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R class data viz.Rmd
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R class data viz.Rmd
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---
title: "Using R to plot Price indexes"
author: "Mike J"
date: "8/19/2021"
output: html_document
---
```{r setup, include=FALSE}
library(knitr)
knitr::opts_chunk$set(echo = TRUE)
```
## CPI Huddle (Lets Learn R!)
```{r packwd, echo=FALSE, warning=FALSE, message=FALSE}
library(tidyverse)
library(readxl)
library(ggplot2)
library(lubridate)
library(zoo)
library(dplyr)
library(scales)
library(cowplot)
library(forcats)
library(ggthemes)
#setwd("G:/WORKING DIRECTORY")
#opts_knit$set(root.dir = "~G:/WORKING DIRECTORY")
#getwd()
```
## Load the data file
```{r import, echo=TRUE }
#load the data (readxl package) and check
df<-read_excel("CPI_Demo.xlsx", sheet = "prices")
tail(df )
#check data
#str(df)
#class(df)
```
```{r transform}
#transform date variable to a usable time series
df<- df%>%mutate(date = mdy(date))
df2<-df%>%select(date,cpi_mfe,cpi)
df3<-df2%>%gather(measure, value, cpi_mfe,cpi,convert = FALSE)
df3<-df3 %>% rename(Prices=measure)
df4<-df3 %>% mutate(Prices = ifelse(Prices == "cpi", "CPI","*CORE CPI"))
```
## CPI Basic line plot using GGPLOT2
<br/>
Displaying CPI and Core CPI (minus food and energy prices)
```{r basicplt}
#making the basic plot
ggplot(df4,aes(x=date, y=value, group=Prices))+
geom_line(aes(linetype=Prices, color=Prices))
```
<br/>
Lets use ggplot2's additional arguments to add details along with the scales package functions (scale_x_date and others) and using the annotate() to add the COVID-19 pandemic period.
<br/>
```{r addcovidpd , warning=FALSE}
ggplot(df4,aes(x=date, y=value, group=Prices))+
#annotate the chart to see the period experiening covid
annotate("rect", xmin = as.Date("2020-03-01"), xmax = as.Date("2021-07-01"), ymin = -Inf, ymax = Inf, fill = "gray", alpha = 0.2)+
geom_line(aes(linetype=Prices, color=Prices))+
#adjust the chart so it ends at the last dates
scale_x_date(date_breaks = "5 months",limits=c(as.Date("2019-01-01"),as.Date("2021-07-01")),labels = date_format("%b/%Y"),expand = c(0, 0))
```
Lets add the Federal Reserve inflation target rate to illustrate a more interesting story with this plot
```{r inflation , warning=FALSE}
ggplot(df4,aes(x=date, y=value, group=Prices))+
#annotate the chart to see the period experiening covid
annotate("rect", xmin = as.Date("2020-03-01"), xmax = as.Date("2021-07-01"), ymin = -Inf, ymax = Inf, fill = "gray", alpha = 0.2)+
geom_line(aes(linetype=Prices, color=Prices))+
#adjust the chart so it ends at the last dates
scale_x_date(date_breaks = "5 months",limits=c(as.Date("2019-01-01"),as.Date("2021-07-01")),labels = date_format("%b/%Y"),expand = c(0, 0))+
#adding the federal reserve board inflation target rate
geom_hline(yintercept=2.0, linetype="dashed",
color = "blue", size=.5)+
scale_linetype_manual(values=c("solid", "twodash"))
```
<br/>
Lets put some text to add details and make the points of interest (COVID 19 pandemic period, Fed Inflation rate more obvious)
<br/>
```{r addtext , warning=FALSE}
ggplot(df4,aes(x=date, y=value, group=Prices))+
#annotate the chart to see the period experiencing covid
annotate("rect", xmin = as.Date("2020-03-01"), xmax = as.Date("2021-07-01"), ymin = -Inf, ymax = Inf, fill = "gray", alpha = 0.2)+
geom_line(aes(linetype=Prices, color=Prices))+
#adjust the chart so it ends at the last dates
scale_x_date(date_breaks = "5 months",limits=c(as.Date("2019-01-01"),as.Date("2021-07-01")),labels = date_format("%b/%Y"),expand = c(0, 0))+
geom_hline(yintercept=2.0, linetype="dashed",
color = "blue", size=.5)+
scale_linetype_manual(values=c("solid", "twodash"))+
#add the inflation traget rate with annotate()
annotate("text", x = as.Date("2020-09-01"), y = 2.6,colour = "blue", label = "Fed Inflation Target Rate",fontface = 'italic', size=2)+
#ADDING TEXT TO MAKE THE ABOVE POINTS OF INTEREST CLEAR!
annotate("segment", x = as.Date("2020-09-01"), xend = as.Date("2020-09-01"), y = 2.05, yend = 2.4,colour = "blue")+
annotate("text", x = as.Date("2020-07-01"), y = 3.4,colour = "black", label = "COVID-19 Pandemic",fontface = 'italic',size = 2.3)
```
```{r addtext2 , warning=FALSE}
line<-ggplot(df4,aes(x=date, y=value, group=Prices))+
#annotate the chart to see the period experiening covid
annotate("rect", xmin = as.Date("2020-03-01"), xmax = as.Date("2021-07-01"), ymin = -Inf, ymax = Inf, fill = "gray", alpha = 0.2)+
geom_line(aes(linetype=Prices, color=Prices))+
#adjust the chart so it ends at the last dates
scale_x_date(date_breaks = "5 months",limits=c(as.Date("2019-01-01"),as.Date("2021-07-01")),labels = date_format("%b/%Y"),expand = c(0, 0))+
geom_hline(yintercept=2.0, linetype="dashed",
color = "blue", size=.5)+
scale_linetype_manual(values=c("solid", "twodash"))+
annotate("text", x = as.Date("2020-09-01"), y = 2.6,colour = "blue", label = "Fed Inflation Target Rate",fontface = 'italic', size=2)+
annotate("segment", x = as.Date("2020-09-01"), xend = as.Date("2020-09-01"), y = 2.05, yend = 2.4,colour = "blue")+
annotate("text", x = as.Date("2020-07-01"), y = 3.4,colour = "black", label = "COVID-19 Pandemic",fontface = 'italic',size = 2.3)+
scale_color_manual(values=c('red','black'))+
scale_y_continuous(limits = c(0, 5.5), breaks = seq(0, 5.5, by = 1),expand = c(0, 0),labels = function(value) paste0(value, "%"))+
labs(title = "The Economic Monitor: Inflation",
caption = "*CORE Consumer Price Index excluding food and energy commodities\nAll Urban Consumers measures\nSource: Bureau Of Labor Statistics")+xlab("")+ylab("") +
theme_bw()+
theme(panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
plot.title = element_text(size = 16, face="bold"),
plot.caption = element_text(hjust = 0,size = 9),
legend.text=element_text(size=8),
axis.text.x = element_text(vjust = -2))+
annotate("text", x = as.Date("2019-08-01"), y = 4.8,colour = "black", label = "Consumer Price Index",fontface = 'plain',size = 4.1)+
#adding few more text to add clarity for our readers
annotate("text", x = as.Date("2019-07-15"), y = 4.5,colour = "black", label = "12-month Percent Change",fontface = 'plain',size = 2.6)
line
#go back to slides- learning points
```
<br/>
##Lets make a Bar plot based on Table 7 data!
<br/>
```{r loadcat}
df2<-read_excel("CPI_Demo.xlsx", sheet = "catJuly")
class(df2)
head(df2)
#using dplyr package to wrangle the data
df3<-df2%>%mutate(rounded = round(Rate, 0))%>%arrange((Rate))
```
```{r barplot }
bar<-df3%>%ggplot(aes(x=reorder(Item, rounded),y=rounded,fill = rounded>0))+
geom_bar(stat="identity",show.legend = FALSE) +coord_flip()+
geom_text(aes(label = rounded),size =3, vjust = 1,hjust=1,family = "sans",fontface ="bold")+
scale_y_continuous(breaks = NULL,limits = c(-10,100), expand = c(0, 0))+labs(title = "12-month change in July's price",subtitle="Unadjusted")+
theme(axis.text.x = element_blank(),axis.title.x = element_blank(),
axis.title.y = element_blank(),plot.caption = element_text(hjust = 0),plot.title = element_text(size = 15,face = "bold"),
axis.ticks.x=element_blank())+
scale_fill_manual(values = c("Red2", "dodgerblue2"))+ theme_excel_new()+xlab("")+ylab("")+
theme(
plot.title = element_text(color="black", size=14, face="bold"))
bar
```
<br/>
## Lets put the 2 plots together to tell a story in one output
<br/>
```{r twoplots ,fig.width=12, warning=FALSE}
plot_grid(line,bar)
#save output to insert image into word, or maybe make a social media post!
#ggsave("CPI August.png")
```