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Commit 35876f15 authored by Francesco Sabatini's avatar Francesco Sabatini
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Updated to exclude plots

parent cb93654e
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......@@ -152,7 +152,8 @@ Pfe-o-05- 1707854
```{r, eval=T}
header0 <- header0 %>%
filter(!PlotObservationID %in% c(1707776, 1707779:1707782, 1707849, 1707854)) %>%
filter(Dataset != "$Coastal_Borja")
filter(Dataset != "$Coastal_Borja") %>%
filter(Dataset != "$Coastal_Poland")
```
......
......@@ -63,19 +63,25 @@ DT0 <- readr::read_delim("../sPlot_data_export/sPlot_3_0_2_species_test.csv",
x_ = col_double()
)
)
nplots <- length(unique(DT0$PlotObservationID))
nspecies <- length(unique(DT0$`Matched concept`))
```
Match plots with those in `header`
```{r}
load("../_output/header_sPlot3.0.RData")
DT0 <- DT0 %>%
filter(PlotObservationID %in% unique(header$PlotObservationID))
nplots <- length(unique(DT0$PlotObservationID))
nspecies <- length(unique(DT0$`Matched concept`))
# Plots in header but not in DT
empty.plots <- header %>%
filter(!PlotObservationID %in% unique(DT2$PlotObservationID)) %>%
pull(PlotObservationID)
```
The DT table includes `r nrow(DT0)` species * plot records, across `r nplots` plots. Before taxonomic resolution, there are `r nspecies` species .
\newline
The DT table includes `r nrow(DT0)` species * plot records, across `r nplots` plots. Before taxonomic resolution, there are `r nspecies` species. There are `r length(empty.plots)`. These are plots where the only species reported in Turboveg 3 are not identified (and not in the taxonomic list). Should these be deleted from `header`?
<br>
```{r, echo=F}
......
......@@ -412,6 +412,14 @@ CWM <- CWM1 %>%
arrange(PlotObservationID)
```
```{r, echo=F}
# align to header (to avoid recalculating cached CWMs)
CWM <- CWM %>%
filter(PlotObservationID %in% header$PlotObservationID)
```
### 2.1 Explore CWM output
```{r, echo=F}
knitr::kable(CWM %>%
......
......@@ -265,7 +265,7 @@ knitr::kable(CWM %>%
latex_options = "basic",
full_width = F, position = "center")
```
The CWM table contains `r nrow(CWM)` entries, i.e., the combination between `r CWM %>% distinct(PlotObservationID) %>% nrow()` unique plots, and `r CWM %>% distinct(variable) %>% nrow()`.
The CWM table contains `r nrow(CWM)` entries, i.e., the combination between `r CWM %>% distinct(PlotObservationID) %>% nrow()` unique plots, and `r CWM %>% distinct(variable) %>% nrow()` traits.
......
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