vectorsurvR

VectorSurv

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.

To install package from CRAN (recommended) run:

install.packages("vectorsurvR")

Or install the developing version from our github run:

devtools::install_github("UCD-DART/vectorsurvR")

Then load the package for use.

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


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_ids = NULL)

Arguments

token: access token retrieved from getToken()
start_year: Beginning of year range
end_year: End of year range
arthropod: Type of pools to retrieve: ‘tick’ , ‘mosquito’
agency_ids: Default to NULL returns data for all available agencies, specifying a vector of agency ids will return data for specific agencies. This parameter is best used by accounts with access to multiple agencies.

#Example
collections = getArthroCollections(token, 2022,2023, 'mosquito',55)

getPools(…)

Description

getPools() similar to getArthroCollections() obtains pools on a year range (start_year, end_year) after supplying a valid token retrieved from getToken(). getPools() can retrieve data for both mosquito and tick pools.

Usage

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

token: access token retrieved from getToken()
start_year: Beginning of year range
end_year: End of year range
arthropod: Type of collections to retrieve: ‘tick’ , ‘mosquito’
agency_ids: Default to NULL returns data for all available agencies, specifying a vector of agency ids will return data for specific agencies. This parameter is best used by accounts with access to multiple agencies.

#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")

Sample Data

The ‘vectorsurvR’ package comes with two sample datasets which can be used in place of real collections and pools data. sample_collections and sample_pools will be used for example purposes in this document.

Data Processing

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(sample_collections) #displays column names and associated index
#>  [1] "agency_code"          "collection_id"        "collection_date"     
#>  [4] "surv_year"            "species_display_name" "sex_type"            
#>  [7] "trap_acronym"         "trap_problem_bit"     "num_trap"            
#> [10] "trap_nights"          "num_count"            "site_code"           
#> [13] "collection_longitude" "collection_latitude"

#Subseting by name
head(sample_collections[c("collection_date", "species_display_name", "num_count")])
#> # A tibble: 6 × 3
#>   collection_date species_display_name num_count
#>   <date>          <chr>                    <int>
#> 1 2015-01-22      Cx pipiens                   1
#> 2 2015-01-10      Cx stigmatosoma              2
#> 3 2015-01-02      Cs inornata                  1
#> 4 2015-01-02      Cs inornata                 22
#> 5 2015-01-29      Cs inornata                 38
#> 6 2015-01-29      Cs incidens                  2

#by index
head(sample_collections[c(2, 4, 10)])
#> # A tibble: 6 × 3
#> # Groups:   surv_year [1]
#>   collection_id surv_year trap_nights
#>           <int>     <dbl>       <int>
#> 1       1374818      2015           6
#> 2       1368247      2015           8
#> 3       1366915      2015          15
#> 4       1366929      2015          15
#> 5       1377676      2015           7
#> 6       1377690      2015           7

#to save a subset
collections_subset = sample_collections[c(2, 4, 10)]

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()
#> Adding missing grouping variables: `surv_year`
#> # A tibble: 6 × 4
#> # Groups:   surv_year [1]
#>   surv_year collection_date species_display_name num_count
#>       <dbl> <date>          <chr>                    <int>
#> 1      2015 2015-01-22      Cx pipiens                   1
#> 2      2015 2015-01-10      Cx stigmatosoma              2
#> 3      2015 2015-01-02      Cs inornata                  1
#> 4      2015 2015-01-02      Cs inornata                 22
#> 5      2015 2015-01-29      Cs inornata                 38
#> 6      2015 2015-01-29      Cs incidens                  2

Below are more examples for filtering data.


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

#filtering multiple arguments using '%in%'
collections_pip_tar = sample_collections %>%
  filter(species_display_name %in% c("Cx pipiens", "Cx tarsalis"))

Grouping and Summarising

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

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

sample_collections %>%
  group_by(collection_date, species_display_name) %>%
  summarise(sum_count = sum(num_count, na.rm = T)) %>%
  head()
#> `summarise()` has grouped output by 'collection_date'. You can override using
#> the `.groups` argument.
#> # A tibble: 6 × 3
#> # Groups:   collection_date [2]
#>   collection_date species_display_name sum_count
#>   <date>          <chr>                    <int>
#> 1 2015-01-02      Cs incidens                 15
#> 2 2015-01-02      Cs inornata                 88
#> 3 2015-01-02      Cx pipiens                  46
#> 4 2015-01-02      Cx tarsalis                  3
#> 5 2015-01-10      An freeborni                 4
#> 6 2015-01-10      Cs incidens                  1


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

sample_collections %>%
  group_by(collection_date, species_display_name) %>%
  summarise(avg_count = mean(num_count, na.rm = T)) %>%
  head()
#> `summarise()` has grouped output by 'collection_date'. You can override using
#> the `.groups` argument.
#> # A tibble: 6 × 3
#> # Groups:   collection_date [2]
#>   collection_date species_display_name avg_count
#>   <date>          <chr>                    <dbl>
#> 1 2015-01-02      Cs incidens                5  
#> 2 2015-01-02      Cs inornata                8  
#> 3 2015-01-02      Cx pipiens                11.5
#> 4 2015-01-02      Cx tarsalis                1.5
#> 5 2015-01-10      An freeborni               4  
#> 6 2015-01-10      Cs incidens                1

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(
  sample_collections,
  names_from = c("species_display_name","sex_type"),
  values_from = "num_count"
)
#> Warning: Values from `num_count` are not uniquely identified; output will contain
#> list-cols.
#> • Use `values_fn = list` to suppress this warning.
#> • Use `values_fn = {summary_fun}` to summarise duplicates.
#> • Use the following dplyr code to identify duplicates.
#>   {data} |>
#>   dplyr::summarise(n = dplyr::n(), .by = c(agency_code, collection_id,
#>   collection_date, surv_year, trap_acronym, trap_problem_bit, num_trap,
#>   trap_nights, site_code, collection_longitude, collection_latitude,
#>   species_display_name, sex_type)) |>
#>   dplyr::filter(n > 1L)

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 = NULL, trap = NULL, separate_by = NULL)

Arguments

collections: Mosquito collections data retrieved from getArthroCollections(...)
interval: Calculation interval for abundance, accepts “collection_date”,“Biweek”,“Week”, and “Month.
species: Species filter for calculating abundance. Species_display_name is the accepted notation. To see a list of species present in your data run unique(collections$species_display_name). If species is unspecified, the default NULL will return data for all species in data.
trap: Trap filter for calculating abundance. Trap_acronym is the is the accepted notation. Run unique(collections$trap_acronym) to see trap types present in your data. If trap is unspecified, the default NULL will return data for all trap types.
separate_by:Separate/group the calculation by ‘trap’,‘species’ or ‘agency’. Default NULL does not separate calculation

getAbundance(
  sample_collections,
  interval = "Biweek",
  species = c("Cx tarsalis", "Cx pipiens"),
  trap = "CO2",
  separate_by = NULL
)
#>                Agency Year Biweek                 Species Count TrapEvents Trap
#> 1            Agency_1 2021      4              Cx pipiens    10          3  CO2
#> 2            Agency_1 2021      7              Cx pipiens     4          3  CO2
#> 3  Agency_1, Agency_2 2021      8 Cx pipiens, Cx tarsalis    23         10  CO2
#> 4            Agency_1 2021     10              Cx pipiens     9          5  CO2
#> 5  Agency_1, Agency_2 2021     11 Cx pipiens, Cx tarsalis    17         13  CO2
#> 6  Agency_1, Agency_2 2021     12 Cx pipiens, Cx tarsalis   474         10  CO2
#> 7  Agency_1, Agency_2 2021     13              Cx pipiens   338         12  CO2
#> 8  Agency_1, Agency_2 2021     14 Cx pipiens, Cx tarsalis    79          9  CO2
#> 9  Agency_1, Agency_2 2021     15 Cx pipiens, Cx tarsalis  3137         10  CO2
#> 10 Agency_1, Agency_2 2021     16 Cx pipiens, Cx tarsalis  2265         12  CO2
#> 11 Agency_1, Agency_2 2021     17 Cx pipiens, Cx tarsalis    23          8  CO2
#> 12 Agency_1, Agency_2 2021     18             Cx tarsalis    49          6  CO2
#> 13 Agency_1, Agency_2 2021     19             Cx tarsalis    35         10  CO2
#> 14 Agency_1, Agency_2 2021     20             Cx tarsalis    39         10  CO2
#> 15           Agency_1 2021     21 Cx pipiens, Cx tarsalis    16          6  CO2
#> 16           Agency_2 2020      9             Cx tarsalis    56          4  CO2
#> 17           Agency_2 2020     10 Cx pipiens, Cx tarsalis    24          6  CO2
#> 18 Agency_1, Agency_2 2020     11 Cx pipiens, Cx tarsalis   204         15  CO2
#> 19 Agency_1, Agency_2 2020     12 Cx pipiens, Cx tarsalis   251         10  CO2
#> 20 Agency_1, Agency_2 2020     13 Cx pipiens, Cx tarsalis   208         14  CO2
#> 21 Agency_1, Agency_2 2020     14 Cx pipiens, Cx tarsalis  2584         12  CO2
#> 22           Agency_1 2020     15 Cx pipiens, Cx tarsalis    38          8  CO2
#> 23 Agency_1, Agency_2 2020     16 Cx pipiens, Cx tarsalis   345          8  CO2
#> 24 Agency_1, Agency_2 2020     17 Cx pipiens, Cx tarsalis  1670         10  CO2
#> 25 Agency_1, Agency_2 2020     18             Cx tarsalis    79          7  CO2
#> 26 Agency_1, Agency_2 2020     19 Cx pipiens, Cx tarsalis   187         13  CO2
#> 27           Agency_1 2020     20             Cx tarsalis    43         12  CO2
#> 28 Agency_1, Agency_2 2020     21 Cx pipiens, Cx tarsalis    61         13  CO2
#> 29 Agency_1, Agency_2 2020     22 Cx pipiens, Cx tarsalis    12         11  CO2
#> 30           Agency_1 2020     23 Cx pipiens, Cx tarsalis     9          8  CO2
#> 31           Agency_1 2020     24              Cx pipiens     3          4  CO2
#> 32           Agency_1 2019      4              Cx pipiens     3          1  CO2
#> 33           Agency_1 2019      9 Cx pipiens, Cx tarsalis    18          9  CO2
#> 34           Agency_1 2019     10 Cx pipiens, Cx tarsalis    22          7  CO2
#> 35 Agency_1, Agency_2 2019     11 Cx pipiens, Cx tarsalis   564         16  CO2
#> 36 Agency_1, Agency_2 2019     12              Cx pipiens    51          4  CO2
#> 37 Agency_1, Agency_2 2019     13 Cx pipiens, Cx tarsalis   881         13  CO2
#> 38           Agency_2 2019     14 Cx pipiens, Cx tarsalis   266          6  CO2
#> 39 Agency_1, Agency_2 2019     15 Cx pipiens, Cx tarsalis  4239         16  CO2
#> 40 Agency_1, Agency_2 2019     16 Cx pipiens, Cx tarsalis    68         14  CO2
#> 41 Agency_1, Agency_2 2019     17 Cx pipiens, Cx tarsalis    77          7  CO2
#> 42           Agency_1 2019     18 Cx pipiens, Cx tarsalis   262          6  CO2
#> 43 Agency_1, Agency_2 2019     19 Cx pipiens, Cx tarsalis    54          9  CO2
#> 44           Agency_1 2019     20 Cx pipiens, Cx tarsalis   214         12  CO2
#> 45           Agency_2 2019     21             Cx tarsalis    37          9  CO2
#> 46 Agency_1, Agency_2 2018     10 Cx pipiens, Cx tarsalis   304         16  CO2
#> 47 Agency_1, Agency_2 2018     11 Cx pipiens, Cx tarsalis    96         16  CO2
#> 48 Agency_1, Agency_2 2018     12 Cx pipiens, Cx tarsalis    90         16  CO2
#> 49 Agency_1, Agency_2 2018     13 Cx pipiens, Cx tarsalis  4016         13  CO2
#> 50 Agency_1, Agency_2 2018     14 Cx pipiens, Cx tarsalis   486          9  CO2
#> 51 Agency_1, Agency_2 2018     15 Cx pipiens, Cx tarsalis   147         11  CO2
#> 52 Agency_1, Agency_2 2018     16 Cx pipiens, Cx tarsalis  1611         14  CO2
#> 53           Agency_1 2018     17 Cx pipiens, Cx tarsalis    41         13  CO2
#> 54 Agency_1, Agency_2 2018     18 Cx pipiens, Cx tarsalis   211         13  CO2
#> 55 Agency_1, Agency_2 2018     19              Cx pipiens   125         10  CO2
#> 56 Agency_1, Agency_2 2018     20 Cx pipiens, Cx tarsalis    47         10  CO2
#> 57 Agency_1, Agency_2 2018     21             Cx tarsalis    11          9  CO2
#> 58           Agency_1 2017      8              Cx pipiens    10          4  CO2
#> 59           Agency_1 2017      9 Cx pipiens, Cx tarsalis    18          8  CO2
#> 60 Agency_1, Agency_2 2017     10 Cx pipiens, Cx tarsalis   149         16  CO2
#> 61 Agency_1, Agency_2 2017     11 Cx pipiens, Cx tarsalis   351         13  CO2
#> 62 Agency_1, Agency_2 2017     12 Cx pipiens, Cx tarsalis    65         17  CO2
#> 63 Agency_1, Agency_2 2017     13 Cx pipiens, Cx tarsalis   209         19  CO2
#> 64 Agency_1, Agency_2 2017     14 Cx pipiens, Cx tarsalis   410         23  CO2
#> 65 Agency_1, Agency_2 2017     15 Cx pipiens, Cx tarsalis   669         10  CO2
#> 66 Agency_1, Agency_2 2017     16 Cx pipiens, Cx tarsalis  2574         13  CO2
#> 67 Agency_1, Agency_2 2017     17 Cx pipiens, Cx tarsalis  4535         22  CO2
#> 68 Agency_1, Agency_2 2017     18 Cx pipiens, Cx tarsalis   462         17  CO2
#> 69 Agency_1, Agency_2 2017     19 Cx pipiens, Cx tarsalis   144         10  CO2
#> 70 Agency_1, Agency_2 2017     20 Cx pipiens, Cx tarsalis    49         22  CO2
#> 71 Agency_1, Agency_2 2017     21             Cx tarsalis   138         16  CO2
#> 72           Agency_1 2016      8             Cx tarsalis    68         12  CO2
#> 73           Agency_1 2016      9 Cx pipiens, Cx tarsalis    13         11  CO2
#> 74 Agency_1, Agency_2 2016     10 Cx pipiens, Cx tarsalis   323          9  CO2
#> 75 Agency_1, Agency_2 2016     11 Cx pipiens, Cx tarsalis    52         15  CO2
#> 76 Agency_1, Agency_2 2016     12 Cx pipiens, Cx tarsalis   559         26  CO2
#> 77 Agency_1, Agency_2 2016     13 Cx pipiens, Cx tarsalis  1189         21  CO2
#> 78           Agency_1 2016     14 Cx pipiens, Cx tarsalis    94         10  CO2
#> 79 Agency_1, Agency_2 2016     15 Cx pipiens, Cx tarsalis  2198         18  CO2
#> 80 Agency_1, Agency_2 2016     16 Cx pipiens, Cx tarsalis  1128         10  CO2
#> 81 Agency_1, Agency_2 2016     17 Cx pipiens, Cx tarsalis  1107         18  CO2
#> 82 Agency_1, Agency_2 2016     18 Cx pipiens, Cx tarsalis   330         16  CO2
#> 83 Agency_1, Agency_2 2016     19 Cx pipiens, Cx tarsalis   160         17  CO2
#> 84 Agency_1, Agency_2 2016     20 Cx pipiens, Cx tarsalis    38         16  CO2
#> 85 Agency_1, Agency_2 2016     21 Cx pipiens, Cx tarsalis     8          9  CO2
#> 86 Agency_1, Agency_2 2015      8              Cx pipiens     8          6  CO2
#> 87           Agency_1 2015      9 Cx pipiens, Cx tarsalis    12         10  CO2
#> 88 Agency_1, Agency_2 2015     10 Cx pipiens, Cx tarsalis   612         27  CO2
#> 89 Agency_1, Agency_2 2015     11 Cx pipiens, Cx tarsalis   318         19  CO2
#> 90 Agency_1, Agency_2 2015     12 Cx pipiens, Cx tarsalis   737         16  CO2
#> 91 Agency_1, Agency_2 2015     13 Cx pipiens, Cx tarsalis   405         17  CO2
#> 92 Agency_1, Agency_2 2015     14 Cx pipiens, Cx tarsalis   531         15  CO2
#> 93 Agency_1, Agency_2 2015     15 Cx pipiens, Cx tarsalis   780         10  CO2
#> 94 Agency_1, Agency_2 2015     16 Cx pipiens, Cx tarsalis   805         18  CO2
#> 95 Agency_1, Agency_2 2015     17 Cx pipiens, Cx tarsalis  2433         15  CO2
#> 96 Agency_1, Agency_2 2015     18 Cx pipiens, Cx tarsalis  2056         20  CO2
#> 97 Agency_1, Agency_2 2015     19 Cx pipiens, Cx tarsalis   164         17  CO2
#> 98 Agency_1, Agency_2 2015     20 Cx pipiens, Cx tarsalis   569         22  CO2
#> 99           Agency_1 2015     21              Cx pipiens    45          5  CO2
#>    Abundance
#> 1       3.33
#> 2       1.33
#> 3       2.30
#> 4       1.80
#> 5       1.31
#> 6      47.40
#> 7      28.17
#> 8       8.78
#> 9     313.70
#> 10    188.75
#> 11      2.88
#> 12      8.17
#> 13      3.50
#> 14      3.90
#> 15      2.67
#> 16     14.00
#> 17      4.00
#> 18     13.60
#> 19     25.10
#> 20     14.86
#> 21    215.33
#> 22      4.75
#> 23     43.12
#> 24    167.00
#> 25     11.29
#> 26     14.38
#> 27      3.58
#> 28      4.69
#> 29      1.09
#> 30      1.12
#> 31      0.75
#> 32      3.00
#> 33      2.00
#> 34      3.14
#> 35     35.25
#> 36     12.75
#> 37     67.77
#> 38     44.33
#> 39    264.94
#> 40      4.86
#> 41     11.00
#> 42     43.67
#> 43      6.00
#> 44     17.83
#> 45      4.11
#> 46     19.00
#> 47      6.00
#> 48      5.62
#> 49    308.92
#> 50     54.00
#> 51     13.36
#> 52    115.07
#> 53      3.15
#> 54     16.23
#> 55     12.50
#> 56      4.70
#> 57      1.22
#> 58      2.50
#> 59      2.25
#> 60      9.31
#> 61     27.00
#> 62      3.82
#> 63     11.00
#> 64     17.83
#> 65     66.90
#> 66    198.00
#> 67    206.14
#> 68     27.18
#> 69     14.40
#> 70      2.23
#> 71      8.62
#> 72      5.67
#> 73      1.18
#> 74     35.89
#> 75      3.47
#> 76     21.50
#> 77     56.62
#> 78      9.40
#> 79    122.11
#> 80    112.80
#> 81     61.50
#> 82     20.62
#> 83      9.41
#> 84      2.38
#> 85      0.89
#> 86      1.33
#> 87      1.20
#> 88     22.67
#> 89     16.74
#> 90     46.06
#> 91     23.82
#> 92     35.40
#> 93     78.00
#> 94     44.72
#> 95    162.20
#> 96    102.80
#> 97      9.65
#> 98     25.86
#> 99      9.00

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 = NULL, trap = NULL, separate_by = NULL)

Arguments

collections: Collections data retrieved from getArthroCollections(...)
interval: Calculation interval for abundance, accepts “collection_date”,“Biweek”,“Week”, and “Month.
target_year: Year to calculate analysis on. Collections data must have a year range of at least (target_year - 5, target_year).
species: Species filter for calculating abundance. Species_display_name is the accepted notation. To see a list of species present in your data run unique(collections$species_display_name). If species is unspecified, the default NULL will return data for all species in data.
trap: Trap filter for calculating abundance. Trap_acronym is the is the accepted notation. Run unique(collections$trap_acronym) to see trap types present in your data. If trap is unspecified, the default NULL will return data for all trap types.
separate_by:Separate/group the calculation by ‘trap’,‘species’ or ‘agency’. Default NULL does not separate calculation


getAbundanceAnomaly(sample_collections,
                    interval = "Biweek",
                    target_year = 2020,
                    species = c("Cx tarsalis", "Cx pipiens"),
                    trap = "CO2",
                    separate_by  = "species") 
#> Warning in getAbundanceAnomaly(sample_collections, interval = "Biweek", : There
#> are years greater than the target year in the data. These years will not be
#> included in the anomaly calculation.
#>    Biweek     Species             Agency Year Count TrapEvents Trap Abundance
#> 1      10  Cx pipiens           Agency_2 2020     1          6  CO2      0.17
#> 2      10 Cx tarsalis           Agency_2 2020    23          6  CO2      3.83
#> 3      11  Cx pipiens Agency_1, Agency_2 2020    24         15  CO2      1.60
#> 4      11 Cx tarsalis Agency_1, Agency_2 2020   180         15  CO2     12.00
#> 5      12  Cx pipiens           Agency_1 2020     5         10  CO2      0.50
#> 6      12 Cx tarsalis Agency_1, Agency_2 2020   246         10  CO2     24.60
#> 7      13  Cx pipiens Agency_1, Agency_2 2020    93         14  CO2      6.64
#> 8      13 Cx tarsalis Agency_1, Agency_2 2020   115         14  CO2      8.21
#> 9      14  Cx pipiens Agency_1, Agency_2 2020   287         12  CO2     23.92
#> 10     14 Cx tarsalis Agency_1, Agency_2 2020  2297         12  CO2    191.42
#> 11     15  Cx pipiens           Agency_1 2020    29          8  CO2      3.62
#> 12     15 Cx tarsalis           Agency_1 2020     9          8  CO2      1.12
#> 13     16  Cx pipiens Agency_1, Agency_2 2020    87          8  CO2     10.88
#> 14     16 Cx tarsalis           Agency_2 2020   258          8  CO2     32.25
#> 15     17  Cx pipiens Agency_1, Agency_2 2020    13         10  CO2      1.30
#> 16     17 Cx tarsalis Agency_1, Agency_2 2020  1657         10  CO2    165.70
#> 17     18 Cx tarsalis Agency_1, Agency_2 2020    79          7  CO2     11.29
#> 18     19  Cx pipiens           Agency_1 2020    44         13  CO2      3.38
#> 19     19 Cx tarsalis Agency_1, Agency_2 2020   143         13  CO2     11.00
#> 20     20 Cx tarsalis           Agency_1 2020    43         12  CO2      3.58
#> 21     21  Cx pipiens           Agency_1 2020    31         13  CO2      2.38
#> 22     21 Cx tarsalis Agency_1, Agency_2 2020    30         13  CO2      2.31
#> 23      9 Cx tarsalis           Agency_2 2020    56          4  CO2     14.00
#>    FiveYearAvg           YearsInAverage   Delta
#> 1       1.8820 2015,2016,2017,2018,2019  -90.97
#> 2      16.1200 2015,2016,2017,2018,2019  -76.24
#> 3       2.3260 2015,2016,2017,2018,2019  -31.21
#> 4      15.3640 2015,2016,2017,2018,2019  -21.90
#> 5       5.3240 2015,2016,2017,2018,2019  -90.61
#> 6      15.7850      2015,2016,2017,2018   55.84
#> 7       5.8140 2015,2016,2017,2018,2019   14.21
#> 8      87.8140 2015,2016,2017,2018,2019  -90.65
#> 9       4.8300 2015,2016,2017,2018,2019  395.24
#> 10     27.3620 2015,2016,2017,2018,2019  599.58
#> 11     22.2620 2015,2016,2017,2018,2019  -83.74
#> 12     86.8040 2015,2016,2017,2018,2019  -98.71
#> 13     26.9020 2015,2016,2017,2018,2019  -59.56
#> 14     68.1900 2015,2016,2017,2018,2019  -52.71
#> 15      8.8460 2015,2016,2017,2018,2019  -85.30
#> 16     79.9520 2015,2016,2017,2018,2019  107.25
#> 17     28.9700 2015,2016,2017,2018,2019  -61.03
#> 18      7.6160 2015,2016,2017,2018,2019  -55.62
#> 19      3.4675      2015,2016,2017,2019  217.23
#> 20      3.8240 2015,2016,2017,2018,2019   -6.38
#> 21      4.5550                2015,2016  -47.75
#> 22      3.6825      2016,2017,2018,2019  -37.27
#> 23      0.7375      2015,2016,2017,2019 1798.31

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 = c(NULL), trap = c(NULL))

Arguments

pools: Pools data retrieved from getPools(...)
interval: Calculation interval for abundance, accepts: “Biweek”,“Week”, and “Month.
target_year: Year to calculate infection rate for. This year must be present in the data.
target_disease: The disease to calculate infection rate for–i.e. “WNV”. Disease acronyms are the accepted input. To see a list of disease acronyms, run unique(pools$target_acronym).
pt_estimate: The estimation type for infection rate. Options include: “mle”,“bc-”mle”, “mir”
scale: Constant to multiply result
species: Species filter for calculating abundance. Species_display_name is the accepted notation. To see a list of species present in your data run unique(pools$species_display_name). If species is unspecified, the default NULL will return data for all species in data.
trap: Trap filter for calculating abundance. Trap_acronym is the is the accepted notation. Run unique(pools$trap_acronym) to see trap types present in your data. If trap is unspecified, the default NULL will return data for all trap types.
wide: T/F Should the data be returned in wide/spreadsheet format

getInfectionRate(sample_pools, 
                      interval = "Week",
                      target_disease = "WNV",
                      pt_estimate = "mle", 
                      scale = 1000,
                      species = c("Cx pipiens", "Cx tarsalis"),
                      trap = c("CO2"),
                      separate_by="species", wide = FALSE )
#> # A tibble: 275 × 9
#> # Groups:   Year, Week [160]
#>     Year  Week Agency        Species Trap  Disease InfectionRate LowerCI UpperCI
#>    <dbl> <dbl> <chr>         <chr>   <chr> <chr>           <dbl>   <dbl>   <dbl>
#>  1  2015    18 Agency_1      Cx pip… CO2   WNV                 0       0       0
#>  2  2015    18 Agency_1      Cx tar… CO2   WNV                 0       0       0
#>  3  2015    19 Agency_1      Cx pip… CO2   WNV                 0       0       0
#>  4  2015    20 Agency_1      Cx tar… CO2   WNV                 0       0       0
#>  5  2015    21 Agency_1      Cx pip… CO2   WNV                 0       0       0
#>  6  2015    21 Agency_1      Cx tar… CO2   WNV                 0       0       0
#>  7  2015    22 Agency_1      Cx pip… CO2   WNV                 0       0       0
#>  8  2015    22 Agency_1      Cx tar… CO2   WNV                 0       0       0
#>  9  2015    23 Agency_1      Cx pip… CO2   WNV                 0       0       0
#> 10  2015    23 Agency_1, Ag… Cx tar… CO2   WNV                 0       0       0
#> # ℹ 265 more rows

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=NULL, trap = NULL,)

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

Note: Years from pools and collections data must overlap

interval: Calculation interval for abundance, accepts “collection_date”,“Biweek”,“Week”, and “Month.
target_disease: The disease to calculate infection rate. Disease acronyms are the accepted input. To see a list of disease acronyms, run unique(pools$target_acronym).
pt_estimate: The estimation type for infection rate. Options include: “mle”,“bc-”mle”, “mir”.
species: Species filter for calculating abundance. Species_display_name is the accepted notation. To see a list of species present in your data run unique(pools$species_display_name). If species is unspecified, the default NULL will return data for all species in data.
trap: Trap filter for calculating abundance. Trap_acronym is the is the accepted notation. Run unique(pools$trap_acronym) to see trap types present in your data. If trap is unspecified, the default NULL will return data for all trap types.
separate_by:Separate/group the calculation by ‘trap’,‘species’ or ‘agency’. Default NULL does not separate calculation
wide: T/F Should the data be returned in wide/spreadsheet format

getVectorIndex(sample_collections, 
               sample_pools,
               interval = "Biweek",
               target_disease = "WNV",
               pt_estimate = "bc-mle", 
              
               separate_by = c("agency","species"),
               wide = FALSE) 
#> Warning: There were 3 warnings in `dplyr::summarise()`.
#> The first warning was:
#> ℹ In argument: `InfectionRate = case_when(...)`.
#> ℹ In group 21: `surv_year = 2015`, `INTERVAL = 16`, `species_display_name = "Cx
#>   pipiens"`, `agency_code = "Agency_1"`.
#> Caused by warning in `sqrt()`:
#> ! NaNs produced
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
#> # A tibble: 1,417 × 13
#>     Year Biweek Agency   Species        Count Trap  TrapEvents Abundance Disease
#>    <dbl>  <dbl> <chr>    <chr>          <int> <chr>      <int>     <dbl> <chr>  
#>  1  2015      1 Agency_1 An freeborni       4 " MM…        186      0.02 <NA>   
#>  2  2015      1 Agency_1 Cs incidens        5 " MM…        186      0.03 <NA>   
#>  3  2015      1 Agency_1 Cs inornata      125 " MM…        186      0.67 <NA>   
#>  4  2015      1 Agency_1 Cx pipiens        41 " MM…        186      0.22 <NA>   
#>  5  2015      1 Agency_1 Cx stigmatoso…     2 " MM…        186      0.01 <NA>   
#>  6  2015      1 Agency_1 Cx tarsalis        4 " MM…        186      0.02 <NA>   
#>  7  2015      2 Agency_1 Cs incidens       10 " MM…        137      0.07 <NA>   
#>  8  2015      2 Agency_1 Cs inornata      147 " MM…        137      1.07 <NA>   
#>  9  2015      2 Agency_1 Cx pipiens        12 " MM…        137      0.09 <NA>   
#> 10  2015      2 Agency_1 Cx stigmatoso…     1 " MM…        137      0.01 <NA>   
#> # ℹ 1,407 more rows
#> # ℹ 4 more variables: InfectionRate <dbl>, LowerCI <dbl>, UpperCI <dbl>,
#> #   VectorIndex <dbl>
sample_collections%>%filter(species_display_name=="Cx tarsalis", trap_acronym=="CO2")
#> # A tibble: 342 × 14
#> # Groups:   surv_year [7]
#>    agency_code collection_id collection_date surv_year species_display_name
#>    <chr>               <int> <date>              <dbl> <chr>               
#>  1 Agency_1          1392822 2015-04-26           2015 Cx tarsalis         
#>  2 Agency_2          1411490 2015-05-17           2015 Cx tarsalis         
#>  3 Agency_1          1412519 2015-05-22           2015 Cx tarsalis         
#>  4 Agency_1          1415729 2015-05-25           2015 Cx tarsalis         
#>  5 Agency_1          1399581 2015-05-04           2015 Cx tarsalis         
#>  6 Agency_2          1413728 2015-05-22           2015 Cx tarsalis         
#>  7 Agency_1          1408922 2015-05-15           2015 Cx tarsalis         
#>  8 Agency_1          1400736 2015-05-08           2015 Cx tarsalis         
#>  9 Agency_1          1402308 2015-05-11           2015 Cx tarsalis         
#> 10 Agency_1          1412359 2015-05-22           2015 Cx tarsalis         
#> # ℹ 332 more rows
#> # ℹ 9 more variables: sex_type <chr>, trap_acronym <chr>,
#> #   trap_problem_bit <lgl>, num_trap <int>, trap_nights <int>, num_count <int>,
#> #   site_code <dbl>, collection_longitude <dbl>, collection_latitude <dbl>

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

pools: Pools data retrieved from getPools(...)
target_disease: The disease to calculate infection rate for–i.e. “WNV”. Disease acronyms are the accepted input. To see a list of disease acronyms, run unique(pools$target_acronym).
separate_by:Separate/group the calculation by ‘trap’,‘species’ or ‘agency’. Default NULL does not separate calculation

getPoolsComparisionTable(
  sample_pools,
  interval = "Week",
  target_disease = "WNV"
)
#> # A tibble: 169 × 6
#> # Groups:   Year, Week [169]
#>     Year  Week Negative Confirmed Total `Percent Positive`
#>    <dbl> <dbl>    <int>     <int> <int>              <dbl>
#>  1  2015    18        3         0     3                  0
#>  2  2015    19        1         0     1                  0
#>  3  2015    20        3         0     3                  0
#>  4  2015    21        9         0     9                  0
#>  5  2015    22        9         0     9                  0
#>  6  2015    23        5         0     5                  0
#>  7  2015    24        6         0     6                  0
#>  8  2015    25        8         0     8                  0
#>  9  2015    26        5         0     5                  0
#> 10  2015    27        9         1    10                 10
#> # ℹ 159 more rows

Styling Dataframes with ‘kable’

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



library(kableExtra)
#> 
#> Attaching package: 'kableExtra'
#> The following object is masked from 'package:dplyr':
#> 
#>     group_rows

AbAnOutput = getAbundance(
  sample_collections,
  interval = "Biweek",
  
  species = c("Cx tarsalis", "Cx pipiens"),
  trap = "CO2",
  separate_by = "species")

head(AbAnOutput)
#>               Agency Year Biweek     Species Count TrapEvents Trap Abundance
#> 1           Agency_1 2021      4  Cx pipiens    10          3  CO2      3.33
#> 2           Agency_1 2021      7  Cx pipiens     4          3  CO2      1.33
#> 3 Agency_1, Agency_2 2021      8  Cx pipiens    10         10  CO2      1.00
#> 4           Agency_2 2021      8 Cx tarsalis    13         10  CO2      1.30
#> 5           Agency_1 2021     10  Cx pipiens     9          5  CO2      1.80
#> 6           Agency_1 2021     11  Cx pipiens     5         13  CO2      0.38

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

AbAnOutput %>%
  kbl() %>%
  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 = "")

Agency	Year	Biweek	Species	Count	TrapEvents	Trap	Abundance
Agency_1	2021	4	Cx pipiens	10	3	CO2	3.33
Agency_1	2021	7	Cx pipiens	4	3	CO2	1.33
Agency_1, Agency_2	2021	8	Cx pipiens	10	10	CO2	1.00
Agency_2	2021	8	Cx tarsalis	13	10	CO2	1.30
Agency_1	2021	10	Cx pipiens	9	5	CO2	1.80
Agency_1	2021	11	Cx pipiens	5	13	CO2	0.38
Agency_1, Agency_2	2021	11	Cx tarsalis	12	13	CO2	0.92
Agency_1	2021	12	Cx pipiens	45	10	CO2	4.50
Agency_1, Agency_2	2021	12	Cx tarsalis	429	10	CO2	42.90
Agency_1, Agency_2	2021	13	Cx pipiens	338	12	CO2	28.17
Agency_2	2021	14	Cx pipiens	40	9	CO2	4.44
Agency_1, Agency_2	2021	14	Cx tarsalis	39	9	CO2	4.33
Agency_1	2021	15	Cx pipiens	23	10	CO2	2.30
Agency_1, Agency_2	2021	15	Cx tarsalis	3114	10	CO2	311.40
Agency_1, Agency_2	2021	16	Cx pipiens	71	12	CO2	5.92
Agency_1, Agency_2	2021	16	Cx tarsalis	2194	12	CO2	182.83
Agency_1	2021	17	Cx pipiens	18	8	CO2	2.25
Agency_1, Agency_2	2021	17	Cx tarsalis	5	8	CO2	0.62
Agency_1, Agency_2	2021	18	Cx tarsalis	49	6	CO2	8.17
Agency_1, Agency_2	2021	19	Cx tarsalis	35	10	CO2	3.50
Agency_1, Agency_2	2021	20	Cx tarsalis	39	10	CO2	3.90
Agency_1	2021	21	Cx pipiens	1	6	CO2	0.17
Agency_1	2021	21	Cx tarsalis	15	6	CO2	2.50
Agency_2	2020	9	Cx tarsalis	56	4	CO2	14.00
Agency_2	2020	10	Cx pipiens	1	6	CO2	0.17
Agency_2	2020	10	Cx tarsalis	23	6	CO2	3.83
Agency_1, Agency_2	2020	11	Cx pipiens	24	15	CO2	1.60
Agency_1, Agency_2	2020	11	Cx tarsalis	180	15	CO2	12.00
Agency_1	2020	12	Cx pipiens	5	10	CO2	0.50
Agency_1, Agency_2	2020	12	Cx tarsalis	246	10	CO2	24.60
Agency_1, Agency_2	2020	13	Cx pipiens	93	14	CO2	6.64
Agency_1, Agency_2	2020	13	Cx tarsalis	115	14	CO2	8.21
Agency_1, Agency_2	2020	14	Cx pipiens	287	12	CO2	23.92
Agency_1, Agency_2	2020	14	Cx tarsalis	2297	12	CO2	191.42
Agency_1	2020	15	Cx pipiens	29	8	CO2	3.62
Agency_1	2020	15	Cx tarsalis	9	8	CO2	1.12
Agency_1, Agency_2	2020	16	Cx pipiens	87	8	CO2	10.88
Agency_2	2020	16	Cx tarsalis	258	8	CO2	32.25
Agency_1, Agency_2	2020	17	Cx pipiens	13	10	CO2	1.30
Agency_1, Agency_2	2020	17	Cx tarsalis	1657	10	CO2	165.70
Agency_1, Agency_2	2020	18	Cx tarsalis	79	7	CO2	11.29
Agency_1	2020	19	Cx pipiens	44	13	CO2	3.38
Agency_1, Agency_2	2020	19	Cx tarsalis	143	13	CO2	11.00
Agency_1	2020	20	Cx tarsalis	43	12	CO2	3.58
Agency_1	2020	21	Cx pipiens	31	13	CO2	2.38
Agency_1, Agency_2	2020	21	Cx tarsalis	30	13	CO2	2.31
Agency_1	2020	22	Cx pipiens	2	11	CO2	0.18
Agency_1, Agency_2	2020	22	Cx tarsalis	10	11	CO2	0.91
Agency_1	2020	23	Cx pipiens	8	8	CO2	1.00
Agency_1	2020	23	Cx tarsalis	1	8	CO2	0.12
Agency_1	2020	24	Cx pipiens	3	4	CO2	0.75
Agency_1	2019	4	Cx pipiens	3	1	CO2	3.00
Agency_1	2019	9	Cx pipiens	15	9	CO2	1.67
Agency_1	2019	9	Cx tarsalis	3	9	CO2	0.33
Agency_1	2019	10	Cx pipiens	8	7	CO2	1.14
Agency_1	2019	10	Cx tarsalis	14	7	CO2	2.00
Agency_1, Agency_2	2019	11	Cx pipiens	25	16	CO2	1.56
Agency_1, Agency_2	2019	11	Cx tarsalis	539	16	CO2	33.69
Agency_1, Agency_2	2019	12	Cx pipiens	51	4	CO2	12.75
Agency_1	2019	13	Cx pipiens	36	13	CO2	2.77
Agency_1, Agency_2	2019	13	Cx tarsalis	845	13	CO2	65.00
Agency_2	2019	14	Cx pipiens	14	6	CO2	2.33
Agency_2	2019	14	Cx tarsalis	252	6	CO2	42.00
Agency_1, Agency_2	2019	15	Cx pipiens	160	16	CO2	10.00
Agency_1, Agency_2	2019	15	Cx tarsalis	4079	16	CO2	254.94
Agency_1, Agency_2	2019	16	Cx pipiens	14	14	CO2	1.00
Agency_1, Agency_2	2019	16	Cx tarsalis	54	14	CO2	3.86
Agency_1	2019	17	Cx pipiens	3	7	CO2	0.43
Agency_1, Agency_2	2019	17	Cx tarsalis	74	7	CO2	10.57
Agency_1	2019	18	Cx pipiens	175	6	CO2	29.17
Agency_1	2019	18	Cx tarsalis	87	6	CO2	14.50
Agency_1, Agency_2	2019	19	Cx pipiens	42	9	CO2	4.67
Agency_1	2019	19	Cx tarsalis	12	9	CO2	1.33
Agency_1	2019	20	Cx pipiens	134	12	CO2	11.17
Agency_1	2019	20	Cx tarsalis	80	12	CO2	6.67
Agency_2	2019	21	Cx tarsalis	37	9	CO2	4.11
Agency_1	2018	10	Cx pipiens	8	16	CO2	0.50
Agency_1, Agency_2	2018	10	Cx tarsalis	296	16	CO2	18.50
Agency_1	2018	11	Cx pipiens	80	16	CO2	5.00
Agency_2	2018	11	Cx tarsalis	16	16	CO2	1.00
Agency_1	2018	12	Cx pipiens	27	16	CO2	1.69
Agency_1, Agency_2	2018	12	Cx tarsalis	63	16	CO2	3.94
Agency_1, Agency_2	2018	13	Cx pipiens	132	13	CO2	10.15
Agency_1, Agency_2	2018	13	Cx tarsalis	3884	13	CO2	298.77
Agency_1, Agency_2	2018	14	Cx pipiens	34	9	CO2	3.78
Agency_1, Agency_2	2018	14	Cx tarsalis	452	9	CO2	50.22
Agency_1, Agency_2	2018	15	Cx pipiens	105	11	CO2	9.55
Agency_1, Agency_2	2018	15	Cx tarsalis	42	11	CO2	3.82
Agency_1, Agency_2	2018	16	Cx pipiens	782	14	CO2	55.86
Agency_1, Agency_2	2018	16	Cx tarsalis	829	14	CO2	59.21
Agency_1	2018	17	Cx pipiens	6	13	CO2	0.46
Agency_1	2018	17	Cx tarsalis	35	13	CO2	2.69
Agency_1, Agency_2	2018	18	Cx pipiens	200	13	CO2	15.38
Agency_1	2018	18	Cx tarsalis	11	13	CO2	0.85
Agency_1, Agency_2	2018	19	Cx pipiens	125	10	CO2	12.50
Agency_2	2018	20	Cx pipiens	31	10	CO2	3.10
Agency_1, Agency_2	2018	20	Cx tarsalis	16	10	CO2	1.60
Agency_1, Agency_2	2018	21	Cx tarsalis	11	9	CO2	1.22
Agency_1	2017	8	Cx pipiens	10	4	CO2	2.50
Agency_1	2017	9	Cx pipiens	11	8	CO2	1.38
Agency_1	2017	9	Cx tarsalis	7	8	CO2	0.88
Agency_1, Agency_2	2017	10	Cx pipiens	11	16	CO2	0.69
Agency_1, Agency_2	2017	10	Cx tarsalis	138	16	CO2	8.62
Agency_1	2017	11	Cx pipiens	33	13	CO2	2.54
Agency_1, Agency_2	2017	11	Cx tarsalis	318	13	CO2	24.46
Agency_1, Agency_2	2017	12	Cx pipiens	43	17	CO2	2.53
Agency_1	2017	12	Cx tarsalis	22	17	CO2	1.29
Agency_1, Agency_2	2017	13	Cx pipiens	135	19	CO2	7.11
Agency_2	2017	13	Cx tarsalis	74	19	CO2	3.89
Agency_1, Agency_2	2017	14	Cx pipiens	86	23	CO2	3.74
Agency_1, Agency_2	2017	14	Cx tarsalis	324	23	CO2	14.09
Agency_1, Agency_2	2017	15	Cx pipiens	230	10	CO2	23.00
Agency_2	2017	15	Cx tarsalis	439	10	CO2	43.90
Agency_1, Agency_2	2017	16	Cx pipiens	295	13	CO2	22.69
Agency_2	2017	16	Cx tarsalis	2279	13	CO2	175.31
Agency_1, Agency_2	2017	17	Cx pipiens	286	22	CO2	13.00
Agency_1, Agency_2	2017	17	Cx tarsalis	4249	22	CO2	193.14
Agency_1, Agency_2	2017	18	Cx pipiens	31	17	CO2	1.82
Agency_1, Agency_2	2017	18	Cx tarsalis	431	17	CO2	25.35
Agency_1, Agency_2	2017	19	Cx pipiens	138	10	CO2	13.80
Agency_2	2017	19	Cx tarsalis	6	10	CO2	0.60
Agency_1, Agency_2	2017	20	Cx pipiens	42	22	CO2	1.91
Agency_1	2017	20	Cx tarsalis	7	22	CO2	0.32
Agency_1, Agency_2	2017	21	Cx tarsalis	138	16	CO2	8.62
Agency_1	2016	8	Cx tarsalis	68	12	CO2	5.67
Agency_1	2016	9	Cx pipiens	6	11	CO2	0.55
Agency_1	2016	9	Cx tarsalis	7	11	CO2	0.64
Agency_1	2016	10	Cx pipiens	59	9	CO2	6.56
Agency_1, Agency_2	2016	10	Cx tarsalis	264	9	CO2	29.33
Agency_2	2016	11	Cx pipiens	8	15	CO2	0.53
Agency_1, Agency_2	2016	11	Cx tarsalis	44	15	CO2	2.93
Agency_1, Agency_2	2016	12	Cx pipiens	17	26	CO2	0.65
Agency_1, Agency_2	2016	12	Cx tarsalis	542	26	CO2	20.85
Agency_1, Agency_2	2016	13	Cx pipiens	91	21	CO2	4.33
Agency_1, Agency_2	2016	13	Cx tarsalis	1098	21	CO2	52.29
Agency_1	2016	14	Cx pipiens	7	10	CO2	0.70
Agency_1	2016	14	Cx tarsalis	87	10	CO2	8.70
Agency_1, Agency_2	2016	15	Cx pipiens	208	18	CO2	11.56
Agency_1, Agency_2	2016	15	Cx tarsalis	1990	18	CO2	110.56
Agency_1, Agency_2	2016	16	Cx pipiens	454	10	CO2	45.40
Agency_1, Agency_2	2016	16	Cx tarsalis	674	10	CO2	67.40
Agency_1, Agency_2	2016	17	Cx pipiens	395	18	CO2	21.94
Agency_1, Agency_2	2016	17	Cx tarsalis	712	18	CO2	39.56
Agency_1, Agency_2	2016	18	Cx pipiens	78	16	CO2	4.88
Agency_1, Agency_2	2016	18	Cx tarsalis	252	16	CO2	15.75
Agency_1, Agency_2	2016	19	Cx pipiens	23	17	CO2	1.35
Agency_1, Agency_2	2016	19	Cx tarsalis	137	17	CO2	8.06
Agency_1, Agency_2	2016	20	Cx pipiens	28	16	CO2	1.75
Agency_1, Agency_2	2016	20	Cx tarsalis	10	16	CO2	0.62
Agency_2	2016	21	Cx pipiens	1	9	CO2	0.11
Agency_1	2016	21	Cx tarsalis	7	9	CO2	0.78
Agency_1, Agency_2	2015	8	Cx pipiens	8	6	CO2	1.33
Agency_1	2015	9	Cx pipiens	1	10	CO2	0.10
Agency_1	2015	9	Cx tarsalis	11	10	CO2	1.10
Agency_1, Agency_2	2015	10	Cx pipiens	14	27	CO2	0.52
Agency_1, Agency_2	2015	10	Cx tarsalis	598	27	CO2	22.15
Agency_1	2015	11	Cx pipiens	38	19	CO2	2.00
Agency_1, Agency_2	2015	11	Cx tarsalis	280	19	CO2	14.74
Agency_1	2015	12	Cx pipiens	144	16	CO2	9.00
Agency_1, Agency_2	2015	12	Cx tarsalis	593	16	CO2	37.06
Agency_1, Agency_2	2015	13	Cx pipiens	80	17	CO2	4.71
Agency_1, Agency_2	2015	13	Cx tarsalis	325	17	CO2	19.12
Agency_1, Agency_2	2015	14	Cx pipiens	204	15	CO2	13.60
Agency_1, Agency_2	2015	14	Cx tarsalis	327	15	CO2	21.80
Agency_1, Agency_2	2015	15	Cx pipiens	572	10	CO2	57.20
Agency_1	2015	15	Cx tarsalis	208	10	CO2	20.80
Agency_1, Agency_2	2015	16	Cx pipiens	172	18	CO2	9.56
Agency_1, Agency_2	2015	16	Cx tarsalis	633	18	CO2	35.17
Agency_1	2015	17	Cx pipiens	126	15	CO2	8.40
Agency_1, Agency_2	2015	17	Cx tarsalis	2307	15	CO2	153.80
Agency_1, Agency_2	2015	18	Cx pipiens	288	20	CO2	14.40
Agency_1, Agency_2	2015	18	Cx tarsalis	1768	20	CO2	88.40
Agency_1, Agency_2	2015	19	Cx pipiens	98	17	CO2	5.76
Agency_1	2015	19	Cx tarsalis	66	17	CO2	3.88
Agency_1, Agency_2	2015	20	Cx pipiens	351	22	CO2	15.95
Agency_1	2015	20	Cx tarsalis	218	22	CO2	9.91
Agency_1	2015	21	Cx pipiens	45	5	CO2	9.00
Table X: Combined biweekly Abundance Calculation for Cx. tarsalis, pipiens in CO2 traps

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", "Species","Trap Type","Trap Events", "Abundance"))


table(vectorsurvR:::testing_collections$trap_acronym, vectorsurvR:::testing_collections$surv_year) %>%
  kbl(align = "c") %>%
  kable_paper(
    full_width = F,
    html_font = "arial",
    lightable_options = "striped",
  ) %>%
  add_header_above(c("Trap Type", "Years" = 6)) %>%
  footnote(general = "Table 3: Traps deployed by year", general_title = "") %>%
  row_spec(c(3, 9, 10), background = "yellow") %>%
  column_spec(c(4), background = "orange")

Trap Type	Years
	2019	2020	2021	2022	2023	2024
BACKPACK	0	0	26	33	11	5
BGSENT	1215	90	5600	7139	6898	9629
BTLJC	30	33	63	0	0	0
CDCAGO	20	0	0	0	0	0
CO2	12143	11622	10901	9719	12364	13437
FLANNEL	3	3	9	14	11	6
GRVD	9547	10181	9221	8611	9069	8820
LCKR	1434	3092	3707	3693	3559	3744
NJLT	3062	844	0	0	0	0
OTHER	0	0	10	11	37	517
OVI	4	0	0	294	0	0
WRKR	0	0	77	0	0	0
Table 3: Traps deployed by year