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

#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

#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

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      4 County 1          No Collections     0          3  CO2
#> 2   AGENCY 2021      6 County 1          No Collections     0          3  CO2
#> 3   AGENCY 2021      7 County 1             Cx tarsalis     2          3  CO2
#> 4   AGENCY 2021      8 County 1 Cx pipiens, Cx tarsalis    50          7  CO2
#> 5   AGENCY 2021      9 County 1          No Collections     0          1  CO2
#> 6   AGENCY 2021     10 County 1 Cx pipiens, Cx tarsalis    79          6  CO2
#> 7   AGENCY 2021     11 County 1 Cx pipiens, Cx tarsalis    41          9  CO2
#> 8   AGENCY 2021     12 County 1 Cx pipiens, Cx tarsalis   549         14  CO2
#> 9   AGENCY 2021     13 County 1             Cx tarsalis  3573          7  CO2
#> 10  AGENCY 2021     14 County 1 Cx pipiens, Cx tarsalis   900          8  CO2
#> 11  AGENCY 2021     15 County 1 Cx pipiens, Cx tarsalis    70          6  CO2
#> 12  AGENCY 2021     16 County 1 Cx pipiens, Cx tarsalis    80         11  CO2
#> 13  AGENCY 2021     17 County 1 Cx pipiens, Cx tarsalis   371          7  CO2
#> 14  AGENCY 2021     18 County 1             Cx tarsalis     8          4  CO2
#> 15  AGENCY 2021     19 County 1             Cx tarsalis    29          6  CO2
#> 16  AGENCY 2021     20 County 1              Cx pipiens    57          9  CO2
#> 17  AGENCY 2021     21 County 1 Cx pipiens, Cx tarsalis    14          3  CO2
#> 18  AGENCY 2021     22 County 1          No Collections     0          1  CO2
#> 19  AGENCY 2020      9 County 1              Cx pipiens     1          3  CO2
#> 20  AGENCY 2020     10 County 1              Cx pipiens     4          7  CO2
#> 21  AGENCY 2020     11 County 1 Cx pipiens, Cx tarsalis    27         11  CO2
#> 22  AGENCY 2020     12 County 1             Cx tarsalis     1          9  CO2
#> 23  AGENCY 2020     13 County 1              Cx pipiens    81          5  CO2
#> 24  AGENCY 2020     14 County 1 Cx pipiens, Cx tarsalis  1731          9  CO2
#> 25  AGENCY 2020     15 County 1             Cx tarsalis    63          6  CO2
#> 26  AGENCY 2020     16 County 1 Cx pipiens, Cx tarsalis   274          8  CO2
#> 27  AGENCY 2020     17 County 1 Cx pipiens, Cx tarsalis    17          6  CO2
#> 28  AGENCY 2020     18 County 1 Cx pipiens, Cx tarsalis   503          4  CO2
#> 29  AGENCY 2020     19 County 1 Cx pipiens, Cx tarsalis    73         14  CO2
#> 30  AGENCY 2020     20 County 1             Cx tarsalis     1          5  CO2
#> 31  AGENCY 2020     21 County 1              Cx pipiens   145          7  CO2
#> 32  AGENCY 2020     22 County 1              Cx pipiens    20          7  CO2
#> 33  AGENCY 2020     23 County 1 Cx pipiens, Cx tarsalis     8          4  CO2
#> 34  AGENCY 2020     24 County 1          No Collections     0          1  CO2
#> 35  AGENCY 2019      9 County 1 Cx pipiens, Cx tarsalis   116         10  CO2
#> 36  AGENCY 2019     10 County 1              Cx pipiens    15          5  CO2
#> 37  AGENCY 2019     11 County 1 Cx pipiens, Cx tarsalis   597         13  CO2
#> 38  AGENCY 2019     12 County 1              Cx pipiens     2          4  CO2
#> 39  AGENCY 2019     13 County 1             Cx tarsalis   596          5  CO2
#> 40  AGENCY 2019     14 County 1             Cx tarsalis    60          4  CO2
#> 41  AGENCY 2019     15 County 1 Cx pipiens, Cx tarsalis  4887         10  CO2
#> 42  AGENCY 2019     16 County 1 Cx pipiens, Cx tarsalis  2192         15  CO2
#> 43  AGENCY 2019     17 County 1 Cx pipiens, Cx tarsalis  2396          6  CO2
#> 44  AGENCY 2019     18 County 1 Cx pipiens, Cx tarsalis  6097         11  CO2
#> 45  AGENCY 2019     19 County 1 Cx pipiens, Cx tarsalis   136          6  CO2
#> 46  AGENCY 2019     20 County 1          No Collections     0          2  CO2
#> 47  AGENCY 2019     21 County 1 Cx pipiens, Cx tarsalis   101          8  CO2
#> 48  AGENCY 2018     10 County 1 Cx pipiens, Cx tarsalis   346          7  CO2
#> 49  AGENCY 2018     11 County 1 Cx pipiens, Cx tarsalis   213         19  CO2
#> 50  AGENCY 2018     12 County 1              Cx pipiens     7         10  CO2
#> 51  AGENCY 2018     13 County 1 Cx pipiens, Cx tarsalis  3024         10  CO2
#> 52  AGENCY 2018     14 County 1 Cx pipiens, Cx tarsalis   231         10  CO2
#> 53  AGENCY 2018     15 County 1 Cx pipiens, Cx tarsalis  1046          7  CO2
#> 54  AGENCY 2018     16 County 1             Cx tarsalis  1649          5  CO2
#> 55  AGENCY 2018     17 County 1 Cx pipiens, Cx tarsalis   126          8  CO2
#> 56  AGENCY 2018     18 County 1             Cx tarsalis   258          7  CO2
#> 57  AGENCY 2018     19 County 1 Cx pipiens, Cx tarsalis   521         10  CO2
#> 58  AGENCY 2018     20 County 1 Cx pipiens, Cx tarsalis    64         10  CO2
#> 59  AGENCY 2018     21 County 1 Cx pipiens, Cx tarsalis    35          7  CO2
#> 60  AGENCY 2017      4 County 1             Cx tarsalis     2          1  CO2
#> 61  AGENCY 2017      8 County 1          No Collections     0          1  CO2
#> 62  AGENCY 2017      9 County 1              Cx pipiens     8         11  CO2
#> 63  AGENCY 2017     10 County 1 Cx pipiens, Cx tarsalis    67         13  CO2
#> 64  AGENCY 2017     11 County 1 Cx pipiens, Cx tarsalis   852          8  CO2
#> 65  AGENCY 2017     12 County 1 Cx pipiens, Cx tarsalis   311         11  CO2
#> 66  AGENCY 2017     13 County 1 Cx pipiens, Cx tarsalis   400          9  CO2
#> 67  AGENCY 2017     14 County 1 Cx pipiens, Cx tarsalis  1269         10  CO2
#> 68  AGENCY 2017     15 County 1 Cx pipiens, Cx tarsalis  2020         16  CO2
#> 69  AGENCY 2017     16 County 1 Cx pipiens, Cx tarsalis   801         10  CO2
#> 70  AGENCY 2017     17 County 1 Cx pipiens, Cx tarsalis  2507         13  CO2
#> 71  AGENCY 2017     18 County 1 Cx pipiens, Cx tarsalis  2108         12  CO2
#> 72  AGENCY 2017     19 County 1 Cx pipiens, Cx tarsalis    52         11  CO2
#> 73  AGENCY 2017     20 County 1 Cx pipiens, Cx tarsalis   141         11  CO2
#> 74  AGENCY 2017     21 County 1 Cx pipiens, Cx tarsalis    60         14  CO2
#> 75  AGENCY 2016      8 County 1 Cx pipiens, Cx tarsalis     3          7  CO2
#> 76  AGENCY 2016      9 County 1 Cx pipiens, Cx tarsalis    46         13  CO2
#> 77  AGENCY 2016     10 County 1 Cx pipiens, Cx tarsalis   126          8  CO2
#> 78  AGENCY 2016     11 County 1 Cx pipiens, Cx tarsalis    19          9  CO2
#> 79  AGENCY 2016     12 County 1 Cx pipiens, Cx tarsalis   254          7  CO2
#> 80  AGENCY 2016     13 County 1 Cx pipiens, Cx tarsalis   741         12  CO2
#> 81  AGENCY 2016     14 County 1 Cx pipiens, Cx tarsalis  2459         13  CO2
#> 82  AGENCY 2016     15 County 1 Cx pipiens, Cx tarsalis  1704         10  CO2
#> 83  AGENCY 2016     16 County 1 Cx pipiens, Cx tarsalis  3327         10  CO2
#> 84  AGENCY 2016     17 County 1 Cx pipiens, Cx tarsalis  2740         15  CO2
#> 85  AGENCY 2016     18 County 1 Cx pipiens, Cx tarsalis    62         11  CO2
#> 86  AGENCY 2016     19 County 1 Cx pipiens, Cx tarsalis    29          7  CO2
#> 87  AGENCY 2016     20 County 1 Cx pipiens, Cx tarsalis    20         16  CO2
#> 88  AGENCY 2016     21 County 1 Cx pipiens, Cx tarsalis   111          6  CO2
#> 89  AGENCY 2015      8 County 1              Cx pipiens     3          4  CO2
#> 90  AGENCY 2015      9 County 1 Cx pipiens, Cx tarsalis    59         12  CO2
#> 91  AGENCY 2015     10 County 1 Cx pipiens, Cx tarsalis   164         15  CO2
#> 92  AGENCY 2015     11 County 1 Cx pipiens, Cx tarsalis   140          5  CO2
#> 93  AGENCY 2015     12 County 1 Cx pipiens, Cx tarsalis   356         11  CO2
#> 94  AGENCY 2015     13 County 1 Cx pipiens, Cx tarsalis   535         17  CO2
#> 95  AGENCY 2015     14 County 1 Cx pipiens, Cx tarsalis   664         10  CO2
#> 96  AGENCY 2015     15 County 1 Cx pipiens, Cx tarsalis  1427         10  CO2
#> 97  AGENCY 2015     16 County 1 Cx pipiens, Cx tarsalis  4855         14  CO2
#> 98  AGENCY 2015     17 County 1 Cx pipiens, Cx tarsalis   118          8  CO2
#> 99  AGENCY 2015     18 County 1              Cx pipiens     9          6  CO2
#> 100 AGENCY 2015     19 County 1 Cx pipiens, Cx tarsalis   223         15  CO2
#> 101 AGENCY 2015     20 County 1 Cx pipiens, Cx tarsalis   107         12  CO2
#> 102 AGENCY 2015     21 County 1 Cx pipiens, Cx tarsalis    76          4  CO2
#> 103 AGENCY 2015     23 County 1          No Collections     0          2  CO2
#>     Abundance
#> 1      0.0000
#> 2      0.0000
#> 3      0.6667
#> 4      7.1429
#> 5      0.0000
#> 6     13.1667
#> 7      4.5556
#> 8     39.2143
#> 9    510.4286
#> 10   112.5000
#> 11    11.6667
#> 12     7.2727
#> 13    53.0000
#> 14     2.0000
#> 15     4.8333
#> 16     6.3333
#> 17     4.6667
#> 18     0.0000
#> 19     0.3333
#> 20     0.5714
#> 21     2.4545
#> 22     0.1111
#> 23    16.2000
#> 24   192.3333
#> 25    10.5000
#> 26    34.2500
#> 27     2.8333
#> 28   125.7500
#> 29     5.2143
#> 30     0.2000
#> 31    20.7143
#> 32     2.8571
#> 33     2.0000
#> 34     0.0000
#> 35    11.6000
#> 36     3.0000
#> 37    45.9231
#> 38     0.5000
#> 39   119.2000
#> 40    15.0000
#> 41   488.7000
#> 42   146.1333
#> 43   399.3333
#> 44   554.2727
#> 45    22.6667
#> 46     0.0000
#> 47    12.6250
#> 48    49.4286
#> 49    11.2105
#> 50     0.7000
#> 51   302.4000
#> 52    23.1000
#> 53   149.4286
#> 54   329.8000
#> 55    15.7500
#> 56    36.8571
#> 57    52.1000
#> 58     6.4000
#> 59     5.0000
#> 60     2.0000
#> 61     0.0000
#> 62     0.7273
#> 63     5.1538
#> 64   106.5000
#> 65    28.2727
#> 66    44.4444
#> 67   126.9000
#> 68   126.2500
#> 69    80.1000
#> 70   192.8462
#> 71   175.6667
#> 72     4.7273
#> 73    12.8182
#> 74     4.2857
#> 75     0.4286
#> 76     3.5385
#> 77    15.7500
#> 78     2.1111
#> 79    36.2857
#> 80    61.7500
#> 81   189.1538
#> 82   170.4000
#> 83   332.7000
#> 84   182.6667
#> 85     5.6364
#> 86     4.1429
#> 87     1.2500
#> 88    18.5000
#> 89     0.7500
#> 90     4.9167
#> 91    10.9333
#> 92    28.0000
#> 93    32.3636
#> 94    31.4706
#> 95    66.4000
#> 96   142.7000
#> 97   346.7857
#> 98    14.7500
#> 99     1.5000
#> 100   14.8667
#> 101    8.9167
#> 102   19.0000
#> 103    0.0000

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


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 tarsalis     0          3  CO2    0.0000
#> 2  AGENCY 2020      9 County 1  Cx pipiens     1          3  CO2    0.3333
#> 3  AGENCY 2020     10 County 1 Cx tarsalis     0          7  CO2    0.0000
#> 4  AGENCY 2020     10 County 1  Cx pipiens     4          7  CO2    0.5714
#> 5  AGENCY 2020     11 County 1 Cx tarsalis     9         11  CO2    0.8182
#> 6  AGENCY 2020     11 County 1  Cx pipiens    18         11  CO2    1.6364
#> 7  AGENCY 2020     12 County 1 Cx tarsalis     1          9  CO2    0.1111
#> 8  AGENCY 2020     12 County 1  Cx pipiens     0          9  CO2    0.0000
#> 9  AGENCY 2020     13 County 1 Cx tarsalis     0          5  CO2    0.0000
#> 10 AGENCY 2020     13 County 1  Cx pipiens    81          5  CO2   16.2000
#> 11 AGENCY 2020     14 County 1 Cx tarsalis  1717          9  CO2  190.7778
#> 12 AGENCY 2020     14 County 1  Cx pipiens    14          9  CO2    1.5556
#> 13 AGENCY 2020     15 County 1 Cx tarsalis    63          6  CO2   10.5000
#> 14 AGENCY 2020     15 County 1  Cx pipiens     0          6  CO2    0.0000
#> 15 AGENCY 2020     16 County 1 Cx tarsalis   207          8  CO2   25.8750
#> 16 AGENCY 2020     16 County 1  Cx pipiens    67          8  CO2    8.3750
#> 17 AGENCY 2020     17 County 1 Cx tarsalis    15          6  CO2    2.5000
#> 18 AGENCY 2020     17 County 1  Cx pipiens     2          6  CO2    0.3333
#> 19 AGENCY 2020     18 County 1 Cx tarsalis   499          4  CO2  124.7500
#> 20 AGENCY 2020     18 County 1  Cx pipiens     4          4  CO2    1.0000
#> 21 AGENCY 2020     19 County 1 Cx tarsalis    58         14  CO2    4.1429
#> 22 AGENCY 2020     19 County 1  Cx pipiens    15         14  CO2    1.0714
#> 23 AGENCY 2020     20 County 1 Cx tarsalis     1          5  CO2    0.2000
#> 24 AGENCY 2020     20 County 1  Cx pipiens     0          5  CO2    0.0000
#> 25 AGENCY 2020     21 County 1 Cx tarsalis     0          7  CO2    0.0000
#> 26 AGENCY 2020     21 County 1  Cx pipiens   145          7  CO2   20.7143
#> 27 AGENCY 2020     22 County 1 Cx tarsalis     0          7  CO2    0.0000
#> 28 AGENCY 2020     22 County 1  Cx pipiens    20          7  CO2    2.8571
#> 29 AGENCY 2020     23 County 1 Cx tarsalis     5          4  CO2    1.2500
#> 30 AGENCY 2020     23 County 1  Cx pipiens     3          4  CO2    0.7500
#> 31 AGENCY 2020     24 County 1 Cx tarsalis     0          1  CO2    0.0000
#> 32 AGENCY 2020     24 County 1  Cx pipiens     0          1  CO2    0.0000
#>    FiveYearAvg           YearsInAverage   Delta
#> 1     3.866350      2015,2016,2017,2019 -100.00
#> 2     1.329275      2015,2016,2017,2019  -74.93
#> 3    14.424880 2015,2016,2017,2018,2019 -100.00
#> 4     2.428300 2015,2016,2017,2018,2019  -76.47
#> 5    23.029360 2015,2016,2017,2018,2019  -96.45
#> 6    15.719580 2015,2016,2017,2018,2019  -89.59
#> 7     9.337660 2015,2016,2017,2018,2019  -98.81
#> 8    10.286760 2015,2016,2017,2018,2019 -100.00
#> 9    99.279740 2015,2016,2017,2018,2019 -100.00
#> 10   12.573280 2015,2016,2017,2018,2019   28.84
#> 11   47.780000 2015,2016,2017,2018,2019  299.28
#> 12   36.330760 2015,2016,2017,2018,2019  -95.72
#> 13  189.793920 2015,2016,2017,2018,2019  -94.47
#> 14   25.701780 2015,2016,2017,2018,2019 -100.00
#> 15  162.067620 2015,2016,2017,2018,2019  -84.03
#> 16   85.036200 2015,2016,2017,2018,2019  -90.15
#> 17   86.616540 2015,2016,2017,2018,2019  -97.11
#> 18   74.452700 2015,2016,2017,2018,2019  -99.55
#> 19  116.765360 2015,2016,2017,2018,2019    6.84
#> 20   38.021200 2015,2016,2017,2018,2019  -97.37
#> 21   10.444840 2015,2016,2017,2018,2019  -60.34
#> 22    9.255860 2015,2016,2017,2018,2019  -88.42
#> 23    2.872200 2015,2016,2017,2018,2019  -93.04
#> 24    3.004780 2015,2016,2017,2018,2019 -100.00
#> 25    3.391660 2015,2016,2017,2018,2019 -100.00
#> 26    8.490480 2015,2016,2017,2018,2019  143.97
#> 27          NA                     <NA>      NA
#> 28          NA                     <NA>      NA
#> 29    0.000000                     2015     Inf
#> 30    0.000000                     2015     Inf
#> 31          NA                     <NA>      NA
#> 32          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

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")
#> Warning: There were 3 warnings in `dplyr::summarise()`.
#> The first warning was:
#> ℹ In argument: `InfectionRate = case_when(...)`.
#> ℹ In group 20: `surv_year = 2017`, `INTERVAL = 32`, `species_display_name = "Cx
#>   tarsalis"`.
#> Caused by warning in `sqrt()`:
#> ! NaNs produced
#> ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.
#> # A tibble: 196 × 9
#> # Groups:   Year, Week [113]
#>     Year  Week Agency Species     Trap  Disease InfectionRate LowerCI UpperCI
#>    <dbl> <dbl> <chr>  <chr>       <chr> <chr>           <dbl>   <dbl>   <dbl>
#>  1  2017    22 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  2  2017    22 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#>  3  2017    23 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  4  2017    23 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#>  5  2017    24 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  6  2017    25 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  7  2017    25 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#>  8  2017    26 AGENCY Cx pipiens  CO2   WNV                 0       0       0
#>  9  2017    26 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#> 10  2017    27 AGENCY Cx tarsalis CO2   WNV                 0       0       0
#> # ℹ 186 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

getVectorIndex(sample_collections, 
               sample_pools,
               interval = "Biweek",
               target_disease = "WNV",
               pt_estimate = "bc-mle", 
              
               separate_by = c("agency","species")) 
#> [1] "Years in Pools and Collections data do not match. Years much match for function to operate."
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

getPoolsComparisionTable(
  sample_pools,
  interval = "Week",
  target_disease = "WNV"
)
#> # A tibble: 119 × 6
#> # Groups:   Year, Week [119]
#>     Year  Week Negative Confirmed Total `Percent Positive`
#>    <dbl> <dbl>    <int>     <int> <int>              <dbl>
#>  1  2017    20        2         0     2                0  
#>  2  2017    21        3         0     3                0  
#>  3  2017    22       10         0    10                0  
#>  4  2017    23        7         0     7                0  
#>  5  2017    24       10         0    10                0  
#>  6  2017    25        8         0     8                0  
#>  7  2017    26        4         0     4                0  
#>  8  2017    27        7         1     8               12.5
#>  9  2017    28       10         0    10                0  
#> 10  2017    29        8         0     8                0  
#> # ℹ 109 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      4 County 1 Cx tarsalis     0          3  CO2    0.0000
#> 2 AGENCY 2021      4 County 1  Cx pipiens     0          3  CO2    0.0000
#> 3 AGENCY 2021      6 County 1 Cx tarsalis     0          3  CO2    0.0000
#> 4 AGENCY 2021      6 County 1  Cx pipiens     0          3  CO2    0.0000
#> 5 AGENCY 2021      7 County 1 Cx tarsalis     2          3  CO2    0.6667
#> 6 AGENCY 2021      7 County 1  Cx pipiens     0          3  CO2    0.0000

#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 4 County 1 Cx tarsalis 0 3 CO2 0.0000
AGENCY 2021 4 County 1 Cx pipiens 0 3 CO2 0.0000
AGENCY 2021 6 County 1 Cx tarsalis 0 3 CO2 0.0000
AGENCY 2021 6 County 1 Cx pipiens 0 3 CO2 0.0000
AGENCY 2021 7 County 1 Cx tarsalis 2 3 CO2 0.6667
AGENCY 2021 7 County 1 Cx pipiens 0 3 CO2 0.0000
AGENCY 2021 8 County 1 Cx tarsalis 31 7 CO2 4.4286
AGENCY 2021 8 County 1 Cx pipiens 19 7 CO2 2.7143
AGENCY 2021 9 County 1 Cx tarsalis 0 1 CO2 0.0000
AGENCY 2021 9 County 1 Cx pipiens 0 1 CO2 0.0000
AGENCY 2021 10 County 1 Cx tarsalis 51 6 CO2 8.5000
AGENCY 2021 10 County 1 Cx pipiens 28 6 CO2 4.6667
AGENCY 2021 11 County 1 Cx tarsalis 23 9 CO2 2.5556
AGENCY 2021 11 County 1 Cx pipiens 18 9 CO2 2.0000
AGENCY 2021 12 County 1 Cx tarsalis 342 14 CO2 24.4286
AGENCY 2021 12 County 1 Cx pipiens 207 14 CO2 14.7857
AGENCY 2021 13 County 1 Cx tarsalis 3573 7 CO2 510.4286
AGENCY 2021 13 County 1 Cx pipiens 0 7 CO2 0.0000
AGENCY 2021 14 County 1 Cx tarsalis 820 8 CO2 102.5000
AGENCY 2021 14 County 1 Cx pipiens 80 8 CO2 10.0000
AGENCY 2021 15 County 1 Cx tarsalis 69 6 CO2 11.5000
AGENCY 2021 15 County 1 Cx pipiens 1 6 CO2 0.1667
AGENCY 2021 16 County 1 Cx tarsalis 7 11 CO2 0.6364
AGENCY 2021 16 County 1 Cx pipiens 73 11 CO2 6.6364
AGENCY 2021 17 County 1 Cx tarsalis 354 7 CO2 50.5714
AGENCY 2021 17 County 1 Cx pipiens 17 7 CO2 2.4286
AGENCY 2021 18 County 1 Cx tarsalis 8 4 CO2 2.0000
AGENCY 2021 18 County 1 Cx pipiens 0 4 CO2 0.0000
AGENCY 2021 19 County 1 Cx tarsalis 29 6 CO2 4.8333
AGENCY 2021 19 County 1 Cx pipiens 0 6 CO2 0.0000
AGENCY 2021 20 County 1 Cx tarsalis 0 9 CO2 0.0000
AGENCY 2021 20 County 1 Cx pipiens 57 9 CO2 6.3333
AGENCY 2021 21 County 1 Cx tarsalis 5 3 CO2 1.6667
AGENCY 2021 21 County 1 Cx pipiens 9 3 CO2 3.0000
AGENCY 2021 22 County 1 Cx tarsalis 0 1 CO2 0.0000
AGENCY 2021 22 County 1 Cx pipiens 0 1 CO2 0.0000
AGENCY 2020 9 County 1 Cx tarsalis 0 3 CO2 0.0000
AGENCY 2020 9 County 1 Cx pipiens 1 3 CO2 0.3333
AGENCY 2020 10 County 1 Cx tarsalis 0 7 CO2 0.0000
AGENCY 2020 10 County 1 Cx pipiens 4 7 CO2 0.5714
AGENCY 2020 11 County 1 Cx tarsalis 9 11 CO2 0.8182
AGENCY 2020 11 County 1 Cx pipiens 18 11 CO2 1.6364
AGENCY 2020 12 County 1 Cx tarsalis 1 9 CO2 0.1111
AGENCY 2020 12 County 1 Cx pipiens 0 9 CO2 0.0000
AGENCY 2020 13 County 1 Cx tarsalis 0 5 CO2 0.0000
AGENCY 2020 13 County 1 Cx pipiens 81 5 CO2 16.2000
AGENCY 2020 14 County 1 Cx tarsalis 1717 9 CO2 190.7778
AGENCY 2020 14 County 1 Cx pipiens 14 9 CO2 1.5556
AGENCY 2020 15 County 1 Cx tarsalis 63 6 CO2 10.5000
AGENCY 2020 15 County 1 Cx pipiens 0 6 CO2 0.0000
AGENCY 2020 16 County 1 Cx tarsalis 207 8 CO2 25.8750
AGENCY 2020 16 County 1 Cx pipiens 67 8 CO2 8.3750
AGENCY 2020 17 County 1 Cx tarsalis 15 6 CO2 2.5000
AGENCY 2020 17 County 1 Cx pipiens 2 6 CO2 0.3333
AGENCY 2020 18 County 1 Cx tarsalis 499 4 CO2 124.7500
AGENCY 2020 18 County 1 Cx pipiens 4 4 CO2 1.0000
AGENCY 2020 19 County 1 Cx tarsalis 58 14 CO2 4.1429
AGENCY 2020 19 County 1 Cx pipiens 15 14 CO2 1.0714
AGENCY 2020 20 County 1 Cx tarsalis 1 5 CO2 0.2000
AGENCY 2020 20 County 1 Cx pipiens 0 5 CO2 0.0000
AGENCY 2020 21 County 1 Cx tarsalis 0 7 CO2 0.0000
AGENCY 2020 21 County 1 Cx pipiens 145 7 CO2 20.7143
AGENCY 2020 22 County 1 Cx tarsalis 0 7 CO2 0.0000
AGENCY 2020 22 County 1 Cx pipiens 20 7 CO2 2.8571
AGENCY 2020 23 County 1 Cx tarsalis 5 4 CO2 1.2500
AGENCY 2020 23 County 1 Cx pipiens 3 4 CO2 0.7500
AGENCY 2020 24 County 1 Cx tarsalis 0 1 CO2 0.0000
AGENCY 2020 24 County 1 Cx pipiens 0 1 CO2 0.0000
AGENCY 2019 9 County 1 Cx tarsalis 101 10 CO2 10.1000
AGENCY 2019 9 County 1 Cx pipiens 15 10 CO2 1.5000
AGENCY 2019 10 County 1 Cx tarsalis 0 5 CO2 0.0000
AGENCY 2019 10 County 1 Cx pipiens 15 5 CO2 3.0000
AGENCY 2019 11 County 1 Cx tarsalis 590 13 CO2 45.3846
AGENCY 2019 11 County 1 Cx pipiens 7 13 CO2 0.5385
AGENCY 2019 12 County 1 Cx tarsalis 0 4 CO2 0.0000
AGENCY 2019 12 County 1 Cx pipiens 2 4 CO2 0.5000
AGENCY 2019 13 County 1 Cx tarsalis 596 5 CO2 119.2000
AGENCY 2019 13 County 1 Cx pipiens 0 5 CO2 0.0000
AGENCY 2019 14 County 1 Cx tarsalis 60 4 CO2 15.0000
AGENCY 2019 14 County 1 Cx pipiens 0 4 CO2 0.0000
AGENCY 2019 15 County 1 Cx tarsalis 4841 10 CO2 484.1000
AGENCY 2019 15 County 1 Cx pipiens 46 10 CO2 4.6000
AGENCY 2019 16 County 1 Cx tarsalis 103 15 CO2 6.8667
AGENCY 2019 16 County 1 Cx pipiens 2089 15 CO2 139.2667
AGENCY 2019 17 County 1 Cx tarsalis 446 6 CO2 74.3333
AGENCY 2019 17 County 1 Cx pipiens 1950 6 CO2 325.0000
AGENCY 2019 18 County 1 Cx tarsalis 4475 11 CO2 406.8182
AGENCY 2019 18 County 1 Cx pipiens 1622 11 CO2 147.4545
AGENCY 2019 19 County 1 Cx tarsalis 93 6 CO2 15.5000
AGENCY 2019 19 County 1 Cx pipiens 43 6 CO2 7.1667
AGENCY 2019 20 County 1 Cx tarsalis 0 2 CO2 0.0000
AGENCY 2019 20 County 1 Cx pipiens 0 2 CO2 0.0000
AGENCY 2019 21 County 1 Cx tarsalis 37 8 CO2 4.6250
AGENCY 2019 21 County 1 Cx pipiens 64 8 CO2 8.0000
AGENCY 2018 10 County 1 Cx tarsalis 343 7 CO2 49.0000
AGENCY 2018 10 County 1 Cx pipiens 3 7 CO2 0.4286
AGENCY 2018 11 County 1 Cx tarsalis 87 19 CO2 4.5789
AGENCY 2018 11 County 1 Cx pipiens 126 19 CO2 6.6316
AGENCY 2018 12 County 1 Cx tarsalis 0 10 CO2 0.0000
AGENCY 2018 12 County 1 Cx pipiens 7 10 CO2 0.7000
AGENCY 2018 13 County 1 Cx tarsalis 3013 10 CO2 301.3000
AGENCY 2018 13 County 1 Cx pipiens 11 10 CO2 1.1000
AGENCY 2018 14 County 1 Cx tarsalis 196 10 CO2 19.6000
AGENCY 2018 14 County 1 Cx pipiens 35 10 CO2 3.5000
AGENCY 2018 15 County 1 Cx tarsalis 601 7 CO2 85.8571
AGENCY 2018 15 County 1 Cx pipiens 445 7 CO2 63.5714
AGENCY 2018 16 County 1 Cx tarsalis 1649 5 CO2 329.8000
AGENCY 2018 16 County 1 Cx pipiens 0 5 CO2 0.0000
AGENCY 2018 17 County 1 Cx tarsalis 6 8 CO2 0.7500
AGENCY 2018 17 County 1 Cx pipiens 120 8 CO2 15.0000
AGENCY 2018 18 County 1 Cx tarsalis 258 7 CO2 36.8571
AGENCY 2018 18 County 1 Cx pipiens 0 7 CO2 0.0000
AGENCY 2018 19 County 1 Cx tarsalis 243 10 CO2 24.3000
AGENCY 2018 19 County 1 Cx pipiens 278 10 CO2 27.8000
AGENCY 2018 20 County 1 Cx tarsalis 2 10 CO2 0.2000
AGENCY 2018 20 County 1 Cx pipiens 62 10 CO2 6.2000
AGENCY 2018 21 County 1 Cx tarsalis 12 7 CO2 1.7143
AGENCY 2018 21 County 1 Cx pipiens 23 7 CO2 3.2857
AGENCY 2017 4 County 1 Cx tarsalis 2 1 CO2 2.0000
AGENCY 2017 4 County 1 Cx pipiens 0 1 CO2 0.0000
AGENCY 2017 8 County 1 Cx tarsalis 0 1 CO2 0.0000
AGENCY 2017 8 County 1 Cx pipiens 0 1 CO2 0.0000
AGENCY 2017 9 County 1 Cx tarsalis 0 11 CO2 0.0000
AGENCY 2017 9 County 1 Cx pipiens 8 11 CO2 0.7273
AGENCY 2017 10 County 1 Cx tarsalis 56 13 CO2 4.3077
AGENCY 2017 10 County 1 Cx pipiens 11 13 CO2 0.8462
AGENCY 2017 11 County 1 Cx tarsalis 434 8 CO2 54.2500
AGENCY 2017 11 County 1 Cx pipiens 418 8 CO2 52.2500
AGENCY 2017 12 County 1 Cx tarsalis 287 11 CO2 26.0909
AGENCY 2017 12 County 1 Cx pipiens 24 11 CO2 2.1818
AGENCY 2017 13 County 1 Cx tarsalis 8 9 CO2 0.8889
AGENCY 2017 13 County 1 Cx pipiens 392 9 CO2 43.5556
AGENCY 2017 14 County 1 Cx tarsalis 543 10 CO2 54.3000
AGENCY 2017 14 County 1 Cx pipiens 726 10 CO2 72.6000
AGENCY 2017 15 County 1 Cx tarsalis 1629 16 CO2 101.8125
AGENCY 2017 15 County 1 Cx pipiens 391 16 CO2 24.4375
AGENCY 2017 16 County 1 Cx tarsalis 341 10 CO2 34.1000
AGENCY 2017 16 County 1 Cx pipiens 460 10 CO2 46.0000
AGENCY 2017 17 County 1 Cx tarsalis 2344 13 CO2 180.3077
AGENCY 2017 17 County 1 Cx pipiens 163 13 CO2 12.5385
AGENCY 2017 18 County 1 Cx tarsalis 1672 12 CO2 139.3333
AGENCY 2017 18 County 1 Cx pipiens 436 12 CO2 36.3333
AGENCY 2017 19 County 1 Cx tarsalis 12 11 CO2 1.0909
AGENCY 2017 19 County 1 Cx pipiens 40 11 CO2 3.6364
AGENCY 2017 20 County 1 Cx tarsalis 101 11 CO2 9.1818
AGENCY 2017 20 County 1 Cx pipiens 40 11 CO2 3.6364
AGENCY 2017 21 County 1 Cx tarsalis 32 14 CO2 2.2857
AGENCY 2017 21 County 1 Cx pipiens 28 14 CO2 2.0000
AGENCY 2016 8 County 1 Cx tarsalis 2 7 CO2 0.2857
AGENCY 2016 8 County 1 Cx pipiens 1 7 CO2 0.1429
AGENCY 2016 9 County 1 Cx tarsalis 34 13 CO2 2.6154
AGENCY 2016 9 County 1 Cx pipiens 12 13 CO2 0.9231
AGENCY 2016 10 County 1 Cx tarsalis 110 8 CO2 13.7500
AGENCY 2016 10 County 1 Cx pipiens 16 8 CO2 2.0000
AGENCY 2016 11 County 1 Cx tarsalis 3 9 CO2 0.3333
AGENCY 2016 11 County 1 Cx pipiens 16 9 CO2 1.7778
AGENCY 2016 12 County 1 Cx tarsalis 1 7 CO2 0.1429
AGENCY 2016 12 County 1 Cx pipiens 253 7 CO2 36.1429
AGENCY 2016 13 County 1 Cx tarsalis 718 12 CO2 59.8333
AGENCY 2016 13 County 1 Cx pipiens 23 12 CO2 1.9167
AGENCY 2016 14 County 1 Cx tarsalis 1105 13 CO2 85.0000
AGENCY 2016 14 County 1 Cx pipiens 1354 13 CO2 104.1538
AGENCY 2016 15 County 1 Cx tarsalis 1419 10 CO2 141.9000
AGENCY 2016 15 County 1 Cx pipiens 285 10 CO2 28.5000
AGENCY 2016 16 County 1 Cx tarsalis 1295 10 CO2 129.5000
AGENCY 2016 16 County 1 Cx pipiens 2032 10 CO2 203.2000
AGENCY 2016 17 County 1 Cx tarsalis 2641 15 CO2 176.0667
AGENCY 2016 17 County 1 Cx pipiens 99 15 CO2 6.6000
AGENCY 2016 18 County 1 Cx tarsalis 9 11 CO2 0.8182
AGENCY 2016 18 County 1 Cx pipiens 53 11 CO2 4.8182
AGENCY 2016 19 County 1 Cx tarsalis 14 7 CO2 2.0000
AGENCY 2016 19 County 1 Cx pipiens 15 7 CO2 2.1429
AGENCY 2016 20 County 1 Cx tarsalis 5 16 CO2 0.3125
AGENCY 2016 20 County 1 Cx pipiens 15 16 CO2 0.9375
AGENCY 2016 21 County 1 Cx tarsalis 2 6 CO2 0.3333
AGENCY 2016 21 County 1 Cx pipiens 109 6 CO2 18.1667
AGENCY 2015 8 County 1 Cx tarsalis 0 4 CO2 0.0000
AGENCY 2015 8 County 1 Cx pipiens 3 4 CO2 0.7500
AGENCY 2015 9 County 1 Cx tarsalis 33 12 CO2 2.7500
AGENCY 2015 9 County 1 Cx pipiens 26 12 CO2 2.1667
AGENCY 2015 10 County 1 Cx tarsalis 76 15 CO2 5.0667
AGENCY 2015 10 County 1 Cx pipiens 88 15 CO2 5.8667
AGENCY 2015 11 County 1 Cx tarsalis 53 5 CO2 10.6000
AGENCY 2015 11 County 1 Cx pipiens 87 5 CO2 17.4000
AGENCY 2015 12 County 1 Cx tarsalis 225 11 CO2 20.4545
AGENCY 2015 12 County 1 Cx pipiens 131 11 CO2 11.9091
AGENCY 2015 13 County 1 Cx tarsalis 258 17 CO2 15.1765
AGENCY 2015 13 County 1 Cx pipiens 277 17 CO2 16.2941
AGENCY 2015 14 County 1 Cx tarsalis 650 10 CO2 65.0000
AGENCY 2015 14 County 1 Cx pipiens 14 10 CO2 1.4000
AGENCY 2015 15 County 1 Cx tarsalis 1353 10 CO2 135.3000
AGENCY 2015 15 County 1 Cx pipiens 74 10 CO2 7.4000
AGENCY 2015 16 County 1 Cx tarsalis 4341 14 CO2 310.0714
AGENCY 2015 16 County 1 Cx pipiens 514 14 CO2 36.7143
AGENCY 2015 17 County 1 Cx tarsalis 13 8 CO2 1.6250
AGENCY 2015 17 County 1 Cx pipiens 105 8 CO2 13.1250
AGENCY 2015 18 County 1 Cx tarsalis 0 6 CO2 0.0000
AGENCY 2015 18 County 1 Cx pipiens 9 6 CO2 1.5000
AGENCY 2015 19 County 1 Cx tarsalis 140 15 CO2 9.3333
AGENCY 2015 19 County 1 Cx pipiens 83 15 CO2 5.5333
AGENCY 2015 20 County 1 Cx tarsalis 56 12 CO2 4.6667
AGENCY 2015 20 County 1 Cx pipiens 51 12 CO2 4.2500
AGENCY 2015 21 County 1 Cx tarsalis 32 4 CO2 8.0000
AGENCY 2015 21 County 1 Cx pipiens 44 4 CO2 11.0000
AGENCY 2015 23 County 1 Cx tarsalis 0 2 CO2 0.0000
AGENCY 2015 23 County 1 Cx pipiens 0 2 CO2 0.0000
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