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Move data-proc code from server to global.R #39

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Jun 8, 2024
Merged

Move data-proc code from server to global.R #39

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21 changes: 21 additions & 0 deletions R/data_processing.R
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Original file line number Diff line number Diff line change
Expand Up @@ -143,10 +143,31 @@ compute_aquatroll <- function(aquatroll, ddt) {
names_to = "variable", values_to = "value") ->
aquatroll_600_long

aquatroll_200_long %>%
arrange(Timestamp) %>%
group_by(Well_Name, variable) %>%
slice_tail(n = 10) %>%
ungroup() %>%
select(Timestamp, Well_Name, Instrument, variable, value, Logger_ID, Plot) ->
aq200_long

aquatroll_600_long %>%
arrange(Timestamp) %>%
group_by(Well_Name, variable) %>%
slice_tail(n = 10) %>%
ungroup() %>%
select(Timestamp, Well_Name, Instrument, variable, value, Logger_ID, Plot) %>%
bind_rows(aq200_long) %>%
# at this point we have the full trolls dataset in long form
pivot_wider(id_cols = c("Well_Name", "variable", "Plot", "Instrument"),
names_from = "Timestamp", values_from = "value") ->
aquatroll_table_data

list(aquatroll_600 = aquatroll$aquatroll_600,
aquatroll_200 = aquatroll$aquatroll_200,
aquatroll_600_long = aquatroll_600_long,
aquatroll_200_long = aquatroll_200_long,
aquatroll_table_data = aquatroll_table_data,
aquatroll_bad_sensors = aquatroll_bad_sensors,
aquatroll_bdg = aquatroll_bdg)
}
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2 changes: 1 addition & 1 deletion global.R
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Expand Up @@ -39,7 +39,7 @@ FLAG_TIME_WINDOW <- 1 # hours back from the dashboard datetime

# The 'no data' graph that's shown if no rows are selected, etc.
NO_DATA_GRAPH <- ggplot() +
annotate("text", x = 1, y = 1, label = "(No data)", size = 12) +
annotate("text", x = 1, y = 1, label = "(No selection)", size = 12) +
theme(axis.title = element_blank(),
axis.text = element_blank(),
)
101 changes: 43 additions & 58 deletions server.R
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Original file line number Diff line number Diff line change
Expand Up @@ -303,14 +303,25 @@ server <- function(input, output, session) {
trees_selected

dropbox_data()[["sapflow"]] %>%
filter(Sapflow_ID %in% trees_selected) %>%
ggplot(aes(Timestamp, Value, group = Sapflow_ID, color = Plot)) +
shaded_flood_rect(ymin = min(SAPFLOW_RANGE), ymax = max(SAPFLOW_RANGE)) +
filter(Sapflow_ID %in% trees_selected) ->
selected_data

b <- ggplot(selected_data,
aes(Timestamp, Value, group = Sapflow_ID)) +
shaded_flood_rect(ymin = min(SAPFLOW_RANGE),
ymax = max(SAPFLOW_RANGE)) +
geom_line() +
xlab("") +
xlim(c(ddt - GRAPH_TIME_WINDOW * 60 * 60, ddt)) +
geom_hline(yintercept = SAPFLOW_RANGE, color = "grey", linetype = 2) ->
b
geom_hline(yintercept = SAPFLOW_RANGE, color = "grey", linetype = 2)
# Try to assign color intelligently. If different plots are selected,
# have that be the color; otherwise by ID
if(length(unique(selected_data$Plot)) > 1) {
b <- b + aes(color = Plot)
} else {
b <- b + aes(color = Sapflow_ID)
}

} else {
b <- NO_DATA_GRAPH
}
Expand All @@ -331,33 +342,28 @@ server <- function(input, output, session) {
ddt <- reactive({ DASHBOARD_DATETIME() })()

dropbox_data()[["teros_table_data"]] %>%
slice(input$teros_table_rows_selected) %>%
select(variable, ID) ->
slice(input$teros_table_rows_selected) ->
tsensor_selected

dropbox_data()[["teros"]] %>%
filter(ID %in% tsensor_selected$ID,
variable %in% tsensor_selected$variable) -> selected_data

b <- ggplot(selected_data,
aes(Timestamp, value, group = interaction(ID, variable))) +
geom_line() +
xlab("") +
xlim(c(ddt - GRAPH_TIME_WINDOW * 60 * 60, ddt))
# Try to assign color intelligently. If different plots are selected,
# have that be the color; otherwise by depth; otherwise by ID
if(length(unique(selected_data$Plot)) > 1) {
b <- ggplot(selected_data,
aes(Timestamp, value, group = interaction(ID, variable),
color = Plot))
b <- b + aes(color = Plot)
} else if(length(unique(selected_data$Depth)) > 1) {
b <- ggplot(selected_data,
aes(Timestamp, value, group = interaction(ID, variable),
color = Depth))
b <- b + aes(color = Depth)
} else {
b <- ggplot(selected_data,
aes(Timestamp, value, group = interaction(ID, variable),
color = ID))
b <- b + aes(color = ID)
}

b <- b + geom_line() +
xlab("") +
xlim(c(ddt - GRAPH_TIME_WINDOW * 60 * 60, ddt))
} else {
b <- NO_DATA_GRAPH
}
Expand All @@ -369,48 +375,15 @@ server <- function(input, output, session) {

output$troll_table <- DT::renderDataTable({
dataInvalidate()

dropbox_data()[["aquatroll_200_long"]] %>%
group_by(Well_Name, variable) %>%
slice_tail(n = 10) %>%
ungroup() %>%
select(Timestamp, Well_Name, Instrument, variable, value, Logger_ID, Plot) ->
aq200_long

dropbox_data()[["aquatroll_600_long"]] %>%
group_by(Well_Name, variable) %>%
slice_tail(n = 10) %>%
ungroup() %>%
select(Timestamp, Well_Name, Instrument, variable, value, Logger_ID, Plot) %>%
bind_rows(aq200_long) %>%
# at this point we have the full trolls dataset in long form
arrange(Timestamp) %>%
pivot_wider(id_cols = c("Well_Name", "variable", "Plot", "Instrument"),
names_from = "Timestamp", values_from = "value")
dropbox_data()[["aquatroll_table_data"]]
})

output$troll_detail_graph <- renderPlotly({
if(length(input$troll_table_rows_selected)) {

ddt <- reactive({ DASHBOARD_DATETIME() })()

dropbox_data()[["aquatroll_200_long"]] %>%
group_by(Well_Name, variable) %>%
slice_tail(n = 10) %>%
ungroup() %>%
select(Timestamp, Well_Name, Instrument, variable, value, Logger_ID, Plot) ->
aq200_long

dropbox_data()[["aquatroll_600_long"]] %>%
group_by(Well_Name, variable) %>%
slice_tail(n = 10) %>%
ungroup() %>%
select(Timestamp, Well_Name, Instrument, variable, value, Logger_ID, Plot) %>%
bind_rows(aq200_long) -> trolls

trolls %>%
pivot_wider(id_cols = c("Well_Name", "variable", "Plot", "Instrument"),
names_from = "Timestamp", values_from = "value") %>%
dropbox_data()[["aquatroll_table_data"]] %>%
slice(input$troll_table_rows_selected) %>%
select(variable, Well_Name) ->
aqsensor_selected
Expand All @@ -420,13 +393,25 @@ server <- function(input, output, session) {
dropbox_data()[["aquatroll_200_long"]] %>%
bind_rows(dropbox_data()[["aquatroll_600_long"]]) %>%
filter(Well_Name %in% aqsensor_selected$Well_Name,
variable %in% aqsensor_selected$variable) %>%
ggplot(aes(Timestamp, value, group = interaction(Well_Name, variable), color = Well_Name)) +
variable %in% aqsensor_selected$variable) -> selected_data

b <- ggplot(selected_data,
aes(Timestamp, value,
group = interaction(Well_Name, variable))) +
geom_line() +
xlab("") +
labs(color = "Well Name") +
xlim(c(ddt - GRAPH_TIME_WINDOW * 60 * 60, ddt)) ->
b
xlim(c(ddt - GRAPH_TIME_WINDOW * 60 * 60, ddt))
# Try to assign color intelligently. If different plots are selected,
# have that be the color; otherwise by variable; otherwise by name
if(length(unique(selected_data$Plot)) > 1) {
b <- b + aes(color = Plot)
} else if(length(unique(selected_data$variable)) > 1) {
b <- b + aes(color = variable)
} else {
b <- b + aes(color = Well_Name)
}

} else {
b <- NO_DATA_GRAPH
}
Expand Down
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