From c641db399d9f8a921271a3600ad77b3aaf4abcc4 Mon Sep 17 00:00:00 2001 From: Alexandre Jesus Date: Sun, 3 May 2020 00:13:30 +0100 Subject: [PATCH] Make exercise 2 of module 3 --- module3/exo2/annual-inc-hist.png | Bin 0 -> 5131 bytes module3/exo2/annual-inc-plot.png | Bin 0 -> 7212 bytes module3/exo2/data.csv | 1536 ++++++++++++++++++++++++++++++ module3/exo2/exercice_R_en.org | 405 ++++++-- module3/exo2/inc-plot-zoom.png | Bin 0 -> 18577 bytes module3/exo2/inc-plot.png | Bin 0 -> 48574 bytes 6 files changed, 1875 insertions(+), 66 deletions(-) create mode 100644 module3/exo2/annual-inc-hist.png create mode 100644 module3/exo2/annual-inc-plot.png create mode 100644 module3/exo2/data.csv create mode 100644 module3/exo2/inc-plot-zoom.png create mode 100644 module3/exo2/inc-plot.png diff --git a/module3/exo2/annual-inc-hist.png b/module3/exo2/annual-inc-hist.png new file mode 100644 index 0000000000000000000000000000000000000000..af05a55c2378d3efb89ed3525867a07dc4cd9f05 GIT binary patch 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+199101,7,15565,10271,20859,27,18,36,FR,France +199052,7,19375,13295,25455,34,23,45,FR,France +199051,7,19080,13807,24353,34,25,43,FR,France +199050,7,11079,6660,15498,20,12,28,FR,France +199049,7,1143,0,2610,2,0,5,FR,France diff --git a/module3/exo2/exercice_R_en.org b/module3/exo2/exercice_R_en.org index 2b73d64..92e0924 100644 --- a/module3/exo2/exercice_R_en.org +++ b/module3/exo2/exercice_R_en.org @@ -1,81 +1,354 @@ -#+TITLE: Your title -#+AUTHOR: Your name -#+DATE: Today's date +#+TITLE: Analysis of the incidence of chickenpox #+LANGUAGE: en -# #+PROPERTY: header-args :eval never-export +#+OPTIONS: *:nil num:1 toc:t -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: -#+HTML_HEAD: +#+PROPERTY: header-args :session :exports both -* Some explanations +* Foreword -This is an org-mode document with code examples in R. Once opened in -Emacs, this document can easily be exported to HTML, PDF, and Office -formats. For more information on org-mode, see -https://orgmode.org/guide/. +For running this analysis, you need the following software: -When you type the shortcut =C-c C-e h o=, this document will be -exported as HTML. All the code in it will be re-executed, and the -results will be retrieved and included into the exported document. If -you do not want to re-execute all code each time, you can delete the # -and the space before ~#+PROPERTY:~ in the header of this document. +** Emacs 25 or higher +Older versions may suffice. For Emacs versions older than 26, org-mode must be updated to version 9.x. -Like we showed in the video, R code is included as follows (and is -exxecuted by typing ~C-c C-c~): +** R 4.0 +We use only basic R functionality, so a earlier version might be OK, but we did not test this. -#+begin_src R :results output :exports both -print("Hello world!") -#+end_src +#+BEGIN_SRC emacs-lisp :results output +(unless (featurep 'ob-R) + (print "Please activate R in org-babel (org-babel-do-languages)!")) +#+END_SRC + +* 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-7.csv + +This is the documentation of the data from [[https://ns.sentiweb.fr/incidence/csv-schema-v1.json][the download site]]: + +| Column name | Description | +|--------------+---------------------------------------------------------------------------------------------------------------------------| +| ~week~ | ISO8601 Yearweek number as numeric (year*100 + week nubmer) | +| ~geo_insee~ | Identifier of the geographic area, from INSEE https://www.insee.fr | +| ~geo_name~ | Geographic label of the area, corresponding to INSEE code. This label is not an id and is only provided for human reading | +| ~indicator~ | Unique identifier of the indicator, see metadata document https://www.sentiweb.fr/meta.json | +| ~inc~ | Estimated incidence value for the time step, in the geographic level | +| ~inc_low~ | Lower bound of the estimated incidence 95% Confidence Interval | +| ~inc_up~ | Upper bound of the estimated incidence 95% Confidence Interval | +| ~inc100~ | Estimated rate incidence per 100,000 inhabitants | +| ~inc100_low~ | Lower bound of the estimated incidence 95% Confidence Interval | +| ~inc100_up~ | Upper bound of the estimated rate incidence 95% Confidence Interval | + +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 +We first check if the data is available in a data file. Otherwise, we +download it into the datafile +#+NAME: data-file +data.csv + +#+BEGIN_SRC R :results output :var data_url=data-url :var data_file=data-file +f <- trimws(data_file) +if(!file.exists(f)) { + download.file(trimws(data_url), f) +} +#+END_SRC + +#+RESULTS: + +After that we read the csv. Note that the first line is skipped, since +it contains a comment. And also, we convert empty strings to NA for +easier processing later. + +#+BEGIN_SRC R :results output :var data_file=data-file +data <- read.csv(trimws(data_file), skip = 1, na.strings=c("", "NA")) +head(data) +#+END_SRC + +#+RESULTS: +#+begin_example + + week indicator inc inc_low inc_up inc100 inc100_low inc100_up geo_insee +1 202017 7 113 0 346 0 0 0 FR +2 202016 7 770 77 1463 1 0 2 FR +3 202015 7 1918 675 3161 3 1 5 FR +4 202014 7 3879 2227 5531 6 3 9 FR +5 202013 7 7326 5236 9416 11 8 14 FR +6 202012 7 8123 5790 10456 12 8 16 FR + geo_name +1 France +2 France +3 France +4 France +5 France +6 France +#+end_example + +** Checking for missing data +Unfortunately there are many ways to indicate the absence of a data +value in a dataset. In the previous second we converted empty strings +to NA, now we will look for NA values. 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. + +#+BEGIN_SRC R :results output +sum(is.na(data)) +#+END_SRC + +#+RESULTS: +: [1] 0 + +Luckily there seems to be no missing data. + +** 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 R :results output +library(dplyr) +data <- select(data, week, inc) +head(data) +tail(data) +#+END_SRC + +#+RESULTS: +#+begin_example + +Attaching package: ‘dplyr’ + +The following objects are masked from ‘package:stats’: + + filter, lag + +The following objects are masked from ‘package:base’: + + intersect, setdiff, setequal, union + + week inc +1 202017 113 +2 202016 770 +3 202015 1918 +4 202014 3879 +5 202013 7326 +6 202012 8123 + + week inc +1529 199102 16277 +1530 199101 15565 +1531 199052 19375 +1532 199051 19080 +1533 199050 11079 +1534 199049 1143 +#+end_example + +** 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 R :results output +is.numeric(data$week) +is.numeric(data$inc) +sum(nchar(data$week) != 6) == 0 +#+END_SRC + +#+RESULTS: +: [1] TRUE +: +: [1] TRUE +: +: [1] TRUE + +No problem - fine! + +** Date conversion +In order to facilitate the subsequent treatment, we replace the ISO +week numbers by the dates of each week's Monday. This is also a good +occasion to sort the lines by increasing data, and to convert the +incidences from strings to integers. + +#+BEGIN_SRC R :results output +library(parsedate) +convert_week = function(w) { + iso <- paste0(substring(w, 1, 4), "-W", substring(w, 5, 6)) + as.character(parse_iso_8601(iso)) +} +data$date <- as.Date(convert_week(data$week)) +class(data$date) +#+END_SRC #+RESULTS: -: [1] "Hello world!" +: +: [1] "Date" + +#+BEGIN_SRC R :results output +data <- select(data, date, inc) +data <- data[order(data$date),] +head(data) +tail(data) +#+END_SRC + +#+RESULTS: +#+begin_example + + date inc +1534 1990-12-03 1143 +1533 1990-12-10 11079 +1532 1990-12-17 19080 +1531 1990-12-24 19375 +1530 1990-12-31 15565 +1529 1991-01-07 16277 + + date inc +6 2020-03-16 8123 +5 2020-03-23 7326 +4 2020-03-30 3879 +3 2020-04-06 1918 +2 2020-04-13 770 +1 2020-04-20 113 +#+end_example -And now the same but in an R session. This is the most frequent -situation, because R is really an interactive language. With a -session, R's state, i.e. the values of all the variables, remains -persistent from one code block to the next. The code is still executed -using ~C-c C-c~. +** Date verification +We do one more verification: our dates must be separated by exactly +one week. -#+begin_src R :results output :session *R* :exports both -summary(cars) -#+end_src +#+BEGIN_SRC R :results output +for(i in 2:nrow(data)) { + if(difftime(data$date[i], data$date[i-1], units="days") != 7) { + print(sprintf("The difference between %s and %s is greater than 7", data$date[i], data$date[i-1])); + } +} +#+END_SRC #+RESULTS: -: speed dist -: Min. : 4.0 Min. : 2.00 -: 1st Qu.:12.0 1st Qu.: 26.00 -: Median :15.0 Median : 36.00 -: Mean :15.4 Mean : 42.98 -: 3rd Qu.:19.0 3rd Qu.: 56.00 -: Max. :25.0 Max. :120.00 - -Finally, an example for graphical output: -#+begin_src R :results output graphics :file "./cars.png" :exports results :width 600 :height 400 :session *R* -plot(cars) -#+end_src + +All okay! + +** Inspection +Finally we can look at a plot of our data! +#+BEGIN_SRC R :results output graphics :file inc-plot.png +plot(data, type="l", xlab="Date", ylab="Weekly incidence") +#+END_SRC + +#+RESULTS: +[[file:inc-plot.png]] + +A zoom on the last few years makes the peaks in winter stand out more +clearly. +#+BEGIN_SRC R :results output graphics :file inc-plot-zoom.png +plot(tail(data, 200), type="l", xlab="Date", ylab="Weekly incidence") +#+END_SRC + +#+RESULTS: +[[file:inc-plot-zoom.png]] + +* Study of the annual incidence + +** Computation of the annual incidence +Since the peaks of the epidemic happen in winter, slightly after the +transition between calendar years, we define the reference period for +the annual incidence from September 1st of year /N/ to September 1st +of year /N+1/. We label this period as year /N+1/ because the peak is +always located in year /N+1/. The very low incidence in summer ensures +that the arbitrariness of the choice of reference period has no impact +on our conclusions. + +This R function computes the annual incidence as defined above: +#+BEGIN_SRC R :results silent +yearly_peak <- function(year) { + start <- paste0(year-1,"-09-01") + end <- paste0(year,"-09-01") + weeks <- data$date > start & data$date <= end + sum(data$inc[weeks], na.rm=TRUE) +} +#+END_SRC + +We must also be careful with the first and last years of the +dataset. The data start in December 1990, meaning that we don't have +all the data for the peak attributed to the year 1990. We therefore +exclude it from the analysis. For the same reason, we define 2019 as +the final year. We can increase this value to 2020 only when all data +up to August 2020 is available. +#+BEGIN_SRC R :results silent +years <- 1991:2019 +#+END_SRC + +We make a new data frame for the annual incidence, applying the +function ~yearly_peak~ to each year: +#+BEGIN_SRC R :results value +annnual_inc <- data.frame(year = years, + incidence = sapply(years, yearly_peak)) +head(annnual_inc) +#+END_SRC + +#+RESULTS: +| 1991 | 553895 | +| 1992 | 834935 | +| 1993 | 642921 | +| 1994 | 662750 | +| 1995 | 651333 | +| 1996 | 564994 | + +** Inspection +A plot of the annual incidence: +#+BEGIN_SRC R :results output graphics :file annual-inc-plot.png +plot(annnual_inc, type="p", xlab="Year", ylab="Annual incidence") +#+END_SRC + +#+RESULTS: +[[file:annual-inc-plot.png]] + +** 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 value +head(annnual_inc[order(-annnual_inc$incidence),]) +#+END_SRC + +#+RESULTS: +| 2009 | 841233 | +| 1992 | 834935 | +| 2010 | 834077 | +| 2016 | 779816 | +| 2004 | 778914 | +| 2003 | 760765 | + +A list sorted by increasing annual incidence makes it easy to find the +years with the least incidence: +#+BEGIN_SRC R :results value +head(annnual_inc[order(annnual_inc$incidence),]) +#+END_SRC + +#+RESULTS: +| 2002 | 515343 | +| 2018 | 539765 | +| 2017 | 552906 | +| 1991 | 553895 | +| 1996 | 564994 | +| 2019 | 584116 | + +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: -[[file:./cars.png]] - -Note the parameter ~:exports results~, which indicates that the code -will not appear in the exported document. We recommend that in the -context of this MOOC, you always leave this parameter setting as -~:exports both~, because we want your analyses to be perfectly -transparent and reproducible. - -Watch out: the figure generated by the code block is /not/ stored in -the org document. It's a plain file, here named ~cars.png~. 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