Medians, ranges, missing counts and one run chart, all produced by a commented R script from the writer's own data, are what this descriptive analysis for NU603 Unit 4 reports. Searches like "nu 603 unit 4 assignment example", "nu603 unit 4 sample" and "nu603 unit 4 example" land here.
Describing Before Interpreting: A Descriptive Analysis of Fasting Times at Check-In, With the R Script That Produces Every Table
[Student Name]
Purdue University Global
NU603: Evaluating and Disseminating Principles of Advanced Nursing Practice - Clinical
Unit 4 Descriptive Data Analysis
[Instructor Name]
[Date]
Every figure is bracketed for the writer's own data; the two input files are de-identified extracts, and the script is described step by step in the appendix.
Data Sources
Two files feed the analysis. The first is an extract of the old check-in intake field for all adult elective cases in the baseline quarter and the [eight] post-launch weeks, with study number, arrival time, arrival date and time since last intake. The second holds the new nine-item check-in forms, which exist only after launch. A third, smaller file holds the weekly call-note audits and the fasting-related delay counts from the operating room log. All three carry study numbers only.
Cleaning
Every later number rests on how odd entries were handled, so cleaning is reported first. The rules come from the Unit 3 coding guide and were fixed before the data were opened.
Old intake field: [2,014] baseline records and [1,236] post-launch records were received. Of these, [31] had no intake time and were coded missing; [9] produced a duration outside [0 to 24] hours after the midnight rule and were checked against nurse notes, of which [6] were corrected from the note and [3] remained flagged and were excluded. Analyzed: [1,991] baseline and [1,227] post-launch.
Check-in form: [1,188] forms were entered. Item 3, last clear liquid, was blank on [44] and "does not remember" on [17]; both groups are reported, not imputed. [12] forms had milk or creamer only and were coded under item 4 as the guide requires. Analyzed for item 3: [1,127].
Missing values were counted and reported, never filled in, because an imputed fasting time would be a guess dressed as data.
Why Medians
Fasting durations are skewed. Most patients cluster within a few hours, and a small number have fasted since the evening before, which pulls a mean upward and makes it describe no one in particular. Medians with interquartile ranges describe the typical patient and the spread of the middle half without that distortion (Altman & Bland, 1994). Every table below uses them.
Table 1: Old Intake Field, Before and After Launch
Overall: baseline median [12.6] hours, interquartile range [10.9 to 14.8], n = [1,991]; post-launch median [5.4] hours, range [3.6 to 10.2], n = [1,227]. Before 07:00: baseline [7.9], range [6.8 to 9.4]; post-launch [7.6], range [6.4 to 9.1]. 07:00 to 11:00: baseline [11.6], range [10.2 to 13.1]; post-launch [4.9], range [3.4 to 8.8]. After 11:00: baseline [13.9], range [12.3 to 15.6]; post-launch [4.8], range [3.1 to 9.7]. Share with more than [eight] hours since last intake: baseline [81] percent; post-launch [34] percent.
This field records intake of anything, so it mixes food and fluid in both phases. It is the only measure with a baseline.
Table 2: Check-In Form, Post-Launch Only
Hours since last clear liquid: median [3.1], range [2.4 to 5.8], n = [1,127]. By band: before 07:00 [6.9]; 07:00 to 11:00 [2.8]; after 11:00 [2.6]. Hours since last solid food: median [14.2], range [12.6 to 16.1]. Thirst, 0 to 10: median [3], range [1 to 5]. Stop time recalled correctly, within [fifteen] minutes of the time documented on the call: [842] of [1,053] who received a call, [80] percent. Instruction source: pre-admission nurse [71] percent; surgeon's office [6] percent; both [18] percent; unsure [5] percent.
The table is labeled post-launch only. Its figures cannot be compared with a baseline, because no such form existed before launch.
Table 3: Process and Balancing Counts
Script line and stop time present: [134] of [160] audited call notes, [84] percent, after excluding [11] notes correctly routed on the exception line. Fasting-related delays and cancellations: [five] in [eight] weeks across [1,236] cases, [4.0] per [1,000]; baseline [15] in the quarter, [11] delays and [4] cancellations, across [2,014] cases, [7.4] per [1,000]. Of the five post-launch events: drank on time [0], drank late [1], ate [4]. Aspiration events reported by anesthesia: [0], listed for completeness and not as a result. Median call length: baseline [4.6] minutes; post-launch [5.9] minutes.
Run Chart
Weekly medians from the old intake field are plotted in time order for the [thirteen] baseline weeks and the [eight] post-launch weeks, with a vertical line at launch and the baseline median drawn across the whole chart as the center line. A run chart of this kind shows whether a shift or trend occurred around a point in time using simple rules about consecutive points, without claiming statistical significance (Perla et al., 2011). Weekly medians are plotted, not individual patients, which keeps the chart readable and avoids showing anyone's times.
No Inferential Test
No test of significance is reported. The samples are uneven, the baseline and outcome measures differ, and the design is a single-site pre-post comparison that could not support a causal claim whatever the p value. A test would add an appearance of certainty without adding information. Interpretation, including rival explanations, is the work of Unit 6.
Appendix: The R Script
The script runs in base R (R Core Team, 2024) with no added packages, so anyone at the site can reproduce every number from the same three files. Its steps, each marked by a comment in the file:
1. Read the three files with read.csv(), setting study number as text so leading zeros survive.
2. Convert arrival and intake times to minutes after midnight with a small helper function, to_min().
3. Compute durations as arrival minus intake; where intake is later than arrival, add 1,440 minutes, applying the midnight rule from the coding guide.
4. Flag any duration below zero or above 1,440 minutes; write flagged rows to a separate file for manual review rather than dropping them.
5. Assign arrival bands with cut() at 07:00 and 11:00.
6. Assign phase, baseline or post-launch, from the arrival date against the launch date held in one variable at the top of the script.
7. Summarize with a function that returns n, median and the 25th and 75th percentiles from quantile(), rounded to one decimal.
8. Build Table 1 by phase and band with aggregate(); add the share above eight hours.
9. Build Table 2 from the form file, reporting blanks and "does not remember" as separate counts.
10. Build Table 3 from the audit and delay file, computing rates per 1,000 cases.
11. Compute weekly medians and draw the run chart with plot() and abline(), marking launch and the baseline center line.
12. Write every table to a CSV file and the chart to a PNG, with the date of the run in each file name.
The file begins with a header comment naming its inputs, outputs and the version of the coding guide it follows.
References
Altman, D. G., & Bland, J. M. (1994). Statistics notes: Quartiles, quintiles, centiles, and other quantiles. BMJ, 309(6960), 996. https://doi.org/10.1136/bmj.309.6960.996
Perla, R. J., Provost, L. P., & Murray, S. K. (2011). The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Quality & Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
How this NU603 Unit 4 example is structured
Description is kept separate from interpretation, so the narrative reports what the numbers are and leaves what they mean to a later unit. Cleaning is reported first, since every later number rests on how impossible or missing times were treated, and the rules come from the coding guide rather than decisions made after seeing the data. Medians and interquartile ranges replace means throughout, because fasting durations are skewed by a few very long fasts. Each table gives its sample size, and the form table is labeled post-launch only. The run chart shows weekly medians rather than individual patients, which keeps the figure readable and protects privacy. The script is included so another person could reproduce every number from the same files. No inferential test appears, for reasons given in a short paragraph: small, uneven samples and a design that could not support a causal claim anyway.
Get an NU603 Unit 4 example written to your instructions
Describe your data, the variables, how many records and where they came from, but keep identifiers out. Include the Unit 4 prompt with its rubric, a copy of the measures you defined, and the software your section prefers. Within 24-48h, a free first custom sample returns tables, a chart and a commented script, each result left in brackets until your figures replace it. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
NU603 Unit 4 questions, answered
Does the analysis have to be done in R?
No. Excel, SPSS, Python or R can all produce the same descriptive tables, and sections often name a preference. A script has one advantage: every step is recorded, so anyone can rerun it and check the numbers. The sample uses R with plain-language comments, and the same logic transfers to whatever tool your course expects.
Why use medians instead of means for practicum data?
Times, lengths of stay and similar measures are often skewed, with a few very long values pulling the mean upward. The median tracks the typical patient more faithfully, and the interquartile range shows spread without being distorted by extremes. The sample gives median and range together and explains the choice once, so readers understand why averages do not appear.
Can the sample include my actual results?
Only you can supply them, and they should come from your own data collection. The sample shows the analysis structure with every figure bracketed, so you can see what a complete table, cleaning report and run chart contain. Replacing the brackets with your own verified numbers, and checking that each matches your file, is part of your work.