Chapter 3 Data summary

load("data/data.Rdata")

Summary of sampled individuals and analysed faecal samples.

#number of samples
length(sample_metadata$EHI_number)

[1] 105

#number of samples by transect
sample_metadata %>%
  group_by(Transect) %>%
  summarise(n_samples = length(EHI_number)) %>%
  tt()

Transect	n_samples
Aisa	22
Aran	37
Sentein	19
Tourmalet	27

#number of samples by transect and elevation
sample_metadata %>%
  group_by(Transect, Elevation) %>%
  summarise(n_samples = length(EHI_number)) %>%
  tt()

Transect	Elevation	n_samples
Aisa	1250	6
Aisa	1450	6
Aisa	1650	4
Aisa	1850	6
Aran	1000	6
Aran	1080	7
Aran	1340	5
Aran	1500	6
Aran	1650	7
Aran	1850	6
Sentein	941	5
Sentein	1260	4
Sentein	1628	5
Sentein	1873	5
Tourmalet	953	5
Tourmalet	1255	4
Tourmalet	1561	4
Tourmalet	1797	4
Tourmalet	2065	2
Tourmalet	2134	3
Tourmalet	2306	5

#n of analysed faecal samples
ncol(read_counts)

[1] 106

Geographical location of sampled lizards in the Pyrenees.

#Summarise for generating map
options(dplyr.summarise.inform = FALSE)
sample_metadata_summary <- sample_metadata %>%
  #Group by geography and count samples
  select(EHI_number, latitude, longitude, Transect) %>%
  group_by(latitude, longitude, Transect) %>%
  summarize(count = n()) %>%
  ungroup()

#plotting on map
## Determine the longitude and latitude ranges
lon_range <- range(sample_metadata_summary$longitude, na.rm = TRUE)
lat_range <- range(sample_metadata_summary$latitude, na.rm = TRUE)

sample_metadata_summary %>%
  ggplot(.) +
  #render map
  geom_map(
    data=map_data("world"),
    map = map_data("world"),
    aes(long, lat, map_id=region),
    color = "white", fill = "#cccccc", linewidth = 0.2
  ) +
  #render points
  geom_point(
    aes(x=longitude,y=latitude, color=Transect),
    alpha=0.5, shape=16) +
  #add general plot layout
  theme_minimal() +
  theme(legend.position = "right",
        axis.title.x=element_blank(),
        axis.title.y=element_blank()
  ) + coord_map("mercator", xlim = lon_range, ylim = lat_range)