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authorKen Kellner <ken.kellner@gmail.com>2016-04-22 16:03:53 (GMT)
committerKen Kellner <ken.kellner@gmail.com>2016-04-22 16:03:53 (GMT)
commite26da6bdfc374b66f11fbf28b8aac41caaf4e7a1 (patch)
treee81c5756f2f0ef0a82c332cf0299dca56e085975
parentb2206bef81fefa2c2ee7a34a664063cd6394ce82 (diff)
Fix table in supplement s1.
-rw-r--r--supplements/supplement_s1.Rmd4
1 files changed, 2 insertions, 2 deletions
diff --git a/supplements/supplement_s1.Rmd b/supplements/supplement_s1.Rmd
index 38c5541..dc3a302 100644
--- a/supplements/supplement_s1.Rmd
+++ b/supplements/supplement_s1.Rmd
@@ -9,7 +9,7 @@ For each visit to each subplot during the study, we measured five interspecific
1. Percent cover of herbaceous vegetation (ordinal scale 0-6)
2. Percent cover of woody vegetation < 2 m in height (ordinal scale 0-6)
-3. Density of woody stems 0-50 cm in height
+3. Density of woody stems 10-50 cm in height
4. Density of woody stems 50-100 cm in height
5. Density of woody stems >100 cm in height
@@ -22,7 +22,7 @@ head(comp)
```{r, echo=FALSE}
comp <- read.csv('../data/competition.csv',header=TRUE)[,c(1,5:10)]
compdisp <- comp[c(1,2,3,5,6,7),]
-knitr::kable(head(compdisp))
+knitr::kable(head(compdisp),row.names=FALSE)
```
The ordinal percent cover variables were converted back to a percent scale by assigning them to the midpoint of the ordinal category percent range: