Graphing time series in ggplot2 with CDC weeks ordered sensibly - ggplot2

Question

I have a data frame ('Example') like this.
n CDCWeek Year Week
25.512324 2011-39 2011 39
26.363035 2011-4 2011 4
25.510500 2011-40 2011 40
25.810663 2011-41 2011 41
25.875451 2011-42 2011 42
25.860873 2011-43 2011 43
25.374876 2011-44 2011 44
25.292944 2011-45 2011 45
24.810807 2011-46 2011 46
24.793090 2011-47 2011 47
22.285000 2011-48 2011 48
23.015480 2011-49 2011 49
26.296376 2011-5 2011 5
22.074581 2011-50 2011 50
22.209183 2011-51 2011 51
22.270705 2011-52 2011 52
25.391377 2011-6 2011 6
25.225481 2011-7 2011 7
24.678918 2011-8 2011 8
24.382214 2011-9 2011 9
I want to plot this as a time series with 'CDCWeek' as the X-axis and 'n' as the Y using this code.
ggplot(Example, aes(CDCWeek, n, group=1)) + geom_line()
The problem I am running into is that it is not graphing CDCWeek in the right order. CDCWeek is the year followed by the week number (1 to 52 or 53 depending on the year). It is being graphed in the order shown in the data frame, with 2011-39 followed by 2011-4, etc. I understand why this is happening but is there anyway to force ggplot2 to use the proper order of weeks?
EDIT: I can't just use the 'week' variable because the actual dataset covers many years.
Thank you

Answer 1

aweek::get_date allows you to get weekly dates only using the year and epiweek.
Here I created a reprex with a sequence of dates (link), extract the epiweek with lubridate::epiweek, defined sunday as start of a week with aweek::set_week_start, summarized weekly values, created a new date vector with aweek::get_date, and plot them.
library(tidyverse)
library(lubridate)
library(aweek)
data_ts <- tibble(date=seq(ymd('2012-04-07'),
ymd('2014-03-22'),
by = '1 day')) %>%
mutate(value = rnorm(n(),mean = 5),
#using aweek
epidate=date2week(date,week_start = 7),
#using lubridate
epiweek=epiweek(date),
dayw=wday(date,label = T,abbr = F),
month=month(date,label = F,abbr = F),
year=year(date)) %>%
print()
#> # A tibble: 715 x 7
#> date value epidate epiweek dayw month year
#> <date> <dbl> <aweek> <dbl> <ord> <dbl> <dbl>
#> 1 2012-04-07 3.54 2012-W14-7 14 sábado 4 2012
#> 2 2012-04-08 5.79 2012-W15-1 15 domingo 4 2012
#> 3 2012-04-09 4.50 2012-W15-2 15 lunes 4 2012
#> 4 2012-04-10 5.44 2012-W15-3 15 martes 4 2012
#> 5 2012-04-11 5.13 2012-W15-4 15 miércoles 4 2012
#> 6 2012-04-12 4.87 2012-W15-5 15 jueves 4 2012
#> 7 2012-04-13 3.28 2012-W15-6 15 viernes 4 2012
#> 8 2012-04-14 5.72 2012-W15-7 15 sábado 4 2012
#> 9 2012-04-15 6.91 2012-W16-1 16 domingo 4 2012
#> 10 2012-04-16 4.58 2012-W16-2 16 lunes 4 2012
#> # ... with 705 more rows
#CORE: Here you set the start of the week!
set_week_start(7) #sunday
get_week_start()
#> [1] 7
data_ts_w <- data_ts %>%
group_by(year,epiweek) %>%
summarise(sum_week_value=sum(value)) %>%
ungroup() %>%
#using aweek
mutate(epi_date=get_date(week = epiweek,year = year),
wik_date=date2week(epi_date)
) %>%
print()
#> # A tibble: 104 x 5
#> year epiweek sum_week_value epi_date wik_date
#> <dbl> <dbl> <dbl> <date> <aweek>
#> 1 2012 1 11.0 2012-01-01 2012-W01-1
#> 2 2012 14 3.54 2012-04-01 2012-W14-1
#> 3 2012 15 34.7 2012-04-08 2012-W15-1
#> 4 2012 16 35.1 2012-04-15 2012-W16-1
#> 5 2012 17 34.5 2012-04-22 2012-W17-1
#> 6 2012 18 34.7 2012-04-29 2012-W18-1
#> 7 2012 19 36.5 2012-05-06 2012-W19-1
#> 8 2012 20 32.1 2012-05-13 2012-W20-1
#> 9 2012 21 35.4 2012-05-20 2012-W21-1
#> 10 2012 22 37.5 2012-05-27 2012-W22-1
#> # ... with 94 more rows
#you can use get_date output with ggplot
data_ts_w %>%
slice(-(1:3)) %>%
ggplot(aes(epi_date, sum_week_value)) +
geom_line() +
scale_x_date(date_breaks="5 week", date_labels = "%Y-%U") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Weekly time serie",
x="Time (Year - CDC epidemiological week)",
y="Sum of weekly values")
ggsave("figure/000-timeserie-week.png",height = 3,width = 10)
Created on 2019-08-12 by the reprex package (v0.3.0)

Answer 2

Convert the Year and Week into a date with dplyr:
df <- df %>%
mutate(date=paste(Year, Week, 1, sep="-") %>%
as.Date(., "%Y-%U-%u"))
ggplot(df, aes(date, n, group=1)) +
geom_line() +
scale_x_date(date_breaks="8 week", date_labels = "%Y-%U")

Answer 3

One option would be to use the Year and Week variables you already have but facet by Year. I changed the Year variable in your data a bit to make my case.
Example$Year = rep(2011:2014, each = 5)
ggplot(Example, aes(x = Week, y = n)) +
geom_line() +
facet_grid(Year~., scales = "free_x")
#facet_grid(.~Year, scales = "free_x")
This has the added advantage of being able to compare across years. If you switch the final line to the option I've commented out then the facets will be horizontal.
Yet another option would be to group by Year as a factor level and include them all on the same figure.
ggplot(Example, aes(x = Week, y = n)) +
geom_line(aes(group = Year, color = factor(Year)))

Answer 4

It turns out I just had to order Example$CDCWeek properly and then ggplot would graph it properly.
1) Put the database in the proper order.
Example <- Example[order(Example$Year, Example$Week), ]
2) Reset the rownames.
row.names(Example) <- NULL
3) Create a new variable with the observation number from the rownames
Example$Obs <- as.numeric(rownames(Example))
4) Order the CDCWeeks variable as a factor according to the observation number
Example$CDCWeek <- factor(Example$CDCWeek, levels=Example$CDCWeek[order(Example$Obs)], ordered=TRUE)
5) Graph it
ggplot(Example, aes(CDCWeek, n, group=1)) + geom_line()
Thanks a lot for the help, everyone!