Load Required Packages

library(tidyverse)

## ── Attaching packages ───────────────────────────── tidyverse 1.2.1 ──

## ✔ ggplot2 3.1.0     ✔ purrr   0.2.5
## ✔ tibble  1.4.2     ✔ dplyr   0.7.8
## ✔ tidyr   0.8.2     ✔ stringr 1.3.1
## ✔ readr   1.1.1     ✔ forcats 0.3.0

## ── Conflicts ──────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(readr)

Some Economic Data

This data came from the Federal Reserve Economic Data system (FRED). It is all quarterly beginning with the first quarter of 1990. The first step is to import the data.

indicators <- read_csv("~/Dropbox/Documents/SMU/CSC 463/indicators.csv",col_types = cols(Date = col_date(format = "%m/%d/%y")))

Examine the data.

str(indicators)

## Classes 'tbl_df', 'tbl' and 'data.frame':    148 obs. of  7 variables:
##  $ Date     : Date, format: "1980-01-01" "1980-04-01" ...
##  $ President: chr  "Reagan" "Reagan" "Reagan" "Reagan" ...
##  $ realgdp  : num  1.3 -7.9 -0.6 7.6 8.5 -2.9 4.7 -4.6 -6.5 2.2 ...
##  $ lfpr25_54: num  78.7 78.6 78.6 78.7 79.1 79.4 79.1 79.5 79.5 79.8 ...
##  $ unrate   : num  6.3 7.3 7.7 7.4 7.4 7.4 7.4 8.2 8.8 9.4 ...
##  $ infcpi   : num  16.7 14.2 7.7 11.7 11.5 8.6 11.6 6.7 3.6 5.9 ...
##  $ newjobs  : int  316 -437 -408 679 390 238 194 -401 -628 -479 ...
##  - attr(*, "spec")=List of 2
##   ..$ cols   :List of 7
##   .. ..$ Date     :List of 1
##   .. .. ..$ format: chr "%m/%d/%y"
##   .. .. ..- attr(*, "class")= chr  "collector_date" "collector"
##   .. ..$ President: list()
##   .. .. ..- attr(*, "class")= chr  "collector_character" "collector"
##   .. ..$ realgdp  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ lfpr25_54: list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ unrate   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ infcpi   : list()
##   .. .. ..- attr(*, "class")= chr  "collector_double" "collector"
##   .. ..$ newjobs  : list()
##   .. .. ..- attr(*, "class")= chr  "collector_integer" "collector"
##   ..$ default: list()
##   .. ..- attr(*, "class")= chr  "collector_guess" "collector"
##   ..- attr(*, "class")= chr "col_spec"

glimpse(indicators)

## Observations: 148
## Variables: 7
## $ Date      <date> 1980-01-01, 1980-04-01, 1980-07-01, 1980-10-01, 198...
## $ President <chr> "Reagan", "Reagan", "Reagan", "Reagan", "Reagan", "R...
## $ realgdp   <dbl> 1.3, -7.9, -0.6, 7.6, 8.5, -2.9, 4.7, -4.6, -6.5, 2....
## $ lfpr25_54 <dbl> 78.7, 78.6, 78.6, 78.7, 79.1, 79.4, 79.1, 79.5, 79.5...
## $ unrate    <dbl> 6.3, 7.3, 7.7, 7.4, 7.4, 7.4, 7.4, 8.2, 8.8, 9.4, 9....
## $ infcpi    <dbl> 16.7, 14.2, 7.7, 11.7, 11.5, 8.6, 11.6, 6.7, 3.6, 5....
## $ newjobs   <int> 316, -437, -408, 679, 390, 238, 194, -401, -628, -47...

One Indicator for One President.

indicators %>% filter(President == "Reagan") %>% 
  ggplot(aes(x=Date,y=realgdp)) +
  geom_point() +
  geom_smooth() +
  ggtitle("Reagan - Real GDP Growth Rate")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Compare the Presidents on Economic Performance

Produce a time series plot of the data for each variable. Map the presidents’ names to color.

indicators %>% ggplot(aes(x=Date,y=realgdp,col=President)) +
  geom_point() +
  geom_smooth(se=FALSE) + ggtitle("Growth Rate of Real GDP") 

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

indicators %>% ggplot(aes(x=Date,y=lfpr25_54,col=President)) +
  geom_point() +
  geom_smooth(se=FALSE) + ggtitle("Labor Force Participation Rate 25 to 54") 

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

indicators %>% ggplot(aes(x=Date,y=unrate,col=President)) +
  geom_point() +
  geom_smooth(se=FALSE) + ggtitle("Unemployment Rate")

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

indicators %>% ggplot(aes(x=Date,y=infcpi,col=President)) +
  geom_point() +
  geom_smooth(se=FALSE) + ggtitle("Inflation (CPI)") 

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

indicators %>% ggplot(aes(x=Date,y=newjobs,col=President)) +
  geom_point() +
  geom_smooth(se=FALSE) + ggtitle("New Jobs") 

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Restructure the Data

We need to create a narrow version of our dataframe in which the indicator names are the contents of a variable rather than variable names. The name of the variable will be “indicator” and the values will be in a variable called “value.” Call the resulting dataframe “indg.”

 indicators %>% gather(indicator,value,c(realgdp,lfpr25_54,unrate,infcpi,newjobs)) -> indg
glimpse(indg)

## Observations: 740
## Variables: 4
## $ Date      <date> 1980-01-01, 1980-04-01, 1980-07-01, 1980-10-01, 198...
## $ President <chr> "Reagan", "Reagan", "Reagan", "Reagan", "Reagan", "R...
## $ indicator <chr> "realgdp", "realgdp", "realgdp", "realgdp", "realgdp...
## $ value     <dbl> 1.3, -7.9, -0.6, 7.6, 8.5, -2.9, 4.7, -4.6, -6.5, 2....

glimpse(indicators)

## Observations: 148
## Variables: 7
## $ Date      <date> 1980-01-01, 1980-04-01, 1980-07-01, 1980-10-01, 198...
## $ President <chr> "Reagan", "Reagan", "Reagan", "Reagan", "Reagan", "R...
## $ realgdp   <dbl> 1.3, -7.9, -0.6, 7.6, 8.5, -2.9, 4.7, -4.6, -6.5, 2....
## $ lfpr25_54 <dbl> 78.7, 78.6, 78.6, 78.7, 79.1, 79.4, 79.1, 79.5, 79.5...
## $ unrate    <dbl> 6.3, 7.3, 7.7, 7.4, 7.4, 7.4, 7.4, 8.2, 8.8, 9.4, 9....
## $ infcpi    <dbl> 16.7, 14.2, 7.7, 11.7, 11.5, 8.6, 11.6, 6.7, 3.6, 5....
## $ newjobs   <int> 316, -437, -408, 679, 390, 238, 194, -401, -628, -47...

First attempt at Facetting

ggplot(indg,aes(x=Date,y=value)) + 
  geom_point() +
  facet_grid(indicator~President)

Fix the Scales

ggplot(indg,aes(x=Date,y=value)) + 
  geom_point() +
  facet_grid(indicator~President,scales = "free")

Fix the Order of the Presidents

The next problem is that the names of the presidents are in alphabetical order. Fix this and redo the graph

indg$President = factor(indg$President,levels = c("Reagan","Bush 41","Clinton","Bush 43","Obama","Trump"))

ggplot(indg,aes(x=Date,y=value)) + 
  geom_point() +
  facet_grid(indicator~President,scales = "free")

Fix the Indicator Labels

indg$indicator=factor(indg$indicator,labels =
  c("Inflation", "Labor Force Participation \n 25-54"," New Jobs","Real GDP \n Growth Rate","Unemployment \n Rate") )
levels(indg$indicator)

## [1] "Inflation"                         
## [2] "Labor Force Participation \n 25-54"
## [3] " New Jobs"                         
## [4] "Real GDP \n Growth Rate"           
## [5] "Unemployment \n Rate"

ggplot(indg,aes(x=Date,y=value)) + 
  geom_point() +
  facet_grid(indicator~President,scales = "free")

Make Indicator Labels Horizontal

ggplot(indg,aes(x=Date,y=value)) + 
  geom_point() +
  facet_grid(indicator~President,scales = "free") +
  theme(strip.text.y = element_text(angle = 0))

Task 10:

Enhance the appearance of the graph by mapping col to the name of the president. The default colors are a bit wimpy, so pick something better using scale_color_brewer.

ggplot(indg,aes(x=Date,y=value,col=President)) + 
  geom_point() +
  facet_grid(indicator~President,scales = "free") +
  theme(strip.text.y = element_text(angle = 0)) +
  scale_color_brewer(palette="Dark2")

Task 11: Eliminate the Legend

ggplot(indg,aes(x=Date,y=value,col=President)) + 
  geom_point() +
  facet_grid(indicator~President,scales = "free") +
  theme(strip.text.y = element_text(angle = 0)) +
  scale_color_brewer(palette="Dark2") +
  theme(legend.position = "none") 

Task 12: Pretty it up and Save.

Play with the parameters to get something you like. Also change the angle of the years on the x-axis to avoid the overprinting. Finally save the result to both a pdf file and a jpg file.

indg %>% ggplot(aes(x=Date,y=value,col=President)) + 
  geom_point(size=.5) +
  geom_smooth(size=.2) +
  facet_grid(indicator~President,scales="free") +
  theme(axis.text.x = element_text(angle=45)) +
  theme(strip.text.y = element_text(angle = 0)) +
  scale_color_brewer(palette="Dark2") +
  theme(legend.position = "none") +
  ggsave("Compare Presidents.jpg" ) +
  ggsave("Compare Presidents.pdf" )

## Saving 8 x 6 in image

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Saving 8 x 6 in image

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'