In Chicago-R-User-Group/RUGtools: Functions useful for organizing R User Group meetups

knitr::opts_chunk$set(echo = TRUE)

library(knitr)

library(RUGtools)

Introduction

R User Group Introduction Slides template

This is a traditional ioslides R Markdown template, but modified to contain default content routinely used when introducing Chicago R user group meetups. Slides are useful because they look good and you won't forget to do important things like thanking the sponsors! Slides can be accessed from within R Studio using the New R Markdown dialog menu, and then selecting From Template. One can also use the draft function, exemplified below.

rmarkdown::draft(file="new_meetup.Rmd", template = "intro_slides", package="RUGtools")

Estimate your pizza order

In Chicago, we think a lot about Pizza. And if one is involved in the local meetup culture, this is doubly so. At a recent meetup group that wasn't ours, I counted nearly 6 large pizzas left over. Struck by an overwhelming sorrow, I vowed the Chicago R User Group shall never partake in such a tragic waste of resources. With a few data points, one can use the pizza_estimate function to arrive at a more efficient order.

kable( pizza_estimate(registered = 120, pizza_diameter = 18, attend_rate = 0.57, 
                      serving = 2, style = "thin") )

Channeling our ever-curious pizza scientist, it turns out the Chicago "party cut" (thin-crust cut into small squares) inherits a few very attractive properties when dividing p pizzas among n guests. Small square pieces allow guests to better estimate pizza consumption, thus decreasing the integer-programming problem exacerbated by large triangular slices. Reducing wasted pizza is not only virtuous, it demonstrates great stewardship of sponsor resources bestowed upon thee.

plot(3.1*c(1, 0, -1, 0), 3.1*c(0, 1, 0, -1) , col = "transparent",
     xaxs="i", yaxs="i", xaxt="n", yaxt="n", ylab="18 inches", xlab="18 inches", 
     main = "The Chicago `Party Cut` Extra-Large Pizza", bg = "white", axes = 0)
polygon(3*sin(seq(0, 2*pi, length=100)), 3*cos(seq(0, 2*pi, length=100)), border = "goldenrod2", lwd=6, col = "lightgoldenrod1")
polygon(2.85*sin(seq(0, 2*pi, length=100)), 2.85*cos(seq(0, 2*pi, length=100)), border = "goldenrod1", lwd=16, col = "lightgoldenrod1")
points(x=jitter(c((1:4)-2.5, seq(from = -2, to =2, length.out=6), (1:6)-3.5, (1:6)-3.5, seq(from = -2, to =2, length.out=6), (1:4)-2.5), factor = 2), 
       y=jitter(c(rep(2.75,4), rep(1.85, 6), rep(1,6), rep(0,6), rep(-0.85, 6), rep(-1.75,4))-0.5, factor=1), pch=19, cex=4, col="red")
points(x=jitter(c((1:4)-2.5, seq(from = -2, to =2, length.out=6), (1:6)-3.5, (1:6)-3.5, seq(from = -2, to =2, length.out=6), (1:4)-2.5), factor = 2), 
       y=jitter(c(rep(2.6,4), rep(1.85, 6), rep(1,6), rep(0,6), rep(-0.85, 6), rep(-1.6,4))-0.5, factor = 2), pch="`", cex=4, col="darkgreen")
points(x=jitter(c(seq(from=-1.5, to=1.5, length.out=12), seq(from = -2, to =2, length.out=18), seq(from=-3, to=3, length.out=24), seq(from=-3, to=3, length.out=24), seq(from = -2, to =2, length.out=18), seq(from=-1.5, to=1.5, length.out=12)), factor = 2), 
       y=jitter(c(rnorm(12, mean = 3, sd=0.5), rnorm(18, mean = 2, sd=0.5), rnorm(24, mean = 1, sd=0.5), rnorm(24, mean = 0, sd=0.5), rnorm(18, mean = -1, sd=0.5), rnorm(12, mean = -2, sd=0.5))-0.5, factor=2), pch=".", cex=1.5, col="black")
abline(h=seq(from=-3.1, to=3.1, length.out=7), col="white", lwd=2.5)
abline(v=seq(from=-3.1, to=3.1, length.out=6), col="white", lwd=2.5)

Data Analysis

Chicago R User Group data is included and downloaded from meetup.com/ChicagoRUG. Personally identifiable information has been removed, data formatted and ready for analysis.

Load the member list data. How many members do we have?

nrow(member_list)

Lets plot the cumulative membership.

plot(y = member_list$Member.ID, x = member_list$Joined.Group.on, type = "l", lwd=2, 
     col = "blue", frame = FALSE, main = "Chicago R User Group members", 
     ylab = "Members", xlab = "", ylim = c(0, 4000), 
     xlim=c(min(member_list$Joined.Group.on), as.Date("2020-01-01")))

How many members joined since January 2017?

nrow(subset(member_list, Joined.Group.on > "2017-01-01"))

What percentage of the Chicago R User Group joined since January 2017?

Percentage <- 100 * NROW(subset(member_list, Joined.Group.on > "2017-01-01")) / NROW(member_list)
round(Percentage, 2)

How many new members usually join between meetups?

First, get a unique ordered list of Meetup dates

Meetup_dates <- sort(unique(member_list$Last.Attended))

new_members <- subset(member_list, Joined.Group.on > Meetup_dates[NROW(Meetup_dates)])

Then count the number of new members joined between the most recent meetup and the one prior to that.

new_members2 <- subset(member_list, Joined.Group.on <= Meetup_dates[NROW(Meetup_dates)] &
                                    Joined.Group.on > Meetup_dates[NROW(Meetup_dates)-1])

nrow(new_members2)

In danger of repeating the above analysis several times over, we created a function new_mem_counter to count the number of new members joined between meetups for all meetups in the data set.

new_members <- new_mem_counter(member_list)
# drop the last observation, as incomplete data leading up to the coming meetup.
new_members <- new_members[-NROW(new_members),]

kable(head(new_members), align = 'l')

kable(tail(new_members), align = 'l')

Which gap between meetups had the most new members?

kable(new_members[new_members$New==max(new_members$New), ], align = 'l')

Note the previous meetup was 6 months prior, so likely this was not due to the topic covered.

What is the average number of new members joined between meetups?

mean(new_members$New)

Plot the new members data.

   # Create Date Range Index
  Date_Index <- as.numeric(row.names(new_members[new_members$Date > "2010-01-01" & new_members$Date <= Sys.Date(),]))

  # Create x-axis labels, using year-month date format
  x_labels <- format(new_members$Date[Date_Index], "%Y-%m")

  # Plot
  barplot(new_members$New[Date_Index], names.arg = x_labels, main = "CRUG members, joined between meetups",  
          ylab = "New Members", xlab = "")

Plot the new members data since 2017.

   # Create Date Range Index
  Date_Index <- as.numeric(row.names(new_members[new_members$Date > "2017-01-01" & new_members$Date <= Sys.Date(),]))

  x_labels <- format(new_members$Date[Date_Index], "%Y-%m")

  # Plot
  barplot(new_members$New[Date_Index], names.arg = x_labels, las=2, main = "CRUG members, joined between meetups",  
          ylab = "New Members", xlab = "")

What is the average number of new members joined between meetups since 2017?

mean(new_members$New[Date_Index])

Membership as time series

Load and use the xts package.

library(xts)

members_xts <- xts(x = member_list$Member.ID, order.by = member_list$Joined.Group.on)
names(members_xts) <- "useRs"

plot(members_xts, col = "blue", grid.col = "white", main = "Chicago R User Group members by join date")

How many members join by month?

members_monthly <- to.monthly(members_xts, OHLC = FALSE)

barplot(diff(members_monthly), col = "lightgrey", main = "Chicago R User Group, new members by month")

What's our percentage growth per month?

Omit the first two months growth outliers.

barplot(100*diff(members_monthly)[-c(1,2)]/members_monthly[-c(1,2)], col = "lightgrey",
        main = "CRUG Growth since inception", ylab="Percent")

Let's view the last 4 years.

members_3_years <- 100*diff(members_monthly["2015-05/"]) / members_monthly["2015-05/"]
barplot(members_3_years, col = "lightgrey", main = "CRUG Growth, last 4 years", ylab="Percent")

Consider seasonal variation.

month_percent_growth <- c(NA, NA, NA, NA, 100*diff(log(coredata(members_monthly))), NA, NA, NA, NA, NA, NA, NA)
seasonal_matrix <- matrix(month_percent_growth, ncol = 12, byrow = TRUE)
colnames(seasonal_matrix) <- month.abb
rownames(seasonal_matrix) <- 2010:2019
seasonal_matrix <- rbind(seasonal_matrix, Median=round(apply(seasonal_matrix, 2, median, na.rm=TRUE), 2))

kable(seasonal_matrix, digits=2, caption = "Percentage Growth per Month")

As one of the largest and oldest R user groups in existence, the Chicago R User Group has matured into a comfortable period of value. Growth rates are lower by percentage, but the group continues to serve a steady group of new useRs.

Chicago-R-User-Group/RUGtools documentation built on April 30, 2023, 7:42 a.m.