In C-Research/c4aRs: Resources for C4 R Users

library(dplyr)
library(stringr)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(c4aRs)

This package is meant to provide support to R users at C4. It contains resources for working with Seizure data, some dplyr tools, the C4 color palettes, and some miscellaneous tools. Below are a few examples. This package is meant as a supplement to existing tidyverse functions.

Seizure Data Resources

seiz_dat <- 
  tribble(
  ~Year, ~Type, ~Seizure, ~Origin, ~Transit, ~Destination,
    2010, 'Wildlife', 'China', 'Mexico', 'Belgium', 'China',
    2010, 'Drugs', 'USA', 'Panama', 'USA', 'China',
    2016, 'Firearms', 'China', 'South Africa', 'Qatar', 'China',
    2019, 'Wildlife', 'China', 'Mexico', 'UAE', 'China',
    )

countryCols <- 
  c('Seizure', 
      'Origin', 
      'Transit', 
      'Destination')

Trafficking Instance Counts

To get TI count for all countries in the dataset, use traffickingInstanceCount without an argument for by_col.

seiz_dat %>% 
  traffickingInstanceCount(cols = countryCols)

Use the by_col to get TI count by that specific column.

seiz_dat %>% 
  traffickingInstanceCount(cols = countryCols, by_col = "Type")

seiz_dat %>% 
  traffickingInstanceCount(cols = countryCols, by_col = "Year")

Trafficking Instances

To get all rows that contain trafficking instances for a given country (or multiple), use selectByMultCols. This selects all rows that have an entry that appears in the values argument in one of the cols.

# get all Mexico TIs
seiz_dat %>% 
  selectByMultCols(cols = countryCols, values = "Mexico")

# get all North American TIs
north_america <- c("USA", "Mexico", "Canada")
seiz_dat %>% 
  selectByMultCols(cols = countryCols, values = north_america)

Mapping/Plotting Resources

leafletLabel and plotlyLabel provide a way of easily adding tooltip labels to maps and graphs. Each adds a label column that will plug into leaflet or plotly. textNames and colNames are required, and there are options to add a title and change font sizes.

locs <- 
  tribble(
  ~Loc, ~Long, ~Lat, ~Type, ~People,
    'A', 2.783629, 50.279807, 'Mine', 67,
    'B', 2.782419, 50.281229, 'Grocery Store', 323,
    'C', 2.780950, 50.287279, 'Train Station', 623,
    'D', 2.777744, 50.291332, 'Place', 1505
    )

library(leaflet)

locs %>% 
  leafletLabel(textNames = c("Place Type", "Number of People"), 
               colNames = c("Type", "People"), 
               titleCol = "Loc",
               titleSize = 2.8) %>% 
  leaflet() %>% 
  addTiles() %>% 
  addMarkers(
    lng = ~Long,
    lat = ~Lat,
    label = ~label %>% lapply(htmltools::HTML)
  )