Welcome to the SangerTools package

This package has been created to provide you convenience functions for working with population health data and is specifically aimed at healthcare providers and services that focus on population health management.

In the next sections we will take you through how you use the tool. This tool will expand over time, as we already have a million ideas of how to adapt and add to the tool.

Loading in the PopHealthData

To load in the PopHealthData attached to the SangerTools package. This is extremely simple, as the data is attached to the package. To load the data into R follow these steps:

health_data <- SangerTools::PopHealthData
glimpse(health_data)
#> Rows: 1,000
#> Columns: 8
#> $ Sex                <chr> "Male", "Female", "Male", "Male", "Male", "Female",~
#> $ Smoker             <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ~
#> $ Diabetes           <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, ~
#> $ AgeBand            <chr> "Children and Young People (0-17)", "Working Age Ad~
#> $ IMD_Decile         <dbl> 9, 5, 3, 3, 4, 6, 3, 7, 6, 4, 9, 2, 1, 9, 2, 3, 4, ~
#> $ Ethnicity          <chr> "White", "White", "White", "White", "White", "White~
#> $ Locality           <chr> "Cheltenham", "Gloucester City", "Cheltenham", "Glo~
#> $ PrimaryCareNetwork <chr> "Cheltenham Central", "Tewkesbury, Newent and Staun~

The data contains:

Sex of the patient (Sex)
Whether the patient is a smoker (Smoker)
Indication by binary flag that the patient is diabetic (Diabetes)
Age band of the patient at the time of presentation to the service (AgeBand)
IMD decile - this is the indices of multiple deprivation and looks at the level of deprivation, by geography, for that patient (IMD_Decile)
Ethnicity of the patient is captured as a nominal representation (Ethnicity)
Locality - the locality the patient resides in (Locality)
Primary Care Network is the network associated to the patients clinical commissioning group (CCG)

With the data we can start to work with the function in the package.

Generating a categorical column chart easily

The package makes it very simple to create a categorical column chart. This will be implemented in the next example:

# Group by Ethnicity
health_data %>% 
  dplyr::filter(Diabetes==1) %>% 
  SangerTools::categorical_col_chart(., Ethnicity)


# Group by Age Band
health_data %>% 
  dplyr::filter(Smoker==1) %>% 
  SangerTools::categorical_col_chart(AgeBand) + labs(x="Ethnicity", y="Patient Number")


# Group by Sex
health_data %>% 
  dplyr::filter(Diabetes==1) %>% 
  SangerTools::categorical_col_chart(Sex) + labs(x="Gender")

It really is that simple to generate very nice looking proportional charts.

Crude prevalence rate outputs

To obtain the crude prevalance rate, this can be achieved below:

crude_prevalence <- SangerTools::crude_pr(health_data, health_data %>% dplyr::filter(Diabetes==1), Locality)
#> Joining, by = "Locality"
# Another way
print(crude_prevalence)
#> # A tibble: 7 x 4
#>   Locality                       demo_pop condition_pop prevalance_1k
#>   <chr>                             <int>         <int>         <dbl>
#> 1 Cheltenham                          249            15          60.2
#> 2 Gloucester City                     272            15          55.1
#> 3 South Cotswolds                      98             5          51.0
#> 4 The Forest of Dean                   81             4          49.4
#> 5 Tewkesbury Newent and Staunton       64             2          31.2
#> 6 Stroud and Berkeley Vale            187             5          26.7
#> 7 North Cotswolds                      49             1          20.4

Other functionality added into the package would be to use the multiple CSV reader and clipboard functions.

Excel Clipboard function

This essentially copies a data frame to the clipboard for you for then pasting into Excel sheets, or csvs, or raw text.

#health_data %>%
 # slice(1:10) %>%
  #SangerTools::excel_clip(row.names = FALSE, col.names = TRUE) #Include column names

There is the potential to read from multiple CSVs as well and then these can be fed into data frames.

Multiple CSV reader

To implement this function you would need to have a number of CSVs contained in a folder. To read these in, follow the below instructions:

file_path = 'my_file_path_where_csvs_are_stored'

if (length(SangerTools::multiple_csv_reader(file_path))==0){
  message("This won't work without changing the variable input to a local file path with CSVs in")
}
#> This won't work without changing the variable input to a local file path with CSVs in

This will need to link to a series of CSV files.

See Brand Colours

This is an anonymous function and can be called without any arguments


show_brand_palette()

See More Colours

This is also an anonymous function; it will show an extended colour palette

show_extended_palette()

Closing

More functions are being added to this tool and a new version of the file will be released on CRAN very soon. Keep an eye out on the associated GitHub for updates, and make sure to pull down the most recent version from CRAN.

SangerTools Vignette