vectorsurvR Package Documentation and User Guide

VectorSurv provides public health agencies the tools to manage, visualize and analyze the spread of vector-borne diseases and make informed decisions to protect public health.

The ‘vectorsurvR’ package is intended for users of VectorSurv, a public health vector borne disease surveillance system. The package contains functions tailored to data retrieved from the VectorSurv database. A valid VectorSurv username and password is required for data retrieval. Those without agency access can use sample datasets in place of real data. This documentation covers the functions in ‘vectorsurvR’ and introduces users to methods of R programming. The purpose of this documentation is to introduce and guide users with limited programming experience.

```{r setup, include = TRUE}

##If not installed ##Install from cran with (recommended) install.packages(“vectorsurvR”)

##Or install developing version from our github with devtools::install_github(“UCD-DART/vectorsurvR”)

##Load package library(vectorsurvR)




## Data Retrieval 

**getToken()**

*Description*

`getToken()` returns a token needed to run `getArthroCollections()` and `getPools()`. The function prompts users for their Gateway credentials. If credentials are accepted, the function returns a user token needed to obtain data and a list of agencies the user has access to. 

*Usage*

`getToken()`

*Arguments*

```{r, results='hide'}

token = getToken()

getArthroCollections(…)

Description

getArthroCollections(...) obtains collections data for a range of years. It prompts the user for their Gateway username and password before retrieving the associated data. You can only retrieve data from agencies linked to your Gateway account.

Usage

getArthroCollections(token,start_year, end_year, arthropod, agency_id = NULL)

Arguments

#Example
collections = getArthroCollections(token, 2022,2023, 'mosquito')

getPools(…)

Description

getPools() similar to getArthroCollections() obtains pools on a year range (start_year, end_year). It prompts the user for their Gateway username and password before retrieving the associated data. getPools() retrieve data for both mosquito and tick pools.

Usage

getPools(token, start_year, end_year, arthropod, agency_id = NULL) Arguments

#Example
pools = getPools(token, 2022,2023, 'mosquito')

Write Data to file

You can save retrieved data as a .csv file in your current directory using write.csv(). That same data can be retrieved using read.csv(). Writing data to a .csv can make the rendering process more efficient when generating reports in R. We recommend that you write the data pulled from our API into a csv and then load that data when generating reports.

#creates a file named "collections_18_23.csv" in your current directory
write.csv(x = collections, file = "collections_22_23.csv")

#loads collections data
collections = read.csv("collections_22_23.csv")

Data Processing

The ‘vectorsurvR’ package comes with two sample datasets which can be used in place of real collections and pools data.

Data can be subset to contain columns of interest. Subsetting can also be used to reorder the columns in a data frame.Do not subset collections or pools data before inputting them into VectorSurv calculator functions to avoid losing essential columns. It is recommended to subset after calculations are complete and before inputting into a table generator. Remember, subsetting, filtering, grouping and summarising will not change the value of the data unless it is reassigned to the same variable name. We recommend creating a new variable for processed data.

Subsetting

#Subset using column names or index number


colnames(collections) #displays column names and associated index



#Subseting by name
head(collections[c("collection_date", "species_display_name", "num_count")])

#by index
head(collections[c(3, 23, 17)])

#to save a subset
collections_subset = collections[c(3, 23, 17)]

Filtering and subsetting in ‘dplyr’

‘dplyr’ is a powerful package for filtering and sub-setting data. It follows logic similar to SQL queries.

For more information on data manipulation using ‘dplyr’ Click Here

‘dplyr’ utilizes the pipe operator %>% to send data into functions. The head() function returns the first few rows of data, specifying head(1) tells the software to return only the first row for viewing purposes. Remove head() to see all the data or reassign the data to a new variable.

#NOTE: library was loaded above
library(dplyr)

#Subsetting columns with 'select()'
sample_collections %>%
  dplyr::select(collection_date, species_display_name, num_count) %>% head()

Below are more examples for filtering data.


#filtering with dplyr 'filter'
collections_pip = collections %>%
  filter(species_display_name == "Cx pipiens")

#filtering multiple arguments using '%in%'
collections_pip_tar = collections %>%
  filter(species_display_name %in% c("Cx pipiens", "Cx tarsalis"),
         site %in% c(119819, 102832)) #filters site codes

Grouping and Summarising

In addition to filtering and sub-setting, data can be group by variables and summarised.

#groups by species and collection date and sums the number counted

collections %>%
  group_by(collection_date, species_display_name) %>%
  summarise(sum_count = sum(num_count, na.rm = T)) %>%
  head()


#groups by species and collection date and takes the average the number counted

collections %>%
  group_by(collection_date, species_display_name) %>%
  summarise(avg_count = mean(num_count, na.rm = T)) %>%
  head()

Pivoting

Data can be manipulated into long and wide (spreadsheet) forms using pivot_wider() and pivot_longer() from the ‘tidyr’ package. By default data from the API is in long form. Here we pivot on species and sex condition names using num_count as values. The end result is data with num_count values in the columns named species_sex. For more on pivoting see ??pivot_longer() and ??pivot_wider().

library(tidyr)

collections_wide = pivot_wider(
  collections,
  names_from = c("species_display_name","sex_name"),
  values_from = "num_count"
)

Calculations

Abundance

getAbundance(…)

Description

getAbundance() uses any amount of mosquito collections data to calculate the abundance for the specified parameters. The function calculates using the methods of the Gateway Abundance calculator.

Usage

getAbundance(collections,interval, species_list = NULL, trap_list = NULL, species_separate = FALSE)

Arguments

getAbundance(
  collections,
  interval = "Biweek",
  species_list = c("Cx tarsalis", "Cx pipiens"),
  trap_list = "CO2",
  species_separate = FALSE
)

Abundance Anomaly (comparison to 5 year average)

getAbundanceAnomaly()

Description

getAbundanceAnomaly(...) requires at least five years prior to the target_year of mosquito collections data to calculate for the specified parameters. The function uses the methods of the Gateway Abundance Anomaly calculator, and will not work if there is fewer than five years of data present.

Usage

getAbundanceAnomaly(collections,interval,target_year, species_list = NULL, trap_list = NULL, species_separate = FALSE)

Arguments

collections_18_23 = getArthroCollections(token, 2018,2023, 'mosquito')

getAbundanceAnomaly(collections_18_23,
                    interval = "Biweek",
                    target_year = 2023,
                    species_list = c("Cx tarsalis", "Cx pipiens"),
                    trap_list = "CO2",
                    species_separate = FALSE)

Infection Rate

getInfectionRate()

Description

getInfectionRate(...) estimates the arbovirus infection rate based on testing pools of mosquitoes.

Usage

getInfectionRate(pools,interval, target_year, target_disease,pt_estimate, scale = 1000, species_list = c(NULL), trap_list = c(NULL))

Arguments

IR = getInfectionRate(pools, 
                      interval = "Week",
                      target_disease = "WNV",
                      pt_estimate = "mle", 
                      scale = 1000,
                      species_list = c("Cx pipiens"),
                      trap_list = c("CO2","GRVD") )
IR

Vector Index

getVectorIndex()

Description

getVectorIndex(...) The vector index is the relative abundance of infected mosquitoes and is a way to quickly estimate the risk of arbovirus transmission in an area. Vector index is the product of the abundance and infection rate for a given time interval: \(Vector Index = Infection Rate * Abundance\)

Usage

getVectorIndex(collections, pools, interval, , target_disease, pt_estimate,species_list=NULL, trap_list = NULL)

Arguments - collections: collections data retrieved from getArthroCollections(...) - pools: Pools data retrieved from getPools(...)

Note: Years from pools and collections data must overlap


pools = getPools(token,2023,2023, 'mosquito')
collections = getArthroCollections(token,2023,2023, 'mosquito')

getVectorIndex(collections, pools, interval = "Biweek",
                           
                           target_disease = "WNV", pt_estimate = "bc-mle",
                           species_list=c("Cx tarsalis"), 
                           
                           trap_list =  c("CO2"))

Tables

getPoolsComparisionTable()

Description

getPoolsComparisionTable() produces a frequency table for positive and negative pools counts by year and species. The more years present in the data, the larger the table.

Usage

getPoolsComparisionTable(pools,target_disease, species_separate=F)

Arguments

getPoolsComparisionTable(
  pools,
  interval = "Week",
  target_disease = "WNV",
  species_separate = T
)

Styling Dataframes with ‘kable’

Professional looking tables can be produced using the ‘kable’ and ‘kableExtra’ packages.



library(kableExtra)

AbAnOutput = getAbundance(
  collections,
  interval = "Biweek",
  
  species_list = c("Cx tarsalis", "Cx pipiens"),
  trap_list = "CO2",
  species_separate = FALSE
)
head(AbAnOutput)

#kable table where column names, font_size, style and much more can be customized

AbAnOutput %>%
  kbl(col.names = c("Disease Year", "Biweek", "Count", "Trap Events", "Abundance")) %>%
  kable_styling(
    bootstrap_options = "striped",
    font_size = 14,
    latex_options = "scale_down"
  ) %>%
  footnote(general = "Table X: Combined biweekly Abundance Calculation for Cx. tarsalis, pipiens in CO2 traps", general_title = "")

Data using ‘datatables’

Interactive html only tables can be produced using the ‘DT’ package. ‘DT’ tables allow for sorting and filtering with in a webpage. These are ideal for viewing data but are not compatible with pdf or word formats.

library(DT)

AbAnOutput %>%
  datatable(colnames =  c("Disease Year", "Biweek", "Count", "Trap Events", "Abundance"))

Charts and Graphs

‘ggplot2’ is a easy to use plotting library in R. ‘ggplot2’ syntax consists of creating a ggplot object with a dataframe and adding subsequent arguments to that object. Aesthetics (aes()) in ggplot represents the data mapping aspect of the plot. A simple example using collections is shown below.

library(ggplot2)
library(lubridate)
#creates a month column and translates numerics
collections$month = as.factor(month(collections$collection_date))


collections_sums = collections %>%
  filter(
    species_display_name %in% c(
      "Cx tarsalis",
      "Cx pipiens",
      "An freeborni",
      "Cs incidens",
      "Ae melanimon",
      "Cs inornata",
      "Cx stigmatosoma",
      "Cx erythrothorax",
      "Ae vexans",
      "I pacificus"
    )
  ) %>%
  group_by(month, species_display_name) %>%
  summarise(sum_count = sum(num_count, na.rm = T)) %>% arrange(desc(sum_count), .by_group = T)



#ggplot with dots a values for each species of interest

ggplot(data = collections_sums,
       aes(x = month, y = sum_count, color = species_display_name)) +
  geom_point()

#bar chart
ggplot(data = collections_sums,
       aes(x = month, y = sum_count, fill = species_display_name)) +
  geom_bar(stat = "identity")


#adding labels
ggplot(data = collections_sums,
       aes(x = month, y = sum_count, fill = species_display_name)) +
  geom_bar(stat = "identity") +
  labs(title = "Mosquito Counts by Month and Species",
       x = "Month",
       y = "Sum of Mosquitoes",
       fill = "Species")

When plotting with libraries in R, it is easiest when the data is prepared in long form. Most calculator outputs from our functions are in wide form. The following wrapper functions help process and plot this data.

processAbunAnom()

Description

processAbunAnom() processes the output returned from getAbundanceAnomaly() into a long form suitable for plotting in ‘ggplot’

Usage

processAbunAnom(AbAnomOutput)

Arguments



collections = getArthroCollections(token, 2018, 2023, 'mosquito')

AbAnOut = getAbundanceAnomaly(
  collections,
  interval = "Biweek",
  target_year = 2023,
  species_list = c("Cx tarsalis", "Cx pipiens"),
  species_separate = TRUE
)



AbAnOut_L = processAbunAnom(AbAnOut)

We can take the output of processAbunAnom() and create a plot comparing the target year abundance to the five year average.



AbAnOut_L %>%  filter(Abundance_Type %in% c("2023_Abundance",
                                            "Five_Year_Avg")) %>%
  ggplot(aes(x = Biweek,
             y = Abundance_Calculation,
             color = Abundance_Type)) +
  geom_point() +
  geom_line() +
  facet_wrap( ~ species_display_name) +
  labs(title = "2023 Abundance Anomaly", y = "")

We can also create a plot which displays the percent change from the five year average.

AbAnOut_L %>%
  filter(Abundance_Type == "Delta") %>%
  mutate(Change = ifelse(Abundance_Calculation > 0, "Increase", "Decrease")) %>%
  ggplot(aes(x = Biweek,
             y = Abundance_Calculation,
             fill = Change)) +
  geom_bar(stat = "identity") +
  facet_wrap( ~ species_display_name) +
  labs(x = "Biweek",
       y = "Percent Change",
       title = "Relative Abundance 2023, % Change from 5-year average",
       fill = "Relative Change")

plotInfectionRate()

Description

plotInfectionRate() plots the output returned from getInfectionRate() with confidence intervals using ‘ggplot2’.

Usage

plotInfectionRate(InfRtOutput, year)

Arguments

IR = getInfectionRate(
  pools,
  interval = "Week",
  target_disease = "WNV",
  pt_estimate = "mle",
  species_list = c("Cx pipiens"),
  trap_list = c("CO2", "GRVD")
)

plotInfectionRate(InfRtOutput = IR, year = 2023)

Additional Table Examples

We can highlight rows and columns, add headers, and customize footnotes. For more information please Click Here

collections = getArthroCollections(token, 2021, 2023, 'mosquito')

table(collections$trap_acronym, collections$surv_year) %>%
  kbl(align = "c") %>%
  kable_paper(
    full_width = F,
    html_font = "arial",
    lightable_options = "striped",
  ) %>%
  add_header_above(c("Trap Type", "Years" = 3)) %>%
  footnote(general = "Table X: Traps deployed by year", general_title = "") %>%
  row_spec(c(3, 9, 10), background = "yellow") %>%
  column_spec(c(4), background = "orange")