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perdida_de_timpo.txt
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\pagebreak
### Red de inversión teniendo en cuenta R2a
```{r rinvestINPPA, echo=FALSE,fig.width=13, fig.height=11,warning=FALSE,strip.white=FALSE}
d2 <- d %>% dplyr::mutate(start = case_when(
R2b == "2" ~ R_Destiny_ID,
TRUE ~ R_Start_ID
),
end = case_when(
R2b == "2" ~ R_Start_ID ,
TRUE ~ R_Destiny_ID
))
# duplicar e invertir las relacions recibio/aporto
dopler <- d2 %>%
dplyr::filter(R2b == "3") %>%
dplyr::mutate(start = R_Destiny_ID,
end = R_Start_ID) %>%
dplyr::select(start, end, R2c, R2a, R2b)
# adicionar filas duplicadas al edgelist
d2 <- d2 %>%
bind_rows(., dopler) %>%
dplyr::select(R_Start_ID = start, R_Destiny_ID = end, R2c, R2a, R2b)
net <- graph_from_data_frame(d= d2,
vertices = groups %>% dplyr::select(ID, V1),
directed=T)%>%
igraph::delete_vertices(., igraph::degree(.) == 0) %>%
igraph::delete_edges(., edges = which(igraph::which_multiple(.)))
colrs <- c("olivedrab3", "tomato", "gold", "gray40")
V(net)$color <- colrs[as.numeric(as.factor(V(net)$V1))]
igraph::add_shape("triangle", clip=igraph::shapes("circle")$clip,
plot=mytriangle)
edge.col <- tibble(var = E(net)$R2a) %>%
dplyr::mutate(col = dplyr::recode(var, "0" = "gray48", "1" = "darkgreen", "2" = "seashell1", "3" = "seashell3" )) %>% pull(col)
igraph::add_shape("triangle", clip=igraph::shapes("circle")$clip,
plot=mytriangle)
par(bg = NA)
plot(net,
layout= layout_with_kk,
edge.arrow.size=0.8,
rescale= TRUE,
edge.color= edge.col,
edge.width = 2.2,
vertex.size = 6.5,
vertex.shape = vtx_shp$shape,
vertex.label.dist=0,
vertex.label= V(net)$name,
vertex.label.family = "Arial",
vertex.label.font = 2,
vertex.label.cex = 0.90,
vertex.label.dist = 150,
vertex.label.degree = -pi/2,
vertex.label.color="black",
asp = 0)
legend("topright" , c("Group A","Group A2", "Group B", "Group C", "SHAPE","Reporter", "Partner", "Both"), pch = c(21, 21, 21,21, 15,15, 17, 16),
col= c("white","black", "black", "black"), pt.bg= c(colrs, "white","black", "black", "black"), pt.cex=2, cex=.8, bty = "n", title="GROUPS")
legend("bottomright", c( "In PPA", "Out PPA", "Não sei dizer", "Outro"), lty = c(1,1, 1, 1), col = c("darkgreen", "gray48", "seashell3", "seashell3"), lwd = c(3,3,3,3), pt.cex=2, cex=0.8, bty="n", title = "R2a")
# frecuencias por cada nodo de los tipos de edge que tiene (R2a)
summ <- tibble(var = E(net)$R2a) %>%
dplyr::mutate( start = igraph::ends(net, es=E(net), names=T)[,1]) %>%
left_join(., groups, by = c("start" = "ID"))
summ %>%
group_by( V1, var) %>%
tally() %>%
dplyr::mutate(freq = round(n/sum(n),2)*100, freq = paste0(freq, "%") , var = case_when(
var == "0" ~ "Out PPA",
var == "1" ~ "In PPA",
var == "3" ~ "Não sei dizer",
TRUE ~ "Outro"
)) %>%
dplyr::select(Group = V1, R2a = var, Count = n, Frequency = freq) %>%
ungroup() %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_R2a_summa", ".csv")) %>%
flextable(.)
summ %>%
left_join(., vtx_shp, by = c("start" = "nodos")) %>%
dplyr::mutate(var = case_when(
var == "0" ~ "Out_PPA",
var == "1" ~ "In_PPA",
var == "2" ~ "Nao_sei_dizer",
TRUE ~ "Outro"
)) %>%
group_by(start, var) %>%
tally() %>%
pivot_wider(., names_from = var, values_from = n) %>%
ungroup() %>%
mutate_all(., .funs = function(i){
x <- ifelse(is.na(i), 0, i)
return(x)
}) %>%
left_join(., groups, by = c("start" = "ID")) %>%
dplyr::mutate(Nao_sei_dizer = if("Nao_sei_dizer" %in% names(.)){Nao_sei_dizer}else{0},
total = (In_PPA + Out_PPA + Nao_sei_dizer),
In_PPA_frq = round(In_PPA/total,2)*100,
Out_PPA_frq = round(Out_PPA/total,2)*100,
Nao_sei_dizer_frq = round(Nao_sei_dizer/total,2)*100) %>%
dplyr::select(ID = start, Group=V1, In_PPA:Nao_sei_dizer, In_PPA_frq:Nao_sei_dizer_frq) %>%
dplyr::left_join(., groups %>% dplyr::select(ID, Nome) , by = ("ID" = "ID")) %>%
dplyr::left_join(., vtx_shp, by = c("ID" = "nodos")) %>%
dplyr::select(ID, Type = shp, -shape, everything()) %>%
dplyr::select(-shape) %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_R2a_summ_by_nod", ".csv")) %>%
flextable(.) %>% set_caption(., caption = "R2a summary by Node")
#Porcentaje in_ppa y out_ppa por cada grupo
red <- "invest"
summ_deg <- summ %>%
dplyr::mutate(var = case_when(
var == "0" ~ "Out PPA",
var == "1" ~ "In PPA",
var == "2" ~ "Não sei dizer",
TRUE ~ "Outro"
)) %>%
pivot_wider(id_cols = Nome, names_from = var, values_from = var, values_fn = list(var = length)) %>%
mutate_if(is.numeric, function(i){ifelse(is.na(i), 0, i)}) %>%
dplyr::left_join(., groups %>% select(Nome, V1, ID), by = c("Nome" = "Nome")) %>%
dplyr::select(ID, group = V1, Nome, in_deg = `In PPA`, out_deg = `Out PPA`, not_avaliable = `Não sei dizer`)
### conteos por cada empresa
summ_deg %>%
dplyr::left_join(., entrevistados, by = c("ID" = "ID")) %>%
dplyr::left_join(., type_org %>% select(ID, V39a_lab, V39b_lab), by = c("ID" = "ID")) %>%
dplyr::mutate(sum_all = in_deg +out_deg+not_avaliable,
percentage_in_ppa = paste0(round((in_deg/sum(sum_all))*100,1), "%"),
percentage_out_ppa = paste0(round((out_deg/sum(sum_all))*100,1),"%"),
percentage_not_av = paste0(round((not_avaliable/sum(sum_all))*100,1),"%")
) %>%
dplyr::select(group, Nome, "count_in_ppa" = in_deg, "count_out_ppa" = out_deg, "count_not_avaliable" = not_avaliable, percentage_in_ppa:percentage_not_av, status:V39b_lab) %>%
write_csv(., path = paste0(tbl_pth, red,"_inPPA_percent_all", ".csv"))
### porcentages por grupo
summ_deg %>%
dplyr::group_by(group) %>%
dplyr::summarise(sum_in_deg = sum(in_deg),
sum_out_deg = sum(out_deg),
sum_not_av = sum(not_avaliable),
sum_all = sum_in_deg+sum_out_deg+sum_not_av) %>%
ungroup() %>%
dplyr::mutate(percentage_in_ppa = paste0(round((sum_in_deg/sum(sum_all))*100,1), "%"),
percentage_out_ppa = paste0(round((sum_out_deg/sum(sum_all))*100,1),"%"),
percentage_not_av = paste0(round((sum_not_av/sum(sum_all))*100,1),"%")) %>%
dplyr::select(group,
Count_links_in_ppa = sum_in_deg,
Count_links_out_ppa = sum_out_deg,
count_links_not_avaliable = sum_not_av,
percentage_in_ppa,
percentage_out_ppa,
percentage_not_av) %>%
write_csv(., path = paste0(tbl_pth, red,"_inPPA_percent", ".csv"))
# por tipo de empresa v39a
summ_deg %>%
dplyr::left_join(., type_org %>% select(ID, V39a_lab), by = c("ID" = "ID")) %>%
dplyr::group_by(group, V39a_lab) %>%
dplyr::summarise(sum_in_deg = sum(in_deg),
sum_out_deg = sum(out_deg),
sum_not_av = sum(not_avaliable),
sum_all = sum_in_deg+sum_out_deg+sum_not_av) %>%
ungroup() %>%
dplyr::mutate(percentage_in_ppa = paste0(round((sum_in_deg/sum(sum_all))*100,1), "%"),
percentage_out_ppa = paste0(round((sum_out_deg/sum(sum_all))*100,1),"%"),
percentage_not_av = paste0(round((sum_not_av/sum(sum_all))*100,1),"%")) %>%
dplyr::select(group,
V39a_lab,
Count_links_in_ppa = sum_in_deg,
Count_links_out_ppa = sum_out_deg,
count_links_not_avaliable = sum_not_av,
percentage_in_ppa,
percentage_out_ppa,
percentage_not_av) %>%
write_csv(., path = paste0(tbl_pth, red,"_inPPA_perc_by_V39a", ".csv"))
# por tipo de empresa v39b
summ_deg %>%
dplyr::left_join(., type_org %>% select(ID, V39b_lab), by = c("ID" = "ID")) %>%
dplyr::group_by(group, V39b_lab) %>%
dplyr::summarise(sum_in_deg = sum(in_deg),
sum_out_deg = sum(out_deg),
sum_not_av = sum(not_avaliable),
sum_all = sum_in_deg+sum_out_deg+sum_not_av) %>%
ungroup() %>%
dplyr::mutate(percentage_in_ppa = paste0(round((sum_in_deg/sum(sum_all))*100,1), "%"),
percentage_out_ppa = paste0(round((sum_out_deg/sum(sum_all))*100,1),"%"),
percentage_not_av = paste0(round((sum_not_av/sum(sum_all))*100,1),"%")) %>%
dplyr::select(group,
V39b_lab,
Count_links_in_ppa = sum_in_deg,
Count_links_out_ppa = sum_out_deg,
count_links_not_avaliable = sum_not_av,
percentage_in_ppa,
percentage_out_ppa,
percentage_not_av) %>%
write_csv(., path = paste0(tbl_pth, red, "_inPPA_perc_by_V39b", ".csv"))
# por tipo de entrevista
summ_deg %>%
dplyr::left_join(., entrevistados, by = c("ID" = "ID")) %>%
dplyr::group_by(group, status) %>%
dplyr::summarise(sum_in_deg = sum(in_deg),
sum_out_deg = sum(out_deg),
sum_not_av = sum(not_avaliable),
sum_all = sum_in_deg+sum_out_deg+sum_not_av) %>%
ungroup() %>%
dplyr::mutate(percentage_in_ppa = paste0(round((sum_in_deg/sum(sum_all))*100,1), "%"),
percentage_out_ppa = paste0(round((sum_out_deg/sum(sum_all))*100,1),"%"),
percentage_not_av = paste0(round((sum_not_av/sum(sum_all))*100,1),"%")) %>%
dplyr::select(group,
status,
Count_links_in_ppa = sum_in_deg,
Count_links_out_ppa = sum_out_deg,
count_links_not_avaliable = sum_not_av,
percentage_in_ppa,
percentage_out_ppa,
percentage_not_av) %>%
write_csv(., path = paste0(tbl_pth, red, "_inPPA_perc_by_status", ".csv"))
```
Colores de los nodos representan los diferentes grupos (A2, A, B y C), el diámetro de los links representan la intensidad de la interacción (1, 2, 3, 4, 5, 6) de la pregunta R2c, la forma del vertice o nodo indica si el actor de la PPA recibe/emite inversión o por el contrario ambas, el valor de 6 (Outro - especificar) fue recodificado de tal manera que su diámetro sea el mas pequeño, esto con la intención de resaltar los links con respuestas definidas y evitar confusiones. Adicionalmente en este groupsáfico se resalta según el color de los links si la interacción se da dentro del contexto de la PPA (pregunta R2a).
\pagebreak
### Red investimento con Todas las características (Overloaded)
```{r rinvestimento, echo=FALSE,fig.width=11, fig.height=11,warning=FALSE,strip.white=FALSE, message = FALSE, cache = TRUE}
d2 <- d %>% dplyr::mutate(start = case_when(
R2b == "2" ~ R_Destiny_ID,
TRUE ~ R_Start_ID
),
end = case_when(
R2b == "2" ~ R_Start_ID ,
TRUE ~ R_Destiny_ID
))
# duplicar e invertir las relacions recibio/aporto
dopler <- d2 %>%
dplyr::filter(R2b == "3") %>%
dplyr::mutate(start = R_Destiny_ID,
end = R_Start_ID) %>%
dplyr::select(start, end, R2c, R2a, R2b)
# adicionar filas duplicadas al edgelist
d2 <- d2 %>%
bind_rows(., dopler) %>%
dplyr::select(R_Start_ID = start, R_Destiny_ID = end, R2c, R2a, R2b)
net <- graph_from_data_frame(d= d2,
vertices = groups %>% dplyr::select(ID, V1),
directed=T)%>%
igraph::delete_vertices(., igraph::degree(.) == 0)
overloaded_net <- net
#save adjacencyu matrix
as_adjacency_matrix(net, type = "both", sparse = F) %>%
as.data.frame() %>%
write_csv(., path = paste0(tbl_pth, "adj_m_invst_over", ".csv"))
## tamaño de los edges
E(net)$width <- as.numeric(E(net)$R2c)
colrs <- c("olivedrab3", "tomato", "gold", "gray40")
V(net)$color <- colrs[as.numeric(as.factor(V(net)$V1))]
## color de los edges
edge.col <- tibble(var = E(net)$R2a) %>%
dplyr::mutate(col = dplyr::recode(var, "0" = "grey70", "1" = "darkgreen", "2" = "orange", "3" = "red" )) %>% pull(col)
#shape de los vertex
vtx_shp <- tibble(nodos = character(0), shp = character(0)) %>%
add_row(nodos = intersect(d2$R_Start_ID, d2$R_Destiny_ID), shp = "both") %>%
add_row(nodos = setdiff(d2$R_Start_ID, d2$R_Destiny_ID), shp = "reporter") %>%
add_row(nodos = setdiff(d2$R_Destiny_ID, d2$R_Start_ID), shp = "partner") %>%
right_join(., tibble(vtx = V(net)$name), by = c("nodos" = "vtx") ) %>%
dplyr::mutate( shape = dplyr::case_when(
shp == "both" ~ "circle",
shp == "reporter" ~ "square",
shp == "partner" ~ "triangle"))
set.seed(123)
igraph::add_shape("triangle", clip=igraph::shapes("circle")$clip,
plot=mytriangle)
par(mar = c(0.5, 0.5, 0.5, 0.5), bg = NA)
plot(net,
layout=layout_with_kk,
edge.arrow.size=0.85,
rescale=T,
edge.color= edge.col,#adjustcolor("gray50", alpha.f = .6),
vertex.size = 6.5,
vertex.shape = vtx_shp$shape,
vertex.label.dist=0,
vertex.label= V(net)$name,
vertex.label.family = "Arial",
vertex.label.font = 2,
vertex.label.cex = 0.7,
vertex.label.dist = 150,
vertex.label.degree = -pi/2,
vertex.label.color="black")
legend("topright" , c("Group A","Group A2", "Group B", "Group C", "SHAPE","Reporter", "Partner", "Both"), pch = c(21, 21, 21,21, 15,15, 17, 16),
col= c("white","black", "black", "black"), pt.bg= c(colrs, "white","black", "black", "black"), pt.cex=2, cex=.8, bty = "n", title="GROUPS")
legend("bottomright", c(as.character(1:6)), lwd = c(2:6,1) , bty = "n", title = "R2c")
legend("bottomleft", c("In PPA", "Out PPA", "Não sei dizer", "Outro"), lty = c(1,1, 1, 1), col = c("darkgreen", "grey70", "orange", "red"), lwd = c(3,3,3,3), pt.cex=2, cex=0.8, bty="n", title = "R2a")
#calculate intensity summaries metrics
mtrs <- tibble(start = ends(net, es = E(net))[,1],
end = ends(net, es = E(net))[,2],
R2c = E(net)$R2c) %>%
left_join(., groups, by = c("start" = "ID") ) %>%
left_join(., vtx_shp, by = c("start" = "nodos"))
# add partnes to data frame
mtrs_partner <- tibble(start = ends(net, es = E(net))[,1],
end = ends(net, es = E(net))[,2],
R2c = E(net)$R2c) %>%
left_join(., groups, by = c("end" = "ID") ) %>%
left_join(., vtx_shp, by = c("end" = "nodos") ) %>%
filter(shp == "partner")
mtrs %>%
bind_rows(mtrs_partner) %>%
group_by(V1, R2c) %>%
tally() %>%
dplyr::mutate(freq = n/sum(n), Frequency = paste0(round(freq*100,1), "%") ) %>%
dplyr::select(Group = V1, Intensity = R2c, Count = n, Frequency) %>%
ungroup() %>%
dplyr::mutate(Intensity = case_when(
Intensity == "1" ~ "Outro - especificar",
Intensity == "2" ~ "Muito baixo - até R$ 100 mil",
Intensity == "3" ~ "Baixo - entre R$ 101 mil e R$ 250 mil",
Intensity == "4" ~"Médio - entre R$ 251 mil e R$ 500 mil",
Intensity == "5" ~"Alto - entre R$ 501 mil e R$ 1 milhão",
Intensity == "6" ~"Muito alto - acima de R$ 1 milhão",
TRUE ~ "Nao identificado"
)) %>%
flextable(.)
mtrs %>%
bind_rows(mtrs_partner) %>%
group_by(V1, shp, R2c) %>%
tally() %>%
dplyr::mutate( freq = n/sum(n), Frequency = paste0(round(freq*100, 1), "%")) %>%
ungroup() %>%
dplyr::mutate(R2c = case_when(
R2c == "1" ~ "Outro - especificar",
R2c == "2" ~ "Muito baixo - até R$ 100 mil",
R2c == "3" ~ "Baixo - entre R$ 101 mil e R$ 250 mil",
R2c == "4" ~"Médio - entre R$ 251 mil e R$ 500 mil",
R2c == "5" ~"Alto - entre R$ 501 mil e R$ 1 milhão",
R2c == "6" ~"Muito alto - acima de R$ 1 milhão",
TRUE ~ "Nao identificado"
)) %>%
dplyr::filter(R2c != "Nao identificado") %>%
dplyr::select(Group = V1, type = shp, Intesity = R2c, Count = n, Frequency) %>%
flextable(.)
```
Colores de los nodos representan los diferentes grupos (A2, A, B y C), el diámetro de los links representan la intensidad de la interacción (1, 2, 3, 4, 5, 6) de la pregunta R2c, la forma del vertice o nodo indica si el actor de la PPA recibe/emite inversión o por el contrario ambas,el valor de 6 (Outro - especificar) fue recodificado de tal manera que su diámetro sea el mas pequeño, esto con la intención de resaltar los links con respuestas definidas y evitar confusiones.
\pagebreak
#### Degree análisis
```{r rinvestalldegSum, echo=FALSE,fig.width=11, fig.height=11,warning=FALSE,strip.white=FALSE, message = FALSE}
summ_deg <- tibble(ID = V(net)$name ,
type = vtx_shp$shp,
group = V(net)$V1,
in_deg = igraph::degree(net, mode = 'in'),
out_deg = igraph::degree(net, mode = 'out'),
all = igraph::degree(net, mode = 'total') ) %>%
dplyr::mutate(in_deg_frq = round(in_deg/all,3)*100,
out_deg_frq = round(out_deg/all,3)*100) %>%
dplyr::left_join(., groups %>% dplyr::select(ID, Nome) , by = ("ID" = "ID"))
summ_deg %>%
dplyr::filter(type == "both") %>%
dplyr::group_by(group) %>%
dplyr::summarise(sum_in_deg = sum(in_deg),
sum_out_deg = sum(out_deg),
sum_all = sum(all)) %>%
dplyr::mutate(in_deg_frq = round(sum_in_deg/sum_all,3)*100,
out_deg_frq = round(sum_out_deg/sum_all,3)*100) %>%
pivot_longer(cols = in_deg_frq:out_deg_frq) %>%
ggplot(aes(x = group, y = value, fill = name))+
geom_bar( stat = "identity")+
geom_text(aes(label = paste0(value, "%")),
position = position_stack(vjust = .5), size = 7)+
labs(x= "Group", y = "Frequency", title = "Degree Composition (Aporto/recebeu)")+
scale_fill_discrete(name = "", labels = c("In-degree", "Out-degree"))+
labs(caption = "ALLIANCE BIOVERSITY CIAT - IPAM- CALPP")+
theme(aspect.ratio = 0.6)+
theme_light()+
theme(legend.position="right", title = element_text(size=25),
axis.text = element_text(size=25),
legend.text = element_text(size=20),
legend.title = element_text(size=25))
summ_deg %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_degree_analysis_overloaded", ".csv")) %>%
flextable(.)
```
\pagebreak
### Metricas Generales Red de investimento (Direccionada/Sin loops)
```{r invsSummMetricsdir, echo=FALSE,warning=FALSE,strip.white=FALSE}
tibble(Metric = c("density", "centraBetw", "centraDeg", "modularidad", "meanDegree", "transitiv", "assorta", "EigenValue"),
Value = c(igraph::edge_density(net_directed, loops = FALSE),
igraph::centralization.betweenness(net_directed)$centralization,
igraph::centralization.degree (net_directed, mode = "all")$centralization,
igraph:: cluster_walktrap(net_directed) %>% igraph::modularity(),
mean(igraph::degree(net_directed, mode="all", normalized = F)),
igraph::transitivity(net_directed),
igraph::assortativity.degree(net_directed, directed = T),
eigen_centrality(net_directed, directed = T)$value
)) %>%
dplyr::mutate_if(is.numeric, round, digits = 2) %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_gnrl_metrics_dir", ".csv")) %>%
flextable(., cwidth = 2)
```
### Metricas por cada Nodo (Direccionada/Sin loops)
```{r invsNodemetricsdir, echo=FALSE,warning=FALSE,fig.width=11, fig.height=11,strip.white=FALSE}
metrics <- tibble(nodes_id = V(net_directed)$name) %>%
dplyr::left_join(., groups, by = c("nodes_id" = "ID")) %>%
dplyr::select(nodes_id, Nome,V1) %>%
dplyr::mutate(degree = igraph::degree(net_directed, mode="all", normalized = F),
betwe = igraph::betweenness(net_directed) %>% round(.,1),
closeness = igraph::closeness(net_directed, mode="all") %>% round(.,4),
ClusWalk = igraph:: cluster_walktrap(net_directed) %>% membership(),
transitivLocal = igraph::transitivity(net_directed, type = "local") %>% round(.,2),
Eigen_centr = igraph::eigen_centrality(net_directed, directed = T)$vector %>% round(.,2)
) %>%
dplyr::mutate( HClust = dplyr::select(., degree, betwe, closeness, Eigen_centr ) %>%
clustering(data =., k = 4, dist.method = "euclidean", scale.unit = T)) %>%
dplyr::left_join(., entrevistados, by = c("nodes_id"= "ID")) %>%
dplyr::left_join(., type_org %>% dplyr::select(ID, V39a_lab, V39b_lab), by = c("nodes_id"= "ID"))
metrics %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_all_metrics_dir", ".csv")) %>%
flextable(.)
metrics %>%
dplyr::group_by(HClust) %>%
dplyr::summarise_if(is.numeric, list(~mean(.),~sd(.))) %T>%
write_csv(., path = paste0(tbl_pth, "investement_Dir_cluster_summary.csv")) %>%
flextable(.)
```
\pagebreak
### Metricas generales Red de Investimento (No direccionada/Sin loops)
```{r invsSummMetricsNOdir, echo=FALSE,warning=FALSE,strip.white=FALSE}
tibble(Metric = c("density", "centraBetw", "centraDeg", "modularidad", "meanDegree", "transitiv", "assorta", "EigenValue"),
Value = c(igraph::edge_density(net_no_dir, loops = FALSE),
igraph::centralization.betweenness(net_no_dir)$centralization,
igraph::centralization.degree (net_no_dir, mode = "all")$centralization,
igraph:: cluster_walktrap(net_no_dir) %>% igraph::modularity(),
mean(igraph::degree(net_no_dir, mode="all", normalized = F)),
igraph::transitivity(net_no_dir),
igraph::assortativity.degree(net_no_dir, directed = F),
igraph::eigen_centrality(net_no_dir, directed = F)$value %>% round(.,2)
)) %>%
dplyr::mutate_if(is.numeric, round, digits = 2) %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_gnr_mtr_Ndir", ".csv")) %>%
flextable(., cwidth = 2)
```
#### Metricas por cada Nodo (No direccionada/Sin loops)
```{r invsNodemetricsNodir, echo=FALSE,warning=FALSE,fig.width=11, fig.height=11,strip.white=FALSE}
metrics <- tibble(nodes_id = V(net_no_dir)$name) %>%
dplyr::left_join(., groups, by = c("nodes_id" = "ID")) %>%
dplyr::select(nodes_id, Nome,V1) %>%
dplyr::mutate(degree = igraph::degree(net_no_dir, mode="all", normalized = F),
betwe = igraph::betweenness(net_no_dir) %>% round(.,1),
closeness = igraph::closeness(net_no_dir, mode="all") %>% round(.,4),
ClusWalk = igraph:: cluster_walktrap(net_no_dir) %>% membership(),
transitivLocal = igraph::transitivity(net_no_dir, type = "local") %>% round(.,2),
Eigen_centr = igraph::eigen_centrality(net_no_dir, directed = F)$vector %>% round(.,2)
) %>%
dplyr::mutate( HClust = dplyr::select(., degree, betwe, closeness, Eigen_centr ) %>% clustering(data =., k = 4, dist.method = "euclidean", scale.unit = T)) %>%
dplyr::left_join(., entrevistados, by = c("nodes_id"= "ID")) %>%
dplyr::left_join(., type_org %>% dplyr::select(ID, V39a_lab, V39b_lab), by = c("nodes_id"= "ID"))
metrics %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_all_metri_Ndir", ".csv")) %>%
flextable(.)
metrics %>%
dplyr::group_by(HClust) %>%
dplyr::summarise_if(is.numeric, list(~mean(.),~sd(.))) %T>%
write_csv(., path = paste0(tbl_pth, "investement_noDir_cluster_summary.csv")) %>%
flextable(.)
```
\pagebreak
### Metricas generales Red de Investimento (Direccionada/Con loops/Overloaded)
```{r invssummMetricsoverloaded, echo=FALSE,warning=FALSE,fig.width=11, fig.height=11,strip.white=FALSE}
tibble(Metric = c("density", "centraBetw", "centraDeg", "modularidad", "meanDegree", "transitiv", "assorta", "EigenValue"),
Value = c(igraph::edge_density(overloaded_net, loops = FALSE),
igraph::centralization.betweenness(overloaded_net)$centralization,
igraph::centralization.degree (overloaded_net, mode = "all")$centralization,
igraph:: cluster_walktrap(overloaded_net) %>% igraph::modularity(),
mean(igraph::degree(overloaded_net, mode="all", normalized = F)),
igraph::transitivity(overloaded_net),
igraph::assortativity.degree(overloaded_net, directed = T),
igraph::eigen_centrality(overloaded_net, directed = T)$value %>% round(.,2)
)) %>%
dplyr::mutate_if(is.numeric, round, digits = 2) %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_gnr_metrics_over", ".csv")) %>%
flextable(., cwidth = 2)
```
#### Metricas por cada Nodo (Direccionada/Con loops/Overloaded)
```{r invsNodemetricsoverloaded, echo=FALSE,warning=FALSE, message=F,fig.width=11, fig.height=11,strip.white=FALSE}
metrics <- tibble(nodes_id = V(overloaded_net)$name) %>%
dplyr::left_join(., groups, by = c("nodes_id" = "ID")) %>%
dplyr::select(nodes_id, V1) %>%
dplyr::mutate(degree = igraph::degree(overloaded_net, mode="all", normalized = F),
betwe = igraph::betweenness(overloaded_net) %>% round(.,1),
closeness = igraph::closeness(overloaded_net, mode="all") %>% round(.,4),
ClusWalk = igraph:: cluster_walktrap(overloaded_net) %>% membership(),
transitivLocal = igraph::transitivity(overloaded_net, type = "local") %>% round(.,2),
Eigen_centr = igraph::eigen_centrality(overloaded_net, directed = T)$vector %>% round(.,2)
) %>%
dplyr::mutate( HClust = dplyr::select(., degree, betwe, closeness, Eigen_centr ) %>% clustering(data =., k = 4, dist.method = "euclidean", scale.unit = T)) %>%
dplyr::left_join(., entrevistados, by = c("nodes_id"= "ID")) %>%
dplyr::left_join(., type_org %>% dplyr::select(ID, V39a_lab, V39b_lab), by = c("nodes_id"= "ID"))
metrics %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "invest_all_metrics_over", ".csv")) %>%
flextable(.)
metrics %>%
dplyr::group_by(HClust) %>%
dplyr::summarise_if(is.numeric, list(~mean(.),~sd(.))) %T>%
write_csv(., path = paste0(tbl_pth, "investement_overloaded_cluster_summary.csv")) %>%
flextable(.)
```
\pagebreak
## Red de inversión más Grupo C (Direccionada)
```{r redinvestimento_grC, echo=FALSE,warning=FALSE, message = FALSE, cache = F, fig.width=15, fig.height=11, dpi =200}
red <- "invest"
nt1 <- read_excel(xls.file, sheet = "R2_investimento")
d <- nt1 %>%
dplyr::select( R_Start_ID, R_Destiny_ID, R2c, R2a, R2b) %>%
dplyr::mutate(R2c = R2c+1, R2c = if_else(R2c == 7, 1, R2c)) %>%
dplyr::mutate_all(as.character)
d2 <- d %>% dplyr::mutate(start = case_when(
R2b == "2" ~ R_Destiny_ID,
TRUE ~ R_Start_ID
),
end = case_when(
R2b == "2" ~ R_Start_ID ,
TRUE ~ R_Destiny_ID
))
# duplicar e invertir las relacions recibio/aporto
dopler <- d2 %>%
dplyr::filter(R2b == "3") %>%
dplyr::mutate(start = R_Destiny_ID,
end = R_Start_ID) %>%
dplyr::select(start, end, R2c, R2a, R2b)
# adicionar filas duplicadas al edgelist
d2 <- d2 %>%
bind_rows(., dopler) %>%
dplyr::select(R_Start_ID = start, R_Destiny_ID = end, R2c, R2a, R2b)
nt1 <- d2 %>%
dplyr::select(R_Start_ID, R_Destiny_ID) %>%
dplyr::mutate_all(as.character)
nt2 <- read_excel(xls.file, sheet = "R5_outras_orgs") %>%
dplyr::select(R_Start_ID, R_Destiny_ID = R5b_Destiny_ID, R5c) %>%
drop_na() %>%
mutate_all(as.character) %>%
dplyr::filter(R5c == "2") %>%
dplyr::select( -R5c)
nt_all <- bind_rows(nt1, nt2)
net <- graph_from_data_frame(d= nt_all,
vertices = groups %>% dplyr::select(ID, V1),
directed= T)%>%
igraph::delete_vertices(., igraph::degree(.) == 0) %>%
igraph::delete_edges(., edges = which(igraph::which_multiple(.)))
#save adjacency matrix
as_adjacency_matrix(net, type = "both", sparse = F) %>%
as.data.frame() %>%
write_csv(., path = paste0(tbl_pth, "adj_m_invst_GrC", ".csv"))
colrs <- c("olivedrab3", "tomato", "gold", "gray40")
V(net)$color <- colrs[as.numeric(as.factor(V(net)$V1))]
#shape de los vertex
vtx_shp <- tibble(nodos = character(0), shp = character(0)) %>%
add_row(nodos = intersect(nt_all$R_Start_ID, nt_all$R_Destiny_ID), shp = "both") %>%
add_row(nodos = setdiff(nt_all$R_Start_ID, nt_all$R_Destiny_ID), shp = "reporter") %>%
add_row(nodos = setdiff(nt_all$R_Destiny_ID, nt_all$R_Start_ID), shp = "partner") %>%
right_join(., tibble(vtx = V(net)$name), by = c("nodos" = "vtx") ) %>%
dplyr::mutate( shape = dplyr::case_when(
shp == "both" ~ "circle",
shp == "reporter" ~ "square",
shp == "partner" ~ "triangle"))
set.seed(123)
igraph::add_shape("triangle", clip=igraph::shapes("circle")$clip,
plot=mytriangle)
par(bg = NA)
plot(net,
layout= layout_with_kk,
edge.arrow.size=0.8,
rescale=T,
edge.color= "gray70",
edge.width = 2.5,
vertex.size = 6.5,
vertex.label.dist=0,
vertex.shape = NULL,
vertex.label= V(net)$name,
vertex.label.family = "Arial",
vertex.label.font = 2,
vertex.label.cex = 0.9,
vertex.label.dist = 150,
vertex.label.degree = -pi/2,
vertex.label.color="black",
asp = 0)
legend("topright" , c("Group A","Group A2", "Group B", "Group C"), pch = c(21, 21, 21,21),
col= c("black","black", "black", "black"), pt.bg= colrs, pt.cex=2, cex=.8, bty = "n", title="GROUPS")
#contar los links para cada nodo según el grupo
as_tibble(ends(net, E(net), names = T)) %>%
rename(start = V1, end = V2) %>%
left_join(., groups, by = c("start" = "ID")) %>%
dplyr::select( ID_start = V1, everything()) %>%
dplyr::left_join(., groups, by = c("end"= "ID")) %>%
dplyr::select(start, end, ID_start,ID_end = V1, everything()) %>%
group_by(ID_start, ID_end) %>%
tally() %>%
ungroup() %>%
dplyr::mutate(Ratio = paste(round(n/sum(n)*100,1), "%")) %>%
dplyr::select(Group_start = ID_start, Group_end = ID_end, Links_count = n, Ratio) %T>%
write_csv(., path = paste0(tbl_pth, paste0(red, "_node_links_count_GrC", ".csv"))) %>%
flextable(.)
#contar total de nodos
tibble(ID = V(net)$name) %>%
left_join(., groups, by = c("ID" = "ID")) %>%
group_by(V1) %>%
tally() %>%
dplyr::select(Group = V1, Node_counts = n) %T>%
write_csv(., path = paste0(tbl_pth, paste0(red, "invest_node_count_GrC", ".csv"))) %>%
flextable(.)
tibble(Metric = c("density", "centraBetw", "centraDeg", "modularidad", "meanDegree", "transitiv", "assorta", "EigenValue"),
Value = c(igraph::edge_density(net, loops = FALSE),
igraph::centralization.betweenness(net)$centralization,
igraph::centralization.degree (net, mode = "all")$centralization,
igraph:: cluster_walktrap(net) %>% igraph::modularity(),
mean(igraph::degree(net, mode="all", normalized = F)),
igraph::transitivity(net),
igraph::assortativity.degree(net),
igraph::eigen_centrality(net, directed = T)$value %>% round(.,2)
)) %>%
dplyr::mutate_if(is.numeric, round, digits = 2) %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, paste0(red, "_gnr_metrics_Grc", ".csv"))) %>%
flextable(., cwidth = 2)
metrics <- tibble(nodes_id = V(net)$name) %>%
dplyr::left_join(., groups, by = c("nodes_id" = "ID")) %>%
dplyr::select(nodes_id, V1) %>%
dplyr::mutate(degree = igraph::degree(net, mode="all", normalized = F),
betwe = igraph::betweenness(net) %>% round(.,1),
closeness = igraph::closeness(net, mode="all") %>% round(.,4),
ClusWalk = igraph:: cluster_walktrap(net) %>% membership(),
transitivLocal = igraph::transitivity(net, type = "local") %>% round(.,2),
Eigen_centr = igraph::eigen_centrality(net, directed = T)$vector %>% round(.,2)
) %>%
dplyr::mutate( HClust = dplyr::select(., degree, betwe, closeness, Eigen_centr ) %>% clustering(data =., k = 4, dist.method = "euclidean", scale.unit = T)) %>%
dplyr::left_join(., entrevistados, by = c("nodes_id"= "ID")) %>%
dplyr::left_join(., type_org %>% dplyr::select(ID, V39a_lab, V39b_lab), by = c("nodes_id"= "ID"))
metrics %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, red, "_all_GrC_metrics_over", ".csv")) %>%
flextable(.)
metrics %>%
dplyr::group_by(HClust) %>%
dplyr::summarise_if(is.numeric, list(~mean(.),~sd(.))) %T>%
write_csv(., path = paste0(tbl_pth, red, "_GrC_cluster_summary.csv")) %>%
flextable(.)
```
\pagebreak
# Red de Cooperación técnica
```{r redcoopTec, echo=FALSE,warning=FALSE, message = FALSE, cache = TRUE}
nt1 <- read_excel(xls.file, sheet = "R3_coop_técnica") %>%
dplyr::filter(R_Start_ID != 25 & R_Destiny_ID != 25) %>%
dplyr::filter(R_Start_ID != 78 & R_Destiny_ID != 75)
d <- nt1 %>%
dplyr::select( R_Start_ID, R_Destiny_ID, R3a, R3d, R3c) %>%
dplyr::mutate(R3c = R3c+1, R3c = if_else(R3c == 7, 1, R3c)) %>%
dplyr::mutate_all(as.character)
net <- graph_from_data_frame(d= d,
vertices = groups %>% dplyr::select(ID, V1),
directed=T)%>%
igraph::delete.vertices(., igraph::degree(.) == 0) %>%
igraph::delete_edges(., edges = which(igraph::which_multiple(.)))
#contar egos
nt1 %>%
dplyr::mutate_all(as.character) %>%
dplyr::left_join(., groups, by = c("R_Start_ID" = "ID")) %>%
dplyr::mutate(dummy = paste0(R_Start_ID, R_Start_ID_ego)) %>%
dplyr::filter(!duplicated(dummy)) %>%
dplyr::group_by(V1, R_Start_ID_ego) %>%
tally() %>%
dplyr::select(Groups = V1, Ego = R_Start_ID_ego, Ego_count = n) %>%
ungroup() %>%
data.frame() %T>%
write_csv(., path = paste0(tbl_pth, "coopTec_ego_by_gr", ".csv")) %>%
flextable(.)
#contar los links para cada nodo según el grupo
as_tibble(ends(net, E(net))) %>%
rename(start = V1, end = V2) %>%
left_join(., groups, by = c("start" = "ID")) %>%
dplyr::select( ID_start = V1, everything()) %>%
dplyr::left_join(., groups, by = c("end"= "ID")) %>%
dplyr::select(start, end, ID_start,ID_end = V1, everything()) %>%
group_by(ID_start, ID_end) %>%
tally() %>%
dplyr::select(Group_start = ID_start, Group_end = ID_end, Links_count = n) %T>%
write_csv(., path = paste0(tbl_pth, "coopTec_node_links_count", ".csv")) %>%
flextable(.)
```
\pagebreak
### Grupo A2
```{r rcoopgrA2, echo=FALSE,fig.width=15, fig.height=11,warning=FALSE,strip.white=FALSE, cache = TRUE}
a2 <- groups %>% filter(V1 == "A2") %>% pull(ID)
d2 <- d %>%
dplyr::mutate(col = case_when( R_Start_ID %in% a2 ~ "orange", TRUE ~ "gray80"))
net <- graph_from_data_frame(d= d2,
vertices = groups %>% dplyr::select(ID, V1),
directed=F)%>%
igraph::delete_vertices(., igraph::degree(.) == 0) %>%
igraph::delete_edges(., edges = which(igraph::which_multiple(.)))
net_no_dir <- net
#save adjacency matrix
as_adjacency_matrix(net, type = "both", sparse = F) %>%
as.data.frame() %>%
write_csv(., path = paste0(tbl_pth, "adj_m_coopTEc_Ndir", ".csv"))
edge.col <- E(net)$col
v.col <- groups %>%
dplyr::mutate(v.col = case_when(
V1 == "A2" ~ "tomato",
TRUE ~ "gray50"
)) %>%
dplyr::filter(ID %in% V(net)$name) %>%
pull(v.col)
par(bg = NA)
plot(net,
layout= layout_with_kk,
edge.arrow.size=0.5,
rescale=T,
edge.color= edge.col,
edge.width = 2.5,
vertex.size = 6.5,
vertex.shape = NULL,
vertex.label.dist=0,
vertex.label= V(net)$name,
vertex.label.family = "Arial",
vertex.label.font = 2,
vertex.label.cex = 0.9,
vertex.label.dist = 150,
vertex.label.degree = -pi/2,
vertex.label.color="black",
vertex.color = v.col,
asp = 0)
legend("topright" , c("Group A","Group A2", "Group B"),
pch = c(21, 21, 21),
col= c("black","black", "black", "black"), pt.bg= c("olivedrab3", "tomato", "gold"),
pt.cex=2, cex=.8, bty = "n", title="GROUPS")
legend("bottomright", c( "A2 net", "Others Net"), lty = c(1,1), col = c("orange", "gray50"), lwd = c(3,3), pt.cex=2, cex=0.8, bty="n")
```
\pagebreak
### Grupo A
```{r rcoopgrA, echo=FALSE,fig.width=15, fig.height=11,warning=FALSE,strip.white=FALSE}
#
set.seed(123)
a2 <- groups %>% filter(V1 == "A") %>% pull(ID)
d2 <- d %>%
dplyr::mutate(col = case_when( R_Start_ID %in% a2 ~ "orange", TRUE ~ "gray80"))
net <- graph_from_data_frame(d= d2,
vertices = groups %>% dplyr::select(ID, V1),
directed=F)%>%
igraph::delete_vertices(., igraph::degree(.) == 0) %>%
igraph::delete_edges(., edges = which(igraph::which_multiple(.)))
edge.col <- E(net)$col
v.col <- groups %>%
dplyr::mutate(v.col = case_when(
V1 == "A" ~ "olivedrab3",
TRUE ~ "gray50"
)) %>%
dplyr::filter(ID %in% V(net)$name) %>%
pull(v.col)
par(bg = NA)
plot(net,
layout= layout_with_kk,
edge.arrow.size=0.5,
rescale=T,
edge.color= edge.col,
edge.width = 2.5,
vertex.size = 6.5,
vertex.shape = NULL,
vertex.label.dist=0,
vertex.label= V(net)$name,
vertex.label.family = "Arial",
vertex.label.font = 2,
vertex.label.cex = 0.9,
vertex.label.dist = 150,
vertex.label.degree = -pi/2,
vertex.label.color="black",
vertex.color = v.col,
asp = 0)
legend("topright" , c("Group A","Group A2", "Group B"),
pch = c(21, 21, 21),
col= c("black","black", "black", "black"), pt.bg= c("olivedrab3", "tomato", "gold"),
pt.cex=2, cex=.8, bty = "n", title="GROUPS")
legend("bottomright", c( "A net", "Others Net"), lty = c(1,1), col = c("orange", "gray50"), lwd = c(3,3), pt.cex=2, cex=0.8, bty="n")
```
\pagebreak
### Grupo B
```{r rcoopgrB, echo=FALSE,fig.width=15, fig.height=11,warning=FALSE,strip.white=FALSE}
set.seed(123)
a2 <- groups %>% filter(V1 == "B") %>% pull(ID)
d2 <- d %>%
dplyr::mutate(col = case_when( R_Start_ID %in% a2 ~ "orange", TRUE ~ "gray80"))
net <- graph_from_data_frame(d= d2,
vertices = groups %>% dplyr::select(ID, V1),
directed=F)%>%
igraph::delete_vertices(., igraph::degree(.) == 0) %>%
igraph::delete_edges(., edges = which(igraph::which_multiple(.)))
edge.col <- E(net)$col
v.col <- groups %>%
dplyr::mutate(v.col = case_when(
V1 == "B" ~ "gold",
TRUE ~ "gray50"
)) %>%