initial commit

.gitattributes

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-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
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-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-data/item_to_item_similarity_dataframe_full.csv filter=lfs diff=lfs merge=lfs -text
-data/ratings_filtered.rds filter=lfs diff=lfs merge=lfs -text
-data/ref_corp_tfidf_new.rds filter=lfs diff=lfs merge=lfs -text
-data/svdf.rds filter=lfs diff=lfs merge=lfs -text
-data/dataset_goodreads_filtered.csv filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1 +0,0 @@
-.DS_Store

Dockerfile

@@ -1,12 +1,5 @@
 FROM rocker/shiny-verse:4.3.0
 
-RUN apt-get update --fix-missing \
-    && apt-get install -y software-properties-common \
-    && rm -rf /var/lib/apt/lists/* 
-RUN add-apt-repository -y ppa:cran/poppler
-RUN apt-get install -y libpoppler-cpp-dev
-
-
 # Workaround for renv cache
 RUN mkdir /.cache
 RUN chmod 777 /.cache
@@ -35,5 +28,4 @@ COPY app/ ./app/
 RUN ls -lah ./data/
 
 RUN mkdir -p ./app_cache/sass
-RUN touch system_recommendations_log.csv
-CMD ["R", "--quiet", "-e", "shiny::runApp(host='0.0.0.0', port=7860)"]
+CMD ["R", "--quiet", "-e", "shiny::runApp(host='0.0.0.0', port=7860)"]

README.md

@@ -1,8 +1,8 @@
 ---
-title: BookRecommenderApp
+title: M9211_projekt
 emoji: 🐠
 colorFrom: green
-colorTo: red
+colorTo: blue
 sdk: docker
 pinned: false
 ---

app/js/BookCard.jsx

@@ -1,72 +0,0 @@
-const { useState } = React;
-import Swal from 'sweetalert2'
-import withReactContent from 'sweetalert2-react-content'
-import { FaStar, FaGoodreads } from "react-icons/fa";
-import { GenreTag } from './GenreTag';
-import Heart from '@react-sandbox/heart'
-
-
-const showDesc = (title, message) => {
-  withReactContent(
-    Swal.fire({
-      title: title,
-      text: message,
-      icon: "info",
-      width: '40rem',
-      grow: "fullscreen"
-    })
-  )
-}
-
-export default function BookCard({ title, author_name, avg_rating, genres, description, imageUrl, url, model }) {
-
-
-  const [active, setActive] = useState(false)
-  return (
-    <div className="book-card__container">
-      <div className="book-card__cover">
-        <img className="book-card__cover__image" src={imageUrl} alt={title} />
-      </div>
-
-      <div className="book-card__info-section">
-        <div className="book-card__info-section__genre-section">
-          {genres.map((genre) => (
-            <GenreTag genre={genre} />
-          ))}
-        </div>
-
-
-        <h3 className="book-card__info-section__title">{title}</h3>
-        <p className="book-card__info-section__author">{author_name}</p>
-        <div className="book-card__info-section__rating">
-          <FaStar className='book-card__info-section__rating-icon' />
-          <span className='book-card__info-section__rating-icon-rating'>{avg_rating}</span>
-
-          <a href={url} target="_blank" rel="noopener noreferrer">
-            <FaGoodreads className='book-card__info-section__rating-icon' />
-          </a>
-        </div>
-        <button
-          className="book-card__info-section__show-description-btn btn btn-sm"
-          onClick={() => showDesc(title, description)}
-        >
-          Show Description
-        </button>
-        <Heart
-        width={34}
-        height={34}
-        active={active}
-        onClick={() => {
-          Shiny.setInputValue("app-recommend_books-myval", {
-            title: title,
-            model: model,
-            genres: genres
-          })
-          console.log()
-          setActive(!active)} 
-        }
-      />
-         </div>
-    </div>
-  );
-}

app/js/GenreTag.jsx

@@ -1,24 +0,0 @@
-
-
-function getClassName(genre) {
-    const classMap = {
-      "children": "children-color",
-      "fantasy": "fantasy-color",
-      "history_biography": "history_biography-color",
-      "graphic_comics": "comics-color",
-      "romance": "romance-color",
-      "poetry": "poetry-color",
-      "ya": "YA-color",
-      "crime": "crime-color"
-    };
-    return classMap[genre.toLowerCase().trim()] || "default_color"; // Use toLowerCase for case-insensitivity
-  }
-
-
-export const GenreTag = (props) => {
-    return (
-      <div className={getClassName(props.genre) + " book-card__info-section__genre-section-container" + " flex-center"}>
-        <span className='book-card__info-section__genre-section-conteiner-text'>#{props.genre}</span>
-      </div>
-    )
-}

app/js/index.js

@@ -1,4 +0,0 @@
-import BookCard from './BookCard';
-
-
-Rhino.registerReactComponents({ BookCard })

app/logic/SVD_model.R

@@ -1,39 +0,0 @@
-box::use(
-  recommenderlab[predict],
-  methods[as],
-  dplyr[select]
-)
-
-
-SVD_predict <- function(books_tab, selected_ids, ratings_tab, SVD_model, select_user_mat, how_many) {
-  found_ids <- selected_ids[selected_ids %in% colnames(ratings_tab)]
-  if (length(found_ids) == 0) {
-    return(NULL)
-  }
-  ratings_line <- ratings_tab[1,]
-  ratings_line[] <- NA
-  ratings_line[found_ids] <- 5
-  
-  select_user_mat <- as.matrix(ratings_line)
-  select_user_mat <- as(select_user_mat, 'realRatingMatrix')
-  
-  predict_SVDF <- predict(SVD_model, 
-                          select_user_mat,
-                          type = "topNList",
-                          n = how_many
-  )
-  
-  
-  predict_SVDF_list <- as(predict_SVDF, 'list')
-  predict_SVDF_list <- lapply(predict_SVDF_list, as.numeric)
-  predict_SVDF_df <- as.data.frame(predict_SVDF_list)
-  names(predict_SVDF_df) <- "book_id"
-  
-  recommendations_SVDF <- merge(predict_SVDF_df, books_tab, by = "book_id")
-  recommendations_tab <- recommendations_SVDF |> select(
-    title, average_rating, description, url, image_url, genres, author_name
-  )
-  recommendations_tab$model <- "SVD"
-  return(recommendations_tab)
-  
-}

app/logic/item_item_model.R

@@ -1,26 +0,0 @@
-box::use(
-  dplyr[filter]
-)
-
-box::use(
-  app/logic/utils[split_number, parse_recommendations]
-)
-
-#' @export
-get_item_item_recommendations <- function(item_item_df, data_tab, ids, how_many) {
-  rows <- item_item_df |> filter(
-    book_id %in% ids
-  )
-  
-  distribution <- split_number(how_many, length(ids))
-  
-  result <- list()
-  for (i in 1:length(distribution)) {
-    result <- append(result, rows[i,which(colnames(item_item_df) == "X1"):(distribution[i]+2)] |> as.vector())
-  }
-  
-  result <- result |> unlist()
-  return(parse_recommendations(result, data_tab, "ITEM-ITEM"))
-}
-
-

app/logic/poisson_model.R

@@ -0,0 +1,504 @@
+box::use(
+  tibble[tibble],
+  dplyr[...],
+  tidyr[...],
+  ggplot2[ggplot, geom_line, aes, geom_contour, labs, ggtitle, geom_point],
+  stats[dgamma, qgamma, dpois],
+  HDInterval[hdi],
+)
+
+
+# POISSON -----------------------------------------------------------------
+
+
+#' get_poisson_prior_tab
+#'
+#' vytvori dataframe s apriornim rozdelenim pravdepodobnosti
+#'
+#' @param theta_start levy konec intervalu parametrickeho prostoru
+#' @param theta_end pravy konec intervalu parametrickeho prostoru
+#' @param a apriorni parametr gamma rozdeleni == celkovy pocet udalosti ve
+#'  fiktivnim apriornim vyber
+#' @param b apriorni parametr gamma rozdeleni == rozsah fiktivniho apriorniho
+#'  vyberu (\strong{degree of belief})
+#' @param theta_res "by" parametr pro \emph{seq} rikajici, jak huste chceme
+#' nasamplovat parametr \theta. Default: 0.01
+#' @param model_name jmeno modelu, ktere se vlozi do sloupce \emph{model}.
+#' Default: "prior"
+#' @return dataframe se sloupci: n | y | p | theta | model
+#' @export
+#'
+#' @examples
+#' get_poisson_prior_tab(0, 5, 2, 1)
+get_poisson_prior_tab <-
+  function(theta_start,
+           theta_end,
+           a,
+           b,
+           theta_res = 0.1,
+           model_name = "prior",
+           is_jeffreys = F
+           ) {
+    theta <- seq(theta_start, theta_end, by = theta_res)
+    
+    prior_tab <- tibble(theta = theta) |>
+      mutate(
+        n = NA,
+        y = NA,
+        p = ifelse(is_jeffreys, 1/sqrt(theta), dgamma(theta, a, b)),
+        model = model_name
+      )
+    return(prior_tab)
+  }
+
+
+
+#'
+#' Funkce vytvori a vrati DataFrame s posteriornim rozdelenim pravdepodobnosti
+#' pro poissonuv model
+#'
+#' @param a apriorni parametr gamma rozdeleni == celkovy pocet udalosti ve
+#'  fiktivnim apriornim vyber
+#' @param b apriorni parametr gamma rozdeleni == rozsah fiktivniho apriorniho
+#'  vyberu (\strong{degree of belief})
+#' @param n = rozsah nahodneho vyberu v experimentu
+#' @param y = celkovy pocet udalosti pozorovanych v experimentu
+#' @param theta vektor nebo list nasamplovanych hodnot parametru theta
+#' @param model_name jmeno modelu, ktere se vlozi do sloupce \emph{model}.
+#' Default: "posterior"
+#'
+#' @return dataframe se sloupci: n | y | p | theta | model
+#' @export
+#'
+#' @examples
+get_poisson_posterior_tab <-
+  function(a, b, n, y, theta, model_name = "posterior") {
+    posterior_tab <- tibble(n = n, y = y, theta = theta) |>
+      mutate(p = dgamma(theta, a + y, b + n),
+             model = model_name)
+  }
+
+
+
+#' get_poisson_likelihood_tab
+#'
+#' Vypocita verohodnostni funkci (likelihood) podle:
+#' (Y \mid \theta) \sim Po(n \theta)
+#' f(y \mid \theta) = \frac{(n\theta)^y}{y!} e^{-n\theta}
+#'
+#'
+#' @param n = rozsah nahodneho vyberu v experimentu
+#' @param y = celkovy pocet udalosti pozorovanych v experimentu
+#' @param theta vektor nebo list nasamplovanych hodnot parametru theta
+#' @param likelihood_multiplier konstanta, kterou vynasobime likelihood pro
+#' lepsi vizualizaci. Vynasobeni konstantou se na poloze verohodnostni funkce
+#' neprojevi
+#' @param model_name jmeno modelu, ktere se vlozi do sloupce \emph{model}.
+#' Default: "likelihood"
+#'
+#' @return dataframe se sloupci: n | y | p | theta | model
+#' @export
+#'
+#' @examples
+get_poisson_likelihood_tab <-
+  function(n,
+           y,
+           theta,
+           likelihood_multiplier = 1,
+           model_name = "likelihood") {
+    likelihood_tab <- tibble(
+      theta = theta,
+      model = model_name,
+      n = n,
+      y = y
+    ) |>
+      mutate(p = dpois(y, n * theta) * likelihood_multiplier)
+  }
+
+
+
+
+#' get_distribution plot
+#'
+#' @param full_tab dataframe, ktery obsahuje sloupce n | y | p | theta | model
+#' a kde model = c("prior", "posterior", "likelihood")
+#' @param linewidth ggplot line witdth
+#'
+#' @return ggplot2 plot posteriorni distribuce, apriorni distribuce a likelihood
+#' @export
+#'
+#' @examples
+get_distribution_plot <- function(full_tab, linewidth) {
+  plot <-
+    full_tab |> ggplot(aes(
+      x = theta,
+      y = p,
+      color = model,
+      group =
+    )) +
+    geom_line(linewidth = linewidth)
+}
+
+
+#' get_prior_poisson_characteristics
+#'
+#' @param prior_a apriorni parametr gamma rozdeleni == celkovy pocet udalosti ve
+#'  fiktivnim apriornim vyber
+#' @param prior_b apriorni parametr gamma rozdeleni == rozsah fiktivniho apriorniho
+#'  vyberu (\strong{degree of belief})
+#'
+#' @return dataframe se sloupci: mean | SD | mode | lwr | upr | HDP.lower | HDP.upper
+#' @export
+#'
+#' @examples
+get_prior_poisson_characteristics <- function(prior_a, prior_b, model_name = "prior") {
+  prior <- list(a = prior_a, b = prior_b)
+  
+  stat.prior <- with(
+    prior,
+    data.frame(
+      model = model_name,
+      MLE = NA,
+      MAP = NA,
+      mean = a / b,
+      SD = sqrt(a / b ^ 2),
+      ETCI.lwr = qgamma(0.025, a, b),
+      ETCI.upr = qgamma(0.975, a, b),
+      HPD = t(hdi(
+        qgamma,
+        credMass = 0.95,
+        shape = a,
+        rate = b
+      ))
+    )
+  )
+}
+
+
+
+#' get_posterior_poisson_characteristics
+#'
+#' @param prior_a apriorni parametr gamma rozdeleni == celkovy pocet udalosti ve
+#'  fiktivnim apriornim vyber
+#' @param prior_b apriorni parametr gamma rozdeleni == rozsah fiktivniho apriorniho
+#'  vyberu (\strong{degree of belief})
+#' @param n rozsah nahodneho vyberu v experimentu
+#' @param y celkovy pocet udalosti pozorovanych v experimentu
+#' @param full_tab dataframe, ktery obsahuje sloupce n | y | p | theta | model
+#' a kde model = c("prior", "posterior", "likelihood")
+#'
+#' @return Dataframe se sloupci: mean | SD | mode | lwr | upr | MLE | HDP.lower | HDP.upper
+#' @export
+#'
+#' @examples
+get_posterior_poisson_characteristics <-
+  function(prior_a,
+           prior_b,
+           n,
+           y,
+           full_tab,
+           model_name = "posterior") {
+    posterior_hyperparameters <- list(a = prior_a + y, b = prior_b + n)
+    MaxL <- slice(.data = full_tab, which.max(full_tab$p))
+    
+    stats_posterior <- with(
+      posterior_hyperparameters,
+      data.frame(
+        model = model_name,
+        MLE = MaxL$theta,
+        MAP = (a - 1) / b,
+        mean = a / b,
+        SD = sqrt(a / (b ^ 2)),
+        ETCI.lwr = qgamma(0.025, a, b),
+        ETCI.upr = qgamma(0.975, a, b),
+        HPD = t(hdi(\(x) qgamma(x, a, b), credMass = 0.95))
+      )
+    )
+  }
+
+
+
+#' calculate_posterior_mean
+#'
+#' @param mean.prior
+#' @param belief == b
+#' @param y
+#' @param n
+#'
+#' @return
+#' @export
+#'
+#' @examples
+calculate_posterior_mean <- function(mean.prior, belief, y, n) {
+  (belief * mean.prior + y) / (belief + n)
+}
+
+
+#' predict_neg_bin_prior
+#'
+#' @param pred_start
+#' @param pred_end
+#' @param prior_a
+#' @param prior_b
+#' @param model_name
+#'
+#' @return
+#' @export
+#'
+#' @examples
+predict_neg_bin_prior <-
+  function(pred_start = 0,
+           pred_end = 10,
+           prior_a,
+           prior_b,
+           model_name = "marginal") {
+    tibble(
+      y.pred = seq(pred_start, pred_end, by = 1),
+      f = dnbinom(y.pred, size = prior_a, prob = prior_b / (prior_b + 1)),
+      model = model_name
+    )
+  }
+
+
+#' predict_neg_bin_marginal_characteristics
+#'
+#' @param prior_a
+#' @param prior_b
+#' @param model_name
+#'
+#' @return
+#' @export
+#'
+#' @examples
+predict_neg_bin_marginal_characteristics <-
+  function(prior_a, prior_b, model_name = "marginal") {
+    marginal <- list(a = prior_a, b = prior_b)
+    with(
+      marginal,
+      data.frame(
+        model = model_name,
+        mean = a / b,
+        sd = sqrt(a * (b + 1) / b ^ 2),
+        lower = qnbinom(0.025, a, b / (b + 1)),
+        upper = qnbinom(0.975, a, b / (b + 1))
+      )
+    )
+  }
+
+
+#' predict_neg_bin_posterior_characteristics
+#'
+#' @param prior_a
+#' @param prior_b
+#' @param y
+#' @param n
+#' @param model_name
+#'
+#' @return
+#' @export
+#'
+#' @examples
+predict_neg_bin_posterior_characteristics <-
+  function(prior_a, prior_b, y, n, model_name = "posterior") {
+    posterior1 <- list(a = prior_a + y, b = prior_b + n)
+    with(
+      posterior1,
+      data.frame(
+        model = model_name,
+        mean = a / b,
+        sd = sqrt(a * (b + 1) / b ^ 2),
+        lower = qnbinom(0.025, a, b / (b + 1)),
+        upper = qnbinom(0.975, a, b / (b + 1))
+      )
+    )
+  }
+
+
+#' predict_neg_bin_posterior
+#'
+#' @param pred_start
+#' @param pred_end
+#' @param prior_a
+#' @param prior_b
+#' @param y
+#' @param n
+#' @param model_name
+#'
+#' @return
+#' @export
+#'
+#' @examples
+predict_neg_bin_posterior <-
+  function(pred_start = 0,
+           pred_end = 10,
+           prior_a,
+           prior_b,
+           y,
+           n,
+           model_name = "posterior") {
+    tibble(
+      y.pred = seq(pred_start, pred_end, by = 1),
+      f = dnbinom(
+        y.pred,
+        size = prior_a + y,
+        prob = (prior_b + n) / (prior_b + n + 1)
+      ),
+      model = model_name
+    )
+  }
+
+
+#' get_predict_dist_plots
+#'
+#' @param predict_tab tabulka se sloupci: y.pred | f | model
+#' @param lab_x popisek osy x
+#' @param lab_y popisek osy y
+#'
+#' @return ggplot2 plot prediktivnich rozdelni
+#' @export
+#'
+#' @examples
+get_predict_dist_plots <-
+  function(predict_tab, lab_x, lab_y = "prediktivni pravdepodobnosti") {
+    predict_tab |> ggplot(aes(
+      x = y.pred,
+      y = f,
+      group = model,
+      colour = model
+    )) +
+      geom_line(linewidth = 0.3, lty = 2) +
+      geom_point(size = 1.5, pch = 19) +
+      labs(x = lab_x, y = lab_y)
+  }
+
+
+#' get_poisson_sensitivity_plot
+#'
+#' @param a apriorni parametr gamma rozdeleni == celkovy pocet udalosti ve
+#'  fiktivnim apriornim vyber
+#' @param b apriorni parametr gamma rozdeleni == rozsah fiktivniho apriorniho
+#'  vyberu (\strong{degree of belief})
+#' @param n = rozsah nahodneho vyberu v experimentu
+#' @param y = celkovy pocet udalosti pozorovanych v experimentu
+#' @param mean_prior_left zacatek intervalu pro samplovani apriorni stredni hodnoty
+#' @param mean_prior_right konec intervalu pro samplovani apriorni stredni hodnoty
+#' @param belief_left zacatek intervalu pro samplovani degree of belief (b)
+#' @param belief_right
+#'
+#' @return
+#' @export
+#'
+#' @examples
+get_poisson_sensitivity_plot <-
+  function(a,
+           b,
+           n,
+           y,
+           mean_prior_left,
+           mean_prior_right,
+           belief_left,
+           belief_right) {
+    da <- expand_grid(
+      mean.prior = seq(mean_prior_left, mean_prior_right, by = 1),
+      belief = seq(belief_left, belief_right, by = 1),
+    ) |> mutate(mean.posterior = calculate_posterior_mean(mean.prior, belief, y, n))
+    # vrstevnicovy graf s barevnymi vrstevnicemi
+    da |> ggplot(aes(x = belief, y = mean.prior, z = mean.posterior)) +
+      geom_contour(binwidth = 0.05, aes(colour = after_stat(level))) +
+      labs(x = "belief", y = "apriorni stredni hodnota", color = "apost.\nstredni\nhodnota") +
+      ggtitle("Aposteriorni stredni hodnota") +
+      geom_point(
+        x = b,
+        y = a / b,
+        color = "red",
+        show.legend = FALSE
+      )
+  }
+
+
+
+# Priklady ----------------------------------------------------------------
+
+
+# 
+# 
+# 
+# 
+# # 1. zeny
+# 
+# load(file = "../../04/deti1.RData", verbose = TRUE)
+# data <- Y
+# 
+# tab <- data |>
+#   filter(FEMALE == 1) |>
+#   filter((YEAR >= 1990 & YEAR < 2000) & (AGE == 40)) |>
+#   select(CHILDS, DEGREE)
+# 
+# tab <- drop_na(tab)
+# 
+# Y1 <- tab |> filter(DEGREE <= 2) |> mutate(edu = "<BC")
+# n1 <- nrow(Y1)
+# y1 <- sum(Y1$CHILDS)
+# 
+# Y2 <- tab |> filter(DEGREE >= 3) |> mutate(edu = "BC+")
+# n2 <- nrow(Y2)
+# y2 <- sum(Y2$CHILDS)
+# 
+# Y <- bind_rows(Y1, Y2)
+# 
+# a <- 2
+# b <- 1
+# 
+# prior_tab_low_ed <- get_poisson_prior_tab(0, 5, a, b, model_name = "BC< prior")
+# prior_tab_high_ed <- get_poisson_prior_tab(0, 5, a, b, model_name = "BC+ prior")
+# 
+# theta <- prior_tab_high_ed$theta
+# prior <- bind_rows(prior_tab_low_ed, prior_tab_high_ed)
+# 
+# 
+# posterior_tab_low_ed <- get_poisson_posterior_tab(a, b, n1, y1, theta, "BC< posterior")
+# posterior_tab_high_ed <- get_poisson_posterior_tab(a, b, n2, y2, theta, "BC+ posterior")
+# 
+# posterior <- bind_rows(posterior_tab_low_ed, posterior_tab_high_ed)
+# 
+# likelihood_tab_low_ed <- get_poisson_likelihood_tab(n1, y1, theta, 10, "BC< likelihood")
+# likelihood_tab_high_ed <- get_poisson_likelihood_tab(n2, y2, theta, 10, "BC+ likelihood")
+# 
+# likelihood <- bind_rows(likelihood_tab_low_ed, likelihood_tab_high_ed)
+# 
+# full_tab <- bind_rows(prior, posterior, likelihood)
+# 
+# low_full_tab <- bind_rows(prior_tab_low_ed, posterior_tab_low_ed, likelihood_tab_low_ed)
+# high_full_tab <- bind_rows(prior_tab_high_ed, posterior_tab_high_ed, likelihood_tab_high_ed)
+# 
+# 
+# dist_plot <- get_distribution_plot(full_tab)
+# dist_plot
+# 
+# characteristics_low <- get_posterior_poisson_characteristics(a, b, n1, y1, low_full_tab)
+# characteristics_high <- get_posterior_poisson_characteristics(a, b, n2, y2, high_full_tab)
+# 
+# print(rbind(characteristics_low, characteristics_high), digits = 3)
+# 
+# sens_plt_low <- get_poisson_sensitivity_plot(a, b, n1, y1,  0, 4, 0, 20)
+# sens_plt_high <- get_poisson_sensitivity_plot(a, b, n2, y2,  0, 4, 0, 20)
+# 
+# gridExtra::grid.arrange(sens_plt_low, sens_plt_high, ncol = 2)
+# 
+# prior_pred <- predict_neg_bin_prior(0, 8, a, b, "marginal")
+# post_pred_low <- predict_neg_bin_posterior(0, 8, a, b, y1, n1, "posterior <BC")
+# post_pred_high <- predict_neg_bin_posterior(0, 8, a, b, y2, n2, "posterior BC+")
+# 
+# pred_full <- rbind(prior_pred, post_pred_low, post_pred_high)
+# 
+# predict_plot <- get_predict_dist_plots(pred_full, "predikovany pocet deti")
+# 
+# # 2. Nemocnice ------------------------------------------------------------
+# 
+# data <- read.csv2(file = "srdce1.csv") |>
+#   tibble::column_to_rownames(var = "nemocnice")
+# print(data)
+
+
+
+

app/logic/recommend_system.R

@@ -1,78 +0,0 @@
-box::use(
-  quanteda[corpus, docnames, dfm, convert, dfm_tfidf, dfm_subset],
-  quanteda.textstats[textstat_simil],
-  utils[head],
-  spacyr[spacy_parse],
-  dplyr[mutate, filter, select],
-)
-
-
-
-create_ref_corpus <- function(data_tab) {
-  corp <- corpus(data_tab, text_field = "description")
-  docnames(corp) <- data_tab$title
-  return(corp)
-}
-
-
-spacy_pipeline <- function(corp) {
-  res <- corp |> spacy_parse() 
-  res_tokens <- res |> 
-    filter(
-    ! pos %in% c("PUNCT", "PART", "NUM", "SYM")
-    ) |>
-    mutate(
-      lemma = tolower(lemma)
-    ) |>
-    as.tokens(
-      use_lemma = TRUE
-    ) |>
-    tokens_remove(stopwords("en"))
-  
-  corp_dfm <- res_tokens |> dfm()
-  
-  saveRDS(corp_dfm, "./data/ref_corp_dfm.rds")
-  
-  
-  corp_tfidf <- corp_dfm |> dfm_tfidf()
-  
-  saveRDS(corp_tfidf, "./data/ref_corp_tfidf.rds")
-}
-
-#' export
-get_recommendations <- function(corp_dfm, query_book_titles, simil_method = "ejaccard", how_many) {
-  query_dfm <- dfm_subset(corp_dfm, docname_ %in% query_book_titles)
-  
-  tstat <- textstat_simil(
-    query_dfm, corp_dfm,
-    margin = "documents",
-    method = simil_method
-  )
-  
-  stat_list <- as.list(tstat)
-  ordered <- sort(unlist(stat_list), decreasing = TRUE)
-  top_n <- head(ordered, n = how_many)
-  names(top_n) <- names(top_n) |> gsub(pattern = "\\..*$", replacement = "")
-  return(names(top_n))
-}
-
-#' export
-parse_recommendations <- function(rec_book_names, data_tab) {
-  subset_books <- data_tab |> 
-    filter(
-      title %in% rec_book_names  
-    ) |>
-    select(
-      title, average_rating, description, url, image_url, genres, author_name
-    )
-  return(subset_books)
-}
-
-# hp3 <- dfm_subset(corp_dfm, docname_ %in% "A Game of Thrones (A Song of Ice and Fire, #1)")
-# tstat <- textstat_simil(hp3, corp_dfm,
-#                         margin = "documents", method = "ejaccard")
-# stat_list <- as.list(tstat)
-# ordered <- sort(unlist(stat_list), decreasing = TRUE)
-# top_ten <- head(ordered, n = 10)
-# names(top_ten) <- names(top_ten) |> gsub(pattern = "\\..*$", replacement = "")
-# top_ten

app/logic/tfidf_model.R

@@ -1,107 +0,0 @@
-box::use(
-  quanteda[corpus, docvars, docnames, dfm, convert, dfm_tfidf, dfm_subset],
-  quanteda.textstats[textstat_simil],
-  utils[head],
-  spacyr[spacy_parse],
-  dplyr[mutate, filter, select],
-  data.table[setorderv],
-)
-
-box::use(
-  app/logic/utils[parse_recommendations]
-)
-
-
-create_ref_corpus <- function(data_tab, field) {
-  corp <- corpus(data_tab, text_field = field)
-  docnames(corp) <- data_tab$book_id
-  return(corp)
-}
-
-
-spacy_pipeline <- function(corp) {
-  browser()
-  res <- corp |> spacy_parse(nounphrase = TRUE) 
-  res_tokens <- res |> 
-    filter(
-      ! pos %in% c("PUNCT", "PART", "NUM", "SYM"),
-      ! entity %in% c("PERSON_B", "PERSON_I") 
-    ) |>
-    mutate(
-      lemma = tolower(lemma)
-    )
-  
-  all <- res_tokens |>
-    group_by(sentence_id, nounphrase) |>
-    mutate(nounphrase_id = cumsum(nounphrase %in% c("beg_root", ""))) |>
-    group_by(sentence_id, nounphrase_id) |>
-    mutate(has_entity = ifelse(entity!= "", 1, 0)) |>
-    as.data.table()
-  
-  
-  phrases <- all |>
-    filter(nounphrase_id == 0, has_entity == 1) 
-  
-  non_phrases <- fsetdiff(all, phrases)
-  
-  phrases <- phrases |>
-    mutate(nounphrase_id = cumsum(nounphrase == "beg"), seq_id = -1)
-  
-  phrases[1, ]$seq_id <- 0
-  
-  phrases$seq_id <- cumsum(c(TRUE, phrases$sentence_id[-1]!= phrases$sentence_id[-nrow(phrases)] | 
-                               phrases$token_id[-1]!= phrases$token_id[-nrow(phrases)] + 1))
-  
-  phrases_concat <- phrases[,c("token", "lemma", "pos", "entity") := 
-                              .(paste(token, collapse = " "), 
-                                paste(lemma, collapse = " "), 
-                                paste(pos, collapse = " "), 
-                                paste(entity, collapse = " ")), 
-                            by =.(nounphrase_id, sentence_id, seq_id)]
-  
-  phrases_concat <- unique(phrases_concat, by = c("sentence_id", "nounphrase_id", "token", "seq_id"))
-  non_phrases[, c("nounphrase_id", "has_entity") := NULL]
-  phrases_concat[, c("nounphrase_id", "has_entity", "seq_id") := NULL]
-  
-  joined <- rbindlist(list(non_phrases, phrases_concat))
-  setorder(joined, doc_id, sentence_id, token_id)
-  
-  class(joined) <- c("spacyr_parsed", class(joined))
-  res_tokens <- joined |> as.tokens(
-    use_lemma = TRUE
-  )
-  
-  corp_dfm <- res_tokens |> dfm()
-  docvars(corp_dfm) <- docvars(corp)
-  
-  saveRDS(corp_dfm, "./data/ref_corp_dfm_new.rds")
-  
-  
-  corp_tfidf <- corp_dfm |> dfm_tfidf()
-  
-  saveRDS(corp_tfidf, "./data/ref_corp_tfidf_new.rds")
-}
-
-#' export
-get_recommendations <- function(corp_dfm, data_tab, query_book_ids, genres, simil_method = "cosine", how_many) {
-  query_dfm <- dfm_subset(corp_dfm, docname_ %in% query_book_ids)
-  if (!is.null(genres)) {
-    corp_dfm <- corp_dfm[grep(genres, paste(docvars(corp_dfm)$genres)),]
-  }
-  rest_dfm <- dfm_subset(corp_dfm, !docname_ %in% query_book_ids)
-  
-
-  tstat <- textstat_simil(
-    query_dfm, rest_dfm,
-    margin = "documents",
-    method = simil_method
-  ) |>
-  as.data.frame()
-  setorderv(tstat, cols = c(simil_method), order = -1)
-  recommendations <- parse_recommendations(tstat[1:how_many,]$document2, data_tab, "TFIDF")
-  return(recommendations)
-}
-
-
-
-

app/logic/utils.R

@@ -1,47 +1,21 @@
 box::use(
-  dplyr[select, filter]
+  dplyr[group_by, summarise, filter],
+  stats[median]
 )
 
 #' @export
-split_number <- function(number, n){
-  # Calculate the base value for each part (integer division)
-  base_part = number %/% n
-  
-  # Initialize the result list
-  result = rep(base_part, n)
-  
-  # Distribute the remainder among the first n-1 elements
-  remainder = number %% n
-  i <- 1
-  for(one in rep(1, remainder)) {
-    result[[i]] <- result[[i]] + one
-    i <- i + 1
-  }
-  
-  return(result)
-}
-
-
-#' @export
-get_random_titles <- function(books_tab, how_many) {
-  rows <- sample(1:nrow(books_tab), how_many)
-  selected <- books_tab[rows,]
-  selected <- selected |>  select(
-    title, average_rating, description, url, image_url, genres, author_name
-  )
-  selected$model <- "random"
-  return(selected)
+get_weekly_tab <- function(daily_data, var) {
+  weekly_tab <- daily_data |> 
+    group_by(week_index) |>
+    summarise(
+      weekly_total = sum(get({{var}})),
+      high_temp_median = median(high_temp_C),
+      low_temp_median = median(low_temp_C),
+      mean_precipitation = mean(precipitation)
+    )
 }
 
 #' @export
-parse_recommendations <- function(rec_book_ids, data_tab, model) {
-  subset_books <- data_tab |> 
-    filter(
-      book_id %in% rec_book_ids 
-    ) |>
-    select(
-      title, average_rating, description, url, image_url, genres, author_name
-    )
-  subset_books$model <- model
-  return(subset_books)
+get_bridge_tab <- function(data, var) {
+  data |> filter(name == var)
 }

app/main.R

@@ -1,132 +1,126 @@
 box::use(
-  utils[head, read.csv2],
-  shiny[div, moduleServer, downloadLink, downloadHandler, sliderInput, tagList, req, reactiveVal, actionButton, fileInput, observeEvent, h1, h3, p, NS, selectizeInput, HTML, tags],
-  bslib[page_fillable, page_sidebar, nav_panel, page_navbar, layout_columns, card, card_header, card_body, layout_column_wrap, value_box, input_dark_mode, nav_item],
-  shinyWidgets[pickerInput],
-  waiter[useWaiter, autoWaiter, waiter_show, spin_fading_circles, waiter_hide, waiterShowOnLoad, waiter_on_busy],
-  spacyr[spacy_install],
-  data.table[fread, fwrite],
+  shiny[tagList, div, reactive, moduleServer, observeEvent, NS, renderUI, tags, uiOutput, numericInput],
+  bslib[page_sidebar, nav_panel, navset_tab],
+  data.table[fread],
+  shinyWidgets[pickerInput, materialSwitch],
+  shinyjs[hidden, disable, enable, useShinyjs]
 )
 
+
 box::use(
-  view / mod_search_books,
-  view / mod_recommend_books,
-  view / mod_data_analysis
+  view/mod_value_boxes,
+  view/mod_poisson_model,
+  logic/utils[get_weekly_tab, get_bridge_tab]
 )
 
-
 #' @export
 ui <- function(id) {
   ns <- NS(id)
-  page_navbar(
-    title = "Book Recommender",
+  page_sidebar(
+    useShinyjs(),
+    title = "New York bridges analysis",
     sidebar = my_sidebar(ns),
-    fillable = FALSE,
-    nav_panel(
-      useWaiter(),
-      waiter_on_busy(html = tagList(div(class = "main-waiter", h3("Give me a second to read all those books..."), spin_fading_circles()))),
-      # waiterShowOnLoad(html = tagList(div(class = "main-waiter", h3("Give me a second to read all those books..."), spin_fading_circles()))),
-      title = "Recommendations",
-      tags$main(
-        class = "main-container",
-        tags$section(
-          h1("Discover books you will love!", class = "align-text-center"),
-          div(class = "text-main align-text-center", p(" Enter books you like and the site will analyse the contents of the books to provide book recommendations and suggestions for what to read next.")),
-          mod_search_books$ui(ns("search_books")),
-          mod_recommend_books$ui(ns("recommend_books"))
-        )
-      )
-    ),
-    nav_panel(
-      "Data analysis",
-      mod_data_analysis$ui(ns("data_analysis"))
-    ),
-    # nav_item(
-    #   fileInput(ns("upload_goodreads"), NULL, buttonLabel = "Upload goodreads", multiple = FALSE)
-    # ),
+    navset_tab(
+      nav_panel(
+        title = "Poisson model",
+        mod_value_boxes$ui(ns("value_boxes")),
+        mod_poisson_model$ui(ns("poisson_model"))
+      ),
+      nav_panel(title = "Poisson regression")
+    )
   )
 }
 
 #' @export
 server <- function(id) {
   moduleServer(id, function(input, output, session) {
-    gargoyle::init("start_recommend_event")
-
-    # LOAD DATA ---------------------------------------------------------------
-
-    data <- load_data("data/dataset_goodreads_filtered.csv")
-    item_item_df <- fread("data/item_to_item_similarity_dataframe_full.csv")
-    user_ratings_tab <- readRDS("data/ratings_filtered.rds")
-    corp_dfm <- readRDS("data/ref_corp_tfidf_new.rds")
-    SVD_model <- readRDS("data/svdf.rds")
-    # spacy_install()
-
-    selected_books_ids <- mod_search_books$server("search_books", data$title, data$image_url, data$book_id)
-    mod_data_analysis$server("data_analysis")
-
-    output$downloadData <- downloadHandler(
-      filename = function() {
-        "system_recommendations_log.csv"
-      },
-      content = function(file) {
-        data <- fread("system_recommendations_log.csv")
-        fwrite(data, file)
-      }
-    )
     
-    observeEvent(input$method, {
-      mod_recommend_books$server(
-        "recommend_books",
-        user_ratings_tab,
-        SVD_model,
-        corp_dfm,
-        item_item_df,
-        selected_books_ids,
-        data,
-        input$how_many_recommends_slider,
-        input$simil_metrics,
-        input$method
-      )
+    ns <- session$ns
+    # Read data ---------------------------------------------------------------
+    
+    data <- reactive({fread("data/bike_data_filtered_long.csv")})
+    
+    recompute_trigger <- reactive({
+      list(input$data_group_input, input$prior_a_input, input$prior_b_input, input$jeffreys_prior)
     })
-
-    observeEvent(input$how_many_recommends_slider, {
-      mod_recommend_books$server(
-        "recommend_books",
-        user_ratings_tab,
-        SVD_model,
-        corp_dfm,
-        item_item_df,
-        selected_books_ids,
-        data,
-        input$how_many_recommends_slider,
-        input$simil_metrics,
-        input$method
-      )
-      gargoyle::trigger("start_recommend_event")
+    
+    observeEvent(input$jeffreys_prior, {
+      if (input$jeffreys_prior) {
+        disable(selector = "div.prior_params_input_container > div.shiny-input-container > input")
+      } else {
+        enable(selector = "div.prior_params_input_container > div.shiny-input-container > input")
+      }
+    })
+    
+    observeEvent(recompute_trigger(), {
+      if (grepl("*Bridge", input$data_group_input)) {
+        run_servers(
+          mod_value_boxes$server,
+          mod_poisson_model$server,
+          data,
+          tolower(gsub(" Bridge", "", input$data_group_input)),
+          input$prior_a_input,
+          input$prior_b_input,
+          input$jeffreys_prior
+        )
+      } else {
+        run_servers(
+          mod_value_boxes$server,
+          mod_poisson_model$server,
+          data,
+          "total",
+          input$prior_a_input,
+          input$prior_b_input,
+          input$jeffreys_prior
+        )
+      }
     })
   })
 }
 
 
-load_data <- function(path) {
-  data <- data.table::fread(path)
-  data[, genres := strsplit(genre, split = ",")]
-  data[, average_rating := as.numeric(average_rating)]
-  data$genre <- NULL
-  data$similar_books <- NULL
-  return(data)
-}
+run_servers <- function(value_box_server, pois_model_server, data_react, column_var, prior_a, prior_b, is_jeffreys) {
+  # weekly_data <- reactive({get_weekly_tab(data_react(), column_var)})
+  bridge_data <- reactive({get_bridge_tab(data_react(), column_var) })
+  value_box_server("value_boxes", bridge_data)
+  pois_model_server("poisson_model", bridge_data, prior_a, prior_b, is_jeffreys)
+} 
 
 
 my_sidebar <- function(ns) {
   tagList(
-    sliderInput(ns("how_many_recommends_slider"), "Number of books to recommend", 1, 100, 10, step = 1),
-    pickerInput(
-      inputId = ns("method"),
-      label = "recommendation method",
-      selected = "ALL",
-      choices = c("SVD", "TFIDF", "item-item", "ALL")
+    div(
+      class = "prior_params_input_container",
+      numericInput(
+        ns("prior_a_input"),
+        "Input prior gamma parameter a",
+        value = 1/2,
+        min = 0
+      ),
+      numericInput(
+        ns("prior_b_input"),
+        "Input prior gamma parameter b",
+        value = 0,
+        min = 0
+      )
     ),
-    downloadLink(ns("downloadData"), "Download logs")
+    materialSwitch(
+      ns("jeffreys_prior"),
+      label = "Jeffreys prior",
+      value = FALSE,
+      status = 'primary'
+    ),
+    pickerInput(
+      inputId = ns("data_group_input"),
+      label = "Select data group",
+      selected = "Brooklyn Bridge",
+      choices = c(
+        "Brooklyn Bridge",
+        "Manhattan Bridge",
+        "Williamsburg Bridge",
+        "Queensboro Bridge",
+        "All"
+      )
+    )
   )
 }

app/static/css/app.min.css

@@ -1 +0,0 @@
-*{box-sizing:border-box}.value-box-showcase{overflow:hidden}.vscomp-option{height:100px !important}.booksearch__img{margin-right:.4rem;height:45px;width:30px}.booksearch__title{margin-left:.4rem;font-size:1rem;font-weight:600}.align-text-center{text-align:center}.text-main{font-size:1.2rem;font-weight:500;margin:1.2rem}.select_books_input_container{display:flex;justify-content:center;align-items:center}.select_books_input_container>div.form-group{width:75vw}.vscomp-options-container{max-height:420px !important}.flex-center{display:flex;flex-direction:column;justify-content:center;align-items:center}.main{height:95vh;margin:0;padding:3rem 0 1rem 0}.main-container{display:flex;flex-direction:column;justify-content:center;align-items:center}.main-waiter{display:flex;flex-direction:column;justify-content:space-between;align-items:center}#app-recommend_books-bookCardsOutput{display:flex;justify-content:space-evenly;align-items:center;flex-wrap:wrap;margin-top:2rem;width:80vw}.book-card__container{display:flex;justify-content:center;align-items:center;margin:1rem;flex-direction:column;width:300px;margin-bottom:1rem;-webkit-box-shadow:10px 10px 22px 4px rgba(0,0,0,.75);-moz-box-shadow:10px 10px 22px 4px rgba(0,0,0,.75);box-shadow:10px 10px 22px 4px rgba(0,0,0,.75)}.book-card__cover{display:flex;justify-content:center;align-items:center;flex-direction:column;height:200px;width:300px;background-color:#fffaf0}.book-card__cover__image{height:180;width:92;-webkit-box-shadow:10px 10px 22px 4px rgba(0,0,0,.75);-moz-box-shadow:10px 10px 22px 4px rgba(0,0,0,.75);box-shadow:10px 10px 22px 4px rgba(0,0,0,.75)}.book-card__info-section{display:flex;justify-content:center;align-items:center;flex-direction:column}.book-card__info-section__genre-section{display:flex;justify-content:space-evenly;align-items:center;flex-direction:row;margin-top:.5rem;margin-bottom:.5rem}.book-card__info-section__genre-section-container{color:#fff;text-align:center;margin-left:.25rem;margin-right:.25rem;border-radius:5px}.book-card__info-section__genre-section-conteiner-text{font-style:italic;font-size:.7rem;padding:5px;font-weight:600}.book-card__info-section__title{display:-webkit-box;-webkit-line-clamp:2;-webkit-box-orient:vertical;overflow:hidden;font-weight:600;font-size:1.2rem;height:3rem;color:#700202;text-align:center;margin-left:.3rem;margin-right:.3rem}.book-card__info-section__show-description-btn{background-color:#700202;color:#fff;margin-bottom:.6rem}.children-color{background-color:#4169e1}.fantasy-color{background-color:#b59410}.history_biography-color{background-color:#228b22}.comics-color{background-color:#000}.romance-color{background-color:maroon}.poetry-color{background-color:#003049}.YA-color{background-color:#f0f}.crime-color{background-color:navy}.book-card__info-section__rating-icon{height:1.5rem;width:1.5rem;color:#000;margin-bottom:5px}.book-card__info-section__rating-icon-rating{font-weight:550;font-size:1.1rem;margin-right:1rem;margin-left:.4rem}.book-card__info-section__author{color:#3d445e;font-weight:500;font-size:1.1rem}.img-card{max-width:450px !important}.img-gallery{display:grid;grid-template-columns:1fr 1fr 1fr}label.checkbtn{display:inline-flex;align-items:center;justify-content:center}.btn-group{flex-direction:column}@media(min-width: 960px){.btn-group{flex-direction:row}}

app/static/js/app.min.js.LICENSE.txt

@@ -1,34 +0,0 @@
-/*!
-* sweetalert2 v11.10.6
-* Released under the MIT License.
-*/
-
-/**
- * @license React
- * react-dom.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-/**
- * @license React
- * react.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */
-
-/**
- * @license React
- * scheduler.production.min.js
- *
- * Copyright (c) Facebook, Inc. and its affiliates.
- *
- * This source code is licensed under the MIT license found in the
- * LICENSE file in the root directory of this source tree.
- */

app/styles/main.scss

@@ -1,259 +0,0 @@
-* {
-  box-sizing: border-box;
-}
-
-.value-box-showcase {
-  overflow: hidden;
-}
-
-.vscomp-option {
-  height: 100px !important;
-}
-
-
-
-.booksearch__img {
-  margin-right: 0.4rem;
-  height: 45px;
-  width: 30px;
-}
-
-.booksearch__title {
-  margin-left: 0.4rem;
-  font-size: 1rem;
-  font-weight: 600;
-}
-
-
-.align-text-center {
-  text-align: center;
-}
-
-.text-main {
-  font-size: 1.2rem;
-  font-weight: 500;
-  margin: 1.2rem;
-}
-
-
-.select_books_input_container {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-
-}
-
-.select_books_input_container > div.form-group {
-  width: 75vw;
-}
-
-.vscomp-options-container {
-  max-height: 420px !important;
-}
-
-
-.flex-center {
-  display: flex;
-  flex-direction: column;
-  justify-content: center;
-  align-items: center;
-}
-
-.main {
-  height: 95vh;
-  margin: 0;
-  padding: 3rem 0 1rem 0;
-}
-
-.main-container {
-  display: flex;
-  flex-direction: column;
-  justify-content: center;
-  align-items: center;
-}
-
-.main-waiter {
-  display: flex;
-  flex-direction: column;
-  justify-content: space-between;
-  align-items: center;
-}
-
-
-#app-recommend_books-bookCardsOutput {
-  display: flex;
-  justify-content: space-evenly;
-  align-items: center;
-  flex-wrap: wrap;
-  margin-top: 2rem;
-  width: 80vw;
-
-}
-
-.book-card__container {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  margin: 1rem;
-  flex-direction: column;
-  width: 300px;
-  margin-bottom: 1rem;
-  -webkit-box-shadow: 10px 10px 22px 4px rgba(0, 0, 0, 0.75);
-  -moz-box-shadow: 10px 10px 22px 4px rgba(0, 0, 0, 0.75);
-  box-shadow: 10px 10px 22px 4px rgba(0, 0, 0, 0.75);
-}
-
-
-.book-card__cover {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  flex-direction: column;
-  height: 200px;
-  width: 300px;
-  background-color: floralwhite;
-}
-
-
-.book-card__cover__image {
-  height: 180;
-  width: 92;
-  -webkit-box-shadow: 10px 10px 22px 4px rgba(0, 0, 0, 0.75);
-  -moz-box-shadow: 10px 10px 22px 4px rgba(0, 0, 0, 0.75);
-  box-shadow: 10px 10px 22px 4px rgba(0, 0, 0, 0.75);
-}
-
-.book-card__info-section {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  flex-direction: column;
-}
-
-.book-card__info-section__genre-section {
-  display: flex;
-  justify-content: space-evenly;
-  align-items: center;
-  flex-direction: row;
-  margin-top: 0.5rem;
-  margin-bottom: 0.5rem;
-
-}
-
-.book-card__info-section__genre-section-container {
-  color: white;
-  text-align: center;
-  margin-left: 0.25rem;
-  margin-right: 0.25rem;
-  border-radius: 5px;
-}
-
-.book-card__info-section__genre-section-conteiner-text {
-  font-style: italic;
-  font-size: 0.7rem;
-  padding: 5px;
-  font-weight: 600;
-}
-
-.book-card__info-section__title {
-  display: -webkit-box;
-  -webkit-line-clamp: 2;
-  -webkit-box-orient: vertical;
-  overflow: hidden;
-  font-weight: 600;
-  font-size: 1.2rem;
-  height: 3rem;
-  color: #700202;
-  text-align: center;
-  margin-left: 0.3rem;
-  margin-right: 0.3rem;
-}
-
-
-.book-card__info-section__show-description-btn {
-  background-color: #700202;
-  color: white;
-  margin-bottom: 0.6rem;
-}
-
-.children-color {
-  background-color: #4169E1;
-}
-
-.fantasy-color {
-  background-color: #B59410;
-}
-
-
-.history_biography-color {
-  background-color: #228B22;
-}
-
-.comics-color {
-  background-color: black;
-}
-
-.romance-color {
-  background-color: #800000;
-}
-
-.poetry-color {
-  background-color: #003049;
-}
-
-.YA-color {
-  background-color: #FF00FF;
-}
-
-.crime-color {
-  background-color: navy;
-}
-
-.book-card__info-section__rating-icon {
-  height: 1.5rem;
-  width: 1.5rem;
-  color: black;
-  margin-bottom: 5px;
-}
-
-.book-card__info-section__rating-icon-rating {
-  font-weight: 550;
-  font-size: 1.1rem;
-  margin-right: 1rem;
-  margin-left: 0.4rem;
-}
-
-
-.book-card__info-section__author {
-  color: #3D445E;
-  font-weight: 500;
-  font-size: 1.1rem;
-}
-
-
-.img-card {
-  max-width: 450px !important;
-}
-
-.img-gallery {
-  display: grid;
-  grid-template-columns: 1fr 1fr 1fr;
-}
-
-
-label.checkbtn {
-  display: inline-flex;
-  align-items: center;
-  justify-content: center;
-}
-
-
-.btn-group {
-  flex-direction: column;
-}
-
-@media (min-width: 960px) {
-  .btn-group {
-    flex-direction: row;
-  }
-}

app/view/mod_data_analysis.R

@@ -1,64 +0,0 @@
-box::use(
-  shiny[moduleServer, fluidRow, NS, uiOutput, div, renderImage, renderUI],
-  bslib[layout_column_wrap, value_box, card, card_header, card_body, layout_columns],
-  bsicons[bs_icon],
-  purrr[map],
-  grDevices[replayPlot]
-)
-
-#' @export
-ui <- function(id) {
-  ns <- NS(id)
-  div(
-    layout_column_wrap(
-      value_box(
-        title = "Number of books",
-        value = "15591",
-        showcase = bs_icon("book-half"),
-        theme = "purple"
-      ),
-      value_box(
-        title = "Average book rating",
-        value = "4.03",
-        showcase = bs_icon("star-fill"),
-        theme = "purple"
-      ),
-      value_box(
-        title = "Number of genres",
-        value = "8",
-        showcase = bs_icon("tags-fill"),
-        theme = "purple"
-      )
-    ),
-    uiOutput(ns("wordcloud_plots"))
-    
-  )
-
-}
-
-#' @export
-server <- function(id) {
-  moduleServer(id, function(input, output, session) {
-    images <- list.files("data/plots", full.names = TRUE)
-    output$wordcloud_plots <- renderUI({
-      div(
-        class = "img-gallery",
-        map(.x = images, .f = create_plot_card)
-      )
-    })
-  })
-}
-
-create_plot_card <- function(plot_img_path) {
-
-  name <- strsplit(plot_img_path, "/")[[1]][3] |> gsub(pattern = ".png*", replacement = "")
-  card(
-    class = "img-card",
-    card_header(name),
-    card_body(
-      renderImage({
-        list(src = plot_img_path, height = "400px")
-      })
-    )
-  )
-}

app/view/mod_poisson_model.R

@@ -0,0 +1,259 @@
+box::use(
+  shiny[observeEvent, tagList, numericInput, reactiveVal, moduleServer, actionButton, reactive, req, NS, plotOutput, renderPlot],
+  bslib[layout_column_wrap, layout_sidebar, sidebar,  card, card_header],
+  dplyr[bind_rows, filter],
+  data.table[setnames],
+  reactable[reactableOutput, reactable, renderReactable, colDef, colFormat, reactableTheme],
+)
+
+
+box::use(
+  app / logic / poisson_model[
+    get_poisson_prior_tab,
+    get_poisson_posterior_tab,
+    get_poisson_likelihood_tab,
+    get_distribution_plot,
+    get_poisson_sensitivity_plot,
+    get_posterior_poisson_characteristics,
+    get_prior_poisson_characteristics
+  ]
+)
+
+#' @export
+ui <- function(id) {
+  ns <- NS(id)
+  layout_column_wrap(
+    plot_card(
+      "Distribution plot",
+      ns("distribution_plot"),
+      settings = tagList(
+        numericInput(
+          ns("theta_start"),
+          "Theta interval start",
+          value = 500,
+          min = 0
+        ),
+        numericInput(
+          ns("theta_end"),
+          "Theta interval end",
+          value = 20000,
+          min = 0
+        ),
+        numericInput(
+          ns("linewidth_dist"),
+          "density line width",
+          value = 1.5,
+          min = 1
+        ),
+      ),
+      ns("btn_redraw_dist"),
+      "Redraw plot"
+    ),
+    table_card(
+      "Model characteristics",
+      ns("characteristics_tab")
+    )
+    # plot_card(
+    #   "Sensitivity plot",
+    #   ns("sensitivity_plot"),
+    #   tagList(
+    #     numericInput(
+    #       ns("prior_mean_start"),
+    #       "Prior mean interval start",
+    #       value = 0,
+    #       min = 0
+    #     ),
+    #     numericInput(
+    #       ns("prior_mean_end"),
+    #       "Prior mean interval end",
+    #       value = 500,
+    #       min = 0
+    #     ),
+    #     numericInput(
+    #       ns("belief_start"),
+    #       "Degree of belief interval start",
+    #       value = 0,
+    #       min = 0
+    #     ),
+    #     numericInput(
+    #       ns("belief_end"),
+    #       "Degree of belief interval end",
+    #       value = 500,
+    #       min = 0
+    #     )
+    #   ),
+    #   ns("btn_redraw_sensitivity"),
+    #   "Redraw plot"
+    # )
+  )
+  
+}
+
+
+#' @export
+server <- function(id, data, prior_a, prior_b, is_jeffreys) {
+  moduleServer(id, function(input, output, session) {
+    
+    full_dist_tab <- reactiveVal() 
+    distribution_plot <- reactiveVal()
+    characteristics_reactable <- reactiveVal()
+    sensitivity_plot <- reactiveVal(
+      # get_poisson_sensitivity_plot(
+      #   prior_a,
+      #   prior_b,
+      #   nrow(weekly_data()),
+      #   sum(weekly_data()$weekly_total),
+      #   input$prior_mean_start,
+      #   input$prior_mean_end,
+      #   input$belief_start,
+      #   input$belief_end
+      # )
+    )
+    
+    recompute_prior_trigger <- reactive({
+      list(data(), input$btn_redraw_dist)
+    })
+    
+    observeEvent(recompute_prior_trigger(), {
+      req(data())
+      prior_tab_dry <- get_poisson_prior_tab(input$theta_start, input$theta_end, prior_a, prior_b, 0.1, "Dry prior", is_jeffreys)
+      prior_tab_rainy <- get_poisson_prior_tab(input$theta_start, input$theta_end, prior_a, prior_b, 0.1, "Rainy prior", is_jeffreys)
+      
+      prior <- bind_rows(prior_tab_dry, prior_tab_rainy)
+      
+      dry_data <- data() |> filter(!is_rainy)
+      wet_data <- data() |> filter(is_rainy)
+      
+      theta <- prior_tab_dry$theta
+      n_dry <- nrow(dry_data)
+      y_dry <- sum(dry_data$value)
+      
+      n_wet <- nrow(wet_data)
+      y_wet <- sum(wet_data$value)
+      
+      posterior_tab_dry <- get_poisson_posterior_tab(prior_a, prior_b, n_dry, y_dry, theta, "Dry posterior")
+      posterior_tab_rainy <- get_poisson_posterior_tab(prior_a, prior_b, n_wet, y_wet, theta, "Rainy posterior")
+      
+      posterior <- bind_rows(posterior_tab_dry, posterior_tab_rainy)
+      
+      likelihood_tab_dry <- get_poisson_likelihood_tab(n_dry, y_dry, theta, 10, "Dry likelihood")
+      likelihood_tab_rainy <- get_poisson_likelihood_tab(n_wet, y_wet, theta, 10, "Rainy likelihood")
+      
+      likelihood <- bind_rows(likelihood_tab_dry, likelihood_tab_rainy)
+      
+      full_tab <- bind_rows(prior, posterior, likelihood)
+      
+      full_dist_tab(full_tab)
+      distribution_plot(get_distribution_plot(full_dist_tab(), input$linewidth_dist))
+      characteristics_reactable(compute_characteristics(prior_a, prior_b, n_dry, y_dry, posterior_tab_dry, n_wet, y_wet, posterior_tab_rainy))
+      #sensitivity_plot(get_poisson_sensitivity_plot(prior_a, prior_b, n, y))
+    })
+    
+    observeEvent(input$btn_redraw_sensitivity, {
+      print("HELLO")
+      n <- nrow(data())
+      y <- sum(data()$value)
+      plt <- get_poisson_sensitivity_plot(
+        prior_a,
+        prior_b,
+        n,
+        y,
+        input$prior_mean_start,
+        input$prior_mean_end,
+        input$belief_start,
+        input$belief_end
+      )
+      sensitivity_plot(plt)
+    })
+    
+    output$characteristics_tab <- renderReactable({
+      characteristics_reactable()
+    })
+    
+    
+    output$distribution_plot <- renderPlot({
+      req(distribution_plot())
+      distribution_plot()
+    })
+    
+    output$sensitivity_plot <- renderPlot({
+      req(sensitivity_plot())
+      sensitivity_plot()
+    })
+    
+  })
+}
+
+
+compute_characteristics <-function(prior_a, prior_b, n_dry, y_dry, dry_post_tab, n_wet, y_wet, rainy_post_tab) {
+  prior_characteristics_df <- get_prior_poisson_characteristics(prior_a, prior_b)
+  dry_characteristics_df <- get_posterior_poisson_characteristics(
+    prior_a,
+    prior_b,
+    n_dry,
+    y_dry,
+    dry_post_tab,
+    "Dry posterior"
+  )
+  
+  rainy_characteristics_df <- get_posterior_poisson_characteristics(
+    prior_a,
+    prior_b,
+    n_wet,
+    y_wet,
+    rainy_post_tab,
+    "Rainy posterior"
+  )
+  
+  characteristics_df <- rbind(prior_characteristics_df, dry_characteristics_df, rainy_characteristics_df)
+  setnames(characteristics_df, c("HPD.lower", "HPD.upper"), c("HPD.lwr", "HPD.upr"))
+  reactable(characteristics_df,
+            resizable = TRUE,
+            compact = TRUE,
+            columns = list(
+              MLE = colDef(format = colFormat(digits = 2)),
+              MAP = colDef(format = colFormat(digits = 2)),
+              mean = colDef(format = colFormat(digits = 2)),
+              SD = colDef(format = colFormat(digits = 2)),
+              ETCI.lwr = colDef(format = colFormat(digits = 2)),
+              ETCI.upr = colDef(format = colFormat(digits = 2)),
+              HPD.lwr = colDef(format = colFormat(digits = 2)),
+              HPD.upr = colDef(format = colFormat(digits = 2))
+            ),
+            defaultColDef = colDef(width = 90, align = "center"),
+            defaultPageSize = 15,
+            theme = reactableTheme(
+              style = list(fontFamily = "-system-ui, -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial, sans-serif", ".reactable-container" = list(width = "auto"))
+            ))
+}
+
+
+plot_card <- function(header, plot_id, settings = NULL, btn_id = NULL, btn_label = NULL) {
+  card(
+    full_screen = TRUE,
+    card_header(
+      class = "bg-dark",
+      header
+    ),
+    layout_sidebar(
+      fillable = TRUE,
+      sidebar = sidebar(
+        open = FALSE,
+        settings,
+        actionButton(btn_id, btn_label),
+      ),
+      plotOutput(plot_id)
+    )
+  )
+} 
+
+table_card <- function(header, table_id) {
+  card(
+    full_screen = TRUE,
+    card_header(
+      class = "bg-dark",
+      header
+    ),
+    reactableOutput(table_id)
+  )
+}

app/view/mod_recommend_books.R

@@ -1,131 +0,0 @@
-box::use(
-  shiny[
-    moduleServer,
-    textOutput,
-    renderText,
-    NS,
-    tags,
-    req,
-    div,
-    actionButton,
-    reactiveVal,
-    tagList,
-    observeEvent,
-    uiOutput,
-    renderUI
-  ],
-  dplyr[select],
-  methods[as],
-  shinyWidgets[checkboxGroupButtons],
-  purrr[pmap],
-  data.table[data.table, fwrite]
-
-)
-
-box::use(
-  app/logic/tfidf_model[get_recommendations],
-  app/logic/SVD_model[SVD_predict],
-  app/logic/item_item_model[get_item_item_recommendations],
-  app/logic/utils[split_number, get_random_titles],
-  app/view/react[BookCard],
-)
-
-#' @export
-ui <- function(id) {
-  ns <- NS(id)
-  div(
-    class = "flex-center",
-    checkboxGroupButtons(
-      inputId = ns("genre_selector"),
-      label = "Choose genres to be included:", 
-      choices = c(`<div class=''>fantasy</div>` = "fantasy",
-                  `<span class=''>children</span>` = "children",
-                  `<span class=''>history & biography</span>` = "history_biography",
-                  `<span class=''>comics</span>` = "comics",
-                  `<span class=''>romance</span>` = "romance",
-                  `<span class=''>poetry</span>` = "poetry",
-                  `<span class=''>YA</span>` = "YA",
-                  `<span class=''>crime</span>` = "crime"
-                  ),
-      justified = TRUE,
-    ),
-    textOutput(ns("mytext1")),
-    
-    
-    actionButton(ns("get_recommend_btn"), "Get Recommendations"),
-    uiOutput(ns("bookCardsOutput"))
-    
-
-  )
-}
-
-#' @export
-server <- function(id, ratings_tab, SVD_model, corp_dfm, item_item_df, query_book_ids, data_tab, how_many, simil_metrics, method = "SVD") {
-  moduleServer(id, function(input, output, session) {
-    book_recommends_tab <- reactiveVal()
-    
-    observeEvent(input$get_recommend_btn, {
-      gargoyle::trigger("start_recommend_event")
-    })
-    
-    observeEvent(gargoyle::watch("start_recommend_event"), {
-      req(query_book_ids())
-      if (method == "SVD") {
-        recommendations <- SVD_predict(data_tab, query_book_ids(), ratings_tab, SVD_model, select_user_mat, how_many = how_many)
-        book_recommends_tab(recommendations)
-      }
-      else if (method == "TFIDF") {
-        recommendations <- get_recommendations(corp_dfm, data_tab, query_book_ids(), input$genre_selector, "cosine", how_many)
-        book_recommends_tab(recommendations)
-      }
-      else if (method == "item-item") {
-        recommendations <- get_item_item_recommendations(item_item_df, data_tab, query_book_ids(), how_many)
-        book_recommends_tab(recommendations)
-      }
-      else {
-        parts <- split_number(how_many, 3)
-        
-        SVD_recommends <- SVD_predict(data_tab, query_book_ids(), ratings_tab, SVD_model, select_user_mat, how_many = parts[1])
-        tfidf_recommends <- get_recommendations(corp_dfm, data_tab, query_book_ids(), input$genre_selector, "cosine", parts[2])
-        item_item_recommendations <- get_item_item_recommendations(item_item_df, data_tab, query_book_ids(), how_many)
-        #random <- get_random_titles(data_tab, parts[3])
-        
-        all_recs <- rbind(SVD_recommends, tfidf_recommends, item_item_recommendations)
-        all_recs <- all_recs[sample(1:nrow(all_recs)), ] 
-        book_recommends_tab(all_recs)
-        
-      }
-    })
-
-    observeEvent(input$myval, {
-      req(input$myval)
-      record <- input$myval
-      record_row <- data.table(
-        query_book_id = query_book_ids(),
-        recommended_title = record$title,
-        model = record$model,
-        datetime = date()
-      )
-      fwrite(record_row, "system_recommendations_log.csv", append = TRUE)
-      
-    })
-    
-    output$bookCardsOutput <- renderUI({
-      req(book_recommends_tab())
-      pmap(.l = book_recommends_tab(), .f = create_card)
-    })
-  })
-}
-
-create_card <- function(title, average_rating, description, url, image_url, genres, author_name, model) {
-  BookCard(title = title,
-           avg_rating = average_rating,
-           genres = as.list(genres),
-           description = description,
-           author_name = author_name,
-           imageUrl = image_url,
-           url = url,
-           model = model
-
-  )
-}

app/view/mod_search_books.R

@@ -1,44 +0,0 @@
-box::use(
-  shiny[moduleServer, observeEvent, reactive, NS, div, uiOutput, renderUI],
-  bslib[card, card_header, card_body],
-  shinyWidgets[virtualSelectInput],
-)
-
-#' @export
-ui <- function(id) {
-  ns <- NS(id)
-  uiOutput(ns("select_books_ui_output"))
-}
-
-#' @export
-server <- function(id, book_titles, book_url_images, book_ids) {
-  moduleServer(id, function(input, output, session) {
-    ns <- session$ns
-    
-    
-    output$select_books_ui_output <- renderUI({
-      res <- div(
-        class = "select_books_input_container",
-        virtualSelectInput(
-          ns("select_books_input"),
-          "",
-          choices = list(label=sprintf("<div class=\"booksearch\" >
-                                        <img class=\"booksearch__img\" src=\"%s\"/>
-                                        <span class=\"booksearch__title\">%s</span>
-                                     </div>", book_url_images, book_titles),
-                         value = book_ids) |> purrr::transpose(),
-          multiple = TRUE,
-          optionsCount = 6,
-          search = TRUE,
-          width = "70vw",
-          showValueAsTags = TRUE,
-          html = TRUE
-        )
-      )
-      res
-    }) 
-
-    return(reactive({input$select_books_input}))
-  })
-
-}

app/view/mod_value_boxes.R

@@ -0,0 +1,93 @@
+box::use(
+  shiny[moduleServer, div, textOutput, p, renderText, h1, NS, uiOutput, renderUI],
+  dplyr[mutate],
+  bslib[layout_column_wrap, value_box],
+  bsicons[bs_icon],
+  plotly[plot_ly, add_lines, add_markers, config, layout],
+)
+
+
+#' @export
+ui <- function(id) {
+  ns <- NS(id)
+  div(
+    uiOutput(ns("value_boxes_uioutput")),
+  )
+  
+}
+
+
+#' @export
+server <- function(id, data) {
+  moduleServer(id, function(input, output, session) {
+    output$value_boxes_uioutput <- renderUI({
+      layout_column_wrap(
+        value_box(
+          title = "Number of measured weeks",
+          value = nrow(data()),
+          showcase = bs_icon("calendar-date"),
+          theme = "blue"
+        ),
+        value_box(
+          title = "mean number of bicycles",
+          value = round(mean(data()$value), 2),
+          showcase = create_sparkline(data()),
+          full_screen = TRUE,
+          theme = "success"
+        )
+      )
+    })
+    
+
+    
+  })
+}
+
+create_sparkline <- function(data) {
+  
+  plot_ly(data) |>
+    add_lines(
+      x = ~V1, y = ~value,
+      span = I(1),
+      color = I("white"),
+      fill = 'tozeroy', alpha = 0.2,
+      showlegend = FALSE
+    ) |>
+    add_markers(
+      x = ~V1, y = ~value,
+      marker = list(
+        symbol = ~ifelse(is_rainy, "triangle-up", "circle"), 
+        color = ~ifelse(is_rainy, "red", "white"),
+        size = 6
+      ),
+      name = ~ifelse(is_rainy, "Rainy", "Dry")
+      
+    ) |>
+    layout(
+      xaxis = list(visible = F, showgrid = F, title = "", tickvals = data$V1,  ticktext = data$date),
+      yaxis = list(visible = F, showgrid = F, title = ""),
+      hovermode = "x",
+      showlegend = TRUE,
+      legend = list(
+        title = list(text = "Day Type"), # Customize legend title if needed 
+        color = ~ifelse(is_rainy, "red", "white"),
+        traceorder = "normal" 
+      ),
+      margin = list(t = 0, r = 0, l = 0, b = 0),
+      font = list(color = "white"),
+      paper_bgcolor = "transparent",
+      plot_bgcolor = "transparent"
+    ) |>
+    config(displayModeBar = F) |>
+    htmlwidgets::onRender(
+      "function(el) {
+      var ro = new ResizeObserver(function() {
+         var visible = el.offsetHeight > 200;
+         Plotly.relayout(el, {'xaxis.visible': visible});
+      });
+      ro.observe(el);
+    }"
+    )
+}
+
+

app/view/react.R

@@ -1,5 +0,0 @@
-box::use(
-  rhino[react_component],
-)
-#' export
-BookCard <- react_component("BookCard")

data/bike_data_filtered.csv

@@ -0,0 +1,215 @@
+"","V1","date","day","brooklyn","manhattan","williamsburg","queensboro","total","precipitation","high_temp_C","low_temp_C","week_index","is_rainy"
+"1",1,"4/1","Saturday",606,1446,1915,1430,5397,0,7.77777777777778,2.77777777777778,0,FALSE
+"2",2,"4/2","Sunday",2021,3943,4207,2862,13033,0,16.7222222222222,5,0,FALSE
+"3",3,"4/3","Monday",2470,4988,5178,3689,16325,0.762,17.2222222222222,10,0,FALSE
+"4",4,"4/4","Tuesday",723,1913,2279,1666,6581,29.972,10.6111111111111,7.77777777777778,0,TRUE
+"5",5,"4/5","Wednesday",2807,5276,5711,4197,17991,0,17.2222222222222,7.77777777777778,0,FALSE
+"6",6,"4/6","Thursday",461,1324,1739,1372,4896,18.542,9.38888888888889,5,0,TRUE
+"7",7,"4/7","Friday",1222,2955,3399,2765,10341,0.254,8.88888888888889,6.11111111111111,0,FALSE
+"8",8,"4/8","Saturday",1674,3163,4082,2691,11610,0,13.2777777777778,4.38888888888889,1,FALSE
+"9",9,"4/9","Sunday",2375,4377,4886,3261,14899,0,18.8888888888889,7.22222222222222,1,FALSE
+"10",10,"4/10","Monday",3324,6359,6881,4731,21295,0,23.2777777777778,12.7777777777778,1,FALSE
+"11",11,"4/11","Tuesday",3887,7247,8079,5501,24714,0,26.7222222222222,16.7222222222222,1,FALSE
+"12",12,"4/12","Wednesday",2565,5633,6620,4537,19355,0.508,23.2777777777778,14.3888888888889,1,FALSE
+"13",13,"4/13","Thursday",3353,6052,6775,4700,20880,0,17.7777777777778,9.38888888888889,1,FALSE
+"14",14,"4/14","Friday",2942,5054,5877,4142,18015,0,18.2777777777778,9.38888888888889,1,FALSE
+"15",15,"4/15","Saturday",2253,3853,4965,3291,14362,0,18.2777777777778,11.1111111111111,2,FALSE
+"16",16,"4/16","Sunday",2877,4585,5259,3687,16408,0.254,29.3888888888889,16.7222222222222,2,FALSE
+"17",17,"4/17","Monday",3152,6352,6924,4658,21086,0.254,23.2777777777778,17.7777777777778,2,FALSE
+"18",18,"4/18","Tuesday",3415,6691,7341,5012,22459,0,18.8888888888889,10,2,FALSE
+"19",19,"4/19","Wednesday",1965,4632,5234,3996,15827,0.254,11.1111111111111,7.22222222222222,2,FALSE
+"20",20,"4/20","Thursday",1567,3365,4302,3056,12290,4.318,18.2777777777778,10,2,TRUE
+"21",21,"4/21","Friday",1426,3142,3674,2672,10914,7.366,11.7222222222222,8.88888888888889,2,TRUE
+"22",22,"4/22","Saturday",1318,2297,2963,2366,8944,2.794,13.2777777777778,11.1111111111111,3,TRUE
+"23",23,"4/23","Sunday",2520,4681,5105,3434,15740,0,18.2777777777778,8.27777777777778,3,FALSE
+"24",24,"4/24","Monday",2544,5366,5762,4183,17855,0.254,15.6111111111111,10,3,FALSE
+"25",25,"4/25","Tuesday",611,1402,1902,1578,5493,23.114,12.2222222222222,10,3,TRUE
+"26",26,"4/26","Wednesday",1247,2774,3429,2369,9819,8.636,15,12.2222222222222,3,TRUE
+"27",27,"4/27","Thursday",2959,5311,6026,4285,18581,0,20,15,3,FALSE
+"28",28,"4/28","Friday",3679,6774,7196,5138,22787,0,28.2777777777778,14.3888888888889,3,FALSE
+"29",29,"4/29","Saturday",3315,5605,6045,4078,19043,1.524,28.8888888888889,17.7777777777778,4,TRUE
+"30",30,"4/30","Sunday",2225,4057,4513,3137,13932,0,17.7777777777778,12.2222222222222,4,FALSE
+"31",31,"5/1","Monday",3084,6506,6793,4820,21203,0,22.2222222222222,10,4,FALSE
+"32",32,"5/2","Tuesday",3423,7035,7789,5449,23696,0,23.2777777777778,19.3888888888889,4,FALSE
+"33",33,"5/3","Wednesday",3342,6984,7445,4804,22575,0,18.2777777777778,14.3888888888889,4,FALSE
+"34",34,"5/4","Thursday",3019,6525,6922,5144,21610,0,17.2222222222222,10,4,FALSE
+"35",35,"5/5","Friday",513,1471,1951,1421,5356,76.708,15,11.1111111111111,4,TRUE
+"36",36,"5/6","Saturday",1892,3815,5502,3965,15174,4.572,18.2777777777778,13.8888888888889,5,TRUE
+"37",37,"5/7","Sunday",3539,3547,4941,3851,15878,0.254,12.2222222222222,9.38888888888889,5,FALSE
+"38",38,"5/8","Monday",2886,5812,6249,4432,19379,0,13.8888888888889,7.22222222222222,5,FALSE
+"39",39,"5/9","Tuesday",2718,6455,6910,5000,21083,0,16.1111111111111,8.88888888888889,5,FALSE
+"40",40,"5/10","Wednesday",2810,6396,7075,4980,21261,0,21.1111111111111,10.6111111111111,5,FALSE
+"41",41,"5/11","Thursday",2657,6179,6750,5139,20725,0,16.1111111111111,11,5,FALSE
+"42",42,"5/12","Friday",2640,5367,6325,4692,19024,0,16.7222222222222,10.6111111111111,5,FALSE
+"43",43,"5/13","Saturday",151,484,874,865,2374,33.274,10.6111111111111,7.22222222222222,6,TRUE
+"44",44,"5/14","Sunday",1452,2704,3320,2389,9865,0.508,18.2777777777778,7.77777777777778,6,FALSE
+"45",45,"5/15","Monday",2685,5865,5981,4550,19081,0,19.3888888888889,13.2777777777778,6,FALSE
+"46",46,"5/16","Tuesday",3666,7847,8420,6209,26142,0,25.6111111111111,14.3888888888889,6,FALSE
+"47",47,"5/17","Wednesday",3535,7888,8540,6121,26084,0,32.2222222222222,18.8888888888889,6,FALSE
+"48",48,"5/18","Thursday",3190,6886,7645,5753,23474,0,33.2777777777778,23.8888888888889,6,FALSE
+"49",49,"5/19","Friday",2952,6432,6859,5272,21515,0,32.2222222222222,24.3888888888889,6,FALSE
+"50",50,"5/20","Saturday",2161,3896,5198,3910,15165,0.254,17.7777777777778,13.2777777777778,7,FALSE
+"51",51,"5/21","Sunday",2612,4656,5634,3782,16684,0,19.3888888888889,12.7777777777778,7,FALSE
+"52",52,"5/22","Monday",768,1633,2116,1609,6126,14.986,16.1111111111111,12.2222222222222,7,TRUE
+"53",53,"5/23","Tuesday",3174,6715,7011,4899,21799,0,20,14.3888888888889,7,FALSE
+"54",54,"5/24","Wednesday",2969,6005,6872,4344,20190,1.016,19.3888888888889,13.8888888888889,7,TRUE
+"55",55,"5/25","Thursday",488,1196,1789,1316,4789,14.732,14.3888888888889,13.2777777777778,7,TRUE
+"56",56,"5/26","Friday",2590,4563,5344,3642,16139,2.54,22.7777777777778,13.2777777777778,7,TRUE
+"57",57,"5/27","Saturday",2609,4013,6105,3703,16430,0,21.7222222222222,16.1111111111111,8,FALSE
+"58",58,"5/28","Sunday",2640,4048,5456,3549,15693,0,21.7222222222222,15,8,FALSE
+"59",59,"5/29","Monday",836,1730,2738,1704,7008,3.302,14.3888888888889,13.2777777777778,8,TRUE
+"60",60,"5/30","Tuesday",2301,5366,5938,4431,18036,1.524,15,13.2777777777778,8,TRUE
+"61",61,"5/31","Wednesday",2689,5717,6523,4575,19504,0.762,23.8888888888889,14.3888888888889,8,FALSE
+"62",62,"6/1","Thursday",3468,7328,8461,6184,25441,0,25.6111111111111,16.7222222222222,8,FALSE
+"63",63,"6/2","Friday",3271,7007,7968,5293,23539,0.254,23.2777777777778,15.6111111111111,8,FALSE
+"64",64,"6/3","Saturday",2589,4510,6210,4084,17393,0.254,22.2222222222222,12.7777777777778,9,FALSE
+"65",65,"6/4","Sunday",1805,3127,4023,3023,11978,2.286,20,15.6111111111111,9,TRUE
+"66",66,"6/5","Monday",2171,4552,5276,3359,15358,0.508,19.3888888888889,15.6111111111111,9,FALSE
+"67",67,"6/6","Tuesday",1193,3021,3807,2572,10593,1.524,13.2777777777778,11.7222222222222,9,TRUE
+"68",68,"6/7","Wednesday",3211,7180,7632,5072,23095,0,19.3888888888889,12.2222222222222,9,FALSE
+"69",69,"6/8","Thursday",3253,7083,7778,5288,23402,0,20,15,9,FALSE
+"70",70,"6/9","Friday",3401,6859,7744,5155,23159,0,26.7222222222222,15,9,FALSE
+"71",71,"6/10","Saturday",3066,5193,6391,4425,19075,0,28.8888888888889,20,10,FALSE
+"72",72,"6/11","Sunday",2465,4388,5153,3178,15184,0,32.2222222222222,22.7777777777778,10,FALSE
+"73",73,"6/12","Monday",2854,6265,7049,5032,21200,0,33.2777777777778,25,10,FALSE
+"74",74,"6/13","Tuesday",2882,6141,7155,5399,21577,0.254,34.3888888888889,25.6111111111111,10,FALSE
+"75",75,"6/14","Wednesday",2596,5630,6619,5152,19997,7.366,28.8888888888889,20.6111111111111,10,TRUE
+"76",76,"6/15","Thursday",3510,7854,8652,6344,26360,0,23.8888888888889,18.8888888888889,10,FALSE
+"77",77,"6/16","Friday",2054,4857,5783,4474,17168,0,20,18.8888888888889,10,FALSE
+"78",78,"6/17","Saturday",1399,2633,3439,2977,10448,35.306,22.7777777777778,19.3888888888889,11,TRUE
+"79",79,"6/18","Sunday",2199,4014,4901,3396,14510,0.254,28.8888888888889,22.2222222222222,11,FALSE
+"80",80,"6/19","Monday",1648,3553,4334,3552,13087,34.29,30.6111111111111,21.1111111111111,11,TRUE
+"81",81,"6/20","Tuesday",3407,7473,7975,6091,24946,0.762,27.7777777777778,22.2222222222222,11,FALSE
+"82",82,"6/21","Wednesday",3304,7416,8754,6159,25633,0,27.7777777777778,22.2222222222222,11,FALSE
+"83",83,"6/22","Thursday",3368,7624,8307,6289,25588,0,27.7777777777778,21.1111111111111,11,FALSE
+"84",84,"6/23","Friday",2283,4683,5611,4330,16907,1.016,28.2777777777778,24.3888888888889,11,TRUE
+"85",85,"6/24","Saturday",2307,3933,5214,3396,14850,32.766,28.2777777777778,21.7222222222222,12,TRUE
+"86",86,"6/25","Sunday",2625,4882,5727,4197,17431,0,27.7777777777778,20.6111111111111,12,FALSE
+"87",87,"6/26","Monday",3386,7217,7974,5845,24422,0,25.6111111111111,18.8888888888889,12,FALSE
+"88",88,"6/27","Tuesday",3182,7317,6501,5580,22580,4.572,24.3888888888889,16.1111111111111,12,TRUE
+"89",89,"6/28","Wednesday",3766,8239,8873,5834,26712,0,25.6111111111111,16.7222222222222,12,FALSE
+"90",90,"6/29","Thursday",3356,7544,8276,5662,24838,0,27.2222222222222,20,12,FALSE
+"91",91,"6/30","Friday",2687,5890,7013,4754,20344,0.254,31.1111111111111,23.2777777777778,12,FALSE
+"92",92,"7/1","Saturday",1848,2958,3845,3216,11867,5.842,29.3888888888889,22.2222222222222,13,TRUE
+"93",93,"7/2","Sunday",2467,3776,4173,3579,13995,0,30.6111111111111,22.7777777777778,13,FALSE
+"94",94,"7/3","Monday",2714,4199,4924,4230,16067,11.43,30.6111111111111,21.7222222222222,13,TRUE
+"95",95,"7/4","Tuesday",2296,4084,3684,3861,13925,0,28.2777777777778,21.1111111111111,13,FALSE
+"96",96,"7/5","Wednesday",3170,6770,7308,5862,23110,0,29.3888888888889,21.7222222222222,13,FALSE
+"97",97,"7/6","Thursday",3065,6243,7302,5251,21861,0.254,23.8888888888889,21.7222222222222,13,FALSE
+"98",98,"7/7","Friday",1513,3567,4421,3304,12805,45.212,26.1111111111111,20,13,TRUE
+"99",99,"7/8","Saturday",2718,4807,5781,3952,17258,0,28.2777777777778,21.1111111111111,14,FALSE
+"100",100,"7/9","Sunday",3048,5446,5782,4044,18320,0,27.2222222222222,20.6111111111111,14,FALSE
+"101",101,"7/10","Monday",3506,7503,8106,5712,24827,0,28.2777777777778,21.7222222222222,14,FALSE
+"102",102,"7/11","Tuesday",2929,6436,7249,5129,21743,0,28.8888888888889,23.8888888888889,14,FALSE
+"103",103,"7/12","Wednesday",2860,6283,7102,4929,21174,0,30.6111111111111,25,14,FALSE
+"104",104,"7/13","Thursday",2563,5150,6116,4461,18290,0,31.7222222222222,25,14,FALSE
+"105",105,"7/14","Friday",907,2182,2974,2147,8210,8.89,20.6111111111111,18.2777777777778,14,TRUE
+"106",106,"7/15","Saturday",2853,4857,5631,3918,17259,0,28.2777777777778,20,15,FALSE
+"107",107,"7/16","Sunday",2917,5190,5370,4062,17539,0,29.3888888888889,21.1111111111111,15,FALSE
+"108",108,"7/17","Monday",3264,7316,7789,5468,23837,0,29.3888888888889,23.2777777777778,15,FALSE
+"109",109,"7/18","Tuesday",3507,7732,8268,5531,25038,0,30.6111111111111,24.3888888888889,15,FALSE
+"110",110,"7/19","Wednesday",3114,6710,7776,5247,22847,0,32.7777777777778,25,15,FALSE
+"111",111,"7/20","Thursday",2840,5980,6781,4804,20405,0.254,33.8888888888889,25.6111111111111,15,FALSE
+"112",112,"7/21","Friday",2751,5469,6548,5005,19773,0,32.7777777777778,25,15,FALSE
+"113",113,"7/22","Saturday",2301,3745,4535,3841,14422,14.478,32.7777777777778,25.6111111111111,16,TRUE
+"114",114,"7/23","Sunday",2321,3756,4096,3124,13297,1.524,25.6111111111111,22.7777777777778,16,TRUE
+"115",115,"7/24","Monday",1576,3584,3867,2835,11862,18.796,20.6111111111111,17.2222222222222,16,TRUE
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data/bike_data_filtered_long.csv

@@ -0,0 +1,1071 @@
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+"95","4/19","Wednesday",0.254,11.1111111111111,7.22222222222222,2,FALSE,"total",15827
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+"124","4/25","Tuesday",23.114,12.2222222222222,10,3,TRUE,"queensboro",1578
+"125","4/25","Tuesday",23.114,12.2222222222222,10,3,TRUE,"total",5493
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+"129","4/26","Wednesday",8.636,15,12.2222222222222,3,TRUE,"queensboro",2369
+"130","4/26","Wednesday",8.636,15,12.2222222222222,3,TRUE,"total",9819
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+"133","4/27","Thursday",0,20,15,3,FALSE,"williamsburg",6026
+"134","4/27","Thursday",0,20,15,3,FALSE,"queensboro",4285
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+"139","4/28","Friday",0,28.2777777777778,14.3888888888889,3,FALSE,"queensboro",5138
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+"145","4/29","Saturday",1.524,28.8888888888889,17.7777777777778,4,TRUE,"total",19043
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+"149","4/30","Sunday",0,17.7777777777778,12.2222222222222,4,FALSE,"queensboro",3137
+"150","4/30","Sunday",0,17.7777777777778,12.2222222222222,4,FALSE,"total",13932
+"151","5/1","Monday",0,22.2222222222222,10,4,FALSE,"brooklyn",3084
+"152","5/1","Monday",0,22.2222222222222,10,4,FALSE,"manhattan",6506
+"153","5/1","Monday",0,22.2222222222222,10,4,FALSE,"williamsburg",6793
+"154","5/1","Monday",0,22.2222222222222,10,4,FALSE,"queensboro",4820
+"155","5/1","Monday",0,22.2222222222222,10,4,FALSE,"total",21203
+"156","5/2","Tuesday",0,23.2777777777778,19.3888888888889,4,FALSE,"brooklyn",3423
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+"159","5/2","Tuesday",0,23.2777777777778,19.3888888888889,4,FALSE,"queensboro",5449
+"160","5/2","Tuesday",0,23.2777777777778,19.3888888888889,4,FALSE,"total",23696
+"161","5/3","Wednesday",0,18.2777777777778,14.3888888888889,4,FALSE,"brooklyn",3342
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+"165","5/3","Wednesday",0,18.2777777777778,14.3888888888889,4,FALSE,"total",22575
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+"169","5/4","Thursday",0,17.2222222222222,10,4,FALSE,"queensboro",5144
+"170","5/4","Thursday",0,17.2222222222222,10,4,FALSE,"total",21610
+"171","5/5","Friday",76.708,15,11.1111111111111,4,TRUE,"brooklyn",513
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+"180","5/6","Saturday",4.572,18.2777777777778,13.8888888888889,5,TRUE,"total",15174
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+"185","5/7","Sunday",0.254,12.2222222222222,9.38888888888889,5,FALSE,"total",15878
+"186","5/8","Monday",0,13.8888888888889,7.22222222222222,5,FALSE,"brooklyn",2886
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+"190","5/8","Monday",0,13.8888888888889,7.22222222222222,5,FALSE,"total",19379
+"191","5/9","Tuesday",0,16.1111111111111,8.88888888888889,5,FALSE,"brooklyn",2718
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+"195","5/9","Tuesday",0,16.1111111111111,8.88888888888889,5,FALSE,"total",21083
+"196","5/10","Wednesday",0,21.1111111111111,10.6111111111111,5,FALSE,"brooklyn",2810
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+"199","5/10","Wednesday",0,21.1111111111111,10.6111111111111,5,FALSE,"queensboro",4980
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+"201","5/11","Thursday",0,16.1111111111111,11,5,FALSE,"brooklyn",2657
+"202","5/11","Thursday",0,16.1111111111111,11,5,FALSE,"manhattan",6179
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+"204","5/11","Thursday",0,16.1111111111111,11,5,FALSE,"queensboro",5139
+"205","5/11","Thursday",0,16.1111111111111,11,5,FALSE,"total",20725
+"206","5/12","Friday",0,16.7222222222222,10.6111111111111,5,FALSE,"brooklyn",2640
+"207","5/12","Friday",0,16.7222222222222,10.6111111111111,5,FALSE,"manhattan",5367
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+"209","5/12","Friday",0,16.7222222222222,10.6111111111111,5,FALSE,"queensboro",4692
+"210","5/12","Friday",0,16.7222222222222,10.6111111111111,5,FALSE,"total",19024
+"211","5/13","Saturday",33.274,10.6111111111111,7.22222222222222,6,TRUE,"brooklyn",151
+"212","5/13","Saturday",33.274,10.6111111111111,7.22222222222222,6,TRUE,"manhattan",484
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+"214","5/13","Saturday",33.274,10.6111111111111,7.22222222222222,6,TRUE,"queensboro",865
+"215","5/13","Saturday",33.274,10.6111111111111,7.22222222222222,6,TRUE,"total",2374
+"216","5/14","Sunday",0.508,18.2777777777778,7.77777777777778,6,FALSE,"brooklyn",1452
+"217","5/14","Sunday",0.508,18.2777777777778,7.77777777777778,6,FALSE,"manhattan",2704
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+"219","5/14","Sunday",0.508,18.2777777777778,7.77777777777778,6,FALSE,"queensboro",2389
+"220","5/14","Sunday",0.508,18.2777777777778,7.77777777777778,6,FALSE,"total",9865
+"221","5/15","Monday",0,19.3888888888889,13.2777777777778,6,FALSE,"brooklyn",2685
+"222","5/15","Monday",0,19.3888888888889,13.2777777777778,6,FALSE,"manhattan",5865
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+"224","5/15","Monday",0,19.3888888888889,13.2777777777778,6,FALSE,"queensboro",4550
+"225","5/15","Monday",0,19.3888888888889,13.2777777777778,6,FALSE,"total",19081
+"226","5/16","Tuesday",0,25.6111111111111,14.3888888888889,6,FALSE,"brooklyn",3666
+"227","5/16","Tuesday",0,25.6111111111111,14.3888888888889,6,FALSE,"manhattan",7847
+"228","5/16","Tuesday",0,25.6111111111111,14.3888888888889,6,FALSE,"williamsburg",8420
+"229","5/16","Tuesday",0,25.6111111111111,14.3888888888889,6,FALSE,"queensboro",6209
+"230","5/16","Tuesday",0,25.6111111111111,14.3888888888889,6,FALSE,"total",26142
+"231","5/17","Wednesday",0,32.2222222222222,18.8888888888889,6,FALSE,"brooklyn",3535
+"232","5/17","Wednesday",0,32.2222222222222,18.8888888888889,6,FALSE,"manhattan",7888
+"233","5/17","Wednesday",0,32.2222222222222,18.8888888888889,6,FALSE,"williamsburg",8540
+"234","5/17","Wednesday",0,32.2222222222222,18.8888888888889,6,FALSE,"queensboro",6121
+"235","5/17","Wednesday",0,32.2222222222222,18.8888888888889,6,FALSE,"total",26084
+"236","5/18","Thursday",0,33.2777777777778,23.8888888888889,6,FALSE,"brooklyn",3190
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+"238","5/18","Thursday",0,33.2777777777778,23.8888888888889,6,FALSE,"williamsburg",7645
+"239","5/18","Thursday",0,33.2777777777778,23.8888888888889,6,FALSE,"queensboro",5753
+"240","5/18","Thursday",0,33.2777777777778,23.8888888888889,6,FALSE,"total",23474
+"241","5/19","Friday",0,32.2222222222222,24.3888888888889,6,FALSE,"brooklyn",2952
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+"244","5/19","Friday",0,32.2222222222222,24.3888888888889,6,FALSE,"queensboro",5272
+"245","5/19","Friday",0,32.2222222222222,24.3888888888889,6,FALSE,"total",21515
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+"249","5/20","Saturday",0.254,17.7777777777778,13.2777777777778,7,FALSE,"queensboro",3910
+"250","5/20","Saturday",0.254,17.7777777777778,13.2777777777778,7,FALSE,"total",15165
+"251","5/21","Sunday",0,19.3888888888889,12.7777777777778,7,FALSE,"brooklyn",2612
+"252","5/21","Sunday",0,19.3888888888889,12.7777777777778,7,FALSE,"manhattan",4656
+"253","5/21","Sunday",0,19.3888888888889,12.7777777777778,7,FALSE,"williamsburg",5634
+"254","5/21","Sunday",0,19.3888888888889,12.7777777777778,7,FALSE,"queensboro",3782
+"255","5/21","Sunday",0,19.3888888888889,12.7777777777778,7,FALSE,"total",16684
+"256","5/22","Monday",14.986,16.1111111111111,12.2222222222222,7,TRUE,"brooklyn",768
+"257","5/22","Monday",14.986,16.1111111111111,12.2222222222222,7,TRUE,"manhattan",1633
+"258","5/22","Monday",14.986,16.1111111111111,12.2222222222222,7,TRUE,"williamsburg",2116
+"259","5/22","Monday",14.986,16.1111111111111,12.2222222222222,7,TRUE,"queensboro",1609
+"260","5/22","Monday",14.986,16.1111111111111,12.2222222222222,7,TRUE,"total",6126
+"261","5/23","Tuesday",0,20,14.3888888888889,7,FALSE,"brooklyn",3174
+"262","5/23","Tuesday",0,20,14.3888888888889,7,FALSE,"manhattan",6715
+"263","5/23","Tuesday",0,20,14.3888888888889,7,FALSE,"williamsburg",7011
+"264","5/23","Tuesday",0,20,14.3888888888889,7,FALSE,"queensboro",4899
+"265","5/23","Tuesday",0,20,14.3888888888889,7,FALSE,"total",21799
+"266","5/24","Wednesday",1.016,19.3888888888889,13.8888888888889,7,TRUE,"brooklyn",2969
+"267","5/24","Wednesday",1.016,19.3888888888889,13.8888888888889,7,TRUE,"manhattan",6005
+"268","5/24","Wednesday",1.016,19.3888888888889,13.8888888888889,7,TRUE,"williamsburg",6872
+"269","5/24","Wednesday",1.016,19.3888888888889,13.8888888888889,7,TRUE,"queensboro",4344
+"270","5/24","Wednesday",1.016,19.3888888888889,13.8888888888889,7,TRUE,"total",20190
+"271","5/25","Thursday",14.732,14.3888888888889,13.2777777777778,7,TRUE,"brooklyn",488
+"272","5/25","Thursday",14.732,14.3888888888889,13.2777777777778,7,TRUE,"manhattan",1196
+"273","5/25","Thursday",14.732,14.3888888888889,13.2777777777778,7,TRUE,"williamsburg",1789
+"274","5/25","Thursday",14.732,14.3888888888889,13.2777777777778,7,TRUE,"queensboro",1316
+"275","5/25","Thursday",14.732,14.3888888888889,13.2777777777778,7,TRUE,"total",4789
+"276","5/26","Friday",2.54,22.7777777777778,13.2777777777778,7,TRUE,"brooklyn",2590
+"277","5/26","Friday",2.54,22.7777777777778,13.2777777777778,7,TRUE,"manhattan",4563
+"278","5/26","Friday",2.54,22.7777777777778,13.2777777777778,7,TRUE,"williamsburg",5344
+"279","5/26","Friday",2.54,22.7777777777778,13.2777777777778,7,TRUE,"queensboro",3642
+"280","5/26","Friday",2.54,22.7777777777778,13.2777777777778,7,TRUE,"total",16139
+"281","5/27","Saturday",0,21.7222222222222,16.1111111111111,8,FALSE,"brooklyn",2609
+"282","5/27","Saturday",0,21.7222222222222,16.1111111111111,8,FALSE,"manhattan",4013
+"283","5/27","Saturday",0,21.7222222222222,16.1111111111111,8,FALSE,"williamsburg",6105
+"284","5/27","Saturday",0,21.7222222222222,16.1111111111111,8,FALSE,"queensboro",3703
+"285","5/27","Saturday",0,21.7222222222222,16.1111111111111,8,FALSE,"total",16430
+"286","5/28","Sunday",0,21.7222222222222,15,8,FALSE,"brooklyn",2640
+"287","5/28","Sunday",0,21.7222222222222,15,8,FALSE,"manhattan",4048
+"288","5/28","Sunday",0,21.7222222222222,15,8,FALSE,"williamsburg",5456
+"289","5/28","Sunday",0,21.7222222222222,15,8,FALSE,"queensboro",3549
+"290","5/28","Sunday",0,21.7222222222222,15,8,FALSE,"total",15693
+"291","5/29","Monday",3.302,14.3888888888889,13.2777777777778,8,TRUE,"brooklyn",836
+"292","5/29","Monday",3.302,14.3888888888889,13.2777777777778,8,TRUE,"manhattan",1730
+"293","5/29","Monday",3.302,14.3888888888889,13.2777777777778,8,TRUE,"williamsburg",2738
+"294","5/29","Monday",3.302,14.3888888888889,13.2777777777778,8,TRUE,"queensboro",1704
+"295","5/29","Monday",3.302,14.3888888888889,13.2777777777778,8,TRUE,"total",7008
+"296","5/30","Tuesday",1.524,15,13.2777777777778,8,TRUE,"brooklyn",2301
+"297","5/30","Tuesday",1.524,15,13.2777777777778,8,TRUE,"manhattan",5366
+"298","5/30","Tuesday",1.524,15,13.2777777777778,8,TRUE,"williamsburg",5938
+"299","5/30","Tuesday",1.524,15,13.2777777777778,8,TRUE,"queensboro",4431
+"300","5/30","Tuesday",1.524,15,13.2777777777778,8,TRUE,"total",18036
+"301","5/31","Wednesday",0.762,23.8888888888889,14.3888888888889,8,FALSE,"brooklyn",2689
+"302","5/31","Wednesday",0.762,23.8888888888889,14.3888888888889,8,FALSE,"manhattan",5717
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+"304","5/31","Wednesday",0.762,23.8888888888889,14.3888888888889,8,FALSE,"queensboro",4575
+"305","5/31","Wednesday",0.762,23.8888888888889,14.3888888888889,8,FALSE,"total",19504
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+"315","6/2","Friday",0.254,23.2777777777778,15.6111111111111,8,FALSE,"total",23539
+"316","6/3","Saturday",0.254,22.2222222222222,12.7777777777778,9,FALSE,"brooklyn",2589
+"317","6/3","Saturday",0.254,22.2222222222222,12.7777777777778,9,FALSE,"manhattan",4510
+"318","6/3","Saturday",0.254,22.2222222222222,12.7777777777778,9,FALSE,"williamsburg",6210
+"319","6/3","Saturday",0.254,22.2222222222222,12.7777777777778,9,FALSE,"queensboro",4084
+"320","6/3","Saturday",0.254,22.2222222222222,12.7777777777778,9,FALSE,"total",17393
+"321","6/4","Sunday",2.286,20,15.6111111111111,9,TRUE,"brooklyn",1805
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+"324","6/4","Sunday",2.286,20,15.6111111111111,9,TRUE,"queensboro",3023
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+"326","6/5","Monday",0.508,19.3888888888889,15.6111111111111,9,FALSE,"brooklyn",2171
+"327","6/5","Monday",0.508,19.3888888888889,15.6111111111111,9,FALSE,"manhattan",4552
+"328","6/5","Monday",0.508,19.3888888888889,15.6111111111111,9,FALSE,"williamsburg",5276
+"329","6/5","Monday",0.508,19.3888888888889,15.6111111111111,9,FALSE,"queensboro",3359
+"330","6/5","Monday",0.508,19.3888888888889,15.6111111111111,9,FALSE,"total",15358
+"331","6/6","Tuesday",1.524,13.2777777777778,11.7222222222222,9,TRUE,"brooklyn",1193
+"332","6/6","Tuesday",1.524,13.2777777777778,11.7222222222222,9,TRUE,"manhattan",3021
+"333","6/6","Tuesday",1.524,13.2777777777778,11.7222222222222,9,TRUE,"williamsburg",3807
+"334","6/6","Tuesday",1.524,13.2777777777778,11.7222222222222,9,TRUE,"queensboro",2572
+"335","6/6","Tuesday",1.524,13.2777777777778,11.7222222222222,9,TRUE,"total",10593
+"336","6/7","Wednesday",0,19.3888888888889,12.2222222222222,9,FALSE,"brooklyn",3211
+"337","6/7","Wednesday",0,19.3888888888889,12.2222222222222,9,FALSE,"manhattan",7180
+"338","6/7","Wednesday",0,19.3888888888889,12.2222222222222,9,FALSE,"williamsburg",7632
+"339","6/7","Wednesday",0,19.3888888888889,12.2222222222222,9,FALSE,"queensboro",5072
+"340","6/7","Wednesday",0,19.3888888888889,12.2222222222222,9,FALSE,"total",23095
+"341","6/8","Thursday",0,20,15,9,FALSE,"brooklyn",3253
+"342","6/8","Thursday",0,20,15,9,FALSE,"manhattan",7083
+"343","6/8","Thursday",0,20,15,9,FALSE,"williamsburg",7778
+"344","6/8","Thursday",0,20,15,9,FALSE,"queensboro",5288
+"345","6/8","Thursday",0,20,15,9,FALSE,"total",23402
+"346","6/9","Friday",0,26.7222222222222,15,9,FALSE,"brooklyn",3401
+"347","6/9","Friday",0,26.7222222222222,15,9,FALSE,"manhattan",6859
+"348","6/9","Friday",0,26.7222222222222,15,9,FALSE,"williamsburg",7744
+"349","6/9","Friday",0,26.7222222222222,15,9,FALSE,"queensboro",5155
+"350","6/9","Friday",0,26.7222222222222,15,9,FALSE,"total",23159
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+"352","6/10","Saturday",0,28.8888888888889,20,10,FALSE,"manhattan",5193
+"353","6/10","Saturday",0,28.8888888888889,20,10,FALSE,"williamsburg",6391
+"354","6/10","Saturday",0,28.8888888888889,20,10,FALSE,"queensboro",4425
+"355","6/10","Saturday",0,28.8888888888889,20,10,FALSE,"total",19075
+"356","6/11","Sunday",0,32.2222222222222,22.7777777777778,10,FALSE,"brooklyn",2465
+"357","6/11","Sunday",0,32.2222222222222,22.7777777777778,10,FALSE,"manhattan",4388
+"358","6/11","Sunday",0,32.2222222222222,22.7777777777778,10,FALSE,"williamsburg",5153
+"359","6/11","Sunday",0,32.2222222222222,22.7777777777778,10,FALSE,"queensboro",3178
+"360","6/11","Sunday",0,32.2222222222222,22.7777777777778,10,FALSE,"total",15184
+"361","6/12","Monday",0,33.2777777777778,25,10,FALSE,"brooklyn",2854
+"362","6/12","Monday",0,33.2777777777778,25,10,FALSE,"manhattan",6265
+"363","6/12","Monday",0,33.2777777777778,25,10,FALSE,"williamsburg",7049
+"364","6/12","Monday",0,33.2777777777778,25,10,FALSE,"queensboro",5032
+"365","6/12","Monday",0,33.2777777777778,25,10,FALSE,"total",21200
+"366","6/13","Tuesday",0.254,34.3888888888889,25.6111111111111,10,FALSE,"brooklyn",2882
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+"368","6/13","Tuesday",0.254,34.3888888888889,25.6111111111111,10,FALSE,"williamsburg",7155
+"369","6/13","Tuesday",0.254,34.3888888888889,25.6111111111111,10,FALSE,"queensboro",5399
+"370","6/13","Tuesday",0.254,34.3888888888889,25.6111111111111,10,FALSE,"total",21577
+"371","6/14","Wednesday",7.366,28.8888888888889,20.6111111111111,10,TRUE,"brooklyn",2596
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+"374","6/14","Wednesday",7.366,28.8888888888889,20.6111111111111,10,TRUE,"queensboro",5152
+"375","6/14","Wednesday",7.366,28.8888888888889,20.6111111111111,10,TRUE,"total",19997
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+"378","6/15","Thursday",0,23.8888888888889,18.8888888888889,10,FALSE,"williamsburg",8652
+"379","6/15","Thursday",0,23.8888888888889,18.8888888888889,10,FALSE,"queensboro",6344
+"380","6/15","Thursday",0,23.8888888888889,18.8888888888889,10,FALSE,"total",26360
+"381","6/16","Friday",0,20,18.8888888888889,10,FALSE,"brooklyn",2054
+"382","6/16","Friday",0,20,18.8888888888889,10,FALSE,"manhattan",4857
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+"384","6/16","Friday",0,20,18.8888888888889,10,FALSE,"queensboro",4474
+"385","6/16","Friday",0,20,18.8888888888889,10,FALSE,"total",17168
+"386","6/17","Saturday",35.306,22.7777777777778,19.3888888888889,11,TRUE,"brooklyn",1399
+"387","6/17","Saturday",35.306,22.7777777777778,19.3888888888889,11,TRUE,"manhattan",2633
+"388","6/17","Saturday",35.306,22.7777777777778,19.3888888888889,11,TRUE,"williamsburg",3439
+"389","6/17","Saturday",35.306,22.7777777777778,19.3888888888889,11,TRUE,"queensboro",2977
+"390","6/17","Saturday",35.306,22.7777777777778,19.3888888888889,11,TRUE,"total",10448
+"391","6/18","Sunday",0.254,28.8888888888889,22.2222222222222,11,FALSE,"brooklyn",2199
+"392","6/18","Sunday",0.254,28.8888888888889,22.2222222222222,11,FALSE,"manhattan",4014
+"393","6/18","Sunday",0.254,28.8888888888889,22.2222222222222,11,FALSE,"williamsburg",4901
+"394","6/18","Sunday",0.254,28.8888888888889,22.2222222222222,11,FALSE,"queensboro",3396
+"395","6/18","Sunday",0.254,28.8888888888889,22.2222222222222,11,FALSE,"total",14510
+"396","6/19","Monday",34.29,30.6111111111111,21.1111111111111,11,TRUE,"brooklyn",1648
+"397","6/19","Monday",34.29,30.6111111111111,21.1111111111111,11,TRUE,"manhattan",3553
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+"399","6/19","Monday",34.29,30.6111111111111,21.1111111111111,11,TRUE,"queensboro",3552
+"400","6/19","Monday",34.29,30.6111111111111,21.1111111111111,11,TRUE,"total",13087
+"401","6/20","Tuesday",0.762,27.7777777777778,22.2222222222222,11,FALSE,"brooklyn",3407
+"402","6/20","Tuesday",0.762,27.7777777777778,22.2222222222222,11,FALSE,"manhattan",7473
+"403","6/20","Tuesday",0.762,27.7777777777778,22.2222222222222,11,FALSE,"williamsburg",7975
+"404","6/20","Tuesday",0.762,27.7777777777778,22.2222222222222,11,FALSE,"queensboro",6091
+"405","6/20","Tuesday",0.762,27.7777777777778,22.2222222222222,11,FALSE,"total",24946
+"406","6/21","Wednesday",0,27.7777777777778,22.2222222222222,11,FALSE,"brooklyn",3304
+"407","6/21","Wednesday",0,27.7777777777778,22.2222222222222,11,FALSE,"manhattan",7416
+"408","6/21","Wednesday",0,27.7777777777778,22.2222222222222,11,FALSE,"williamsburg",8754
+"409","6/21","Wednesday",0,27.7777777777778,22.2222222222222,11,FALSE,"queensboro",6159
+"410","6/21","Wednesday",0,27.7777777777778,22.2222222222222,11,FALSE,"total",25633
+"411","6/22","Thursday",0,27.7777777777778,21.1111111111111,11,FALSE,"brooklyn",3368
+"412","6/22","Thursday",0,27.7777777777778,21.1111111111111,11,FALSE,"manhattan",7624
+"413","6/22","Thursday",0,27.7777777777778,21.1111111111111,11,FALSE,"williamsburg",8307
+"414","6/22","Thursday",0,27.7777777777778,21.1111111111111,11,FALSE,"queensboro",6289
+"415","6/22","Thursday",0,27.7777777777778,21.1111111111111,11,FALSE,"total",25588
+"416","6/23","Friday",1.016,28.2777777777778,24.3888888888889,11,TRUE,"brooklyn",2283
+"417","6/23","Friday",1.016,28.2777777777778,24.3888888888889,11,TRUE,"manhattan",4683
+"418","6/23","Friday",1.016,28.2777777777778,24.3888888888889,11,TRUE,"williamsburg",5611
+"419","6/23","Friday",1.016,28.2777777777778,24.3888888888889,11,TRUE,"queensboro",4330
+"420","6/23","Friday",1.016,28.2777777777778,24.3888888888889,11,TRUE,"total",16907
+"421","6/24","Saturday",32.766,28.2777777777778,21.7222222222222,12,TRUE,"brooklyn",2307
+"422","6/24","Saturday",32.766,28.2777777777778,21.7222222222222,12,TRUE,"manhattan",3933
+"423","6/24","Saturday",32.766,28.2777777777778,21.7222222222222,12,TRUE,"williamsburg",5214
+"424","6/24","Saturday",32.766,28.2777777777778,21.7222222222222,12,TRUE,"queensboro",3396
+"425","6/24","Saturday",32.766,28.2777777777778,21.7222222222222,12,TRUE,"total",14850
+"426","6/25","Sunday",0,27.7777777777778,20.6111111111111,12,FALSE,"brooklyn",2625
+"427","6/25","Sunday",0,27.7777777777778,20.6111111111111,12,FALSE,"manhattan",4882
+"428","6/25","Sunday",0,27.7777777777778,20.6111111111111,12,FALSE,"williamsburg",5727
+"429","6/25","Sunday",0,27.7777777777778,20.6111111111111,12,FALSE,"queensboro",4197
+"430","6/25","Sunday",0,27.7777777777778,20.6111111111111,12,FALSE,"total",17431
+"431","6/26","Monday",0,25.6111111111111,18.8888888888889,12,FALSE,"brooklyn",3386
+"432","6/26","Monday",0,25.6111111111111,18.8888888888889,12,FALSE,"manhattan",7217
+"433","6/26","Monday",0,25.6111111111111,18.8888888888889,12,FALSE,"williamsburg",7974
+"434","6/26","Monday",0,25.6111111111111,18.8888888888889,12,FALSE,"queensboro",5845
+"435","6/26","Monday",0,25.6111111111111,18.8888888888889,12,FALSE,"total",24422
+"436","6/27","Tuesday",4.572,24.3888888888889,16.1111111111111,12,TRUE,"brooklyn",3182
+"437","6/27","Tuesday",4.572,24.3888888888889,16.1111111111111,12,TRUE,"manhattan",7317
+"438","6/27","Tuesday",4.572,24.3888888888889,16.1111111111111,12,TRUE,"williamsburg",6501
+"439","6/27","Tuesday",4.572,24.3888888888889,16.1111111111111,12,TRUE,"queensboro",5580
+"440","6/27","Tuesday",4.572,24.3888888888889,16.1111111111111,12,TRUE,"total",22580
+"441","6/28","Wednesday",0,25.6111111111111,16.7222222222222,12,FALSE,"brooklyn",3766
+"442","6/28","Wednesday",0,25.6111111111111,16.7222222222222,12,FALSE,"manhattan",8239
+"443","6/28","Wednesday",0,25.6111111111111,16.7222222222222,12,FALSE,"williamsburg",8873
+"444","6/28","Wednesday",0,25.6111111111111,16.7222222222222,12,FALSE,"queensboro",5834
+"445","6/28","Wednesday",0,25.6111111111111,16.7222222222222,12,FALSE,"total",26712
+"446","6/29","Thursday",0,27.2222222222222,20,12,FALSE,"brooklyn",3356
+"447","6/29","Thursday",0,27.2222222222222,20,12,FALSE,"manhattan",7544
+"448","6/29","Thursday",0,27.2222222222222,20,12,FALSE,"williamsburg",8276
+"449","6/29","Thursday",0,27.2222222222222,20,12,FALSE,"queensboro",5662
+"450","6/29","Thursday",0,27.2222222222222,20,12,FALSE,"total",24838
+"451","6/30","Friday",0.254,31.1111111111111,23.2777777777778,12,FALSE,"brooklyn",2687
+"452","6/30","Friday",0.254,31.1111111111111,23.2777777777778,12,FALSE,"manhattan",5890
+"453","6/30","Friday",0.254,31.1111111111111,23.2777777777778,12,FALSE,"williamsburg",7013
+"454","6/30","Friday",0.254,31.1111111111111,23.2777777777778,12,FALSE,"queensboro",4754
+"455","6/30","Friday",0.254,31.1111111111111,23.2777777777778,12,FALSE,"total",20344
+"456","7/1","Saturday",5.842,29.3888888888889,22.2222222222222,13,TRUE,"brooklyn",1848
+"457","7/1","Saturday",5.842,29.3888888888889,22.2222222222222,13,TRUE,"manhattan",2958
+"458","7/1","Saturday",5.842,29.3888888888889,22.2222222222222,13,TRUE,"williamsburg",3845
+"459","7/1","Saturday",5.842,29.3888888888889,22.2222222222222,13,TRUE,"queensboro",3216
+"460","7/1","Saturday",5.842,29.3888888888889,22.2222222222222,13,TRUE,"total",11867
+"461","7/2","Sunday",0,30.6111111111111,22.7777777777778,13,FALSE,"brooklyn",2467
+"462","7/2","Sunday",0,30.6111111111111,22.7777777777778,13,FALSE,"manhattan",3776
+"463","7/2","Sunday",0,30.6111111111111,22.7777777777778,13,FALSE,"williamsburg",4173
+"464","7/2","Sunday",0,30.6111111111111,22.7777777777778,13,FALSE,"queensboro",3579
+"465","7/2","Sunday",0,30.6111111111111,22.7777777777778,13,FALSE,"total",13995
+"466","7/3","Monday",11.43,30.6111111111111,21.7222222222222,13,TRUE,"brooklyn",2714
+"467","7/3","Monday",11.43,30.6111111111111,21.7222222222222,13,TRUE,"manhattan",4199
+"468","7/3","Monday",11.43,30.6111111111111,21.7222222222222,13,TRUE,"williamsburg",4924
+"469","7/3","Monday",11.43,30.6111111111111,21.7222222222222,13,TRUE,"queensboro",4230
+"470","7/3","Monday",11.43,30.6111111111111,21.7222222222222,13,TRUE,"total",16067
+"471","7/4","Tuesday",0,28.2777777777778,21.1111111111111,13,FALSE,"brooklyn",2296
+"472","7/4","Tuesday",0,28.2777777777778,21.1111111111111,13,FALSE,"manhattan",4084
+"473","7/4","Tuesday",0,28.2777777777778,21.1111111111111,13,FALSE,"williamsburg",3684
+"474","7/4","Tuesday",0,28.2777777777778,21.1111111111111,13,FALSE,"queensboro",3861
+"475","7/4","Tuesday",0,28.2777777777778,21.1111111111111,13,FALSE,"total",13925
+"476","7/5","Wednesday",0,29.3888888888889,21.7222222222222,13,FALSE,"brooklyn",3170
+"477","7/5","Wednesday",0,29.3888888888889,21.7222222222222,13,FALSE,"manhattan",6770
+"478","7/5","Wednesday",0,29.3888888888889,21.7222222222222,13,FALSE,"williamsburg",7308
+"479","7/5","Wednesday",0,29.3888888888889,21.7222222222222,13,FALSE,"queensboro",5862
+"480","7/5","Wednesday",0,29.3888888888889,21.7222222222222,13,FALSE,"total",23110
+"481","7/6","Thursday",0.254,23.8888888888889,21.7222222222222,13,FALSE,"brooklyn",3065
+"482","7/6","Thursday",0.254,23.8888888888889,21.7222222222222,13,FALSE,"manhattan",6243
+"483","7/6","Thursday",0.254,23.8888888888889,21.7222222222222,13,FALSE,"williamsburg",7302
+"484","7/6","Thursday",0.254,23.8888888888889,21.7222222222222,13,FALSE,"queensboro",5251
+"485","7/6","Thursday",0.254,23.8888888888889,21.7222222222222,13,FALSE,"total",21861
+"486","7/7","Friday",45.212,26.1111111111111,20,13,TRUE,"brooklyn",1513
+"487","7/7","Friday",45.212,26.1111111111111,20,13,TRUE,"manhattan",3567
+"488","7/7","Friday",45.212,26.1111111111111,20,13,TRUE,"williamsburg",4421
+"489","7/7","Friday",45.212,26.1111111111111,20,13,TRUE,"queensboro",3304
+"490","7/7","Friday",45.212,26.1111111111111,20,13,TRUE,"total",12805
+"491","7/8","Saturday",0,28.2777777777778,21.1111111111111,14,FALSE,"brooklyn",2718
+"492","7/8","Saturday",0,28.2777777777778,21.1111111111111,14,FALSE,"manhattan",4807
+"493","7/8","Saturday",0,28.2777777777778,21.1111111111111,14,FALSE,"williamsburg",5781
+"494","7/8","Saturday",0,28.2777777777778,21.1111111111111,14,FALSE,"queensboro",3952
+"495","7/8","Saturday",0,28.2777777777778,21.1111111111111,14,FALSE,"total",17258
+"496","7/9","Sunday",0,27.2222222222222,20.6111111111111,14,FALSE,"brooklyn",3048
+"497","7/9","Sunday",0,27.2222222222222,20.6111111111111,14,FALSE,"manhattan",5446
+"498","7/9","Sunday",0,27.2222222222222,20.6111111111111,14,FALSE,"williamsburg",5782
+"499","7/9","Sunday",0,27.2222222222222,20.6111111111111,14,FALSE,"queensboro",4044
+"500","7/9","Sunday",0,27.2222222222222,20.6111111111111,14,FALSE,"total",18320
+"501","7/10","Monday",0,28.2777777777778,21.7222222222222,14,FALSE,"brooklyn",3506
+"502","7/10","Monday",0,28.2777777777778,21.7222222222222,14,FALSE,"manhattan",7503
+"503","7/10","Monday",0,28.2777777777778,21.7222222222222,14,FALSE,"williamsburg",8106
+"504","7/10","Monday",0,28.2777777777778,21.7222222222222,14,FALSE,"queensboro",5712
+"505","7/10","Monday",0,28.2777777777778,21.7222222222222,14,FALSE,"total",24827
+"506","7/11","Tuesday",0,28.8888888888889,23.8888888888889,14,FALSE,"brooklyn",2929
+"507","7/11","Tuesday",0,28.8888888888889,23.8888888888889,14,FALSE,"manhattan",6436
+"508","7/11","Tuesday",0,28.8888888888889,23.8888888888889,14,FALSE,"williamsburg",7249
+"509","7/11","Tuesday",0,28.8888888888889,23.8888888888889,14,FALSE,"queensboro",5129
+"510","7/11","Tuesday",0,28.8888888888889,23.8888888888889,14,FALSE,"total",21743
+"511","7/12","Wednesday",0,30.6111111111111,25,14,FALSE,"brooklyn",2860
+"512","7/12","Wednesday",0,30.6111111111111,25,14,FALSE,"manhattan",6283
+"513","7/12","Wednesday",0,30.6111111111111,25,14,FALSE,"williamsburg",7102
+"514","7/12","Wednesday",0,30.6111111111111,25,14,FALSE,"queensboro",4929
+"515","7/12","Wednesday",0,30.6111111111111,25,14,FALSE,"total",21174
+"516","7/13","Thursday",0,31.7222222222222,25,14,FALSE,"brooklyn",2563
+"517","7/13","Thursday",0,31.7222222222222,25,14,FALSE,"manhattan",5150
+"518","7/13","Thursday",0,31.7222222222222,25,14,FALSE,"williamsburg",6116
+"519","7/13","Thursday",0,31.7222222222222,25,14,FALSE,"queensboro",4461
+"520","7/13","Thursday",0,31.7222222222222,25,14,FALSE,"total",18290
+"521","7/14","Friday",8.89,20.6111111111111,18.2777777777778,14,TRUE,"brooklyn",907
+"522","7/14","Friday",8.89,20.6111111111111,18.2777777777778,14,TRUE,"manhattan",2182
+"523","7/14","Friday",8.89,20.6111111111111,18.2777777777778,14,TRUE,"williamsburg",2974
+"524","7/14","Friday",8.89,20.6111111111111,18.2777777777778,14,TRUE,"queensboro",2147
+"525","7/14","Friday",8.89,20.6111111111111,18.2777777777778,14,TRUE,"total",8210
+"526","7/15","Saturday",0,28.2777777777778,20,15,FALSE,"brooklyn",2853
+"527","7/15","Saturday",0,28.2777777777778,20,15,FALSE,"manhattan",4857
+"528","7/15","Saturday",0,28.2777777777778,20,15,FALSE,"williamsburg",5631
+"529","7/15","Saturday",0,28.2777777777778,20,15,FALSE,"queensboro",3918
+"530","7/15","Saturday",0,28.2777777777778,20,15,FALSE,"total",17259
+"531","7/16","Sunday",0,29.3888888888889,21.1111111111111,15,FALSE,"brooklyn",2917
+"532","7/16","Sunday",0,29.3888888888889,21.1111111111111,15,FALSE,"manhattan",5190
+"533","7/16","Sunday",0,29.3888888888889,21.1111111111111,15,FALSE,"williamsburg",5370
+"534","7/16","Sunday",0,29.3888888888889,21.1111111111111,15,FALSE,"queensboro",4062
+"535","7/16","Sunday",0,29.3888888888889,21.1111111111111,15,FALSE,"total",17539
+"536","7/17","Monday",0,29.3888888888889,23.2777777777778,15,FALSE,"brooklyn",3264
+"537","7/17","Monday",0,29.3888888888889,23.2777777777778,15,FALSE,"manhattan",7316
+"538","7/17","Monday",0,29.3888888888889,23.2777777777778,15,FALSE,"williamsburg",7789
+"539","7/17","Monday",0,29.3888888888889,23.2777777777778,15,FALSE,"queensboro",5468
+"540","7/17","Monday",0,29.3888888888889,23.2777777777778,15,FALSE,"total",23837
+"541","7/18","Tuesday",0,30.6111111111111,24.3888888888889,15,FALSE,"brooklyn",3507
+"542","7/18","Tuesday",0,30.6111111111111,24.3888888888889,15,FALSE,"manhattan",7732
+"543","7/18","Tuesday",0,30.6111111111111,24.3888888888889,15,FALSE,"williamsburg",8268
+"544","7/18","Tuesday",0,30.6111111111111,24.3888888888889,15,FALSE,"queensboro",5531
+"545","7/18","Tuesday",0,30.6111111111111,24.3888888888889,15,FALSE,"total",25038
+"546","7/19","Wednesday",0,32.7777777777778,25,15,FALSE,"brooklyn",3114
+"547","7/19","Wednesday",0,32.7777777777778,25,15,FALSE,"manhattan",6710
+"548","7/19","Wednesday",0,32.7777777777778,25,15,FALSE,"williamsburg",7776
+"549","7/19","Wednesday",0,32.7777777777778,25,15,FALSE,"queensboro",5247
+"550","7/19","Wednesday",0,32.7777777777778,25,15,FALSE,"total",22847
+"551","7/20","Thursday",0.254,33.8888888888889,25.6111111111111,15,FALSE,"brooklyn",2840
+"552","7/20","Thursday",0.254,33.8888888888889,25.6111111111111,15,FALSE,"manhattan",5980
+"553","7/20","Thursday",0.254,33.8888888888889,25.6111111111111,15,FALSE,"williamsburg",6781
+"554","7/20","Thursday",0.254,33.8888888888889,25.6111111111111,15,FALSE,"queensboro",4804
+"555","7/20","Thursday",0.254,33.8888888888889,25.6111111111111,15,FALSE,"total",20405
+"556","7/21","Friday",0,32.7777777777778,25,15,FALSE,"brooklyn",2751
+"557","7/21","Friday",0,32.7777777777778,25,15,FALSE,"manhattan",5469
+"558","7/21","Friday",0,32.7777777777778,25,15,FALSE,"williamsburg",6548
+"559","7/21","Friday",0,32.7777777777778,25,15,FALSE,"queensboro",5005
+"560","7/21","Friday",0,32.7777777777778,25,15,FALSE,"total",19773
+"561","7/22","Saturday",14.478,32.7777777777778,25.6111111111111,16,TRUE,"brooklyn",2301
+"562","7/22","Saturday",14.478,32.7777777777778,25.6111111111111,16,TRUE,"manhattan",3745
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+"564","7/22","Saturday",14.478,32.7777777777778,25.6111111111111,16,TRUE,"queensboro",3841
+"565","7/22","Saturday",14.478,32.7777777777778,25.6111111111111,16,TRUE,"total",14422
+"566","7/23","Sunday",1.524,25.6111111111111,22.7777777777778,16,TRUE,"brooklyn",2321
+"567","7/23","Sunday",1.524,25.6111111111111,22.7777777777778,16,TRUE,"manhattan",3756
+"568","7/23","Sunday",1.524,25.6111111111111,22.7777777777778,16,TRUE,"williamsburg",4096
+"569","7/23","Sunday",1.524,25.6111111111111,22.7777777777778,16,TRUE,"queensboro",3124
+"570","7/23","Sunday",1.524,25.6111111111111,22.7777777777778,16,TRUE,"total",13297
+"571","7/24","Monday",18.796,20.6111111111111,17.2222222222222,16,TRUE,"brooklyn",1576
+"572","7/24","Monday",18.796,20.6111111111111,17.2222222222222,16,TRUE,"manhattan",3584
+"573","7/24","Monday",18.796,20.6111111111111,17.2222222222222,16,TRUE,"williamsburg",3867
+"574","7/24","Monday",18.796,20.6111111111111,17.2222222222222,16,TRUE,"queensboro",2835
+"575","7/24","Monday",18.796,20.6111111111111,17.2222222222222,16,TRUE,"total",11862
+"576","7/25","Tuesday",0,21.7222222222222,17.7777777777778,16,FALSE,"brooklyn",3191
+"577","7/25","Tuesday",0,21.7222222222222,17.7777777777778,16,FALSE,"manhattan",7201
+"578","7/25","Tuesday",0,21.7222222222222,17.7777777777778,16,FALSE,"williamsburg",7496
+"579","7/25","Tuesday",0,21.7222222222222,17.7777777777778,16,FALSE,"queensboro",5698
+"580","7/25","Tuesday",0,21.7222222222222,17.7777777777778,16,FALSE,"total",23586
+"581","7/26","Wednesday",0,24.3888888888889,18.8888888888889,16,FALSE,"brooklyn",3821
+"582","7/26","Wednesday",0,24.3888888888889,18.8888888888889,16,FALSE,"manhattan",7987
+"583","7/26","Wednesday",0,24.3888888888889,18.8888888888889,16,FALSE,"williamsburg",8605
+"584","7/26","Wednesday",0,24.3888888888889,18.8888888888889,16,FALSE,"queensboro",6556
+"585","7/26","Wednesday",0,24.3888888888889,18.8888888888889,16,FALSE,"total",26969
+"586","7/27","Thursday",0.254,25,19.3888888888889,16,FALSE,"brooklyn",3287
+"587","7/27","Thursday",0.254,25,19.3888888888889,16,FALSE,"manhattan",6624
+"588","7/27","Thursday",0.254,25,19.3888888888889,16,FALSE,"williamsburg",7221
+"589","7/27","Thursday",0.254,25,19.3888888888889,16,FALSE,"queensboro",5997
+"590","7/27","Thursday",0.254,25,19.3888888888889,16,FALSE,"total",23129
+"591","7/28","Friday",0,29.3888888888889,22.7777777777778,16,FALSE,"brooklyn",3123
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+"593","7/28","Friday",0,29.3888888888889,22.7777777777778,16,FALSE,"williamsburg",6987
+"594","7/28","Friday",0,29.3888888888889,22.7777777777778,16,FALSE,"queensboro",5496
+"595","7/28","Friday",0,29.3888888888889,22.7777777777778,16,FALSE,"total",21741
+"596","7/29","Saturday",0,24.3888888888889,20,17,FALSE,"brooklyn",2074
+"597","7/29","Saturday",0,24.3888888888889,20,17,FALSE,"manhattan",3787
+"598","7/29","Saturday",0,24.3888888888889,20,17,FALSE,"williamsburg",5242
+"599","7/29","Saturday",0,24.3888888888889,20,17,FALSE,"queensboro",3711
+"600","7/29","Saturday",0,24.3888888888889,20,17,FALSE,"total",14814
+"601","7/30","Sunday",0,27.2222222222222,18.2777777777778,17,FALSE,"brooklyn",3331
+"602","7/30","Sunday",0,27.2222222222222,18.2777777777778,17,FALSE,"manhattan",5282
+"603","7/30","Sunday",0,27.2222222222222,18.2777777777778,17,FALSE,"williamsburg",5759
+"604","7/30","Sunday",0,27.2222222222222,18.2777777777778,17,FALSE,"queensboro",4324
+"605","7/30","Sunday",0,27.2222222222222,18.2777777777778,17,FALSE,"total",18696
+"606","7/31","Monday",0,31.1111111111111,19.3888888888889,17,FALSE,"brooklyn",3560
+"607","7/31","Monday",0,31.1111111111111,19.3888888888889,17,FALSE,"manhattan",7404
+"608","7/31","Monday",0,31.1111111111111,19.3888888888889,17,FALSE,"williamsburg",7546
+"609","7/31","Monday",0,31.1111111111111,19.3888888888889,17,FALSE,"queensboro",5774
+"610","7/31","Monday",0,31.1111111111111,19.3888888888889,17,FALSE,"total",24284
+"611","8/1","Tuesday",0,32.7777777777778,22.2222222222222,17,FALSE,"brooklyn",3492
+"612","8/1","Tuesday",0,32.7777777777778,22.2222222222222,17,FALSE,"manhattan",7290
+"613","8/1","Tuesday",0,32.7777777777778,22.2222222222222,17,FALSE,"williamsburg",7686
+"614","8/1","Tuesday",0,32.7777777777778,22.2222222222222,17,FALSE,"queensboro",6050
+"615","8/1","Tuesday",0,32.7777777777778,22.2222222222222,17,FALSE,"total",24518
+"616","8/2","Wednesday",2.286,30,20.6111111111111,17,TRUE,"brooklyn",2637
+"617","8/2","Wednesday",2.286,30,20.6111111111111,17,TRUE,"manhattan",5966
+"618","8/2","Wednesday",2.286,30,20.6111111111111,17,TRUE,"williamsburg",6407
+"619","8/2","Wednesday",2.286,30,20.6111111111111,17,TRUE,"queensboro",5288
+"620","8/2","Wednesday",2.286,30,20.6111111111111,17,TRUE,"total",20298
+"621","8/3","Thursday",0,30,21.1111111111111,17,FALSE,"brooklyn",3346
+"622","8/3","Thursday",0,30,21.1111111111111,17,FALSE,"manhattan",6728
+"623","8/3","Thursday",0,30,21.1111111111111,17,FALSE,"williamsburg",7297
+"624","8/3","Thursday",0,30,21.1111111111111,17,FALSE,"queensboro",5835
+"625","8/3","Thursday",0,30,21.1111111111111,17,FALSE,"total",23206
+"626","8/4","Friday",3.81,28.2777777777778,21.1111111111111,17,TRUE,"brooklyn",2400
+"627","8/4","Friday",3.81,28.2777777777778,21.1111111111111,17,TRUE,"manhattan",4447
+"628","8/4","Friday",3.81,28.2777777777778,21.1111111111111,17,TRUE,"williamsburg",5158
+"629","8/4","Friday",3.81,28.2777777777778,21.1111111111111,17,TRUE,"queensboro",4427
+"630","8/4","Friday",3.81,28.2777777777778,21.1111111111111,17,TRUE,"total",16432
+"631","8/5","Saturday",7.62,25,21.1111111111111,18,TRUE,"brooklyn",3409
+"632","8/5","Saturday",7.62,25,21.1111111111111,18,TRUE,"manhattan",4520
+"633","8/5","Saturday",7.62,25,21.1111111111111,18,TRUE,"williamsburg",5169
+"634","8/5","Saturday",7.62,25,21.1111111111111,18,TRUE,"queensboro",3907
+"635","8/5","Saturday",7.62,25,21.1111111111111,18,TRUE,"total",17005
+"636","8/6","Sunday",0,24.3888888888889,17.7777777777778,18,FALSE,"brooklyn",3130
+"637","8/6","Sunday",0,24.3888888888889,17.7777777777778,18,FALSE,"manhattan",5088
+"638","8/6","Sunday",0,24.3888888888889,17.7777777777778,18,FALSE,"williamsburg",5634
+"639","8/6","Sunday",0,24.3888888888889,17.7777777777778,18,FALSE,"queensboro",4207
+"640","8/6","Sunday",0,24.3888888888889,17.7777777777778,18,FALSE,"total",18059
+"641","8/7","Monday",19.304,21.7222222222222,18.2777777777778,18,TRUE,"brooklyn",804
+"642","8/7","Monday",19.304,21.7222222222222,18.2777777777778,18,TRUE,"manhattan",1809
+"643","8/7","Monday",19.304,21.7222222222222,18.2777777777778,18,TRUE,"williamsburg",2669
+"644","8/7","Monday",19.304,21.7222222222222,18.2777777777778,18,TRUE,"queensboro",2441
+"645","8/7","Monday",19.304,21.7222222222222,18.2777777777778,18,TRUE,"total",7723
+"646","8/8","Tuesday",0,25,18.8888888888889,18,FALSE,"brooklyn",3598
+"647","8/8","Tuesday",0,25,18.8888888888889,18,FALSE,"manhattan",7176
+"648","8/8","Tuesday",0,25,18.8888888888889,18,FALSE,"williamsburg",7571
+"649","8/8","Tuesday",0,25,18.8888888888889,18,FALSE,"queensboro",5932
+"650","8/8","Tuesday",0,25,18.8888888888889,18,FALSE,"total",24277
+"651","8/9","Wednesday",0,28.2777777777778,18.8888888888889,18,FALSE,"brooklyn",3893
+"652","8/9","Wednesday",0,28.2777777777778,18.8888888888889,18,FALSE,"manhattan",7791
+"653","8/9","Wednesday",0,28.2777777777778,18.8888888888889,18,FALSE,"williamsburg",8589
+"654","8/9","Wednesday",0,28.2777777777778,18.8888888888889,18,FALSE,"queensboro",6582
+"655","8/9","Wednesday",0,28.2777777777778,18.8888888888889,18,FALSE,"total",26855
+"656","8/10","Thursday",0,28.2777777777778,20.6111111111111,18,FALSE,"brooklyn",3423
+"657","8/10","Thursday",0,28.2777777777778,20.6111111111111,18,FALSE,"manhattan",7378
+"658","8/10","Thursday",0,28.2777777777778,20.6111111111111,18,FALSE,"williamsburg",7850
+"659","8/10","Thursday",0,28.2777777777778,20.6111111111111,18,FALSE,"queensboro",6290
+"660","8/10","Thursday",0,28.2777777777778,20.6111111111111,18,FALSE,"total",24941
+"661","8/11","Friday",0.254,27.2222222222222,21.1111111111111,18,FALSE,"brooklyn",3148
+"662","8/11","Friday",0.254,27.2222222222222,21.1111111111111,18,FALSE,"manhattan",6295
+"663","8/11","Friday",0.254,27.2222222222222,21.1111111111111,18,FALSE,"williamsburg",7068
+"664","8/11","Friday",0.254,27.2222222222222,21.1111111111111,18,FALSE,"queensboro",5669
+"665","8/11","Friday",0.254,27.2222222222222,21.1111111111111,18,FALSE,"total",22180
+"666","8/12","Saturday",2.794,24.3888888888889,18.2777777777778,19,TRUE,"brooklyn",4146
+"667","8/12","Saturday",2.794,24.3888888888889,18.2777777777778,19,TRUE,"manhattan",4593
+"668","8/12","Saturday",2.794,24.3888888888889,18.2777777777778,19,TRUE,"williamsburg",5443
+"669","8/12","Saturday",2.794,24.3888888888889,18.2777777777778,19,TRUE,"queensboro",4548
+"670","8/12","Saturday",2.794,24.3888888888889,18.2777777777778,19,TRUE,"total",18730
+"671","8/13","Sunday",0,27.7777777777778,21.7222222222222,19,FALSE,"brooklyn",3274
+"672","8/13","Sunday",0,27.7777777777778,21.7222222222222,19,FALSE,"manhattan",5086
+"673","8/13","Sunday",0,27.7777777777778,21.7222222222222,19,FALSE,"williamsburg",5419
+"674","8/13","Sunday",0,27.7777777777778,21.7222222222222,19,FALSE,"queensboro",4430
+"675","8/13","Sunday",0,27.7777777777778,21.7222222222222,19,FALSE,"total",18209
+"676","8/14","Monday",0,26.7222222222222,21.1111111111111,19,FALSE,"brooklyn",3291
+"677","8/14","Monday",0,26.7222222222222,21.1111111111111,19,FALSE,"manhattan",6717
+"678","8/14","Monday",0,26.7222222222222,21.1111111111111,19,FALSE,"williamsburg",7301
+"679","8/14","Monday",0,26.7222222222222,21.1111111111111,19,FALSE,"queensboro",5850
+"680","8/14","Monday",0,26.7222222222222,21.1111111111111,19,FALSE,"total",23159
+"681","8/15","Tuesday",11.43,22.7777777777778,20.6111111111111,19,TRUE,"brooklyn",2149
+"682","8/15","Tuesday",11.43,22.7777777777778,20.6111111111111,19,TRUE,"manhattan",4987
+"683","8/15","Tuesday",11.43,22.7777777777778,20.6111111111111,19,TRUE,"williamsburg",5976
+"684","8/15","Tuesday",11.43,22.7777777777778,20.6111111111111,19,TRUE,"queensboro",4711
+"685","8/15","Tuesday",11.43,22.7777777777778,20.6111111111111,19,TRUE,"total",17823
+"686","8/16","Wednesday",0,29.3888888888889,21.1111111111111,19,FALSE,"brooklyn",3685
+"687","8/16","Wednesday",0,29.3888888888889,21.1111111111111,19,FALSE,"manhattan",7191
+"688","8/16","Wednesday",0,29.3888888888889,21.1111111111111,19,FALSE,"williamsburg",7852
+"689","8/16","Wednesday",0,29.3888888888889,21.1111111111111,19,FALSE,"queensboro",6231
+"690","8/16","Wednesday",0,29.3888888888889,21.1111111111111,19,FALSE,"total",24959
+"691","8/17","Thursday",0,27.7777777777778,21.7222222222222,19,FALSE,"brooklyn",3637
+"692","8/17","Thursday",0,27.7777777777778,21.7222222222222,19,FALSE,"manhattan",6972
+"693","8/17","Thursday",0,27.7777777777778,21.7222222222222,19,FALSE,"williamsburg",7871
+"694","8/17","Thursday",0,27.7777777777778,21.7222222222222,19,FALSE,"queensboro",6129
+"695","8/17","Thursday",0,27.7777777777778,21.7222222222222,19,FALSE,"total",24609
+"696","8/18","Friday",22.352,27.2222222222222,22.7777777777778,19,TRUE,"brooklyn",1064
+"697","8/18","Friday",22.352,27.2222222222222,22.7777777777778,19,TRUE,"manhattan",2309
+"698","8/18","Friday",22.352,27.2222222222222,22.7777777777778,19,TRUE,"williamsburg",3113
+"699","8/18","Friday",22.352,27.2222222222222,22.7777777777778,19,TRUE,"queensboro",2819
+"700","8/18","Friday",22.352,27.2222222222222,22.7777777777778,19,TRUE,"total",9305
+"701","8/19","Saturday",0,29.3888888888889,22.7777777777778,20,FALSE,"brooklyn",4693
+"702","8/19","Saturday",0,29.3888888888889,22.7777777777778,20,FALSE,"manhattan",4871
+"703","8/19","Saturday",0,29.3888888888889,22.7777777777778,20,FALSE,"williamsburg",5612
+"704","8/19","Saturday",0,29.3888888888889,22.7777777777778,20,FALSE,"queensboro",4958
+"705","8/19","Saturday",0,29.3888888888889,22.7777777777778,20,FALSE,"total",20134
+"706","8/20","Sunday",0,27.2222222222222,21.1111111111111,20,FALSE,"brooklyn",2822
+"707","8/20","Sunday",0,27.2222222222222,21.1111111111111,20,FALSE,"manhattan",4582
+"708","8/20","Sunday",0,27.2222222222222,21.1111111111111,20,FALSE,"williamsburg",5182
+"709","8/20","Sunday",0,27.2222222222222,21.1111111111111,20,FALSE,"queensboro",4188
+"710","8/20","Sunday",0,27.2222222222222,21.1111111111111,20,FALSE,"total",16774
+"711","8/21","Monday",0,29.3888888888889,22.7777777777778,20,FALSE,"brooklyn",3088
+"712","8/21","Monday",0,29.3888888888889,22.7777777777778,20,FALSE,"manhattan",6400
+"713","8/21","Monday",0,29.3888888888889,22.7777777777778,20,FALSE,"williamsburg",7141
+"714","8/21","Monday",0,29.3888888888889,22.7777777777778,20,FALSE,"queensboro",5666
+"715","8/21","Monday",0,29.3888888888889,22.7777777777778,20,FALSE,"total",22295
+"716","8/22","Tuesday",7.62,31.1111111111111,23.8888888888889,20,TRUE,"brooklyn",2983
+"717","8/22","Tuesday",7.62,31.1111111111111,23.8888888888889,20,TRUE,"manhattan",6151
+"718","8/22","Tuesday",7.62,31.1111111111111,23.8888888888889,20,TRUE,"williamsburg",7146
+"719","8/22","Tuesday",7.62,31.1111111111111,23.8888888888889,20,TRUE,"queensboro",5795
+"720","8/22","Tuesday",7.62,31.1111111111111,23.8888888888889,20,TRUE,"total",22075
+"721","8/23","Wednesday",0.254,26.7222222222222,21.7222222222222,20,FALSE,"brooklyn",2994
+"722","8/23","Wednesday",0.254,26.7222222222222,21.7222222222222,20,FALSE,"manhattan",6586
+"723","8/23","Wednesday",0.254,26.7222222222222,21.7222222222222,20,FALSE,"williamsburg",7297
+"724","8/23","Wednesday",0.254,26.7222222222222,21.7222222222222,20,FALSE,"queensboro",6122
+"725","8/23","Wednesday",0.254,26.7222222222222,21.7222222222222,20,FALSE,"total",22999
+"726","8/24","Thursday",0,26.1111111111111,18.8888888888889,20,FALSE,"brooklyn",3688
+"727","8/24","Thursday",0,26.1111111111111,18.8888888888889,20,FALSE,"manhattan",7061
+"728","8/24","Thursday",0,26.1111111111111,18.8888888888889,20,FALSE,"williamsburg",7849
+"729","8/24","Thursday",0,26.1111111111111,18.8888888888889,20,FALSE,"queensboro",6289
+"730","8/24","Thursday",0,26.1111111111111,18.8888888888889,20,FALSE,"total",24887
+"731","8/25","Friday",0,25.6111111111111,17.7777777777778,20,FALSE,"brooklyn",3144
+"732","8/25","Friday",0,25.6111111111111,17.7777777777778,20,FALSE,"manhattan",6296
+"733","8/25","Friday",0,25.6111111111111,17.7777777777778,20,FALSE,"williamsburg",7318
+"734","8/25","Friday",0,25.6111111111111,17.7777777777778,20,FALSE,"queensboro",5786
+"735","8/25","Friday",0,25.6111111111111,17.7777777777778,20,FALSE,"total",22544
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+"738","8/26","Saturday",0,25,16.7222222222222,21,FALSE,"williamsburg",5537
+"739","8/26","Saturday",0,25,16.7222222222222,21,FALSE,"queensboro",4670
+"740","8/26","Saturday",0,25,16.7222222222222,21,FALSE,"total",17509
+"741","8/27","Sunday",0,25,17.2222222222222,21,FALSE,"brooklyn",2676
+"742","8/27","Sunday",0,25,17.2222222222222,21,FALSE,"manhattan",4785
+"743","8/27","Sunday",0,25,17.2222222222222,21,FALSE,"williamsburg",5145
+"744","8/27","Sunday",0,25,17.2222222222222,21,FALSE,"queensboro",4159
+"745","8/27","Sunday",0,25,17.2222222222222,21,FALSE,"total",16765
+"746","8/28","Monday",0,23.8888888888889,17.2222222222222,21,FALSE,"brooklyn",3332
+"747","8/28","Monday",0,23.8888888888889,17.2222222222222,21,FALSE,"manhattan",6908
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+"749","8/28","Monday",0,23.8888888888889,17.2222222222222,21,FALSE,"queensboro",5917
+"750","8/28","Monday",0,23.8888888888889,17.2222222222222,21,FALSE,"total",23500
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+"752","8/29","Tuesday",2.54,20,16.7222222222222,21,TRUE,"manhattan",3206
+"753","8/29","Tuesday",2.54,20,16.7222222222222,21,TRUE,"williamsburg",3776
+"754","8/29","Tuesday",2.54,20,16.7222222222222,21,TRUE,"queensboro",3563
+"755","8/29","Tuesday",2.54,20,16.7222222222222,21,TRUE,"total",12017
+"756","8/30","Wednesday",0.254,24.3888888888889,16.1111111111111,21,FALSE,"brooklyn",3468
+"757","8/30","Wednesday",0.254,24.3888888888889,16.1111111111111,21,FALSE,"manhattan",7159
+"758","8/30","Wednesday",0.254,24.3888888888889,16.1111111111111,21,FALSE,"williamsburg",7656
+"759","8/30","Wednesday",0.254,24.3888888888889,16.1111111111111,21,FALSE,"queensboro",5858
+"760","8/30","Wednesday",0.254,24.3888888888889,16.1111111111111,21,FALSE,"total",24141
+"761","8/31","Thursday",0,27.2222222222222,17.7777777777778,21,FALSE,"brooklyn",3279
+"762","8/31","Thursday",0,27.2222222222222,17.7777777777778,21,FALSE,"manhattan",6705
+"763","8/31","Thursday",0,27.2222222222222,17.7777777777778,21,FALSE,"williamsburg",7536
+"764","8/31","Thursday",0,27.2222222222222,17.7777777777778,21,FALSE,"queensboro",5934
+"765","8/31","Thursday",0,27.2222222222222,17.7777777777778,21,FALSE,"total",23454
+"766","9/1","Friday",0,21.1111111111111,12.7777777777778,21,FALSE,"brooklyn",2945
+"767","9/1","Friday",0,21.1111111111111,12.7777777777778,21,FALSE,"manhattan",5611
+"768","9/1","Friday",0,21.1111111111111,12.7777777777778,21,FALSE,"williamsburg",6576
+"769","9/1","Friday",0,21.1111111111111,12.7777777777778,21,FALSE,"queensboro",5398
+"770","9/1","Friday",0,21.1111111111111,12.7777777777778,21,FALSE,"total",20530
+"771","9/2","Saturday",13.462,19.3888888888889,12.2222222222222,22,TRUE,"brooklyn",1876
+"772","9/2","Saturday",13.462,19.3888888888889,12.2222222222222,22,TRUE,"manhattan",3121
+"773","9/2","Saturday",13.462,19.3888888888889,12.2222222222222,22,TRUE,"williamsburg",3625
+"774","9/2","Saturday",13.462,19.3888888888889,12.2222222222222,22,TRUE,"queensboro",3325
+"775","9/2","Saturday",13.462,19.3888888888889,12.2222222222222,22,TRUE,"total",11947
+"776","9/3","Sunday",18.796,20.6111111111111,15.6111111111111,22,TRUE,"brooklyn",1004
+"777","9/3","Sunday",18.796,20.6111111111111,15.6111111111111,22,TRUE,"manhattan",1867
+"778","9/3","Sunday",18.796,20.6111111111111,15.6111111111111,22,TRUE,"williamsburg",2479
+"779","9/3","Sunday",18.796,20.6111111111111,15.6111111111111,22,TRUE,"queensboro",1711
+"780","9/3","Sunday",18.796,20.6111111111111,15.6111111111111,22,TRUE,"total",7061
+"781","9/4","Monday",0,26.1111111111111,16.7222222222222,22,FALSE,"brooklyn",2866
+"782","9/4","Monday",0,26.1111111111111,16.7222222222222,22,FALSE,"manhattan",4766
+"783","9/4","Monday",0,26.1111111111111,16.7222222222222,22,FALSE,"williamsburg",4882
+"784","9/4","Monday",0,26.1111111111111,16.7222222222222,22,FALSE,"queensboro",4346
+"785","9/4","Monday",0,26.1111111111111,16.7222222222222,22,FALSE,"total",16860
+"786","9/5","Tuesday",0.254,28.8888888888889,21.1111111111111,22,FALSE,"brooklyn",3244
+"787","9/5","Tuesday",0.254,28.8888888888889,21.1111111111111,22,FALSE,"manhattan",7244
+"788","9/5","Tuesday",0.254,28.8888888888889,21.1111111111111,22,FALSE,"williamsburg",7537
+"789","9/5","Tuesday",0.254,28.8888888888889,21.1111111111111,22,FALSE,"queensboro",5906
+"790","9/5","Tuesday",0.254,28.8888888888889,21.1111111111111,22,FALSE,"total",23931
+"791","9/6","Wednesday",10.668,21.1111111111111,16.7222222222222,22,TRUE,"brooklyn",1232
+"792","9/6","Wednesday",10.668,21.1111111111111,16.7222222222222,22,TRUE,"manhattan",2672
+"793","9/6","Wednesday",10.668,21.1111111111111,16.7222222222222,22,TRUE,"williamsburg",3310
+"794","9/6","Wednesday",10.668,21.1111111111111,16.7222222222222,22,TRUE,"queensboro",2951
+"795","9/6","Wednesday",10.668,21.1111111111111,16.7222222222222,22,TRUE,"total",10165
+"796","9/7","Thursday",0.254,21.7222222222222,15,22,FALSE,"brooklyn",3249
+"797","9/7","Thursday",0.254,21.7222222222222,15,22,FALSE,"manhattan",7059
+"798","9/7","Thursday",0.254,21.7222222222222,15,22,FALSE,"williamsburg",7717
+"799","9/7","Thursday",0.254,21.7222222222222,15,22,FALSE,"queensboro",5923
+"800","9/7","Thursday",0.254,21.7222222222222,15,22,FALSE,"total",23948
+"801","9/8","Friday",0,21.1111111111111,15,22,FALSE,"brooklyn",3234
+"802","9/8","Friday",0,21.1111111111111,15,22,FALSE,"manhattan",6850
+"803","9/8","Friday",0,21.1111111111111,15,22,FALSE,"williamsburg",7739
+"804","9/8","Friday",0,21.1111111111111,15,22,FALSE,"queensboro",5836
+"805","9/8","Friday",0,21.1111111111111,15,22,FALSE,"total",23659
+"806","9/9","Saturday",0,20.6111111111111,12.7777777777778,23,FALSE,"brooklyn",2609
+"807","9/9","Saturday",0,20.6111111111111,12.7777777777778,23,FALSE,"manhattan",5035
+"808","9/9","Saturday",0,20.6111111111111,12.7777777777778,23,FALSE,"williamsburg",5849
+"809","9/9","Saturday",0,20.6111111111111,12.7777777777778,23,FALSE,"queensboro",4471
+"810","9/9","Saturday",0,20.6111111111111,12.7777777777778,23,FALSE,"total",17964
+"811","9/10","Sunday",0,22.2222222222222,13.8888888888889,23,FALSE,"brooklyn",4960
+"812","9/10","Sunday",0,22.2222222222222,13.8888888888889,23,FALSE,"manhattan",5673
+"813","9/10","Sunday",0,22.2222222222222,13.8888888888889,23,FALSE,"williamsburg",5773
+"814","9/10","Sunday",0,22.2222222222222,13.8888888888889,23,FALSE,"queensboro",4381
+"815","9/10","Sunday",0,22.2222222222222,13.8888888888889,23,FALSE,"total",20787
+"816","9/11","Monday",0,24.3888888888889,12.7777777777778,23,FALSE,"brooklyn",3657
+"817","9/11","Monday",0,24.3888888888889,12.7777777777778,23,FALSE,"manhattan",7783
+"818","9/11","Monday",0,24.3888888888889,12.7777777777778,23,FALSE,"williamsburg",8254
+"819","9/11","Monday",0,24.3888888888889,12.7777777777778,23,FALSE,"queensboro",5986
+"820","9/11","Monday",0,24.3888888888889,12.7777777777778,23,FALSE,"total",25680
+"821","9/12","Tuesday",0,25.6111111111111,16.1111111111111,23,FALSE,"brooklyn",3497
+"822","9/12","Tuesday",0,25.6111111111111,16.1111111111111,23,FALSE,"manhattan",7983
+"823","9/12","Tuesday",0,25.6111111111111,16.1111111111111,23,FALSE,"williamsburg",8756
+"824","9/12","Tuesday",0,25.6111111111111,16.1111111111111,23,FALSE,"queensboro",6386
+"825","9/12","Tuesday",0,25.6111111111111,16.1111111111111,23,FALSE,"total",26622
+"826","9/13","Wednesday",1.524,27.7777777777778,18.2777777777778,23,TRUE,"brooklyn",2994
+"827","9/13","Wednesday",1.524,27.7777777777778,18.2777777777778,23,TRUE,"manhattan",7461
+"828","9/13","Wednesday",1.524,27.7777777777778,18.2777777777778,23,TRUE,"williamsburg",8095
+"829","9/13","Wednesday",1.524,27.7777777777778,18.2777777777778,23,TRUE,"queensboro",6213
+"830","9/13","Wednesday",1.524,27.7777777777778,18.2777777777778,23,TRUE,"total",24763
+"831","9/14","Thursday",0.508,27.2222222222222,21.1111111111111,23,FALSE,"brooklyn",3013
+"832","9/14","Thursday",0.508,27.2222222222222,21.1111111111111,23,FALSE,"manhattan",6703
+"833","9/14","Thursday",0.508,27.2222222222222,21.1111111111111,23,FALSE,"williamsburg",7629
+"834","9/14","Thursday",0.508,27.2222222222222,21.1111111111111,23,FALSE,"queensboro",5697
+"835","9/14","Thursday",0.508,27.2222222222222,21.1111111111111,23,FALSE,"total",23042
+"836","9/15","Friday",0,27.2222222222222,19.3888888888889,23,FALSE,"brooklyn",3344
+"837","9/15","Friday",0,27.2222222222222,19.3888888888889,23,FALSE,"manhattan",6998
+"838","9/15","Friday",0,27.2222222222222,19.3888888888889,23,FALSE,"williamsburg",7749
+"839","9/15","Friday",0,27.2222222222222,19.3888888888889,23,FALSE,"queensboro",5853
+"840","9/15","Friday",0,27.2222222222222,19.3888888888889,23,FALSE,"total",23944
+"841","9/16","Saturday",0,27.7777777777778,21.1111111111111,24,FALSE,"brooklyn",2560
+"842","9/16","Saturday",0,27.7777777777778,21.1111111111111,24,FALSE,"manhattan",4860
+"843","9/16","Saturday",0,27.7777777777778,21.1111111111111,24,FALSE,"williamsburg",5568
+"844","9/16","Saturday",0,27.7777777777778,21.1111111111111,24,FALSE,"queensboro",4532
+"845","9/16","Saturday",0,27.7777777777778,21.1111111111111,24,FALSE,"total",17520
+"846","9/17","Sunday",0,26.7222222222222,21.1111111111111,24,FALSE,"brooklyn",2676
+"847","9/17","Sunday",0,26.7222222222222,21.1111111111111,24,FALSE,"manhattan",4935
+"848","9/17","Sunday",0,26.7222222222222,21.1111111111111,24,FALSE,"williamsburg",5599
+"849","9/17","Sunday",0,26.7222222222222,21.1111111111111,24,FALSE,"queensboro",4032
+"850","9/17","Sunday",0,26.7222222222222,21.1111111111111,24,FALSE,"total",17242
+"851","9/18","Monday",0,22.7777777777778,20.6111111111111,24,FALSE,"brooklyn",2673
+"852","9/18","Monday",0,22.7777777777778,20.6111111111111,24,FALSE,"manhattan",6338
+"853","9/18","Monday",0,22.7777777777778,20.6111111111111,24,FALSE,"williamsburg",6708
+"854","9/18","Monday",0,22.7777777777778,20.6111111111111,24,FALSE,"queensboro",4864
+"855","9/18","Monday",0,22.7777777777778,20.6111111111111,24,FALSE,"total",20583
+"856","9/19","Tuesday",5.588,25.6111111111111,20.6111111111111,24,TRUE,"brooklyn",2012
+"857","9/19","Tuesday",5.588,25.6111111111111,20.6111111111111,24,TRUE,"manhattan",4726
+"858","9/19","Tuesday",5.588,25.6111111111111,20.6111111111111,24,TRUE,"williamsburg",5729
+"859","9/19","Tuesday",5.588,25.6111111111111,20.6111111111111,24,TRUE,"queensboro",4171
+"860","9/19","Tuesday",5.588,25.6111111111111,20.6111111111111,24,TRUE,"total",16638
+"861","9/20","Wednesday",0,25.6111111111111,21.7222222222222,24,FALSE,"brooklyn",3296
+"862","9/20","Wednesday",0,25.6111111111111,21.7222222222222,24,FALSE,"manhattan",6826
+"863","9/20","Wednesday",0,25.6111111111111,21.7222222222222,24,FALSE,"williamsburg",7518
+"864","9/20","Wednesday",0,25.6111111111111,21.7222222222222,24,FALSE,"queensboro",5570
+"865","9/20","Wednesday",0,25.6111111111111,21.7222222222222,24,FALSE,"total",23210
+"866","9/21","Thursday",0,26.7222222222222,21.7222222222222,24,FALSE,"brooklyn",3317
+"867","9/21","Thursday",0,26.7222222222222,21.7222222222222,24,FALSE,"manhattan",6663
+"868","9/21","Thursday",0,26.7222222222222,21.7222222222222,24,FALSE,"williamsburg",8151
+"869","9/21","Thursday",0,26.7222222222222,21.7222222222222,24,FALSE,"queensboro",5792
+"870","9/21","Thursday",0,26.7222222222222,21.7222222222222,24,FALSE,"total",23923
+"871","9/22","Friday",0,27.7777777777778,18.8888888888889,24,FALSE,"brooklyn",3297
+"872","9/22","Friday",0,27.7777777777778,18.8888888888889,24,FALSE,"manhattan",6272
+"873","9/22","Friday",0,27.7777777777778,18.8888888888889,24,FALSE,"williamsburg",7570
+"874","9/22","Friday",0,27.7777777777778,18.8888888888889,24,FALSE,"queensboro",5547
+"875","9/22","Friday",0,27.7777777777778,18.8888888888889,24,FALSE,"total",22686
+"876","9/23","Saturday",0,30,20,25,FALSE,"brooklyn",2810
+"877","9/23","Saturday",0,30,20,25,FALSE,"manhattan",4940
+"878","9/23","Saturday",0,30,20,25,FALSE,"williamsburg",6167
+"879","9/23","Saturday",0,30,20,25,FALSE,"queensboro",4635
+"880","9/23","Saturday",0,30,20,25,FALSE,"total",18552
+"881","9/24","Sunday",0,32.2222222222222,20.6111111111111,25,FALSE,"brooklyn",2543
+"882","9/24","Sunday",0,32.2222222222222,20.6111111111111,25,FALSE,"manhattan",4500
+"883","9/24","Sunday",0,32.2222222222222,20.6111111111111,25,FALSE,"williamsburg",5083
+"884","9/24","Sunday",0,32.2222222222222,20.6111111111111,25,FALSE,"queensboro",3977
+"885","9/24","Sunday",0,32.2222222222222,20.6111111111111,25,FALSE,"total",16103
+"886","9/25","Monday",0,30.6111111111111,22.2222222222222,25,FALSE,"brooklyn",3276
+"887","9/25","Monday",0,30.6111111111111,22.2222222222222,25,FALSE,"manhattan",7227
+"888","9/25","Monday",0,30.6111111111111,22.2222222222222,25,FALSE,"williamsburg",7708
+"889","9/25","Monday",0,30.6111111111111,22.2222222222222,25,FALSE,"queensboro",5941
+"890","9/25","Monday",0,30.6111111111111,22.2222222222222,25,FALSE,"total",24152
+"891","9/26","Tuesday",0,27.7777777777778,20.6111111111111,25,FALSE,"brooklyn",3157
+"892","9/26","Tuesday",0,27.7777777777778,20.6111111111111,25,FALSE,"manhattan",7676
+"893","9/26","Tuesday",0,27.7777777777778,20.6111111111111,25,FALSE,"williamsburg",8150
+"894","9/26","Tuesday",0,27.7777777777778,20.6111111111111,25,FALSE,"queensboro",6179
+"895","9/26","Tuesday",0,27.7777777777778,20.6111111111111,25,FALSE,"total",25162
+"896","9/27","Wednesday",0,29.3888888888889,21.7222222222222,25,FALSE,"brooklyn",3216
+"897","9/27","Wednesday",0,29.3888888888889,21.7222222222222,25,FALSE,"manhattan",7313
+"898","9/27","Wednesday",0,29.3888888888889,21.7222222222222,25,FALSE,"williamsburg",7983
+"899","9/27","Wednesday",0,29.3888888888889,21.7222222222222,25,FALSE,"queensboro",6309
+"900","9/27","Wednesday",0,29.3888888888889,21.7222222222222,25,FALSE,"total",24821
+"901","9/28","Thursday",0,25.6111111111111,18.8888888888889,25,FALSE,"brooklyn",3421
+"902","9/28","Thursday",0,25.6111111111111,18.8888888888889,25,FALSE,"manhattan",7301
+"903","9/28","Thursday",0,25.6111111111111,18.8888888888889,25,FALSE,"williamsburg",8118
+"904","9/28","Thursday",0,25.6111111111111,18.8888888888889,25,FALSE,"queensboro",6277
+"905","9/28","Thursday",0,25.6111111111111,18.8888888888889,25,FALSE,"total",25117
+"906","9/29","Friday",0,19.3888888888889,12.7777777777778,25,FALSE,"brooklyn",2988
+"907","9/29","Friday",0,19.3888888888889,12.7777777777778,25,FALSE,"manhattan",6221
+"908","9/29","Friday",0,19.3888888888889,12.7777777777778,25,FALSE,"williamsburg",7659
+"909","9/29","Friday",0,19.3888888888889,12.7777777777778,25,FALSE,"queensboro",5677
+"910","9/29","Friday",0,19.3888888888889,12.7777777777778,25,FALSE,"total",22545
+"911","9/30","Saturday",0,17.7777777777778,13.2777777777778,26,FALSE,"brooklyn",1903
+"912","9/30","Saturday",0,17.7777777777778,13.2777777777778,26,FALSE,"manhattan",3507
+"913","9/30","Saturday",0,17.7777777777778,13.2777777777778,26,FALSE,"williamsburg",4749
+"914","9/30","Saturday",0,17.7777777777778,13.2777777777778,26,FALSE,"queensboro",3729
+"915","9/30","Saturday",0,17.7777777777778,13.2777777777778,26,FALSE,"total",13888
+"916","10/1","Sunday",0,19.3888888888889,10,26,FALSE,"brooklyn",2297
+"917","10/1","Sunday",0,19.3888888888889,10,26,FALSE,"manhattan",4540
+"918","10/1","Sunday",0,19.3888888888889,10,26,FALSE,"williamsburg",5046
+"919","10/1","Sunday",0,19.3888888888889,10,26,FALSE,"queensboro",4092
+"920","10/1","Sunday",0,19.3888888888889,10,26,FALSE,"total",15975
+"921","10/2","Monday",0,22.2222222222222,11.1111111111111,26,FALSE,"brooklyn",3387
+"922","10/2","Monday",0,22.2222222222222,11.1111111111111,26,FALSE,"manhattan",7059
+"923","10/2","Monday",0,22.2222222222222,11.1111111111111,26,FALSE,"williamsburg",7616
+"924","10/2","Monday",0,22.2222222222222,11.1111111111111,26,FALSE,"queensboro",5722
+"925","10/2","Monday",0,22.2222222222222,11.1111111111111,26,FALSE,"total",23784
+"926","10/3","Tuesday",0,21.1111111111111,13.8888888888889,26,FALSE,"brooklyn",3386
+"927","10/3","Tuesday",0,21.1111111111111,13.8888888888889,26,FALSE,"manhattan",7370
+"928","10/3","Tuesday",0,21.1111111111111,13.8888888888889,26,FALSE,"williamsburg",8161
+"929","10/3","Tuesday",0,21.1111111111111,13.8888888888889,26,FALSE,"queensboro",6363
+"930","10/3","Tuesday",0,21.1111111111111,13.8888888888889,26,FALSE,"total",25280
+"931","10/4","Wednesday",0,23.8888888888889,13.2777777777778,26,FALSE,"brooklyn",3412
+"932","10/4","Wednesday",0,23.8888888888889,13.2777777777778,26,FALSE,"manhattan",7691
+"933","10/4","Wednesday",0,23.8888888888889,13.2777777777778,26,FALSE,"williamsburg",8136
+"934","10/4","Wednesday",0,23.8888888888889,13.2777777777778,26,FALSE,"queensboro",6238
+"935","10/4","Wednesday",0,23.8888888888889,13.2777777777778,26,FALSE,"total",25477
+"936","10/5","Thursday",0,27.7777777777778,18.2777777777778,26,FALSE,"brooklyn",3312
+"937","10/5","Thursday",0,27.7777777777778,18.2777777777778,26,FALSE,"manhattan",7034
+"938","10/5","Thursday",0,27.7777777777778,18.2777777777778,26,FALSE,"williamsburg",7598
+"939","10/5","Thursday",0,27.7777777777778,18.2777777777778,26,FALSE,"queensboro",5998
+"940","10/5","Thursday",0,27.7777777777778,18.2777777777778,26,FALSE,"total",23942
+"941","10/6","Friday",0,27.2222222222222,20.6111111111111,26,FALSE,"brooklyn",2982
+"942","10/6","Friday",0,27.2222222222222,20.6111111111111,26,FALSE,"manhattan",6204
+"943","10/6","Friday",0,27.2222222222222,20.6111111111111,26,FALSE,"williamsburg",7336
+"944","10/6","Friday",0,27.2222222222222,20.6111111111111,26,FALSE,"queensboro",5675
+"945","10/6","Friday",0,27.2222222222222,20.6111111111111,26,FALSE,"total",22197
+"946","10/7","Saturday",0,26.7222222222222,18.8888888888889,27,FALSE,"brooklyn",2750
+"947","10/7","Saturday",0,26.7222222222222,18.8888888888889,27,FALSE,"manhattan",5019
+"948","10/7","Saturday",0,26.7222222222222,18.8888888888889,27,FALSE,"williamsburg",5873
+"949","10/7","Saturday",0,26.7222222222222,18.8888888888889,27,FALSE,"queensboro",4576
+"950","10/7","Saturday",0,26.7222222222222,18.8888888888889,27,FALSE,"total",18218
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data/dataset_goodreads_filtered.csv

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data/item_to_item_similarity_dataframe_full.csv

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data/ratings_filtered.rds

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data/svdf.rds

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dependencies.R

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 # This file allows packrat (used by rsconnect during deployment) to pick up dependencies.
 library(bsicons)
 library(bslib)
+library(data.table)
 library(dplyr)
-library(gargoyle)
-library(quanteda)
-library(quanteda.corpora)
-library(quanteda.textplots)
-library(quanteda.textstats)
-library(readtext)
+library(ggplot2)
+library(HDInterval)
+library(htmlwidgets)
+library(plotly)
+library(reactable)
+library(readODS)
 library(rhino)
-library(shiny)
-library(shiny.react)
+library(shinyjs)
 library(shinyWidgets)
-library(spacyr)
-library(waiter)
+library(tidyr)
+library(VGAM)

package-lock.json

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renv.lock

@@ -4,7 +4,7 @@
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@@ -535,14 +435,14 @@
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@@ -574,14 +474,14 @@
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@@ -753,7 +590,7 @@
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@@ -900,42 +725,6 @@
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@@ -1185,36 +936,15 @@
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