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Updated modern slavery stats to 2023 Q3
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,225 @@ | ||
| library(tidyverse) | ||
| library(lubridate) | ||
| library(devtools) | ||
| library(readODS) | ||
| library(httr) | ||
|
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||
| load_all() | ||
|
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||
| query_url <- | ||
| query_urls |> | ||
| filter(data_set == "nrm_2023_q4") |> | ||
| pull(query_url) | ||
|
|
||
| GET( | ||
| query_url, | ||
| write_disk(tf <- tempfile(fileext = ".ods")) | ||
| ) | ||
|
|
||
| # ---- Table 11: NRM referrals by government agency first responder, exploitation type, age at exploitation, gender and nationality ---- | ||
| nrm_referrals_govt_2023_q4_raw <- | ||
| read_ods(tf, sheet = "Table_11", skip = 5) | ||
|
|
||
| nrm_referrals_govt_2023_q4 <- | ||
| nrm_referrals_govt_2023_q4_raw |> | ||
| as_tibble() |> | ||
|
|
||
| # Replace dots in column names with a space | ||
| rename_with(~str_replace_all(.x, "[\\.]+", " ")) |> | ||
|
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||
| # The first row of each new type of first responder is a total - make that clear in the data | ||
| mutate( | ||
| `Exploitation type` = if_else(!is.na(`Government agency first responder`), "Total", `Exploitation type`), | ||
| `Age at exploitation` = if_else(!is.na(`Government agency first responder`), "Total", `Age at exploitation`), | ||
| `Gender` = if_else(!is.na(`Government agency first responder`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| #... same for exploitation type | ||
| mutate( | ||
| `Age at exploitation` = if_else(!is.na(`Exploitation type`), "Total", `Age at exploitation`), | ||
| `Gender` = if_else(!is.na(`Exploitation type`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| #... and same for age | ||
| mutate( | ||
| `Gender` = if_else(!is.na(`Age at exploitation`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| # Fill in all the other blanks | ||
| fill(`Government agency first responder`, `Exploitation type`, `Age at exploitation`, Gender) |> | ||
|
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||
| # Rename total row | ||
| mutate(`Government agency first responder` = if_else(`Government agency first responder` == "Total", "Government agency total", `Government agency first responder`)) |> | ||
| # filter(`Government agency first responder` != "Total") |> | ||
|
|
||
| rename(`First responder` = `Government agency first responder`) |> | ||
| mutate(`First responder type` = "Government agency") |> | ||
| relocate(`First responder type`) | ||
|
|
||
| # Make nationality a single column | ||
| nrm_referrals_govt_2023_q4 <- | ||
| nrm_referrals_govt_2023_q4 |> | ||
| pivot_longer(cols = -c(`First responder type`:Gender), names_to = "Nationality", values_to = "People") | ||
|
|
||
| # ---- Table 12: NRM referrals by NGO and third sector organisation first responder, exploitation type, age at exploitation, gender and nationality ---- | ||
| nrm_referrals_ngo_2023_q4_raw <- | ||
| read_ods(tf, sheet = "Table_12", skip = 5) | ||
|
|
||
| nrm_referrals_ngo_2023_q4 <- | ||
| nrm_referrals_ngo_2023_q4_raw |> | ||
| as_tibble() |> | ||
|
|
||
| # Replace dots in column names with a space | ||
| rename_with(~str_replace_all(.x, "[\\.]+", " ")) |> | ||
|
|
||
| # The first row of each new type of first responder is a total - make that clear in the data | ||
| mutate( | ||
| `Exploitation type` = if_else(!is.na(`NGO and third sector first responder`), "Total", `Exploitation type`), | ||
| `Age at exploitation` = if_else(!is.na(`NGO and third sector first responder`), "Total", `Age at exploitation`), | ||
| `Gender` = if_else(!is.na(`NGO and third sector first responder`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| #... same for exploitation type | ||
| mutate( | ||
| `Age at exploitation` = if_else(!is.na(`Exploitation type`), "Total", `Age at exploitation`), | ||
| `Gender` = if_else(!is.na(`Exploitation type`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| #... and same for age | ||
| mutate( | ||
| `Gender` = if_else(!is.na(`Age at exploitation`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| # Fill in all the other blanks | ||
| fill(`NGO and third sector first responder`, `Exploitation type`, `Age at exploitation`, Gender) |> | ||
|
|
||
| # Rename total row | ||
| mutate(`NGO and third sector first responder` = if_else(`NGO and third sector first responder` == "Total", "NGO and third sector total", `NGO and third sector first responder`)) |> | ||
| # filter(`NGO and third sector first responder` != "Total") |> | ||
|
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||
| rename(`First responder` = `NGO and third sector first responder`) |> | ||
| mutate(`First responder type` = "NGO and third sector") |> | ||
| relocate(`First responder type`) | ||
|
|
||
| # Make nationality a single column | ||
| nrm_referrals_ngo_2023_q4 <- | ||
| nrm_referrals_ngo_2023_q4 |> | ||
| pivot_longer(cols = -c(`First responder type`:Gender), names_to = "Nationality", values_to = "People") | ||
|
|
||
| # ---- Table 13: NRM referrals by police first responder, exploitation type, age at exploitation, gender and nationality ---- | ||
| nrm_referrals_police_2023_q4_raw <- | ||
| read_ods(tf, sheet = "Table_13", skip = 5) | ||
|
|
||
| nrm_referrals_police_2023_q4 <- | ||
| nrm_referrals_police_2023_q4_raw |> | ||
| as_tibble() |> | ||
|
|
||
| # Replace dots in column names with a space | ||
| rename_with(~str_replace_all(.x, "[\\.]+", " ")) |> | ||
|
|
||
| # The first row of each new type of first responder is a total - make that clear in the data | ||
| mutate( | ||
| `Exploitation type` = if_else(!is.na(`Police first responder`), "Total", `Exploitation type`), | ||
| `Age at exploitation` = if_else(!is.na(`Police first responder`), "Total", `Age at exploitation`), | ||
| `Gender` = if_else(!is.na(`Police first responder`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| #... same for exploitation type | ||
| mutate( | ||
| `Age at exploitation` = if_else(!is.na(`Exploitation type`), "Total", `Age at exploitation`), | ||
| `Gender` = if_else(!is.na(`Exploitation type`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| #... and same for age | ||
| mutate( | ||
| `Gender` = if_else(!is.na(`Age at exploitation`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| # Fill in all the other blanks | ||
| fill(`Police first responder`, `Exploitation type`, `Age at exploitation`, Gender) |> | ||
|
|
||
| # Rename total row | ||
| mutate(`Police first responder` = if_else(`Police first responder` == "Total", "Police total", `Police first responder`)) |> | ||
| # filter(`Police first responder` != "Total") |> | ||
|
|
||
| rename(`First responder` = `Police first responder`) |> | ||
| mutate(`First responder type` = "Police") |> | ||
| relocate(`First responder type`) | ||
|
|
||
| # Make nationality a single column | ||
| nrm_referrals_police_2023_q4 <- | ||
| nrm_referrals_police_2023_q4 |> | ||
| pivot_longer(cols = -c(`First responder type`:Gender), names_to = "Nationality", values_to = "People") | ||
|
|
||
| # ---- Table 14: NRM referrals from local authority first responder by exploitation type, age at exploitation, gender and nationality ---- | ||
| nrm_referrals_la_2023_q4_raw <- | ||
| read_ods(tf, sheet = "Table_14", skip = 5) | ||
|
|
||
| nrm_referrals_la_2023_q4 <- | ||
| nrm_referrals_la_2023_q4_raw |> | ||
| as_tibble() |> | ||
|
|
||
| # Replace dots in column names with a space | ||
| rename_with(~str_replace_all(.x, "[\\.]+", " ")) |> | ||
|
|
||
| # The first row of each new type of exploitation type is a total - make that clear in the data | ||
| mutate( | ||
| `Age at exploitation` = if_else(!is.na(`Exploitation type`), "Total", `Age at exploitation`), | ||
| `Gender` = if_else(!is.na(`Exploitation type`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| #... and same for age | ||
| mutate( | ||
| `Gender` = if_else(!is.na(`Age at exploitation`), "Total", `Gender`) | ||
| ) |> | ||
|
|
||
| # Fill in all the other blanks | ||
| fill(`Exploitation type`, `Age at exploitation`, Gender) |> | ||
|
|
||
| mutate( | ||
| `First responder type` = "Local Authority", | ||
| `First responder` = "Local Authority" | ||
| ) |> | ||
| relocate(`First responder type`, `First responder`) |> | ||
|
|
||
| # Rename total row | ||
| mutate(`First responder` = if_else(`Exploitation type` == "Total", "Local Authority total", `First responder`)) | ||
|
|
||
| # Make nationality a single column | ||
| nrm_referrals_la_2023_q4 <- | ||
| nrm_referrals_la_2023_q4 |> | ||
| pivot_longer(cols = -c(`First responder type`:Gender), names_to = "Nationality", values_to = "People") | ||
|
|
||
| # ---- Combine NRM referrals into a single dataframe ---- | ||
| nrm_referrals_2023_q4 <- | ||
| bind_rows( | ||
| nrm_referrals_govt_2023_q4, | ||
| nrm_referrals_ngo_2023_q4, | ||
| nrm_referrals_police_2023_q4 |> filter(str_detect(`First responder`, "total")), # Just take totals for Police | ||
| nrm_referrals_la_2023_q4 | ||
| ) | ||
|
|
||
| # ---- Table 41: DtN reports by nationality ---- | ||
| nrm_duty_to_notify_2023_q4 <- | ||
| read_ods(tf, sheet = "Table_41", skip = 5) |> | ||
| as_tibble() | ||
|
|
||
| # ---- Save output to data/ folder ---- | ||
| usethis::use_data(nrm_referrals_2023_q4, overwrite = TRUE) | ||
| readr::write_csv(nrm_referrals_2023_q4, "data-raw/nrm_referrals_2023_q4.csv") | ||
|
|
||
| usethis::use_data(nrm_referrals_govt_2023_q4, overwrite = TRUE) | ||
| readr::write_csv(nrm_referrals_govt_2023_q4, "data-raw/nrm_referrals_government_2023_q4.csv") | ||
|
|
||
| usethis::use_data(nrm_referrals_ngo_2023_q4, overwrite = TRUE) | ||
| readr::write_csv(nrm_referrals_ngo_2023_q4, "data-raw/nrm_referrals_ngo_2023_q4.csv") | ||
|
|
||
| usethis::use_data(nrm_referrals_police_2023_q4, overwrite = TRUE) | ||
| readr::write_csv(nrm_referrals_police_2023_q4, "data-raw/nrm_referrals_police_2023_q4.csv") | ||
|
|
||
| usethis::use_data(nrm_referrals_la_2023_q4, overwrite = TRUE) | ||
| readr::write_csv(nrm_referrals_la_2023_q4, "data-raw/nrm_referrals_local-authority_2023_q4.csv") | ||
|
|
||
| usethis::use_data(nrm_duty_to_notify_2023_q4, overwrite = TRUE) | ||
| readr::write_csv(nrm_duty_to_notify_2023_q4, "data-raw/nrm_duty_to_notify_2023_q4.csv") | ||
|
|
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