The data on the incidence of chickenpox illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). 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 1990 and ending with a recent week, is available for download. The URL is:
| `week` | ISO8601 Yearweek number as numeric (year*100 + week nubmer) |
| `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 |
| `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 |
### If the local file does not exist, download the data and put them into the local file
```{r}
destfile = "incidence-PAY-7.csv"
if(!file.exists(destfile)){
res <- tryCatch(download.file(data_url,
destfile,
method="auto"),
error=function(e) 1)
}
```
### Read the local CSV file.
The first line of the CSV file is a comment, which we ignore with `skip=1`.
```{r}
data = read.csv(destfile, skip=1)
```
Let's have a look at what we got:
```{r}
head(data)
tail(data)
```
Are there missing data points?
```{r}
na_records = apply(data, 1, function (x) any(is.na(x)))
data[na_records,]
```
The two relevant columns for us are `week` and `inc`. Let's verify their classes:
```{r}
class(data$week)
class(data$inc)
```
Integers, fine!
### Conversion of the week numbers
Date handling is always a delicate subject. There are many conventions that are easily confused. Our dataset uses the [ISO-8601](https://en.wikipedia.org/wiki/ISO_8601) week number format, which is popular in Europe but less so in North America. In `R`, it is handled by the library [parsedate](https://cran.r-project.org/package=parsedate):
This is an R Markdown document that you can easily export to HTML, PDF, and MS Word formats. For more information on R Markdown, see <http://rmarkdown.rstudio.com>.
When you click on the button **Knit**, the document will be compiled in order to re-execute the R code and to include the results into the final document. As we have shown in the video, R code is inserted as follows:
In order to facilitate the subsequent treatment, we replace the ISO week numbers by the dates of each week's Monday. This function does it for one value:
```{r cars}
```{r}
summary(cars)
convert_week = function(w) {
ws = paste(w)
iso = paste0(substring(ws, 1, 4), "-W", substring(ws, 5, 6))
as.character(parse_iso_8601(iso))
}
```
```
It is also straightforward to include figures. For example:
We apply it to all points, creating a new column `date` in our data frame:
```{r}
data$date = as.Date(convert_week(data$week))
```
```{r pressure, echo=FALSE}
Let's check that is has class `Date`:
plot(pressure)
```{r}
class(data$date)
```
```
Note the parameter `echo = FALSE` that indicates that the code will not appear in the final version of the document. We recommend not to use this parameter in the context of this MOOC, because we want your data analyses to be perfectly transparent and reproducible.
The points are in inverse chronological order, so it's preferable to sort them:
```{r}
data = data[order(data$date),]
```
Since the results are not stored in Rmd files, you should generate an HTML or PDF version of your exercises and commit them. Otherwise reading and checking your analysis will be difficult for anyone else but you.
That's a good occasion for another check: our dates should be separated by exactly seven days:
```{r}
all(diff(data$date) == 7)
```
Now it's your turn! You can delete all this information and replace it by your computational document.
Since the peaks of the epidemic happen in winter, near 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.
The argument `na.rm=True` in the sum indicates that missing data points are removed. This is a reasonable choice since there is only one missing point, whose impact cannot be very strong.
```{r}
yearly_peak = function(year) {
start = paste0(year-1,"-09-01")
end = paste0(year,"-09-01")
semaines = data$date > start & data$date <= end
sum(data$inc[semaines], na.rm=TRUE)
}
```
We must also be careful with the first and last years of the dataset. The data start in December 1990, meaning that we don't have all the data for the peak attributed to the year 1990. We therefore exclude it from the analysis. For the same reason, we define 2020 as the final year. We can increase this value to 2021 only when all data up to August 2021 is available.
```{r}
years = 1991:2020
```
We make a new data frame for the annual incidence, applying the function `yearly_peak` to each year:
### Identification of the strongest and the weakest epidemics
A list sorted by decreasing annual incidence makes it easy to find the most important ones:
```{r}
head(annnual_inc[order(-annnual_inc$incidence),])
```
A list sorted by decreasing annual incidence makes it easy to find the least important ones:
```{r}
head(annnual_inc[order(annnual_inc$incidence),])
```
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.
