 1 100.00%
## 15 Uterus 1 2.22% 0 0.00% 0 0.00% 0 0.00% 1 100.00%
## 16 Prosta… 1 2.22% 0 0.00% 1 100.00% 0 0.00% 0 0.00%
## # … with 2 more variables: H_I <dbl>, H_I_p <formttbl>, and abbreviated
## # variable name ¹Animal_p# Condense the table
Cancer_sites_artcount <- Cancer_sites_artcount %>% mutate(precl = Chem+Cell+Animal,
precl_p = Chem_p+Cell_p+Animal_p) %>%
select(c(1,2,3, 14,15, 10,11,12,13))
Cancer_sites_artcount## # A tibble: 16 × 9
## Site Total Total_p precl precl_p H_O H_O_p H_I H_I_p
## <chr> <int> <formttbl> <dbl> <formttbl> <dbl> <formttbl> <dbl> <formtt>
## 1 Various 9 20.00% 0 0.00% 2 22.22% 7 77.78%
## 2 Breast 8 17.78% 4 50.00% 3 37.50% 1 12.50%
## 3 Liver 5 11.11% 2 40.00% 2 40.00% 1 20.00%
## 4 Blood 4 8.89% 2 50.00% 0 0.00% 2 50.00%
## 5 Colorectal 2 4.44% 0 0.00% 2 100.00% 0 0.00%
## 6 Lung 2 4.44% 0 0.00% 2 100.00% 0 0.00%
## 7 Ovary 2 4.44% 0 0.00% 2 100.00% 0 0.00%
## 8 Stomach 2 4.44% 1 50.00% 1 50.00% 0 0.00%
## 9 Skin 2 4.44% 1 50.00% 1 50.00% 0 0.00%
## 10 Cervical 1 2.22% 0 0.00% 0 0.00% 1 100.00%
## 11 Head & Neck 1 2.22% 0 0.00% 0 0.00% 1 100.00%
## 12 Bile duct 1 2.22% 0 0.00% 1 100.00% 0 0.00%
## 13 Pancreatic 1 2.22% 0 0.00% 1 100.00% 0 0.00%
## 14 Umbilical 1 2.22% 0 0.00% 1 100.00% 0 0.00%
## 15 Uterus 1 2.22% 0 0.00% 1 100.00% 0 0.00%
## 16 Prostate 1 2.22% 1 100.00% 0 0.00% 0 0.00%# create a new list of nodes from the column names row 10 to 36
onodes <- benefit_list %>% colnames()
onodes <- onodes[c(10:35)]
onodes <- data.frame(label = onodes, group="Outcome", level=3)
onodes## label group level
## 1 Mast cells Outcome 3
## 2 Macrophages Outcome 3
## 3 Neutrophils Outcome 3
## 4 Dendritic Cells Outcome 3
## 5 T & B Cells Outcome 3
## 6 Natural Killer Cells Outcome 3
## 7 Eosinophils Outcome 3
## 8 Lymphocytes Outcome 3
## 9 Cytokines Outcome 3
## 10 Nitric Oxide Production Outcome 3
## 11 Cancer Proliferation Outcome 3
## 12 Tumor Markers Outcome 3
## 13 Apoptosis Outcome 3
## 14 Gene Expression Outcome 3
## 15 Immunoglobulins Outcome 3
## 16 Histamine Outcome 3
## 17 Inflammatory Markers Outcome 3
## 18 Hematopoietic tissues Outcome 3
## 19 Oxidative Stress Markers Outcome 3
## 20 Survival Rate Outcome 3
## 21 Incidence Rate Outcome 3
## 22 Treatment Response Outcome 3
## 23 Quality of Life Assessment Outcome 3
## 24 Chemo Side Effects Outcome 3
## 25 Liver Function Markers Outcome 3
## 26 Safety & Adverse Events Outcome 3# Create edges as a data frame
edges2 <- data.frame(fromA= character(0), toA= character(0), cond = character(0), art = character(0), cat = numeric(0) )
# Edges are defined through iterating the co-occurrence of a beneficial effect and an outcome
for (a in unique(benefit_list$BeneficialEffect)) {
i_list <- benefit_list %>% filter(BeneficialEffect == a)
len <- nrow(i_list)
for(l in c(10:35)) {
n <- names(benefit_list)[l]
for (i in c(1:len)) {
if (!is.na(i_list[i,l]) & i_list[i,l] == "Yes") {
new <- c(a, n, i_list[i, 5], i_list[i, 1], i_list[i, 4])
edges2[nrow(edges2) + 1,] <- new
}
}
}
}
edges2## fromA toA
## 1 Immunomodulation Macrophages
## 2 Immunomodulation Macrophages
## 3 Immunomodulation Macrophages
## 4 Immunomodulation Macrophages
## 5 Immunomodulation Macrophages
## 6 Immunomodulation Macrophages
## 7 Immunomodulation Macrophages
## 8 Immunomodulation Macrophages
## 9 Immunomodulation Macrophages
## 10 Immunomodulation Macrophages
## 11 Immunomodulation Macrophages
## 12 Immunomodulation Macrophages
## 13 Immunomodulation Macrophages
## 14 Immunomodulation Neutrophils
## 15 Immunomodulation Dendritic Cells
## 16 Immunomodulation Dendritic Cells
## 17 Immunomodulation Dendritic Cells
## 18 Immunomodulation Dendritic Cells
## 19 Immunomodulation Dendritic Cells
## 20 Immunomodulation T & B Cells
## 21 Immunomodulation T & B Cells
## 22 Immunomodulation T & B Cells
## 23 Immunomodulation T & B Cells
## 24 Immunomodulation T & B Cells
## 25 Immunomodulation T & B Cells
## 26 Immunomodulation T & B Cells
## 27 Immunomodulation T & B Cells
## 28 Immunomodulation T & B Cells
## 29 Immunomodulation T & B Cells
## 30 Immunomodulation Natural Killer Cells
## 31 Immunomodulation Natural Killer Cells
## 32 Immunomodulation Natural Killer Cells
## 33 Immunomodulation Natural Killer Cells
## 34 Immunomodulation Natural Killer Cells
## 35 Immunomodulation Natural Killer Cells
## 36 Immunomodulation Natural Killer Cells
## 37 Immunomodulation Natural Killer Cells
## 38 Immunomodulation Natural Killer Cells
## 39 Immunomodulation Natural Killer Cells
## 40 Immunomodulation Natural Killer Cells
## 41 Immunomodulation Natural Killer Cells
## 42 Immunomodulation Natural Killer Cells
## 43 Immunomodulation Natural Killer Cells
## 44 Immunomodulation Natural Killer Cells
## 45 Immunomodulation Lymphocytes
## 46 Immunomodulation Lymphocytes
## 47 Immunomodulation Lymphocytes
## 48 Immunomodulation Lymphocytes
## 49 Immunomodulation Lymphocytes
## 50 Immunomodulation Cytokines
## 51 Immunomodulation Cytokines
## 52 Immunomodulation Cytokines
## 53 Immunomodulation Cytokines
## 54 Immunomodulation Cytokines
## 55 Immunomodulation Cytokines
## 56 Immunomodulation Cytokines
## 57 Immunomodulation Cytokines
## 58 Immunomodulation Cytokines
## 59 Immunomodulation Cytokines
## 60 Immunomodulation Cytokines
## 61 Immunomodulation Cytokines
## 62 Immunomodulation Cytokines
## 63 Immunomodulation Nitric Oxide Production
## 64 Immunomodulation Nitric Oxide Production
## 65 Immunomodulation Nitric Oxide Production
## 66 Immunomodulation Nitric Oxide Production
## 67 Immunomodulation Tumor Markers
## 68 Immunomodulation Gene Expression
## 69 Immunomodulation Gene Expression
## 70 Immunomodulation Inflammatory Markers
## 71 Immunomodulation Inflammatory Markers
## 72 Immunomodulation Survival Rate
## 73 Immunomodulation Survival Rate
## 74 Immunomodulation Treatment Response
## 75 Immunomodulation Quality of Life Assessment
## 76 Immunomodulation Liver Function Markers
## 77 Immunomodulation Liver Function Markers
## 78 Immunomodulation Liver Function Markers
## 79 Immunomodulation Safety & Adverse Events
## 80 Immunomodulation Safety & Adverse Events
## 81 Immunomodulation Safety & Adverse Events
## 82 Immunomodulation Safety & Adverse Events
## 83 Immunomodulation Safety & Adverse Events
## 84 Immunomodulation Safety & Adverse Events
## 85 Immunomodulation Safety & Adverse Events
## 86 Anticancer Natural Killer Cells
## 87 Anticancer Natural Killer Cells
## 88 Anticancer Cytokines
## 89 Anticancer Cytokines
## 90 Anticancer Cancer Proliferation
## 91 Anticancer Cancer Proliferation
## 92 Anticancer Cancer Proliferation
## 93 Anticancer Cancer Proliferation
## 94 Anticancer Cancer Proliferation
## 95 Anticancer Cancer Proliferation
## 96 Anticancer Cancer Proliferation
## 97 Anticancer Cancer Proliferation
## 98 Anticancer Cancer Proliferation
## 99 Anticancer Apoptosis
## 100 Anticancer Apoptosis
## 101 Anticancer Gene Expression
## 102 Anticancer Gene Expression
## 103 Anticancer Oxidative Stress Markers
## 104 Anticancer Survival Rate
## 105 Anticancer Survival Rate
## 106 Anticancer Survival Rate
## 107 Anticancer Survival Rate
## 108 Anticancer Survival Rate
## 109 Anticancer Safety & Adverse Events
## 110 Antiallergy Mast cells
## 111 Antiallergy Mast cells
## 112 Antiallergy Mast cells
## 113 Antiallergy Mast cells
## 114 Antiallergy Eosinophils
## 115 Antiallergy Cytokines
## 116 Antiallergy Cytokines
## 117 Antiallergy Immunoglobulins
## 118 Antiallergy Immunoglobulins
## 119 Antiallergy Histamine
## 120 Antiallergy Histamine
## 121 Antiallergy Inflammatory Markers
## 122 Antiallergy Inflammatory Markers
## 123 Noncytotoxic Safety & Adverse Events
## 124 Antioxidant Cytokines
## 125 Antioxidant Cancer Proliferation
## 126 Antioxidant Apoptosis
## 127 Antioxidant Apoptosis
## 128 Antioxidant Gene Expression
## 129 Antioxidant Gene Expression
## 130 Antioxidant Inflammatory Markers
## 131 Antioxidant Inflammatory Markers
## 132 Antioxidant Hematopoietic tissues
## 133 Antioxidant Oxidative Stress Markers
## 134 Antioxidant Oxidative Stress Markers
## 135 Antioxidant Oxidative Stress Markers
## 136 Antioxidant Oxidative Stress Markers
## 137 Antioxidant Oxidative Stress Markers
## 138 Antioxidant Oxidative Stress Markers
## 139 Antioxidant Liver Function Markers
## 140 Chemoprevention Lymphocytes
## 141 Chemoprevention Cancer Proliferation
## 142 Chemoprevention Cancer Proliferation
## 143 Chemoprevention Cancer Proliferation
## 144 Chemoprevention Apoptosis
## 145 Chemoprevention Apoptosis
## 146 Chemoprevention Gene Expression
## 147 Chemoprevention Inflammatory Markers
## 148 Chemoprevention Inflammatory Markers
## 149 Chemoprevention Incidence Rate
## 150 Chemoprevention Liver Function Markers
## 151 Chemoprevention Liver Function Markers
## 152 Synergistic anticancer effect Cancer Proliferation
## 153 Synergistic anticancer effect Cancer Proliferation
## 154 Synergistic anticancer effect Cancer Proliferation
## 155 Synergistic anticancer effect Cancer Proliferation
## 156 Synergistic anticancer effect Cancer Proliferation
## 157 Synergistic anticancer effect Cancer Proliferation
## 158 Synergistic anticancer effect Cancer Proliferation
## 159 Synergistic anticancer effect Cancer Proliferation
## 160 Synergistic anticancer effect Cancer Proliferation
## 161 Synergistic anticancer effect Tumor Markers
## 162 Synergistic anticancer effect Tumor Markers
## 163 Synergistic anticancer effect Tumor Markers
## 164 Synergistic anticancer effect Tumor Markers
## 165 Synergistic anticancer effect Tumor Markers
## 166 Synergistic anticancer effect Tumor Markers
## 167 Synergistic anticancer effect Tumor Markers
## 168 Synergistic anticancer effect Apoptosis
## 169 Synergistic anticancer effect Apoptosis
## 170 Synergistic anticancer effect Apoptosis
## 171 Synergistic anticancer effect Apoptosis
## 172 Synergistic anticancer effect Apoptosis
## 173 Synergistic anticancer effect Apoptosis
## 174 Synergistic anticancer effect Gene Expression
## 175 Synergistic anticancer effect Gene Expression
## 176 Synergistic anticancer effect Gene Expression
## 177 Synergistic anticancer effect Survival Rate
## 178 Synergistic anticancer effect Survival Rate
## 179 Synergistic anticancer effect Survival Rate
## 180 Synergistic anticancer effect Survival Rate
## 181 Synergistic anticancer effect Survival Rate
## 182 Synergistic anticancer effect Survival Rate
## 183 Synergistic anticancer effect Survival Rate
## 184 Synergistic anticancer effect Survival Rate
## 185 Synergistic anticancer effect Survival Rate
## 186 Synergistic anticancer effect Survival Rate
## 187 Synergistic anticancer effect Incidence Rate
## 188 Synergistic anticancer effect Treatment Response
## 189 Synergistic anticancer effect Treatment Response
## 190 Synergistic anticancer effect Treatment Response
## 191 Synergistic anticancer effect Treatment Response
## 192 Synergistic anticancer effect Treatment Response
## 193 Synergistic anticancer effect Treatment Response
## 194 Synergistic anticancer effect Treatment Response
## 195 Synergistic anticancer effect Treatment Response
## 196 Synergistic anticancer effect Treatment Response
## 197 Synergistic anticancer effect Quality of Life Assessment
## 198 Synergistic anticancer effect Quality of Life Assessment
## 199 Synergistic anticancer effect Quality of Life Assessment
## 200 Synergistic anticancer effect Quality of Life Assessment
## 201 Synergistic anticancer effect Quality of Life Assessment
## 202 Synergistic anticancer effect Quality of Life Assessment
## 203 Synergistic anticancer effect Liver Function Markers
## 204 Synergistic anticancer effect Liver Function Markers
## 205 Synergistic anticancer effect Safety & Adverse Events
## 206 Hepatoprotection Lymphocytes
## 207 Hepatoprotection Cytokines
## 208 Hepatoprotection Cytokines
## 209 Hepatoprotection Cytokines
## 210 Hepatoprotection Cytokines
## 211 Hepatoprotection Tumor Markers
## 212 Hepatoprotection Apoptosis
## 213 Hepatoprotection Gene Expression
## 214 Hepatoprotection Gene Expression
## 215 Hepatoprotection Gene Expression
## 216 Hepatoprotection Inflammatory Markers
## 217 Hepatoprotection Inflammatory Markers
## 218 Hepatoprotection Inflammatory Markers
## 219 Hepatoprotection Inflammatory Markers
## 220 Hepatoprotection Inflammatory Markers
## 221 Hepatoprotection Oxidative Stress Markers
## 222 Hepatoprotection Survival Rate
## 223 Hepatoprotection Treatment Response
## 224 Hepatoprotection Quality of Life Assessment
## 225 Hepatoprotection Quality of Life Assessment
## 226 Hepatoprotection Liver Function Markers
## 227 Hepatoprotection Liver Function Markers
## 228 Hepatoprotection Liver Function Markers
## 229 Hepatoprotection Liver Function Markers
## 230 Hepatoprotection Liver Function Markers
## 231 Hepatoprotection Liver Function Markers
## 232 Hepatoprotection Liver Function Markers
## 233 Hepatoprotection Liver Function Markers
## 234 Hepatoprotection Liver Function Markers
## 235 Hepatoprotection Liver Function Markers
## 236 Hepatoprotection Liver Function Markers
## 237 Hepatoprotection Liver Function Markers
## 238 Hepatoprotection Liver Function Markers
## 239 Hepatoprotection Safety & Adverse Events
## 240 Hepatoprotection Safety & Adverse Events
## 241 No significant effect Safety & Adverse Events
## 242 No significant effect Safety & Adverse Events
## 243 Psychoneuroimmuno-modulation Natural Killer Cells
## 244 Psychoneuroimmuno-modulation Cytokines
## 245 Psychoneuroimmuno-modulation Tumor Markers
## 246 Psychoneuroimmuno-modulation Survival Rate
## 247 Psychoneuroimmuno-modulation Treatment Response
## 248 Psychoneuroimmuno-modulation Treatment Response
## 249 Psychoneuroimmuno-modulation Quality of Life Assessment
## 250 Psychoneuroimmuno-modulation Quality of Life Assessment
## 251 Psychoneuroimmuno-modulation Quality of Life Assessment
## 252 Psychoneuroimmuno-modulation Quality of Life Assessment
## 253 Psychoneuroimmuno-modulation Quality of Life Assessment
## 254 Psychoneuroimmuno-modulation Quality of Life Assessment
## 255 Psychoneuroimmuno-modulation Quality of Life Assessment
## 256 Psychoneuroimmuno-modulation Quality of Life Assessment
## 257 Psychoneuroimmuno-modulation Chemo Side Effects
## 258 Psychoneuroimmuno-modulation Chemo Side Effects
## 259 Psychoneuroimmuno-modulation Safety & Adverse Events
## 260 Psychoneuroimmuno-modulation Safety & Adverse Events
## 261 Antiflu Incidence Rate
## 262 Antiflu Incidence Rate
## 263 Antiflu Treatment Response
## 264 Antiflu Treatment Response
## 265 Antiwasting Chemo Side Effects
## 266 Antiwasting Safety & Adverse Events
## 267 Memory enhancer Gene Expression
## 268 Memory enhancer Inflammatory Markers
## 269 Memory enhancer Oxidative Stress Markers
## 270 Antibacterial Neutrophils
## 271 Antibacterial T & B Cells
## 272 Antibacterial Lymphocytes
## 273 Antibacterial Cytokines
## 274 Antibacterial Cytokines
## 275 Antibacterial Survival Rate
## 276 Antibacterial Treatment Response
## 277 Antibacterial Liver Function Markers
## 278 Radioprotection Apoptosis
## 279 Radioprotection Inflammatory Markers
## 280 Radioprotection Hematopoietic tissues
## 281 Radioprotection Oxidative Stress Markers
## 282 Radioprotection Oxidative Stress Markers
## 283 Radioprotection Chemo Side Effects
## 284 Radioprotection Safety & Adverse Events
## 285 Antiinflammation Mast cells
## 286 Antiinflammation Lymphocytes
## 287 Antiinflammation Cytokines
## 288 Antiinflammation Cytokines
## 289 Antiinflammation Cytokines
## 290 Antiinflammation Apoptosis
## 291 Antiinflammation Gene Expression
## 292 Antiinflammation Inflammatory Markers
## 293 Antiinflammation Inflammatory Markers
## 294 Antiinflammation Inflammatory Markers
## 295 Antiinflammation Inflammatory Markers
## 296 Antiinflammation Inflammatory Markers
## 297 Antiinflammation Inflammatory Markers
## 298 Antiinflammation Oxidative Stress Markers
## 299 Antiinflammation Survival Rate
## 300 Antiinflammation Treatment Response
## 301 Antiinflammation Quality of Life Assessment
## 302 Antiinflammation Liver Function Markers
## 303 Antiinflammation Liver Function Markers
## 304 Antirheumatic effect Inflammatory Markers
## 305 Antirheumatic effect Treatment Response
## 306 Antirheumatic effect Quality of Life Assessment
## 307 Gastroprotection Chemo Side Effects
## 308 Gastroprotection Chemo Side Effects
## 309 Gastroprotection Safety & Adverse Events
## 310 Gastroprotection Safety & Adverse Events
## 311 Chemoprotection Survival Rate
## 312 Chemoprotection Incidence Rate
## 313 Chemoprotection Liver Function Markers
## 314 Antiasthma Eosinophils
## 315 Antiasthma Histamine
## 316 Antiasthma Inflammatory Markers
## 317 Antifatigue Treatment Response
## 318 Antifatigue Treatment Response
## 319 Endothelial improvement Nitric Oxide Production
## 320 Antimetastatic effect Tumor Markers
## 321 Antiviral Treatment Response
## 322 Antiviral Quality of Life Assessment
## 323 Antiviral Liver Function Markers
## 324 Antiviral Safety & Adverse Events
## cond art cat
## 1 Healthy / Nonspecific Chae et al. (2004) 8
## 2 Healthy / Nonspecific Ghoneum & Matsuura (2004) 8
## 3 Healthy / Nonspecific Kang et al. (2022) 8
## 4 Healthy / Nonspecific Kim D.J. et al. (2011b) 7
## 5 Healthy / Nonspecific Kim H.Y. et al. (2005) 7
## 6 Healthy / Nonspecific Kim H.Y. et al. (2005) 8
## 7 Healthy / Nonspecific Kim S.P. et al. (2013) 8
## 8 Healthy / Nonspecific Kim S.P. et al. (2014) 8
## 9 Healthy / Nonspecific Kim S.P. et al. (2018) 8
## 10 Healthy / Nonspecific Miura et al. (2004/2013) 8
## 11 Healthy / Nonspecific Pérez-Martínez et al. (2015) 8
## 12 Cancer Pérez-Martínez et al. (2015) 7
## 13 Healthy / Nonspecific Yu et al. (2004) 8
## 14 Cancer Golombick et al. (2016) 3
## 15 Healthy / Nonspecific Cholujova et al. (2009) 8
## 16 Cancer Cholujova et al. (2013) 1
## 17 Healthy / Nonspecific Ghoneum & Agrawal (2011) 8
## 18 Healthy / Nonspecific Ghoneum & Agrawal (2014) 8
## 19 Healthy / Nonspecific Kim S.P. et al. (2014) 8
## 20 Healthy / Nonspecific Bae et al. (2004) 7
## 21 Healthy / Nonspecific Chae et al. (2004) 8
## 22 Healthy / Nonspecific Ghoneum & Agrawal (2011) 8
## 23 Healthy / Nonspecific Ghoneum & Agrawal (2014) 8
## 24 Cancer Ghoneum & Brown (1999) 3
## 25 Cancer Ghoneum (1999) 3
## 26 Healthy / Nonspecific Giese et al. (2008) 7
## 27 Healthy / Nonspecific Kang et al. (2022) 8
## 28 Healthy / Nonspecific Kim H.Y. et al. (2005) 8
## 29 Cancer Lissoni et al. (2008) 3
## 30 Healthy / Nonspecific Ali et al. (2012) 1
## 31 Cancer Cholujova et al. (2013) 1
## 32 Geriatric Elsaid et al. (2018) 1
## 33 Geriatric Elsaid et al. (2021) 1
## 34 Geriatric Ghoneum & Abedi (2004) 7
## 35 Geriatric Ghoneum & Abedi (2004) 8
## 36 Cancer Ghoneum & Brown (1999) 3
## 37 Healthy / Nonspecific Ghoneum & Jewett (2000) 8
## 38 Healthy / Nonspecific Ghoneum (1998b) 3
## 39 Cancer Ghoneum (1999) 3
## 40 Chemical exposure Ghoneum (1999) 3
## 41 Healthy / Nonspecific Giese et al. (2008) 7
## 42 Irritable bowel syndrome Kamiya et al. (2014) 1
## 43 Healthy / Nonspecific Pérez-Martínez et al. (2015) 8
## 44 Cancer Pérez-Martínez et al. (2015) 7
## 45 Irritable bowel syndrome Kamiya et al. (2014) 1
## 46 Healthy / Nonspecific Kim D.J. et al. (2011b) 7
## 47 Healthy / Nonspecific Kim H.Y. et al. (2005) 7
## 48 HIV Lewis et al. (2020b) 1
## 49 Cancer Lissoni et al. (2008) 3
## 50 Healthy / Nonspecific Ali et al. (2012) 1
## 51 Healthy / Nonspecific Chae et al. (2004) 8
## 52 Healthy / Nonspecific Choi et al. (2014) 1
## 53 Cancer Cholujova et al. (2013) 1
## 54 Healthy / Nonspecific Ghoneum & Agrawal (2011) 8
## 55 Healthy / Nonspecific Ghoneum & Agrawal (2014) 8
## 56 Healthy / Nonspecific Ghoneum & Jewett (2000) 8
## 57 Healthy / Nonspecific Ghoneum & Matsuura (2004) 8
## 58 Healthy / Nonspecific Ghoneum (1998b) 3
## 59 Healthy / Nonspecific Giese et al. (2008) 7
## 60 Healthy / Nonspecific Kang et al. (2022) 8
## 61 Healthy / Nonspecific Kim D.J. et al. (2011b) 7
## 62 Healthy / Nonspecific Kim S.P. et al. (2018) 8
## 63 Healthy / Nonspecific Chae et al. (2004) 8
## 64 Healthy / Nonspecific Ghoneum & Matsuura (2004) 8
## 65 Healthy / Nonspecific Kang et al. (2022) 8
## 66 Healthy / Nonspecific Kim S.P. et al. (2018) 8
## 67 Cancer Okamura (2004) 5
## 68 Geriatric Elsaid et al. (2021) 1
## 69 Healthy / Nonspecific Kim S.P. et al. (2018) 8
## 70 Cancer Golombick et al. (2016) 3
## 71 Irritable bowel syndrome Kamiya et al. (2014) 1
## 72 Cancer Ghoneum & Brown (1999) 3
## 73 Cancer Okamura (2004) 5
## 74 Irritable bowel syndrome Kamiya et al. (2014) 1
## 75 Cancer Okamura (2004) 5
## 76 Geriatric Elsaid et al. (2018) 1
## 77 HIV Lewis et al. (2020b) 1
## 78 Cancer Okamura (2004) 5
## 79 Healthy / Nonspecific Ali et al. (2012) 1
## 80 Healthy / Nonspecific Choi et al. (2014) 1
## 81 Geriatric Elsaid et al. (2021) 1
## 82 Healthy / Nonspecific Ghoneum (1998b) 3
## 83 Cancer Itoh et al. (2015) 1
## 84 Irritable bowel syndrome Kamiya et al. (2014) 1
## 85 HIV Lewis et al. (2020b) 1
## 86 Cancer Badr El-Din et al. (2008) 7
## 87 Cancer Kim H.Y. et al. (2007) 7
## 88 Cancer Badr El-Din et al. (2008) 7
## 89 Cancer Ghoneum et al. (2000) 8
## 90 Cancer An (2011) 7
## 91 Cancer Badr El-Din et al. (2008) 7
## 92 Cancer Bae et al. (2004) 7
## 93 Cancer Brush et al. (2010) 8
## 94 Cancer Ghoneum et al. (2000) 8
## 95 Cancer Kim D.J. et al. (2011a) 7
## 96 Cancer Kim H.Y. et al. (2007) 7
## 97 Cancer Markus et al. (2006) 6
## 98 Cancer Noaman et al. (2008) 7
## 99 Cancer Ghoneum & Gollapudi (2003) 8
## 100 Cancer Noaman et al. (2008) 7
## 101 Cancer Ghoneum & Gollapudi (2003) 8
## 102 Cancer Noaman et al. (2008) 7
## 103 Cancer Noaman et al. (2008) 7
## 104 Cancer An (2011) 7
## 105 Cancer Bae et al. (2004) 7
## 106 Cancer Kim D.J. et al. (2011a) 7
## 107 Cancer Kim H.Y. et al. (2007) 7
## 108 Cancer Markus et al. (2006) 6
## 109 Cancer Badr El-Din et al. (2008) 7
## 110 Allergy An (2011) 7
## 111 Allergy Bae et al. (2004) 7
## 112 Healthy / Nonspecific Hoshino et al. (2010) 8
## 113 Healthy / Nonspecific Kim D.J. et al. (2011a) 8
## 114 Allergy Kambayashi & Endo (2002) 7
## 115 Allergy An (2011) 7
## 116 Healthy / Nonspecific Hoshino et al. (2010) 8
## 117 Allergy An (2011) 7
## 118 Allergy Bae et al. (2004) 7
## 119 Allergy Bae et al. (2004) 7
## 120 Allergy Kambayashi & Endo (2002) 7
## 121 Healthy / Nonspecific Hoshino et al. (2010) 8
## 122 Allergy Kambayashi & Endo (2002) 7
## 123 Healthy / Nonspecific An (2011) 8
## 124 Endotoxemia Kim S.P. et al. (2013) 7
## 125 Cancer Noaman et al. (2008) 7
## 126 Cancer Noaman et al. (2008) 7
## 127 Gastroenteritis Zhao et al. (2020) 7
## 128 Alzheimer's disease Ghoneum & El Sayed (2021) 7
## 129 Cancer Noaman et al. (2008) 7
## 130 Alzheimer's disease Ghoneum & El Sayed (2021) 7
## 131 Gastroenteritis Zhao et al. (2020) 7
## 132 Healthy / Nonspecific Ghoneum et al. (2013) 7
## 133 Alzheimer's disease Ghoneum & El Sayed (2021) 7
## 134 Healthy / Nonspecific Ghoneum et al. (2013) 7
## 135 Endotoxemia Kim S.P. et al. (2013) 7
## 136 Cancer Noaman et al. (2008) 7
## 137 Oxidative stress Tazawa et al. (2000) 9
## 138 Gastroenteritis Zhao et al. (2020) 7
## 139 Endotoxemia Kim S.P. et al. (2013) 7
## 140 Cancer Badr El-Din et al. (2016a) 7
## 141 Cancer Badr El-Din et al. (2016a) 7
## 142 Cancer Badr El-Din et al. (2016c) 7
## 143 Cancer Badr El-Din et al. (2020) 7
## 144 Cancer Badr El-Din et al. (2016a) 7
## 145 Cancer Badr El-Din et al. (2020) 7
## 146 Cancer Badr El-Din et al. (2020) 7
## 147 Cancer Badr El-Din et al. (2016c) 7
## 148 Cancer Badr El-Din et al. (2020) 7
## 149 Cancer Badr El-Din et al. (2016a) 7
## 150 Cancer Badr El-Din et al. (2016c) 7
## 151 Cancer Badr El-Din et al. (2020) 7
## 152 Cancer Badr El-Din et al. (2016b) 7
## 153 Cancer Badr El-Din et al. (2019) 7
## 154 Cancer Ghoneum et al. (2014) 8
## 155 Cancer Hajtó & Kirsch (2013) 5
## 156 Cancer Hajtó (2017) 6
## 157 Cancer Hajtó (2018) 6
## 158 Cancer Hajtó et al. (2015) 6
## 159 Cancer Hajtó et al. (2016a) 6
## 160 Cancer Kaketani (2004) 6
## 161 Cancer Bang et al. (2010) 1
## 162 Cancer Hajtó & Kirsch (2013) 5
## 163 Cancer Hajtó (2017) 6
## 164 Cancer Hajtó (2018) 6
## 165 Cancer Meshitsuka (2013) 6
## 166 Cancer Okamura (2004) 5
## 167 Cancer Tsunekawa (2004) 5
## 168 Cancer Badr El-Din et al. (2016b) 7
## 169 Cancer Badr El-Din et al. (2019) 7
## 170 Cancer Ghoneum & Gollapudi (2005a) 8
## 171 Cancer Ghoneum & Gollapudi (2005b) 8
## 172 Cancer Ghoneum & Gollapudi (2011) 8
## 173 Cancer Ghoneum et al. (2014) 8
## 174 Cancer Badr El-Din et al. (2016b) 7
## 175 Cancer Badr El-Din et al. (2019) 7
## 176 Cancer Ghoneum & Gollapudi (2011) 8
## 177 Cancer Bang et al. (2010) 1
## 178 Cancer Ghoneum et al. (2014) 8
## 179 Cancer Gollapudi & Ghoneum (2008) 8
## 180 Cancer Hajtó & Kirsch (2013) 5
## 181 Cancer Hajtó et al. (2015) 6
## 182 Cancer Hajtó et al. (2016a) 6
## 183 Cancer Kaketani (2004) 6
## 184 Cancer Kawai (2004) 6
## 185 Cancer Okamura (2004) 5
## 186 Cancer Tsunekawa (2004) 5
## 187 Cancer Bang et al. (2010) 1
## 188 Cancer Bang et al. (2010) 1
## 189 Cancer Hajtó & Kirsch (2013) 5
## 190 Cancer Hajtó (2017) 6
## 191 Cancer Hajtó (2018) 6
## 192 Cancer Hajtó et al. (2015) 6
## 193 Cancer Hajtó et al. (2016a) 6
## 194 Cancer Kaketani (2004) 6
## 195 Cancer Meshitsuka (2013) 6
## 196 Cancer Tan & Flores (2020) 1
## 197 Cancer Hajtó & Kirsch (2013) 5
## 198 Cancer Hajtó et al. (2015) 6
## 199 Cancer Hajtó et al. (2016a) 6
## 200 Cancer Kawai (2004) 6
## 201 Cancer Okamura (2004) 5
## 202 Cancer Tsunekawa (2004) 5
## 203 Cancer Hajtó & Kirsch (2013) 5
## 204 Cancer Okamura (2004) 5
## 205 Cancer Bang et al. (2010) 1
## 206 Hepatitis / Liver Disease Lewis et al. (2020c) 1
## 207 Hepatitis / Liver Disease Egashira et al. (2013) 7
## 208 Hepatitis / Liver Disease Kim S.P. et al. (2013) 7
## 209 Hepatitis / Liver Disease Lewis et al. (2020c) 1
## 210 Hepatitis / Liver Disease Zheng et al. (2012a) 7
## 211 Cancer Okamura (2004) 5
## 212 Cancer Badr El-Din et al. (2020) 7
## 213 Cancer Badr El-Din et al. (2020) 7
## 214 Hepatitis / Liver Disease Chung et al. (2015) 7
## 215 Hepatitis / Liver Disease Zheng et al. (2012b) 7
## 216 Cancer Badr El-Din et al. (2016c) 7
## 217 Cancer Badr El-Din et al. (2020) 7
## 218 Hepatitis / Liver Disease Chung et al. (2015) 7
## 219 Hepatitis / Liver Disease Egashira et al. (2013) 7
## 220 Hepatitis / Liver Disease Zheng et al. (2012b) 7
## 221 Hepatitis / Liver Disease Kim S.P. et al. (2013) 7
## 222 Cancer Okamura (2004) 5
## 223 Hepatitis / Liver Disease Salama et al. (2016) 1
## 224 Cancer Okamura (2004) 5
## 225 Hepatitis / Liver Disease Salama et al. (2016) 1
## 226 Cancer Badr El-Din et al. (2016c) 7
## 227 Cancer Badr El-Din et al. (2020) 7
## 228 Healthy / Nonspecific Chung et al. (2015) 8
## 229 Hepatitis / Liver Disease Chung et al. (2015) 7
## 230 Hepatitis / Liver Disease Daizo et al. (2001) 7
## 231 Hepatitis / Liver Disease Egashira et al. (2013) 7
## 232 Hepatitis / Liver Disease Kim S.P. et al. (2013) 7
## 233 Hepatitis / Liver Disease Lewis et al. (2020c) 1
## 234 Cancer Okamura (2004) 5
## 235 Hepatitis / Liver Disease Salama et al. (2016) 1
## 236 Hepatitis / Liver Disease Yamada et al. (2002) 7
## 237 Hepatitis / Liver Disease Zheng et al. (2012a) 7
## 238 Hepatitis / Liver Disease Zheng et al. (2012b) 7
## 239 Hepatitis / Liver Disease Lewis et al. (2020c) 1
## 240 Hepatitis / Liver Disease Salama et al. (2016) 1
## 241 HIV Cadden et al. (2020) 1
## 242 Chronic fatigue syndrome McDermott et al. (2006) 1
## 243 Cancer Takahara & Sano (2004) 1
## 244 Cancer Kim J.M. et al.(2020) 2
## 245 Cancer Tsunekawa (2004) 5
## 246 Cancer Tsunekawa (2004) 5
## 247 Cancer Hajtó et al. (2016b) 4
## 248 Cancer Takahara & Sano (2004) 1
## 249 Geriatric Elsaid et al. (2020) 1
## 250 Cancer Hajtó et al. (2016b) 4
## 251 Cancer Kim J.M. et al.(2020) 2
## 252 Cancer Masood et al. (2013) 1
## 253 Cancer Petrovics et al. (2016) 1
## 254 Cancer Takahara & Sano (2004) 1
## 255 Cancer Tan & Flores (2020) 1
## 256 Cancer Tsunekawa (2004) 5
## 257 Cancer Hajtó et al. (2016b) 4
## 258 Cancer Masood et al. (2013) 1
## 259 Cancer Hajtó et al. (2016b) 4
## 260 Cancer Masood et al. (2013) 1
## 261 Cold / Flu Elsaid et al. (2021) 1
## 262 Cold / Flu Tazawa et al. (2003) 1
## 263 Cold / Flu Elsaid et al. (2021) 1
## 264 Cold / Flu Tazawa et al. (2003) 1
## 265 Healthy / Nonspecific Endo & Kanbayashi (2003) 7
## 266 Healthy / Nonspecific Endo & Kanbayashi (2003) 7
## 267 Alzheimer's disease Ghoneum & El Sayed (2021) 7
## 268 Alzheimer's disease Ghoneum & El Sayed (2021) 7
## 269 Alzheimer's disease Ghoneum & El Sayed (2021) 7
## 270 Healthy / Nonspecific Ghoneum et al. (2008) 8
## 271 Bacterial infection Kim S.P. et al. (2014) 7
## 272 Bacterial infection Kim S.P. et al. (2014) 7
## 273 Healthy / Nonspecific Ghoneum et al. (2008) 8
## 274 Bacterial infection Kim S.P. et al. (2014) 7
## 275 Bacterial infection Kim S.P. et al. (2014) 7
## 276 Gastroenteritis Kim S.P. et al. (2018) 7
## 277 Bacterial infection Kim S.P. et al. (2014) 7
## 278 Gastroenteritis Zhao et al. (2020) 7
## 279 Gastroenteritis Zhao et al. (2020) 7
## 280 Healthy / Nonspecific Ghoneum et al. (2013) 7
## 281 Healthy / Nonspecific Ghoneum et al. (2013) 7
## 282 Gastroenteritis Zhao et al. (2020) 7
## 283 Cancer Tan & Flores (2020) 1
## 284 Cancer Tan & Flores (2020) 1
## 285 Healthy / Nonspecific Hoshino et al. (2010) 8
## 286 Allergy Kim D.J. et al. (2011a) 7
## 287 Healthy / Nonspecific Hoshino et al. (2010) 8
## 288 Endotoxemia Sudo et al. (2001) 7
## 289 Hepatitis / Liver Disease Zheng et al. (2012a) 7
## 290 Gastroenteritis Zhao et al. (2020) 7
## 291 Hepatitis / Liver Disease Zheng et al. (2012b) 7
## 292 Healthy / Nonspecific Hoshino et al. (2010) 8
## 293 Rheumatism Ichihashi (2004) 5
## 294 Allergy Kim D.J. et al. (2011a) 7
## 295 Endotoxemia Sudo et al. (2001) 7
## 296 Gastroenteritis Zhao et al. (2020) 7
## 297 Hepatitis / Liver Disease Zheng et al. (2012b) 7
## 298 Gastroenteritis Zhao et al. (2020) 7
## 299 Endotoxemia Sudo et al. (2001) 7
## 300 Rheumatism Ichihashi (2004) 5
## 301 Rheumatism Ichihashi (2004) 5
## 302 Hepatitis / Liver Disease Zheng et al. (2012a) 7
## 303 Hepatitis / Liver Disease Zheng et al. (2012b) 7
## 304 Rheumatism Ichihashi (2004) 5
## 305 Rheumatism Ichihashi (2004) 5
## 306 Rheumatism Ichihashi (2004) 5
## 307 Gastroenteritis Itoh et al. (2015) 1
## 308 Healthy / Nonspecific Jacoby et al. (2001) 7
## 309 Gastroenteritis Itoh et al. (2015) 1
## 310 Healthy / Nonspecific Jacoby et al. (2001) 7
## 311 Healthy / Nonspecific Jacoby et al. (2001) 7
## 312 Healthy / Nonspecific Jacoby et al. (2001) 7
## 313 Healthy / Nonspecific Jacoby et al. (2001) 7
## 314 Allergy Kambayashi & Endo (2002) 7
## 315 Allergy Kambayashi & Endo (2002) 7
## 316 Allergy Kambayashi & Endo (2002) 7
## 317 Chronic fatigue syndrome Kenyon (2001) 3
## 318 Chronic fatigue syndrome Petrovics et al. (2016) 1
## 319 HIV Lewis et al. (2020a) 1
## 320 Cancer Pescatore et al. (2022) 3
## 321 Hepatitis / Liver Disease Salama et al. (2016) 1
## 322 Hepatitis / Liver Disease Salama et al. (2016) 1
## 323 Hepatitis / Liver Disease Salama et al. (2016) 1
## 324 Hepatitis / Liver Disease Salama et al. (2016) 1#calculate the value of outcome nodes using the edges
edges2_dist <- edges2 %>% select(toA, art, cat) %>% distinct_all()
lonodes <- onodes %>% left_join(edges2_dist, by = c("label" = "toA")) %>%
group_by(label, group, level, cat) %>% summarise(value = n()) %>%
arrange(desc(value))## `summarise()` has grouped output by 'label', 'group', 'level'. You can override
## using the `.groups` argument.lonodes## # A tibble: 80 × 5
## # Groups: label, group, level [26]
## label group level cat value
## <chr> <chr> <dbl> <dbl> <int>
## 1 Safety & Adverse Events Outcome 3 1 13
## 2 Cancer Proliferation Outcome 3 7 11
## 3 Liver Function Markers Outcome 3 7 11
## 4 Cytokines Outcome 3 8 10
## 5 Inflammatory Markers Outcome 3 7 10
## 6 Macrophages Outcome 3 8 10
## 7 Cytokines Outcome 3 7 9
## 8 Treatment Response Outcome 3 1 8
## 9 Gene Expression Outcome 3 7 7
## 10 Survival Rate Outcome 3 7 7
## # … with 70 more rowsoutcome_list_artcount <- lonodes %>% select(label, N=value, cat) %>%
mutate(n_Chemical = ifelse(cat == 9, N, 0))%>%
mutate(n_Cell = ifelse(cat == 8, N, 0))%>%
mutate(n_Animal = ifelse(cat == 7, N, 0))%>%
mutate(n_Observational = ifelse(cat > 3 & cat < 7, N, 0))%>%
mutate(n_Interventional = ifelse(cat <= 3, N, 0))%>%
select(-c("cat")) %>%
arrange(desc(N))## Adding missing grouping variables: `group`, `level`outcome_list_artcount <- outcome_list_artcount %>%
group_by(label) %>%
summarise(Total = sum(N),Chem = sum(n_Chemical), Cell = sum(n_Cell), Animal = sum(n_Animal), H_O = sum(n_Observational), H_I = sum(n_Interventional)) %>%
arrange(desc(Total), desc(H_I),desc(H_O), desc(Animal), desc(Cell), desc(Chem) )
outcome_list_artcount <- outcome_list_artcount %>%
mutate(Total_p = percent(Total /N_art) ) %>%
mutate(Chem_p = percent(Chem /Total) ) %>%
mutate(Cell_p = percent(Cell /Total) ) %>%
mutate(Animal_p = percent(Animal /Total) ) %>%
mutate(H_O_p = percent(H_O /Total) ) %>%
mutate(H_I_p = percent(H_I /Total) ) %>%
select(c(1,2,8,3,9,4,10,5,11,6,12,7,13))
outcome_list_artcount## # A tibble: 26 × 13
## label Total Total_p Chem Chem_p Cell Cell_p Animal Anima…¹ H_O H_O_p
## <chr> <int> <formt> <dbl> <form> <dbl> <form> <dbl> <formt> <dbl> <form>
## 1 Cytokines 25 25.51% 0 0.00% 10 40.00% 9 36.00% 0 0.00%
## 2 Cancer P… 21 21.43% 0 0.00% 3 14.29% 11 52.38% 7 33.33%
## 3 Safety &… 19 19.39% 0 0.00% 1 5.26% 3 15.79% 1 5.26%
## 4 Treatmen… 19 19.39% 0 0.00% 0 0.00% 1 5.26% 9 47.37%
## 5 Survival… 19 19.39% 0 0.00% 2 10.53% 7 36.84% 8 42.11%
## 6 Liver Fu… 18 18.37% 0 0.00% 1 5.56% 11 61.11% 2 11.11%
## 7 Natural … 17 17.35% 0 0.00% 3 17.65% 5 29.41% 0 0.00%
## 8 Quality … 15 15.31% 0 0.00% 0 0.00% 0 0.00% 8 53.33%
## 9 Inflamma… 14 14.29% 0 0.00% 1 7.14% 10 71.43% 1 7.14%
## 10 Macropha… 13 13.27% 0 0.00% 10 76.92% 3 23.08% 0 0.00%
## # … with 16 more rows, 2 more variables: H_I <dbl>, H_I_p <formttbl>, and
## # abbreviated variable name ¹Animal_p# Condense the table
outcome_list_artcount <- outcome_list_artcount %>% mutate(precl = Chem+Cell+Animal,
precl_p = Chem_p+Cell_p+Animal_p) %>%
select(c(1,2,3, 14,15, 10,11,12,13))
outcome_list_artcount## # A tibble: 26 × 9
## label Total Total_p precl precl_p H_O H_O_p H_I H_I_p
## <chr> <int> <formt> <dbl> <formt> <dbl> <form> <dbl> <form>
## 1 Cytokines 25 25.51% 19 76.00% 0 0.00% 6 24.00%
## 2 Cancer Proliferation 21 21.43% 14 66.67% 7 33.33% 0 0.00%
## 3 Safety & Adverse Events 19 19.39% 4 21.05% 1 5.26% 14 73.68%
## 4 Treatment Response 19 19.39% 1 5.26% 9 47.37% 9 47.37%
## 5 Survival Rate 19 19.39% 9 47.37% 8 42.11% 2 10.53%
## 6 Liver Function Markers 18 18.37% 12 66.67% 2 11.11% 4 22.22%
## 7 Natural Killer Cells 17 17.35% 8 47.06% 0 0.00% 9 52.94%
## 8 Quality of Life Assess… 15 15.31% 0 0.00% 8 53.33% 7 46.67%
## 9 Inflammatory Markers 14 14.29% 11 78.57% 1 7.14% 2 14.29%
## 10 Macrophages 13 13.27% 13 100.00% 0 0.00% 0 0.00%
## # … with 16 more rows# Combined Beneficial effects and Positive outcomes
lonodes <- lonodes %>% group_by(label, group) %>% summarise(value = sum(value)) %>% arrange(desc(value)) %>%
mutate(group = factor(group, levels=c("Condition", "Effect", "Outcome"))) %>%
mutate(level = as.numeric(group)) %>%
rowid_to_column("id") %>%
mutate(id = id + nrow(nodes)) %>%
mutate(font.size = 20+(value)/2)## `summarise()` has grouped output by 'label'. You can override using the
## `.groups` argument.lonodes## # A tibble: 26 × 6
## # Groups: label [26]
## id label group value level font.size
## <int> <chr> <fct> <int> <dbl> <dbl>
## 1 47 Cytokines Outcome 25 3 32.5
## 2 48 Cancer Proliferation Outcome 21 3 30.5
## 3 49 Safety & Adverse Events Outcome 19 3 29.5
## 4 50 Survival Rate Outcome 19 3 29.5
## 5 51 Treatment Response Outcome 19 3 29.5
## 6 52 Liver Function Markers Outcome 18 3 29
## 7 53 Natural Killer Cells Outcome 17 3 28.5
## 8 54 Quality of Life Assessment Outcome 15 3 27.5
## 9 55 Inflammatory Markers Outcome 14 3 27
## 10 56 Macrophages Outcome 13 3 26.5
## # … with 16 more rowsall_nodes <- rbind(nodes, lonodes)
all_nodes## # A tibble: 72 × 6
## id label value group level font.size
## <int> <chr> <int> <fct> <dbl> <dbl>
## 1 1 Cancer 45 Condition 1 42.5
## 2 2 Healthy / Nonspecific 31 Condition 1 35.5
## 3 3 Hepatitis / Liver Disease 9 Condition 1 24.5
## 4 4 Geriatric 6 Condition 1 23
## 5 5 HIV 4 Condition 1 22
## 6 6 Allergy 4 Condition 1 22
## 7 7 Chronic fatigue syndrome 3 Condition 1 21.5
## 8 8 Gastroenteritis 3 Condition 1 21.5
## 9 9 Cold / Flu 2 Condition 1 21
## 10 10 Diabetes mellitus 2 Condition 1 21
## # … with 62 more rows# Edges are defined as the effect->outcome within the same article with Study Design as the category
edges2 <- edges2 %>% left_join(all_nodes, by = c("fromA"= "label")) %>% rename(from=id)
edges2 <- edges2 %>% left_join(all_nodes, by = c("toA"= "label")) %>% rename(to=id)
edges2 <- edges2 %>% select(from, to, cat, cond)
edges2## from to cat cond
## 1 18 56 8 Healthy / Nonspecific
## 2 18 56 8 Healthy / Nonspecific
## 3 18 56 8 Healthy / Nonspecific
## 4 18 56 7 Healthy / Nonspecific
## 5 18 56 7 Healthy / Nonspecific
## 6 18 56 8 Healthy / Nonspecific
## 7 18 56 8 Healthy / Nonspecific
## 8 18 56 8 Healthy / Nonspecific
## 9 18 56 8 Healthy / Nonspecific
## 10 18 56 8 Healthy / Nonspecific
## 11 18 56 8 Healthy / Nonspecific
## 12 18 56 7 Cancer
## 13 18 56 8 Healthy / Nonspecific
## 14 18 70 3 Cancer
## 15 18 64 8 Healthy / Nonspecific
## 16 18 64 1 Cancer
## 17 18 64 8 Healthy / Nonspecific
## 18 18 64 8 Healthy / Nonspecific
## 19 18 64 8 Healthy / Nonspecific
## 20 18 59 7 Healthy / Nonspecific
## 21 18 59 8 Healthy / Nonspecific
## 22 18 59 8 Healthy / Nonspecific
## 23 18 59 8 Healthy / Nonspecific
## 24 18 59 3 Cancer
## 25 18 59 3 Cancer
## 26 18 59 7 Healthy / Nonspecific
## 27 18 59 8 Healthy / Nonspecific
## 28 18 59 8 Healthy / Nonspecific
## 29 18 59 3 Cancer
## 30 18 53 1 Healthy / Nonspecific
## 31 18 53 1 Cancer
## 32 18 53 1 Geriatric
## 33 18 53 1 Geriatric
## 34 18 53 7 Geriatric
## 35 18 53 8 Geriatric
## 36 18 53 3 Cancer
## 37 18 53 8 Healthy / Nonspecific
## 38 18 53 3 Healthy / Nonspecific
## 39 18 53 3 Cancer
## 40 18 53 3 Chemical exposure
## 41 18 53 7 Healthy / Nonspecific
## 42 18 53 1 Irritable bowel syndrome
## 43 18 53 8 Healthy / Nonspecific
## 44 18 53 7 Cancer
## 45 18 60 1 Irritable bowel syndrome
## 46 18 60 7 Healthy / Nonspecific
## 47 18 60 7 Healthy / Nonspecific
## 48 18 60 1 HIV
## 49 18 60 3 Cancer
## 50 18 47 1 Healthy / Nonspecific
## 51 18 47 8 Healthy / Nonspecific
## 52 18 47 1 Healthy / Nonspecific
## 53 18 47 1 Cancer
## 54 18 47 8 Healthy / Nonspecific
## 55 18 47 8 Healthy / Nonspecific
## 56 18 47 8 Healthy / Nonspecific
## 57 18 47 8 Healthy / Nonspecific
## 58 18 47 3 Healthy / Nonspecific
## 59 18 47 7 Healthy / Nonspecific
## 60 18 47 8 Healthy / Nonspecific
## 61 18 47 7 Healthy / Nonspecific
## 62 18 47 8 Healthy / Nonspecific
## 63 18 66 8 Healthy / Nonspecific
## 64 18 66 8 Healthy / Nonspecific
## 65 18 66 8 Healthy / Nonspecific
## 66 18 66 8 Healthy / Nonspecific
## 67 18 61 5 Cancer
## 68 18 58 1 Geriatric
## 69 18 58 8 Healthy / Nonspecific
## 70 18 55 3 Cancer
## 71 18 55 1 Irritable bowel syndrome
## 72 18 50 3 Cancer
## 73 18 50 5 Cancer
## 74 18 51 1 Irritable bowel syndrome
## 75 18 54 5 Cancer
## 76 18 52 1 Geriatric
## 77 18 52 1 HIV
## 78 18 52 5 Cancer
## 79 18 49 1 Healthy / Nonspecific
## 80 18 49 1 Healthy / Nonspecific
## 81 18 49 1 Geriatric
## 82 18 49 3 Healthy / Nonspecific
## 83 18 49 1 Cancer
## 84 18 49 1 Irritable bowel syndrome
## 85 18 49 1 HIV
## 86 21 53 7 Cancer
## 87 21 53 7 Cancer
## 88 21 47 7 Cancer
## 89 21 47 8 Cancer
## 90 21 48 7 Cancer
## 91 21 48 7 Cancer
## 92 21 48 7 Cancer
## 93 21 48 8 Cancer
## 94 21 48 8 Cancer
## 95 21 48 7 Cancer
## 96 21 48 7 Cancer
## 97 21 48 6 Cancer
## 98 21 48 7 Cancer
## 99 21 57 8 Cancer
## 100 21 57 7 Cancer
## 101 21 58 8 Cancer
## 102 21 58 7 Cancer
## 103 21 63 7 Cancer
## 104 21 50 7 Cancer
## 105 21 50 7 Cancer
## 106 21 50 7 Cancer
## 107 21 50 7 Cancer
## 108 21 50 6 Cancer
## 109 21 49 7 Cancer
## 110 25 67 7 Allergy
## 111 25 67 7 Allergy
## 112 25 67 8 Healthy / Nonspecific
## 113 25 67 8 Healthy / Nonspecific
## 114 25 71 7 Allergy
## 115 25 47 7 Allergy
## 116 25 47 8 Healthy / Nonspecific
## 117 25 69 7 Allergy
## 118 25 69 7 Allergy
## 119 25 68 7 Allergy
## 120 25 68 7 Allergy
## 121 25 55 8 Healthy / Nonspecific
## 122 25 55 7 Allergy
## 123 46 49 8 Healthy / Nonspecific
## 124 24 47 7 Endotoxemia
## 125 24 48 7 Cancer
## 126 24 57 7 Cancer
## 127 24 57 7 Gastroenteritis
## 128 24 58 7 Alzheimer's disease
## 129 24 58 7 Cancer
## 130 24 55 7 Alzheimer's disease
## 131 24 55 7 Gastroenteritis
## 132 24 72 7 Healthy / Nonspecific
## 133 24 63 7 Alzheimer's disease
## 134 24 63 7 Healthy / Nonspecific
## 135 24 63 7 Endotoxemia
## 136 24 63 7 Cancer
## 137 24 63 9 Oxidative stress
## 138 24 63 7 Gastroenteritis
## 139 24 52 7 Endotoxemia
## 140 27 60 7 Cancer
## 141 27 48 7 Cancer
## 142 27 48 7 Cancer
## 143 27 48 7 Cancer
## 144 27 57 7 Cancer
## 145 27 57 7 Cancer
## 146 27 58 7 Cancer
## 147 27 55 7 Cancer
## 148 27 55 7 Cancer
## 149 27 65 7 Cancer
## 150 27 52 7 Cancer
## 151 27 52 7 Cancer
## 152 19 48 7 Cancer
## 153 19 48 7 Cancer
## 154 19 48 8 Cancer
## 155 19 48 5 Cancer
## 156 19 48 6 Cancer
## 157 19 48 6 Cancer
## 158 19 48 6 Cancer
## 159 19 48 6 Cancer
## 160 19 48 6 Cancer
## 161 19 61 1 Cancer
## 162 19 61 5 Cancer
## 163 19 61 6 Cancer
## 164 19 61 6 Cancer
## 165 19 61 6 Cancer
## 166 19 61 5 Cancer
## 167 19 61 5 Cancer
## 168 19 57 7 Cancer
## 169 19 57 7 Cancer
## 170 19 57 8 Cancer
## 171 19 57 8 Cancer
## 172 19 57 8 Cancer
## 173 19 57 8 Cancer
## 174 19 58 7 Cancer
## 175 19 58 7 Cancer
## 176 19 58 8 Cancer
## 177 19 50 1 Cancer
## 178 19 50 8 Cancer
## 179 19 50 8 Cancer
## 180 19 50 5 Cancer
## 181 19 50 6 Cancer
## 182 19 50 6 Cancer
## 183 19 50 6 Cancer
## 184 19 50 6 Cancer
## 185 19 50 5 Cancer
## 186 19 50 5 Cancer
## 187 19 65 1 Cancer
## 188 19 51 1 Cancer
## 189 19 51 5 Cancer
## 190 19 51 6 Cancer
## 191 19 51 6 Cancer
## 192 19 51 6 Cancer
## 193 19 51 6 Cancer
## 194 19 51 6 Cancer
## 195 19 51 6 Cancer
## 196 19 51 1 Cancer
## 197 19 54 5 Cancer
## 198 19 54 6 Cancer
## 199 19 54 6 Cancer
## 200 19 54 6 Cancer
## 201 19 54 5 Cancer
## 202 19 54 5 Cancer
## 203 19 52 5 Cancer
## 204 19 52 5 Cancer
## 205 19 49 1 Cancer
## 206 20 60 1 Hepatitis / Liver Disease
## 207 20 47 7 Hepatitis / Liver Disease
## 208 20 47 7 Hepatitis / Liver Disease
## 209 20 47 1 Hepatitis / Liver Disease
## 210 20 47 7 Hepatitis / Liver Disease
## 211 20 61 5 Cancer
## 212 20 57 7 Cancer
## 213 20 58 7 Cancer
## 214 20 58 7 Hepatitis / Liver Disease
## 215 20 58 7 Hepatitis / Liver Disease
## 216 20 55 7 Cancer
## 217 20 55 7 Cancer
## 218 20 55 7 Hepatitis / Liver Disease
## 219 20 55 7 Hepatitis / Liver Disease
## 220 20 55 7 Hepatitis / Liver Disease
## 221 20 63 7 Hepatitis / Liver Disease
## 222 20 50 5 Cancer
## 223 20 51 1 Hepatitis / Liver Disease
## 224 20 54 5 Cancer
## 225 20 54 1 Hepatitis / Liver Disease
## 226 20 52 7 Cancer
## 227 20 52 7 Cancer
## 228 20 52 8 Healthy / Nonspecific
## 229 20 52 7 Hepatitis / Liver Disease
## 230 20 52 7 Hepatitis / Liver Disease
## 231 20 52 7 Hepatitis / Liver Disease
## 232 20 52 7 Hepatitis / Liver Disease
## 233 20 52 1 Hepatitis / Liver Disease
## 234 20 52 5 Cancer
## 235 20 52 1 Hepatitis / Liver Disease
## 236 20 52 7 Hepatitis / Liver Disease
## 237 20 52 7 Hepatitis / Liver Disease
## 238 20 52 7 Hepatitis / Liver Disease
## 239 20 49 1 Hepatitis / Liver Disease
## 240 20 49 1 Hepatitis / Liver Disease
## 241 31 49 1 HIV
## 242 31 49 1 Chronic fatigue syndrome
## 243 22 53 1 Cancer
## 244 22 47 2 Cancer
## 245 22 61 5 Cancer
## 246 22 50 5 Cancer
## 247 22 51 4 Cancer
## 248 22 51 1 Cancer
## 249 22 54 1 Geriatric
## 250 22 54 4 Cancer
## 251 22 54 2 Cancer
## 252 22 54 1 Cancer
## 253 22 54 1 Cancer
## 254 22 54 1 Cancer
## 255 22 54 1 Cancer
## 256 22 54 5 Cancer
## 257 22 62 4 Cancer
## 258 22 62 1 Cancer
## 259 22 49 4 Cancer
## 260 22 49 1 Cancer
## 261 30 65 1 Cold / Flu
## 262 30 65 1 Cold / Flu
## 263 30 51 1 Cold / Flu
## 264 30 51 1 Cold / Flu
## 265 40 62 7 Healthy / Nonspecific
## 266 40 49 7 Healthy / Nonspecific
## 267 42 58 7 Alzheimer's disease
## 268 42 55 7 Alzheimer's disease
## 269 42 63 7 Alzheimer's disease
## 270 28 70 8 Healthy / Nonspecific
## 271 28 59 7 Bacterial infection
## 272 28 60 7 Bacterial infection
## 273 28 47 8 Healthy / Nonspecific
## 274 28 47 7 Bacterial infection
## 275 28 50 7 Bacterial infection
## 276 28 51 7 Gastroenteritis
## 277 28 52 7 Bacterial infection
## 278 26 57 7 Gastroenteritis
## 279 26 55 7 Gastroenteritis
## 280 26 72 7 Healthy / Nonspecific
## 281 26 63 7 Healthy / Nonspecific
## 282 26 63 7 Gastroenteritis
## 283 26 62 1 Cancer
## 284 26 49 1 Cancer
## 285 23 67 8 Healthy / Nonspecific
## 286 23 60 7 Allergy
## 287 23 47 8 Healthy / Nonspecific
## 288 23 47 7 Endotoxemia
## 289 23 47 7 Hepatitis / Liver Disease
## 290 23 57 7 Gastroenteritis
## 291 23 58 7 Hepatitis / Liver Disease
## 292 23 55 8 Healthy / Nonspecific
## 293 23 55 5 Rheumatism
## 294 23 55 7 Allergy
## 295 23 55 7 Endotoxemia
## 296 23 55 7 Gastroenteritis
## 297 23 55 7 Hepatitis / Liver Disease
## 298 23 63 7 Gastroenteritis
## 299 23 50 7 Endotoxemia
## 300 23 51 5 Rheumatism
## 301 23 54 5 Rheumatism
## 302 23 52 7 Hepatitis / Liver Disease
## 303 23 52 7 Hepatitis / Liver Disease
## 304 37 55 5 Rheumatism
## 305 37 51 5 Rheumatism
## 306 37 54 5 Rheumatism
## 307 32 62 1 Gastroenteritis
## 308 32 62 7 Healthy / Nonspecific
## 309 32 49 1 Gastroenteritis
## 310 32 49 7 Healthy / Nonspecific
## 311 41 50 7 Healthy / Nonspecific
## 312 41 65 7 Healthy / Nonspecific
## 313 41 52 7 Healthy / Nonspecific
## 314 38 71 7 Allergy
## 315 38 68 7 Allergy
## 316 38 55 7 Allergy
## 317 29 51 3 Chronic fatigue syndrome
## 318 29 51 1 Chronic fatigue syndrome
## 319 36 66 1 HIV
## 320 34 61 3 Cancer
## 321 35 51 1 Hepatitis / Liver Disease
## 322 35 54 1 Hepatitis / Liver Disease
## 323 35 52 1 Hepatitis / Liver Disease
## 324 35 49 1 Hepatitis / Liver Diseasenodes2 <- all_nodes %>% filter(group != "Condition")
nodes2## # A tibble: 55 × 6
## id label value group level font.size
## <int> <chr> <int> <fct> <dbl> <dbl>
## 1 18 Immunomodulation 36 Effect 2 38
## 2 19 Synergistic anticancer effect 19 Effect 2 29.5
## 3 20 Hepatoprotection 13 Effect 2 26.5
## 4 21 Anticancer 10 Effect 2 25
## 5 22 Psychoneuroimmuno-modulation 8 Effect 2 24
## 6 23 Antiinflammation 7 Effect 2 23.5
## 7 24 Antioxidant 7 Effect 2 23.5
## 8 25 Antiallergy 5 Effect 2 22.5
## 9 26 Radioprotection 3 Effect 2 21.5
## 10 27 Chemoprevention 3 Effect 2 21.5
## # … with 45 more rows# Display the raw network diagram
visNetwork(nodes2, edges2) #collapse the edges by creating a value to each edge based on count of co-occurance
edges3 <- edges2 %>% filter(from != to) %>% group_by(from, to, cat) %>% summarise(value = n())## `summarise()` has grouped output by 'from', 'to'. You can override using the
## `.groups` argument.edges3 <- edges3%>%
left_join(cat_color_list, by = c("cat"= "id"))
edges3## # A tibble: 184 × 5
## # Groups: from, to [126]
## from to cat value color
## <int> <int> <dbl> <int> <chr>
## 1 18 47 1 3 #FF0000
## 2 18 47 3 1 #FF0000
## 3 18 47 7 2 #00EEFF
## 4 18 47 8 7 #00EEFF
## 5 18 49 1 6 #FF0000
## 6 18 49 3 1 #FF0000
## 7 18 50 3 1 #FF0000
## 8 18 50 5 1 #00FF00
## 9 18 51 1 1 #FF0000
## 10 18 52 1 2 #FF0000
## # … with 174 more rows# Plot the network
visNetwork(nodes2, edges3) %>%
visNodes(scaling = list(min = 5, max = 35), borderWidth=0) %>%
visEdges(arrows = "to", scaling = list(min = 1, max = 10)) %>%
visPhysics(stabilization= FALSE, solver = "forceAtlas2Based")# visIgraphLayout()all_edges <- rbind(edges1, edges3)
visNetwork(all_nodes, all_edges) %>%
visNodes(scaling = list(min = 5, max = 35), borderWidth=0) %>%
visEdges(arrows = "to", scaling = list(min = 1, max = 10)) %>%
visPhysics(stabilization= FALSE, solver = "forceAtlas2Based")visNetwork(all_nodes, all_edges) %>%
visEdges(arrows = "to", smooth = TRUE, color=all_edges$category) %>%
visHierarchicalLayout(direction = "LR", levelSeparation = 500, nodeSpacing = 500)%>%
visNodes(scaling = list(min = 10, max = 20), borderWidth=0, shape = "dot")# Filter all benefits that link to cancer
cancer_node <- all_nodes %>% filter(label == "Cancer")
cancer_benefit_edges <- all_edges %>% filter(from == cancer_node$id )
cancer_benefit_nodes <- all_nodes %>% filter(id %in% cancer_benefit_edges$to) %>% rbind(cancer_node)
# Filter all outcomes that link to benefits cancer
cancer_outcome_edges <- edges2 %>% filter(cond == "Cancer") %>% filter(from != to) %>% group_by(from, to, cat) %>%
summarise(value = n()) %>%
left_join(cat_color_list, by = c("cat"= "id"))## `summarise()` has grouped output by 'from', 'to'. You can override using the
## `.groups` argument.cancer_outcome_nodes <- all_nodes %>% filter(id %in% cancer_outcome_edges$to) %>% rbind(cancer_benefit_nodes) %>% distinct_all()
cancer_benefit_edges <- cancer_benefit_edges %>% mutate(to1=from, from1=to) %>% mutate(to=to1, from = from1) %>% select(-c(to1, from1))
cancer_outcome_edges <- cancer_outcome_edges %>% mutate(to1=from, from1=to) %>% mutate(to=to1, from = from1) %>% select(-c(to1, from1))
benefits_outcomes_edges <- rbind(cancer_benefit_edges, cancer_outcome_edges) # %>% select(-c(category))
cancer_outcome_nodes## # A tibble: 30 × 6
## id label value group level font.size
## <int> <chr> <int> <fct> <dbl> <dbl>
## 1 47 Cytokines 25 Outcome 3 32.5
## 2 48 Cancer Proliferation 21 Outcome 3 30.5
## 3 49 Safety & Adverse Events 19 Outcome 3 29.5
## 4 50 Survival Rate 19 Outcome 3 29.5
## 5 51 Treatment Response 19 Outcome 3 29.5
## 6 52 Liver Function Markers 18 Outcome 3 29
## 7 53 Natural Killer Cells 17 Outcome 3 28.5
## 8 54 Quality of Life Assessment 15 Outcome 3 27.5
## 9 55 Inflammatory Markers 14 Outcome 3 27
## 10 56 Macrophages 13 Outcome 3 26.5
## # … with 20 more rowsbenefits_outcomes_edges## # A tibble: 111 × 6
## # Groups: from, to [69]
## from to category value cat color
## <int> <int> <fct> <int> <dbl> <chr>
## 1 18 1 Randomised controlled trial 2 1 #FF0000
## 2 18 1 Before and after study 4 3 #FF0000
## 3 18 1 Case series 1 5 #00FF00
## 4 18 1 Animal 1 7 #00EEFF
## 5 19 1 Randomised controlled trial 2 1 #FF0000
## 6 19 1 Case series 3 5 #00FF00
## 7 19 1 Case report 7 6 #00FF00
## 8 19 1 Animal 2 7 #00EEFF
## 9 19 1 Cell 5 8 #00EEFF
## 10 20 1 Case series 1 5 #00FF00
## # … with 101 more rowsvisNetwork(cancer_outcome_nodes, benefits_outcomes_edges, main="Beneficial actions of RBAC against cancer and its positive outcomes", height=800, width="100%") %>%
visEdges(arrows = "to", smooth = TRUE, color=benefits_outcomes_edges$category, dashes = TRUE) %>%
visHierarchicalLayout(direction = "LR", levelSeparation = 500, nodeSpacing = 1500)%>%
visNodes(scaling = list(min = 10, max = 100), borderWidth=1, shape = "box")# Filter all benefits that link to healthy or Geriatric
healthy_node <- all_nodes %>% filter(label %in% c("Healthy / Nonspecific" , "Geriatric") )
healthy_benefit_edges <- all_edges %>% filter(from %in% healthy_node$id )
healthy_benefit_nodes <- all_nodes %>% filter(id %in% healthy_benefit_edges$to) %>% rbind(healthy_node)
# Filter all outcomes that link to benefits cancer
healthy_outcome_edges <- edges2 %>% filter(cond %in% c("Healthy / Nonspecific" , "Geriatric")) %>% filter(from != to) %>% group_by(from, to, cat) %>% summarise(value = n()) %>%
left_join(cat_color_list, by = c("cat"= "id"))## `summarise()` has grouped output by 'from', 'to'. You can override using the
## `.groups` argument.healthy_outcome_nodes <- all_nodes %>% filter(id %in% healthy_outcome_edges$to) %>% rbind(healthy_benefit_nodes) %>% distinct_all()
healthy_benefit_edges <- healthy_benefit_edges %>% mutate(to1=from, from1=to) %>% mutate(to=to1, from = from1) %>% select(-c(to1, from1))
healthy_outcome_edges <- healthy_outcome_edges %>% mutate(to1=from, from1=to) %>% mutate(to=to1, from = from1) %>% select(-c(to1, from1))
healthy_benefits_outcomes_edges <- rbind(healthy_benefit_edges, healthy_outcome_edges) %>% select(-c(category))
healthy_outcome_nodes## # A tibble: 34 × 6
## id label value group level font.size
## <int> <chr> <int> <fct> <dbl> <dbl>
## 1 47 Cytokines 25 Outcome 3 32.5
## 2 49 Safety & Adverse Events 19 Outcome 3 29.5
## 3 50 Survival Rate 19 Outcome 3 29.5
## 4 52 Liver Function Markers 18 Outcome 3 29
## 5 53 Natural Killer Cells 17 Outcome 3 28.5
## 6 54 Quality of Life Assessment 15 Outcome 3 27.5
## 7 55 Inflammatory Markers 14 Outcome 3 27
## 8 56 Macrophages 13 Outcome 3 26.5
## 9 58 Gene Expression 11 Outcome 3 25.5
## 10 59 T & B Cells 11 Outcome 3 25.5
## # … with 24 more rowshealthy_benefits_outcomes_edges## # A tibble: 62 × 5
## # Groups: from, to [46]
## from to value cat color
## <int> <int> <int> <dbl> <chr>
## 1 18 2 2 1 #FF0000
## 2 18 2 1 3 #FF0000
## 3 18 2 4 7 #00EEFF
## 4 18 2 14 8 #00EEFF
## 5 20 2 1 8 #00EEFF
## 6 23 2 1 8 #00EEFF
## 7 24 2 1 7 #00EEFF
## 8 24 2 1 9 #00EEFF
## 9 25 2 2 8 #00EEFF
## 10 26 2 1 7 #00EEFF
## # … with 52 more rowsvisNetwork(healthy_outcome_nodes, healthy_benefits_outcomes_edges, main="Beneficial actions of RBAC in healthy or aged adults and its positive outcomes", height=800, width="100%") %>%
visEdges(arrows = "to", smooth = TRUE, color=benefits_outcomes_edges$category, dashes = TRUE) %>%
visHierarchicalLayout(direction = "LR", levelSeparation = 500, nodeSpacing = 1500)%>%
visNodes(scaling = list(min = 10, max = 100), borderWidth=1, shape = "box")# Filter all benefits that link to Hepatitis / Liver Disease
liver_node <- all_nodes %>% filter(label %in% c("Hepatitis / Liver Disease") )
liver_benefit_edges <- all_edges %>% filter(from %in% liver_node$id )
liver_benefit_nodes <- all_nodes %>% filter(id %in% liver_benefit_edges$to) %>% rbind(liver_node)
# Filter all outcomes that link to benefits cancer
liver_outcome_edges <- edges2 %>% filter(cond %in% c("Hepatitis / Liver Disease")) %>% filter(from != to) %>% group_by(from, to, cat) %>% summarise(value = n()) %>%
left_join(cat_color_list, by = c("cat"= "id"))## `summarise()` has grouped output by 'from', 'to'. You can override using the
## `.groups` argument.liver_outcome_nodes <- all_nodes %>% filter(id %in% liver_outcome_edges$to) %>% rbind(liver_benefit_nodes) %>% distinct_all()
liver_benefit_edges <- liver_benefit_edges %>% mutate(to1=from, from1=to) %>% mutate(to=to1, from = from1) %>% select(-c(to1, from1))
liver_outcome_edges <- liver_outcome_edges %>% mutate(to1=from, from1=to) %>% mutate(to=to1, from = from1) %>% select(-c(to1, from1))
liver_benefits_outcomes_edges <- rbind(liver_benefit_edges, liver_outcome_edges) %>% select(-c(category))
liver_outcome_nodes## # A tibble: 13 × 6
## id label value group level font.size
## <int> <chr> <int> <fct> <dbl> <dbl>
## 1 47 Cytokines 25 Outcome 3 32.5
## 2 49 Safety & Adverse Events 19 Outcome 3 29.5
## 3 51 Treatment Response 19 Outcome 3 29.5
## 4 52 Liver Function Markers 18 Outcome 3 29
## 5 54 Quality of Life Assessment 15 Outcome 3 27.5
## 6 55 Inflammatory Markers 14 Outcome 3 27
## 7 58 Gene Expression 11 Outcome 3 25.5
## 8 60 Lymphocytes 9 Outcome 3 24.5
## 9 63 Oxidative Stress Markers 6 Outcome 3 23
## 10 20 Hepatoprotection 13 Effect 2 26.5
## 11 23 Antiinflammation 7 Effect 2 23.5
## 12 35 Antiviral 1 Effect 2 20.5
## 13 3 Hepatitis / Liver Disease 9 Condition 1 24.5liver_benefits_outcomes_edges## # A tibble: 23 × 5
## # Groups: from, to [20]
## from to value cat color
## <int> <int> <int> <dbl> <chr>
## 1 20 3 2 1 #FF0000
## 2 20 3 7 7 #00EEFF
## 3 23 3 2 7 #00EEFF
## 4 35 3 1 1 #FF0000
## 5 47 20 1 1 #FF0000
## 6 47 20 3 7 #00EEFF
## 7 49 20 2 1 #FF0000
## 8 51 20 1 1 #FF0000
## 9 52 20 2 1 #FF0000
## 10 52 20 7 7 #00EEFF
## # … with 13 more rowsvisNetwork(liver_outcome_nodes, liver_benefits_outcomes_edges, main="Beneficial actions of RBAC in liver diseases and its positive outcomes", height=800, width="100%") %>%
visEdges(arrows = "to", smooth = TRUE, color=benefits_outcomes_edges$category, dashes = TRUE) %>%
visHierarchicalLayout(direction = "LR", levelSeparation = 500, nodeSpacing = 1500)%>%
visNodes(scaling = list(min = 10, max = 100), borderWidth=1, shape = "box")