From 5de05a4fc50489b13898176ee463c9b8b8ecbb21 Mon Sep 17 00:00:00 2001 From: annaramji Date: Tue, 2 Jul 2024 22:10:19 +0000 Subject: [PATCH] made placeholder for int folder, adjusted file path for new ILO data in livelihood_dataprep --- globalprep/le/v2024/int/int_placeholder.txt | 0 globalprep/le/v2024/livelihood_dataprep.Rmd | 6 +++--- 2 files changed, 3 insertions(+), 3 deletions(-) create mode 100644 globalprep/le/v2024/int/int_placeholder.txt diff --git a/globalprep/le/v2024/int/int_placeholder.txt b/globalprep/le/v2024/int/int_placeholder.txt new file mode 100644 index 00000000..e69de29b diff --git a/globalprep/le/v2024/livelihood_dataprep.Rmd b/globalprep/le/v2024/livelihood_dataprep.Rmd index 3967ec8b..5ddc4227 100644 --- a/globalprep/le/v2024/livelihood_dataprep.Rmd +++ b/globalprep/le/v2024/livelihood_dataprep.Rmd @@ -226,7 +226,7 @@ wage_data_clean <- ilo_wage_data %>% # set column name to year rename(year = time) %>% # filter to the cap date range from the jobs data -- 2019 - filter(year %in% c(2014:2019)) %>% + filter(year %in% c(2009:2019)) %>% # add iso3 column, tourism sector label mutate(iso3 = country_regex_to_iso3c(ref_area_label), sector = "tour") %>% @@ -239,7 +239,7 @@ wage_data_clean <- ilo_wage_data %>% years_df <- tibble(iso3 = wage_data_clean$iso3) %>% group_by(iso3) %>% - summarize(year = seq(2014, 2019)) + summarize(year = seq(2009, 2019)) wage_data_years <- left_join(years_df, wage_data_clean, by = c("iso3", "year")) @@ -308,7 +308,7 @@ paste0("proportion of countries/regions with only 1 data point: ", round(((num_n ```{r} # preliminary plot line_plot <- plotly::plot_ly(wage_gf, x = ~year, y = ~appx_wage_fill, color = ~ref_area_label, type = "scatter", mode = "lines") %>% - layout(title = "All Regions: monthly Service Wages (USD)", + layout(title = "All Regions: monthly Service Wages (USD (2017) PPP-Adjusted)", xaxis = list(title = "Year"), yaxis = list(title = "monthly Wages (USD)"))