diff --git a/Backend/Dashboard/.Rhistory b/Backend/Dashboard/.Rhistory
new file mode 100644
index 0000000..88d6729
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+++ b/Backend/Dashboard/.Rhistory
@@ -0,0 +1,512 @@
+(player_name %in% c("Anna Adelusi", "Terry Ruth Enweonwu") & efficienza >= percent(0.27)) |
+(player_name %in% c("Anna Haak", "Lena Stigrot") & efficienza >= percent(0.34)) |
+(player_name %in% c("Alice Tanase", "Madison Kubik") & efficienza >= percent(0.24)) |
+(player_name %in% c("Saly Thior", "Amandha Sylves", "Anna Hall", "Beatrice Molinaro") & efficienza >= percent(0.44)) |
+efficienza >= percent(0.42) ~ "lightgreen",
+(player_name %in% c("Anna Adelusi", "Terry Ruth Enweonwu") & efficienza > percent(0.24) & efficienza < percent(0.27)) |
+(player_name %in% c("Anna Haak", "Lena Stigrot") & efficienza > percent(0.30) & efficienza < percent(0.34)) |
+(player_name %in% c("Alice Tanase", "Madison Kubik") & efficienza > percent(0.20) & efficienza < percent(0.24)) |
+(player_name %in% c("Saly Thior", "Amandha Sylves", "Anna Hall", "Beatrice Molinaro") & efficienza > percent(0.38) & efficienza < percent(0.44)) |
+(efficienza > percent(0.41) & efficienza < percent(0.42)) ~ "orange",
+(player_name %in% c("Anna Adelusi", "Terry Ruth Enweonwu") & efficienza <= percent(0.24)) |
+(player_name %in% c("Anna Haak", "Lena Stigrot") & efficienza <= percent(0.30)) |
+(player_name %in% c("Alice Tanase", "Madison Kubik") & efficienza <= percent(0.20)) |
+(player_name %in% c("Saly Thior", "Amandha Sylves", "Anna Hall", "Beatrice Molinaro") & efficienza <= percent(0.38)) |
+efficienza < percent(0.41) ~ "lightcoral",
+TRUE ~ NA_character_
+))
+fig <- plot_ly(
+x = data_plot$player_name,
+y = round(data_plot$efficienza, digits = 2),
+marker = list(color = data_plot$color),
+name = "Efficienza in Attacco",
+type = "bar",
+text = round(data_plot$efficienza, digits = 2), textposition = 'auto', marker = list(color = 'rgb(158,202,225)', line = list(color = 'rgb(8,48,107)', width = 1.5)))
+fig <- fig %>% layout(title = "Efficienza in Attacco",
+xaxis = list(title = ""),
+yaxis = list(title = ""))
+fig
+saveWidget(fig, "Attacco.html")
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate")
+View(attack_rate)
+View(px)
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == 1) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate")
+View(attack_rate)
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName) %>%
+group_by(team, start_zone, visiting_setter_position) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate")
+View(attack_rate)
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == 1) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate")
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == 2) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate")
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == 3) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == 3) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+#ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) + scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == 3) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+#ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) + scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+saveWidget(fig, "Distribuzione3.html")
+for (i in 1:6){
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == i) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+#ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) + scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+html_name <- paste0("Distribuzione", i, ".html")
+saveWidget(fig, html_name)
+}
+for (i in 1:6){
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == i) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+#ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) + scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+fig <- ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+html_name <- paste0("Distribuzione", i, ".html")
+saveWidget(fig, html_name)
+}
+for (i in 1:6){
+attack_rate <- px %>%
+dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == i) %>%
+group_by(team, start_zone) %>%
+dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>%
+ungroup
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+fig <- ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+html_name <- paste0("Distribuzione", i, ".html")
+saveWidget(fig, html_name)
+}
+for (i in 1:6){
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == i) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+#ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) + scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+fig <- ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+html_name <- paste0("Distribuzione", i, ".html")
+#saveWidget(fig, html_name)
+}
+for (i in 1:6){
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == i) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+#ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) + scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+fig <- ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+html_name <- paste0("Distribuzione", i, ".html")
+saveWidget(fig, html_name)
+}
+for (i in 1:6){
+attack_rate <- px %>% dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == i) %>%
+group_by(team, start_zone) %>% dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>% ungroup
+## add x, y coordinates associated with the zones
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+## for team 2, these need to be on the top half of the diagram
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+#ggplot(attack_rate, aes(x, y, fill = rate)) + geom_tile() + ggcourt(labels = teams(px)) + scale_fill_gradient2(name = "Attack rate")
+# Round the rate to the second digit
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+# Plotting with rounded percentage on each position
+ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+html_name <- paste0("Distribuzione", i, ".html")
+saveWidget(fig, html_name)
+}
+fig
+saveWidget(fig, html_name)
+saveWidget(fig, "Distribuzione")
+saveWidget(fig, "Distribuzione.html")
+saveWidget(fig, "Distr.html")
+library(ggplot2)
+library(htmlwidgets)
+for (i in 1:6) {
+attack_rate <- px %>%
+dplyr::filter(skill == "Attack", team == teamName, visiting_setter_position == i) %>%
+group_by(team, start_zone) %>%
+dplyr::summarize(n_attacks = n()) %>%
+mutate(rate = n_attacks/sum(n_attacks)) %>%
+ungroup
+attack_rate <- cbind(attack_rate, dv_xy(attack_rate$start_zone, end = "lower"))
+tm2i <- attack_rate$team == teams(px)[2]
+attack_rate[tm2i, c("x", "y")] <- dv_flip_xy(attack_rate[tm2i, c("x", "y")])
+attack_rate$rate_rounded <- round(attack_rate$rate, 2)
+fig <- ggplot(attack_rate, aes(x, y, fill = rate_rounded)) +
+geom_tile() +
+geom_text(aes(label = paste0(round(rate_rounded * 100, 2), "%")), color = "black", size = 3) +
+ggcourt(labels = teams(px)) +
+scale_fill_gradient2(name = "Attack rate (%)", labels = scales::percent_format(accuracy = 0.01))
+html_name <- paste0("Distribuzione", i, ".html")
+# Save the plot as an HTML file
+saveWidget(fig, file = html_name)
+}
+install.packages("datavolley", repos = 'https://cloud.r-project.org')
+library(blackmarbler)
+library(geodata)
+library(sf)
+library(terra)
+library(ggplot2)
+library(tidyterra)
+library(lubridate)
+#### Define NASA bearer token
+token <- "eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIjoiVXNlciIsInVpZCI6Im1pcmtvOTgiLCJleHAiOjE3MjUzNTk1MTMsImlhdCI6MTcyMDE3NTUxMywiaXNzIjoiRWFydGhkYXRhIExvZ2luIn0.6KrS82Gb6rjTbIaBXROQrNfJRXCH7NH3si_UQgz5h3h5lsEiemz63LJL1X1YnMqpMAr0Pik2_LbA5PDKRtLqgaBKEnMFSsppuDHu4p0Pa7OBTWNBbYCEsuIZ2yfwVIc2sfr0g37g2hSaHqDcXMBGrppzrGCZtx10pDbmrrIdfnVE7tsFADhdPGryIgjs0XrDjSzSvLt9Z9MaPo-fXrWItTRoaSpa8aEaAsB3w7E4XX_wnLw54xfighLY-19ERWoEMyHGc6UaZGpkMZk3zyvVTGJnkmqJL1QvW3w0DfSRx0kW8TnFlfPrQ7Ay2N5t-n-wpApvPu-d_qloBNNtZx1eew"
+bearer <- token
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A1",
+date = "2019-06-01",
+bearer = bearer)
+r_20240510 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2024-05-10",
+bearer = bearer)
+#### Setup
+# Load packages
+library(blackmarbler)
+library(geodata)
+library(sf)
+library(terra)
+library(ggplot2)
+library(tidyterra)
+library(lubridate)
+#### Define NASA bearer token
+token <- "eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIjoiVXNlciIsInVpZCI6Im1pcmtvOTgiLCJleHAiOjE3MjUzNTk1MTMsImlhdCI6MTcyMDE3NTUxMywiaXNzIjoiRWFydGhkYXRhIExvZ2luIn0.6KrS82Gb6rjTbIaBXROQrNfJRXCH7NH3si_UQgz5h3h5lsEiemz63LJL1X1YnMqpMAr0Pik2_LbA5PDKRtLqgaBKEnMFSsppuDHu4p0Pa7OBTWNBbYCEsuIZ2yfwVIc2sfr0g37g2hSaHqDcXMBGrppzrGCZtx10pDbmrrIdfnVE7tsFADhdPGryIgjs0XrDjSzSvLt9Z9MaPo-fXrWItTRoaSpa8aEaAsB3w7E4XX_wnLw54xfighLY-19ERWoEMyHGc6UaZGpkMZk3zyvVTGJnkmqJL1QvW3w0DfSRx0kW8TnFlfPrQ7Ay2N5t-n-wpApvPu-d_qloBNNtZx1eew"
+bearer <- token
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A1",
+date = "2019-06-01",
+bearer = bearer)
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-01",
+bearer = bearer)
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-02",
+bearer = bearer)
+#### Setup
+# Load packages
+library(blackmarbler)
+library(geodata)
+library(sf)
+library(terra)
+library(ggplot2)
+library(tidyterra)
+library(lubridate)
+#### Define NASA bearer token
+token <- "eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIjoiVXNlciIsInVpZCI6Im1pcmtvOTgiLCJleHAiOjE3MjUzNTk1MTMsImlhdCI6MTcyMDE3NTUxMywiaXNzIjoiRWFydGhkYXRhIExvZ2luIn0.6KrS82Gb6rjTbIaBXROQrNfJRXCH7NH3si_UQgz5h3h5lsEiemz63LJL1X1YnMqpMAr0Pik2_LbA5PDKRtLqgaBKEnMFSsppuDHu4p0Pa7OBTWNBbYCEsuIZ2yfwVIc2sfr0g37g2hSaHqDcXMBGrppzrGCZtx10pDbmrrIdfnVE7tsFADhdPGryIgjs0XrDjSzSvLt9Z9MaPo-fXrWItTRoaSpa8aEaAsB3w7E4XX_wnLw54xfighLY-19ERWoEMyHGc6UaZGpkMZk3zyvVTGJnkmqJL1QvW3w0DfSRx0kW8TnFlfPrQ7Ay2N5t-n-wpApvPu-d_qloBNNtZx1eew"
+bearer <- token
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-01",
+bearer = bearer)
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+source("C:/Users/mirko/Downloads/light_pollution.R")
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+library(blackmarbler)
+library(geodata)
+library(sf)
+library(terra)
+library(ggplot2)
+library(tidyterra)
+library(lubridate)
+#### Define NASA bearer token
+token <- "eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIjoiVXNlciIsInVpZCI6Im1pcmtvOTgiLCJleHAiOjE3MjUzNTk1MTMsImlhdCI6MTcyMDE3NTUxMywiaXNzIjoiRWFydGhkYXRhIExvZ2luIn0.6KrS82Gb6rjTbIaBXROQrNfJRXCH7NH3si_UQgz5h3h5lsEiemz63LJL1X1YnMqpMAr0Pik2_LbA5PDKRtLqgaBKEnMFSsppuDHu4p0Pa7OBTWNBbYCEsuIZ2yfwVIc2sfr0g37g2hSaHqDcXMBGrppzrGCZtx10pDbmrrIdfnVE7tsFADhdPGryIgjs0XrDjSzSvLt9Z9MaPo-fXrWItTRoaSpa8aEaAsB3w7E4XX_wnLw54xfighLY-19ERWoEMyHGc6UaZGpkMZk3zyvVTGJnkmqJL1QvW3w0DfSRx0kW8TnFlfPrQ7Ay2N5t-n-wpApvPu-d_qloBNNtZx1eew"
+bearer <- token
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-01",
+bearer = bearer)
+remove.packages("sf")
+install.packages("sf")
+install.packages("sf")
+#### Setup
+# Load packages
+library(blackmarbler)
+library(geodata)
+library(sf)
+library(terra)
+library(ggplot2)
+library(tidyterra)
+library(lubridate)
+#### Define NASA bearer token
+token <- "eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIjoiVXNlciIsInVpZCI6Im1pcmtvOTgiLCJleHAiOjE3MjUzNTk1MTMsImlhdCI6MTcyMDE3NTUxMywiaXNzIjoiRWFydGhkYXRhIExvZ2luIn0.6KrS82Gb6rjTbIaBXROQrNfJRXCH7NH3si_UQgz5h3h5lsEiemz63LJL1X1YnMqpMAr0Pik2_LbA5PDKRtLqgaBKEnMFSsppuDHu4p0Pa7OBTWNBbYCEsuIZ2yfwVIc2sfr0g37g2hSaHqDcXMBGrppzrGCZtx10pDbmrrIdfnVE7tsFADhdPGryIgjs0XrDjSzSvLt9Z9MaPo-fXrWItTRoaSpa8aEaAsB3w7E4XX_wnLw54xfighLY-19ERWoEMyHGc6UaZGpkMZk3zyvVTGJnkmqJL1QvW3w0DfSRx0kW8TnFlfPrQ7Ay2N5t-n-wpApvPu-d_qloBNNtZx1eew"
+bearer <- token
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-01",
+bearer = bearer)
+library(blackmarbler)
+library(geodata)
+library(sf)
+library(terra)
+library(ggplot2)
+library(tidyterra)
+library(lubridate)
+#### Define NASA bearer token
+token <- "eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIjoiVXNlciIsInVpZCI6Im1pcmtvOTgiLCJleHAiOjE3MjUzNTk1MTMsImlhdCI6MTcyMDE3NTUxMywiaXNzIjoiRWFydGhkYXRhIExvZ2luIn0.6KrS82Gb6rjTbIaBXROQrNfJRXCH7NH3si_UQgz5h3h5lsEiemz63LJL1X1YnMqpMAr0Pik2_LbA5PDKRtLqgaBKEnMFSsppuDHu4p0Pa7OBTWNBbYCEsuIZ2yfwVIc2sfr0g37g2hSaHqDcXMBGrppzrGCZtx10pDbmrrIdfnVE7tsFADhdPGryIgjs0XrDjSzSvLt9Z9MaPo-fXrWItTRoaSpa8aEaAsB3w7E4XX_wnLw54xfighLY-19ERWoEMyHGc6UaZGpkMZk3zyvVTGJnkmqJL1QvW3w0DfSRx0kW8TnFlfPrQ7Ay2N5t-n-wpApvPu-d_qloBNNtZx1eew"
+bearer <- token
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-01",
+bearer = bearer)
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=2, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-01",
+bearer = bearer)
+library(blackmarbler)
+library(geodata)
+library(sf)
+library(terra)
+library(ggplot2)
+library(tidyterra)
+library(lubridate)
+#### Define NASA bearer token
+token <- "eyJ0eXAiOiJKV1QiLCJvcmlnaW4iOiJFYXJ0aGRhdGEgTG9naW4iLCJzaWciOiJlZGxqd3RwdWJrZXlfb3BzIiwiYWxnIjoiUlMyNTYifQ.eyJ0eXBlIjoiVXNlciIsInVpZCI6Im1pcmtvOTgiLCJleHAiOjE3MjUzNTk1MTMsImlhdCI6MTcyMDE3NTUxMywiaXNzIjoiRWFydGhkYXRhIExvZ2luIn0.6KrS82Gb6rjTbIaBXROQrNfJRXCH7NH3si_UQgz5h3h5lsEiemz63LJL1X1YnMqpMAr0Pik2_LbA5PDKRtLqgaBKEnMFSsppuDHu4p0Pa7OBTWNBbYCEsuIZ2yfwVIc2sfr0g37g2hSaHqDcXMBGrppzrGCZtx10pDbmrrIdfnVE7tsFADhdPGryIgjs0XrDjSzSvLt9Z9MaPo-fXrWItTRoaSpa8aEaAsB3w7E4XX_wnLw54xfighLY-19ERWoEMyHGc6UaZGpkMZk3zyvVTGJnkmqJL1QvW3w0DfSRx0kW8TnFlfPrQ7Ay2N5t-n-wpApvPu-d_qloBNNtZx1eew"
+bearer <- token
+####  ITALY
+### ROI
+# Define region of interest (roi). The roi must be (1) an sf polygon and (2)
+# in the WGS84 (epsg:4326) coordinate reference system. Here, we use the
+# getData function to load a polygon of Ghana
+roi_sf <- gadm(country = "ITA", level=1, path = tempdir())
+### Daily data
+r_20190601 <- bm_raster(roi_sf = roi_sf,
+product_id = "VNP46A2",
+date = "2019-06-01",
+bearer = bearer)
+remotes::install_github("openvolley/datavolley")
+remotes::install_github("openvolley/datavolley")
+install.packages(c("cli", "hdf5r", "knitr", "sp", "yaml"))
+install.packages(c("boot", "class", "cluster", "codetools", "foreign", "KernSmooth", "lattice", "mgcv", "nlme", "nnet", "rpart", "spatial", "survival"), lib="C:/Program Files/R/R-4.3.0/library")
+library(cli)
+remove.packages("cli")
+install.packages("cli")
+remotes::install_github("openvolley/datavolley")
+shiny::runApp('GitHub/CuneoWebsite.io/Backend')
+rsconnect::setAccountInfo(name='mirko98', token='A45152018784DEF748AE689356AEDDCB', secret='Di9j9QiHvpabWeNNYRg9Scn5hlguSwsQJBoswcs/')
+library(rsconnect)
+deployApp('C:\Users\mirko\Documents\GitHub\CuneoWebsite.io\Backend')
+deployApp('C:/Users/mirko/Documents/GitHub/CuneoWebsite.io/Backend')
+remotes::install_github("openvolley/ovlytics")
+deployApp('C:/Users/mirko/Documents/GitHub/CuneoWebsite.io/Backend')
+deployApp('C:/Users/mirko/Documents/GitHub/CuneoWebsite.io/Backend/Dashboard')
+runApp('GitHub/CuneoWebsite.io/Backend')
+setwd('C:/Users/mirko/Documents/GitHub/CuneoWebsite.io/Backend/Dashboard')
+runApp()
+runApp()
+runApp()
+runApp()
+runApp()
diff --git a/.github/workflows/update.yml b/Old Files/update.yml
similarity index 100%
rename from .github/workflows/update.yml
rename to Old Files/update.yml