How to calculate indicators with `coresoi`

library(coresoi)

Where is data?

Data form BNDCP has been carefully aggregated and inserted in this package.

… todo

Quick data exploration

… todo

Calculate an indicator

Let’s say we are interested in calculating and indicator say number 3, namely Economic deviation across the crisis. The indicator aims at quantifying the difference between the sum foreseen in the contract and the actual payment by the C.A. ( ita S.A. Stazione Appaltante) prior and after the emergency outbreak. This is extremely convenient since we may expect that the ratio between what’s should be paid and what it is actually paid (in other words the the proportion of the contract value left to be paid) needs to remain just as equal in the two adjacent periods. We may also suppose that this does not happen due to the fact that during emergency outbreaks inspections and controls are wicker and less in-depth. This may give the chance to fraudsters to get money quicker than before and runaway with 💰.

All we need to do is to load data sample mock_data_core and supply related variables to function ind_3(). Check out the documentation for function ind_3() to get a grasp on that.

data("mock_data_core", package = "coresoi")

ind_3(
  data = mock_data_core,
  award_value=importo_aggiudicazione, 
  sums_paid=importo_lotto,
  cf_amministrazione_appaltante,
  publication_date=data_pubblicazione,
  emergency_name = "coronavirus"
  ) |>
  head(10)
#> # A tibble: 10 × 12
#>    indicator_id indica…¹ indic…² aggre…³ aggre…⁴ aggre…⁵ emerg…⁶ emerg…⁷ count…⁸
#>           <dbl> <chr>      <dbl> <chr>   <chr>   <chr>     <int> <chr>   <chr>  
#>  1            3 Economi…   0.5   000647… ISTAT1  cf_amm…       1 Corona… 1      
#>  2            3 Economi…   0     000759… ISTAT1  cf_amm…       1 Corona… 1      
#>  3            3 Economi…   0.5   000982… ISTAT1  cf_amm…       1 Corona… 1      
#>  4            3 Economi…   1     001043… ISTAT1  cf_amm…       1 Corona… 1      
#>  5            3 Economi…   1     001086… ISTAT1  cf_amm…       1 Corona… 1      
#>  6            3 Economi…   0.794 001155… ISTAT1  cf_amm…       1 Corona… 1      
#>  7            3 Economi…   1     001184… ISTAT1  cf_amm…       1 Corona… 1      
#>  8            3 Economi…   1     001188… ISTAT1  cf_amm…       1 Corona… 1      
#>  9            3 Economi…   0.429 001246… ISTAT1  cf_amm…       1 Corona… 1      
#> 10            3 Economi…   0.5   001323… ISTAT1  cf_amm…       1 Corona… 1      
#> # … with 3 more variables: country_name <chr>, indicator_last_update <dttm>,
#> #   data_last_update <dttm>, and abbreviated variable names ¹​indicator_name,
#> #   ²​indicator_value, ³​aggregation_name, ⁴​aggregation_id, ⁵​aggregation_type,
#> #   ⁶​emergency_id, ⁷​emergency_name, ⁸​country_id

The output from ind_3 is a schema which shows

… we may want also to be interested in…

… ggplot2 graphs and minimal model…