---
title: "GDELT Events: Russia"
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
---
# **Section 1. GDELT Events Recorded in Russia: 2020 - 2023**
## **1.01 Breakdown of events by keywords**
#### **Keywords: State actions**
- ARREST
- CONFISCATION
- PERSECUTION
- SEIZE
#### **Keywords: Non-state actions**
- BLACK_MARKET
- CRIME_CARTELS
- DRUG_TRADE
- HUMAN_TRAFFICKING
- MIL_SELF_IDENTIFIED_ARMS_DEAL
- ORGANIZED_CRIME
```{r, error = F, message = F, warning = F}
rm(list = ls())
#if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse, data.table, vtable,
lubridate, ggcharts, plyr,
gridExtra, RgoogleMaps, ggmap, mapproj)
## GDELT dataframe:
df <- fread("~/Desktop/soc_ace_2024/data/gdelt/gkg_v2/gkg_themes_russia_cleaned.csv")
## Create different dataframes for each event types based on keywords:
df_arrest <- df[df$count_arrest >= 1, ]
df_confiscation <- df[df$count_confiscation >= 1, ]
df_persecution <- df[df$count_persecution >= 1, ]
df_seize <- df[df$count_seize >= 1, ]
df_black_market <- df[df$count_black_market >= 1, ]
df_crime_cartels <- df[df$count_crime_cartels >= 1, ]
df_drug_trade <- df[df$count_drug_trade >= 1, ]
df_human_trafficking <- df[df$count_human_trafficking >= 1, ]
df_arms_deal <- df[df$count_arms_deal >= 1, ]
df_organized_crime <- df[df$count_organized_crime >= 1, ]
df_backup <- df
df$arrest <- ifelse(df$count_arrest >=1, 1, 0)
df$confiscation <- ifelse(df$count_confiscation >=1, 1, 0)
df$persecution <- ifelse(df$count_persecution >=1, 1, 0)
df$seize <- ifelse(df$count_seize >=1, 1, 0)
df$black_market <- ifelse(df$count_black_market >=1, 1, 0)
df$crime_cartels <- ifelse(df$count_crime_cartels >=1, 1, 0)
df$drug_trade <- ifelse(df$count_drug_trade >=1, 1, 0)
df$human_trafficking <- ifelse(df$count_human_trafficking >=1, 1, 0)
df$arms_deal <- ifelse(df$count_arms_deal >=1, 1, 0)
df$organized_crime <- ifelse(df$count_organized_crime >=1, 1, 0)
## table(df$arrest)
## table(df$confiscation)
## table(df$persecution)
## table(df$seize)
## table(df$black_market)
## table(df$crime_cartels)
## table(df$drug_trade)
## table(df$human_trafficking)
## table(df$arms_deal)
## table(df$organized_crime)
## State Actions:
dfstate <- cbind.data.frame(df$arrest, df$confiscation, df$persecution, df$seize)
colnames(dfstate)[1:4] <- c("Arrest", "Confiscation", "Persecution", "Seize")
dfstate[] <- sapply(dfstate, as.numeric)
dfstate[is.na(dfstate)] <- 0
tab01 <- data.frame(values=colSums(dfstate,
na.rm=TRUE),
names = names(dfstate))
p01 <- ggplot(data=tab01,
aes(x=names,
y=values)) +
geom_bar(stat="identity",
fill = "black",
color = "black",
size = 1.5,
width = 0.75)+
ylim(0, 14000)+
labs(title = paste("Russia: state actions"),
x = "",
y = "Count")+
theme_bw()
p01 <- p01+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
# p01
## Non-nonstate Actions:
dfnonstate <- cbind.data.frame(df$black_market, df$crime_cartels, df$drug_trade,
df$human_trafficking, df$arms_deal, df$organized_crime)
colnames(dfnonstate)[1:6] <- c("Black_market", "Cartels", "Drug_trade",
"Human_trafficking", "Arms_deal", "Organized_crime")
dfnonstate[] <- sapply(dfnonstate, as.numeric)
dfnonstate[is.na(dfnonstate)] <- 0
tab02 <- data.frame(values=colSums(dfnonstate,
na.rm=TRUE),
names = names(dfnonstate))
p02 <- ggplot(data=tab02,
aes(x=names,
y=values)) +
geom_bar(stat="identity",
fill = "black",
color = "black",
size = 1.5,
width = 0.75)+
ylim(0, 14000)+
labs(title = paste("Russia: non-state actions"),
x = "",
y = "Count")+
theme_bw()
p02 <- p02+ theme(axis.text.x = element_text(angle = 45, hjust = 1))
p01 | p02
```
## **1.02 Maps of events by keyword**
The maps below display the count of event types by keyword in Russia.
Bigger circles around specific coordinates means higher count of events. <br>
(Larger circles = more events in that area).
**Blank map:**
```{r, error = F, message = F, warning = F}
# Register ggmap API for mapping:
# summary(df$ActionGeo_Lat)
# summary(df$ActionGeo_Long)
ggmap::register_stadiamaps(key = "ecb62ab3-c884-4be1-9e19-4a49f5f8bed0")
mapoutline <- get_map(location = c(left = 20.00, ## bottom left (lon)
bottom = 47.00, ## bottom left (lat)
right = 179.90, # bottom right (lon)
top = 76.00),
maptype = "outdoors",
source = "stadia",
color="bw")
print(ggmap(mapoutline))
```
## **1.03 Event type: all keywords**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
df$Event_count <- 1
df01 <- ddply(df,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: all keywords in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
df2020 <- df[df$year == "2020", ]
df2021 <- df[df$year == "2021", ]
df2022 <- df[df$year == "2022", ]
df2023 <- df[df$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: all keywords in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: all keywords in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: all keywords in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: all keywords in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.04 Event type: state actions**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
df$state <- ifelse(df$arrest == 1, 1,
ifelse(df$confiscation == 1, 1,
ifelse(df$persecution == 1, 1,
ifelse(df$seize == 1, 1, 0))))
dfstate <- df[(df$state == 1), ]
dfstate$Event_count <- 1
df01 <- ddply(dfstate,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: state actions 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())
```
### 2020
```{r, error = F, message = F, warning = F}
df2020 <- dfstate[dfstate$year == "2020", ]
df2021 <- dfstate[dfstate$year == "2021", ]
df2022 <- dfstate[dfstate$year == "2022", ]
df2023 <- dfstate[dfstate$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: state actions in Russia, 2020"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: state actions in Russia, 2021"))+
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())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: state actions in Russia, 2022"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: state actions in Russia, 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())
```
## **1.05 Event type: non-state actions**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
df$nonstate <- ifelse(df$black_market == 1, 1,
ifelse(df$crime_cartels == 1, 1,
ifelse(df$drug_trade == 1, 1,
ifelse(df$human_trafficking == 1, 1,
ifelse(df$arms_deal == 1, 1,
ifelse(df$organized_crime == 1, 1, 0))))))
dfnonstate <- df[(df$nonstate == 1), ]
dfnonstate$Event_count <- 1
df01 <- ddply(dfnonstate,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: non-state actions in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
df2020 <- dfnonstate[dfnonstate$year == "2020", ]
df2021 <- dfnonstate[dfnonstate$year == "2021", ]
df2022 <- dfnonstate[dfnonstate$year == "2022", ]
df2023 <- dfnonstate[dfnonstate$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: non-state actions in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: non-state actions in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: non-state actions in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: non-state actions in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.06 Event type: Arrest**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfarrest <- df[(df$arrest == 1), ]
dfarrest$Event_count <- 1
df01 <- ddply(dfarrest,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arrest in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfarrest[dfarrest$year == "2020", ]
df2021 <- dfarrest[dfarrest$year == "2021", ]
df2022 <- dfarrest[dfarrest$year == "2022", ]
df2023 <- dfarrest[dfarrest$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arrest in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arrest in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arrest in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arrest in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.07 Event type: Confiscation**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfconfiscation <- df[(df$confiscation == 1), ]
dfconfiscation$Event_count <- 1
df01 <- ddply(dfconfiscation,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: confiscation in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfconfiscation[dfconfiscation$year == "2020", ]
df2021 <- dfconfiscation[dfconfiscation$year == "2021", ]
df2022 <- dfconfiscation[dfconfiscation$year == "2022", ]
df2023 <- dfconfiscation[dfconfiscation$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: confiscation in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: confiscation in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: confiscation in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: confiscation in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.08 Event type: Persecution**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfpersecution <- df[(df$persecution == 1), ]
dfpersecution$Event_count <- 1
df01 <- ddply(dfpersecution,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: persecution in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfpersecution[dfpersecution$year == "2020", ]
df2021 <- dfpersecution[dfpersecution$year == "2021", ]
df2022 <- dfpersecution[dfpersecution$year == "2022", ]
df2023 <- dfpersecution[dfpersecution$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: persecution in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: persecution in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: persecution in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: persecution in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.09 Event type: Seize**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfseize <- df[(df$seize == 1), ]
dfseize$Event_count <- 1
df01 <- ddply(dfseize,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: seize in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfseize[dfseize$year == "2020", ]
df2021 <- dfseize[dfseize$year == "2021", ]
df2022 <- dfseize[dfseize$year == "2022", ]
df2023 <- dfseize[dfseize$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: seize in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: seize in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: seize in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: seize in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.10 Event type: Black market**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfblack_market <- df[(df$black_market == 1), ]
dfblack_market$Event_count <- 1
df01 <- ddply(dfblack_market,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: black_market in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfblack_market[dfblack_market$year == "2020", ]
df2021 <- dfblack_market[dfblack_market$year == "2021", ]
df2022 <- dfblack_market[dfblack_market$year == "2022", ]
df2023 <- dfblack_market[dfblack_market$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: black_market in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: black_market in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: black_market in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: black_market in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.11 Event type: Cartels**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfcrime_cartels <- df[(df$crime_cartels == 1), ]
dfcrime_cartels$Event_count <- 1
df01 <- ddply(dfcrime_cartels,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: crime_cartels in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfcrime_cartels[dfcrime_cartels$year == "2020", ]
df2021 <- dfcrime_cartels[dfcrime_cartels$year == "2021", ]
df2022 <- dfcrime_cartels[dfcrime_cartels$year == "2022", ]
df2023 <- dfcrime_cartels[dfcrime_cartels$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: crime_cartels in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: crime_cartels in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: crime_cartels in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: crime_cartels in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.12 Event type: Drug trade**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfdrug_trade <- df[(df$drug_trade == 1), ]
dfdrug_trade$Event_count <- 1
df01 <- ddply(dfdrug_trade,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: drug_trade in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfdrug_trade[dfdrug_trade$year == "2020", ]
df2021 <- dfdrug_trade[dfdrug_trade$year == "2021", ]
df2022 <- dfdrug_trade[dfdrug_trade$year == "2022", ]
df2023 <- dfdrug_trade[dfdrug_trade$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: drug_trade in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: drug_trade in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: drug_trade in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: drug_trade in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.13 Event type: Human trafficking**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfhuman_trafficking <- df[(df$human_trafficking == 1), ]
dfhuman_trafficking$Event_count <- 1
df01 <- ddply(dfhuman_trafficking,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: human_trafficking in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfhuman_trafficking[dfhuman_trafficking$year == "2020", ]
df2021 <- dfhuman_trafficking[dfhuman_trafficking$year == "2021", ]
df2022 <- dfhuman_trafficking[dfhuman_trafficking$year == "2022", ]
df2023 <- dfhuman_trafficking[dfhuman_trafficking$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: human_trafficking in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: human_trafficking in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: human_trafficking in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: human_trafficking in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.14 Event type: Arms deal**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dfarms_deal <- df[(df$arms_deal == 1), ]
dfarms_deal$Event_count <- 1
df01 <- ddply(dfarms_deal,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arms_deal in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dfarms_deal[dfarms_deal$year == "2020", ]
df2021 <- dfarms_deal[dfarms_deal$year == "2021", ]
df2022 <- dfarms_deal[dfarms_deal$year == "2022", ]
df2023 <- dfarms_deal[dfarms_deal$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arms_deal in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arms_deal in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arms_deal in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: arms_deal in Russia, 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
## **1.15 Event type: Organized crime**
### 2020 - 2023
```{r, error = F, message = F, warning = F}
dforganized_crime <- df[(df$organized_crime == 1), ]
dforganized_crime$Event_count <- 1
df01 <- ddply(dforganized_crime,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = df01,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: organized_crime in Russia, 2020 - 2023"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2020
```{r, error = F, message = F, warning = F}
### 2020
df2020 <- dforganized_crime[dforganized_crime$year == "2020", ]
df2021 <- dforganized_crime[dforganized_crime$year == "2021", ]
df2022 <- dforganized_crime[dforganized_crime$year == "2022", ]
df2023 <- dforganized_crime[dforganized_crime$year == "2023", ]
## 2020:
df2020$Event_count <- 1
df202001 <- ddply(df2020,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map02 <- print(ggmap(mapoutline) +
geom_point(data = df202001,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: organized_crime in Russia, 2020"))+
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())
```
### 2021
```{r, error = F, message = F, warning = F}
## 2021:
df2021$Event_count <- 1
df202101 <- ddply(df2021,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map03 <- print(ggmap(mapoutline) +
geom_point(data = df202101,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: organized_crime in Russia, 2021"))+
theme_bw()+
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank())
```
### 2022
```{r, error = F, message = F, warning = F}
## 2022:
df2022$Event_count <- 1
df202201 <- ddply(df2022,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map04 <- print(ggmap(mapoutline) +
geom_point(data = df202201,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: organized_crime in Russia, 2022"))+
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())
```
### 2023
```{r, error = F, message = F, warning = F}
## 2023:
df2023$Event_count <- 1
df202301 <- ddply(df2023,
.(ActionGeo_Lat, ActionGeo_Long),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map05 <- print(ggmap(mapoutline) +
geom_point(data = df202301,
aes(x = ActionGeo_Long,
y = ActionGeo_Lat,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event type: organized_crime in Russia, 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())
```
