Exercise 3 Part 1

module3/exo1/influenza-like-illness-analysis.org

@@ -39,9 +39,12 @@ We use only basic R functionality, so a earlier version might be OK, but we did
   (print "Please activate R in org-babel (org-babel-do-languages)!"))
 #+END_SRC
 
+#+RESULTS:
+
 * Data preprocessing
 
 The data on the incidence of influenza-like illness are available from the Web site of the [[http://www.sentiweb.fr/][Réseau Sentinelles]]. We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download. The URL is:
+
 #+NAME: data-url
 http://www.sentiweb.fr/datasets/incidence-PAY-3.csv
 
@@ -63,12 +66,27 @@ This is the documentation of the data from [[https://ns.sentiweb.fr/incidence/cs
 The [[https://en.wikipedia.org/wiki/ISO_8601][ISO-8601]] format is popular in Europe, but less so in North America. This may explain why few software packages handle this format. The Python language does it since version 3.6. We therefore use Python for the pre-processing phase, which has the advantage of not requiring any additional library. (Note: we will explain in module 4 why it is desirable for reproducibility to use as few external libraries as possible.)
 
 ** Download
-After downloading the raw data, we extract the part we are interested in. We first split the file into lines, of which we discard the first one that contains a comment. We then split the remaining lines into columns.
+To make the data locally available we first download the data and save it for furture use. The data of the retrieval is output as result.
+
+#+BEGIN_SRC python :results output :var data_url=data-url
+data_file = 'syndrome_grippal.csv'
 
-#+BEGIN_SRC python :results silent :var data_url=data-url
-from urllib.request import urlopen
+import datetime
+from urllib.request import urlretrieve
+import os
+
+if not os.path.exists(data_file):
+    urlretrieve(data_url, data_file)
 
-data = urlopen(data_url).read()
+print(f'Data is retrieved at {datetime.datetime.utcnow()} UTC')
+#+END_SRC
+
+#+RESULTS:
+: Data is retrieved at 2020-09-16 21:52:00.330209 UTC
+
+Now we extract the interesting part of the data.
+#+BEGIN_SRC python :results silent
+data = open(data_file, 'rb').read()
 lines = data.decode('latin-1').strip().split('\n')
 data_lines = lines[1:]
 table = [line.split(',') for line in data_lines]
@@ -79,6 +97,13 @@ Let's have a look at what we have so far:
 table[:5]
 #+END_SRC
 
+#+RESULTS:
+|   week | indicator |   inc | inc_low | inc_up | inc100 | inc100_low | inc100_up | geo_insee | geo_name |
+| 202037 |         3 | 22799 |   18087 |  27511 |     35 |         28 |        42 | FR        | France   |
+| 202036 |         3 | 10847 |    8019 |  13675 |     16 |         12 |        20 | FR        | France   |
+| 202035 |         3 |  9918 |    6842 |  12994 |     15 |         10 |        20 | FR        | France   |
+| 202034 |         3 |  6084 |    3090 |   9078 |      9 |          4 |        14 | FR        | France   |
+
 ** Checking for missing data
 Unfortunately there are many ways to indicate the absence of a data value in a dataset. Here we check for a common one: empty fields. For completeness, we should also look for non-numerical data in numerical columns. We don't do this here, but checks in later processing steps would catch such anomalies.
 
@@ -93,6 +118,9 @@ for row in table:
         valid_table.append(row)
 #+END_SRC
 
+#+RESULTS:
+: ['198919', '3', '0', '', '', '0', '', '', 'FR', 'France']
+
 ** Extraction of the required columns
 There are only two columns that we will need for our analysis: the first (~"week"~) and the third (~"inc"~). We check the names in the header to be sure we pick the right data. We make a new table containing just the two columns required, without the header.
 #+BEGIN_SRC python :results silent
@@ -110,6 +138,21 @@ Let's look at the first and last lines. We insert ~None~ to indicate to org-mode
 [('week', 'inc'), None] + data[:5] + [None] + data[-5:]
 #+END_SRC
 
+#+RESULTS:
+|   week |    inc |
+|--------+--------|
+| 202037 |  22799 |
+| 202036 |  10847 |
+| 202035 |   9918 |
+| 202034 |   6084 |
+| 202033 |   6106 |
+|--------+--------|
+| 198448 |  78620 |
+| 198447 |  72029 |
+| 198446 |  87330 |
+| 198445 | 135223 |
+| 198444 |  68422 |
+
 ** Verification
 It is always prudent to verify if the data looks credible. A simple fact we can check for is that weeks are given as six-digit integers (four for the year, two for the week), and that the incidence values are positive integers.
 #+BEGIN_SRC python :results output
@@ -120,6 +163,8 @@ for week, inc in data:
         print("Suspicious value in column 'inc': ", (week, inc))
 #+END_SRC
 
+#+RESULTS:
+
 No problem - fine!
 
 ** Date conversion
@@ -139,6 +184,21 @@ str_data = [(str(date), str(inc)) for date, inc in converted_data]
 [('date', 'inc'), None] + str_data[:5] + [None] + str_data[-5:]
 #+END_SRC
 
+#+RESULTS:
+|       date |    inc |
+|------------+--------|
+| 1984-10-29 |  68422 |
+| 1984-11-05 | 135223 |
+| 1984-11-12 |  87330 |
+| 1984-11-19 |  72029 |
+| 1984-11-26 |  78620 |
+|------------+--------|
+| 2020-08-10 |   6106 |
+| 2020-08-17 |   6084 |
+| 2020-08-24 |   9918 |
+| 2020-08-31 |  10847 |
+| 2020-09-07 |  22799 |
+
 ** Date verification
 We do one more verification: our dates must be separated by exactly one week, except around the missing data point.
 #+BEGIN_SRC python :results output
@@ -148,6 +208,9 @@ for date1, date2 in zip(dates[:-1], dates[1:]):
         print(f"The difference between {date1} and {date2} is {date2-date1}")
 #+END_SRC
 
+#+RESULTS:
+: The difference between 1989-05-01 and 1989-05-15 is 14 days, 0:00:00
+
 ** Transfer Python -> R
 We switch to R for data inspection and analysis, because the code is more concise in R and requires no additional libraries.
 
@@ -163,17 +226,31 @@ data$date <- as.Date(data$date)
 summary(data)
 #+END_SRC
 
+#+RESULTS:
+: 
+:       date                 inc         
+:  Min.   :1984-10-29   Min.   :      0  
+:  1st Qu.:1993-10-21   1st Qu.:   5016  
+:  Median :2002-10-07   Median :  15962  
+:  Mean   :2002-10-06   Mean   :  61490  
+:  3rd Qu.:2011-09-22   3rd Qu.:  50052  
+:  Max.   :2020-09-07   Max.   :1001824
+
 ** Inspection
 Finally we can look at a plot of our data!
-#+BEGIN_SRC R :results output graphics :file inc-plot.png
-plot(data, type="l", xlab="Date", ylab="Weekly incidence")
+#+BEGIN_SRC R :exports results :results graphics :file inc-plot.png
+with(data, plot(date, inc, type="l", xlab="Date", ylab="Weekly incidence"))
 #+END_SRC
 
+#+RESULTS:
+
 A zoom on the last few years makes the peaks in winter stand out more clearly.
 #+BEGIN_SRC R :results output graphics :file inc-plot-zoom.png
 plot(tail(data, 200), type="l", xlab="Date", ylab="Weekly incidence")
 #+END_SRC
 
+#+RESULTS:
+
 * Study of the annual incidence
 
 ** Computation of the annual incidence
@@ -201,19 +278,40 @@ annnual_inc = data.frame(year = years,
 head(annnual_inc)
 #+END_SRC
 
+#+RESULTS:
+| 1986 | 5100540 |
+| 1987 | 2861556 |
+| 1988 | 2766142 |
+| 1989 | 5460155 |
+| 1990 | 5233987 |
+| 1991 | 1660832 |
+
 ** Inspection
 A plot of the annual incidence:
 #+BEGIN_SRC R :results output graphics :file annual-inc-plot.png
 plot(annnual_inc, type="p", xlab="Année", ylab="Annual incidence")
 #+END_SRC
 
+#+RESULTS:
+
 ** Identification of the strongest epidemics
 A list sorted by decreasing annual incidence makes it easy to find the most important ones:
 #+BEGIN_SRC R :results output
 head(annnual_inc[order(-annnual_inc$incidence),])
 #+END_SRC
 
+#+RESULTS:
+:    year incidence
+: 4  1989   5460155
+: 5  1990   5233987
+: 1  1986   5100540
+: 28 2013   4182265
+: 25 2010   4085126
+: 14 1999   3897443
+
 Finally, a histogram clearly shows the few very strong epidemics, which affect about 10% of the French population, but are rare: there were three of them in the course of 35 years. The typical epidemic affects only half as many people.
 #+BEGIN_SRC R :results output graphics :file annual-inc-hist.png
 hist(annnual_inc$incidence, breaks=10, xlab="Annual incidence", ylab="Number of observations", main="")
 #+END_SRC
+
+#+RESULTS: