OHI Science | Citation policy
This script generates the “access” layer for the artisanal opportunities goal. This prep uses the UN sustainable development goal 14.b.1, “Degree of application of a legal/regulatory/policy/institutional framework which recognizes and protects access rights for small-scale fisheries (level of implementation: 1 lowest to 5 highest)”. We will rescale the scores to be between 0 and 1, match to OHI regions, and gapfill based on larger regions within the data.
Link: https://www.fao.org/sustainable-development-goals/indicators/14b1/en/
2023 Minor code reorganization
2022 Use the UN SDG API to access data for SDG 14.b.1.
2021 New data source, SDG 14.b.1.
Reference: Food and Agriculture Organization of the United Nations, 2020. Progress in the degree of implementation of international instruments to promote and protect small-scale fisheries, 2020.
Downloaded: 7/10/2023
Description: Progress by countries in the degree of application of a legal/regulatory/policy/institutional framework which recognizes and protects access rights for small-scale fisheries. It is a composite indicator based on FAO member country responses to the Code of Conduct for Responsible Fisheries (CCRF) survey questionnaire which is circulated by FAO every two years to members and IGOs and INGOs. This indicator is calculated on the basis of the efforts being made by countries to implement selected key provisions of the Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines), as reported in a given year of the survey.
Time range: 2018, 2020, 2022
Download link: We now use the API to access data (v2022) but alternatively you can access data here: https://unstats.un.org/sdgs/dataportal/database; Click “select indicators and country or area” and select indicator 14.b. Download all data for this indicator.
Note: This data prep is also used for “Artisanal
fisheries management effectiveness” (fp_artisinal). This means that when
done with the data prep, you will need to update
ohi-global/metadata_documentation/layers_eez_base.csv,
ao_access and fp_artisanal. You will also need
to update ohi-global/eez/conf/scenario_data_years.csv twice
at ao_access and fp_artisinal. Each one should
be run separately to see the effect of each update on the scores.
knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.path = 'figs/', message = FALSE, warning = FALSE)
if (!require(ohicore)){
devtools::install_github('ohi-science/ohicore@dev')
library(ohicore)
}
if (!require(librarian)){
install.packages("librarian")
library(librarian)
}
librarian::shelf(
tidyverse,
here,
janitor,
jsonlite,
plotly
)
### directory paths and relevant files
source(here::here('workflow', 'R', 'common.R'))API query builder: https://unstats.un.org/SDGAPI/swagger/
### Update me!
current_year <- 2023
version_year <- paste0("v", current_year)
#update with the current and previous data year, updated every two years
current_data_year <- 2022
previous_data_year <- 2022
### This is the list of UN M49 codes for regional groupings (not countries)
### These will be used for gapfilling purposes. More info: https://en.wikipedia.org/wiki/UN_M49
### 830 is Channel Islands - considered region by UN, we call it country and split it to islands later
### 57 is Micronesia
### 583 is Micronesia Federated states of
regions <- c(
1, 2, 5, 9, 11, 13, 14, 15, 17, 18,
19, 21, 29, 30, 34, 35, 39, 53, 54, 61, #57,
62, 127, 135, 142, 143, 145, 150, 151, 154, 155,
199, 202, 223, 419, 432, 485, 513, 514, 515, 518,
543, 583, 722, 738, 746, 747, 753 #, 830
)
## Here is the link to the countries that fall under each code (saved in the "raw" folder as a csv):
## https://unstats.un.org/unsd/methodology/m49/
## this shows the different over arching regions for each country
region_info <- here::here("globalprep", "ao", version_year, "raw", "un_region_info_keep.csv") %>%
readr::read_csv(col_types = cols(`M49 Code` = col_integer())) %>%
janitor::clean_names() %>%
dplyr::mutate(
country_or_area = dplyr::case_when(
country_or_area == "Bonaire Sint Eustatius and Saba" ~ "Bonaire, Sint Eustatius and Saba",
# country_or_area == "Côte d’Ivoire" ~ "Ivory Coast",
TRUE ~ country_or_area)) %>%
dplyr::filter(!m49_code %in% c(344, 446)) # filter out hongkong/macao, they are NA anyways
## Query the API for new data (released every 2 years, i.e. 2018, 2020, 2022)
## page size arbitrarily large in order to ensure all data is downloaded in a single page
## v2023 returned 849 rows of data (with countries and region groupings)
url <- "https://unstats.un.org/SDGAPI/v1/sdg/Indicator/Data?indicator=14.b.1&pageSize=10000"
api_get_indicator_data <- jsonlite::fromJSON(url)
clean_data <- api_get_indicator_data$data %>%
dplyr::tibble() %>%
tidyr::unnest(cols = c(goal, target, indicator, footnotes, attributes, dimensions)) %>% # unnest list and df columns then save raw file to csv before cleaning
readr::write_csv(here::here("globalprep", "ao", version_year, "raw", "raw_sdg_14_data.csv")) %>%
janitor::clean_names() %>%
dplyr::mutate(
time_detail = time_period_start,
geo_area_code = as.integer(geo_area_code),
score = dplyr::case_when( # Rescale values between 0 and 1
value == 1 ~ 0.2,
value == 2 ~ 0.4,
value == 3 ~ 0.6,
value == 4 ~ 0.8,
value == 5 ~ 1),
region_type = case_when(
geo_area_code %in% regions ~ "region",
TRUE ~ "country")) %>%
dplyr::filter(!(geo_area_code %in% c(344, 446))) %>% # filter out hongkong/macao, they are NA anyways
dplyr::left_join(dplyr::select(region_info, -country_or_area), by = c("geo_area_code" = "m49_code")) %>%
dplyr::select(region_type, geo_area_code, geo_area_name, time_detail, value, score, region_code,
region_name, sub_region_code, sub_region_name, intermediate_region_code,
intermediate_region_name, iso_alpha3_code, small_island_developing_states_sids)
## Now we have a dataset with all of the information we need to begin
## save a large region data frame for use in gapfilling later
region_df <- clean_data %>%
dplyr::filter(region_type == "region")
## make sure no region has more than 3 observations (2018, 2020, 2022, 2023)
#will increase with each new year of data
duplicate_check <- clean_data %>%
dplyr::group_by(geo_area_name) %>%
dplyr::summarise(n= dplyr::n())
unique(duplicate_check$n)
## Now lets check which OHI regions we are missing, so that we can gapfill them later on. We want a score for every OHI region
region_check <- clean_data %>%
dplyr::filter(region_type == "country")
region_data() ## load rgns_eez from common.R
## Find the differences between OHI regions and UN regions
setdiff(rgns_eez$rgn_name, region_check$geo_area_name)
## it looks like we are missing quite a few... 64 in v2023
## however, many of these are name mis-matches or regions that need to be split. We will fix these below.### not quite what we need I think, but maybe useful with more exploration - v2022
### Perhaps try to integrate in future workflows
# url_regions <- "https://unstats.un.org/SDGAPI/v1/sdg/GeoArea/Tree"
# api_get_geoarea_tree <- jsonlite::fromJSON(url_regions)
#
# un_regions <- api_get_geoarea_tree %>%
## filter(geoAreaName == "World (total) by SDG regions") %>%
# unnest() %>% # unnest the tree structure *3 into a dataframe
# unnest() %>%
# unnest() %>%
# select(-children) %>%
# clean_names() %>%
# filter(geo_area_code3 %in% clean_data$geo_area_code)Use the name2rgn function to fix some of the name mismatches. Additionally, we will manually split some regions.
Name to region function (in OHI core package) reports regions that don’t have a match in OHI region list. Here we report certain reported regions at a higher spatial scale, based on the listed regions in the error message.
#rename any regions with different spelling than ohi regions
country_df <- clean_data %>%
dplyr::filter(region_type == "country") %>%
dplyr::mutate(geo_area_name = case_when(
geo_area_name == "Curaçao" ~ "Curacao",
geo_area_name == "Réunion" ~ "Reunion",
geo_area_name == "Côte d'Ivoire" ~ "Ivory Coast",
geo_area_name == "Saint Martin (French Part)" ~ "Northern Saint-Martin",
geo_area_name == "Svalbard and Jan Mayen Islands" ~ "Jan Mayen",
geo_area_name == "Türkiye" ~ "Turkey",
geo_area_name == "Netherlands (Kingdom of the)" ~ "Netherlands",
TRUE ~ geo_area_name
)) %>%
dplyr::select(
geo_area_name, time_detail, region_name, sub_region_name,
intermediate_region_name, small_island_developing_states_sids, region_type, score)
## Report these regions at higher spatial resolution:
#these are countries that are included as one in the data but split in ohi regions
country_df_clean <- country_df %>%
mutate(split_id = case_when(geo_area_name ==
"Bonaire, Sint Eustatius and Saba" ~
"Bonaire, Saba, Sint Eustatius",
geo_area_name == "French Southern Territories" ~
"Glorioso Islands, Juan de Nova Island, Bassas da India, Ile Europa, Ile Tromelin, Crozet Islands, Amsterdam Island and Saint Paul Island, Kerguelen Islands",
geo_area_name == "United States Minor Outlying Islands" ~ "Wake Island, Jarvis Island, Palmyra Atoll, Howland Island and Baker Island, Johnston Atoll",
geo_area_name == "China" ~ "China, Taiwan"
)) %>%
separate_longer_delim(cols = split_id, delim = ", ") %>%
mutate(geo_area_name = case_when(is.na(split_id) ~ geo_area_name,
TRUE ~ split_id)) %>%
select(-split_id)
## lots of landlocked countries included here
match_country_data <- ohicore::name_2_rgn(
df_in = country_df_clean,
fld_name='geo_area_name',
flds_unique=c('time_detail'))
## v2023
# removed
## Aland Islands (not OHI),
## Channel Islands, in 2023 this was already present as Jersey and Guernsey, so didn't need to be split (check next year)
## Eswatini (not OHI),
## Isle of man (not OHI),
## North Macedonia (land locked),
## Saint Barthelemy (not OHI),
## Palestine (not OHI),
## removed landlocked countries ie. Afghanistan, Andorra... Zimbabwe
## deal with the duplicates - v2023 this brought from 633 rows to 624 rows
fix_dups <- match_country_data %>%
dplyr::group_by(rgn_id, time_detail, rgn_name, region_type) %>%
dplyr::summarise(score = mean(score, na.rm =TRUE)) %>%
dplyr::ungroup() %>%
dplyr::mutate(score = ifelse(is.nan(score), NA, score)) %>%
dplyr::select(rgn_id, rgn_name, time_detail, region_type, score)
## add in the larger regions associated with each ohi region
rgns_data <- match_country_data %>%
dplyr::distinct(
rgn_name, rgn_id, region_type, intermediate_region_name,
sub_region_name, region_name, small_island_developing_states_sids) %>%
dplyr::left_join(fix_dups) %>%
dplyr::filter(rgn_id <= 250) %>%
dplyr::left_join(rgns_eez) %>%
dplyr::select(1:9)
## There is still a region "Kiribati" which needs to be split. For some reason, trying to do so with match2rgn does not work.
## filter for these regions in country_df and prep so they match rgns_data. What we have to do is:
## Split "Kiribati" into "Line Islands (Kiribati)", "Phoenix Islands (Kiribati)", and "Gilbert Islands (Kiribati)"
all_rgns_data <- rgns_data %>%
mutate(split_id = case_when(rgn_name ==
"Kiribati" ~ "Line Islands (Kiribati), Phoenix Islands (Kiribati), Gilbert Islands (Kiribati)")) %>%
separate_longer_delim(split_id, delim = ", ") %>%
mutate(rgn_name = case_when(is.na(split_id) ~ rgn_name,
TRUE ~ split_id),
rgn_id = case_when(rgn_name == "Line Islands (Kiribati)" ~ 148,
rgn_name == "Phoenix Islands (Kiribati)" ~157,
rgn_name == "Gilbert Islands (Kiribati)" ~ 212,
TRUE ~ rgn_id)) %>%
dplyr::select(
region_name, sub_region_name, intermediate_region_name, region_type, rgn_id,
rgn_name, time_detail, score, small_island_developing_states_sids)
## Now lets look to see what OHI regions are still missing
sort(setdiff(rgns_eez$rgn_name, all_rgns_data$rgn_name))
# "Andaman and Nicobar" (include)
# "Antarctica" (don't include, not in OHI)
# "Ascension" (include)
# "Azores" (include)
# "Bouvet Island" (don't include, uninhabited)
# "Canary Islands" (include, same as Spain)
# "Clipperton Island" (don't include, uninhabited)
# "Macquarie Island" (don't include, uninhabited)
# "Madeira" (include, same as Portugal)
# "Oecussi Ambeno" (include)
# "Prince Edward Islands"(include)
# "Tristan da Cunha" (include)
## None of these are located in the raw UN data.
## We will have to manually assign them the appropriate larger regions by googling.
remaining_rgns <- dplyr::tibble(
geo_area_name = c("Andaman and Nicobar",
"Ascension","Azores","Canary Islands",
"Madeira","Oecussi Ambeno",
"Prince Edward Islands","Tristan da Cunha"),
region_name = c("Asia","Africa","Europe",
"Europe","Europe","Asia","Americas",
"Africa"),
sub_region_name = c("South-eastern Asia",NA,
"Western Europe","Southern Europe",
"Southern Europe","South-eastern Asia",
"Northern America",NA),
intermediate_region_name = c(NA,"Western Africa",NA,NA,NA,NA,NA,NA),
small_island_developing_states_sids = c("x", "x", NA, NA, NA, "x", NA, "x")) %>%
dplyr::mutate(region_type = "country", score = NA) %>%
tidyr::crossing(time_detail = unique(all_rgns_data$time_detail))
## Now run the match2rgn function to get OHI regions
match_remaining <- name_2_rgn(df_in = remaining_rgns,
fld_name='geo_area_name',
flds_unique=c('time_detail')) %>%
dplyr::select(-geo_area_name)
## Now join with final dataset
all_rgns_data <- rbind(all_rgns_data, match_remaining)
## Now check to see what OHI regions are missing (should be uninhabited regions)
sort(setdiff(rgns_eez$rgn_name, all_rgns_data$rgn_name))
# "Antarctica" "Bouvet Island" "Clipperton Island" "Macquarie Island" - perfect .. these places are uninhabited anywaysData types:
1: Countries with all years of data
2: Countries with at least one year of data
3: Countries with no data
4: Regional averages (i.e. “East Africa”, “Asia”)Steps to gapfill:
- Determine level of completeness for each country and each region
- Gapfill data type 2 with the closest value from that country
- Gapfill data type 4 with the closest value from that region
- Gapfill data type 3 with values from data type 4 (the regional average)
- First try “intermediate regions” (i.e. Caribbean countries filled with Caribbean islands mean)
- Then try “sub regions” (i.e. “Southern Europe”, “Western Asia”)
- Finally try continental regions (i.e. “Europe”, “Asia”)
- If there remain any that can’t be gapfilled in one of the regions, gapfill with the world score
Note: d. Is an agressive strategy but also an unlikely scenario that will ensure complete data
Determine level of completeness for each country and each region
### Step 1 - Determine level of completeness for each country and each region
### Explore country data
country_score_summary <- all_rgns_data %>%
dplyr::group_by(rgn_name) %>%
dplyr::mutate(
has_data = ifelse(!is.na(score), 1, 0),
data_completeness = paste0(sum(has_data), "/", n())) %>%
dplyr::ungroup()
# View(distinct(country_score_summary, rgn_name, data_completeness))
### Explore regional data
region_score_summary <- region_df %>%
dplyr::select(geo_area_name, time_detail, score) %>%
dplyr::group_by(geo_area_name) %>%
dplyr::mutate(
has_data = ifelse(!is.na(score), 1, 0),
data_completeness = paste0(sum(has_data), "/", n())) %>%
dplyr::ungroup()
# View(distinct(region_score_summary, geo_area_name, data_completeness))Gapfill data type 2 with the closest value from that country
gf_step_2_df <- country_score_summary %>%
dplyr::group_by(rgn_name) %>%
tidyr::fill(score, .direction = "downup") %>%
dplyr::mutate(
gapfilled = dplyr::case_when(
has_data == 0 & !is.na(score) ~ 1,
TRUE ~ 0),
method = dplyr::case_when(
sum(has_data) == n() ~ "Data complete",
gapfilled == 1 ~ "Used closest year score",
gapfilled == 0 & !is.na(score) & data_completeness != paste0(n(),"/",n()) ~ "No gapfill needed",
TRUE ~ as.character(NA))) %>%
dplyr::ungroup()Gapfill data type 4 with the closest value from that region
## Gapfill the regional data by the closest value from that region
region_score_complete <- region_score_summary %>%
dplyr::select(geo_area_name, time_detail, score) %>%
dplyr::group_by(geo_area_name) %>%
tidyr::fill(score, .direction = "down") %>%
dplyr::ungroup() %>%
dplyr::mutate( ## correct a typo
geo_area_name = stringr::str_replace_all(
string = geo_area_name,
pattern = "South-Eastern Asia",
replacement = "South-eastern Asia"))
## Join the intermediate region, sub-region, and regional scores to the country scores
gf_step_3_df <- gf_step_2_df %>%
dplyr::left_join(
region_score_complete,
by = c("time_detail", "intermediate_region_name" = "geo_area_name"),
suffix = c("", "_intermediate")) %>%
dplyr::left_join(
region_score_complete,
by = c("time_detail", "sub_region_name" = "geo_area_name"),
suffix = c("", "_sub")) %>%
dplyr::left_join(
region_score_complete,
by = c("time_detail", "region_name" = "geo_area_name"),
suffix = c("", "_regional"))Gapfill data type 3 with values from data type 4 (the regional average)
First try “intermediate regions” (i.e. Caribbean countries filled with Caribbean islands mean).
We first fill missing data in small island developing states (SIDS) with the averages scores from other SIDS. We then move on to intermediate regions. We can find the SIDS average and round to the nearest score value (0.2, 0.4, 0.6, 0.8, 1) by dividing the mean score of the SIDS by 2, rounding to the nearest tenth, and multiplying by 2 (i.e. 2 * round(mean(score)/2, digits = 1)).
sids_score = gf_step_3_df %>%
dplyr::filter(small_island_developing_states_sids == "x") %>%
dplyr::summarise(score = mean(score, na.rm = TRUE) / 2) %>%
dplyr::pull(score) %>%
round(digits = 1) * 2
gf_step_4a_df <- gf_step_3_df %>%
dplyr::mutate(
### Use SIDS first
score = dplyr::case_when(
small_island_developing_states_sids == "x" & is.na(score) ~ sids_score,
TRUE ~ score),
gapfilled = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ 1,
TRUE ~ gapfilled),
method = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ "Used SIDS score",
TRUE ~ method),
### Use intermediate region score otherwise
score = dplyr::case_when(
is.na(score) ~ score_intermediate,
TRUE ~ score),
gapfilled = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ 1,
TRUE ~ gapfilled),
method = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ "Used intermediate region score",
TRUE ~ method)
)Then try “sub regions” (i.e. “Southern Europe”, “Western Asia”)
gf_step_4b_df <- gf_step_4a_df %>%
dplyr::mutate(
score = dplyr::case_when(
is.na(score) ~ score_sub,
TRUE ~ score),
gapfilled = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ 1,
TRUE ~ gapfilled),
method = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ "Used sub-region score",
TRUE ~ method))Finally try continental regions (i.e. “Europe”, “Asia”)
gf_step_4c_df <- gf_step_4b_df %>%
dplyr::mutate(
score = dplyr::case_when(
is.na(score) ~ score_regional,
TRUE ~ score),
gapfilled = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ 1,
TRUE ~ gapfilled),
method = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ "Used region score",
TRUE ~ method))
## In v2023, all scores were filled with this methodIf there remain any that can’t be gapfilled in one of the regions, gapfill with the world score
Note: d. Is an agressive strategy but also an unlikely scenario that will ensure complete data
world_score = gf_step_4c_df %>%
dplyr::summarise(score = mean(score, na.rm = TRUE) / 2) %>%
dplyr::pull(score) %>%
round(digits = 1) * 2
gf_step_4d_df <- gf_step_4c_df %>%
dplyr::mutate(
score = dplyr::case_when(
is.na(score) ~ world_score,
TRUE ~ score),
gapfilled = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ 1,
TRUE ~ gapfilled),
method = dplyr::case_when(
has_data == 0 & !is.na(score) & is.na(method) ~ "Used world score",
TRUE ~ method))
## v2022 this did not fill anything (already done by step 4.c)final_gf_df <- gf_step_4d_df %>%
dplyr::select(rgn_id, year = time_detail, value = score,
completeness_pre_gf = data_completeness, gapfilled, method)
## save gapfilling flag dataset
final_gf_flags <- final_gf_df %>%
dplyr::select(-value) %>%
readr::write_csv(here::here("globalprep", "ao", version_year, "output", "sdg_14_b_1_ao_gf.csv"))
## save value dataset
final_data <- final_gf_df %>%
dplyr::select(rgn_id, year, value) %>%
readr::write_csv(here::here("globalprep", "ao", version_year, "output", "sdg_14_b_1_ao.csv"))Lets compare to last years AO data.
## This section is checking if we get the same values as last years assesment
## This is primarily to see how the gapfilling changed the data that matches
## from this assessment to last years assessment
version_year_new <- paste0("v", current_year - 0 )
version_year_old <- paste0("v", current_year - 1 )
region_data()
new_data <- here::here("globalprep", "ao", version_year_new, "output", "sdg_14_b_1_ao.csv") %>%
readr::read_csv() %>%
dplyr::left_join(rgns_eez)
old_data <- here::here("globalprep", "ao", version_year_old, "output", "sdg_14_b_1_ao.csv") %>%
readr::read_csv() %>%
dplyr::left_join(rgns_eez)
compare <- new_data %>%
dplyr::filter(year < current_year) %>%
dplyr::left_join(old_data, by = c("rgn_id", "year", "rgn_name", "admin_country_name"),
suffix = c(paste0("_", version_year_new), paste0("_", version_year_old))) %>%
dplyr::mutate(difference = !!rlang::sym(paste0("value_", version_year_old)) - !!rlang::sym(paste0("value_", version_year_new)))
compare_diff <- compare %>%
dplyr::filter(difference != 0) %>%
dplyr::select(rgn_id, rgn_name, admin_country_name, year, value_v2022, value_v2023, difference)
plot_diff <-
ggplot2::ggplot(
compare,
ggplot2::aes(x = !!rlang::sym(paste0("value_", version_year_old)),
y = !!rlang::sym(paste0("value_", version_year_new)),
color = as.factor(year),
text = rgn_name,
label = rgn_id), color = "black") +
ggplot2::geom_jitter(width = 0.025, height = .025) +
ggplot2::geom_abline() +
ggplot2::labs(title = paste0("SDG 14.b.1 values (", version_year_old, " vs. ", version_year_new, ")"),
x = paste(version_year_old, "values"),
y = paste(version_year_new, "values"),
color = "Data year") +
ggplot2::theme_bw()
plotly::ggplotly(plot_diff, tooltip = c("as.factor(year)", "rgn_id", "rgn_name", "x", "y"))old_data_2 <- old_data %>%
dplyr::filter(year == current_data_year) %>% # Every two years
dplyr::mutate(version = version_year_old)
new_data_2 <- new_data %>%
dplyr::filter(year == previous_data_year) %>%
dplyr::mutate(version = version_year_new)
data_year_old = max(old_data_2$year)
data_year_new = max(new_data_2$year)
compare_2 <- rbind(new_data_2, old_data_2) %>%
dplyr::distinct(.keep_all = T) %>%
tidyr::pivot_wider(id_cols = rgn_name, names_from = version, values_from = value) %>%
dplyr::mutate(difference = !!rlang::sym(version_year_old) - !!rlang::sym(version_year_new))
## look at countries that changed from last year to this year
compare_diff_2 <- compare_2 %>%
dplyr::filter(difference != 0)
plot_diff <-
ggplot2::ggplot(
compare_2,
ggplot2::aes(
x = !!rlang::sym(version_year_old),
y = !!rlang::sym(version_year_new),
text = rgn_name)) +
ggplot2::geom_jitter(width = 0.025, height = .025) +
ggplot2::geom_abline() +
ggplot2::labs(
title = paste0("SDG 14.b.1 values (", version_year_old, " vs. ", version_year_new, ")"),
x = paste(data_year_old, "data from", version_year_old),
y = paste(data_year_new, "data from", version_year_new)) +
ggplot2::theme_bw()
plotly::ggplotly(plot_diff, tooltip = c("rgn_name", "x", "y"))