---
title: "Soc-Ace: POLECAT Data Analysis [V2]"
author: Gagan Atreya
date: today
format:
html:
toc: true
toc-location: left
toc-depth: 4
theme: lumen
fontsize: large
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---
# **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("BLR", "GEO", "KAZ",
"POL", "RUS", "UKR")
df01 <- df[df$Country %in% country_list, ]
df <- df01
rm(list = setdiff(ls(), c("df")))
table(df$Country)
df$Year <- year(df$Event.Date)
table(df$Year)
## Register stadiamaps api:
ggmap::register_stadiamaps(key = "ecb62ab3-c884-4be1-9e19-4a49f5f8bed0")
```
# **Section 2. Maps: Belarus**
## **2.1. Belarus: arrests**
```{r, error = F, message = F, warning = F}
dfblr <- df[df$Country == "BLR", ]
dfblr <- dfblr[dfblr$Event.Mode == "arrest", ]
dfblr$Latitude <- as.numeric(dfblr$Latitude)
dfblr$Longitude <- as.numeric(dfblr$Longitude)
dfblr$Event_count <- 1
df01 <- ddply(dfblr,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
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")
## 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())
```
## **2.2. Belarus: arrests per year**
### Table:
```{r, error = F, message = F, warning = F}
dfblr20 <- dfblr[dfblr$Year == "2020", ]
dfblr21 <- dfblr[dfblr$Year == "2021", ]
dfblr22 <- dfblr[dfblr$Year == "2022", ]
dfblr23 <- dfblr[dfblr$Year == "2023", ]
table(dfblr$Year)
```
### Maps:
```{r, error = F, message = F, warning = F}
dfblr20 <- ddply(dfblr20,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfblr21 <- ddply(dfblr21,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfblr22 <- ddply(dfblr22,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfblr23 <- ddply(dfblr23,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr20,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Belarus, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr21,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Belarus, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr22,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Belarus, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr23,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Belarus, 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())
```
## **2.3. Belarus: illegal drugs**
```{r, error = F, message = F, warning = F}
dfblr <- df[df$Country == "BLR", ]
dfblr$drugs <- ifelse(str_detect(dfblr$Contexts, "illegal_drugs") == T, 1, 0)
dfblr <- dfblr[dfblr$drugs == 1, ]
dfblr$Longitude <- as.numeric(dfblr$Longitude)
dfblr$Latitude <- as.numeric(dfblr$Latitude)
dfblr$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 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())
```
## **2.4. Belarus: illegal_drugs per year**
### Table:
```{r, error = F, message = F, warning = F}
dfblr20 <- dfblr[dfblr$Year == "2020", ]
dfblr21 <- dfblr[dfblr$Year == "2021", ]
dfblr22 <- dfblr[dfblr$Year == "2022", ]
dfblr23 <- dfblr[dfblr$Year == "2023", ]
table(dfblr$Year)
```
### Maps:
```{r, error = F, message = F, warning = F}
dfblr20 <- ddply(dfblr20,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfblr21 <- ddply(dfblr21,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfblr22 <- ddply(dfblr22,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfblr23 <- ddply(dfblr23,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr20,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event contexts tagged as illegal_drugs in Belarus, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr21,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Belarus, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr22,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Belarus, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfblr23,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Belarus, 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: Georgia**
## **3.1. Georgia: arrests**
```{r, error = F, message = F, warning = F}
dfgeo <- df[df$Country == "GEO", ]
dfgeo <- dfgeo[dfgeo$Event.Mode == "arrest", ]
dfgeo$Latitude <- as.numeric(dfgeo$Latitude)
dfgeo$Longitude <- as.numeric(dfgeo$Longitude)
dfgeo$Event_count <- 1
df01 <- ddply(dfgeo,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
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")
## 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())
```
## **3.2. Georgia: arrests per year**
### Table:
```{r, error = F, message = F, warning = F}
dfgeo20 <- dfgeo[dfgeo$Year == "2020", ]
dfgeo21 <- dfgeo[dfgeo$Year == "2021", ]
dfgeo22 <- dfgeo[dfgeo$Year == "2022", ]
dfgeo23 <- dfgeo[dfgeo$Year == "2023", ]
table(dfgeo$Year)
```
### Maps:
```{r, error = F, message = F, warning = F}
dfgeo20 <- ddply(dfgeo20,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfgeo21 <- ddply(dfgeo21,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfgeo22 <- ddply(dfgeo22,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfgeo23 <- ddply(dfgeo23,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo20,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Georgia, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo21,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Georgia, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo22,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Georgia, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo23,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Georgia, 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())
```
## **3.3. Georgia: illegal drugs**
```{r, error = F, message = F, warning = F}
dfgeo <- df[df$Country == "GEO", ]
dfgeo$drugs <- ifelse(str_detect(dfgeo$Contexts, "illegal_drugs") == T, 1, 0)
dfgeo <- dfgeo[dfgeo$drugs == 1, ]
dfgeo$Longitude <- as.numeric(dfgeo$Longitude)
dfgeo$Latitude <- as.numeric(dfgeo$Latitude)
dfgeo$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 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())
```
## **3.4. Georgia: illegal_drugs per year**
### Table:
```{r, error = F, message = F, warning = F}
dfgeo20 <- dfgeo[dfgeo$Year == "2020", ]
dfgeo21 <- dfgeo[dfgeo$Year == "2021", ]
dfgeo22 <- dfgeo[dfgeo$Year == "2022", ]
dfgeo23 <- dfgeo[dfgeo$Year == "2023", ]
table(dfgeo$Year)
```
### Maps:
```{r, error = F, message = F, warning = F}
dfgeo20 <- ddply(dfgeo20,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfgeo21 <- ddply(dfgeo21,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfgeo22 <- ddply(dfgeo22,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfgeo23 <- ddply(dfgeo23,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo20,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event contexts tagged as illegal_drugs in Georgia, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo21,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Georgia, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo22,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Georgia, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfgeo23,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Georgia, 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: Kazakhstan**
## **4.1. Kazakhstan: arrests**
```{r, error = F, message = F, warning = F}
dfkaz <- df[df$Country == "KAZ", ]
dfkaz <- dfkaz[dfkaz$Event.Mode == "arrest", ]
dfkaz$Latitude <- as.numeric(dfkaz$Latitude)
dfkaz$Longitude <- as.numeric(dfkaz$Longitude)
dfkaz$Event_count <- 1
df01 <- ddply(dfkaz,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
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")
## 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())
```
## **4.2. Kazakhstan: arrests per year**
### Table:
```{r, error = F, message = F, warning = F}
dfkaz20 <- dfkaz[dfkaz$Year == "2020", ]
dfkaz21 <- dfkaz[dfkaz$Year == "2021", ]
dfkaz22 <- dfkaz[dfkaz$Year == "2022", ]
dfkaz23 <- dfkaz[dfkaz$Year == "2023", ]
table(dfkaz$Year)
```
### Maps:
```{r, error = F, message = F, warning = F}
dfkaz20 <- ddply(dfkaz20,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfkaz21 <- ddply(dfkaz21,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfkaz22 <- ddply(dfkaz22,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfkaz23 <- ddply(dfkaz23,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz20,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Kazakhstan, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz21,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Kazakhstan, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz22,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Kazakhstan, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz23,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event mode tagged as arrests in Kazakhstan, 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())
```
## **4.3. Kazakhstan: illegal drugs**
```{r, error = F, message = F, warning = F}
dfkaz <- df[df$Country == "KAZ", ]
dfkaz$drugs <- ifelse(str_detect(dfkaz$Contexts, "illegal_drugs") == T, 1, 0)
dfkaz <- dfkaz[dfkaz$drugs == 1, ]
dfkaz$Longitude <- as.numeric(dfkaz$Longitude)
dfkaz$Latitude <- as.numeric(dfkaz$Latitude)
dfkaz$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 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())
```
## **4.4. Kazakhstan: illegal_drugs per year**
### Table:
```{r, error = F, message = F, warning = F}
dfkaz20 <- dfkaz[dfkaz$Year == "2020", ]
dfkaz21 <- dfkaz[dfkaz$Year == "2021", ]
dfkaz22 <- dfkaz[dfkaz$Year == "2022", ]
dfkaz23 <- dfkaz[dfkaz$Year == "2023", ]
table(dfkaz$Year)
```
### Maps:
```{r, error = F, message = F, warning = F}
dfkaz20 <- ddply(dfkaz20,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfkaz21 <- ddply(dfkaz21,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfkaz22 <- ddply(dfkaz22,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
dfkaz23 <- ddply(dfkaz23,
.(Latitude, Longitude),
summarize,
Event_count = sum(Event_count))
## Fill up the map with our data:
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz20,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("Event contexts tagged as illegal_drugs in Kazakhstan, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz21,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Kazakhstan, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz22,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Kazakhstan, 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())
map01 <- print(ggmap(mapoutline) +
geom_point(data = dfkaz23,
aes(x = Longitude,
y = Latitude,
size = Event_count),
alpha = 0.5,
colour="black")+
ggtitle("vent contexts tagged as illegal_drugs in Kazakhstan, 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())
```
