report.Rmd

@@ -65,25 +65,12 @@ participant_organizations <- read_csv(participants_path)
 species_planted <- read_csv(species_path)
 vendors <- read_csv(vendors_path)
 
-# Clean column names by removing (Required) and (Optional) and trimming whitespace
-# Define a function to clean column names
-clean_column_names <- function(df) {
-  colnames(df) <- gsub("\\s*\\(Required\\)|\\s*\\(Optional\\)", "", colnames(df))
-  colnames(df) <- str_trim(colnames(df))
-  return(df)
-}
-
-# Apply the function to the relevant tibbles
-survey_data <- clean_column_names(survey_data)
-species_planted <- clean_column_names(species_planted)
-participant_organizations <- clean_column_names(participant_organizations)
-
 # Convert relevant date columns to datetime format and recode planting agency responses to standardized labels
 survey_data <- survey_data %>%
   mutate(CreationDate = mdy_hms(CreationDate)) %>%
-  mutate(`Start Date of Planting` = mdy_hms(`Start Date of Planting`)) %>%
-  mutate(`End Date of Planting` = mdy_hms(`End Date of Planting`)) %>%
-  mutate(`Who Planted The Tree(s)?` = recode(`Who Planted The Tree(s)?`,
+  mutate(`Start Date of Planting (Required)` = mdy_hms(`Start Date of Planting (Required)`)) %>%
+  mutate(`End Date of Planting (Required)` = mdy_hms(`End Date of Planting (Required)`)) %>%
+  mutate(`Who Planted The Tree(s)? (Required)` = recode(`Who Planted The Tree(s)? (Required)`,
     "agency" = "State Agency",
     "community" = "Community Organization",
     "landowner" = "Private Landowner",
@@ -98,7 +85,7 @@ survey_data <- survey_data %>%
     
     Submitted_Date_Str = if_else(
       !is.na(Submitted_Date_Str),
-      paste0("20", Submitted_Date_Str),
+      paste0("20", Submitted_Date_Str),  # add "20" prefix to "24-11-07"
       NA_character_
     ),
 
@@ -132,30 +119,30 @@ subtitle: "`r format(min(survey_data$CreationDate, na.rm = TRUE), "%B %d, %Y")`
 
 ## Key Findings
 ```{r key-findings-summary}
-kf_date_planting_start <- format(min(survey_data$`Start Date of Planting`, na.rm = TRUE), "%B %d, %Y")
-kf_date_planting_end   <- format(max(survey_data$`End Date of Planting`, na.rm = TRUE), "%B %d, %Y")
-kf_total_trees <- format(sum(survey_data$`Number of Trees Planted`), big.mark = ",")
+kf_date_planting_start <- format(min(survey_data$`Start Date of Planting (Required)`, na.rm = TRUE), "%B %d, %Y")
+kf_date_planting_end   <- format(max(survey_data$`End Date of Planting (Required)`, na.rm = TRUE), "%B %d, %Y")
+kf_total_trees <- format(sum(survey_data$`Number of Trees Planted (Required)`), big.mark = ",")
 
 kf_region_total_trees_ranked <- survey_data %>%
   group_by(Region) %>%
-  summarise(Total_Trees = sum(`Number of Trees Planted`, na.rm = TRUE)) %>%
+  summarise(Total_Trees = sum(`Number of Trees Planted (Required)`, na.rm = TRUE)) %>%
   arrange(desc(Total_Trees))
 
 kf_participant_total_trees_ranked <- survey_data %>%
-  group_by(`Who Planted The Tree(s)?`) %>%
-  summarise(Total_Trees = sum(`Number of Trees Planted`, na.rm = TRUE)) %>%
+  group_by(`Who Planted The Tree(s)? (Required)`) %>%
+  summarise(Total_Trees = sum(`Number of Trees Planted (Required)`, na.rm = TRUE)) %>%
   arrange(desc(Total_Trees))
 
-kf_dac_total_trees <- sum(survey_data$`Number of Trees Planted`[!is.na(survey_data$`Disadvantaged Communities Indicator`)], na.rm = TRUE)
-kf_dac_percent <- (kf_dac_total_trees / sum(survey_data$`Number of Trees Planted`, na.rm = TRUE)) * 100
+kf_dac_total_trees <- sum(survey_data$`Number of Trees Planted (Required)`[!is.na(survey_data$`Disadvantaged Communities Indicator`)], na.rm = TRUE)
+kf_dac_percent <- (kf_dac_total_trees / sum(survey_data$`Number of Trees Planted (Required)`, na.rm = TRUE)) * 100
 kf_dac_percent_display <- round(kf_dac_percent, 1)
 
 kf_generic_tree_type_ranked <- species_planted %>%
-  filter(!is.na(`Generic Type of Tree`)) %>%
-  count(`Generic Type of Tree`, name = "Survey_Count") %>%
+  filter(!is.na(`Generic Type of Tree (Optional)`)) %>%
+  count(`Generic Type of Tree (Optional)`, name = "Survey_Count") %>%
   arrange(desc(Survey_Count))
 
-kf_most_common_generic_tree_type <- kf_generic_tree_type_ranked$`Generic Type of Tree`[1]
+kf_most_common_generic_tree_type <- kf_generic_tree_type_ranked$`Generic Type of Tree (Optional)`[1]
 kf_most_common_generic_tree_type_count <- kf_generic_tree_type_ranked$Survey_Count[1]
 kf_most_common_generic_tree_type_count_formatted <- format(kf_most_common_generic_tree_type_count, big.mark = ",")
 
@@ -174,9 +161,9 @@ Between **`r kf_date_planting_start` and `r kf_date_planting_end`**, a total of
 These efforts reflect broad collaboration between **municipal governments**, **community organizations**, **private landowners**, and other stakeholders.
 
 - **Most Trees Planted**: The highest number of trees were reported in **`r kf_region_total_trees_ranked$Region[1]`**, followed by **`r kf_region_total_trees_ranked$Region[2]`**.
-- **Top Planting Groups**: The most trees, approximately **`r scales::comma(kf_participant_total_trees_ranked$Total_Trees[1])`**, were planted by **`r kf_participant_total_trees_ranked$"Who Planted The Tree(s)?"[1]`**, followed by **`r kf_participant_total_trees_ranked$"Who Planted The Tree(s)?"[2]`**, which contributed **`r scales::comma(kf_participant_total_trees_ranked$Total_Trees[2])`** trees.
+- **Top Planting Groups**: The most trees, approximately **`r scales::comma(kf_participant_total_trees_ranked$Total_Trees[1])`**, were planted by **`r kf_participant_total_trees_ranked$"Who Planted The Tree(s)? (Required)"[1]`**, followed by **`r kf_participant_total_trees_ranked$"Who Planted The Tree(s)? (Required)"[2]`**, which contributed **`r scales::comma(kf_participant_total_trees_ranked$Total_Trees[2])`** trees.
 - **Disadvantaged Communities**: Approximately **`r kf_dac_percent_display`%** of all trees were planted in **Disadvantaged Communities**, as defined by New York State’s Climate Act.
-- **Most Reported Tree**: **`r kf_most_common_generic_tree_type`** appeared most frequently, reported in **`r kf_most_common_generic_tree_type_count_formatted`** surveys.
+- **Most Reported Tree Genus**: **`r kf_most_common_generic_tree_type`** appeared most frequently, reported in **`r kf_most_common_generic_tree_type_count_formatted`** surveys.
 - The project received data from **`r kf_total_surveys_formatted` unique surveys**, representing **`r kf_unique_counties_formatted` counties** and **`r kf_unique_municipalities_formatted` municipalities**.
 
 These findings help track progress toward equity-centered climate goals, highlight areas of strong participation, and support data-driven planning for future tree planting across the state.
@@ -412,8 +399,8 @@ calculate_response_rates <- function(survey_data, fields, caption) {
 ```
 
 ```{r response-rate-table-optional, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
-fields <- c("Planter Contact Email", "Funding Source", "Land Ownership", 
-            "Tree Size Planted", "Source of Trees", "Total Number of Species Planted")
+fields <- c("Planter Contact Email (Optional)", "Funding Source (Optional)", "Land Ownership (Optional)", 
+            "Tree Size Planted (Optional)", "Source of Trees (Optional)", "Total Number of Species Planted")
 calculate_response_rates(survey_data, fields, "Response Rates for Key Survey Questions")
 ```
 
@@ -474,7 +461,7 @@ create_histogram <- function(data, field, x_labels = NULL, color_palette = c("#1
 
 create_histogram(
   survey_data, 
-  field = "Who Planted The Tree(s)?", 
+  field = "Who Planted The Tree(s)? (Required)", 
   x_labels = c(
     "agency" = "State Agency",
     "community" = "Community Organization",
@@ -537,8 +524,8 @@ create_bar_chart <- function(data, field, sum_field = NULL, x_labels = NULL, col
 
 create_bar_chart(
   survey_data, 
-  field = "Who Planted The Tree(s)?", 
-  sum_field = "Number of Trees Planted",
+  field = "Who Planted The Tree(s)? (Required)", 
+  sum_field = "Number of Trees Planted (Required)",
   x_labels = c(
     "agency" = "State Agency",
     "community" = "Community Organization",
@@ -599,7 +586,7 @@ This table presents a detailed summary of tree planting activity by participant
 
 ```{r participant-type-table, echo=TRUE}
 survey_data %>%
-  create_summary_table("Who Planted The Tree(s)?", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  create_summary_table("Who Planted The Tree(s)? (Required)", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 ## Named User Activity
@@ -609,7 +596,7 @@ This table breaks down the number of submissions and trees planted by named user
 ```{r named-user-activity-table}
 survey_data %>%
   mutate(Creator = ifelse(is.na(Creator), "Public User", Creator)) %>%
-  create_summary_table("Creator", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  create_summary_table("Creator", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 ## Unique E-mail Activity
@@ -618,8 +605,8 @@ This table summarizes the planting activity associated with unique email address
 
 ```{r unique-email-activity-table}
 survey_data %>%
-  mutate(`Planter Contact Email` = ifelse(is.na(`Planter Contact Email`), "Not Provided", `Planter Contact Email`)) %>%
-  create_summary_table("Planter Contact Email", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  mutate(`Planter Contact Email (Optional)` = ifelse(is.na(`Planter Contact Email (Optional)`), "Not Provided", `Planter Contact Email (Optional)`)) %>%
+  create_summary_table("Planter Contact Email (Optional)", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 ### Municipal Activity
@@ -628,13 +615,13 @@ This table presents the number of trees planted by self-reported municipality. I
 
 ```{r municipal-activity-table}
 survey_data %>%
-  mutate(`Participant Municipality` = case_when(
-    str_starts(`Participant Municipality`, "c_") ~ str_replace(`Participant Municipality`, "^c_", "") %>% paste0(" (city)"),
-    str_starts(`Participant Municipality`, "v_") ~ str_replace(`Participant Municipality`, "^v_", "") %>% paste0(" (village)"),
-    str_starts(`Participant Municipality`, "t_") ~ str_replace(`Participant Municipality`, "^t_", "") %>% paste0(" (town)"),
-    TRUE ~ `Participant Municipality`
+  mutate(`Participant Municipality (Optional)` = case_when(
+    str_starts(`Participant Municipality (Optional)`, "c_") ~ str_replace(`Participant Municipality (Optional)`, "^c_", "") %>% paste0(" (city)"),
+    str_starts(`Participant Municipality (Optional)`, "v_") ~ str_replace(`Participant Municipality (Optional)`, "^v_", "") %>% paste0(" (village)"),
+    str_starts(`Participant Municipality (Optional)`, "t_") ~ str_replace(`Participant Municipality (Optional)`, "^t_", "") %>% paste0(" (town)"),
+    TRUE ~ `Participant Municipality (Optional)`
   )) %>%
-  create_summary_table("Participant Municipality", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  create_summary_table("Participant Municipality (Optional)", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 
 ```
 
@@ -645,15 +632,15 @@ This table highlights planting contributions by named organizations, either sele
 ```{r organization-activity-table}
 survey_data %>%
   inner_join(participant_organizations, by = c("GlobalID" = "ParentGlobalID")) %>%
-  filter(!(is.na(`Participant Organization`) & is.na(`Other`))) %>%
-  filter(!(tolower(`Participant Organization`) == "other" & is.na(`Other`))) %>%
-  mutate(`Participant Organization` = ifelse(
-    tolower(`Participant Organization`) == "other" & !is.na(`Other`),
-    `Other`,
-    `Participant Organization`
+  filter(!(is.na(`Participant Organization (Optional)`) & is.na(`Other (Optional)`))) %>%
+  filter(!(tolower(`Participant Organization (Optional)`) == "other" & is.na(`Other (Optional)`))) %>%
+  mutate(`Participant Organization (Optional)` = ifelse(
+    tolower(`Participant Organization (Optional)`) == "other" & !is.na(`Other (Optional)`),
+    `Other (Optional)`,
+    `Participant Organization (Optional)`
   )) %>%
-  mutate(`Participant Organization` = str_replace_all(`Participant Organization`, "_", " ")) %>%
-  create_summary_table("Participant Organization", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  mutate(`Participant Organization (Optional)` = str_replace_all(`Participant Organization (Optional)`, "_", " ")) %>%
+  create_summary_table("Participant Organization (Optional)", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 # Location Analysis {.tabset}
@@ -713,7 +700,7 @@ Use this map to identify which regions are leading in planting activity, and whe
 ```{r create-region-choropleth-map, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 survey_data_aggregated <- survey_data %>%
   group_by(Region) %>%
-  summarise(total_trees = sum(`Number of Trees Planted`, na.rm = TRUE))
+  summarise(total_trees = sum(`Number of Trees Planted (Required)`, na.rm = TRUE))
 
 shapefile_path <- "/home/nick/gitea/tree-tracker-report/data/redc/redc.shp"
 
@@ -745,7 +732,7 @@ plot_geographic_data(joined_data = survey_data_joined,
 The table below breaks down the total number of trees planted by region. It also shows each region’s percentage contribution to overall planting activity across New York State.
 
 ```{r create-summary-table-region, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
-create_summary_table(survey_data, "Region", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+create_summary_table(survey_data, "Region", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 ## By County
@@ -757,7 +744,7 @@ This visual helps uncover local patterns within regions, and may guide localized
 ```{r create-county-choropleth-map, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 survey_data_aggregated <- survey_data %>%
   group_by(County) %>%
-  summarise(total_trees = sum(`Number of Trees Planted`, na.rm = TRUE))
+  summarise(total_trees = sum(`Number of Trees Planted (Required)`, na.rm = TRUE))
 
 geographic_data <- counties(state = "NY", cb = TRUE, progress = FALSE) %>% 
   st_as_sf() %>%
@@ -782,7 +769,7 @@ plot_geographic_data(joined_data = survey_data_joined,
 This table provides a detailed breakdown of trees planted by county. Use it alongside the map to validate trends or investigate specific areas.
 
 ```{r create-summary-table-county, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
-create_summary_table(survey_data, "County", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+create_summary_table(survey_data, "County", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 # Tree Analysis {.tabset}
@@ -860,7 +847,7 @@ This table summarizes the number and percentage of surveys by **tree genus**. It
 * **"Not Provided"**: Includes submissions where the genus was not specified.
 
 ```{r create-summary-table-genus, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
-create_species_summary_table(species_planted, "Generic Type of Tree", "Tree Genus")
+create_species_summary_table(species_planted, "Generic Type of Tree (Optional)", "Tree Genus")
 ```
 
 ---
@@ -874,7 +861,7 @@ This table provides a breakdown of survey submissions by **tree species**. It of
 * **"Not Provided"**: Surveys that omitted species details.
 
 ```{r create-summary-table-species, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
-create_species_summary_table(species_planted, "Tree Species", "Tree Species")
+create_species_summary_table(species_planted, "Tree Species (Optional)", "Tree Species")
 ```
 
 # Disadvantaged Communities {.tabset}
@@ -896,7 +883,7 @@ This table presents the total number of trees planted within DACs, grouped by Ne
 ```{r create-summary-table-region-dac, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 survey_data %>%
   filter(!is.na(`Disadvantaged Communities Indicator`)) %>%
-  create_summary_table("Region", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  create_summary_table("Region", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 ---
@@ -908,7 +895,7 @@ This table summarizes tree planting within DACs by **county**. It provides a mor
 ```{r create-summary-table-county-dac, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 survey_data %>%
   filter(!is.na(`Disadvantaged Communities Indicator`)) %>%
-  create_summary_table("County", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  create_summary_table("County", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```
 
 ---
@@ -920,5 +907,5 @@ This table breaks down the number of trees planted within DACs by **municipality
 ```{r create-summary-table-county-municipality, echo=TRUE, message=FALSE, fig.height=6, fig.width=8}
 survey_data %>%
   filter(!is.na(`Disadvantaged Communities Indicator`)) %>%
-  create_summary_table("Municipality", "Number of Trees Planted", remove_na = FALSE, table_font_size = 16)
+  create_summary_table("Municipality", "Number of Trees Planted (Required)", remove_na = FALSE, table_font_size = 16)
 ```