Concept

Exploration of pop dynamic patterns at The Portal Project. How do counts of rodents like Dipodomys species change through time.

In this document I will:

  1. Load data from Portal project
  2. Process it into time series.
  3. Make initial visualizations
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.4.4
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.4.4

Data

data<- read.csv("https://ndownloader.figshare.com/files/2292172")
head(data)
##   record_id month day year plot_id species_id sex hindfoot_length weight
## 1         1     7  16 1977       2         NL   M              32     NA
## 2         2     7  16 1977       3         NL   M              33     NA
## 3         3     7  16 1977       2         DM   F              37     NA
## 4         4     7  16 1977       7         DM   M              36     NA
## 5         5     7  16 1977       3         DM   M              35     NA
## 6         6     7  16 1977       1         PF   M              14     NA

the data includes 49 species

Get time-series of counts for Dipodomys species.

time_series<- data %>% 
  group_by(species_id, year) %>% 
  summarize(count = n()) %>% 
  filter(species_id %in% c('DM', 'DO', 'DS')) %>% 
  na.omit()
## Warning: package 'bindrcpp' was built under R version 3.4.4
head(time_series)
## # A tibble: 6 x 3
## # Groups:   species_id [1]
##   species_id  year count
##   <fct>      <int> <int>
## 1 DM          1977   264
## 2 DM          1978   389
## 3 DM          1979   209
## 4 DM          1980   493
## 5 DM          1981   559
## 6 DM          1982   609

Data visualization