Written by Rick Pack

Meetup API tracking with R-Ladies meetupr: Analytics>Forward in RTP, NC – Part 1

Zillow $1M machine learning winner, Jordan Meyer, keynoting this Durham, NC March 9, 2019 event. Register using this link!

Using the lessons learned from last year’s study of the R Ladies meetupr R package, and tracking Analytics>Forward 2018 registrations with the Meetup.com API, I quickly adjusted my code (at bottom) to explore this year’s event registrations.

20190225 update:
All of the R code used for my latest ggplot2 data visualizations (and the top animation) are saved as a Github gist here and at the bottom of this blog post (also see part 2 of this series). The data can be found in this Github folder. I would love to see others’ versions.

It may be hard to see that we (A>F planning committee) started later (created the Meetup event) than prior years. Registrations are still tracking with last year’s (2018) record number of registrations.

When the new Research Triangle Analysts president, Eric Yount, created the event and therefore triggered an email notification to Research Triangle Analysts members, we were impressed by how many people registered within 24-hours. Notice how we started the event very early in 2018 (light blue line) and registrations remained flat until I think Dr. Zeydy Ortiz disseminated the press release to multiple outlets around January 22nd. The peak registrations in that circle of about 20 is a bit higher than what we see for 2015, when like 2019, the event was announced later than usual.

We can conclude that rushing to finalize Analytics>Forward elements so we can announce the event is not important. On the other hand, finalizing those elements so the planning committee can operated in a more relaxed fashion with a speaker secured and space reserved (thank you Aaron Terry and Blue Cross Blue Shield of North Carolina) is a different subject. 🙂

We worked aggressively last year to market the event with blog posts, social media activity, emails to colleagues, and fliers in nail salons and coffee shops.

Aside: Nail salon employees were quite receptive to the display of our flier, perhaps because the topic of “analytics” is not controversial and Research Triangle Analysts is a non-profit organization.

Will we work with similar ardor this year? We shall see! I do know that we have another great keynote speaker. Mara Averick spoke to rave reviews last year and we noted that a beautifully increased diversity of attendees. That momentum will help us this year.

This year, our speaker is Jordan Meyer, a member of a 3-person international team that won $1 Million in a Zillow estimated housing cost (zestimate) contest. This was a nice article about the work by this DataRobot employee and his team. I look forward to learning more about the deep neural networks used to create the winning algorithm and how they stayed motivated over what I am guessing was a tedious duration of massive reiterations and hyperparameter tweaking.

Thank you to machine learning entrepreneur, Dr. Zeydy Ortiz, for securing this excellent speaker!

I speak for myself when I say that Analytics>Forward is a special, welcoming event that brings together people of widely varying experiences with data. Kudos to former RTA president, Dr. Melinda Thielbar, and others on the RTA board who have worked hard since 2015 to coordinate this event.

The Analytics>Forward planning committee loves when prior attendees post comments about their prior experiences, or just give a social media “Like”!

Register at http://bit.ly/AF2019Signup


Thank you sponsors Blue Cross NC, Advance Auto Parts, NTT DATA Services, National Consortium for Data Science / RENCI, Valassis Digital, Rho, and Talking Leaves. 


    

      

        

  

    
    


        



  


  

        

          
          library(httr)
        

        

          
          library(tidyverse)
        

        

          
          library(jsonlite)
        

        

          
          library(meetupr)
        

        

          
          library(lubridate)
        

        

          
          library(magick)
        

        

          
          library(directlabels)
        

        

          
          

        

        

          
          ###################################
        

        

          
          ## If need to use local CSV file ##
        

        

          
          ## rather than use Meetup API    ##
        

        

          
          ###################################
        

        

          
          local_file <- TRUE
        

        

          
          local_filename <- 'C:/Users/Packr1/Documents/Personal/RTA/AF/AnalyticsForward_2019_02_27_AM.csv'
        

        

          
          

        

        

          
          if (!local_file) {
        

        

          
          AMPM <- "AM"
        

        

          
          todaydt <- str_replace_all(as.character(Sys.Date()), "-","_")
        

        

          
          

        

        

          
          # Get your Meetup key by going to
        

        

          
          # https://secure.meetup.com/meetup_api/key/
        

        

          
          Sys.setenv(MEETUP_KEY = "NOT_SHOWN")
        

        

          
          

        

        

          
          # What is the Meetup group name?
        

        

          
          # Find this in the Meetup URL.
        

        

          
          # For example, in https://www.meetup.com/Research-Triangle-Analysts/events/258165094/
        

        

          
          # it is:
        

        

          
          # 'Research-Triangle-Analysts'
        

        

          
          meetupgrp_name <- 'Research-Triangle-Analysts'
        

        

          
          

        

        

          
          # Get the Yes RSVPs for a specified event
        

        

          
          # id_name is a descriptor of your choice
        

        

          
          # and id appears at the end of the Meetup.com URL
        

        

          
          # e.g., the number in
        

        

          
          # https://www.meetup.com/Research-Triangle-Analysts/
        

        

          
          # events/246678392/
        

        

          
          meetup_yes_RSVPs <- function(id_name, id){
        

        

          
              rladiesfrm <- meetupr::get_event_rsvps(meetupgrp_name, id) %>%
        

        

          
                  dplyr::filter(response == "yes") %>%
        

        

          
                  mutate(rsvp_yes_row = 1 + guests) %>% 
        

        

          
                  mutate(dates_yes = date(ymd_hms(created)))
        

        

          
              
        

        

          
              df_content <-   rladiesfrm %>%
        

        

          
                  group_by(dates_yes) %>%
        

        

          
                  summarise(rsvp_yes_count = sum(rsvp_yes_row)) %>%
        

        

          
                  ungroup()
        

        

          
              id_name      <- as.character(id_name)
        

        

          
              dates_yes    <- unique(df_content$dates_yes)
        

        

          
              rsvp_yes_count <- df_content$rsvp_yes_count
        

        

          
              # stringsAsFactors = FALSE to avoid warnings
        

        

          
              # about factor level differences when I bind_rows later
        

        

          
              df_out       <- data.frame(id_name, dates_yes, rsvp_yes_count, id,
        

        

          
                                         stringsAsFactors = FALSE)
        

        

          
              invisible(df_out)
        

        

          
          }
        

        

          
          # manually extracted from URLs like
        

        

          
          # https://www.meetup.com/Research-Triangle-Analysts/events/246678392/
        

        

          
          AF19_id <-  '258165094'
        

        

          
          AF18_id  <- '246678392'
        

        

          
          AF17_id  <- '237118943'
        

        

          
          AF16_id  <- '228455037'
        

        

          
          AF15_id  <- '219885748'
        

        

          
          AF19_frm <- meetup_yes_RSVPs("AF19", AF19_id)
        

        

          
          AF18_frm <- meetup_yes_RSVPs("AF18", AF18_id)
        

        

          
          AF17_frm <- meetup_yes_RSVPs("AF17", AF17_id)
        

        

          
          AF16_frm <- meetup_yes_RSVPs("AF16", AF16_id)
        

        

          
          AF15_frm <- meetup_yes_RSVPs("AF15", AF15_id)
        

        

          
          

        

        

          
          date_fill <- function(df){
        

        

          
              df 
        

        

          
          }
        

        

          
          

        

        

          
          allAF_frm_rladies <- bind_rows(AF15_frm, AF16_frm, AF17_frm, AF18_frm, AF19_frm)
        

        

          
          

        

        

          
          allRTA_events_future <- get_events('Research-Triangle-Analysts')
        

        

          
          allRTA_events_past   <- get_events('Research-Triangle-Analysts', "past")
        

        

          
          allRTA_events        <- rbind(allRTA_events_future, allRTA_events_past)
        

        

          
          AF_events            <- allRTA_events[grepl("FORWARD", toupper(allRTA_events$name)),]
        

        

          
          # Do not include preparation meeting by frequent
        

        

          
          # high Analytics>Forward vote-earner, Mark Hutchinson
        

        

          
          AF_events            <- AF_events %>% dplyr::filter(!grepl("PREPARATION", toupper(name)))
        

        

          
          # local_date loads as a date column
        

        

          
          AF_events_dates_yes  <- AF_events %>% select(id, local_date, created) %>%
        

        

          
              # strip off time and only keep date
        

        

          
              mutate(created = str_sub(created, 1, 10)) %>%
        

        

          
              # renaming columns from Meetup API for easier identification
        

        

          
              rename(creation_date_per_AF_year = created,
        

        

          
                     event_date = local_date) %>%
        

        

          
              group_by(id) %>%
        

        

          
              # seq function below needs from and to arguments to be date variables
        

        

          
              # so converting character to date with lubridate ymd function
        

        

          
              mutate(event_date = ymd(event_date),
        

        

          
                     creation_date_per_AF_year = ymd(creation_date_per_AF_year)) %>%
        

        

          
              mutate(yes_year = lubridate::year(event_date))
        

        

          
          

        

        

          
          # revised until no NAs, added dates with tidyr::complete
        

        

          
          # will only contain non-NA values for group_by columns
        

        

          
          allAF_frm <- left_join(allAF_frm_rladies, AF_events_dates_yes) %>%
        

        

          
              group_by(id_name, event_date, creation_date_per_AF_year,
        

        

          
                       id, yes_year) %>%
        

        

          
              mutate(max_date_per_AF_year = ymd(max(dates_yes))) %>%
        

        

          
              # https://blog.exploratory.io/populating-missing-dates-with-complete-and-fill-functions-in-r-and-exploratory-79f2a321e6b5
        

        

          
              # Notice use of unique because seq.Date needs one value for 'from' and 'to' arguments
        

        

          
              tidyr::complete(dates_yes = seq.Date(unique(creation_date_per_AF_year), 
        

        

          
                                                   unique(max_date_per_AF_year), by = "day")) %>%
        

        

          
              replace_na(list(rsvp_yes_count = 0)) %>%
        

        

          
              mutate(dates_yes_cumsum = cumsum(rsvp_yes_count)) %>%
        

        

          
              ungroup() %>%
        

        

          
              # dropping so data frame can be provided to others and not cause questions about NAs
        

        

          
              # produced by spanning out dates with seq.Date
        

        

          
              select(-max_date_per_AF_year) %>%
        

        

          
              mutate(days_to_event = event_date - dates_yes) %>% 
        

        

          
              arrange(id_name, desc(days_to_event))
        

        

          
          

        

        

          
          

        

        

          
          # add days and month column as inspired by
        

        

          
          # StackOverflow user NicE on
        

        

          
          # https://stackoverflow.com/questions/28503262/using-lubridate-and-ggplot2-effectively-for-date-axis
        

        

          
          allAF_frm$yes_day   <- lubridate::day(allAF_frm$dates_yes)
        

        

          
          allAF_frm$yes_month <- lubridate::month(allAF_frm$dates_yes)
        

        

          
          allAF_frm$yes_year  <- lubridate::year(allAF_frm$dates_yes)
        

        

          
          allAF_frm$dates_yes_otheryear <- as.Date(
        

        

          
              format(
        

        

          
                  # 2019 used in below line because future year
        

        

          
                  # denotes false year imposed to homogenize
        

        

          
                  # year for ggplot printing on same axes
        

        

          
                  allAF_frm$dates_yes,"%d-%m-2020"),
        

        

          
              format = "%d-%m-%y")
        

        

          
          # Get the RSVP yes count as of the latest day of the most recent event
        

        

          
          # so years are ordered at farthest-right point for the year of 
        

        

          
          # greatest interest - the present year
        

        

          
          today_days_to_event      <- allAF_frm %>%
        

        

          
              dplyr::filter(yes_year == max(yes_year)) %>%
        

        

          
              mutate(min_days_event = min(days_to_event)) %>%
        

        

          
              distinct(min_days_event) %>%
        

        

          
              pull(min_days_event)
        

        

          
          

        

        

          
          today_relative_yes_count <- allAF_frm %>%
        

        

          
              group_by(yes_year) %>%
        

        

          
              dplyr::filter(days_to_event == today_days_to_event) %>%
        

        

          
              # relative to today
        

        

          
              rename(relative_today_cumsum = dates_yes_cumsum) %>%
        

        

          
              ungroup() %>%
        

        

          
              select(id, relative_today_cumsum)
        

        

          
          

        

        

          
          allAF_frm            <- allAF_frm %>%
        

        

          
              left_join(., today_relative_yes_count) %>%
        

        

          
              mutate(yes_year_factor = fct_reorder(as.factor(yes_year), relative_today_cumsum, 
        

        

          
                                                   .desc = TRUE))
        

        

          
          }
        

        

          
          

        

        

          
          if (local_file) {
        

        

          
              allAF_frm <- readr::read_csv(local_filename) %>%
        

        

          
                  mutate(yes_year_factor = fct_reorder(as.factor(yes_year), relative_today_cumsum, 
        

        

          
                                                       .desc = TRUE))
        

        

          
              
        

        

          
              today_days_to_event      <- allAF_frm %>%
        

        

          
                  dplyr::filter(yes_year == max(yes_year)) %>%
        

        

          
                  mutate(min_days_event = min(days_to_event)) %>%
        

        

          
                  distinct(min_days_event) %>%
        

        

          
                  pull(min_days_event)
        

        

          
          }
        

        

          
          

        

        

          
          allAF_frm_weekday <- allAF_frm %>%
        

        

          
              mutate(yes_weekday = weekdays(ymd(dates_yes))) %>%
        

        

          
              group_by(yes_year, yes_weekday) %>%
        

        

          
              summarise(dates_yes_cumsum_week = sum(rsvp_yes_count)) %>%
        

        

          
              ungroup() %>%
        

        

          
              mutate(yes_weekday_factor  = factor(yes_weekday, levels = 
        

        

          
                                                      c("Monday", "Tuesday", "Wednesday",
        

        

          
                                                        "Thursday", "Friday" ,"Saturday", "Sunday"),
        

        

          
                                                  ordered = TRUE),
        

        

          
                     # forcats::fct_rev used to reverse order 
        

        

          
                     # (later year will come first)
        

        

          
                     # so in stacked bar graph later year is on far-right
        

        

          
                     yes_year_factor = fct_rev(factor(yes_year))) %>%
        

        

          
              select(-yes_year, -yes_weekday)
        

        

          
          

        

        

          
          # same as above but exclude the last week, which is when
        

        

          
          # registrations dramatically increase
        

        

          
          # last day of last captured week is Saturday
        

        

          
          allAF_frm_weekday_not_lastweek <- allAF_frm %>%
        

        

          
              dplyr::filter(days_to_event > 6) %>%
        

        

          
              mutate(yes_weekday = weekdays(ymd(dates_yes))) %>%
        

        

          
              group_by(yes_year, yes_weekday) %>%
        

        

          
              summarise(dates_yes_cumsum_week = sum(rsvp_yes_count)) %>%
        

        

          
              ungroup() %>%
        

        

          
              mutate(yes_weekday_factor  = factor(yes_weekday, levels = 
        

        

          
                                                      c("Monday", "Tuesday", "Wednesday",
        

        

          
                                                        "Thursday", "Friday" ,"Saturday", "Sunday"),
        

        

          
                                                  ordered = TRUE),
        

        

          
                     # forcats::fct_rev used to reverse order 
        

        

          
                     # (later year will come first)
        

        

          
                     # so in stacked bar graph later year is on far-right
        

        

          
                     yes_year_factor = fct_rev(factor(yes_year))) %>%
        

        

          
              select(-yes_year, -yes_weekday)
        

        

          
          

        

        

          
          # same as above but only the week prior to Analytics>Forward,
        

        

          
          # to help with a marketing plan for that week
        

        

          
          # Last day of last captured week is Saturday
        

        

          
          allAF_frm_weekday_penult_week <- allAF_frm %>%
        

        

          
              dplyr::filter(days_to_event > 6 & days_to_event < 14) %>%
        

        

          
              mutate(yes_weekday = weekdays(ymd(dates_yes))) %>%
        

        

          
              group_by(yes_year, yes_weekday) %>%
        

        

          
              summarise(dates_yes_cumsum_week = sum(rsvp_yes_count)) %>%
        

        

          
              ungroup() %>%
        

        

          
              mutate(yes_weekday_factor  = factor(yes_weekday, levels = 
        

        

          
                                                      c("Monday", "Tuesday", "Wednesday",
        

        

          
                                                        "Thursday", "Friday" ,"Saturday", "Sunday"),
        

        

          
                                                  ordered = TRUE),
        

        

          
                     # forcats::fct_rev used to reverse order 
        

        

          
                     # (later year will come first)
        

        

          
                     # so in stacked bar graph later year is on far-right
        

        

          
                     yes_year_factor = fct_rev(factor(yes_year))) %>%
        

        

          
              select(-yes_year, -yes_weekday)
        

        

          
          

        

        

          
          if (!local_file) {
        

        

          
              write.csv(allAF_frm, paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AnalyticsForward_", todaydt, "_", AMPM, ".csv"), row.names = FALSE)
        

        

          
              write.csv(allAF_frm_weekday, paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AnalyticsForward_weekday_", todaydt, "_", AMPM, ".csv"), row.names = FALSE)
        

        

          
          }
        

        

          
          

        

        

          
          p1 <- 
        

        

          
              ggplot(data = allAF_frm,
        

        

          
                     aes(x = dates_yes_otheryear,
        

        

          
                         y = dates_yes_cumsum,
        

        

          
                         colour = yes_year_factor)) +
        

        

          
              geom_line(size = 1) +
        

        

          
              scale_x_date(date_labels = "%b") +
        

        

          
              xlab("Month of year") +
        

        

          
              ylab("YES (will attend) RSVPs") +
        

        

          
              labs(colour = "Year") + 
        

        

          
              ggtitle(label = paste0("Research Triangle Analysts 'Analytics>Forward' Registrations as of\n",
        

        

          
                                     Sys.Date()),
        

        

          
                      subtitle = str_glue("Zillow Data Science (Kaggle) winner, Jordan Meyer", 
        

        

          
                                          " keynoting March 9, 2019\n",
        

        

          
                                          as.numeric(today_days_to_event), " days remaining\nGraph 1 of 5")) +
        

        

          
              directlabels::geom_dl(aes(label = yes_year), method = list("last.points",rot = -30)) +
        

        

          
              theme(plot.title = element_text(hjust = 0.5, size = 16, lineheight = .8, face = "bold"),
        

        

          
                    plot.subtitle = element_text(hjust = 0.5, size = 12),
        

        

          
                    axis.text.x = element_text(face = "bold.italic", color = "red", size = 16),
        

        

          
                    axis.text.y = element_text(face = "bold.italic", color = "red", size = 16)) +
        

        

          
              labs(colour = "Year") + 
        

        

          
              # http://colorbrewer2.org/#type=qualitative&scheme=Set2&n=5
        

        

          
              scale_colour_manual(values = c("#66c2a5", "#fc8d62", "#8da0cb", "#e78ac3", "#a6d854")) +
        

        

          
              NULL
        

        

          
          

        

        

          
          # annotate("text", x = ymd('2020-02-12'), y = 90,
        

        

          
          #          label = "2019 registrations recently went flat",
        

        

          
          #          size = 5)
        

        

          
          

        

        

          
          p2 <- 
        

        

          
              ggplot(data = allAF_frm,
        

        

          
                     aes(x = as.numeric(days_to_event),
        

        

          
                         y = dates_yes_cumsum,
        

        

          
                         colour = yes_year_factor)) +
        

        

          
              geom_line(size = 1) +
        

        

          
              xlab("Days to Event") +
        

        

          
              ylab("YES (will attend) RSVPs") +
        

        

          
              labs(colour = "Year") + 
        

        

          
              ggtitle(label = paste0("Research Triangle Analysts 'Analytics>Forward' as of\n",
        

        

          
                                     Sys.Date(), "\nGraph 2 of 5"),
        

        

          
                      subtitle = str_glue("Zillow Data Science (Kaggle) winner, Jordan Meyer", 
        

        

          
                                          " keynoting March 9, 2019\n",
        

        

          
                                          as.numeric(today_days_to_event), " days remaining")) +
        

        

          
              directlabels::geom_dl(aes(label = yes_year), method = list("last.points",rot = -30)) +
        

        

          
              theme(plot.title = element_text(hjust = 0.5, size = 16, lineheight = .8, face = "bold"),
        

        

          
                    plot.subtitle = element_text(hjust = 0.5, size = 12),
        

        

          
                    axis.text.x = element_text(face = "bold.italic", color = "red", size = 16),
        

        

          
                    axis.text.y = element_text(face = "bold.italic", color = "red", size = 16)) +
        

        

          
              # http://colorbrewer2.org/#type=qualitative&scheme=Set2&n=5
        

        

          
              scale_colour_manual(values = c("#66c2a5", "#fc8d62", "#8da0cb", "#e78ac3", "#a6d854")) +
        

        

          
              scale_x_reverse() +
        

        

          
              # annotate("text", x = 30, y = 90,
        

        

          
              #          label = "2019 registrations recently went flat",
        

        

          
              #          size = 5) +
        

        

          
              NULL
        

        

          
          

        

        

          
          p3 <-
        

        

          
              ggplot(data = allAF_frm_weekday,
        

        

          
                     aes(y = dates_yes_cumsum_week,
        

        

          
                         x = yes_weekday_factor,
        

        

          
                         fill = yes_year_factor)) +
        

        

          
              geom_bar(position = "stack", stat = "identity") +
        

        

          
              ylab("Yes RSVP Count") +
        

        

          
              xlab("Day of Week") +
        

        

          
              labs(fill = "Year") + 
        

        

          
              coord_flip() + 
        

        

          
              scale_fill_viridis_d() +
        

        

          
              ggtitle(label = paste0("Research Triangle Analysts 'Analytics>Forward' as of\n",
        

        

          
                                     Sys.Date(), "\nGraph 3 of 5"),
        

        

          
                      subtitle = str_glue("Zillow Data Science (Kaggle) winner, Jordan Meyer", 
        

        

          
                                          " keynoting March 9, 2019\nAll weeks shown.\n",
        

        

          
                                          as.numeric(today_days_to_event), " days remaining")) +
        

        

          
              theme(plot.title = element_text(hjust = 0.5, size = 16, lineheight = .8, face = "bold"),
        

        

          
                    plot.subtitle = element_text(hjust = 0.5, size = 12),
        

        

          
                    axis.text.x = element_text(face = "bold.italic", color = "red", size = 16),
        

        

          
                    axis.text.y = element_text(face = "bold.italic", color = "red", size = 16))
        

        

          
          

        

        

          
          p4 <-
        

        

          
              ggplot(data = allAF_frm_weekday_not_lastweek,
        

        

          
                     aes(y = dates_yes_cumsum_week,
        

        

          
                         x = yes_weekday_factor,
        

        

          
                         fill = yes_year_factor)) +
        

        

          
              geom_bar(position = "stack", stat = "identity") +
        

        

          
              ylab("Yes RSVP Count") +
        

        

          
              xlab("Day of Week") +
        

        

          
              labs(fill = "Year") + 
        

        

          
              coord_flip() + 
        

        

          
              scale_fill_viridis_d() +
        

        

          
              ggtitle(label = paste0("Research Triangle Analysts 'Analytics>Forward' as of\n",
        

        

          
                                     Sys.Date(), "\nGraph 4 of 5"),
        

        

          
                      subtitle = str_glue("Zillow Data Science (Kaggle) winner, Jordan Meyer", 
        

        

          
                                          " keynoting March 9, 2019\nEXCLUDES the week of the event.\n",
        

        

          
                                          as.numeric(today_days_to_event), " days remaining")) +
        

        

          
              theme(plot.title = element_text(hjust = 0.5, size = 16, lineheight = .8, face = "bold"),
        

        

          
                    plot.subtitle = element_text(hjust = 0.5, size = 12),
        

        

          
                    axis.text.x = element_text(face = "bold.italic", color = "red", size = 16),
        

        

          
                    axis.text.y = element_text(face = "bold.italic", color = "red", size = 16))
        

        

          
          

        

        

          
          p5 <-
        

        

          
              ggplot(data = allAF_frm_weekday_penult_week,
        

        

          
                     aes(y = dates_yes_cumsum_week,
        

        

          
                         x = yes_weekday_factor,
        

        

          
                         fill = yes_year_factor)) +
        

        

          
              geom_bar(position = "stack", stat = "identity") +
        

        

          
              ylab("Yes RSVP Count") +
        

        

          
              xlab("Day of Week") +
        

        

          
              labs(fill = "Year") + 
        

        

          
              coord_flip() + 
        

        

          
              scale_fill_viridis_d() +
        

        

          
              ggtitle(label = paste0("Research Triangle Analysts 'Analytics>Forward' as of\n",
        

        

          
                                     Sys.Date(), "\nGraph 5 of 5"),
        

        

          
                      subtitle = str_glue("Zillow Data Science (Kaggle) winner, Jordan Meyer", 
        

        

          
                                          " keynoting March 9, 2019\nONLY the week prior to the event.\n",
        

        

          
                                          as.numeric(today_days_to_event), " days remaining")) +
        

        

          
              theme(plot.title = element_text(hjust = 0.5, size = 16, lineheight = .8, face = "bold"),
        

        

          
                    plot.subtitle = element_text(hjust = 0.5, size = 12),
        

        

          
                    axis.text.x = element_text(face = "bold.italic", color = "red", size = 16),
        

        

          
                    axis.text.y = element_text(face = "bold.italic", color = "red", size = 16))
        

        

          
          

        

        

          
          

        

        

          
          ggsave(p1, file = paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AF_",
        

        

          
                                   todaydt, "_1", AMPM, ".png"), height = 5, width = 8)
        

        

          
          ggsave(p2, file = paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AF_",
        

        

          
                                   todaydt, "_2", AMPM, ".png"), height = 5, width = 8)
        

        

          
          ggsave(p3, file = paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AF_",
        

        

          
                                   todaydt, "_3", AMPM, ".png"), height = 5, width = 8)
        

        

          
          ggsave(p4, file = paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AF_",
        

        

          
                                   todaydt, "_4", AMPM, ".png"), height = 5, width = 8)
        

        

          
          ggsave(p5, file = paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AF_",
        

        

          
                                   todaydt, "_5", AMPM, ".png"), height = 5, width = 8)
        

        

          
          # Stitch images for gif using Magick package
        

        

          
          # Use 600 x 322 for LinkedIn
        

        

          
          # Otherwise, 1200 x 720
        

        

          
          # 
        

        

          
          img <- image_graph(width = 1200, height = 720, res = 96)
        

        

          
          print(p3)
        

        

          
          print(p4)
        

        

          
          print(p5)
        

        

          
          print(p1)
        

        

          
          print(p2)
        

        

          
          dev.off()
        

        

          
          

        

        

          
          animation <- image_animate(image_scale(img), fps = 0.25)
        

        

          
          image_write(animation, paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AF_",
        

        

          
                                        todaydt, AMPM, "_animate.gif"))
        

        

          
          dev.off()
        

        

          
          

        

        

          
          img <- image_graph(width = 600, height = 322, res = 96)
        

        

          
          print(p3)
        

        

          
          print(p4)
        

        

          
          print(p5)
        

        

          
          print(p1)
        

        

          
          print(p2)
        

        

          
          dev.off()
        

        

          
          

        

        

          
          animation <- image_animate(image_scale(img), fps = 0.25)
        

        

          
          image_write(animation, paste0("C:/Users/rick2/Documents/Rick/AnalyticsForward/AF_",
        

        

          
                                        todaydt, AMPM, "_LinkedIn_animate.gif"))
        

        

          
          dev.off()
        

        

          
          

        

        

          
          message("Total registrations")
        

        

          
          allAF_frm %>% group_by(yes_year) %>% 
        

        

          
              dplyr::filter(dates_yes ==max(dates_yes)) %>%
        

        

          
              select(id_name, yes_year, dates_yes_cumsum)
        

  





    


  




      

      

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          Meetup_RSVP_Yes_latest.R
        
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4 thoughts on “Meetup API tracking with R-Ladies meetupr: Analytics>Forward in RTP, NC – Part 1”

  1. Pingback: #AnalyticsForward 2019: Meetup API registration tracking in R (#meetupr, @JordanCMeyer) – part 2 – rickpackblog

  2. For the chart, “Research Triangle Analysts ‘Analytics>Forward’ as of “…

    The labels on the X-axis are a bit confusing. Please consider revising the current X-axis to list the “days until the event” – similar to a countdown until a rocket blasts off. (e.g. T-60 days… 59… 58…) This would reduce the issue of an extra day during a leap year, and the particular Saturday in March chosen. This year A>F is scheduled on the 68th day of the year. The March 10, 2018, event was on the 69th day of the year. March 11, 2017 = 70. March 12, 2016 = 72, March 14, 2015 = 73. This revised counting method would also align the “day of the week” across each year, possibly yielding some insight.

    Has the maximum # of Yes RSVPs been the same each year? If the locations were maxed at 100 earlier and are at 200 now, then maybe an _additional_ graph where the Y-axis is % of allowed max Yes RSVPs allowed would be useful.

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