1. Economic Data

One possible single plot view:

library(ggplot2)
library(lattice)
sts <- ts(scale(economics[-1], center = FALSE),
          start = c(1967, 7), frequency = 12)
xyplot(sts, superpose = TRUE)

An alternative scaling that emphasizes the behavior around 2008:

sec2008 <- as.numeric(economics[487, -1])
sec <- scale(economics[-1], center = FALSE, scale = sec2008)
xyplot(ts(sec, start = c(1967, 7), frequency = 12), superpose = TRUE)

An alternative is to use multiple panels:

library(zoo)
ets <- ts(economics[-1], start = c(1967, 7), frequency = 12)
autoplot(as.zoo(ets)) + facet_grid(Series ~ ., scales = 'free')

Some notes:

2. Unemployment Rates

The state code for Iowa is 19. This can be used to filter down to the Iowa data. An alternative is to split the Title variable into county name cname and state code scode components:

library(dplyr)
lausUS <- mutate(lausUS,
                 cname = sub(",.*", "", Title),
                 scode = sub(".*, ", "", Title))

You could also use tidyr::separate().

Extract the Iowa Data:

lausIA <- filter(lausUS, scode == "IA")

Compute average unemployment rates and identify counties with highest and lowest rates:

avgIA <- summarize(group_by(lausIA, cname, State, County),
                   avg_unemp = mean(UnempRate))
avgIA <- ungroup(avgIA)
hi <- top_n(ungroup(avgIA), 1, avg_unemp)
lo <- top_n(ungroup(avgIA), 1, desc(avg_unemp))
hi$cname
## [1] "Marshall County"
lo$cname
## [1] "Lyon County"

The monthly data for these counties can be extracted with

mmIA <- filter(lausIA, cname %in% c(lo$cname, hi$cname))

Using ggplot and an ordered factor for the period:

library(ggplot2)
library(forcats)
ggplot(mutate(mmIA, Period = fct_inorder(Period)),
       aes(x = Period, y = UnempRate, group = cname, color = cname)) +
    geom_line()

The dates can also be computed using ymd() and months():

library(lubridate)
mmIA$date <- rep(ymd("2021-01-01") + months(1 : 14), each = 2)

ggplot(mmIA, aes(date, UnempRate, group = Title, color = Title)) +
    geom_line()

Parsing the dates in thePeriod variable is also possible, but needs care if you want your code to work in an non-English locale. A vignette for the readr package provides some background. One of many possibilities is to use parse_date_time() from lubridate:

library(lubridate)
mmIA <- mutate(mmIA, date = parse_date_time(Period, "my"))

You can also create a time series object, for example as

library(tidyr)
mmIAts <- select(mmIA, UnempRate, cname, Period) %>%
    pivot_wider(names_from=cname, values_from=UnempRate) %>%
    select(-Period) %>%
    ts(start = c(2021, 1), frequency = 12)

and take advantage of the autoplot() methods for time series provided in the forecast package:

library(forecast)
autoplot(mmIAts)

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