From 5389c00574850d568d1cf46721cbe33c02be1840 Mon Sep 17 00:00:00 2001 From: Roman Eisner Date: Wed, 10 Aug 2022 14:29:55 -0600 Subject: [PATCH] move from private repo --- .gitignore | 13 + Dockerfile | 92 +++ README.md | 38 + dashboard_utils.R | 761 ++++++++++++++++++ data_download_module.R | 159 ++++ elasticsearch/.gitignore | 1 + elasticsearch/README.md | 111 +++ elasticsearch/certificates/.gitkeep | 0 global.R | 38 + neighbourhood_map_module.R | 81 ++ run_text_depot_dashboard.R | 1 + search_bar_module.R | 211 +++++ search_results_table_module.R | 271 +++++++ sentiment_histogram_module.R | 100 +++ sentiment_likert_module.R | 108 +++ sentiment_module.R | 165 ++++ sentiment_neighbourhoods_module.R | 241 ++++++ sentiment_timeline_likert_module.R | 126 +++ sentiment_timeline_module.R | 128 +++ server.R | 141 ++++ ui.R | 78 ++ volume_sentiment_module.R | 103 +++ volume_timeline_module.R | 145 ++++ workflow.png | Bin 0 -> 50204 bytes www/text_depot_icon/Text Depot Colours.jpg | Bin 0 -> 626599 bytes www/text_depot_icon/TextDepotIcon_Image.eps | Bin 0 -> 1909398 bytes www/text_depot_icon/TextDepotIcon_Image.png | Bin 0 -> 19701 bytes www/text_depot_icon/TextDepotIcon_Image_L.jpg | Bin 0 -> 110973 bytes www/text_depot_icon/TextDepotIcon_Image_M.jpg | Bin 0 -> 58724 bytes www/text_depot_icon/TextDepotIcon_Image_S.jpg | Bin 0 -> 37823 bytes .../TextDepotIcon_TextBeside.eps | Bin 0 -> 2376630 bytes .../TextDepotIcon_TextBeside.png | Bin 0 -> 34927 bytes .../TextDepotIcon_TextBeside_L.jpg | Bin 0 -> 165022 bytes .../TextDepotIcon_TextBeside_M.jpg | Bin 0 -> 76059 bytes .../TextDepotIcon_TextBeside_S.jpg | Bin 0 -> 40628 bytes .../TextDepotIcon_TextImage.eps | Bin 0 -> 2238022 bytes .../TextDepotIcon_TextImage.png | Bin 0 -> 37746 bytes .../TextDepotIcon_TextImage_L.jpg | Bin 0 -> 163384 bytes .../TextDepotIcon_TextImage_M.jpg | Bin 0 -> 78836 bytes .../TextDepotIcon_TextImage_S.jpg | Bin 0 -> 45456 bytes 40 files changed, 3112 insertions(+) create mode 100644 .gitignore create mode 100644 Dockerfile create mode 100644 README.md create mode 100644 dashboard_utils.R create mode 100644 data_download_module.R create mode 100644 elasticsearch/.gitignore create mode 100644 elasticsearch/README.md create mode 100644 elasticsearch/certificates/.gitkeep create mode 100644 global.R create mode 100644 neighbourhood_map_module.R create mode 100644 run_text_depot_dashboard.R create mode 100644 search_bar_module.R create mode 100644 search_results_table_module.R create mode 100644 sentiment_histogram_module.R create mode 100644 sentiment_likert_module.R create mode 100644 sentiment_module.R create mode 100644 sentiment_neighbourhoods_module.R create mode 100644 sentiment_timeline_likert_module.R create mode 100644 sentiment_timeline_module.R create mode 100644 server.R create mode 100644 ui.R create mode 100644 volume_sentiment_module.R create mode 100644 volume_timeline_module.R create mode 100644 workflow.png create mode 100755 www/text_depot_icon/Text Depot Colours.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_Image.eps create mode 100755 www/text_depot_icon/TextDepotIcon_Image.png create mode 100755 www/text_depot_icon/TextDepotIcon_Image_L.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_Image_M.jpg create mode 100644 www/text_depot_icon/TextDepotIcon_Image_S.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_TextBeside.eps create mode 100755 www/text_depot_icon/TextDepotIcon_TextBeside.png create mode 100755 www/text_depot_icon/TextDepotIcon_TextBeside_L.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_TextBeside_M.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_TextBeside_S.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_TextImage.eps create mode 100755 www/text_depot_icon/TextDepotIcon_TextImage.png create mode 100755 www/text_depot_icon/TextDepotIcon_TextImage_L.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_TextImage_M.jpg create mode 100755 www/text_depot_icon/TextDepotIcon_TextImage_S.jpg diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..8b604f4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,13 @@ +.DS_Store +.Rproj.user +*.Rproj +app_key_dir +app_vault_dir +.deploy_vars +.configs +decode.sh +.devcontainer/ +.bash_history +.local/ +.rstudio/ +.dockerignore \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..996fb4a --- /dev/null +++ b/Dockerfile @@ -0,0 +1,92 @@ +# syntax = docker/dockerfile:1.0-experimental # https://docs.docker.com/develop/develop-images/build_enhancements/ +FROM rocker/r-ver:4.0.3 + +RUN export DEBIAN_FRONTEND=noninteractive && apt-get -y update \ + && apt-get install -y \ + alien \ + bzip2 \ + cmake \ + curl \ + file \ + gdal-bin \ + gnupg2 \ + libaio1 \ + libapparmor1 \ + libcairo2 \ + libcairo2-dev \ + libcurl4-openssl-dev \ + libedit2 \ + libgdal-dev \ + libglpk-dev \ + libpoppler-cpp-dev \ + libproj-dev \ + libsqliteodbc \ + libssl-dev \ + libudunits2-dev \ + libxml2-dev \ + libxt-dev \ + libxt6 \ + lsb-release \ + odbc-postgresql \ + openjdk-8-jdk \ + openssh-client \ + pandoc \ + pandoc-citeproc \ + postgresql \ + procps \ + psmisc \ + r-cran-cairo \ + swaks \ + tcl-dev \ + tk-dev \ + unixodbc \ + zlib1g-dev \ + && rm -rf /var/lib/apt/lists/* + +# get from https://packagemanager.rstudio.com/client/#/repos/1/overview +# Freezing packages to April 22, 2021: +RUN echo "options(repos = c(REPO_NAME = 'https://packagemanager.rstudio.com/all/__linux__/focal/2511902'))" >> $R_HOME/etc/Rprofile.site + +RUN R -e "install.packages(c('assertthat', \ + 'data.table', \ + 'dplyr', \ + 'DT', \ + 'elastic', \ + 'future', \ + 'future.callr', \ + 'ggplot2', \ + 'ggthemes', \ + 'httr', \ + 'jsonlite', \ + 'leaflet', \ + 'lubridate', \ + 'memoise', \ + 'plotly', \ + 'promises', \ + 'rmarkdown', \ + 'rgdal', \ + 'shiny', \ + 'shinycssloaders', \ + 'shinydashboard', \ + 'shinyjs', \ + 'shinyWidgets', \ + 'sf', \ + 'stringr', \ + 'timetk', \ + 'htmltools'))" + +# Add certs for accessing elastic search servers that require them +COPY elasticsearch/certificates/*.crt /usr/local/share/ca-certificates/ +RUN update-ca-certificates + +RUN mkdir /shinyapp +WORKDIR /shinyapp/ +ADD ./ ./ + +RUN useradd shiny -u 5000 -m -b /home +RUN chown -R shiny:shiny /shinyapp +USER shiny + +EXPOSE 3838 + +CMD ["R", "-e", "shiny::runApp('/shinyapp', host = '0.0.0.0', port = 3838)"] diff --git a/README.md b/README.md new file mode 100644 index 0000000..a5767d0 --- /dev/null +++ b/README.md @@ -0,0 +1,38 @@ + + +Text Depot is a tool to search and analyze topics of interest within a large database of text data. The Text Depot dashboard (this repo) provides a front-end to a set of indexes in ElasticSearch. To use this repository, you must provide one or more [Elastic Search](www.elastic.co) indexes in a particular format. + +## Local Machine Setup + +1. Clone this repo. +2. Run `cp .configs_sample .configs` and fill in the relevant values. + +### Running Locally + +1. Install any missing libraries with `install.packages("DT")` (for example). A list of required libraries can be found in the included `Dockerfile` +2. Run `Rscript run_text_depot_dashboard.R` + +### Running via Docker + +1. Optionally, create a `.dockerignore` file to exclude any local files. +2. Use the provided `Dockerfile` to build and run the app: + +``` +$ DOCKER_BUILDKIT=1 docker build -t text_depot_dashboard . +$ docker run -it -p 8080:3838 text_depot_dashboard +``` + +3. Open the dashboard on your browser: [http://localhost:8080](http://localhost:8080) + +## ElasticSearch + +Each data source should be stored in its own Elastic Search index. For more information, see [elasticsearch/](elasticsearch/) + +## Notes + +Our workflow contained the following components: + +![Overall Workflow](workflow.png) + +This repository contains the dashboard code (Blue above) for Text Depot. The green components were scheduled with cron jobs, and keep the indexes up-to-date in the ElasticSearch Database. We wrote a custom Parser for each data source, and a single Annotator class that adds the fields below to each document before insertion. The orange components were added for authentication and embeddings-based search (add to `embedding_api_host` in `.configs`) for your dashboard. + diff --git a/dashboard_utils.R b/dashboard_utils.R new file mode 100644 index 0000000..009547f --- /dev/null +++ b/dashboard_utils.R @@ -0,0 +1,761 @@ +library(httr, warn.conflicts = FALSE) +library(memoise, warn.conflicts = FALSE) +library(jsonlite, warn.conflicts = FALSE) +library(stringr) + +search_fields <- function() { + list("Document Text" = "text", + "Source Title" = "source_title", + "Parent Source Title" = "parent_source_title") +} + +get_configs_from_file <- function() { + if (file.exists('.configs')) { + prop <- yaml::read_yaml('.configs') + } else { + if (file.exists('decode.sh')) { + result = system('./decode.sh ./app_vault_dir/.configs.enc ./app_key_dir/key.bin', intern = TRUE) + configs_str = paste0(result, collapse = "\n") + prop = yaml::yaml.load(configs_str) + } else { + stop(".configs file not found!") + } + } + + prop +} +get_configs = memoise(get_configs_from_file) + +connect_ES <- function() { + prop <- get_configs() + x <- elastic::connect(host = prop$apiurl, + user = prop$apiuser, + pwd = prop$apipassword, + port = "", + path = "", + errors = "complete", + transport_schema = "https" + ) +} + +get_aliases <- function(session = NULL) { + aliases <- c() + if ("SHINYPROXY_USERGROUPS" %in% (Sys.getenv() %>% names())) { + # Get Aliases from assigned roles for user (passed to shiny proxy from Keycloak) + aliases <- Sys.getenv("SHINYPROXY_USERGROUPS") %>% + stringr::str_split(pattern = ",") %>% + unlist() + + print("reading SHINYPROXY_USERGROUPS") + } else if (!is.null(session)) { + if (is.character(session$clientData$shinyproxy_roles)){ + print(paste0("user: ", session$clientData$shinyproxy_user)) + print(paste0("user roles: ", session$clientData$shinyproxy_roles)) + aliases <- session$clientData$shinyproxy_roles + } + } + + if (length(aliases) > 0) { + aliases <- aliases %>% + tolower() %>% + stringr::str_subset("text_depot_alias") %>% + stringr::str_remove("text_depot_alias_") + } + + # If nothing was specified via the env variables, then use the default from configs + if (length(aliases) == 0) { + prop <- get_configs() + aliases <- prop$default_index_aliases + cat("Using alias list from configs:", paste(aliases, collapse = "; "), "\n") + } + + if (length(aliases) == 0) { + stop("User does not have permission to access any aliases") + } + return(aliases) +} + +alias_to_colours_mapping <- function(aliases) { + # List of colours available at: https://stackoverflow.com/a/56495465/786540 + # NOTE: Dont use RED for a dataset colour! (Confusing when paired with sentiment) + # second element of google colour list should be "#dc3912" (red) + # If we ever have more than 30 indexes, we will run out of colours, and will need to cycle through... + available_colours = c("#3366cc", "#ff9900", "#109618", "#990099", "#0099c6", "#dd4477", "#66aa00", "#b82e2e", "#316395", "#994499", "#22aa99", "#aaaa11", "#6633cc", "#e67300", "#8b0707", "#651067", "#329262", "#5574a6", "#3b3eac", "#b77322", "#16d620", "#b91383", "#f4359e", "#9c5935", "#a9c413", "#2a778d", "#668d1c", "#bea413", "#0c5922", "#743411") + + data.frame( + alias_name = aliases, + colours = available_colours[1:length(aliases)], + stringsAsFactors = FALSE + ) +} + +# Return a data frame with two columns: +# index_name - name of the internal ES index +# alias_name - alias used to point at the index +index_to_alias_mapping <- function(es, alias_names) { + mapping = elastic::aliases_get(es, index = alias_names) + + mapping_df = tibble::enframe(mapping, name = "index_name") %>% + tidyr::unnest_wider(value) %>% + filter(str_detect(index_name, "coe_td_")) + + if ("aliases" %in% names(mapping_df)) { + mapping_df = mapping_df %>% + tidyr::unnest_longer(aliases, indices_to = "alias_name") %>% + select(-aliases) %>% + mutate(alias_name = dplyr::coalesce(alias_name, index_name)) # use index name if alias name not present + } else { + # None of the specified datasets had an alias, so the column doesnt exist: + mapping_df$alias_name = mapping_df$index_name + } + + assertthat::assert_that(all(alias_names %in% union(mapping_df$alias_name, mapping_df$index_name)), msg = "some requested aliases/indices are not in Elastic Search") + + mapping_df <- mapping_df %>% + filter(alias_name %in% alias_names | index_name %in% alias_names) + + return(mapping_df) +} + + +query_count <- function(conn, index) { + elastic::count(conn, index = index) +} + +search_body <- function(query, + search_fields, + min_score = 0, + min_date = "2000-01-01", + max_date = Sys.Date(), + use_embedding_search = FALSE) { + fields_str = paste0('"', search_fields, '"', collapse = ", ") + + filter_str = glue::glue(' + "range": { + "date": { + "gte": "<{min_date}>", + "lte": "<{max_date}>" + } + } + ', .open = "<{", .close = "}>") + + if (use_embedding_search) { + # min_score should be in [0, 1] + # min_score = 0.55 # This suggests using at least 0.5 for Doc2Vec: https://radimrehurek.com/gensim/auto_examples/howtos/run_doc2vec_imdb.html + min_score = 0.35 # FastText needs a lower min_score + embedding_search_body(query, fields_str, filter_str, min_score, get_configs()$embedding_api_host, get_configs()$embedding_api_user, get_configs()$embedding_api_password) + } else { + standard_search_body(query, fields_str, filter_str, min_score) + } +} + +embedding_search_body <- function(query, + fields_str, + filter_str, + min_score, + api_url, + api_user, + api_password, + vector_field = 'source_title_vector') { + vector = get_embedding_vector(query, api_url, api_user, api_password) + vector_str = paste0("[", paste0(vector, collapse = ", "), "]") + + # https://www.elastic.co/guide/en/elasticsearch/reference/7.3/query-dsl-script-score-query.html#vector-functions + min_score = min_score + 1.0 # Cosine similarity function below adds 1.0 to score + + # We have to filter to those that have the vector, since some documents might not be embedded. + glue::glue(' + "min_score": <{min_score}>, + "query": { + "script_score": { + "query": { + "bool": { + "filter": [ + { + <{filter_str}> + }, + { + "exists": { "field": "<{vector_field}>" } + } + ] + } + }, + "script": { + "source": "cosineSimilarity(params.queryVector, \'<{vector_field}>\') + 1.0", + "params": {"queryVector": <{vector_str}>} + } + } + } + ', .open = "<{", .close = "}>") +} + +standard_search_body <- function(query, fields_str, filter_str, min_score = 0) { + query = glue::double_quote(query) + glue::glue(' + "min_score": <{min_score}>, + "query": { + "bool": { + "must": { + "simple_query_string" : { + "query": <{query}>, + "fields": [ <{fields_str}> ], + "default_operator": "and" + } + }, + "filter": { + <{filter_str}> + } + } + } + ', .open = "<{", .close = "}>") +} + + +source_body <- function(source_cols) { + if (length(source_cols) == 0) return(c()) + + source_cols <- source_cols %>% + glue::double_quote() %>% + glue::glue_collapse(sep = ",") %>% + (function(x) glue::glue("[", x, "]")) + + glue::glue('"_source": <{source_cols}>', .open = "<{", .close = "}>") +} + +highlights_body <- function(highlight_cols = c("text", "source_title", "parent_source_title"), + n_char_no_match = 100, + n_char_highlights = 100) { + if (length(highlight_cols) == 0) { + return(c()) + } + + no_match_str = glue::glue(': {"no_match_size" : <{n_char_no_match}>, + "fragment_size" : <{n_char_highlights}> }', .open = "<{", .close = "}>") + + highlight_cols <- highlight_cols %>% + glue::double_quote() %>% + glue::glue_collapse(sep = paste0(no_match_str, ",")) %>% + (function(x) glue::glue("<{x}>", no_match_str, .open = "<{", .close = "}>")) + + glue::glue( + '"highlight": { + "fields": { + <{highlight_cols}> + } + } + ', .open = "<{", .close = "}>") +} + +stats_for_field <- function(conn, index, field, numeric = FALSE) { + aggs = glue::glue( + '{ + "aggs": { + "fieldstats": { + "stats": { + "field": "<{field}>" + } + } + } + }', .open = "<{", .close = "}>") + + result <- elastic::Search(conn, index = index, body = aggs, size = 0) + + if (numeric) { + return(list(min = result$aggregations$fieldstats$min, + max = result$aggregations$fieldstats$max, + avg = result$aggregations$fieldstats$avg, + sum = result$aggregations$fieldstats$sum)) + } else { + return(list(min = result$aggregations$fieldstats$min_as_string, + max = result$aggregations$fieldstats$max_as_string, + avg = result$aggregations$fieldstats$avg_as_string, + sum = result$aggregations$fieldstats$sum_as_string)) + } +} + +combine_query <- function(...) { + query_parts <- c(...) + + query_parts %>% + glue::glue_collapse(sep = ",") %>% + (function(x) glue::glue("{ <{x}>}", .open = "<{", .close = "}>")) %>% + jsonlite::prettify() +} + +query_text_depot <- function(query_info = NULL, + aggregates_json = NULL, + source_json = NULL, + highlights_json = NULL, + from = 0, + size = 0) { + conn = query_info$conn + query_str = query_info$query + index = query_info$index + search_fields = query_info$search_fields + min_score = query_info$min_score + min_date = query_info$min_date + max_date = query_info$max_date + use_embeddings = query_info$use_embeddings + query_search <- search_body( + query = query_str, + search_fields = search_fields, + min_score = min_score, + min_date = min_date, + max_date = max_date, + use_embedding_search = use_embeddings + ) + + # if part of query is not defined, it will be default NULL value, and that is elegantly handled by combine_query: + query <- combine_query(query_search, + aggregates_json, + source_json, + highlights_json + ) + + results <- elastic::Search(conn = conn, + index = index, + body = query, + asdf = TRUE, + from = from, + size = size + ) + + if (results$`_shards`$failed > 0) { return(paste0("Error! Shard Failed! ", paste(results$`_shards`$failures, collapse = "; "))) } + + results +} + +get_embedding_vector <- function(query, api_url, api_user, api_password) { + query = URLencode(query) + res = api_url %>% + paste0("/embeddings_api/v1/embed_query?query=", query) %>% + httr::GET(authenticate(api_user, api_password)) %>% + httr::content() + unlist(res$embedding_vector) +} + +parse_hits <- function(es_results, index_to_db_map) { + hits = es_results$hits$hits %>% as_tibble() # Only current page + + rows = nrow(hits) + if (is.null(rows)) { return(data.frame()) } + if (rows == 0) { return(data.frame()) } + + # Hits contains four columns: + # _id -- Elastic Search ID + # _type -- "_doc" -- not used. + # _index: Source index for the hit (e.g. "council_report_files") + # _score: match score + # _source: the document itself. + hits_df <- hits %>% + group_by(`_id`) %>% + add_cols(c("_source.date", "_source.text", "_source.num_sentences", "_source.sentiment_subjectivity", + "_id", "_source.parent_source_url", "_source.num_chars", "_source.parent_source_title", + "_source.source_url", "_source.sentiment_polarity", "_source.source_title", "highlight.text", + "highlight.source_title", "highlight.parent_source_title")) %>% # add any columns that did not return - with multiple indexes, some may be blank + mutate(match_text = format_matches_for_hit(text_list = highlight.text, + source_title = `_source.source_title`, + source_url = `_source.source_url`, + parent_source_title = `_source.parent_source_title`, + parent_source_url = `_source.parent_source_url`)) %>% + ungroup() %>% + rename(id = `_id`, + similarity = `_score`, + index_name = `_index`, + date = `_source.date`, + text = `_source.text`, + source_url = `_source.source_url`, + source_title = `_source.source_title`, + parent_source_url = `_source.parent_source_url`, + parent_source_title = `_source.parent_source_title`, + num_chars = `_source.num_chars`, + num_sentences = `_source.num_sentences`, + sentiment = `_source.sentiment_polarity`, + sentiment_subj = `_source.sentiment_subjectivity`, + matches = match_text) %>% + left_join(index_to_db_map, by = c("index_name" = "index_name")) %>% + mutate_if(is.numeric, .funs = function(x) round(x, 3)) %>% + mutate(date = as.Date(date, format = "%Y-%m-%d")) + + hits_df +} + +parse_aggregates <- function(es_results) { + keys <- es_results$aggregations$group_by_index$buckets$key + + counts_by_index <- bind_cols(index = es_results$aggregations$group_by_index$buckets$key, + doc_count = es_results$aggregations$group_by_index$buckets$doc_count) + + aggregate_names <- setdiff(names(es_results$aggregations$group_by_index$buckets), c("key", "doc_count")) + results <- es_results$aggregations$group_by_index$buckets[aggregate_names] + + results_parsed <- purrr::map(results, function(x) { + names(x) <- keys + bind_rows(x, .id = "index") + }) + + results_parsed <- c(list(counts_by_index = counts_by_index), results_parsed) + + return(results_parsed) +} + +get_document_by_id <- function(connection, + id, + index) { + source_body = source_body(c("text", "neighbourhoods")) + query = glue::glue(' + { + "query": { + "terms": {"_id": ["<{id}>"]} + }, + <{source_body}> + }', .open = "<{", .close = "}>") + + results = elastic::Search(connection, + index = index, + size = 1, + body = query) + + fields = results$hits$hits[[1]]$`_source` + neighbourhoods = fields$neighbourhoods + if (length(neighbourhoods) > 0) { + neighbourhoods = rbindlist(neighbourhoods) + } + + return(list( + "text" = fields$text, + "neighbourhoods" = neighbourhoods$name + )) +} + +# text_list can contain several matching snippets for this document. +format_matches_for_hit <- function(text_list, + source_title, + source_url, + parent_source_title, + parent_source_url, + add_link = FALSE) { + formatted_parent_title = format_title(parent_source_title, parent_source_url, add_link) + formatted_title = format_title(source_title, source_url, add_link) + formatted_highlights = collapse_highlights(text_list, collapse = " ... ", rm_em = FALSE, color_em = TRUE) + + formatted_match = paste0(strong(formatted_parent_title), + br(), + formatted_title, + br(), + formatted_highlights, + br()) + + formatted_match +} + +format_title <- function(title_str, title_url, add_link) { + if (is.na(title_str)) { return("") } + + link_present = TRUE + if (is.na(title_url)) { + link_present = FALSE + } else if (title_url == "") { + link_present = FALSE + } + + if (link_present & add_link) { + formatted_str = a(title_str, href = title_url, target = "_blank") + } else { + formatted_str = span(title_str, style = "color: #0275d8;") + } + + return(formatted_str) +} + +collapse_highlights <- function(..., + collapse = "
...
", + rm_em = FALSE, + bold = FALSE, + color_em = FALSE) { + out <- list(...) %>% + unlist() %>% + (function(x) Filter(Negate(is.na), x)) %>% # remove NA elements + paste(collapse = collapse) + + if (rm_em == TRUE) { + out <- out %>% + stringr::str_remove_all("") %>% + stringr::str_remove_all("") + } + + if (color_em) { + #out = str_replace_all(out, "", '') + out = str_replace_all(out, "", '') + } + + out +} + +add_cols <- function(data, col_name) { + add <- col_name[!col_name %in% names(data)] + + if (length(add) != 0) data[add] <- NA + + data +} + +## This function is coppied from Timetk package, since it is not exposed by the +# API: +# https://github.com/business-science/timetk/blob/master/R/plot-time_series.R +# it relies on 2 functions that are exposed by the API, so we still have the +# timetk package requirement +# A wrapper for smooth_vec() that handles changes in grouped idx's +auto_smooth <- function(idx, x, + smooth_period, + smooth_span, + smooth_degree, + smooth_message) { + + if (length(x) < 2) return(NA) + + if (all(c(is.null(smooth_span), is.numeric(idx)))) { + # Numeric index + smooth_span <- 0.75 + } + + if (all(c(!is.null(smooth_period), is.null(smooth_span)))) { + # smooth_period = some value, and smooth span is NULL + + if (tolower(smooth_period) == "auto") { + smooth_period <- ceiling(length(idx) * 0.75) + } + + smooth_period <- timetk::tk_get_trend( + idx = idx, + period = smooth_period, + message = smooth_message + ) + + smooth_span <- NULL + } else { + # smooth span overrides smooth period + smooth_period <- NULL + smooth_span <- as.numeric(smooth_span) + # if (smooth_message) message(stringr::str_glue()) + } + + ret <- timetk::smooth_vec( + x = x, + period = smooth_period, + span = smooth_span, + degree = smooth_degree) + + return(ret) +} + +plot_timeseries_td <- function(plot_data, date_var, value_var, group_var, colour_var, data_set_info, show_trend = TRUE, scales = "fixed", date_format = "%Y-%b") { + + if (show_trend == TRUE) { + plot_data <- plot_data %>% + group_by({{group_var}}) %>% + mutate(smooth_value = auto_smooth( + idx = {{date_var}}, + x = {{value_var}}, + smooth_period = "auto", + smooth_span = NULL, + smooth_degree = 2, + smooth_message = FALSE) + ) %>% + dplyr::ungroup() + } + + p <- plot_data %>% + ggplot2::ggplot(mapping = aes(x = {{date_var}}, y = {{value_var}}, colour = {{colour_var}})) + + if (show_trend == TRUE) { + p <- p + + ggplot2::geom_line( + ggplot2::aes(y = smooth_value), + color = "dark grey", + size = 1, + alpha = 0.8) + } + + p <- p + + geom_line() + + facet_wrap(vars({{group_var}}), ncol = 1, scales = scales) + + scale_color_manual(breaks = pull(data_set_info, {{group_var}}), + values = data_set_info$colours) + + ggthemes::theme_gdocs() + + theme(legend.position = "none") + + scale_x_date(date_labels = date_format) + + return(p) +} + +# https://stackoverflow.com/questions/3245862/format-numbers-to-significant-figures-nicely-in-r +sigfig <- function(vec, n=3){ + ### function to round values to N significant digits + # input: vec vector of numeric + # n integer is the required sigfig + # output: outvec vector of numeric rounded to N sigfig + + formatC(signif(vec,digits = n), digits = n,format = "fg", flag = "#") + +} # end of function sigfig + +get_sentiment_colourmap_for_value <- function(sentiment_value) { + get_sentiment_colourmap() %>% + filter(sentiment_value >= lower) %>% + filter(sentiment_value < upper) +} + +get_sentiment_colourmap <- function() { + # order is used in the data wrangling functions to ensure that colours + # show up in correct order in chart + colour_map <- tibble(colour = c("#C00000", "#C47070", "#C4BDBD", "#C4BDBD", "#7287FF", "#0027FF"), + legend_text_colour = c("#101010", "#101010", "#101010", "#101010", "#101010", "#C4BDBD"), + name = c("very_negative", "negative", "neutral_negative", "neutral_positive", "positive", "very_positive"), + label = c("Very Negative", "Negative", "Neutral", "Neutral", "Positive", "Very Positive"), + lower = c(-100, -0.3, -0.1, 0, 0.1, 0.3), # Inclusive + upper = c(-0.3, -0.1, 0, 0.1, 0.3, 100), # Exclusive + order = c(1,2,3,6,5,4)) + + return(colour_map) +} + +# Note that this aggregation includes the from value and excludes the to value for each range. +# https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-range-aggregation.html +build_likert_bucket <- function(name, lower, upper) { + filter_str = glue::glue('{ "key": "<{name}>", "from": <{lower}>, "to": <{upper}> }', + .open = "<{", .close = "}>") + +} + +build_likert_bins <- function(mapping) { + bucket_str = mapping %>% + group_by(name) %>% + mutate(bucket_string = build_likert_bucket(name, lower, upper)) %>% + pull(bucket_string) %>% paste0(collapse = ",\n") +} + +plot_likert <- function(plot_data, colour_map, x_var, fractions_var, fill_name, group_var = NULL, hover_label) { + p <- plot_data %>% + ggplot2::ggplot(mapping = aes(x = {{x_var}}, + y = {{fractions_var}} * 100, + fill = {{fill_name}}, + text = {{hover_label}})) + + ggplot2::geom_bar(stat = "identity", + position = "stack") + + scale_fill_manual(breaks = colour_map$name, + values = colour_map$colour, + labels = colour_map$label) + + ggplot2::geom_hline(yintercept = 0, colour = "white") + + ggthemes::theme_few() + + theme(legend.position="none") + + xlab("") + ylab("Proportion of Matching Documents (%)") + + if(!is.null(group_var)) { + p <- p + ggplot2::facet_wrap(group_var, ncol = 1) + }else { + p <- p + ggplot2::coord_flip() + + ylim(-100,100) # set y limits for facetted plots to maximize space + } + + return(p) +} + +wrangle_likert_data <- function(input_data, colour_map, data_set_info) { + + if(!"Date" %in% names(input_data)) input_data$Date = "dummy" # dummy date to make grouping work for both likert plot types + + out <- input_data %>% + # the following line redistributes the two neutral categories equally among neutral- positive and negative, such that the bar is centred on zero + mutate(sentiment.buckets.neutral_negative.doc_count = (sentiment.buckets.neutral_negative.doc_count + sentiment.buckets.neutral_positive.doc_count) / 2, + sentiment.buckets.neutral_positive.doc_count = sentiment.buckets.neutral_negative.doc_count) %>% + as_tibble() %>% + tidyr::pivot_longer(starts_with("sentiment.buckets."), + names_to = "sentiment_bucket", + names_prefix = "sentiment.buckets.", + values_to = "Count") %>% + mutate(sentiment_bucket = str_remove(sentiment_bucket, ".doc_count")) %>% + group_by(index_name, Date) %>% + mutate(total = sum(Count), + frac = Count / total, + frac_mod = ifelse(str_detect(sentiment_bucket, "negative"), + yes = -1 * frac, + no = frac)) %>% + ungroup() %>% + mutate(Sentiment = factor(sentiment_bucket, + levels = colour_map$name)) %>% # turn into factor to make sure plot in correct order + left_join(select(colour_map, sentiment_bucket = name, sentiment_label = label), by = "sentiment_bucket") %>% + left_join(select(data_set_info, index_name, display_name), by = "index_name") %>% # to get display name + group_by(display_name, sentiment_label, Date) %>% + mutate(display_count = sum(Count), + display_frac = display_count / total) %>% # this is a hack to aggregate content for the labels - there are two neutral categories, that we want to combine for the labels + ungroup() + + if(!is.Date(out$Date)) out$Date = NULL + + return(out) +} + +sentiment_legend <- function() { + + colour_mapping <- get_sentiment_colourmap() %>% + filter(name != "neutral_negative") + + colour_tags <- colour_mapping %>% + group_by(label) %>% + mutate(tag_list = list(tags$td( + style = paste0("background-color: ", colour, "; padding: 15px; font-size: 20px; color: ", legend_text_colour, ";"), + label) + )) %>% + ungroup + + tags$table( + style = 'font-family: "Open Sans"', + tags$tr( + colour_tags$tag_list + ) + ) +} + +do_health_check <- function(config) { + status = "healthy" + info = "" + + aliases = get_aliases() + es_connection = connect_ES() + if (length(aliases) == 0) { + status = "error" + info = "no aliases found" + } + + return(list( + health_status = status, + health_info = info, + performance = "", + performance_info = "" + )) +} + +# Adds health check API endpoint +add_health_check <- function(config, route = "/__health-check__") { + httpHandler = function(req) { + info = do_health_check(config) + + return(list(status = 200L, + headers = list('Content-Type' = 'application/json'), + body = jsonlite::toJSON(info, + pretty = TRUE, + auto_unbox = TRUE))) + } + + shiny:::handlerManager$addHandler(handler = shiny:::routeHandler(route, httpHandler), + key = "unique_id_for_health_check") +} + +# Runs expr after all inputs load the first time (i.e. after all the renderUI calls) +# From: https://github.com/rstudio/shiny/issues/3348#issuecomment-810727477 +execute_at_first_input <- function(expr, session = getDefaultReactiveDomain()) { + observeEvent(once = TRUE, reactiveValuesToList(session$input), { + force(expr) + }, ignoreInit = TRUE) +} \ No newline at end of file diff --git a/data_download_module.R b/data_download_module.R new file mode 100644 index 0000000..5760f75 --- /dev/null +++ b/data_download_module.R @@ -0,0 +1,159 @@ +dataDownloadUI <- function(id){ + ns <- NS(id) + + tagList( + fluidRow( + column( + " + Click the button below to download your search results. After + clicking the button, your search results will be + gathered and prepared for download. You can continue using + the application during this process, and you will be notified + when your download is ready. + ", + width = 6 + ) + ), + br(), br(), + actionButton(ns("download_search_data"), label = "Prepare Download"), + textOutput(ns("data_loading_message")) + ) +} + +dataDownload <- function(input, output, session, + query_info, data_set_info) { + shinyjs::hide("data_loading_message") # initially hide data loading message (will show when user is downloading dataset) + + observeEvent(input$download_search_data, { + req(query_info) + req(query_info()$num_hits > 0) + + search_data(NULL) + + # hide data download button while data is prepared, so that user cannot download multiple at same time, replace with message + shinyjs::hide("download_search_data") + #shinyjs::hide("ttt_input") + shinyjs::show("data_loading_message") + # The async functionality here is "intra-session", and is not officially supported yet. + # However, the following issue has a good discussion and solution that we use here (see where future call is piped): + # https://github.com/rstudio/promises/issues/23 + + # Note - all variables used within the future below must be "taken out" of their reactive contexts here, + # b/c inside the future is not considered a reactive context and therefore it throws an error. + es_connection <- query_info()$conn + sources <- query_info()$index + query_text <- query_info()$query + search_fields <- query_info()$search_fields + min_date <- query_info()$min_date + max_date <- query_info()$max_date + data_set_info <- data_set_info() + ttt_input <- FALSE #input$ttt_input + query_info <- query_info() + + future({ + # TODO: Should we remove refs to Text Themer? + if (ttt_input == TRUE) { + # the Text Theming Tool output is currently disabled, since the results are not looking great. + data_return = query_text_depot(query_info, + source_json = source_body(c("id", + "text", + "date")), + highlights_json = NULL, + from = 0, + size = 500) %>% + parse_hits(data_set_info) %>% + select(response_id = id, + date, + text) %>% + mutate(text = gsub("^((\\w+\\W+){100}\\w+).*$","\\1",text)) + } else { + data_return = query_text_depot(query_info, + source_json = source_body(c("id", + "num_chars", + "num_sentences", + "sentiment_polarity", + "sentiment_subjectivity", + "date", + "source_url", + "source_title", + "parent_source_url", + "parent_source_title")), + highlights_json = highlights_body(c("text", + "source_title", + "parent_source_title")), + from = 0, + size = 10000) %>% + parse_hits(data_set_info) %>% + group_by(`id`) %>% + mutate(highlights = collapse_highlights(highlight.source_title, + highlight.parent_source_title, + highlight.text, + collapse = " ... ", + rm_em = TRUE)) %>% + ungroup() %>% + data.table() %>% + select(Type = display_name, + Date = date, + `Source Title` = source_title, + `Source URL` = source_url, + `Parent Source Title` = parent_source_title, + `Parent Source URL` = parent_source_url, + `Document Sentiment` = sentiment, + `Document Sentiment Subjectivity` = sentiment_subj, + `Search Similarity` = similarity, + `Matching Text` = highlights) + } + data_return + }) %...>% + search_data() %...!% + (function(e){ + search_data(NULL) + warning(e) + }) + NULL + }) + + search_data <- reactiveVal() + + output$data_loading_message <- renderText("Preparing your data - this may take a few minutes. You can not download more than one file at a time...") + + # This observe event is needed to get around some peculiar behaviour + # of downloadHandler that stops control being given back to the application + # during the computation of the future (despite the considerations in + # https://github.com/rstudio/promises/issues/23) We are effectively + # preventing the the filehandler from being called until search_data is + # finished computing. + observeEvent({input$download_search_data; search_data()}, { + req(nrow(search_data()) > 0) + output$download_search <- downloadHandler(contentType = "text/csv", + filename = function() { + paste0(query_info()$query, '_search_responses.csv') + }, + content = function(file) { + shinyjs::hide("download_search") # stop data download being attempted twice once modal is shown + + # reset data download button + shinyjs::hide("data_loading_message") + shinyjs::show("download_search_data") + #shinyjs::show("ttt_input") + + search_data() %>% + write.csv(file = file, + row.names = FALSE) + search_data(NULL) + } + ) + + modalDialog( + title = "Download Data", + size = "l", + downloadButton(session$ns("download_search"), label = 'Download Data'), + easyClose = TRUE + ) %>% + showModal() + # just to catch all, when modal closes, reset data download button again + shinyjs::hide("data_loading_message") + shinyjs::show("download_search_data") + #shinyjs::show("ttt_input") + }) +} \ No newline at end of file diff --git a/elasticsearch/.gitignore b/elasticsearch/.gitignore new file mode 100644 index 0000000..19d5ca2 --- /dev/null +++ b/elasticsearch/.gitignore @@ -0,0 +1 @@ +certificates/*.crt diff --git a/elasticsearch/README.md b/elasticsearch/README.md new file mode 100644 index 0000000..f5b72f2 --- /dev/null +++ b/elasticsearch/README.md @@ -0,0 +1,111 @@ +## SSL Certificates + +If your ElasticSearch server is behind HTTPS and you require certificates, then add the `.crt` files in the `elasticsearch/certificates` directory, and they will be installed in the docker image. + +## Data Format + +Each data source should be stored in its own Elastic Search index. The index must be defined as the following (note that the `*_vector` fields are optional, and only used in the embedding search). Use the following mappings to [create](https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-create-index.html#mappings) your indexes: + +``` +{ + "properties": { + "date": { + "type": "date" + }, + "id": { + "type": "long" + }, + "neighbourhoods": { + "type": "nested", + "properties": { + "count": { + "type": "long" + }, + "name": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "ratio": { + "type": "float" + } + } + }, + "parent_source_title_vector": { + "type": "dense_vector", + "dims": 256 + }, + "source_title_vector": { + "type": "dense_vector", + "dims": 256 + }, + "text_vector": { + "type": "dense_vector", + "dims": 256 + }, + "num_chars": { + "type": "long" + }, + "num_sentences": { + "type": "long" + }, + "parent_source_title": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "parent_source_url": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "sentiment_polarity": { + "type": "float" + }, + "sentiment_subjectivity": { + "type": "float" + }, + "source_title": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "source_url": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + }, + "text": { + "type": "text", + "fields": { + "keyword": { + "type": "keyword", + "ignore_above": 256 + } + } + } + } +} +``` + +Then, add your indexes/aliases to the `default_index_aliases` parameter in `.configs`. diff --git a/elasticsearch/certificates/.gitkeep b/elasticsearch/certificates/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/global.R b/global.R new file mode 100644 index 0000000..92deb15 --- /dev/null +++ b/global.R @@ -0,0 +1,38 @@ +library(data.table, warn.conflicts = FALSE) +library(dplyr, warn.conflicts = FALSE) +library(DT, warn.conflicts = FALSE) +library(elastic, warn.conflicts = FALSE) +library(future) +library(future.callr) +library(ggplot2) +library(leaflet) +library(lubridate, warn.conflicts = FALSE) +library(plotly, warn.conflicts = FALSE) +library(promises) +library(rgdal, warn.conflicts = FALSE) +library(shiny) +library(shinydashboard, warn.conflicts = FALSE) +library(shinyjs, warn.conflicts = FALSE) + +options(dplyr.summarise.inform = FALSE) + +plan(callr) + +source('dashboard_utils.R') +source('sentiment_timeline_module.R') +source('sentiment_timeline_likert_module.R') +source('sentiment_likert_module.R') +source('sentiment_module.R') +source('volume_timeline_module.R') +source('sentiment_histogram_module.R') +source('neighbourhood_map_module.R') +source('sentiment_neighbourhoods_module.R') +source('search_results_table_module.R') +source('search_bar_module.R') +source('data_download_module.R') + +RESULTS_PER_PAGE = as.numeric(get_configs()$results_per_page) +N_CHAR_NO_MATCH = as.numeric(get_configs()$n_char_no_match) +N_CHAR_HIGHLIGHTS = as.numeric(get_configs()$n_char_highlights) + +add_health_check(config) diff --git a/neighbourhood_map_module.R b/neighbourhood_map_module.R new file mode 100644 index 0000000..8531b7e --- /dev/null +++ b/neighbourhood_map_module.R @@ -0,0 +1,81 @@ +neighbourhoodMapUI <- function(id){ + ns <- NS(id) + + fluidPage( + tagList( + fluidRow( + style = "max-width: 1200px;margin-top:-20px;", + column( + width = 2, + shinyWidgets::pickerInput( + inputId = ns("select_map"), + inline = TRUE, + label = "", + choices = c("Count", + "Sentiment"), + selected = "Count" + ) + ), + column( + width = 5, + br(), + tabsetPanel( + id = ns("map_descriptions"), + type = "hidden", + tabPanelBody(ns("map"), shiny::uiOutput(ns("description_ui"))) + ) + ), + column( + width = 1, + br(), + tabsetPanel( + id = ns("map_exclude"), + type = "hidden", + tabPanelBody(ns("map"), shiny::uiOutput(ns("exclude_ui"))) + ) + ) + ), + br(), br(), + tabsetPanel( + id = ns("map_tabs"), + type = "hidden", + tabPanelBody(ns("map"), shiny::uiOutput(ns("tabs_ui"))) + ) + ) + ) +} + +neighbourhoodMap <- function(input, output, session, + query_info) { + + observeEvent(input$select_map, { + updateTabsetPanel(session, "map_tabs", selected = input$select_map) + }) + + observeEvent(input$select_map, { + updateTabsetPanel(session, "map_descriptions", selected = input$select_map) + }) + + observeEvent(input$select_map, { + shiny::callModule(module = mapPlot, + id = "map_plot", + query_info = query_info, + selected = input$select_map) + }) + + output$description_ui = renderUI({ + req(input$select_map) + mapPlotUI(session$ns("map_plot"), input$select_map)[["description"]] + }) + + output$exclude_ui = renderUI({ + req(input$select_map) + mapPlotUI(session$ns("map_plot"), input$select_map)[["exclude"]] + }) + + output$tabs_ui = renderUI({ + req(input$select_map) + mapPlotUI(session$ns("map_plot"), input$select_map)[["plot"]] + }) + +} diff --git a/run_text_depot_dashboard.R b/run_text_depot_dashboard.R new file mode 100644 index 0000000..c8ff604 --- /dev/null +++ b/run_text_depot_dashboard.R @@ -0,0 +1 @@ +shiny::runApp(host = '0.0.0.0', port = 8068) diff --git a/search_bar_module.R b/search_bar_module.R new file mode 100644 index 0000000..0348d59 --- /dev/null +++ b/search_bar_module.R @@ -0,0 +1,211 @@ +searchBarUI <- function(id){ + ns <- NS(id) + + tagList( + fluidRow( + column( + fluidRow( + column( + uiOutput(ns("query_text_ui")), + width = 9 + ), + column( + # Cant use "dropdownButton" because of https://github.com/dreamRs/shinyWidgets/issues/38 + shinyWidgets::dropdown( + a("Click here for documentation on Advanced Queries", + href = 'https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-simple-query-string-query.html#simple-query-string-syntax', + target = "_blank", + style = "color: blue; font-weight: bold"), + + br(), br(), + + shinyWidgets::checkboxGroupButtons(ns("search_fields"), + "Search Fields", + choices = search_fields(), + status = 'info', + selected = search_fields(), + checkIcon = list(yes = icon("ok", lib = "glyphicon"), + no = icon("remove", lib = "glyphicon"))), + uiOutput(ns("date_range_ui"), style = "padding-left: 10px"), + uiOutput(ns("data_source_ui")), + + # Only turn on AI search if we've specified an API Host: + if (!is.null(get_configs()$embedding_api_host)) { + tagList( + shinyWidgets::switchInput(ns("use_embeddings"), + label = "🤖 AI Search 🤖", + labelWidth = "110px", + value = FALSE, + inline = TRUE), + span("BETA", class = "label label-info") # Bootstrap label + ) + }, + + circle = FALSE, + status = "primary", + icon = icon("gear"), + width = "600px", + tooltip = shinyWidgets::tooltipOptions(title = "Advanced Options") + ), + width = 3 + ), + style = " + background-color: #D3EDF0; + margin: 0 0 0 0; + padding: 20px 0 5px 0; + border-radius: 10px; + vertical-align = center; + min-height: 75px; // Should be 50 + padding + " + ), + width = 6 + ) + ) + ) +} + +searchBar <- function(input, output, session, + es_connection, + data_set_info, + query_param) { + output$query_text_ui <- renderUI({ + shinyWidgets::searchInput(inputId = session$ns("query_text"), + label = NULL, + value = query_param, + placeholder = "Type Search Here", + btnSearch = icon("search"), + btnReset = icon("remove"), + width = "100%") + }) + + # Set the focus on the search bar, once input is loaded + execute_at_first_input(runjs(sprintf( + 'document.getElementById("%s").focus();', session$ns("query_text_text") + ))) + + output$data_source_ui <- renderUI({ + choices = data_set_info()$alias_name + names(choices) = paste0(data_set_info()$display_name, " (", data_set_info()$db_size, " docs)") + shinyWidgets::pickerInput(session$ns("data_sources"), + label = "Data Sources", + choices = choices, + selected = choices, + multiple = TRUE, + options = shinyWidgets::pickerOptions( + actionsBox = TRUE, + title = "Please Select a Data Source" + )) + }) + + # Setting initial value so that we dont have to load the UI + # element before setting the date ranges: + date_stats <- reactive({ + stats_for_field(es_connection, data_set_info()$alias_name, "date") + }) + + selected_date_range <- reactive({ + range = c(as.Date(date_stats()$min), as.Date(date_stats()$max)) + + # Check that UI element exists and that valid dates are there: + if (!is.null(input$date_range)) { + if ((!is.na(input$date_range[1])) & + (!is.na(input$date_range[2]))) { + range = input$date_range + } + } + + return(range) + }) + + output$date_range_ui <- renderUI({ + min = as.Date(date_stats()$min) + max = as.Date(date_stats()$max) + dateRangeInput( + inputId = session$ns("date_range"), + label = "Date Range", + start = min, + end = max) + }) + + selected_data_sources <- reactive({ + data_sets <- data_set_info()$alias_name + # On page load, input$data_sources is null because it's buried in the dropdown options + if (!is.null(input$data_sources)) { + data_sets = input$data_sources + } + return(data_sets) + }) + + cleaned_query <- reactive({ + req(input$query_text) + query = input$query_text + query = gsub('"', '\"', query) + query = gsub("'", "\"", query) + + query + }) + + aggregations <- reactiveVal(c()) + + use_embeddings_setting <- reactive({ + ifelse(is.null(input$use_embeddings), FALSE, input$use_embeddings) + }) + + # folowing is for computing num_hits. This is used in this module, but also in other modules. + # Get aggregates for ALL results when search is clicked: + observe({ + aggregations(c()) + req(nchar(cleaned_query()) > 0) + req(length(selected_data_sources()) > 0) + req(length(input$search_fields) > 0) + + aggregations = query_text_depot(query_info = list(conn = es_connection, + query = cleaned_query(), + index = selected_data_sources(), + search_fields = input$search_fields, + min_score = 0, + min_date = selected_date_range()[1], + max_date = selected_date_range()[2], + use_embeddings = use_embeddings_setting()), + aggregates_json = statsPerIndexQuery()) + + aggregations = parse_aggregates(aggregations) + + if (is.character(aggregations)) { + # There was an error + shinyjs::alert(aggregations) + aggregations(c()) + } else { + parsed <- parse_aggregates(aggregations) + + aggregations(aggregations) + } + + }) + + query_info <- reactive({ + list(conn = es_connection, + query = cleaned_query(), + index = selected_data_sources(), + search_fields = input$search_fields, + min_score = 0, + min_date = selected_date_range()[1], + max_date = selected_date_range()[2], + num_hits = sum(aggregations()$counts_by_index$doc_count), + use_embeddings = use_embeddings_setting()) + }) + + return(query_info) +} + +statsPerIndexQuery <- function(){ + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + } + } + } + ' +} diff --git a/search_results_table_module.R b/search_results_table_module.R new file mode 100644 index 0000000..32b486c --- /dev/null +++ b/search_results_table_module.R @@ -0,0 +1,271 @@ +searchResultsTableUI <- function(id, include_table_options = FALSE){ + ns <- NS(id) + + tagList( + div( + id = ns("results_wrapper"), + class = "shinyjs-hide", # Hide the div initially + if (include_table_options) { + shinyWidgets::dropdown( + uiOutput(ns("column_picker")), + label = "table options", + circle = FALSE, + status = "primary", + icon = icon("gear"), + size = "xs", + inputId = ns("table_options")) + } + ), + DT::dataTableOutput(ns("results")) %>% shinycssloaders::withSpinner() + ) +} + +searchResultsTable <- function(input, output, session, + query_info, + results_per_page, + data_set_info, + n_char_no_match, + n_char_highlights) { + hits <- reactiveVal(c()) + aggregations <- reactiveVal(c()) + rowstart <- reactiveVal(c()) + default_columns = c("Matching Text", "Type", "Date") + column_mapping = c( + 'Matching Text' = 'matches', + 'Type' = 'display_name', + 'Date' = 'date', + 'Characters' = 'num_chars', + 'Sentences' = 'num_sentences', + 'Sentiment' = 'sentiment', + 'Similarity' = 'similarity' + ) + + # When search is clicked, get the first page of hits (plus 1 to enable paging buttons). + observeEvent(query_info(), { + query_info <- query_info() + hits(c()) # Remove previous hits + rowstart(0) # Reset place in datatable output + + if ( (nchar(query_info$query) == 0) | + (length(query_info$index) == 0) | + (length(query_info$search_fields) == 0) | + (query_info$num_hits == 0)) { + shinyjs::hideElement(session$ns("results_wrapper"), asis = TRUE) + } else { + shinyjs::showElement(session$ns("results_wrapper"), asis = TRUE) + + req(query_info) + + result <- query_text_depot(query_info, + source_json = source_body(c("id", + "num_chars", + "num_sentences", + "sentiment_polarity", + "sentiment_subjectivity", + "date", + "source_url", + "source_title", + "parent_source_url", + "parent_source_title")), + highlights_json = highlights_body(highlight_cols = c("text", + "source_title", + "parent_source_title"), + n_char_no_match = n_char_no_match, + n_char_highlights = n_char_highlights), + from = 0, + size = (results_per_page + 1)) + + if (is.character(result)) { + # There was an error + shinyjs::alert(result) + } else { + hits_df = parse_hits(result, data_set_info()) + hits(hits_df) + } + } + }, ignoreNULL = FALSE) + + # This is triggered when the user clicks on "Next" to go to the next page. It is also + # triggered again when the hits() data is updated. results_rows_current contains the indices + # of the rows that are being displayed on the current page. + observeEvent(input$results_rows_current, { + req(query_info) + query_info <- query_info() + req(nrow(hits()) > results_per_page) # Return if we have a small number of results + req(input$results_rows_current) + isolate(total_rows <- query_info()$num_hits) # isolate because sometimes aggregates is done before hits, which causes error in if below + + # for first page + # length(input$results_rows_current) = 30, nrow(hits()) = 31, results_per_page = 30 >> if statement FALSE + # for second page + # length(input$results_rows_current) = 1, nrow(hits()) = 31, results_per_page = 30 >> if statement TRUE, initially + # after new hits are pulled, second page: + # length(input$results_rows_current) = 30, nrow(hits()) = 61, results_per_page = 30 >> if statement FALSE + # + # If the current page is not a full page and we have not loaded all data, then load more data! + if ( (length(input$results_rows_current) < results_per_page) & + (nrow(hits()) < total_rows)) { + rowstart(input$results_rows_current[1] - 1) # results_rows_current starts indexing at 1, rowstart starts at 0. + + next_chunk = query_text_depot(query_info(), + source_json = source_body(c("id", + "num_chars", + "num_sentences", + "sentiment_polarity", + "sentiment_subjectivity", + "date", + "source_url", + "source_title", + "parent_source_url", + "parent_source_title")), + highlights_json = highlights_body(highlight_cols = c("text", + "source_title", + "parent_source_title"), + n_char_no_match = n_char_no_match, + n_char_highlights = n_char_highlights), + from = nrow(hits()), + size = results_per_page) + + if (is.character(next_chunk)) { + # There was an error + shinyjs::alert(next_chunk) + hits(c()) + } else { + next_chunk_hits = parse_hits(next_chunk, data_set_info()) + hits(bind_rows(hits(), next_chunk_hits)) + } + } + }) + + formatted_hits <- reactive({ + hits = hits() + req(hits) + req(nrow(hits) > 0) + + hits %>% + arrange(desc(similarity)) + }) + + output$column_picker <- renderUI({ + shinyWidgets::pickerInput(inputId = session$ns("show_columns"), + label = "Show Columns", + choices = names(column_mapping), + selected = default_columns, + multiple = TRUE) + }) + + # Need a reactive function here, because if the "table options" has not + # been clicked yet, then input$show_columns will be NULL + chosen_cols <- reactive({ + if (!is.null(input$show_columns)) { + return(input$show_columns) + } else { + return(default_columns) + } + }) + + # This is regenerated when hits() is updated. rowstart() keeps track of which + # row (and therefore page) we are now on. + output$results <- DT::renderDataTable({ + isolate(row_start <- rowstart()) + total_rows = query_info()$num_hits + req(formatted_hits) + req(data_set_info) + + results_table <- formatted_hits() %>% + data.table::data.table() %>% + dplyr::select(all_of(column_mapping)) %>% # Rename columns to human-readable + select(all_of(chosen_cols())) %>% + DT::datatable(rownames = FALSE, + selection = 'single', + escape = FALSE, + options = list( + ordering = FALSE, + pagingType = 'simple', + pageLength = results_per_page, + displayStart = row_start, + dom = 'Btip', + language = list( + info = paste0('Showing _START_ to _END_ of ', total_rows, ' entries')# (loaded _TOTAL_) to show total in data frame + ), + headerCallback = JS(" + function(thead, data, start, end, display) { + $(thead).remove(); + } + ") + ) + ) + + if ("Type" %in% chosen_cols()) { + results_table = DT::formatStyle(results_table, + "Type", + color = DT::styleEqual(data_set_info()$display_name, data_set_info()$colours), + "white-space" = "nowrap") + } + + if ("Date" %in% chosen_cols()) { + results_table = DT::formatStyle(results_table, + "Date", + "white-space" = "nowrap") + } + + results_table + }) + + # Triggered when a row is clicked + observeEvent(input$results_rows_selected, { + row = formatted_hits()[input$results_rows_selected, ] + + color_row = get_sentiment_colourmap_for_value(row$sentiment) + doc = get_document_by_id(query_info()$conn, row$id, row$alias_name) + matching_text = collapse_highlights(row$highlight.source_title, + row$highlight.parent_source_title, + row$highlight.text, + collapse = " ...
") + neighbourhoods = ifelse(length(doc$neighbourhoods) > 0, paste0(doc$neighbourhoods, collapse = ", "), "None") + + display_text = doc$text %>% + gsub("\n\\s*\n\\s*\n\\s*", "\n\n\n" ,.) %>% # Replace 3+ newlines with just 3 newlines + str_replace_all("\t", "    ") %>% + str_replace_all(" ", " ") %>% + str_replace_all("\n", "
") %>% + HTML() + + shinyWidgets::sendSweetAlert( + session = session, + title = "Matching Document Information", + text = tagList( + strong(sprintf('Query: "%s"', query_info()$query), + style = "font-size: 14px; padding: 5px"), + div( + style = "background-color: #D3EDF0; border-radius: 10px; padding: 5px; margin: 10px; font-size: 14px", + tags$u(format_title(row$parent_source_title, row$parent_source_url, add_link = TRUE), style = "font-size: 16px;"), br(), + span(format_title(row$source_title, row$source_url, add_link = TRUE), style = "font-size: 16px;"), br(), + br(), + strong("Date:"), row$date, br(), + strong("Data Set:"), row$display_name, br(), + HTML(sprintf("Sentiment: %s (%s)", color_row$colour, color_row$label, row$sentiment)), br(), + strong("Detected Neighbourhoods:"), neighbourhoods, br(), + br(), + strong(sprintf("Full Document Text (%s Characters, %s Sentences)", row$num_chars, row$num_sentences)), + div( + style = " + margin: auto; + text-align: left; + font-size: 14px; + background-color: #f0f0f0; + overflow: scroll; + height: 300px; + width: 90%; + border: 4px double #e8cdc3; + ", + display_text + ) + ) + ), + width = 1000, + html = TRUE + ) + }) + +} diff --git a/sentiment_histogram_module.R b/sentiment_histogram_module.R new file mode 100644 index 0000000..6c566a8 --- /dev/null +++ b/sentiment_histogram_module.R @@ -0,0 +1,100 @@ +sentimentHistogramUI <- function(id){ + ns <- NS(id) + + tagList( + description = div( + " + Sentiment histogram for matching documents. This chart shows how many + documents have each sentiment value, per data set. Sentiment + above zero is positive (e.g. happy, positive, satisfied), + and below zero is negative (e.g. angry, disappointed, sad). Note + that the sentiment is predicted using AI, and may be incorrect + in some cases (e.g. when sarcasm is used). + " + ), + plot = div( + style = "max-width: 1200px;", + shiny::uiOutput(ns("sentiment_histogram_ui")) %>% + shinycssloaders::withSpinner() + ) + ) +} + +sentimentHistogram <- function(input, output, session, + data_set_info, + query_info) { + + plot_data <- reactive({ + req(query_info()$num_hits > 0) + + aggregations = query_text_depot(query_info = query_info(), + aggregates_json = volumeSentimentQuery()) + aggregations = parse_aggregates(es_results = aggregations) + + plot_hits = aggregations$sentiment.buckets %>% + transmute(Sentiment = key, Count = doc_count, index_name = index) %>% + left_join(select(data_set_info(), index_name, display_name), by = "index_name") %>% # to get display name + arrange(display_name) + + return(plot_hits) + + }) + + output$sentiment_histogram <- plotly::renderPlotly({ + + req(plot_data) + + p <- plot_data() %>% + ggplot2::ggplot(mapping = aes(x = Sentiment, y = Count, fill = display_name)) + + facet_wrap("display_name", scales = "free_y", ncol = 1) + + # hack: blank dummy line with text aesthetic to define the tooltip text that + # is shown in the plotly plot: + # https://stackoverflow.com/questions/44569551/date-format-in-hover-for-ggplot2-and-plotly + suppressWarnings( + geom_col(aes(text = paste0("", display_name, '\n', + "Sentiment: ", sigfig(Sentiment, n = 2), '\n', + "Document Count: ", Count))) + ) + + scale_fill_manual(breaks = data_set_info()$display_name, + values = data_set_info()$colours) + ggthemes::theme_gdocs() + theme(legend.position = "none") + + p1 <- plotly::ggplotly(p, tooltip = c("text"), dynamicTicks = TRUE) %>% + layout(margin = list(l = 75, r = 75)) + + for (x in names(p1$x$layout)[grepl("yaxis", names(p1$x$layout))]) { + p1[["x"]][["layout"]][[x]][["fixedrange"]] <- TRUE + } + + return(p1) + + }) + + output$sentiment_histogram_ui <- renderUI({ + n_facets <- n_distinct(plot_data()$display_name) + plotly::plotlyOutput(session$ns("sentiment_histogram"), height = 200 + (100 * n_facets)) + }) +} + +volumeSentimentQuery <- function() { + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + }, + "aggs" : { + "sentiment" : { + "histogram" : { + "field" : "sentiment_polarity", + "interval" : 0.05, + "extended_bounds" : { + "min" : -1, + "max" : 1 + } + } + } + } + } + } + ' +} diff --git a/sentiment_likert_module.R b/sentiment_likert_module.R new file mode 100644 index 0000000..15fef02 --- /dev/null +++ b/sentiment_likert_module.R @@ -0,0 +1,108 @@ +sentimentLikertUI <- function(id){ + ns <- NS(id) + + tagList( + description = div( + "This chart uses a Likert scale to show the spread of sentiment for each dataset. Positive and Negative sentiment are indicated by blue and red respectively. The height of each bar indicates the percentage of responses that fell into the respective sentiment category." + ), + plot = div( + div( + style = "max-width: 1200px;", + shiny::uiOutput(ns("sentiment_likert_plot_ui")) %>% + shinycssloaders::withSpinner() + ), + br(),br(), + div( + align="center", + style = "max-width: 1200px;", + sentiment_legend() + ) + ) + ) +} + +sentimentLikert <- function(input, output, session, + data_set_info, + query_info) { + + plot_data_likert <- reactive({ + + req(query_info()$num_hits > 0) + + colour_map <- get_sentiment_colourmap() %>% + arrange(order) + + aggregations <- query_text_depot(query_info = query_info(), + aggregates_json = sentimentBucketsQuery(colour_map)) + + plotting_data <- aggregations$aggregations$group_by_index$buckets %>% + select(index_name = key, doc_count, ends_with(".doc_count")) %>% + wrangle_likert_data(colour_map = colour_map, data_set_info = data_set_info()) + + return(plotting_data) + }) + + output$sentiment_likert_plotly <- plotly::renderPlotly({ + + req(plot_data_likert) + + colour_map <- get_sentiment_colourmap() + + p <- plot_data_likert() %>% + mutate(hover_label = paste0("", display_name, '\n', + "Sentiment Category: ", sentiment_label, '\n', + "Document Count: ", display_count, '\n', + "Document Percent: ", sigfig(display_frac * 100, n = 2), '%', '\n')) %>% + plot_likert(colour_map = get_sentiment_colourmap(), + x_var = display_name, + fractions_var = frac_mod, + fill_name = Sentiment, + hover_label = hover_label) + + p <- p + scale_x_discrete(limits = rev) + + p1 <- plotly::ggplotly(p, tooltip = c("text")) %>% + layout(margin = list(l = 75, r = 75)) + + p1 + }) + + output$sentiment_colour_scale <- renderPlot({ + colour_map <- get_sentiment_colourmap() + sentimentLegendPlot(colour_mapping = colour_map) + }) + + output$sentiment_likert_plot_ui <- renderUI({ + n_facets <- n_distinct(plot_data_likert()$display_name) + plotly::plotlyOutput(session$ns("sentiment_likert_plotly")) + }) + +} + +sentimentBucketsQuery <- function(mapping) { + + bucket_str = build_likert_bins(mapping) + + glue::glue( + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + }, + "aggs" : { + "sentiment" : { + "range" : { + "field" : "sentiment_polarity", + "keyed" : true, + "ranges" : [ + <{bucket_str}> + ] + } + } + } + } + } + ', + .open = "<{", .close = "}>") +} diff --git a/sentiment_module.R b/sentiment_module.R new file mode 100644 index 0000000..3082955 --- /dev/null +++ b/sentiment_module.R @@ -0,0 +1,165 @@ + +sentimentUI <- function(id){ + ns <- NS(id) + + sentiment_timeline <- sentimentTimelineUI(ns("sentiment_timeline")) + + fluidPage( + tagList( + fluidRow( + style = "max-width: 1200px;", + column( + width = 3, + shinyWidgets::pickerInput( + inputId = ns("select_chart"), + inline = TRUE, + label = "", + choices = c("Average Sentiment over Time" = ns("pick_sentiment_timeline"), + "Distribution of Sentiment" = ns("pick_sentiment_histogram"), + "Sentiment Categories" = ns("pick_sentiment_likert"), + "Sentiment Categories over Time"=ns("pick_sentiment_timeline_likert")), + selected = "pick_sentiment_timeline" + ) + ), + column( + width = 9, + br(), + tabsetPanel( + id = ns("sentiment_descriptions"), + type = "hidden", + #tabPanelBody(ns("pick_sentiment_timeline"), sentimentTimelineUI(ns("sentiment_timeline"))[["description"]]), + tabPanelBody(ns("pick_sentiment_timeline"), sentiment_timeline[["description"]]), + tabPanelBody(ns("pick_sentiment_histogram"), sentimentHistogramUI(ns("sentiment_histogram"))[["description"]]), + tabPanelBody(ns("pick_sentiment_likert"), sentimentLikertUI(ns("sentiment_likert"))[["description"]]), + tabPanelBody(ns("pick_sentiment_timeline_likert"), sentimentTimelineLikertUI(ns("sentiment_timeline_likert"))[["description"]]) + ) + ) + ), + tabsetPanel( + id = ns("sentiment_tabs"), + type = "hidden", + tabPanelBody(ns("pick_sentiment_timeline"), sentimentTimelineUI(ns("sentiment_timeline"))[["plot"]]), + tabPanelBody(ns("pick_sentiment_histogram"), sentimentHistogramUI(ns("sentiment_histogram"))[["plot"]]), + tabPanelBody(ns("pick_sentiment_likert"), sentimentLikertUI(ns("sentiment_likert"))[["plot"]]), + tabPanelBody(ns("pick_sentiment_timeline_likert"), sentimentTimelineLikertUI(ns("sentiment_timeline_likert"))[["plot"]]) + ) + ) + ) +} + +sentiment <- function(input, output, session, + data_set_info, + query_info) { + + observeEvent(input$select_chart, { + updateTabsetPanel(session, "sentiment_tabs", selected = input$select_chart) + }) + + observeEvent(input$select_chart, { + updateTabsetPanel(session, "sentiment_descriptions", selected = input$select_chart) + }) + + shiny::callModule(module = sentimentTimeline, + id = "sentiment_timeline", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentimentTimelineLikert, + id = "sentiment_timeline_likert", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentimentHistogram, + id = "sentiment_histogram", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentimentLikert, + id = "sentiment_likert", + data_set_info = data_set_info, + query_info = query_info) + +} +sentimentUI <- function(id){ + ns <- NS(id) + + # different parts of the following UI elements used in 2 places below, so compute once here: + sentiment_timeline <- sentimentTimelineUI(ns("sentiment_timeline")) + sentiment_histogram <- sentimentHistogramUI(ns("sentiment_histogram")) + sentiment_likert <- sentimentLikertUI(ns("sentiment_likert")) + sentiment_likert_timeline <- sentimentTimelineLikertUI(ns("sentiment_timeline_likert")) + + fluidPage( + tagList( + fluidRow( + style = "max-width: 1200px;", + column( + width = 3, + shinyWidgets::pickerInput( + inputId = ns("select_chart"), + inline = TRUE, + label = "", + choices = c("Average Sentiment over Time" = ns("pick_sentiment_timeline"), + "Distribution of Sentiment" = ns("pick_sentiment_histogram"), + "Sentiment Categories" = ns("pick_sentiment_likert"), + "Sentiment Categories over Time"=ns("pick_sentiment_timeline_likert")), + selected = "pick_sentiment_timeline" + ) + ), + column( + width = 9, + br(), + tabsetPanel( + id = ns("sentiment_descriptions"), + type = "hidden", + tabPanelBody(ns("pick_sentiment_timeline"), sentiment_timeline[["description"]]), + tabPanelBody(ns("pick_sentiment_histogram"), sentiment_histogram[["description"]]), + tabPanelBody(ns("pick_sentiment_likert"), sentiment_likert[["description"]]), + tabPanelBody(ns("pick_sentiment_timeline_likert"), sentiment_likert_timeline[["description"]]) + ) + ) + ), + tabsetPanel( + id = ns("sentiment_tabs"), + type = "hidden", + tabPanelBody(ns("pick_sentiment_timeline"), sentiment_timeline[["plot"]]), + tabPanelBody(ns("pick_sentiment_histogram"), sentiment_histogram[["plot"]]), + tabPanelBody(ns("pick_sentiment_likert"), sentiment_likert[["plot"]]), + tabPanelBody(ns("pick_sentiment_timeline_likert"), sentiment_likert_timeline[["plot"]]) + ) + )) +} + +sentiment <- function(input, output, session, + data_set_info, + query_info) { + + observeEvent(input$select_chart, { + updateTabsetPanel(session, "sentiment_tabs", selected = input$select_chart) + }) + + observeEvent(input$select_chart, { + updateTabsetPanel(session, "sentiment_descriptions", selected = input$select_chart) + }) + + shiny::callModule(module = sentimentTimeline, + id = "sentiment_timeline", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentimentTimelineLikert, + id = "sentiment_timeline_likert", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentimentHistogram, + id = "sentiment_histogram", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentimentLikert, + id = "sentiment_likert", + data_set_info = data_set_info, + query_info = query_info) + +} \ No newline at end of file diff --git a/sentiment_neighbourhoods_module.R b/sentiment_neighbourhoods_module.R new file mode 100644 index 0000000..2cb8d20 --- /dev/null +++ b/sentiment_neighbourhoods_module.R @@ -0,0 +1,241 @@ +mapPlotUI <- function(id, selected){ + ns <- NS(id) + + if (selected == "Sentiment") { + tagList( + description = div( + "This map shows the search results that mention each neighbourhood. The colors correspond to the average sentiment expressed in each of those documents." + ), + plot = div( + div( + style = "max-width: 1200px;", + shiny::uiOutput(ns("map_plot_output_ui")) %>% + shinycssloaders::withSpinner() + ), + div( + align="center", + style = "max-width: 1200px;", + sentiment_legend() + ) + ), + exclude = div( + div( + style = "max-width: 1200px;", + uiOutput(ns("neighbourhoods_exclude_ui")), + ) + ) + ) + } else if (selected == "Count") { + tagList( + description = div( + "This map shows the number of documents in the search results that mention each neighbourhood." + ), + plot = div( + div( + style = "max-width: 1200px;", + shiny::uiOutput(ns("map_plot_output_ui")) %>% + shinycssloaders::withSpinner() + ) + ), + exclude = div( + div( + style = "max-width: 1200px;", + uiOutput(ns("neighbourhoods_exclude_ui")), + ) + ) + ) + } +} + +mapPlot <- function(input, output, session, + query_info, selected) { + + neighbourhoods = sf::st_read(get_configs()$neighbourhoods_file, quiet = TRUE) + + output$neighbourhoods_exclude_ui <- renderUI({ + shinyWidgets::dropdownButton( + 'Some neighbourhoods can overshadow results for other neighbourhoods. You can exclude neighbourhoods from this analysis by entering them in the box below.', + br(), + selectizeInput(session$ns("neighbourhoods_exclude"), + label = "Exclude Neighbourhoods:", + choices = sort(neighbourhoods$descriptive_name), + selected = c(), + multiple = TRUE, + options = list(placeholder = 'Type here')), + circle = FALSE, + status = "primary", + icon = icon("gear"), + width = "300px", + tooltip = shinyWidgets::tooltipOptions(title = "Map Options") + ) + }) + + plot_alt_map = reactive({ + req(query_info()$num_hits > 0) + + colour_map = get_sentiment_colourmap() + + aggregations = query_text_depot(query_info = query_info(), + aggregates_json = sentimentNeighbourhoodsQuery()) + aggregations = parse_aggregates(es_results = aggregations) + + hood_stats = aggregations$hoods.names.buckets %>% + group_by(key) %>% + summarize( + doc_count = sum(doc_count), + avg_sentiment = mean(neighbourhood_to_sentiment.avg_sentiment.value), + .groups = "drop" + ) %>% + rename(name = key) %>% + mutate(label1 = paste0(name, "
", "Doc Count: ", doc_count, "
", + "Sentiment: ", round(avg_sentiment, 2))) %>% + mutate(label2 = paste0(name, "
", "Doc Count: ", doc_count)) + + hoods_df = neighbourhoods %>% + left_join(hood_stats, by = c("descriptive_name" = "name")) + na_ind = which(is.na(hoods_df$avg_sentiment)) + hoods_df$doc_count[na_ind] = 0 + hoods_df$label1[na_ind] = paste0(hoods_df$name[na_ind], ": ", NA) + hoods_df$label2[na_ind] = paste0(hoods_df$name[na_ind], ": ", NA) + + if (length(input$neighbourhoods_exclude) > 0) { + hoods_df = dplyr::filter(hoods_df, !(descriptive_name %in% input$neighbourhoods_exclude)) + } + + bbox = sf::st_bbox(neighbourhoods) + min.lat = as.numeric(bbox$ymin) + max.lat = as.numeric(bbox$ymax) + min.lng = as.numeric(bbox$xmin) + max.lng = as.numeric(bbox$xmax) + pal = colorNumeric(palette = "Blues", domain = hoods_df$doc_count, reverse = FALSE) + + colours = get_sentiment_colourmap() + + hoods_df$label = ifelse(!is.na(hoods_df$avg_sentiment), + as.character(cut(hoods_df$avg_sentiment, + breaks = c(colour_map$lower, Inf), + labels = colour_map$label, + include.lowest = TRUE, + right = TRUE)), + NA) + hoods_df = dplyr::left_join(hoods_df, colours, by = c("label" = "label")) + + if (selected == "Sentiment") { + m = leaflet() %>% + addProviderTiles(providers$Stamen.TonerLite, options = providerTileOptions(noWrap = TRUE)) %>% + fitBounds(min.lng, min.lat, max.lng, max.lat) %>% + addPolygons( + data = hoods_df, + # fillColor = ~pal(sentiment_mean), + fillColor = ~colour, + weight = 2, + opacity = 1, + color = "white", + dashArray = "3", + fillOpacity = 0.7, + highlight = highlightOptions( + weight = 3, + color = "#666666", + dashArray = "", + fillOpacity = 0.7, + bringToFront = TRUE + ), + label = ~lapply(label1, htmltools::HTML), + labelOptions = labelOptions( + style = list("font-weight" = "normal", padding = "3px 8px"), + textsize = "15px", + direction = "auto" + ) + ) + } else if (selected == "Count") { + m = leaflet() %>% + addProviderTiles(providers$Stamen.TonerLite, options = providerTileOptions(noWrap = TRUE)) %>% + fitBounds(min.lng, min.lat, max.lng, max.lat) %>% + addPolygons( + data = hoods_df, + fillColor = ~pal(doc_count), + weight = 2, + opacity = 1, + color = "white", + dashArray = "3", + fillOpacity = 0.7, + highlight = highlightOptions( + weight = 3, + color = "#666666", + dashArray = "", + fillOpacity = 0.7, + bringToFront = TRUE + ), + label = ~lapply(label2, htmltools::HTML), + labelOptions = labelOptions( + style = list("font-weight" = "normal", padding = "3px 8px"), + textsize = "15px", + direction = "auto" + ) + ) + } + + return(m) + }) + + output$map_plot_output <- leaflet::renderLeaflet({ + plot_alt_map() + }) + + output$map_plot_output_ui <- renderUI({ + leaflet::leafletOutput(session$ns("map_plot_output"), height = 500) + }) + + output$sentiment_colour_scale <- renderPlot({ + colour_map <- get_sentiment_colourmap() + sentimentLegendPlot(colour_mapping = colour_map) + }) +} + +sentimentNeighbourhoodsQuery <- function() { + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + }, + "aggs" : { + "hoods": { + "nested": { + "path": "neighbourhoods" + }, + "aggs": { + "names": { + "terms": { + "size": 500, + "field": "neighbourhoods.name.keyword" + }, + "aggs": { + "count_sum": { + "sum": { + "field": "neighbourhoods.count" + } + }, + "ratio_sum": { + "sum": { + "field": "neighbourhoods.ratio" + } + }, + "neighbourhood_to_sentiment": { + "reverse_nested": {}, + "aggs": { + "avg_sentiment": { + "avg": { + "field": "sentiment_polarity" + } + } + } + } + } + } + } + } + } + } + }' +} \ No newline at end of file diff --git a/sentiment_timeline_likert_module.R b/sentiment_timeline_likert_module.R new file mode 100644 index 0000000..104b64c --- /dev/null +++ b/sentiment_timeline_likert_module.R @@ -0,0 +1,126 @@ +sentimentTimelineLikertUI <- function(id){ + ns <- NS(id) + + tagList( + description = div( + "This chart uses a Likert scale, rotated 90°, to show the spread of sentiment for each dataset by month. Positive and Negative sentiment are indicated by blue and red respectively. The height of each bar indicates the percentage of responses that fell into the respective sentiment category." + ), + plot = div( + div( + style = "max-width: 1200px;", + shiny::uiOutput(ns("sentiment_timeline_likert_plot_ui")) %>% + shinycssloaders::withSpinner() + ), + div( + id = ns("sentiment_legend"), + style = "max-width: 1200px;", + align="center", + sentiment_legend() + ) + ) + ) +} + +sentimentTimelineLikert <- function(input, output, session, + data_set_info, + query_info) { + + plot_data_likert <- reactive({ + req(query_info()$num_hits > 0) + + # arrange by order to make sure colours show up in correct order in chart + colour_map <- get_sentiment_colourmap() %>% + arrange(order) + + aggregations = query_text_depot(query_info = query_info(), + aggregates_json = sentimentBucketsTimelineQuery(colour_map)) + aggregations = parse_aggregates(es_results = aggregations) + + plotting_data <- aggregations$month_counts.buckets %>% + select(index_name = index, key_as_string, key, doc_count, ends_with(".doc_count")) %>% + mutate(Date = as.Date(stringr::str_sub(key_as_string, 1, 10))) %>% + select(-key_as_string) %>% + wrangle_likert_data(colour_map = colour_map, data_set_info = data_set_info()) + + return(plotting_data) + }) + + output$sentiment_timeline_likert_plotly <- plotly::renderPlotly({ + + req(plot_data_likert) + + p <- plot_data_likert() %>% + mutate(hover_label = paste0("", display_name, '\n', + "Sentiment Category: ", sentiment_label, '\n', + "Document Count: ", display_count, '\n', + "Document Percent: ", sigfig(display_frac * 100, n = 2), '%', '\n', + "Date: ", format(Date, format = "%b-%Y"))) %>% + tidyr::replace_na(list(frac_mod=0)) %>% # this enables proper scaling in the plotly conversion of ggplot (plotly ignores NAs) + plot_likert(colour_map = get_sentiment_colourmap(), + x_var = Date, + fractions_var = frac_mod, + fill_name = Sentiment, + group_var = "display_name", + hover_label = hover_label) + + p1 <- plotly::ggplotly(p, tooltip = c("text"),dynamicTicks = "x") %>% + layout(margin = list(l = 75, r = 75), + xaxis = list( + type = 'date', + tickformat = "%b
%Y" + )) + + for (x in names(p1$x$layout)[grepl("yaxis", names(p1$x$layout))]) { + p1[["x"]][["layout"]][[x]][["fixedrange"]] <- TRUE + } + + p1 + }) + + output$sentiment_colour_scale <- renderPlot({ + colour_map <- get_sentiment_colourmap() + sentimentLegendPlot(colour_mapping = colour_map) + }) + + output$sentiment_timeline_likert_plot_ui <- renderUI({ + n_facets <- n_distinct(plot_data_likert()$display_name) + plotly::plotlyOutput(session$ns("sentiment_timeline_likert_plotly"), height = 200 + (150 * n_facets)) + }) + +} + +sentimentBucketsTimelineQuery <- function(mapping) { + + bucket_str = build_likert_bins(mapping) + + glue::glue( + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + }, + "aggs" : { + "month_counts": { + "date_histogram" : { + "field" : "date", + "calendar_interval" : "month" + }, + "aggs" : { + "sentiment" : { + "range" : { + "field" : "sentiment_polarity", + "keyed" : true, + "ranges" : [ + <{bucket_str}> + ] + } + } + } + } + } + } + } + ', + .open = "<{", .close = "}>") +} \ No newline at end of file diff --git a/sentiment_timeline_module.R b/sentiment_timeline_module.R new file mode 100644 index 0000000..d963093 --- /dev/null +++ b/sentiment_timeline_module.R @@ -0,0 +1,128 @@ +sentimentTimelineUI <- function(id){ + ns <- NS(id) + + tagList( + description = div( + "This chart shows the average sentiment of matching search results for each month, by data set." + ), + plot = div( + style = "max-width: 1200px;", + fluidRow( + column( + offset = 8, # this puts the switch over to the right + width = 3, + shinyWidgets::materialSwitch(inputId = ns("show_trend"), + value = FALSE, + label = "Show Trend Line?", + status = "primary", + right = TRUE) + ) + ), + shiny::uiOutput(ns("sentiment_timeline_plot_ui")) %>% + shinycssloaders::withSpinner(id = ns("this spinner")) + ) + ) +} + +sentimentTimeline <- function(input, output, session, + data_set_info, + query_info) { + + plot_data <- reactive({ + req(query_info()$num_hits > 0) + + aggregations = query_text_depot(query_info = query_info(), + aggregates_json = sentimentTimelineQuery()) + aggregations = parse_aggregates(es_results = aggregations) + + plot_hits = aggregations$month_counts.buckets %>% + transmute(Date = as.Date(stringr::str_sub(key_as_string, 1, 10)), Sentiment = sentiment.value, index_name = index, Count = doc_count) %>% + left_join(select(data_set_info(), index_name, display_name), by = "index_name") %>% # to get display name + mutate(Count = ifelse(Count == 0, NA, Count)) %>% + arrange(Date) %>% + filter(!is.na(Sentiment)) %>% + arrange(display_name) + + return(plot_hits) + }) + + output$sentiment_timeline_plotly <- plotly::renderPlotly({ + req(plot_data) + + p <- plot_data() %>% + plot_timeseries_td( + date_var = Date, + value_var = Sentiment, + group_var = display_name, + colour_var = display_name, + data_set_info = data_set_info(), + scales = "free_y", + show_trend = input$show_trend, + date_format = "%Y-%b" + ) + + # hack: blank dummy line with text aesthetic to define the tooltip text that + # is shown in the plotly plot: + # https://stackoverflow.com/questions/44569551/date-format-in-hover-for-ggplot2-and-plotly + p <- p + + geom_line(aes(text = paste0("", display_name, '\n', + "Average Sentiment: ", sigfig(Sentiment, n = 2), '\n', + "Document Count: ", Count, '\n', + "Date: ", format(Date, format = "%b-%Y")))) %>% + suppressWarnings() + + not_enough_data <- plot_data() %>% + group_by(display_name) %>% + filter(n() <= 1) + + if (nrow(not_enough_data) > 0) { + p <- p + geom_point(data = not_enough_data, mapping = aes(text = paste0("", display_name, '\n', + "Average Sentiment: ", sigfig(Sentiment, n = 2), '\n', + "Document Count: ", Count, '\n', + "Date: ", format(Date, format = "%b-%Y")))) %>% + suppressWarnings() + } + + p1 <- plotly::ggplotly(p, tooltip = c("text"), dynamicTicks = TRUE) %>% + layout(margin = list(l = 75, r = 75)) + + for (x in names(p1$x$layout)[grepl("yaxis", names(p1$x$layout))]) { + p1[["x"]][["layout"]][[x]][["fixedrange"]] <- TRUE + } + + return(p1) + }) + + output$sentiment_timeline_plot_ui <- renderUI({ + n_facets <- n_distinct(plot_data()$display_name) + plotly::plotlyOutput(session$ns("sentiment_timeline_plotly"), height = 200 + (100 * n_facets)) + }) + +} + +sentimentTimelineQuery <- function() { + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + }, + "aggs" : { + "month_counts": { + "date_histogram" : { + "field" : "date", + "calendar_interval" : "month" + }, + "aggs" : { + "sentiment" : { + "avg" : { + "field" : "sentiment_polarity" + } + } + } + } + } + } + } + ' +} diff --git a/server.R b/server.R new file mode 100644 index 0000000..0cc39c4 --- /dev/null +++ b/server.R @@ -0,0 +1,141 @@ +function(input, output, session) { + params = isolate(parseQueryString(session$clientData$url_search)) + if ("query" %in% names(params)) { # Did the user specify a query in the URL? + query_param = params$query + } else { + query_param = "" + } + + es_connection <- connect_ES() + + # The mapping from actual Elastic Search index to alias name. + # This is needed because when Elastic Search returns results, it returns the + # index name for each result, not the alias that was originally passed into + # the query. We dont want to expose the underlying index name to the end user, + # so we map back to the original name with this data structure. + data_set_info <- reactive({ + aliases <- get_aliases(session) + + dataset_info <- index_to_alias_mapping(es_connection, aliases) %>% + mutate(display_name = tools::toTitleCase(gsub("_", " ", alias_name, fixed = TRUE))) %>% + mutate(db_size = purrr::map_dbl(alias_name, function(index) { + query_count(es_connection, index = index) + })) + + dataset_info <- dataset_info %>% + left_join(alias_to_colours_mapping(dataset_info$alias_name), by = "alias_name") + + # computing max/min dates for each index to be used in plotting functions... + dataset_info <- dataset_info %>% + group_by(alias_name) %>% + mutate(date_range = list(stats_for_field(es_connection, index_name, "date")[c("min", "max")])) %>% + tidyr::unnest_wider(date_range, names_sep = "_") %>% + mutate(date_range_min = as.Date(date_range_min), + date_range_max = as.Date(date_range_max)) %>% + ungroup() + + dataset_info + }) + + observeEvent(input$more_info, { + shinyWidgets::sendSweetAlert( + session = session, + title = "Additional Information", + text = tagList( + div(sprintf(" + Text Depot is a tool for searching and analyzing text data (documents, survey responses etc.) + at %s. Text Depot has gathered, organized and securely stored multiple text datasets. You can + instantly search the data and analyze the results. You may also download the results for your + own further analyses. + ", get_configs()$location_name), + br(), br(), + sprintf(" + You have access to the following %s data sources: + ", length(data_set_info()$alias_name)), + style = "text-align: left" + ), + br(), br(), + tableOutput("index_stats") %>% shinycssloaders::withSpinner(proxy.height = "50px"), + br(), br(), + "Have another data set to suggest? Email ", a(get_configs()$contact_email, href = paste0("mailto:", get_configs()$contact_email)) + ), + width = 1000, + html = TRUE + ) + + output$index_stats <- renderTable({ + req(data_set_info) + + data_set_info() %>% + group_by(alias_name) %>% + mutate(avg_sentiment = stats_for_field(es_connection, index_name, "sentiment_polarity", numeric = TRUE)[['avg']], + avg_sentences = stats_for_field(es_connection, index_name, "num_sentences", numeric = TRUE)[['avg']]) %>% + ungroup() %>% + transmute("Data Source" = tools::toTitleCase(gsub("_", " ", display_name)), + "Min. Date" = as.character(date_range_min), + "Max. Date" = as.character(date_range_max), + Documents = format(db_size, nsmall = 0), + 'Avg. Sentence Count' = avg_sentences, + "Avg. Sentiment" = avg_sentiment) %>% + arrange(`Data Source`) + + }, align = "cccrrr") + }) + + query_info <- shiny::callModule(module = searchBar, + id = "search_bar", + es_connection = es_connection, + data_set_info = data_set_info, + query_param = query_param) + + #------------------------------------ + # query summary - possible additional module + query_db_size <- reactive({ + req(query_info) + if (length(query_info()$index) == 0) return(0) + query_count(es_connection, index = query_info()$index) + }) + + output$query_summary <- renderText({ + req(query_info) + req(nchar(query_info()$query) > 0) + + output_str = paste0(query_info()$num_hits, " out of ", query_db_size(), " documents matched your search.") + + output_str + }) + #------------------------------------ + + shiny::callModule(module = dataDownload, + id = "data_download", + query_info = query_info, + data_set_info = data_set_info) + + shiny::callModule(module = searchResultsTable, + id = "search_results_table", + query_info = query_info, + results_per_page = RESULTS_PER_PAGE, + data_set_info = data_set_info, + n_char_no_match = N_CHAR_NO_MATCH, + n_char_highlights = N_CHAR_HIGHLIGHTS) + + shiny::callModule(module = volumeTimeline, + id = "volume_timeline", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentimentTimeline, + id = "sentiment_timeline", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = sentiment, + id = "sentiment", + data_set_info = data_set_info, + query_info = query_info) + + shiny::callModule(module = neighbourhoodMap, + id = "neighbourhood_map", + query_info = query_info) + +} diff --git a/ui.R b/ui.R new file mode 100644 index 0000000..56af6c6 --- /dev/null +++ b/ui.R @@ -0,0 +1,78 @@ +ui <- fluidPage( + tags$link(rel = "stylesheet", type = "text/css", href = "https://fonts.googleapis.com/css?family=Open+Sans:400,700"), + + useShinyjs(), + + # Allow user to search using enter button: + tags$head(HTML(' + + ')), + + fluidRow( + column( + a(img(src = "text_depot_icon/TextDepotIcon_Image_S.jpg", width = 105, style = "float: left; padding: 10px 10px 0 0"), + href = "https://github.com/CityofEdmonton/text_depot"), + span("TEXT DEPOT", style = 'font-weight: 700; + font-size: 35px; + font-family: "Open Sans"; + margin: 20px 0 20px 0; + color: #32566F'), + br(), + span(paste0("A simplified way to search and analyze topics of interest related to ", + get_configs()$location_name), + style = "color: #32566F"), + br(), + actionLink("more_info", paste0("Click for More Info")), + width = 12 + ) + ), + + br(), + + searchBarUI("search_bar"), + + br(), + + fluidRow( + column( + textOutput("query_summary"), + width = 6 + ) + ), + + br(), + + tabsetPanel( + id = "main_tabset", + tabPanel("Matching Documents", + br(), + searchResultsTableUI("search_results_table"), + icon = icon("list") + ), + tabPanel("Timeline", + br(), + volumeTimelineUI("volume_timeline"), + icon = icon("chart-line") + ), + tabPanel("Sentiment", + sentimentUI("sentiment"), + icon = icon("heartbeat") + ), + tabPanel("Neighbourhoods", + br(), + neighbourhoodMapUI("neighbourhood_map"), + icon = icon("city") + ), + tabPanel("Download", + br(), + dataDownloadUI("data_download"), + icon = icon("download") + ) + ), + + br() +) diff --git a/volume_sentiment_module.R b/volume_sentiment_module.R new file mode 100644 index 0000000..b297ef7 --- /dev/null +++ b/volume_sentiment_module.R @@ -0,0 +1,103 @@ +volumeSentimentUI <- function(id){ + ns <- NS(id) + + fluidPage( + tagList( + " + Sentiment histogram for matching documents. This chart shows how many + documents have each sentiment value, per data set. Sentiment + above zero is positive (e.g. happy, positive, satisfied), + and below zero is negative (e.g. angry, disappointed, sad). Note + that the sentiment is predicted using AI, and may be incorrect + in some cases (e.g. when sarcasm is used). + " %>% + div( + style = "max-width: 1200px;" + ), + br(), br(), br(), + shiny::uiOutput(ns("sentiment_histogram_ui")) %>% + shinycssloaders::withSpinner() %>% + div( + style = "max-width: 1200px;" + ) + )) +} + +volumeSentiment <- function(input, output, session, + data_set_info, + query_info) { + + plot_data <- reactive({ + req(query_info()$num_hits > 0) + + aggregations = query_text_depot(query_info = query_info(), + aggregates_json = volumeSentimentQuery()) + aggregations = parse_aggregates(es_results = aggregations) + + plot_hits = aggregations$sentiment.buckets %>% + transmute(Sentiment = key, Count = doc_count, index_name = index) %>% + left_join(select(data_set_info(), index_name, display_name), by = "index_name") %>% # to get display name + arrange(display_name) + + return(plot_hits) + + }) + + output$sentiment_histogram <- plotly::renderPlotly({ + + req(plot_data) + + p <- plot_data() %>% + ggplot2::ggplot(mapping = aes(x = Sentiment, y = Count, fill = display_name)) + + facet_wrap("display_name", scales = "free_y", ncol = 1) + + # hack: blank dummy line with text aesthetic to define the tooltip text that + # is shown in the plotly plot: + # https://stackoverflow.com/questions/44569551/date-format-in-hover-for-ggplot2-and-plotly + suppressWarnings( + geom_col(aes(text = paste0("", display_name, '\n', + "Sentiment: ", sigfig(Sentiment, n = 2), '\n', + "Document Count: ", Count))) + ) + + scale_fill_manual(breaks = data_set_info()$display_name, + values = data_set_info()$colours) + ggthemes::theme_gdocs() + theme(legend.position = "none") + + p1 <- plotly::ggplotly(p, tooltip = c("text"), dynamicTicks = TRUE) %>% + layout(margin = list(l = 75, r = 75)) + + for (x in names(p1$x$layout)[grepl("yaxis", names(p1$x$layout))]) { + p1[["x"]][["layout"]][[x]][["fixedrange"]] <- TRUE + } + + return(p1) + + }) + + output$sentiment_histogram_ui <- renderUI({ + n_facets <- n_distinct(plot_data()$display_name) + plotly::plotlyOutput(session$ns("sentiment_histogram"), height = 200 + (100 * n_facets)) + }) +} + +volumeSentimentQuery <- function() { + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + }, + "aggs" : { + "sentiment" : { + "histogram" : { + "field" : "sentiment_polarity", + "interval" : 0.05, + "extended_bounds" : { + "min" : -1, + "max" : 1 + } + } + } + } + } + } + ' +} diff --git a/volume_timeline_module.R b/volume_timeline_module.R new file mode 100644 index 0000000..ea76416 --- /dev/null +++ b/volume_timeline_module.R @@ -0,0 +1,145 @@ +volumeTimelineUI <- function(id){ + ns <- NS(id) + + tagList( + div( + style = "max-width: 1200px;", + fluidRow( + column( + width = 7, + "This chart shows the number of matching search results for each month. Click and drag to zoom, and double-click to reset the zoom level." + ), + column( + width = 3, + shinyWidgets::materialSwitch(inputId = ns("show_trend"), + value = FALSE, + label = "Show Trend Line?", + status = "primary", + right = TRUE), + ) + ) + ), + div( + style = "max-width: 1200px;", + shiny::uiOutput(ns("volume_timeline_plot_ui")) %>% + shinycssloaders::withSpinner() + ) + ) +} + +volumeTimeline <- function(input, output, session, + data_set_info, + query_info) { + + plot_data <- reactive({ + req(query_info()$num_hits > 0) + + aggregations <- query_text_depot(query_info = query_info(), + aggregates_json = volumeTimelineQuery()) + aggregations = parse_aggregates(es_results = aggregations) + + plot_hits <- aggregations$month_counts.buckets %>% + transmute(Date = as.Date(key_as_string), Count = doc_count, index_name = index) %>% + left_join(select(data_set_info(), index_name, display_name), by = "index_name") %>% # to get display name + mutate(Date = strftime(Date, format = "%Y-%m")) %>% + mutate(Date = as.Date(paste0(Date, "-01"))) %>% + arrange(display_name) + + # pad months: + # I can't find a way to do this in the ES query, bc the query first does the + # search (so returns a subset of docs), then does the aggregation. + # This means that for histogram aggregation, it never returns bins before the + # first, or after the last non-zero value + all_months <- data_set_info() %>% + group_by(display_name) %>% + transmute(Date = list( seq(lubridate::floor_date(date_range_min, unit = "month"), + lubridate::ceiling_date(date_range_max, unit = "month"), + by = "months")) ) %>% + ungroup() %>% + tidyr::unnest_longer(Date) %>% + mutate(fill_value = 0) + + plot_hits <- plot_hits %>% + right_join(all_months, by = c("Date" = "Date", "display_name" = "display_name")) %>% + mutate(Count = dplyr::coalesce(Count, fill_value)) # when count is NA, use the fill value + + return(plot_hits) + + }) + + output$volume_timeline_plot <- plotly::renderPlotly({ + + req(plot_data) + req(data_set_info) + + p <- plot_data() %>% + plot_timeseries_td( + date_var = Date, + value_var = Count, + group_var = display_name, + colour_var = display_name, + data_set_info = data_set_info(), + scales = "free_y", + show_trend = input$show_trend, + date_format = "%Y-%b" + ) + + # leaving in here some code to set ggplot facets to start x at zero. plotly conversion messes all this up unfortunately + ## not working: + # dummy_data <- tidyr::crossing(display_name = unique(plot_data()$display_name), Count = c(0,50), Date = median(plot_data()$Date)) + # p <- p + geom_blank(data = dummy_data) + # working: + # p <- p + scale_y_continuous(limits=c(0,max(plot_data()$Count))) + #p <- p + scale_y_continuous(limits=c(0,NA)) + # issue is the plotly reverses the above... + + # hack: blank dummy line with text aesthetic to define the tooltip text that + # is shown in the plotly plot: + # https://stackoverflow.com/questions/44569551/date-format-in-hover-for-ggplot2-and-plotly + p <- p + + geom_line(aes(text = paste0("", display_name, '\n', + "Document Count: ", Count, '\n', + "Date: ", format(Date, format = "%b-%Y")))) %>% + suppressWarnings() + + p1 <- plotly::ggplotly(p, tooltip = c("text"), dynamicTicks = TRUE) %>% + layout(margin = list(l = 75, r = 75)) + + # loop over y axes (facets) in the plotly plot: + for (x in names(p1$x$layout)[grepl("yaxis", names(p1$x$layout))]) { + # turn off y zoom for each y axis + p1[["x"]][["layout"]][[x]][["fixedrange"]] <- TRUE + } + + return(p1) + + }) + + + output$volume_timeline_plot_ui <- renderUI({ + n_facets <- n_distinct(plot_data()$display_name) + plotly::plotlyOutput(session$ns("volume_timeline_plot"), height = 200 + (100 * n_facets)) + }) + + +} + +volumeTimelineQuery <- function() { + ' + "aggs": { + "group_by_index": { + "terms": { + "field": "_index" + }, + "aggs" : { + "month_counts" : { + "date_histogram" : { + "field" : "date", + "calendar_interval" : "month" + } + } + } + } + } + ' +} diff --git a/workflow.png 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