X-Git-Url: https://code.communitydata.science/stats_class_2020.git/blobdiff_plain/4e1671977b9073cc4c6d46ab614a3767008ad6c6..37924e44ba85abcf6fa154c46d9d686c926aa4f3:/r_tutorials/w05a-R_tutorial.html?ds=inline diff --git a/r_tutorials/w05a-R_tutorial.html b/r_tutorials/w05a-R_tutorial.html index 137badd..3c76d1d 100644 --- a/r_tutorials/w05a-R_tutorial.html +++ b/r_tutorials/w05a-R_tutorial.html @@ -1549,8 +1549,8 @@ MTS 525
This is a supplement to the Week 5 R tutorial focused on elaborating some examples of time series plots and more polished plots using ggplot2
. Iâll work some data on state-level COVID-19 in the United States published by The New York Times (NYT). You can access the data an details about the sources, measurement, and different datasets available via the NYT github repository.
To start, Iâll load up the tidyverse
library and also attach the lubridate
package to help handle dates and times. Then Iâll import the âraw csvâ from the web, and take a look at the dataset:
This is a supplement to the Week 5 R tutorial focused on elaborating some examples of time series plots and more polished plots using ggplot2
. Iâll work with some data on state-level COVID-19 in the United States published by The New York Times (NYT). You can access the data as well as details about the sources, measurement, and related available datasets via the NYT github repository.
To start, Iâll load up the tidyverse
library and also attach the lubridate
package, which can help to handle dates and times. Then Iâll import the âraw csvâ of my dataset from the web, and take a look at it:
library(tidyverse)
library(lubridate)
@@ -1559,7 +1559,7 @@ data_url <- url("https://raw.githubusercontent.com/nytimes/covid-19-data
d <- read_csv(data_url)
d
-## # A tibble: 12,004 x 5
+## # A tibble: 12,334 x 5
## date state fips cases deaths
## <date> <chr> <chr> <dbl> <dbl>
## 1 2020-01-21 Washington 53 1 0
@@ -1572,69 +1572,71 @@ d
## 8 2020-01-25 Washington 53 1 0
## 9 2020-01-26 Arizona 04 1 0
## 10 2020-01-26 California 06 2 0
-## # ⦠with 11,994 more rows
-For the sake of my examples, Iâm planning to work with the date
, state
, cases
, and deaths
variables. Notice that by using the read_csv()
function to import the data, R already recognizes the date
column as dates. It looks like I need to convert the state variable to a factor, however. After I do that I can get a quick sense of how much data I have for each state with a univariate table that just counts the number of observations (rows) for each value of state
.
For the sake of my examples, Iâm planning to work with the date
, state
, cases
, and deaths
variables. Notice that by using the read_csv()
function to import the data, R already recognizes the date
column as dates. Also notice that the column names for cases and deaths donât reflect the fact that both variables are cumulative counts. Also also, notice that it looks like I will want to convert the state variable to a factor (since thatâs a more accurate representation of the data and it will likely make my analysis/plotting work easier later on). Iâll start there and then get a quick sense of how much data I have for each state with a univariate table.
d$state <- factor(d$state)
table(d$state)
##
## Alabama Alaska Arizona
-## 208 209 255
+## 214 215 261
## Arkansas California Colorado
-## 210 256 216
+## 216 262 222
## Connecticut Delaware District of Columbia
-## 213 210 214
+## 219 216 220
## Florida Georgia Guam
-## 220 219 206
+## 226 225 212
## Hawaii Idaho Illinois
-## 215 208 257
+## 221 214 263
## Indiana Iowa Kansas
-## 215 213 214
+## 221 219 220
## Kentucky Louisiana Maine
-## 215 212 209
+## 221 218 215
## Maryland Massachusetts Michigan
-## 216 249 211
+## 222 255 217
## Minnesota Mississippi Missouri
-## 215 210 214
+## 221 216 220
## Montana Nebraska Nevada
-## 208 233 216
+## 214 239 222
## New Hampshire New Jersey New Mexico
-## 219 217 210
+## 225 223 216
## New York North Carolina North Dakota
-## 220 218 210
+## 226 224 216
## Northern Mariana Islands Ohio Oklahoma
-## 193 212 215
+## 199 218 221
## Oregon Pennsylvania Puerto Rico
-## 222 215 208
+## 228 221 214
## Rhode Island South Carolina South Dakota
-## 220 215 211
+## 226 221 217
## Tennessee Texas Utah
-## 216 238 225
+## 222 244 231
## Vermont Virgin Islands Virginia
-## 214 207 214
+## 220 213 220
## Washington West Virginia Wisconsin
-## 260 204 245
+## 266 210 251
## Wyoming
-## 210
+## 216
+Two things to point out here: (1) not all of our âstatesâ are technically states (e.g., Puerto Rico, District of Columbia, Virgin Islands, Northern Mariana Islands, Guam). I prefer to think of this as the NYT data scientist team quietly reminding us that the United States maintains a number of colonial properties without formal political representation! The second thing (2) is that not all states have the same number of observations/rows. You can probably figure out exactly why this might be the case from the documentation of the data sources and or from thinking more carefully about the context (e.g., some states had cases much earlier in 2020 than others). Anyhow, just some things to be aware of as we move forward with our analysis.
I recommend using geom_path()
to create univariate time series plots. Specifically, Iâll call geom_line()
, which is a specialized version of geom_path()
that connects observations in order according to the values of variable that is mapped to the x-axis. By convention, a univariate time series maps dates to the x-axis, so this will just plot a line connecting the dots over time.
For my first example, I want to build up a plot of weekly case counts in Illinois. I can start off by just plotting the cumulative cases for all of the states and work my way towards the specific plot I want from there:
+A univariate time series is just a fancy term for a plot of a single variable for which you have repeated observations collected over time. I recommend using geom_path()
(thatâs a hyperlink to the documentation) to create univariate time series plots. Specifically, Iâll call geom_line()
, which is a specialized (masked) version of geom_path()
that connects observations in order according to the values of variable that is mapped to the x-axis. By convention, a univariate time series maps dates to the x-axis, so this will just plot a line connecting the values of my y-values over time.
For a univariate example, letâs build a plot of weekly case counts in Illinois.
+I can start by just plotting the cumulative cases for all of the states and work towards the specific plot we want from there:
ggplot(data = d, aes(date, cases)) +
geom_line()
-
-Notice that ggplot handles the date
variable quite well by default! It recognizes the units of time and generates axis labels in terms of months. Also notice that ggplot handles the axis labels for the cases
variableâ¦less well. I donât know about you, but my brain doesnât parse scientific notation quickly/easily.
Okay, letâs get to work cleaning all this up. At this point, my next steps are to (1) restrict the data to the Illinois cases; (2) reorganize the cumulative daily case counts into weekly counts; and (3) plot it again with better axis labels and a nice title.
+ +Notice that ggplot handles the date
variable quite well by default! It recognizes the units of time and generates axis labels in terms of months. Also notice that ggplot handles the axis labels for the cases
variableâ¦less well. I donât know about you, but my brain doesnât parse scientific notation quickly/easily. Finally, the fact that this figure incorporates all the state-level observations as cumulative counts means that there is just a huge clutter of points/lines in this figure. Itâs impossible to really figure out whatâs going on, much less learn anything other than the cumulative number of cases within states appears to have increased over time (thanks for nothing, ggplot).
Okay, letâs get to work cleaning this up. At this point, my next steps are to (1) restrict the data to the Illinois cases; (2) reorganize the cumulative daily case counts into weekly counts; and (3) plot it again with better axis labels and a nice title.
I can restrict the data to Illinois in a few ways. Since Iâm using ggplot, Iâll work with Tidyverse âpipesâ (%>%
) and âverbsâ (in this case, filter
):
d %>%
filter(state == "Illinois") %>%
ggplot(aes(date, cases)) +
geom_line()
-
-Thatâs already much less cluttered. Inserting a call to the Tidyverse mutate
, group_by
, and summarize
verbs can help me generate the weekly counts Iâm looking for. Hereâs the code to produce a new object. Iâll walk through it below:
Thatâs already much less cluttered and much clearer. It also looks plausibly accurate (itâs always good to sanity check your data visualizations as you goâweird anomalies in a graph are usually a good indicator of something weird happening in the underlying code and/or data.
+Now onwards to converting my cumulative case counts into weekly case counts. When I wrote this tutorial, the first way I thought to do this involved making calls to the Tidyverse mutate
, group_by
, and summarize
verbs. After a little trial and error, I got it to work with the following code (which Iâll walk through in detail below):
il_weekly_cases <- d %>%
filter(state == "Illinois") %>%
mutate(
@@ -1645,7 +1647,7 @@ table(d$state)
summarize(new_cases = sum(diff_cases, na.rm = T), )
il_weekly_cases
-## # A tibble: 38 x 2
+## # A tibble: 39 x 2
## weekdate new_cases
## <fct> <dbl>
## 1 2020-01-20 1
@@ -1658,18 +1660,23 @@ il_weekly_cases
## 8 2020-03-09 87
## 9 2020-03-16 953
## 10 2020-03-23 3568
-## # ⦠with 28 more rows
-Thereâs quite a lot happening there. Iâll go through it verb-by-verb.
-First, I use mutate
to create a diff_cases
variable that disaggregates the cumulative values of cases
(read the documentation for diff
to learn more about this one). Differenced values alone wouldnât produce the same number of items (try running length(1:10)
and compare that with length(diff(1:10, 1))
to see what I mean), so I stores the first value of my cases
variable and then append the differenced values after that. Within the same call to mutate I also create a new variable weekdate
that collapses the dates into weeks (see the documentation for cut.Date
) and stores the resulting strings as factors (e.g., a factor where the levels correspond to a series of Mondays: â2020-01-20â, â2020-01-27ââ¦). Hopefully, so far so good?
Next, I use group_by
to aggregate everything by my weekdate
factor values.
Finally I use summarize
to reshape my data and collapse everything into weekly counts of new cases (notice that I use sum
inside the summarize
call to add up the case counts within the grouping variable). Okay, letâs see about plotting this now:
Hmm. looks like I have a problem with my dates. Letâs troubleshoot this:
+## # ⦠with 29 more rows +Thereâs quite a lot happening there so letâs go through it verb-by-verb.
+First, I filter
my cases to restrict the set to Illinois data. Then I use mutate
to create a diff_cases
variable that disaggregates the cumulative values of cases
(read the documentation for diff
to learn more about this one). Differenced values alone wouldnât produce the correct number of items (try running length(1:10)
and compare that with length(diff(1:10, 1))
to see what I mean), so I store the first value of my cases
variable and then append the differenced values after that (Note that this assumes and takes advantage of the fact that the data is sorted by date. I could add a call to arrange(-desc())
before doing my mutation to ensure the correct ordering, but wonât bother with that for now). Within the same call to mutate I also create a new variable weekdate
that collapses the dates into weeks (see the documentation for cut.Date
) and stores the resulting strings as factors (e.g., a factor where the levels correspond to a series of Mondays: â2020-01-20â, â2020-01-27ââ¦). Hopefully, so far so good?
Next, I use group_by
to aggregate everything by my weekdate
factor values. This is essentially creating conditional groupings of the data that I can then summarize in my next command.
Finally I use summarize
to reshape my data and collapse everything into weekly counts of new cases (notice that I use sum
inside the summarize
call to add up the case counts within the grouping variable). The result is a brand new two-column tibble consisting of weekdates and weekly counts of new cases. Excellent!
Okay, letâs see about plotting this now:
+il_weekly_cases %>%
+ ggplot(aes(weekdate, new_cases)) +
+ geom_line()
+
+Hmm. looks like I have a problem here. My first guess is that thereâs something funny going on with my weekdate
variable because it looks very different on the x-axis. Letâs troubleshoot:
class(il_weekly_cases$weekdate)
## [1] "factor"
-Whoops. It looks like I need to convert that weekdate
variable into an object of class âdateâ so that it will work with ggplot. There are a number of ways I could do this, but Iâll just make a new variable by first converting weekdate
to a character vector and then converting that into a date using as.Date
(and remember that it is sometimes easier to read these ânestedâ commands from the inside-out).
Whoops. Indeed, I need to convert that weekdate
variable back into an object of class âdateâ so that it will work with ggplot. There are a number of ways I could do this, but Iâll just make a new variable by first coercing weekdate
to a character vector and then coercing that into a date using as.Date
(and remember that it is sometimes easier to read these ânestedâ commands from the inside-out).
il_weekly_cases$date <- as.Date(as.character((il_weekly_cases$weekdate)))
il_weekly_cases
-## # A tibble: 38 x 3
+## # A tibble: 39 x 3
## weekdate new_cases date
## <fct> <dbl> <date>
## 1 2020-01-20 1 2020-01-20
@@ -1682,45 +1689,49 @@ il_weekly_cases
## 8 2020-03-09 87 2020-03-09
## 9 2020-03-16 953 2020-03-16
## 10 2020-03-23 3568 2020-03-23
-## # ⦠with 28 more rows
-That ought to work now:
+## # ⦠with 29 more rows +That ought to work for plotting now:
plot1 <- il_weekly_cases %>%
ggplot(aes(date, new_cases)) +
geom_line()
plot1
-
-Much better! Notice that the final week of the data appears to fall off a cliff. Thatâs just an artifact of the way that the NYT has published the data for part of the most recent week. Once it updates, the case count probably wonât drop like that (yikes). Anyhow, onwards to cleaning things up and adding a title.
+ +Much better! Notice that the final week of the data appears to fall off a cliff. Thatâs just an artifact of the way that the NYT has published the data for part of the most recent week. Once it updates, the case count probably wonât tumble like that (yikes).
As I mentioned briefly in class ggplot2
treats labels, titles, and scales as âlayersâ within itâs âgrammar of graphicsâ (and yes, Iâm rolling my eyes as I type those scare-quotes). For the purposes of our example here Iâm going to use scale_date
to work with the x-axis, scale_continuous
to work with the y-axis, and labs
to clean up the title and axis labels.
For starters, letâs see whether there might be any way I want to improve the axis labels. The ggplot defaults for my date
variable are pretty good already, but maybe I want to incorporate a label/break for each month as well as a more granular grid in the background that shows the weeks? Hereâs what all of that looks like:
Now we can style the plot. As I mentioned briefly in class ggplot2
treats labels, titles, and scales as âlayersâ within itâs âgrammar of graphicsâ (that sound you hear is me rolling my eyes as I type those scare-quotes). For the purposes of our example here Iâm going to use scale_date
to work with the x-axis, scale_continuous
to work with the y-axis, and labs
to clean up the title and axis labels. Each of those have documentation and should appear on the ggplot2
cheatsheet available via RStudio/Tidyverse.
To start, letâs see whether there might be any way I want to improve the x-axis labels. The ggplot defaults for my date
variable are pretty good already, but maybe I want to incorporate a label (âbreakâ) for each month as well as a more granular grid in the background (âminor_breaksâ) that shows the weeks? Also, I like the date labels along the axis as abbreviations of the month names, so Iâll keep that with a call to date_labels
. Hereâs what all of that looks like:
plot2 <- plot1 + scale_x_date(date_labels = "%b", date_breaks = "1 month", date_minor_breaks = "1 week")
plot2
-
-The ggplot documentation for scale_date
can give you some other examples and ideas. Also, notice how I appended the scale_date
layer to my existing plot and stored it as a new object? This can make it easier to work iteratively without losing any of my earlier layers along the way.
Now I can fix up the y-axis labels a bit using a call to the labels
argument after I load the scales
package.
The ggplot documentation for scale_date
can give you some other examples and ideas. Also, notice how I appended the scale_date
layer to my existing plot and stored it as a new object? This can make it easier to work iteratively on a single plot, adding new layers as I go without losing existing material along the way.
Now I can fix up the y-axis labels a bit using a call to the labels
argument after I load the scales
package (why doesnât ggplot support this kind of labeling itself? I have no clue).
library(scales)
plot3 <- plot2 + scale_y_continuous(label = comma)
plot3
-
-Nearly done. All thatâs left is a title and better axis names. Iâll do that with yet another layer.
+ +Nearly done. All thatâs left is a title and better axis names. Iâll do that with yet another layer call to labs
. The arguments here are pretty intuitive.
plot4 <- plot3 + labs(x = "Week (in 2020)", y = "New cases", title = "COVID-19 cases in Illinois")
plot4
-
-Last, but not least, I mentioned in our class session that ggplot also has âthemesâ that can be useful for styling plots. One I have used for publications is the âlightâ theme. Hereâs how to apply that:
+ +Last, but not least, I mentioned in our class session that ggplot also has âthemesâ that can be useful for styling plots. One I have used for publications is the âlightâ theme. Here I apply that theme asâ¦yet another layer:
plot4 + theme_light()
-
+
Thatâs looking much better than when we started! If you wanted to export it as a standalone file (e.g., .png, .pdf, or whatever), I recommend looking at the documentation for the ggsave()
function, which is available via ggplot2. Base R also has a save()
function that you can work with, although it can be a bit more complicated to get comfortable with.
So what if you wanted to plot a multivariate time series (e.g., the same plot for more than one state and/or for more than one measure)? As always, you have a number of options, but the most effective way to achieve this with ggplot involves learning to work with âlongâ format data.
-Thus far, we have worked mostly with âwideâ format data where (nearly) every row corresponds to a single unit/observation and every column corresponds to a variable (for which we usually have no more than one value attributed to any unit/observation). Wide format data is great for many things, but it turns out that learning to work with long format data can be super helpful for a number of purposes. Producing richer, multidimensional ggplot visualizations is one of them.
+Okay, thatâs a lovely univariate time series plot. Now letâs make this more sophisticated and interesting by incorporating more data, more dimensions, and more variables. In order to do that, I want to start with a little detour into data structures. Try to stay with meâthis turns out to be super important for working more efficiently with tools like ggplot as well as learning to manage more complex statistical analysis strategies (that we wonât really cover in the course, but so be it).
+So now you want to plot a multivariate time series (e.g., the same plot for more than one state and/or for more than one measure). As always, you have a number of options, but the most effective way to achieve this with ggplot involves learning to work with âlongerâ data.
+Thus far, we have worked mostly with âwideâ format data where (nearly) every row corresponds to a single unit/observation and every column corresponds to a distinct variable (for which we usually have no more than one value attributed to any unit/observation). This often results in wider format data that is great for many things. However, it turns out that longer format data can be super helpful for a number of purposes. Producing richer, multidimensional ggplot visualizations is one of them.
Consider the format of my tidied dataframe that I used for plotting:
il_weekly_cases
-## # A tibble: 38 x 3
+## # A tibble: 39 x 3
## weekdate new_cases date
## <fct> <dbl> <date>
## 1 2020-01-20 1 2020-01-20
@@ -1733,11 +1744,11 @@ plot4
## 8 2020-03-09 87 2020-03-09
## 9 2020-03-16 953 2020-03-16
## 10 2020-03-23 3568 2020-03-23
-## # ⦠with 28 more rows
-This dataframe is in a âwideâ format. Each row is a week and each column is a variable unique to that week.
-Our original dataframe was a bit âlongerâ:
+## # ⦠with 29 more rows +This dataframe is in a pretty âlongâ format. Each row is a week and each column is a variable unique to that week (okay, I could consolidate my weekdate
and date
columns into just one, but thatâs not really the point here. The idea is that thereâs minimal redundant information in the rows and in the columns).
Our original dataframe was also pretty âlongâ:
d
-## # A tibble: 12,004 x 5
+## # A tibble: 12,334 x 5
## date state fips cases deaths
## <date> <fct> <chr> <dbl> <dbl>
## 1 2020-01-21 Washington 53 1 0
@@ -1750,8 +1761,10 @@ plot4
## 8 2020-01-25 Washington 53 1 0
## 9 2020-01-26 Arizona 04 1 0
## 10 2020-01-26 California 06 2 0
-## # ⦠with 11,994 more rows
-We see multiple observations per state (I think I would say the units or rows correspond to âstate-datesâ or something like that). Itâs not completely âlongâ however, because we also have multiple columns corresponding to the two variables of interest: cases
and deaths
. The point I want to make is that there are a number of ways we can make this data âlonger.â For the purposes of producing a multi-state plot like the one above, the most important of these is going to involve dropping the step where I filtered by state=="Illinois"
and replacing by a group_by
step before I create my weekdate
variable. Iâm also going to go ahead and drop the date
and fips
variables because theyâre just getting in my way at this point. Iâll start there
Here we have multiple observations per state (I think I would say the units or rows correspond to âstate-datesâ or something like that). Itâs not as âlongâ as possible, though, because we also have multiple columns corresponding to the two variables of interest: cases
and deaths
.
For the purposes of producing a multi-state and multivariate set of plots, the most important thing I want to do is consolidate my dataset into a format where I have the following columns: date
(collapsed into weeks), state
, variable
(which will either have a value of new cases
or new deaths
), and a column for value
that will hold the corresponding state-week count for the variable in each row. If that doesnât make sense, donât worry, weâll get there soon enough.
Doing this involves a different approach to tidying up my data. Iâll start by dropping the step where I filtered by state=="Illinois"
and replacing it with a group_by
step before I create my weekdate
variable. Iâm also going to go ahead and drop the date
and fips
variables because theyâre just getting in my way.
weekly <- d %>%
group_by(state) %>%
mutate(
@@ -1759,7 +1772,7 @@ plot4
) %>%
select(state, cases, deaths, weekdate)
weekly
-## # A tibble: 12,004 x 4
+## # A tibble: 12,334 x 4
## # Groups: state [55]
## state cases deaths weekdate
## <fct> <dbl> <dbl> <fct>
@@ -1773,17 +1786,19 @@ weekly
## 8 Washington 1 0 2020-01-20
## 9 Arizona 1 0 2020-01-20
## 10 California 2 0 2020-01-20
-## # ⦠with 11,994 more rows
-Iâm getting somewhere with this, I promise. One of the principles of âtidyâ data is to make it so that every variable has a column, every observation has a row, and every value has a cell. Right now, Iâve got multiple observations for each state-week spread across multiple rows. Remember that my cases
and deaths
variables are actually cumulative counts, so I really only need to store the maximum value for each state-week in order to calculate the new cases per state-week. Letâs see what to do about that:
Now Iâve got multiple observations for each state-week spread across multiple rows (because my rows were structured around a more granular measure of time). My next move is to collapse these into a single observation for each state-week. Remember that my cases
and deaths
variables are still cumulative counts, so as I do this aggregation by week I will only need to store the maximum value for each state-week in order to calculate the number of new cases per state-week.
tidy_weekly <- weekly %>%
group_by(state, weekdate) %>%
summarize(
cum_cases = max(cases, na.rm = T),
cum_deaths = max(deaths, na.rm = T)
)
-tidy_weekly$weekdate <- as.Date(as.character(tidy_weekly$weekdate))
-
-tidy_weekly <- tidy_weekly %>%
+Notice that the call to group_by
groups by multiple variables. The order here matters! If I reversed it to read group_by(weekdate, state)
the results would be very different. With the correct ordering, I have things bundled up into state-week sub-groups and then I move on to calculate the maximum value of cumulative cases within each bundle.
+Next, I can fix up my weekdate
variable again so that it is a Date object.
+tidy_weekly$weekdate <- as.Date(as.character(tidy_weekly$weekdate))
+This will allow me to do some sorting within my state-week bundles to ensure things are in the proper order before I convert my weekly cumulative case count into weekly new case counts.
+tidy_weekly <- tidy_weekly %>%
group_by(state) %>%
arrange(-desc(weekdate)) %>%
mutate(
@@ -1792,7 +1807,7 @@ tidy_weekly <- tidy_weekly %>%
)
tidy_weekly
-## # A tibble: 1,780 x 6
+## # A tibble: 1,835 x 6
## # Groups: state [55]
## state weekdate cum_cases cum_deaths new_cases new_deaths
## <fct> <date> <dbl> <dbl> <dbl> <dbl>
@@ -1806,8 +1821,9 @@ tidy_weekly
## 8 Massachusetts 2020-01-27 1 0 1 0
## 9 Washington 2020-01-27 1 0 0 0
## 10 Arizona 2020-02-03 1 0 0 0
-## # ⦠with 1,770 more rows
-This is headed in the right direction. For some purposes, though, itâs still not quite âlongâ enough For starters, I can drop the cumulative cases and deaths columns. The other thing I can do is âpivotâ the data to organize the new_cases
and new_deaths
measures a little differently. To manage this, Iâll use the pivot_longer()
function (part of the tidyr
package from the tidyverse). I will also go ahead and coerce my weekdate
into a Date object again:
+## # ⦠with 1,825 more rows
+Weâre much closer to our goal now!
+I can go ahead and drop the cumulative cases and deaths columns with a call to select
in my next step. Then the big next (and nearly final) step is to âpivotâ the data to organize the new_cases
and new_deaths
measures in the way I described above. To manage this, Iâll use the pivot_longer()
function (part of the tidyr
package from the tidyverse):
long_weekly <- tidy_weekly %>%
select(state, weekdate, new_cases, new_deaths) %>%
pivot_longer(
@@ -1817,7 +1833,7 @@ tidy_weekly
)
long_weekly
-## # A tibble: 3,560 x 4
+## # A tibble: 3,670 x 4
## # Groups: state [55]
## state weekdate variable value
## <fct> <date> <chr> <dbl>
@@ -1831,38 +1847,39 @@ long_weekly
## 8 Washington 2020-01-20 new_deaths 0
## 9 Arizona 2020-01-27 new_cases 0
## 10 Arizona 2020-01-27 new_deaths 0
-## # ⦠with 3,550 more rows
-Can you see what that did? I now have two rows of data for every state-week. One that contains a value for new_cases
and one that contains a value for new_deaths
. Both of those variables have been âpivotedâ into a single variable
column.
Before we move forward Iâm going to clean up the values of variable
.
Can you see what that did? I now have two rows of data for every state-week. One row contains a value for new_cases
and one contains a value for new_deaths
. Both of those variables have been âpivotedâ into a single variable
column and their corresponding values recorded in another new column. Note that this makes our dataframe a little longer even though it does not technically reduce the âwidthâ of this particular dataset (because weâve taken two columns and pivoted them to createâ¦two different columns). However, consider that we could accommodate as many additional numerical variables and values as we might like in this manner and you can start to see how this pivoting step could result in much longer data (the length becomes a function of the number of units in your dataset and the variables you include in your pivoting step).
Before we move forward Iâm also going to clean up the values of variable
. This turns out to be helpful later on when weâre plotting, but makes more sense to implement here before I start creating any plot layers.
long_weekly <- long_weekly %>%
mutate(
variable = recode(variable, new_cases = "new cases", new_deaths = "new deaths")
)
-Okay, prepared with my tidy_weekly
and my long_weekly
tibbles, Iâm now ready to generate some more interesting multidimensional plots. Letâs start with the same sort of time series of new cases we made for Illinois before so we can see how to replicate that with this new data structure:
Okay, prepared with my long_weekly
tibble, Iâm now ready to generate some more interesting and multidimensional plots. Letâs start with the same univariate time series of new cases we made for Illinois before so we can see how to replicate that figure with this new data structure:
long_weekly %>%
filter(
state == "Illinois" & variable == "new cases"
) %>%
ggplot(aes(weekdate, value)) +
geom_line()
-
-Now we can easily plot Illinois cases against deaths from the same tibble:
+ +With our âlongerâ data format, we can plot Illinois cases against deaths from the same tibble by incorporating a color=variable
argument :
long_weekly %>%
filter(state == "Illinois") %>%
ggplot(aes(weekdate, value, color = variable)) +
geom_line()
-
-That plot isnât so great because the death counts are dwarfed by the case counts. Thank goodness!
-Now letâs compare Illinois case counts against some its neighbors in the upper midwest:
+ +Unfortunately, that plot isnât so great because the death counts are dwarfed by the case counts (thank goodness!).
+Now letâs compare Illinois case counts against some the neighboring states in the upper midwest:
upper_midwest <- c("Illinois", "Michigan", "Wisconsin", "Iowa", "Minnesota")
long_weekly %>%
filter(state %in% upper_midwest & variable == "new cases") %>%
ggplot(aes(weekdate, value, color = state)) +
geom_line()
-
-Now thatâs getting a bit more interesting.
-What about finding some way to also incorporate the death counts? Well, ggplot has another layer option called âfacetsâ that can help produce multiple plots and present them alongside each other (or in a grid). Hereâs an example that creates a faceted âgridâ (really just a side-by-side comparison) of case counts and deaths for the same five states.
+ +Notice that I use the %in%
operator to filter for the values of the state
vector that are âinâ the upper_midwest
vector (see help(%in%)
for more).
Also notice that we now have ourselves a multivariate time series!
+So now how about finding some way to also incorporate those death counts? If I just add them to this same plot weâll run into the same issue we did with the Illinois data because the death counts look tiny plotted on the same scale as the case counts. A good solution in such a situation is to create a second plot for weekly deaths that we can display together with this weekly cases plot that uses a differently scaled y-axis. The ggplot way to do this involves another type of layer called âfacets.â Hereâs an example that creates a faceted âgridâ (noy much of a grid since there are only two variables or categories weâre using to do the faceting) of weekly case counts and deaths for the same five states.
midwest_plot <- long_weekly %>%
filter(state %in% upper_midwest) %>%
ggplot(aes(weekdate, value, color = state)) +
@@ -1870,10 +1887,12 @@ long_weekly %>%
facet_grid(rows = vars(variable), scales = "free_y")
midwest_plot
-
-Now we can clean up some of the other elements we worked on with the original plot (axes, title, etc.). Iâll bake that into a single chunk below.
+ +Nice! Now we can clean up some of the other elements we worked on with the original plot (axes, title, etc.). Iâll bake that into a single chunk below.
midwest_plot + scale_x_date(date_labels = "%b", date_breaks = "1 month", date_minor_breaks = "1 week") + scale_y_continuous(label = comma) + labs(x = "Week (in 2020)", y = "", title = "COVID-19 cases in the Upper Midwest") + theme_light()
-
+
+Thatâs it! Mission accomplished. Weâve got ourselves a nice concise visualization of weekly COVID-19 cases and deaths across five upper midwest states over nearly 8 months of the pandemic.
+