Reading in Data
reading_in_data.Rmd
library(STAVE)Example workflow
Input data take the form of three tables. We will use the pre-loaded example data here, if you are using your own data then you need to match this input format:
Create a STAVE object and append the data:
# create new object
s <- STAVE_object$new()
# append data using a member function
s$append_data(studies_dataframe = example_input$studies,
surveys_dataframe = example_input$surveys,
counts_dataframe = example_input$counts)
#> data correctly appended
# check how many studies are now loaded
s
#> Studies: 7
#> Surveys: 24Once data are loaded, we can always view the different tables using get functions. However, we cannot alter the values directly.
s$get_studies() |> head()
#> # A tibble: 6 × 6
#> study_id study_name study_type authors publication_year url
#> <chr> <chr> <chr> <chr> <dbl> <chr>
#> 1 wwarn_10297_Nelson wwarn_10297_Nel… peer_revi… Nelson 1000 http…
#> 2 wwarn_10814_Dama wwarn_10814_Dama peer_revi… Dama 1000 http…
#> 3 wwarn_10992_Mallick wwarn_10992_Mal… peer_revi… Mallick 1000 http…
#> 4 wwarn_11208_Kunasol wwarn_11208_Kun… peer_revi… Kunasol 1000 http…
#> 5 wwarn_11435_Henry wwarn_11435_Hen… peer_revi… Henry 1000 http…
#> 6 wwarn_11720_Ould wwarn_11720_Ould peer_revi… Ould 1000 http…
s$get_surveys() |> head()
#> # A tibble: 6 × 11
#> study_key survey_id country_name site_name latitude longitude spatial_notes
#> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
#> 1 wwarn_10297… wwarn_10… Thailand Kanchana… 15.3 98.5 wwarn lat an…
#> 2 wwarn_10814… wwarn_10… Mali Koulikoro 12.6 -8.14 wwarn lat an…
#> 3 wwarn_10992… wwarn_10… India Chhattis… 20.1 80.8 wwarn lat an…
#> 4 wwarn_10992… wwarn_10… India Goa 15.3 74.1 wwarn lat an…
#> 5 wwarn_10992… wwarn_10… India Gujarat 23.0 73.6 wwarn lat an…
#> 6 wwarn_10992… wwarn_10… India Heilongj… 88.3 27.2 wwarn lat an…
#> # ℹ 4 more variables: collection_start <chr>, collection_end <chr>,
#> # collection_day <chr>, time_notes <chr>
s$get_counts() |> head()
#> # A tibble: 6 × 4
#> survey_key variant_string variant_num total_num
#> <chr> <chr> <dbl> <dbl>
#> 1 wwarn_10297_Nelson_Sangkhlaburi_2002 mdr1:184:F 27 49
#> 2 wwarn_10297_Nelson_Sangkhlaburi_2002 mdr1:86:Y 4 49
#> 3 wwarn_10814_Dama_Bamako_2014 crt:76:T 130 170
#> 4 wwarn_10814_Dama_Bamako_2014 mdr1:86:Y 46 158
#> 5 wwarn_10992_Mallick_Assam_2002 crt:76:T 26 26
#> 6 wwarn_10992_Mallick_Assam_2002 mdr1:86:Y 19 25We can calculate the prevalence of any variant using
get_prevalence():
p <- s$get_prevalence("mdr1:184:F")
p
#> # A tibble: 24 × 21
#> study_id study_name study_type authors publication_year url survey_id
#> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 wwarn_10297_N… wwarn_102… peer_revi… Nelson 1000 http… wwarn_10…
#> 2 wwarn_10814_D… wwarn_108… peer_revi… Dama 1000 http… wwarn_10…
#> 3 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> 4 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> 5 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> 6 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> 7 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> 8 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> 9 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> 10 wwarn_10992_M… wwarn_109… peer_revi… Mallick 1000 http… wwarn_10…
#> # ℹ 14 more rows
#> # ℹ 14 more variables: country_name <chr>, site_name <chr>, latitude <dbl>,
#> # longitude <dbl>, spatial_notes <chr>, collection_start <chr>,
#> # collection_end <chr>, collection_day <chr>, time_notes <chr>,
#> # numerator <dbl>, denominator <dbl>, prevalence <dbl>,
#> # prevalence_lower <dbl>, prevalence_upper <dbl>We can also return a list of all variants in the data:
s$get_variants()
#> [1] "crt:76:T" "k13:469:F" "k13:469:Y" "k13:539:T" "k13:580:Y"
#> [6] "k13:675:V" "mdr1:184:F" "mdr1:86:Y"Finally, we can selectively drop studies from the data using their study ID:
s$drop_study(drop_study_id = "wwarn_10297_Nelson")
s
#> Studies: 6
#> Surveys: 23