---
title: "ExportGenius Trade Analysis: 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
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wrap: 72
---
# **Section 1. Gold**
```{r, error = F, message = F, warning = F}
rm(list = ls())
pacman::p_load(readxl, vtable, tidyverse,
lubridate, data.table, zoo,
readODS, patchwork)
df <- fread("/home/gagan/Desktop/soc_ace_2024/data/exportgenius/gold_price/goldprice.csv")
ggplot(df,
aes(x = date,
y = price_gram)) +
geom_line() +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
labs(title = "International gold price per gram",
x = "Date",
y = "Price (USD)",
caption = "Source: macrotrends & auronum.co.uk") +
theme_minimal()
```
## **1.1 Gold exports**
```{r, error = F, message = F, warning = F}
rm(list = ls())
df <- fread("/home/gagan/Desktop/soc_ace_2024/data/exportgenius/gold_rus/Russia-DetailedExport-Gold.csv")
df$date <- dmy(df$Date)
df$description <- df$`Product Description`
df$destination_country <- df$`Destination Country`
df$exporter <- df$Exporter
df$buyer <- df$Buyer
df$total_value <- df$`Total Value USD`
df$price_invoice <- df$total_value/df$Quantity
df <- df[, c("date", "description", "destination_country",
"exporter", "buyer", "price_invoice", "total_value")]
df02 <- fread("~/Desktop/soc_ace_2024/scripts/exportgenius/gold_analyses_latest/goldprice.csv")
# head(df02$date)
df02$date <- ymd(df02$date)
df <- merge(df, df02, by = "date")
df$price_benchmark <- df$price_gram
plot01 <- ggplot(df,
aes(x = date,
y = total_value)) +
geom_line() +
geom_point() +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
# ylim(15, 120) +
labs(title = "Russia Gold Exports",
x = "Date",
y = "Total Value") +
theme_bw()
plot01
plot02 <- df %>%
ggplot( aes(x = date,
y = total_value,
color = destination_country))+
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
labs(title = "Russia Gold Exports",
y = "Total value",
x = "Date")+
theme_bw()
plot02
plot03 <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~destination_country, ncol = 2)+
labs(title = "Russia Gold Exports by Destination",
x = "Date",
y = "Total Value") +
theme_bw()
plot03
plot03a <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~exporter, ncol = 2)+
labs(title = "Russia Gold Exports by Exporter",
x = "Date",
y = "Total Value") +
theme_bw()
plot03a
plot03b <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~buyer, ncol = 2)+
labs(title = "Russia Gold Exports by Buyer",
x = "Date",
y = "Total Value") +
theme_bw()
plot03b
max_price_index <- which.max(df$price_invoice)
# Remove the row with the highest value in the 'price' column
df <- df[-max_price_index, ]
df$filter01 <- ifelse(df$price_invoice > 150, 1, 0)
#table(df$filter01)
df <- df[df$price_invoice <=120, ]
df$price_difference <- df$price_invoice - df$price_benchmark
df03 <- df[, c("date", "price_invoice", "price_benchmark")]
df_long <- reshape2::melt(df03,
id.vars = "date",
variable.name = "price_type",
value.name = "price")
ggplot(df_long,
aes(x = date,
y = price,
linetype = price_type)) +
geom_line(size = 0.75,
color = "black")+
labs(title = "Russia Gold Imports",
x = "Date",
y = "Price") +
theme_bw()
#df <- df[df$Origin_Country == "China", ]
df03 <- df[, c("date", "price_invoice", "price_benchmark")]
df$price_difference <- scale(df$price_invoice - df$price_benchmark)
plot04 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Exports: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE)
plot04
plot05 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Exports by Destination: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~destination_country,
ncol = 2)
plot05
plot06 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Exports by Exporter: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~exporter,
ncol = 2)
plot06
plot07 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Exports by Buyer: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~buyer,
ncol = 3)
plot07
```
## **1.2 Gold imports**
```{r, error = F, message = F, warning = F}
rm(list = ls())
df <- read_ods("/home/gagan/Desktop/soc_ace_2024/data/exportgenius/gold_rus/Russia-DetailedImport-Gold.ods")
df$date <- dmy(df$Date)
df$date <- df$date
df$description <- df$Product_Description
df$origin_country <- df$Origin_Country
df$supplier <- df$Supplier
df$importer <- df$Importer
df$price_invoice <- df$Total_Value/df$Quantity
df$total_value <- df$Total_Value
df <- df[, c("date", "description", "origin_country",
"supplier", "importer", "price_invoice", "total_value")]
df100 <- read_ods("/home/gagan/Desktop/soc_ace_2024/data/exportgenius/gold_rus/Russia-MirrorImport-Gold.ods")
df100$date <- dmy(df100$Date)
df100$description <- df100$Product_Description
df100$origin_country <- df100$Origin_Country
df100$supplier <- df100$Supplier
df100$importer <- df100$Importer
df100$total_value <- (df100$Total_Value)
df100$price_invoice <- df100$total_value/df100$Quantity
df100 <- df100[, c("date", "description", "origin_country",
"supplier", "importer", "price_invoice", "total_value")]
df <- rbind(df, df100)
df02 <- fread("~/Desktop/soc_ace_2024/scripts/exportgenius/gold_analyses_latest/goldprice.csv")
df02$date <- ymd(df02$date)
df <- merge(df, df02, by = "date")
df$price_benchmark <- df$price_gram
#df[sapply(df, is.infinite)] <- NA
df <- df[!(df$price_invoice > 1000),]
plot01 <- ggplot(df,
aes(x = date,
y = total_value)) +
geom_line() +
geom_point() +
# ylim(15, 120) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
labs(title = "Russia Gold Imports",
x = "Date",
y = "Total Value") +
theme_bw()
plot01
plot02 <- df %>%
ggplot( aes(x = date,
y = total_value,
color = origin_country))+
geom_line()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
geom_point()+
labs(title = "Russia Gold Imports",
x = "Date",
y = "Total value")+
theme_bw()
plot02
plot03 <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~origin_country, ncol = 2)+
labs(title = "Russia Gold Imports by Origin",
x = "Date",
y = "Total Value") +
theme_bw()
plot03
max_price_index <- which.max(df$price_invoice)
# Remove the row with the highest value in the 'price' column
df <- df[-max_price_index, ]
df$filter01 <- ifelse(df$price_invoice > 150, 1, 0)
# table(df$filter01)
df <- df[df$price_invoice <=120, ]
df$price_difference <- df$price_invoice - df$price_benchmark
#df <- df[df$Origin_Country == "China", ]
df03 <- df[, c("date", "price_invoice", "price_benchmark")]
df_long <- reshape2::melt(df03,
id.vars = "date",
variable.name = "price_type",
value.name = "price")
ggplot(df_long,
aes(x = date,
y = price,
linetype = price_type)) +
geom_line(size = 0.75,
color = "black")+
labs(title = "Russia Gold Imports",
x = "Date",
y = "Price") +
theme_bw()
df$price_difference <- df$price_invoice - df$price_benchmark
df$price_difference2 <- scale(df$price_difference)
df$price_benchmark2 <- scale(df$price_benchmark)
plot04 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Imports: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE)
plot04
plot05 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Imports by Origin: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~origin_country,
ncol = 2)
plot05
plot06 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Imports by Importer: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~importer,
ncol = 4)
plot06
plot07 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Gold Imports by Supplier: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~supplier,
ncol = 5)
plot07
```
# **Section 2. Tobacco**
## **2.1 Tobacco exports**
```{r, error = F, message = F, warning = F}
rm(list = ls())
df <- fread("/home/gagan/Desktop/soc_ace_2024/data/exportgenius/gold_rus/Russia-DetailedExport-Tobacco.csv")
df$date <- dmy(df$Date)
df$description <- df$`Product Description`
df$destination_country <- df$`Destination Country`
df$exporter <- df$Exporter
df$buyer <- df$Buyer
df$total_value <- df$`Total Value USD`
df$price_invoice <- df$total_value/df$Quantity
df <- df[, c("date", "description", "destination_country",
"exporter", "buyer", "price_invoice", "total_value")]
df02 <- fread("~/Desktop/soc_ace_2024/scripts/exportgenius/gold_analyses_latest/goldprice.csv")
# head(df02$date)
df02$date <- ymd(df02$date)
df <- merge(df, df02, by = "date")
df$price_benchmark <- df$price_gram
plot01 <- ggplot(df,
aes(x = date,
y = total_value)) +
geom_line() +
geom_point() +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
# ylim(15, 120) +
labs(title = "Russia Tobacco Exports",
x = "Date",
y = "Total Value") +
theme_bw()
plot01
plot02 <- df %>%
ggplot( aes(x = date,
y = total_value,
color = destination_country))+
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
labs(title = "Russia Tobacco Exports",
y = "Total value",
x = "Date")+
theme_bw()
plot02
plot03 <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~destination_country, ncol = 2)+
labs(title = "Russia Tobacco Exports by Destination",
x = "Date",
y = "Total Value") +
theme_bw()
plot03
plot03a <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~exporter, ncol = 2)+
labs(title = "Russia Tobacco Exports by Exporter",
x = "Date",
y = "Total Value") +
theme_bw()
plot03a
plot03b <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~buyer, ncol = 2)+
labs(title = "Russia Tobacco Exports by Buyer",
x = "Date",
y = "Total Value") +
theme_bw()
plot03b
max_price_index <- which.max(df$price_invoice)
# Remove the row with the highest value in the 'price' column
df <- df[-max_price_index, ]
df$filter01 <- ifelse(df$price_invoice > 150, 1, 0)
#table(df$filter01)
df <- df[df$price_invoice <=120, ]
df$price_difference <- df$price_invoice - df$price_benchmark
df03 <- df[, c("date", "price_invoice", "price_benchmark")]
df_long <- reshape2::melt(df03,
id.vars = "date",
variable.name = "price_type",
value.name = "price")
ggplot(df_long,
aes(x = date,
y = price,
linetype = price_type)) +
geom_line(size = 0.75,
color = "black")+
labs(title = "Russia Tobacco Imports",
x = "Date",
y = "Price") +
theme_bw()
#df <- df[df$Origin_Country == "China", ]
df03 <- df[, c("date", "price_invoice", "price_benchmark")]
df$price_difference <- scale(df$price_invoice - df$price_benchmark)
plot04 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Exports: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE)
plot04
plot05 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Exports by Destination: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~destination_country,
ncol = 2)
plot05
plot06 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Exports by Exporter: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~exporter,
ncol = 2)
plot06
plot07 <- ggplot(df, aes(x = date,
y = price_difference)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference,
color = price_difference > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Exports by Buyer: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~buyer,
ncol = 3)
plot07
```
## **2.2 Tobacco imports**
```{r, error = F, message = F, warning = F}
rm(list = ls())
pacman::p_load(readxl, vtable, tidyverse,
lubridate, data.table, zoo,
readODS, patchwork)
rm(list = ls())
df <- fread("/home/gagan/Desktop/soc_ace_2024/data/exportgenius/gold_rus/Russia-DetailedImport-Tobacco.csv")
df$date <- dmy(df$Date)
df$date <- df$date
df$description <- df$`Product Description`
df$origin_country <- df$`Origin Country`
df$supplier <- df$Supplier
df$importer <- df$Importer
df$price_invoice <- df$`Total Value USD`/df$Quantity
df$total_value <- df$`Total Value USD`
df <- df[, c("date", "description", "origin_country",
"supplier", "importer", "price_invoice", "total_value")]
df02 <- fread("~/Desktop/soc_ace_2024/scripts/exportgenius/gold_analyses_latest/goldprice.csv")
df02$date <- ymd(df02$date)
df <- merge(df, df02, by = "date")
df$price_benchmark <- df$price_gram
#df[sapply(df, is.infinite)] <- NA
df <- df[!(df$price_invoice > 1000),]
plot01 <- ggplot(df,
aes(x = date,
y = total_value)) +
geom_line() +
geom_point() +
# ylim(15, 120) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
labs(title = "Russia Tobacco Imports",
x = "Date",
y = "Total Value") +
theme_bw()
plot01
plot02 <- df %>%
ggplot( aes(x = date,
y = total_value,
color = origin_country))+
geom_line()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
geom_point()+
labs(title = "Russia Tobacco Imports",
x = "Date",
y = "Total value")+
theme_bw()
plot02
plot03 <- df %>%
ggplot( aes(x = date,
y = total_value)) +
geom_line()+
geom_point()+
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
facet_wrap(~origin_country, ncol = 2)+
labs(title = "Russia Tobacco Imports by Origin",
x = "Date",
y = "Total Value") +
theme_bw()
plot03
max_price_index <- which.max(df$price_invoice)
# Remove the row with the highest value in the 'price' column
df <- df[-max_price_index, ]
df$filter01 <- ifelse(df$price_invoice > 150, 1, 0)
# table(df$filter01)
df <- df[df$price_invoice <=120, ]
df$price_difference <- df$price_invoice - df$price_benchmark
#df <- df[df$Origin_Country == "China", ]
df03 <- df[, c("date", "price_invoice", "price_benchmark")]
df_long <- reshape2::melt(df03,
id.vars = "date",
variable.name = "price_type",
value.name = "price")
ggplot(df_long,
aes(x = date,
y = price,
linetype = price_type)) +
geom_line(size = 0.75,
color = "black")+
labs(title = "Russia Tobacco Imports",
x = "Date",
y = "Price") +
theme_bw()
df$price_difference <- df$price_invoice - df$price_benchmark
df$price_difference2 <- scale(df$price_difference)
df$price_benchmark2 <- scale(df$price_benchmark)
plot04 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Imports: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE)
plot04
plot05 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Imports by Origin: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~origin_country,
ncol = 2)
plot05
plot06 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Imports by Importer: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~importer,
ncol = 4)
plot06
plot07 <- ggplot(df, aes(x = date,
y = price_difference2)) +
geom_segment(aes(x = date,
xend = date,
y = 0,
yend = price_difference2,
color = price_difference2 > 0)) +
geom_point(color = "black", size = 2) +
geom_vline(xintercept = as.Date("2022-02-24"),
linetype = "dashed",
color = "black",
size = 0.45) +
theme_bw() +
scale_color_manual(values = c("red", "blue")) +
labs(title = "Russia Tobacco Imports by Supplier: Price Differentials Over Time",
x = "Date",
y = "Price differential (standardized)") +
guides(color = FALSE) +
facet_wrap(~supplier,
ncol = 5)
plot07
```
