--- title: "Soc-Ace: POLECAT Data Analysis" author: Gagan Atreya date: today format: html: toc: true toc-location: left toc-depth: 4 theme: lumen fontsize: large code-fold: true code-tools: true code-summary: "Display code" code-overflow: wrap editor: markdown: wrap: 72 editor_options: chunk_output_type: console --- # **Section 1. POLECAT Events By Country: 2020 - 2023** ## **1.1 Breakdown of Events by Country** ```{r, error = F, warning = F, message = F} ## Gagan Atreya ## POLECAT Analysis rm(list = ls()) pacman::p_load(data.table, tidyverse, lubridate, vtable, stringi, stringr, ggcharts, plyr, gridExtra, RgoogleMaps, ggmap, mapproj) df_2020 <- as.data.table(read.delim("~/Desktop/soc_ace_2024/data/polecat/ngecEvents.DV.2020.txt")) df_2021 <- as.data.table(read.delim("~/Desktop/soc_ace_2024/data/polecat/ngecEvents.DV.2021.txt")) df_2022 <- as.data.table(read.delim("~/Desktop/soc_ace_2024/data/polecat/ngecEvents.DV.2022.txt")) df_2023 <- as.data.table(read.delim("~/Desktop/soc_ace_2024/data/polecat/ngecEvents.DV.2023.txt")) df <- rbindlist(list(df_2020, df_2021, df_2022, df_2023)) country_list <- c("ARM", "BLR", "GEO", "HUN", "KAZ", "KGZ", "MDA", "POL", "ROU", "RUS", "SVK", "UKR") df01 <- df[df$Country %in% country_list, ] df <- df01 rm(list = setdiff(ls(), c("df"))) table(df$Country) df_agg <- df[, .(value = .N), by = Country] ggplot(df_agg, aes(x = reorder(Country, value), y = value)) + geom_segment(aes(xend = Country, yend = 0), color = "grey") + # Line segment geom_point(size = 3, color = "blue") + # Lollipop coord_flip() + # Flip coordinates for better readability labs(title = "Country Events in POLECAT", x = "Country", y = "Value") + theme_minimal() ``` ## **1.2 Breakdown of Events by Contexts** - Crime- Corruption- Illegal Drugs- Trafficking```{r, error = F, warning = F, message = F} #### Contexts ##### context_list <- c("crime", "corruption", "illegal drugs", "trafficking") df[, crime := as.integer(str_detect(Contexts, "crime"))] # table(df$crime) df[, corruption := as.integer(str_detect(Contexts, "corruption"))] # table(df$corruption) df[, trafficking := as.integer(str_detect(Contexts, "trafficking"))] # table(df$trafficking) df[, drugs := as.integer(str_detect(Contexts, "illegal_drugs"))] table(df$drugs) df_drugs <- df[df$drugs==1,] ``` ```{r, error=F, warning=F, message=F} df_agg_drugs <- df_drugs[, .(value = .N), by = Country] ggplot(df_agg_drugs, aes(x = reorder(Country, value), y = value)) + geom_segment(aes(xend = Country, yend = 0), color = "grey") + # Line segment geom_point(size = 3, color = "blue") + # Lollipop coord_flip() + # Flip coordinates for better readability labs(title = "Events related to \"illegal drugs\"", x = "Country", y = "Value") + theme_minimal() ``` ```{r} : = as.integer (str_detect (Contexts, "economic" ))]# table(df$economic) : = as.integer (str_detect (Contexts, "legal" ))]table (df$ legal)<- df[df$ legal== 1 ,]<- df_legal[, .(value = .N), by = Country] ggplot (df_agg_legal, aes (x = reorder (Country, value), y = value)) + geom_segment (aes (xend = Country, yend = 0 ), color = "grey" ) + # Line segment geom_point (size = 3 , color = "blue" ) + # Lollipop coord_flip () + # Flip coordinates for better readability labs (title = "Events related to \" legal \" " ,x = "Country" ,y = "Value" ) + theme_minimal ()``` ## **1.3. Other potential variables** ```{r, error = F, warning = F, message = F} table(df$Event.Type) ``` ```{r, error = F, warning = F, message = F} table(df$Event.Mode) ## Visualise for "arrest" ``` ## **1.4. "Arrest" across countries and years** ### Arrest vs countries ```{r, error = F, warning = F, message = F} df[, arrest := as.integer(str_detect(Event.Mode, "arrest"))] df_arrest<- df[df$arrest==1,] df_agg_arrest <- df_arrest[, .(value = .N), by = Country] ggplot(df_agg_arrest, aes(x = reorder(Country, value), y = value)) + geom_segment(aes(xend = Country, yend = 0), color = "grey") + # Line segment geom_point(size = 3, color = "blue") + # Lollipop coord_flip() + # Flip coordinates for better readability labs(title = "Event Mode described as \"arrest\"", x = "Country", y = "Value") + theme_minimal() ``` ### Arrest vs years ```{r, error = F, warning = F, message = F} df$year <- year(df$Event.Date) df <- df[df$year > 2019, ] df[, arrest := as.integer(str_detect(Event.Mode, "arrest"))] df_arrest <- df[df$arrest==1,] table(df_arrest$year) df_agg_arrest <- df_arrest[, .(value = .N), by = year] # Create a lollipop chart ggplot(df_agg_arrest, aes(x = factor(year), y = value)) + geom_segment(aes(x = factor(year), xend = factor(year), y = 0, yend = value), color = "gray") + geom_point(size = 4, color = "blue") + labs(title = "Event mode: \"arrest\" across years", x = "Year", y = "Count") + coord_flip()+ theme_minimal() ``` ### Arrest vs country vs years ```{r, error = F, warning = F, message = F} df11 <- df_arrest[, .(year, Country)] df11_agg <- df11[, .N, by = .(Country, year)] # .N counts occurrences of each country per year # Create a lollipop chart facetted by year ggplot(df11_agg, aes(x = Country, y = N)) + geom_segment(aes(x = Country, xend = Country, y = 0, yend = N), color = "gray") + geom_point(size = 2, color = "blue") + labs(title = "Arrest across countries by year", x = "Country", y = "Count") + theme_minimal() + coord_flip()+ facet_wrap(~ year) ``` ### Arrest vs country vs date ```{r, error = F, warning = F, message = F} df12 <- df_arrest[, .(Event.Date, Country)] df12$Event.Date <- ymd(df12$Event.Date) df12[, Year := year(Event.Date)] df12a <- df12[df12$Country %in% c("ARM", "BLR", "GEO", "HUN", "KAZ", "KGZ")] df12b <- df12[df12$Country %in% c("MDA", "POL", "ROU", "RUS", "SVK", "UKR")] # Plot the data ggplot(df12a, aes(x = Event.Date)) + geom_bar(position = "stack", color = "black", fill = "blue") + # Default fill for better readability scale_x_date(date_labels = "%Y", date_breaks = "12 months") + # Show every 12 months on x-axis labs(title = "Country Occurrences for \"arrest\" Over Time", x = "Date", y = "Count") + facet_wrap(~Country, ncol = 2) + # Facet by country with 4 columns theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis l # Plot the data ggplot(df12b, aes(x = Event.Date)) + geom_bar(position = "stack", color = "black", fill = "blue") + # Default fill for better readability scale_x_date(date_labels = "%Y", date_breaks = "12 months") + # Show every 12 months on x-axis labs(title = "Country Occurrences for \"arrest\" Over Time", x = "Date", y = "Count") + facet_wrap(~Country, ncol = 2) + # Facet by country with 4 columns theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1)) # Rotate x-axis l ``` # **Section 2. Maps: Armenia** ```{r, error = F, message = F, warning = F} ggmap::register_stadiamaps(key = "ecb62ab3-c884-4be1-9e19-4a49f5f8bed0") ``` ## **2.1. Armenia: arrests** ```{r, error = F, message = F, warning = F} dfarm <- df[df$Country == "ARM", ] dfarm$Latitude <- as.numeric(dfarm$Latitude) dfarm$Longitude <- as.numeric(dfarm$Longitude) # summary(dfarm$Latitude) # summary(dfarm$Longitude) mapoutline <- get_map(location = c(left = 44.05, # lon min bottom = 39.00, # lat min right = 46.75, # lon max top = 40.75), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfarm <- dfarm[dfarm$Event.Mode == "arrest", ] dfarm$Event_count <- 1 df01 <- ddply(dfarm, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Armenia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **2.2. Armenia: illegal drugs** ```{r, error = F, message = F, warning = F} dfarm$drugs <- ifelse(str_detect(dfarm$Contexts, "illegal_drugs") == T, 1, 0) dfarm <- dfarm[dfarm$drugs == 1, ] dfarm$Event_count <- 1 df01 <- ddply(dfarm, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfarm$Longitude <- as.numeric(dfarm$Longitude) dfarm$Latitude <- as.numeric(dfarm$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Armenia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 3. Maps: Belarus** ## **3.1. Belarus: arrests** ```{r, error = F, message = F, warning = F} dfblr <- df[df$Country == "BLR", ] dfblr$Latitude <- as.numeric(dfblr$Latitude) dfblr$Longitude <- as.numeric(dfblr$Longitude) #summary(dfblr$Longitude) #summary(dfblr$Latitude) mapoutline <- get_map(location = c(left = 23.00, # lon min bottom = 51.00, # lat min right = 32.75, # lon max top = 56.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfblr <- dfblr[dfblr$Event.Mode == "arrest", ] dfblr$Event_count <- 1 df01 <- ddply(dfblr, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Belarus, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **3.2. Belarus: illegal drugs** ```{r, error = F, message = F, warning = F} dfblr$drugs <- ifelse(str_detect(dfblr$Contexts, "illegal_drugs") == T, 1, 0) dfblr <- dfblr[dfblr$drugs == 1, ] dfblr$Event_count <- 1 df01 <- ddply(dfblr, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfblr$Longitude <- as.numeric(dfblr$Longitude) dfblr$Latitude <- as.numeric(dfblr$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Belarus, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 4. Maps: Georgia** ## **4.1. Georgia: arrests** ```{r, error = F, message = F, warning = F} dfgeo <- df[df$Country == "GEO", ] dfgeo$Latitude <- as.numeric(dfgeo$Latitude) dfgeo$Longitude <- as.numeric(dfgeo$Longitude) # summary(dfgeo$Longitude) # summary(dfgeo$Latitude) mapoutline <- get_map(location = c(left = 40.00, # lon min bottom = 41.00, # lat min right = 46.50, # lon max top = 43.50), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfgeo <- dfgeo[dfgeo$Event.Mode == "arrest", ] dfgeo$Event_count <- 1 df01 <- ddply(dfgeo, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Georgia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **4.2. Georgia: illegal drugs** ```{r, error = F, message = F, warning = F} dfgeo$drugs <- ifelse(str_detect(dfgeo$Contexts, "illegal_drugs") == T, 1, 0) dfgeo <- dfgeo[dfgeo$drugs == 1, ] dfgeo$Event_count <- 1 df01 <- ddply(dfgeo, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfgeo$Longitude <- as.numeric(dfgeo$Longitude) dfgeo$Latitude <- as.numeric(dfgeo$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Georgia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 5. Maps: Hungary** ## **5.1. Hungary: arrests** ```{r, error = F, message = F, warning = F} dfhun <- df[df$Country == "HUN", ] dfhun$Latitude <- as.numeric(dfhun$Latitude) dfhun$Longitude <- as.numeric(dfhun$Longitude) # summary(dfhun$Longitude) # summary(dfhun$Latitude) mapoutline <- get_map(location = c(left = 15.50, # lon min bottom = 45.55, # lat min right = 22.50, # lon max top = 49.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfhun <- dfhun[dfhun$Event.Mode == "arrest", ] dfhun$Event_count <- 1 df01 <- ddply(dfhun, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Hungary, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **5.2. Hungary: illegal drugs** ```{r, error = F, message = F, warning = F} dfhun$drugs <- ifelse(str_detect(dfhun$Contexts, "illegal_drugs") == T, 1, 0) dfhun <- dfhun[dfhun$drugs == 1, ] dfhun$Event_count <- 1 df01 <- ddply(dfhun, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfhun$Longitude <- as.numeric(dfhun$Longitude) dfhun$Latitude <- as.numeric(dfhun$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Hungary, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 6. Maps: Kazakhstan** ## **6.1. Kazakhstan: arrests** ```{r, error = F, message = F, warning = F} dfkaz <- df[df$Country == "KAZ", ] dfkaz$Latitude <- as.numeric(dfkaz$Latitude) dfkaz$Longitude <- as.numeric(dfkaz$Longitude) # summary(dfkaz$Longitude) # summary(dfkaz$Latitude) mapoutline <- get_map(location = c(left = 47.00, # lon min bottom = 40.00, # lat min right = 85.350, # lon max top = 55.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfkaz <- dfkaz[dfkaz$Event.Mode == "arrest", ] dfkaz$Event_count <- 1 df01 <- ddply(dfkaz, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Kazakhstan, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **6.2. Kazakhstan: illegal drugs** ```{r, error = F, message = F, warning = F} dfkaz$drugs <- ifelse(str_detect(dfkaz$Contexts, "illegal_drugs") == T, 1, 0) dfkaz <- dfkaz[dfkaz$drugs == 1, ] dfkaz$Event_count <- 1 df01 <- ddply(dfkaz, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfkaz$Longitude <- as.numeric(dfkaz$Longitude) dfkaz$Latitude <- as.numeric(dfkaz$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Kazakhstan, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 7. Maps: Kyrgyzstan** ## **7.1. Kyrgyzstan: arrests** ```{r, error = F, message = F, warning = F} dfkgz <- df[df$Country == "KGZ", ] dfkgz$Latitude <- as.numeric(dfkgz$Latitude) dfkgz$Longitude <- as.numeric(dfkgz$Longitude) # summary(dfkgz$Longitude) # summary(dfkgz$Latitude) mapoutline <- get_map(location = c(left = 69.00, # lon min bottom = 39.00, # lat min right = 79.50, # lon max top = 44.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfkgz <- dfkgz[dfkgz$Event.Mode == "arrest", ] dfkgz$Event_count <- 1 df01 <- ddply(dfkgz, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Kyrgyzstan, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **7.2. Kyrgyzstan: illegal drugs** ```{r, error = F, message = F, warning = F} dfkgz$drugs <- ifelse(str_detect(dfkgz$Contexts, "illegal_drugs") == T, 1, 0) dfkgz <- dfkgz[dfkgz$drugs == 1, ] dfkgz$Event_count <- 1 df01 <- ddply(dfkgz, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfkgz$Longitude <- as.numeric(dfkgz$Longitude) dfkgz$Latitude <- as.numeric(dfkgz$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Kyrgyzstan, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 8. Maps: Moldova** ## **8.1. Moldova: arrests** ```{r, error = F, message = F, warning = F} dfmda <- df[df$Country == "MDA", ] dfmda$Latitude <- as.numeric(dfmda$Latitude) dfmda$Longitude <- as.numeric(dfmda$Longitude) # summary(dfmda$Longitude) # summary(dfmda$Latitude) mapoutline <- get_map(location = c(left = 26.00, # lon min bottom = 45.00, # lat min right = 31.00, # lon max top = 49.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfmda <- dfmda[dfmda$Event.Mode == "arrest", ] dfmda$Event_count <- 1 df01 <- ddply(dfmda, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Moldova, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **8.2. Moldova: illegal drugs** ```{r, error = F, message = F, warning = F} dfmda$drugs <- ifelse(str_detect(dfmda$Contexts, "illegal_drugs") == T, 1, 0) dfmda <- dfmda[dfmda$drugs == 1, ] dfmda$Event_count <- 1 df01 <- ddply(dfmda, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfmda$Longitude <- as.numeric(dfmda$Longitude) dfmda$Latitude <- as.numeric(dfmda$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Moldova, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 9. Maps: Poland** ## **9.1. Poland: arrests** ```{r, error = F, message = F, warning = F} dfpol <- df[df$Country == "POL", ] dfpol$Latitude <- as.numeric(dfpol$Latitude) dfpol$Longitude <- as.numeric(dfpol$Longitude) # summary(dfpol$Longitude) # summary(dfpol$Latitude) mapoutline <- get_map(location = c(left = 13.50, # lon min bottom = 48.50, # lat min right = 24.75, # lon max top = 55.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfpol <- dfpol[dfpol$Event.Mode == "arrest", ] dfpol$Event_count <- 1 df01 <- ddply(dfpol, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Poland, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **9.2. Poland: illegal drugs** ```{r, error = F, message = F, warning = F} dfpol$drugs <- ifelse(str_detect(dfpol$Contexts, "illegal_drugs") == T, 1, 0) dfpol <- dfpol[dfpol$drugs == 1, ] dfpol$Event_count <- 1 df01 <- ddply(dfpol, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfpol$Longitude <- as.numeric(dfpol$Longitude) dfpol$Latitude <- as.numeric(dfpol$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Poland, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 10. Maps: Romania** ## **10.1. Romania: arrests** ```{r, error = F, message = F, warning = F} dfrou <- df[df$Country == "ROU", ] dfrou$Latitude <- as.numeric(dfrou$Latitude) dfrou$Longitude <- as.numeric(dfrou$Longitude) # summary(dfrou$Longitude) # summary(dfrou$Latitude) mapoutline <- get_map(location = c(left = 20.00, # lon min bottom = 43.00, # lat min right = 31.00, # lon max top = 49.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfrou <- dfrou[dfrou$Event.Mode == "arrest", ] dfrou$Event_count <- 1 df01 <- ddply(dfrou, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Romania, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **10.2. Romania: illegal drugs** ```{r, error = F, message = F, warning = F} dfrou$drugs <- ifelse(str_detect(dfrou$Contexts, "illegal_drugs") == T, 1, 0) dfrou <- dfrou[dfrou$drugs == 1, ] dfrou$Event_count <- 1 df01 <- ddply(dfrou, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfrou$Longitude <- as.numeric(dfrou$Longitude) dfrou$Latitude <- as.numeric(dfrou$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Romania, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 11. Maps: Russia** ## **11.1. Russia: arrests** ```{r, error = F, message = F, warning = F} dfrus <- df[df$Country == "RUS", ] dfrus$Latitude <- as.numeric(dfrus$Latitude) dfrus$Longitude <- as.numeric(dfrus$Longitude) # summary(dfrus$Longitude) # summary(dfrus$Latitude) mapoutline <- get_map(location = c(left = 20.00, # lon min bottom = 42.00, # lat min right = 160.00, # lon max top = 78.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfrus <- dfrus[dfrus$Event.Mode == "arrest", ] dfrus$Event_count <- 1 df01 <- ddply(dfrus, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Russia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **11.2. Russia: illegal drugs** ```{r, error = F, message = F, warning = F} dfrus$drugs <- ifelse(str_detect(dfrus$Contexts, "illegal_drugs") == T, 1, 0) dfrus <- dfrus[dfrus$drugs == 1, ] dfrus$Event_count <- 1 df01 <- ddply(dfrus, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfrus$Longitude <- as.numeric(dfrus$Longitude) dfrus$Latitude <- as.numeric(dfrus$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Russia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 12. Maps: Slovakia** ## **12.1. Slovakia: arrests** ```{r, error = F, message = F, warning = F} dfsvk <- df[df$Country == "SVK", ] dfsvk$Latitude <- as.numeric(dfsvk$Latitude) dfsvk$Longitude <- as.numeric(dfsvk$Longitude) # summary(dfsvk$Longitude) # summary(dfsvk$Latitude) mapoutline <- get_map(location = c(left = 16.00, # lon min bottom = 47.00, # lat min right = 23.00, # lon max top = 51.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfsvk <- dfsvk[dfsvk$Event.Mode == "arrest", ] dfsvk$Event_count <- 1 df01 <- ddply(dfsvk, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Slovakia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **12.2. Slovakia: illegal drugs** ```{r, error = F, message = F, warning = F} dfsvk$drugs <- ifelse(str_detect(dfsvk$Contexts, "illegal_drugs") == T, 1, 0) dfsvk <- dfsvk[dfsvk$drugs == 1, ] dfsvk$Event_count <- 1 df01 <- ddply(dfsvk, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfsvk$Longitude <- as.numeric(dfsvk$Longitude) dfsvk$Latitude <- as.numeric(dfsvk$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Slovakia, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` # **Section 13. Maps: Ukraine** ## **13.1. Ukraine: arrests** ```{r, error = F, message = F, warning = F} dfukr <- df[df$Country == "UKR", ] dfukr$Latitude <- as.numeric(dfukr$Latitude) dfukr$Longitude <- as.numeric(dfukr$Longitude) # summary(dfukr$Longitude) # summary(dfukr$Latitude) mapoutline <- get_map(location = c(left = 21.00, # lon min bottom = 43.00, # lat min right = 41.00, # lon max top = 54.00), # lat max maptype = "outdoors", source = "stadia", color="bw") # ggmap(mapoutline) ``` ```{r, error = F, message = F, warning = F} dfukr <- dfukr[dfukr$Event.Mode == "arrest", ] dfukr$Event_count <- 1 df01 <- ddply(dfukr, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event mode tagged as arrests in Ukraine, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` ## **13.2. Ukraine: illegal drugs** ```{r, error = F, message = F, warning = F} dfukr$drugs <- ifelse(str_detect(dfukr$Contexts, "illegal_drugs") == T, 1, 0) dfukr <- dfukr[dfukr$drugs == 1, ] dfukr$Event_count <- 1 df01 <- ddply(dfukr, .(Latitude, Longitude), summarize, Event_count = sum(Event_count)) dfukr$Longitude <- as.numeric(dfukr$Longitude) dfukr$Latitude <- as.numeric(dfukr$Latitude) ## Fill up the map with our data: map01 <- print(ggmap(mapoutline) + geom_point(data = df01, aes(x = Longitude, y = Latitude, size = Event_count), alpha = 0.5, colour="black")+ ggtitle("Event contexts tagged as illegal_drugs in Ukraine, 2020 - 2023"))+ theme_bw()+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(), axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) ``` 
