Project Scratch Work

Scratch Work

I use this Rmd to work out my coding since I’m more familiar with Rmd.

Found Issues: - ggplot is not rendering in Shiny Why? - Not sure yet. No error given but shows blank plot. This might be dat$Dat column classified as chr or Posixt. I already added in code to convert to as.date but it’s not reading it?? - ylab in ggplot

To get done this week: -Fix issues, once I get this issue solved, the rest will be easier - Currently working on Tables. I found new functions for the tables and will cbind the others. -I want to divide State, County and Metro. - Some aesthetics: Bold, Underline, …etc - Add multiple selected Area Names to same ggplot

using lubridate for date conversion

• Since as.date wasn’t working.
• Turn into function when finished. (But shiny isn’t rendering correctly, No errors)

library(ggplot2)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

library(readxl)

dat <- read_excel("~/Desktop/Stat 128/Local Area Employment/Employment/Local_Area_Unemployment_Statistics__LAUS_.xlsx")

#p = dat[order(as.Date(dat$Date, format="%Y/%m/%d")), ]

# dat$Date <- as.Date(dat$Date , format = "%m/%d/%Y")
# p = dat[order(dat$Date ), ]

dat$Date <- ymd(dat$Date)


 p2 = dat[c(dat$Area_Name == "California"), ]
 p3 = p2[p2$Date >= "1990-01-01" & p2$Date <= "2020-01-01", ]
 p4 = p3[, c("Date", "Labor_Force", "Unemployment", "Employment", "Unemployment_Rate")]
 



g = ggplot(p4, mapping = aes(Date, Labor_Force)) + geom_line(mapping = aes(Date, Unemployment), color = "blue")

print(g)

Cummulation rolled up in years.

sum = aggregate(list(Total_Unemploy = p3$Unemployment), by = list(Year = p3$Year), FUN=sum)

sum

##    Year Total_Unemploy
## 1  1990       20994700
## 2  1991       28022900
## 3  1992       34153500
## 4  1993       34695200
## 5  1994       31460600
## 6  1995       28744100
## 7  1996       26918700
## 8  1997       24124600
## 9  1998       23032300
## 10 1999       20668900
## 11 2000       20038000
## 12 2001       22326700
## 13 2002       27556200
## 14 2003       28209200
## 15 2004       25894700
## 16 2005       22721600
## 17 2006       20790300
## 18 2007       23047700
## 19 2008       31823300
## 20 2009       48766900
## 21 2010       53808300
## 22 2011       51773000
## 23 2012       46126800
## 24 2013       39986100
## 25 2014       33687500
## 26 2015       28013100
## 27 2016       25014700
## 28 2017       22058500
## 29 2018       19695600
## 30 2019       18825000
## 31 2020        1593300

#Need to add Avg Unemplo_Rate
g2 = ggplot(data = sum, mapping = aes(x = Year, y = Total_Unemploy)) + geom_line()

print(g2)