I’m building a React Native application and recently updated to v0.31.0 and at first things were working well debugging on the device benefiting from a feature of the react-native-xcode.sh script. The script copies your Dev machine’s IP address to a text file called ip.txt which is used for establishing the connection to your machine from your device since localhost points to the wrong place. Here’s the line of code in ./node_modules/react-native/packager/react-native-xcode.sh:
In my previous post I wrote about the process that lead me to build a dashboard but first I want to talk a bit about the structure of the data in the Google Sheet where the whole process started. I first started by looking to quickly create a few charts to visualize some of our KPI‘s. To source the data I created a text file containing the SQL statements and used psql to fetch Postgres data which I dumped it to data into .CSV files for import into separate “data” only tabs in Google Sheets.
The first tab was the “primary” dataset which contained a wide (A to AX) set of columns with a blend of content from the various linked “data” tabs and is where I derived all of the pivot tables with a primary key in the first column and with this initial set of at I was able to start building charts to help visualize the data.
Of course, once you’ve answered one question it leads to follow-on questions which require more data leading to more questions. Before long I was querying a dozen tables from Postgres and MSSQL and importing the data into these “data” tabs. For data tabs with a 1-1 relationship based on primary key I would aggregate the data onto the main sheet with a formula like “Imported Data’!B4” or in cases where not all keys were present via a lookup like =IFERROR(VLOOKUP($A:$A,”Data Sheet”!$A:$E,3,FALSE),0) setting the result accordingly when the primary key wasn’t found.
Ultimately, flattening the data made it easy to construct pivot tables for aggregate totals, averages, counts, and median values etc. from which I could build a variety of charts a sampling of which I’ve included below.Here’s a small sample of the kinds of charts built from pivot tables. Yes, I’ve clipped/changed some of the legends knowingly obscuring the underlying meaning of the chart.
Here’s a small sample of the kinds of charts built from pivot tables. Yes, I’ve clipped/changed some of the legends knowingly obscuring the underlying meaning of the chart.
I built a variety of pivot tables for the Wanderful Marketing team (sans charts) for easy analysis of Cash Dash campaigns from a variety of angles such as by a given retailer by offer type, amount, reward, launch day of the week and a variety of campaign performance metrics that I’d calculated within the sheet. Ultimately, the usefulness of this data caught on and a number of teams were not only reviewing the data but asking for additional analysis and updates.
While I was able to automate some portions of updating this sheet, its associated tabs etc. Google Sheet’s charts and pivot tables don’t automatically expand as the size of your data grows which made it a laborious task to “re-scope” them as more data was added not to mention I knew the 2M cell limit was looming in the distance.
In a follow-on post I’ll talk about how I began the shift to automating this using R and a Shiny Dashboard running on an OSX Mac mini.
Working on building mobile apps for the last several years I thought I would publish a list of some of the things I’ve learned here in the mobile trenches. Without further adieu and in no particular order… Btw, welcome your feedback/additions. Continue reading Lessons Learned in a Mobile Startup→
Over the past several months I’ve been building what’s become a large Google Sheet (which is now at the 2M cell limit) tracking Find&Save Cash Dash offers. Like a lot of data projects this one started out small but over many months grew to a point where it no longer makes sense to cut/paste SQL data into a Google Sheet for pivot tables and charts. I wanted a solution that could pull data from REST APIs, MSSQL and Postgres and present data in an internal dashboard. Unfortunately, Google Sheet’s JDBC support doesn’t include Postgres otherwise Google Apps Script might have been a viable choice for automating data collection notwithstanding size limitations. Finally, another issue with Sheets was that I couldn’t seem to get pivot tables to resize automatically as more data was added. So, 3 strikes and Google Sheets was out.
Part of building mobile web apps is understanding the myriad of mobile analytics and in part visualizing the data to shed light on trends that my otherwise be difficult to see in tabular data or even a colorful cohort table. I’ve been building a dashboard using R, RStudio, Shiny, and Shiny Dashboards aggregating data from MSSQL, Postgres, Google Analytics, and Localytics.
Below is the main function to fetch the Localytics sample data and convert it into a data frame that’s suitable for plotting. Now, admittedly I’m not an R expert so there may well be better ways to slice this JSON response but this is a fairly straight forward approach. Essentially, this fetches the data, converts it from JSON to an R object, extracts the weeks, preallocates a matrix and then iterates over the data filling the matrix to build a data frame.
retentionDF <- function() {
# Example data from: http://docs.localytics.com/dev/query-api.html#query-api-example-users-by-week-and-birth_week
localyticsExampleJSON <- getURL('https://gist.githubusercontent.com/strefethen/180efcc1ecda6a02b1351418e95d0a29/raw/1ad93c22488e48b5e62b017dc5428765c5c3ba0f/localyticsexampledata.json')
cohort <- fromJSON(localyticsExampleJSON)
weeks <- unique(cohort$results$week)
numweeks <- length(weeks)
# Take the JSON response and convert it to a retention matrix (all numeric for easy conversion to a dataframe) like so:
# Weekly.Cohort Users Week.1
# 1 2014-12-29 7187 4558
# 2 2015-01-05 5066 NA
i <- 1
# Create a matrix big enough to hold all of the data
m <- matrix(nrow=numweeks, ncol=numweeks + 1)
for (week in weeks) {
# Get data for all weeks of this cohort
d <- cohort$results[cohort$results$birth_week==week,][,2]
lencohort <- length(d)
for (n in 1:lencohort) {
# Skip the first column using "+ 1" below which will be Weekly.Cohort (date)
m[i,n + 1] <- d[n]
}
i <- i + 1
}
# Convert matrix to a dataframe
df <- as.data.frame(m)
# Set values of the first column to the cohort dates
df$V1 <- weeks
# Set the column names accordingly
colnames(df) <- c("Weekly.Cohort", "Users", paste0("Week.", rep(1:(numweeks-1))))
return(df)
}
To make things easy I put together a gist and if you’re using R you can runGist it yourself. It requires several other packages so be sure to check the sources in case you’re missing any. Fair warning the Localytics API demo has very limited data so the chart, let’s just say simplistic however given many weeks worth of data it will fill out nicely (see example below).
