vehicle sale share.Rmd

---
title: "vehicle sale share"
author: "Andrew Dunn"
date: "`r Sys.Date()`"
output: html_document
---

```{r setup}

# data from page 182
# https://tedb.ornl.gov/wp-content/uploads/2021/02/TEDB_Ed_39.pdf#page=182

library(readxl)
library(dplyr)
library(ggplot2)
library(knitr)
library(CPRDataViz)
library(ggrepel)
library(tidyverse)

#setwd("~/Desktop/CPR/electric vehicle tax credits Nov 2022")

# Prior data
full_df <- read_excel("Hybrid and Plug-In Vehicle Sales.xlsx")

df <- full_df %>%
  select(1, 6:8) %>%
  rename("Year" = "Calendar year", "Hybrid" = "Hybrid share of all light vehicles",
         "Plugin" = "Plug-in hybrid share of all light vehicles", 
         "Electric" = "All-electric share of all light vehicles")

# Old data projections from:
# https://www.alternativasostenibile.it/sites/default/files/BCG-The-Electric-Car-Tipping-Point-Jan-2018_tcm9-180862.pdf

# Data projections from Exhibit 1:
# https://www.bcg.com/publications/2022/electric-cars-finding-next-gear

# showing the math 
battery_2025 = .19
hybrid_2025 = .10 + .14
plugin_2025 = .04

battery_2030 = .47
hybrid_2030 = .25 + .09
plugin_2030 = .06

battery_2035 = .68
hybrid_2035 = .21 + .06 
plugin_2035 = .04

# add the rows
df[nrow(df) + 1,] = list(2025, hybrid_2025, plugin_2025, battery_2025)
df[nrow(df) + 1,] = list(2030, hybrid_2030, plugin_2030, battery_2030)
df[nrow(df) + 1,] = list(2035, hybrid_2035, plugin_2035, battery_2035)

# Zai Rutter ~ adding final points per Mehul's Request

#data_ends <- pivot_longer(cols = !Year, names_to = "type", values_to = "value",df)
data_ends <- df %>% filter(Year == 2035)
data_ends <- pivot_longer(cols = !Year, names_to = "type", values_to = "value",data_ends)

```


```{r graph, echo=FALSE}
ggplot(df, aes(x = Year, Hybrid)) +
  #theme_cpr() +
  
  # Data lines
  geom_line(aes(y = Hybrid), color = "darkred") + 
  geom_text(data = df %>% filter(Year == last(Year)), aes(label = "Hybrid", 
                                                               x = Year + 1.2, 
                                                               y = Hybrid) , size = 3) + 
  geom_line(aes(y = Plugin), color = "steelblue") + 
  geom_text(data = df %>% filter(Year == last(Year)), aes(label = "Plug-in", 
                                                          x = Year + 1.2, 
                                                          y = Plugin) , size = 3) +
  geom_line(aes(y = Electric), color = "darkgreen") +
  geom_text(data = df %>% filter(Year == last(Year)), aes(label = "Electric", 
                                                          x = Year + 1.2, 
                                                          y = Electric) , size = 3) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits = c(0, .7)) +
  scale_x_continuous(limits = c(2010, 2037), breaks = seq(2010, 2035, 5)) +
  
  # The vertical line
  geom_segment(aes(x = 2022, y = 0, xend = 2022, yend = .65), linetype="dotted", color = "black", size= .5) +
  annotate("text", x = 2024.5, y = .53, label = "Inflation Reduction \n Act passed in 2022", size = 3) +
  
  # Labels and whole graph formatting
  labs(x = "Year", 
       y = "Share of All Light Vehicles", 
       title = "Electric Vehicle Sales are Projected to Grow Rapidly in the Future",
       subtitle = "Historic and Projected Clean Vehicle Adoption Rates (2010-35)",
       caption = c("\n Sources: Transportation Energy Book Data Book, Edition 39 (2021) \n 
                   Electric Cars Are Finding Their Next Gear (2022) \n
                   Author: Andrew Dunn \n 
                   Editor: Zai Rutter \n 
                   Date: Nov 29, 2022"),
       color = 'Committees') +
  
  theme(plot.caption = element_text(lineheight = 0.5, size = 7),
        plot.caption.position = "plot",
        plot.title = element_text(hjust = 0.5), 
        plot.subtitle = element_text(hjust = 0.5, size = 9),
        axis.title = element_text(size = 9),
        panel.grid.minor = element_blank(), 
        panel.grid.major.y = element_blank(),
        panel.background = element_blank()) 
  
  # Zai Rutter adding final data points per request
#geom_text_repel(
 #  aes(label = value), data = data_ends,
  # fontface ="plain", color = "black", size = 3
  #)
  
  #scale_y_continuous(sec.axis = sec_axis(~ ., breaks = data_ends$value))
  #Now he wants mid marks instead..


# save the graph
ggsave("clean_vehicle_graph.png")
```

Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.