Necessary Installations

As mentioned on the github page:

install.packages("devtools")
devtools::install_github("rladies/meetupr")
Sys.setenv(MEETUP_KEY = "ENTER YOUR API_KEY HERE")

This will let R/RStudio talk to Meetup.com’s API. Once the connection is established we are now ready to pull the information we need.

library(meetupr)
urlname <- "rladies-boston"
members <- get_members(urlname)
## Downloading 663 record(s)...
members$DOJ<-as.Date(members$joined)

I have only pulled memver info, this package however also allows getting information about boards, events, attendees etc. using corresponding functions available here: https://github.com/rladies/meetupr

Before I plot I also am using tidyverse ecosystem to summarise the data.

#members$month<-format(members$DOJ,"%m")
#members$month<-months(members$DOJ)
members$month<-lubridate::month(members$DOJ, label = TRUE)
library(dplyr)
membersByMon <- members %>%
  group_by(month) %>%
  summarise(countMembers = n())

membersByMon<- membersByMon %>% 
  mutate(totalMembers = cumsum(countMembers))

Finally, replicating the graph created by Dr. Jen but for R-Ladies Boston.

library(lattice)
xyplot(membersByMon$totalMembers ~ membersByMon$month, type = c("p", "l"), pch = 19,col="#562457",
       xlab="Month",ylab="R-Ladies Boston Membership")

I also think an alternate visual could also be useful to understand the overall trend.

#An additional visuals from my side 
library(ggplot2)
ggplot(data=members, aes(x=DOJ)) + 
  geom_line(stat="bin", binwidth=100,color="#562457") + 
  ggtitle("Rising Membership for R-Ladies Boston") +
  xlab("Date of Joining")  + 
  ylab("R-Ladies Boston Membership") + 
  theme_classic()