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] "agency_id"            "county"               "collection_longitude"
#> [16] "collection_latitude"  "spatial_feature"      "multiple_features"

#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
#>   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()
#> # 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

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                 36
#> 3 2015-01-02      Cx pipiens                   1
#> 4 2015-01-02      Cx tarsalis                  3
#> 5 2015-01-10      An freeborni                 4
#> 6 2015-01-10      Cs inornata                108


#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                4.5
#> 3 2015-01-02      Cx pipiens                 1  
#> 4 2015-01-02      Cx tarsalis                1.5
#> 5 2015-01-10      An freeborni               4  
#> 6 2015-01-10      Cs inornata               36

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, agency_id, county, collection_longitude,
#>   collection_latitude, spatial_feature, multiple_features,
#>   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   County                 Species Count TrapEvents Trap
#> 1  AGENCY 2021      7 County 1             Cx tarsalis     2          3  CO2
#> 2  AGENCY 2021      8 County 1 Cx pipiens, Cx tarsalis    50          7  CO2
#> 3  AGENCY 2021     10 County 1 Cx pipiens, Cx tarsalis    79          6  CO2
#> 4  AGENCY 2021     11 County 1 Cx pipiens, Cx tarsalis    41          9  CO2
#> 5  AGENCY 2021     12 County 1 Cx pipiens, Cx tarsalis   549         14  CO2
#> 6  AGENCY 2021     13 County 1             Cx tarsalis  3573          7  CO2
#> 7  AGENCY 2021     14 County 1 Cx pipiens, Cx tarsalis   900          8  CO2
#> 8  AGENCY 2021     15 County 1 Cx pipiens, Cx tarsalis    70          6  CO2
#> 9  AGENCY 2021     16 County 1 Cx pipiens, Cx tarsalis    80         11  CO2
#> 10 AGENCY 2021     17 County 1 Cx pipiens, Cx tarsalis   371          7  CO2
#> 11 AGENCY 2021     18 County 1             Cx tarsalis     8          4  CO2
#> 12 AGENCY 2021     19 County 1             Cx tarsalis    29          6  CO2
#> 13 AGENCY 2021     20 County 1              Cx pipiens    57          9  CO2
#> 14 AGENCY 2021     21 County 1 Cx pipiens, Cx tarsalis    14          3  CO2
#> 15 AGENCY 2020      9 County 1              Cx pipiens     1          3  CO2
#> 16 AGENCY 2020     10 County 1              Cx pipiens     4          7  CO2
#> 17 AGENCY 2020     11 County 1 Cx pipiens, Cx tarsalis    27         11  CO2
#> 18 AGENCY 2020     12 County 1             Cx tarsalis     1          9  CO2
#> 19 AGENCY 2020     13 County 1              Cx pipiens    81          5  CO2
#> 20 AGENCY 2020     14 County 1 Cx pipiens, Cx tarsalis  1731          9  CO2
#> 21 AGENCY 2020     15 County 1             Cx tarsalis    63          6  CO2
#> 22 AGENCY 2020     16 County 1 Cx pipiens, Cx tarsalis   274          8  CO2
#> 23 AGENCY 2020     17 County 1 Cx pipiens, Cx tarsalis    17          6  CO2
#> 24 AGENCY 2020     18 County 1 Cx pipiens, Cx tarsalis   503          4  CO2
#> 25 AGENCY 2020     19 County 1 Cx pipiens, Cx tarsalis    73         14  CO2
#> 26 AGENCY 2020     20 County 1             Cx tarsalis     1          5  CO2
#> 27 AGENCY 2020     21 County 1              Cx pipiens   145          7  CO2
#> 28 AGENCY 2020     22 County 1              Cx pipiens    20          7  CO2
#> 29 AGENCY 2020     23 County 1 Cx pipiens, Cx tarsalis     8          4  CO2
#> 30 AGENCY 2019      9 County 1 Cx pipiens, Cx tarsalis   116         10  CO2
#> 31 AGENCY 2019     10 County 1              Cx pipiens    15          5  CO2
#> 32 AGENCY 2019     11 County 1 Cx pipiens, Cx tarsalis   597         13  CO2
#> 33 AGENCY 2019     12 County 1              Cx pipiens     2          4  CO2
#> 34 AGENCY 2019     13 County 1             Cx tarsalis   596          5  CO2
#> 35 AGENCY 2019     14 County 1             Cx tarsalis    60          4  CO2
#> 36 AGENCY 2019     15 County 1 Cx pipiens, Cx tarsalis  4887         10  CO2
#> 37 AGENCY 2019     16 County 1 Cx pipiens, Cx tarsalis  2192         15  CO2
#> 38 AGENCY 2019     17 County 1 Cx pipiens, Cx tarsalis  2396          6  CO2
#> 39 AGENCY 2019     18 County 1 Cx pipiens, Cx tarsalis  6097         11  CO2
#> 40 AGENCY 2019     19 County 1 Cx pipiens, Cx tarsalis   136          6  CO2
#> 41 AGENCY 2019     21 County 1 Cx pipiens, Cx tarsalis   101          8  CO2
#> 42 AGENCY 2018     10 County 1 Cx pipiens, Cx tarsalis   346          7  CO2
#> 43 AGENCY 2018     11 County 1 Cx pipiens, Cx tarsalis   213         19  CO2
#> 44 AGENCY 2018     12 County 1              Cx pipiens     7         10  CO2
#> 45 AGENCY 2018     13 County 1 Cx pipiens, Cx tarsalis  3024         10  CO2
#> 46 AGENCY 2018     14 County 1 Cx pipiens, Cx tarsalis   231         10  CO2
#> 47 AGENCY 2018     15 County 1 Cx pipiens, Cx tarsalis  1046          7  CO2
#> 48 AGENCY 2018     16 County 1             Cx tarsalis  1649          5  CO2
#> 49 AGENCY 2018     17 County 1 Cx pipiens, Cx tarsalis   126          8  CO2
#> 50 AGENCY 2018     18 County 1             Cx tarsalis   258          7  CO2
#> 51 AGENCY 2018     19 County 1 Cx pipiens, Cx tarsalis   521         10  CO2
#> 52 AGENCY 2018     20 County 1 Cx pipiens, Cx tarsalis    64         10  CO2
#> 53 AGENCY 2018     21 County 1 Cx pipiens, Cx tarsalis    35          7  CO2
#> 54 AGENCY 2017      4 County 1             Cx tarsalis     2          1  CO2
#> 55 AGENCY 2017      9 County 1              Cx pipiens     8         11  CO2
#> 56 AGENCY 2017     10 County 1 Cx pipiens, Cx tarsalis    67         13  CO2
#> 57 AGENCY 2017     11 County 1 Cx pipiens, Cx tarsalis   852          8  CO2
#> 58 AGENCY 2017     12 County 1 Cx pipiens, Cx tarsalis   311         11  CO2
#> 59 AGENCY 2017     13 County 1 Cx pipiens, Cx tarsalis   400          9  CO2
#> 60 AGENCY 2017     14 County 1 Cx pipiens, Cx tarsalis  1269         10  CO2
#> 61 AGENCY 2017     15 County 1 Cx pipiens, Cx tarsalis  2020         16  CO2
#> 62 AGENCY 2017     16 County 1 Cx pipiens, Cx tarsalis   801         10  CO2
#> 63 AGENCY 2017     17 County 1 Cx pipiens, Cx tarsalis  2507         13  CO2
#> 64 AGENCY 2017     18 County 1 Cx pipiens, Cx tarsalis  2108         12  CO2
#> 65 AGENCY 2017     19 County 1 Cx pipiens, Cx tarsalis    52         11  CO2
#> 66 AGENCY 2017     20 County 1 Cx pipiens, Cx tarsalis   141         11  CO2
#> 67 AGENCY 2017     21 County 1 Cx pipiens, Cx tarsalis    60         14  CO2
#> 68 AGENCY 2016      8 County 1 Cx pipiens, Cx tarsalis     3          7  CO2
#> 69 AGENCY 2016      9 County 1 Cx pipiens, Cx tarsalis    46         13  CO2
#> 70 AGENCY 2016     10 County 1 Cx pipiens, Cx tarsalis   126          8  CO2
#> 71 AGENCY 2016     11 County 1 Cx pipiens, Cx tarsalis    19          9  CO2
#> 72 AGENCY 2016     12 County 1 Cx pipiens, Cx tarsalis   254          7  CO2
#> 73 AGENCY 2016     13 County 1 Cx pipiens, Cx tarsalis   741         12  CO2
#> 74 AGENCY 2016     14 County 1 Cx pipiens, Cx tarsalis  2459         13  CO2
#> 75 AGENCY 2016     15 County 1 Cx pipiens, Cx tarsalis  1704         10  CO2
#> 76 AGENCY 2016     16 County 1 Cx pipiens, Cx tarsalis  3327         10  CO2
#> 77 AGENCY 2016     17 County 1 Cx pipiens, Cx tarsalis  2740         15  CO2
#> 78 AGENCY 2016     18 County 1 Cx pipiens, Cx tarsalis    62         11  CO2
#> 79 AGENCY 2016     19 County 1 Cx pipiens, Cx tarsalis    29          7  CO2
#> 80 AGENCY 2016     20 County 1 Cx pipiens, Cx tarsalis    20         16  CO2
#> 81 AGENCY 2016     21 County 1 Cx pipiens, Cx tarsalis   111          6  CO2
#> 82 AGENCY 2015      8 County 1              Cx pipiens     3          4  CO2
#> 83 AGENCY 2015      9 County 1 Cx pipiens, Cx tarsalis    59         12  CO2
#> 84 AGENCY 2015     10 County 1 Cx pipiens, Cx tarsalis   164         15  CO2
#> 85 AGENCY 2015     11 County 1 Cx pipiens, Cx tarsalis   140          5  CO2
#> 86 AGENCY 2015     12 County 1 Cx pipiens, Cx tarsalis   356         11  CO2
#> 87 AGENCY 2015     13 County 1 Cx pipiens, Cx tarsalis   535         17  CO2
#> 88 AGENCY 2015     14 County 1 Cx pipiens, Cx tarsalis   664         10  CO2
#> 89 AGENCY 2015     15 County 1 Cx pipiens, Cx tarsalis  1427         10  CO2
#> 90 AGENCY 2015     16 County 1 Cx pipiens, Cx tarsalis  4855         14  CO2
#> 91 AGENCY 2015     17 County 1 Cx pipiens, Cx tarsalis   118          8  CO2
#> 92 AGENCY 2015     18 County 1              Cx pipiens     9          6  CO2
#> 93 AGENCY 2015     19 County 1 Cx pipiens, Cx tarsalis   223         15  CO2
#> 94 AGENCY 2015     20 County 1 Cx pipiens, Cx tarsalis   107         12  CO2
#> 95 AGENCY 2015     21 County 1 Cx pipiens, Cx tarsalis    76          4  CO2
#>    Abundance
#> 1       0.67
#> 2       7.14
#> 3      13.17
#> 4       4.56
#> 5      39.21
#> 6     510.43
#> 7     112.50
#> 8      11.67
#> 9       7.27
#> 10     53.00
#> 11      2.00
#> 12      4.83
#> 13      6.33
#> 14      4.67
#> 15      0.33
#> 16      0.57
#> 17      2.45
#> 18      0.11
#> 19     16.20
#> 20    192.33
#> 21     10.50
#> 22     34.25
#> 23      2.83
#> 24    125.75
#> 25      5.21
#> 26      0.20
#> 27     20.71
#> 28      2.86
#> 29      2.00
#> 30     11.60
#> 31      3.00
#> 32     45.92
#> 33      0.50
#> 34    119.20
#> 35     15.00
#> 36    488.70
#> 37    146.13
#> 38    399.33
#> 39    554.27
#> 40     22.67
#> 41     12.62
#> 42     49.43
#> 43     11.21
#> 44      0.70
#> 45    302.40
#> 46     23.10
#> 47    149.43
#> 48    329.80
#> 49     15.75
#> 50     36.86
#> 51     52.10
#> 52      6.40
#> 53      5.00
#> 54      2.00
#> 55      0.73
#> 56      5.15
#> 57    106.50
#> 58     28.27
#> 59     44.44
#> 60    126.90
#> 61    126.25
#> 62     80.10
#> 63    192.85
#> 64    175.67
#> 65      4.73
#> 66     12.82
#> 67      4.29
#> 68      0.43
#> 69      3.54
#> 70     15.75
#> 71      2.11
#> 72     36.29
#> 73     61.75
#> 74    189.15
#> 75    170.40
#> 76    332.70
#> 77    182.67
#> 78      5.64
#> 79      4.14
#> 80      1.25
#> 81     18.50
#> 82      0.75
#> 83      4.92
#> 84     10.93
#> 85     28.00
#> 86     32.36
#> 87     31.47
#> 88     66.40
#> 89    142.70
#> 90    346.79
#> 91     14.75
#> 92      1.50
#> 93     14.87
#> 94      8.92
#> 95     19.00

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.
#>    Agency Year Biweek   County     Species Count TrapEvents Trap Abundance
#> 1  AGENCY 2020      9 County 1  Cx pipiens     1          3  CO2      0.33
#> 2  AGENCY 2020     10 County 1  Cx pipiens     4          7  CO2      0.57
#> 3  AGENCY 2020     11 County 1  Cx pipiens    18         11  CO2      1.64
#> 4  AGENCY 2020     11 County 1 Cx tarsalis     9         11  CO2      0.82
#> 5  AGENCY 2020     12 County 1 Cx tarsalis     1          9  CO2      0.11
#> 6  AGENCY 2020     13 County 1  Cx pipiens    81          5  CO2     16.20
#> 7  AGENCY 2020     14 County 1  Cx pipiens    14          9  CO2      1.56
#> 8  AGENCY 2020     14 County 1 Cx tarsalis  1717          9  CO2    190.78
#> 9  AGENCY 2020     15 County 1 Cx tarsalis    63          6  CO2     10.50
#> 10 AGENCY 2020     16 County 1  Cx pipiens    67          8  CO2      8.38
#> 11 AGENCY 2020     16 County 1 Cx tarsalis   207          8  CO2     25.88
#> 12 AGENCY 2020     17 County 1  Cx pipiens     2          6  CO2      0.33
#> 13 AGENCY 2020     17 County 1 Cx tarsalis    15          6  CO2      2.50
#> 14 AGENCY 2020     18 County 1  Cx pipiens     4          4  CO2      1.00
#> 15 AGENCY 2020     18 County 1 Cx tarsalis   499          4  CO2    124.75
#> 16 AGENCY 2020     19 County 1  Cx pipiens    15         14  CO2      1.07
#> 17 AGENCY 2020     19 County 1 Cx tarsalis    58         14  CO2      4.14
#> 18 AGENCY 2020     20 County 1 Cx tarsalis     1          5  CO2      0.20
#> 19 AGENCY 2020     21 County 1  Cx pipiens   145          7  CO2     20.71
#> 20 AGENCY 2020     22 County 1  Cx pipiens    20          7  CO2      2.86
#> 21 AGENCY 2020     23 County 1  Cx pipiens     3          4  CO2      0.75
#> 22 AGENCY 2020     23 County 1 Cx tarsalis     5          4  CO2      1.25
#>    FiveYearAvg           YearsInAverage  Delta
#> 1       1.3300      2015,2016,2017,2019 -75.19
#> 2       2.4300 2015,2016,2017,2018,2019 -76.54
#> 3      15.7200 2015,2016,2017,2018,2019 -89.57
#> 4      23.0280 2015,2016,2017,2018,2019 -96.44
#> 5      15.5600           2015,2016,2017 -99.29
#> 6      15.7175      2015,2016,2017,2018   3.07
#> 7      45.4125      2015,2016,2017,2018 -96.56
#> 8      47.7800 2015,2016,2017,2018,2019 299.29
#> 9     189.7940 2015,2016,2017,2018,2019 -94.47
#> 10    106.2950      2015,2016,2017,2019 -92.12
#> 11    162.0680 2015,2016,2017,2018,2019 -84.03
#> 12     74.4520 2015,2016,2017,2018,2019 -99.56
#> 13     86.6160 2015,2016,2017,2018,2019 -97.11
#> 14     47.5250      2015,2016,2017,2019 -97.90
#> 15    145.9575      2016,2017,2018,2019 -14.53
#> 16      9.2560 2015,2016,2017,2018,2019 -88.44
#> 17     10.4440 2015,2016,2017,2018,2019 -60.36
#> 18      3.5900      2015,2016,2017,2018 -94.43
#> 19      8.4920 2015,2016,2017,2018,2019 143.88
#> 20          NA                     <NA>     NA
#> 21          NA                     <NA>     NA
#> 22          NA                     <NA>     NA

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.

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")
#> # 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 Cx pipiens  CO2   WNV                 0       0       0
#>  2  2015    18 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#>  3  2015    19 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  4  2015    20 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#>  5  2015    21 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  6  2015    21 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#>  7  2015    22 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  8  2015    22 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#>  9  2015    23 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#> 10  2015    23 AGENCY Cx tarsalis 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

getVectorIndex(sample_collections, 
               sample_pools,
               interval = "Biweek",
               target_disease = "WNV",
               pt_estimate = "bc-mle", 
              
               separate_by = c("agency","species")) 
#> # A tibble: 1,114 × 13
#>     Year Biweek Agency Species         Count Trap   TrapEvents Abundance Disease
#>    <dbl>  <dbl> <chr>  <chr>           <int> <chr>       <int>     <dbl> <chr>  
#>  1  2015      1 AGENCY An freeborni        4 " MMT…        122      0.03 WNV    
#>  2  2015      1 AGENCY Cs incidens         1 " MMT…        122      0.01 WNV    
#>  3  2015      1 AGENCY Cs inornata       117 " MMT…        122      0.96 WNV    
#>  4  2015      1 AGENCY Cx pipiens         40 " MMT…        122      0.33 WNV    
#>  5  2015      1 AGENCY Cx stigmatosoma     2 " MMT…        122      0.02 WNV    
#>  6  2015      1 AGENCY Cx tarsalis         1 " MMT…        122      0.01 WNV    
#>  7  2015      2 AGENCY Cs incidens        10 " MMT…        110      0.09 WNV    
#>  8  2015      2 AGENCY Cs inornata        92 " MMT…        110      0.84 WNV    
#>  9  2015      2 AGENCY Cx pipiens         11 " MMT…        110      0.1  WNV    
#> 10  2015      2 AGENCY Cx stigmatosoma     1 " MMT…        110      0.01 WNV    
#> # ℹ 1,104 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: 222 × 18
#>    agency_code collection_id collection_date surv_year species_display_name
#>    <chr>               <int> <date>              <dbl> <chr>               
#>  1 AGENCY            1392963 2015-04-26           2015 Cx tarsalis         
#>  2 AGENCY            1395391 2015-04-26           2015 Cx tarsalis         
#>  3 AGENCY            1392822 2015-04-26           2015 Cx tarsalis         
#>  4 AGENCY            1398260 2015-05-03           2015 Cx tarsalis         
#>  5 AGENCY            1409186 2015-05-15           2015 Cx tarsalis         
#>  6 AGENCY            1400680 2015-05-08           2015 Cx tarsalis         
#>  7 AGENCY            1411484 2015-05-17           2015 Cx tarsalis         
#>  8 AGENCY            1402328 2015-05-11           2015 Cx tarsalis         
#>  9 AGENCY            1425396 2015-06-07           2015 Cx tarsalis         
#> 10 AGENCY            1419375 2015-06-01           2015 Cx tarsalis         
#> # ℹ 212 more rows
#> # ℹ 13 more variables: sex_type <chr>, trap_acronym <chr>,
#> #   trap_problem_bit <lgl>, num_trap <int>, trap_nights <int>, num_count <int>,
#> #   site_code <dbl>, agency_id <dbl>, county <chr>, collection_longitude <dbl>,
#> #   collection_latitude <dbl>, spatial_feature <chr>, multiple_features <lgl>

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   County     Species Count TrapEvents Trap Abundance
#> 1 AGENCY 2021      7 County 1 Cx tarsalis     2          3  CO2      0.67
#> 2 AGENCY 2021      8 County 1  Cx pipiens    19          7  CO2      2.71
#> 3 AGENCY 2021      8 County 1 Cx tarsalis    31          7  CO2      4.43
#> 4 AGENCY 2021     10 County 1  Cx pipiens    28          6  CO2      4.67
#> 5 AGENCY 2021     10 County 1 Cx tarsalis    51          6  CO2      8.50
#> 6 AGENCY 2021     11 County 1  Cx pipiens    18          9  CO2      2.00

#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	County	Species	Count	TrapEvents	Trap	Abundance
AGENCY	2021	7	County 1	Cx tarsalis	2	3	CO2	0.67
AGENCY	2021	8	County 1	Cx pipiens	19	7	CO2	2.71
AGENCY	2021	8	County 1	Cx tarsalis	31	7	CO2	4.43
AGENCY	2021	10	County 1	Cx pipiens	28	6	CO2	4.67
AGENCY	2021	10	County 1	Cx tarsalis	51	6	CO2	8.50
AGENCY	2021	11	County 1	Cx pipiens	18	9	CO2	2.00
AGENCY	2021	11	County 1	Cx tarsalis	23	9	CO2	2.56
AGENCY	2021	12	County 1	Cx pipiens	207	14	CO2	14.79
AGENCY	2021	12	County 1	Cx tarsalis	342	14	CO2	24.43
AGENCY	2021	13	County 1	Cx tarsalis	3573	7	CO2	510.43
AGENCY	2021	14	County 1	Cx pipiens	80	8	CO2	10.00
AGENCY	2021	14	County 1	Cx tarsalis	820	8	CO2	102.50
AGENCY	2021	15	County 1	Cx pipiens	1	6	CO2	0.17
AGENCY	2021	15	County 1	Cx tarsalis	69	6	CO2	11.50
AGENCY	2021	16	County 1	Cx pipiens	73	11	CO2	6.64
AGENCY	2021	16	County 1	Cx tarsalis	7	11	CO2	0.64
AGENCY	2021	17	County 1	Cx pipiens	17	7	CO2	2.43
AGENCY	2021	17	County 1	Cx tarsalis	354	7	CO2	50.57
AGENCY	2021	18	County 1	Cx tarsalis	8	4	CO2	2.00
AGENCY	2021	19	County 1	Cx tarsalis	29	6	CO2	4.83
AGENCY	2021	20	County 1	Cx pipiens	57	9	CO2	6.33
AGENCY	2021	21	County 1	Cx pipiens	9	3	CO2	3.00
AGENCY	2021	21	County 1	Cx tarsalis	5	3	CO2	1.67
AGENCY	2020	9	County 1	Cx pipiens	1	3	CO2	0.33
AGENCY	2020	10	County 1	Cx pipiens	4	7	CO2	0.57
AGENCY	2020	11	County 1	Cx pipiens	18	11	CO2	1.64
AGENCY	2020	11	County 1	Cx tarsalis	9	11	CO2	0.82
AGENCY	2020	12	County 1	Cx tarsalis	1	9	CO2	0.11
AGENCY	2020	13	County 1	Cx pipiens	81	5	CO2	16.20
AGENCY	2020	14	County 1	Cx pipiens	14	9	CO2	1.56
AGENCY	2020	14	County 1	Cx tarsalis	1717	9	CO2	190.78
AGENCY	2020	15	County 1	Cx tarsalis	63	6	CO2	10.50
AGENCY	2020	16	County 1	Cx pipiens	67	8	CO2	8.38
AGENCY	2020	16	County 1	Cx tarsalis	207	8	CO2	25.88
AGENCY	2020	17	County 1	Cx pipiens	2	6	CO2	0.33
AGENCY	2020	17	County 1	Cx tarsalis	15	6	CO2	2.50
AGENCY	2020	18	County 1	Cx pipiens	4	4	CO2	1.00
AGENCY	2020	18	County 1	Cx tarsalis	499	4	CO2	124.75
AGENCY	2020	19	County 1	Cx pipiens	15	14	CO2	1.07
AGENCY	2020	19	County 1	Cx tarsalis	58	14	CO2	4.14
AGENCY	2020	20	County 1	Cx tarsalis	1	5	CO2	0.20
AGENCY	2020	21	County 1	Cx pipiens	145	7	CO2	20.71
AGENCY	2020	22	County 1	Cx pipiens	20	7	CO2	2.86
AGENCY	2020	23	County 1	Cx pipiens	3	4	CO2	0.75
AGENCY	2020	23	County 1	Cx tarsalis	5	4	CO2	1.25
AGENCY	2019	9	County 1	Cx pipiens	15	10	CO2	1.50
AGENCY	2019	9	County 1	Cx tarsalis	101	10	CO2	10.10
AGENCY	2019	10	County 1	Cx pipiens	15	5	CO2	3.00
AGENCY	2019	11	County 1	Cx pipiens	7	13	CO2	0.54
AGENCY	2019	11	County 1	Cx tarsalis	590	13	CO2	45.38
AGENCY	2019	12	County 1	Cx pipiens	2	4	CO2	0.50
AGENCY	2019	13	County 1	Cx tarsalis	596	5	CO2	119.20
AGENCY	2019	14	County 1	Cx tarsalis	60	4	CO2	15.00
AGENCY	2019	15	County 1	Cx pipiens	46	10	CO2	4.60
AGENCY	2019	15	County 1	Cx tarsalis	4841	10	CO2	484.10
AGENCY	2019	16	County 1	Cx pipiens	2089	15	CO2	139.27
AGENCY	2019	16	County 1	Cx tarsalis	103	15	CO2	6.87
AGENCY	2019	17	County 1	Cx pipiens	1950	6	CO2	325.00
AGENCY	2019	17	County 1	Cx tarsalis	446	6	CO2	74.33
AGENCY	2019	18	County 1	Cx pipiens	1622	11	CO2	147.45
AGENCY	2019	18	County 1	Cx tarsalis	4475	11	CO2	406.82
AGENCY	2019	19	County 1	Cx pipiens	43	6	CO2	7.17
AGENCY	2019	19	County 1	Cx tarsalis	93	6	CO2	15.50
AGENCY	2019	21	County 1	Cx pipiens	64	8	CO2	8.00
AGENCY	2019	21	County 1	Cx tarsalis	37	8	CO2	4.62
AGENCY	2018	10	County 1	Cx pipiens	3	7	CO2	0.43
AGENCY	2018	10	County 1	Cx tarsalis	343	7	CO2	49.00
AGENCY	2018	11	County 1	Cx pipiens	126	19	CO2	6.63
AGENCY	2018	11	County 1	Cx tarsalis	87	19	CO2	4.58
AGENCY	2018	12	County 1	Cx pipiens	7	10	CO2	0.70
AGENCY	2018	13	County 1	Cx pipiens	11	10	CO2	1.10
AGENCY	2018	13	County 1	Cx tarsalis	3013	10	CO2	301.30
AGENCY	2018	14	County 1	Cx pipiens	35	10	CO2	3.50
AGENCY	2018	14	County 1	Cx tarsalis	196	10	CO2	19.60
AGENCY	2018	15	County 1	Cx pipiens	445	7	CO2	63.57
AGENCY	2018	15	County 1	Cx tarsalis	601	7	CO2	85.86
AGENCY	2018	16	County 1	Cx tarsalis	1649	5	CO2	329.80
AGENCY	2018	17	County 1	Cx pipiens	120	8	CO2	15.00
AGENCY	2018	17	County 1	Cx tarsalis	6	8	CO2	0.75
AGENCY	2018	18	County 1	Cx tarsalis	258	7	CO2	36.86
AGENCY	2018	19	County 1	Cx pipiens	278	10	CO2	27.80
AGENCY	2018	19	County 1	Cx tarsalis	243	10	CO2	24.30
AGENCY	2018	20	County 1	Cx pipiens	62	10	CO2	6.20
AGENCY	2018	20	County 1	Cx tarsalis	2	10	CO2	0.20
AGENCY	2018	21	County 1	Cx pipiens	23	7	CO2	3.29
AGENCY	2018	21	County 1	Cx tarsalis	12	7	CO2	1.71
AGENCY	2017	4	County 1	Cx tarsalis	2	1	CO2	2.00
AGENCY	2017	9	County 1	Cx pipiens	8	11	CO2	0.73
AGENCY	2017	10	County 1	Cx pipiens	11	13	CO2	0.85
AGENCY	2017	10	County 1	Cx tarsalis	56	13	CO2	4.31
AGENCY	2017	11	County 1	Cx pipiens	418	8	CO2	52.25
AGENCY	2017	11	County 1	Cx tarsalis	434	8	CO2	54.25
AGENCY	2017	12	County 1	Cx pipiens	24	11	CO2	2.18
AGENCY	2017	12	County 1	Cx tarsalis	287	11	CO2	26.09
AGENCY	2017	13	County 1	Cx pipiens	392	9	CO2	43.56
AGENCY	2017	13	County 1	Cx tarsalis	8	9	CO2	0.89
AGENCY	2017	14	County 1	Cx pipiens	726	10	CO2	72.60
AGENCY	2017	14	County 1	Cx tarsalis	543	10	CO2	54.30
AGENCY	2017	15	County 1	Cx pipiens	391	16	CO2	24.44
AGENCY	2017	15	County 1	Cx tarsalis	1629	16	CO2	101.81
AGENCY	2017	16	County 1	Cx pipiens	460	10	CO2	46.00
AGENCY	2017	16	County 1	Cx tarsalis	341	10	CO2	34.10
AGENCY	2017	17	County 1	Cx pipiens	163	13	CO2	12.54
AGENCY	2017	17	County 1	Cx tarsalis	2344	13	CO2	180.31
AGENCY	2017	18	County 1	Cx pipiens	436	12	CO2	36.33
AGENCY	2017	18	County 1	Cx tarsalis	1672	12	CO2	139.33
AGENCY	2017	19	County 1	Cx pipiens	40	11	CO2	3.64
AGENCY	2017	19	County 1	Cx tarsalis	12	11	CO2	1.09
AGENCY	2017	20	County 1	Cx pipiens	40	11	CO2	3.64
AGENCY	2017	20	County 1	Cx tarsalis	101	11	CO2	9.18
AGENCY	2017	21	County 1	Cx pipiens	28	14	CO2	2.00
AGENCY	2017	21	County 1	Cx tarsalis	32	14	CO2	2.29
AGENCY	2016	8	County 1	Cx pipiens	1	7	CO2	0.14
AGENCY	2016	8	County 1	Cx tarsalis	2	7	CO2	0.29
AGENCY	2016	9	County 1	Cx pipiens	12	13	CO2	0.92
AGENCY	2016	9	County 1	Cx tarsalis	34	13	CO2	2.62
AGENCY	2016	10	County 1	Cx pipiens	16	8	CO2	2.00
AGENCY	2016	10	County 1	Cx tarsalis	110	8	CO2	13.75
AGENCY	2016	11	County 1	Cx pipiens	16	9	CO2	1.78
AGENCY	2016	11	County 1	Cx tarsalis	3	9	CO2	0.33
AGENCY	2016	12	County 1	Cx pipiens	253	7	CO2	36.14
AGENCY	2016	12	County 1	Cx tarsalis	1	7	CO2	0.14
AGENCY	2016	13	County 1	Cx pipiens	23	12	CO2	1.92
AGENCY	2016	13	County 1	Cx tarsalis	718	12	CO2	59.83
AGENCY	2016	14	County 1	Cx pipiens	1354	13	CO2	104.15
AGENCY	2016	14	County 1	Cx tarsalis	1105	13	CO2	85.00
AGENCY	2016	15	County 1	Cx pipiens	285	10	CO2	28.50
AGENCY	2016	15	County 1	Cx tarsalis	1419	10	CO2	141.90
AGENCY	2016	16	County 1	Cx pipiens	2032	10	CO2	203.20
AGENCY	2016	16	County 1	Cx tarsalis	1295	10	CO2	129.50
AGENCY	2016	17	County 1	Cx pipiens	99	15	CO2	6.60
AGENCY	2016	17	County 1	Cx tarsalis	2641	15	CO2	176.07
AGENCY	2016	18	County 1	Cx pipiens	53	11	CO2	4.82
AGENCY	2016	18	County 1	Cx tarsalis	9	11	CO2	0.82
AGENCY	2016	19	County 1	Cx pipiens	15	7	CO2	2.14
AGENCY	2016	19	County 1	Cx tarsalis	14	7	CO2	2.00
AGENCY	2016	20	County 1	Cx pipiens	15	16	CO2	0.94
AGENCY	2016	20	County 1	Cx tarsalis	5	16	CO2	0.31
AGENCY	2016	21	County 1	Cx pipiens	109	6	CO2	18.17
AGENCY	2016	21	County 1	Cx tarsalis	2	6	CO2	0.33
AGENCY	2015	8	County 1	Cx pipiens	3	4	CO2	0.75
AGENCY	2015	9	County 1	Cx pipiens	26	12	CO2	2.17
AGENCY	2015	9	County 1	Cx tarsalis	33	12	CO2	2.75
AGENCY	2015	10	County 1	Cx pipiens	88	15	CO2	5.87
AGENCY	2015	10	County 1	Cx tarsalis	76	15	CO2	5.07
AGENCY	2015	11	County 1	Cx pipiens	87	5	CO2	17.40
AGENCY	2015	11	County 1	Cx tarsalis	53	5	CO2	10.60
AGENCY	2015	12	County 1	Cx pipiens	131	11	CO2	11.91
AGENCY	2015	12	County 1	Cx tarsalis	225	11	CO2	20.45
AGENCY	2015	13	County 1	Cx pipiens	277	17	CO2	16.29
AGENCY	2015	13	County 1	Cx tarsalis	258	17	CO2	15.18
AGENCY	2015	14	County 1	Cx pipiens	14	10	CO2	1.40
AGENCY	2015	14	County 1	Cx tarsalis	650	10	CO2	65.00
AGENCY	2015	15	County 1	Cx pipiens	74	10	CO2	7.40
AGENCY	2015	15	County 1	Cx tarsalis	1353	10	CO2	135.30
AGENCY	2015	16	County 1	Cx pipiens	514	14	CO2	36.71
AGENCY	2015	16	County 1	Cx tarsalis	4341	14	CO2	310.07
AGENCY	2015	17	County 1	Cx pipiens	105	8	CO2	13.12
AGENCY	2015	17	County 1	Cx tarsalis	13	8	CO2	1.62
AGENCY	2015	18	County 1	Cx pipiens	9	6	CO2	1.50
AGENCY	2015	19	County 1	Cx pipiens	83	15	CO2	5.53
AGENCY	2015	19	County 1	Cx tarsalis	140	15	CO2	9.33
AGENCY	2015	20	County 1	Cx pipiens	51	12	CO2	4.25
AGENCY	2015	20	County 1	Cx tarsalis	56	12	CO2	4.67
AGENCY	2015	21	County 1	Cx pipiens	44	4	CO2	11.00
AGENCY	2015	21	County 1	Cx tarsalis	32	4	CO2	8.00
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	2318	90	11158	14163	13535	18943
BTLJC	30	33	63	0	0	0
CDCAGO	20	0	0	0	0	0
CO2	12201	11622	10901	9719	12364	13475
FLANNEL	3	3	9	14	11	6
GRVD	9560	10184	9221	8611	9069	8910
LCKR	1434	3119	3707	3693	3559	3831
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