The file used in this example can be found here. The information in the file is regularly updated, so do not be concerned if your numerical values are slightly different when following along. Differences can be caused by addition of new data.
mydata <- read.csv("C:/Users/baile/Desktop/MBID/CS_6440/Practicum/COVID-19_Reported_Patient_Impact_and_Hospital_Capacity_by_State_Timeseries.csv")
This will read the data from the file and store it in the variable mydata. But what is my data? That we can figure out by using class().
class(mydata)
"data.frame"
mydata is a data.frame. You may have worked with them before in other languages such as Python. If we want to look at this data.frame, we can use View().
View(mydata)
View invokes a data viewer that is in a spreadsheet style, allowing us to look at matrix-like R objects.
Now that we have the data imported and we know the format, we can start working with the data.frame to do an analysis!!
To get specific information out of a data.frame, we use $. See the example below, where I take the critical_staffing_shortage_anticipated_within_week_yes column and store it in the variable crit_short. This then allows us to do a variety of analyses on the crit_short variable, such as mean, standard deviation, range, etc.
crit_short <- mydata$critical_staffing_shortage_anticipated_within_week_yes
mean(crit_short)
sd(crit_short)
Based on the results from the above code, the average number of hospitals reporting a critical staffing shortage anticipated within the week across all states and dates is 13.27 +/- 24.24 (mean +/- std).
We can also look at other items regarding this data, such as the range.
range(crit_short)
0 201
This tells us that the number of hospitals reporting critical staffing shortages across all states and dates is between 0 and 201.
Another thing we might want to look at, is how many times each of the states appears in the spreadsheet. To do this, we can use table() to get a count.
table(mydata$state)
AK AL AR AZ CA CO CT DC DE FL GA HI IA ID
384 466 401 405 411 393 389 379 380 395 406 466 406 394
IL IN KS KY LA MA MD ME MI MN MO MS MT NC
418 466 435 406 411 380 406 406 406 466 413 417 466 466
ND NE NH NJ NM NV NY OH OK OR PA PR RI SC
406 406 380 406 393 448 393 406 406 412 406 411 404 405
SD TN TX UT VA VI VT WA WI WV WY
382 393 466 387 405 368 390 408 406 416 406
You can also store this table as a variable and do further analysis on it.
One of the ways to do a quick analysis of all the columns and see general information is to use summary().
summary(mydata)
state date
Length:21746 Length:21746
Class :character Class :character
Mode :character Mode :character
critical_staffing_shortage_today_yes
Min. : 0.00
1st Qu.: 0.00
Median : 3.00
Mean : 11.08
3rd Qu.: 14.00
Max. :188.00
critical_staffing_shortage_today_no
Min. : 0.00
1st Qu.: 0.00
Median : 28.00
Mean : 53.52
3rd Qu.: 84.00
Max. :490.00
critical_staffing_shortage_today_not_reported
Min. : 0.00
1st Qu.: 2.00
Median : 8.00
Mean : 33.41
3rd Qu.: 37.00
Max. :492.00
critical_staffing_shortage_anticipated_within_week_yes
Min. : 0.00
1st Qu.: 0.00
Median : 4.00
Mean : 13.23
3rd Qu.: 18.00
Max. :201.00
critical_staffing_shortage_anticipated_within_week_no
Min. : 0.0
1st Qu.: 0.0
Median : 29.0
Mean : 51.5
3rd Qu.: 80.0
Max. :473.0
critical_staffing_shortage_anticipated_within_week_not_reported
Min. : 0.00
1st Qu.: 2.00
Median : 8.00
Mean : 33.27
3rd Qu.: 36.00
Max. :492.00
..........
Here we get a look at each of the columns, which can be very useful in your initial analysis!