Commit b27b157e authored by Dorchies David's avatar Dorchies David
Browse files

fix(doc): encoding issue in Rmd file on Linux

Refs #14
parent 137d23f1
Pipeline #22652 failed with stage
in 1 minute and 15 seconds
......@@ -7,9 +7,9 @@ output:
html_document:
toc: true
self_contained: no
css:
css:
- "pagedown_inrae.css"
includes:
includes:
in_header: header_iframe_resizer.html
bibliography: IRMaRA.bib
---
......@@ -29,11 +29,11 @@ Resulting time series of reservoir storage is statistically analysed in order to
# Main features
Two visualisation modes are available:
Two visualisation modes are available:
## Instant risk overview
This feature shows risk rates of failing objectives in the future considering the current storage state of the reservoir, the policy constraints and the date. The bar plot show the first ten objectives sorted by decreasing order of failure risk rate.
This feature shows risk rates of failing objectives in the future considering the current storage state of the reservoir, the policy constraints and the date. The bar plot show the first ten objectives sorted by decreasing order of failure risk rate.
The storage state of the reservoir can be filled in 3 ways:
......@@ -43,7 +43,7 @@ The storage state of the reservoir can be filled in 3 ways:
## One objective focus
This feature shows a heat map of the failure risk rates for each possible storage state in function of the day of the year for one particular objective and a chosen policy constraints.
This feature shows a heat map of the failure risk rates for each possible storage state in function of the day of the year for one particular objective and a chosen policy constraints.
The chart also displays the theoretical filling curve.
......@@ -62,11 +62,11 @@ Nogent-sur-Seine 25 20 17 16 180 280 420
Gurgy (Yonne) 14 12.5 11 9.2 220 340 400
Courlon sur Yonne 23 16 13 11 550 700 900
Alfortville (Seine) 64 48 41 36 850 1200 1400
Châlons-sur-Marne 12 11 9 8 330 520 700
Châlons-sur-Marne 12 11 9 8 330 520 700
Noisiel (Marne) 32 23 20 17 350 500 650
Paris (Seine) 81 60 51 45 950 1600 2000"
df <- read.csv(text = s, sep = "\t")
knitr::kable(df,
knitr::kable(df,
col.names = strsplit("Monitoring station Low-flow Vigilance Low-flow Alert Low-flow Reinforced alert Low-flow Crises High-flow Vigilance High-flow Regular High-flow Exceptional", "\t")[[1]],
caption = "The thresholds at the monitoring stations in m<sup>3</sup>/s (after @dorchiesClimateChangeImpacts2014)")
```
......
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