R Programming Archives | Ericonanalytics

Finding a Marketing Mix with Google Analytics Multi Channel Funnels and R

Google Analytics Multi Channel Analysis

Online marketing channels such as Paid Search and Display Advertising are used by scores of organizations to improve outreach. Having the ability to improve your visibility by simply purchasing traffic is very useful. What is not useful, at least for most marketing organizations is having to come to grips around whether the money for said traffic was spent as efficiently as possible. Most organizations will simply use the Acquisition reporting in Google Analytics to learn of how much traffic their marketing campaigns generate (bad). Some will even venture to see how many conversions or revenue they produce (better but still bad).

Only the savvy organization will employ the technique known as “multi-session marketing analytics.” This technique uses user and session data in order to analyze the activity of users across multiple sessions. This improves on the simplistic “last click” attribution model used for the regular Google Analytics Acquisition reporting. Google has provided some reporting tools for this type of analysis in the Multi-Channel Funnels and Attribution reporting found under the “Conversions” tab in Google Analytics. The Model Comparison Tool report can even be used to compare different models (i.e. “last click”, “first click”, etc.).

multi session marketing analysis — Model Comparison Tool in Google Analytics

The unfortunate thing about using these models is that there is no such thing as a one-size fits all model for analyzing a site’s marketing channels. Each site has it’s own flavor and it’s own user base with it’s own marketing behavior.

In order to deal with this issue, an organization could either pay for Google Premium (which uses machine learning algorithm to predict the best model to use) or it could run a probabilistic model on its GA data. Markov Chain is one of the easier models to use. It is also a model that is used by a number of marketing attribution analytics consultancies.

I’m no statistician, but I believe the simplest way to explain Markov Chain is that is way of describing the probability of events based on the most previous event state. In this case each event is a session with an assigned medium and the result is the re-assigning of values based on the highest probabilities of conversion. Read more on Markov Chain here.

Markov Chain for Marketing Analysis — Markov Chain Illustration

Good thing for those of us that have no advanced math degree, there is a package for R called ChannelAttribution which allows us to run the Markov Chain model on data direct from the Google Analytics API. It also compares the Markov Chain model to other models such as last touch, first touch and linear without much fuss. There is a great tutorial on using ChannelAttribution on the Lunametrics Blog. Read below to see how this technique can be used with data direct from the Google Analytics API in R. The script seems a bit verbose, but it works very well, thanks to Kaelin Harmon!

library(RGA)
library(ChannelAttribution)
library(data.table)
library(ggplot2)
library(scales)

profile <- {{replace this with your google analytics view id}}

start.date <- {{replace with your start date in YYYY-MM-DD}}
end.date <- {{replace with your end date in YYYY-MM-DD}}

#pull path data from Google Analytics
mcf_data <- get_mcf(profile, start.date, end.date,
                     metrics = "mcf:totalConversions,mcf:totalConversionValue",
                     dimensions = "mcf:basicChannelGroupingPath",
                     fetch.by = "day")

#limit to ecommerce transactions only
setDT(mcf_data)
mcf_data <- mcf_data[conversionType=="Transaction"]

#create path length metric
mcf_data$pathlength <- stringr::str_count(mcf_data$basicChannelGroupingPath, ">") + 1

#remove multi word channel names (problematic for ChannelAttribution script)
mcf_data$basicChannelGroupingPath <- gsub("(?<=\\w)\\s(?=\\w|\\$)", "", mcf_data$basicChannelGroupingPath, perl = TRUE)

#create heuristic multi-channel models
H <- heuristic_models(mcf_data, 'basicChannelGroupingPath', 'totalConversions', var_value = 'totalConversionValue')

#round to convert from scientific notation (if needed)
H$linear_touch_conversions <- round(H$linear_touch_conversions,0)
H$linear_touch_value <- round(H$linear_touch_value,0)

#create markov model
M <- markov_model(mcf_data, 'basicChannelGroupingPath', 'totalConversions', var_value = 'totalConversionValue', order = 1) 
M$total_conversion <- round(M$total_conversion,0)
M$total_conversion_value <- round(M$total_conversion_value,0)

#merge data frames and order by total conversion value
R <- merge(H, M, by = 'channel_name') 
R <- R[order(-R$total_conversion_value),]

#select and rename columns
R1 <- R[, (colnames(R) %in% c('channel_name', 'first_touch_conversions', 'last_touch_conversions', 'linear_touch_conversions', 'total_conversion'))]
colnames(R1) <- c('channel_name', 'first_touch', 'last_touch', 'linear_touch', 'markov_model') 

#transform data from wide to long version
R1 <- melt(R1, id = 'channel_name')

# Plot the total conversions
ggplot(R1, aes(channel_name, value, fill = variable)) +
  geom_bar(stat = 'identity', position = 'dodge') +
  ggtitle('TOTAL CONVERSIONS') + 
  theme(axis.title.x = element_text(vjust = -2)) +
  theme(axis.title.y = element_text(vjust = +2)) +
  theme(title = element_text(size = 16)) +
  theme(plot.title = element_text(size = 20)) +
  ylab("") + 
  scale_y_continuous(labels = comma)

library(RGA)

library(ChannelAttribution)

library(data.table)

library(ggplot2)

library(scales)

profile <- {{replace this with your google analytics view id}}

start.date <- {{replace with your start date in YYYY-MM-DD}}

end.date <- {{replace with your end date in YYYY-MM-DD}}

#pull path data from Google Analytics

mcf_data <- get_mcf(profile, start.date, end.date,

metrics = "mcf:totalConversions,mcf:totalConversionValue",

dimensions = "mcf:basicChannelGroupingPath",

fetch.by = "day")

#limit to ecommerce transactions only

setDT(mcf_data)

mcf_data <- mcf_data[conversionType=="Transaction"]

#create path length metric

mcf_data$pathlength <- stringr::str_count(mcf_data$basicChannelGroupingPath, ">") + 1

#remove multi word channel names (problematic for ChannelAttribution script)

mcf_data$basicChannelGroupingPath <- gsub("(?<=\\w)\\s(?=\\w|\\$)", "", mcf_data$basicChannelGroupingPath, perl = TRUE)

#create heuristic multi-channel models

H <- heuristic_models(mcf_data, 'basicChannelGroupingPath', 'totalConversions', var_value = 'totalConversionValue')

#round to convert from scientific notation (if needed)

H$linear_touch_conversions <- round(H$linear_touch_conversions,0)

H$linear_touch_value <- round(H$linear_touch_value,0)

#create markov model

M <- markov_model(mcf_data, 'basicChannelGroupingPath', 'totalConversions', var_value = 'totalConversionValue', order = 1)

M$total_conversion <- round(M$total_conversion,0)

M$total_conversion_value <- round(M$total_conversion_value,0)

#merge data frames and order by total conversion value

R <- merge(H, M, by = 'channel_name')

R <- R[order(-R$total_conversion_value),]

#select and rename columns

R1 <- R[, (colnames(R) %in% c('channel_name', 'first_touch_conversions', 'last_touch_conversions', 'linear_touch_conversions', 'total_conversion'))]

colnames(R1) <- c('channel_name', 'first_touch', 'last_touch', 'linear_touch', 'markov_model')

#transform data from wide to long version

R1 <- melt(R1, id = 'channel_name')

# Plot the total conversions

ggplot(R1, aes(channel_name, value, fill = variable)) +

geom_bar(stat = 'identity', position = 'dodge') +

ggtitle('TOTAL CONVERSIONS') +

theme(axis.title.x = element_text(vjust = -2)) +

theme(axis.title.y = element_text(vjust = +2)) +

theme(title = element_text(size = 16)) +

theme(plot.title = element_text(size = 20)) +

ylab("") +

scale_y_continuous(labels = comma)

This produces a dataframe and a plot which compares each of the heuristic models (last touch, first touch, linear) and the Markov Model.

Multi Session Analysis in R — Heuristic Models vs Markov Model

If you liked this post, you might want to take a look at my last post on using the GA API and R as an alternative to Google Analytics Premium or just leave me a comment below.

An Alternative to Google Analytics BigQuery Export Using R and Google Tag Manager

Google Analytics has proven to be one of the most influential tools ever created for marketing analysis. Google is pretty unrelenting in their pursuit of innovation for Google Analytics and that innovation shows in the number of other tools they’ve built for analysts. From Google Sheets to BigQuery to Google Data Studio, the complementary tools built are a great aid for dealing with the dearth of data that can be mined from Google Analytics. One of the little known yet game-breaking tools available for use with Google Analytics data is the Google Analytics BigQuery Export.

Google Analytics Premium BigQuery Export

This tool, which is only available for users of Google Analytics premium product, is in essence a raw data export of a website’s Google Analytics data. This unlocks any analyst with a decent knowledge of SQL from the shackles of Google canned reports. This also allows an analyst to create much more robust logic for creating reports. For instance, if an analyst wanted to create a report for all users that viewed a particular page during their session and returned to the site within 6 days, they would only be limited by only their knowledge SQL and their ability to fork out the $150K Google charges for their premium product!!!

Google Analytics does not provide data at the user level out of the box, however, with the aid of a process outlined in Simo Ahava’s tremendously useful blog, you can use Google Tag Manager to pull Google’s user and session IDs out of the cookie (also known as Client ID) and feed them back to the interface in custom dimension or event. This gives an analyst the ability to report on user activity at the user ID level.

Remember: passing personally identifying information to Google Analytics is a violation of the terms of service, so don’t pass any personal identifying information to Google if you might have it, like email addresses.

Below are the steps I use for passing pageview data along with user and session data to the GTM data layer for logging in Google Analytics, however, you could technically use a slightly different process to pass ecommerce, event, goal, custom metric/dimension data as well. I’ll cover that in a later post. I’ll assume that the reader has already tagged all of their pages with a Google Tag Manager container, but if not, start by reading this post and make sure to tag your pages.

Steps:

Create a Custom Dimension by going to the admin page in your Google Analytics view:Under “Custom Definintions” select “Custom Dimensions”, create a dimension and call it “Client ID” or whatever name you prefer. This dimension will have a scope of “Session”. Make note of the dimension index (you’ll need to enter that later).

Create a Custom JavaScript Variable in Google Tag Manager and give it a title such as {{Set Client ID in Dimension 1}}.
Here is the code:

function() {
  // Modify customDimensionIndex to match the index number you want to send the data to
  var customDimensionIndex = 1;
  return function(model) {
    model.set('dimension' + customDimensionIndex, model.get('clientId'));
  }
}

function() {

// Modify customDimensionIndex to match the index number you want to send the data to

var customDimensionIndex = 1;

return function(model) {

model.set('dimension' + customDimensionIndex, model.get('clientId'));

}

Make sure to include the correct index to the customDimensionIndex variable. If you’ve completed this step correctly, you will be able to see the ClientId being passed under whichever custom dimension you have set it up for in the Google Analytics Debugger tool.

If everything shows up, move back to Google Tag Manager and edit the pageview Tag for your site. Under “Fields to Set”, type “customTask” and under “Value” use the dropdown to select the variable we created in step B, {{Set Client ID in Dimension 1}}.

Now that concludes the first part of the process. Once you’ve reached this step, you could technically start playing with the user and session Client ID dimension in Google Analytics’ custom reports.Pull Client ID Custom Dimension DataSo we’ve tagged our site to send user and session data to Google Analytics and have dealt with sampling, now for the fun part. This string pulls page URLs, user and session IDs by date based on the dimensions detailed above. Where pro

ga_data <- get_ga(<replace with your view id>, 
       start.date = "2017-01-01",
       end.date = "2017-01-02",
       dimensions = "ga:date, ga:pagePath, ga:dimension1",
       metrics = "ga:pageviews",
       fetch.by = “day”)

ga_data <- get_ga(<replace with your view id>,

start.date = "2017-01-01",

end.date = "2017-01-02",

dimensions = "ga:date, ga:pagePath, ga:dimension1",

metrics = "ga:pageviews",

fetch.by = “day”)

Run some other scripts

Using some other scripts, an analyst can answer a number of other questions, like how long does it take a new user to become a repeat user. These scripts rely heavily on the data.table syntax instead using base R. Please take a look at my prior post on using data.table to learn why I do so.

ga_data <- get_ga(<replace with your view id>, 
                  start.date = "2017-06-18",
                  end.date = "2017-06-24",
                  dimensions = "ga:date, ga:hour, ga:minute, ga:medium, ga:dimension1",
                  metrics = "ga:sessions",
                  fetch.by = "day")

ga_data <- get_ga(<replace with your view id>,

start.date = "2017-06-18",

end.date = "2017-06-24",

dimensions = "ga:date, ga:hour, ga:minute, ga:medium, ga:dimension1",

metrics = "ga:sessions",

fetch.by = "day")

Then run the rest:

#split client ID into user and session IDs

library(data.table)
a <- do.call(rbind, sapply(ga_data$dimension1, function(x) strsplit(as.character(x),"\\.")))
ga_data <- cbind(ga_data, a)

#count sessions for each user in sequence
ga_data$userseq <- sequence(rle(as.character(ga_data$user_id))$lengths)

setDT(ga_data)

#since the dimension is a "hit" based dimension, data will be returned for all hits.  This script limits data to "session" data only and sorts by user, session and date.
ga_data <- ga_data[sessions > 0][order(user_id,session_id,date,hour,minute)]

#grab the date for the first session for each user
ga_data[,mindate := min(date),by = "user_id"]

#calculate the number of days since the first user session for each subsequent session
ga_data[, datediff := as.numeric(difftime(date,mindate,units = "days"))]

#calculate and return the average
ga_data[,mean(datediff)]

#split client ID into user and session IDs

library(data.table)

a <- do.call(rbind, sapply(ga_data$dimension1, function(x) strsplit(as.character(x),"\\.")))

ga_data <- cbind(ga_data, a)

#count sessions for each user in sequence

ga_data$userseq <- sequence(rle(as.character(ga_data$user_id))$lengths)

setDT(ga_data)

#since the dimension is a "hit" based dimension, data will be returned for all hits. This script limits data to "session" data only and sorts by user, session and date.

ga_data <- ga_data[sessions > 0][order(user_id,session_id,date,hour,minute)]

#grab the date for the first session for each user

ga_data[,mindate := min(date),by = "user_id"]

#calculate the number of days since the first user session for each subsequent session

ga_data[, datediff := as.numeric(difftime(date,mindate,units = "days"))]

#calculate and return the average

ga_data[,mean(datediff)]

This will return the number of days on average it takes a new user to become a repeat user.

There are a number of other uses for the client ID data in GA. For instance, a marketer might want to do some attribution modelling or a content manager would want to know if viewing an article in one session might effect subsequent sessions.

One consideration around doing this type of analysis is scale. Most smaller websites won’t pose an issue, but some larger sites (like the one I currently work on) will. Pulling an individual non-aggregated row for every session, page or event can yield some extremely large datasets. In this case, it would make sense to send the data to a cloud storage data warehouse such as BigQuery. Want to learn more on using R to solve for this? Stay tuned…

Which is Faster?: R Data Manipulation with data.table vs dplyr

I started my voyage into learning R by taking Datacamp’s online courses. After finishing courses on data manipulation in both base R and dplyr, I stumbled upon a course on using the data.table library. I was taken back a bit after learning that data.table using a different syntax than base R. This was unnerving as I didn’t know what I would gain from learning data manipulation in yet another syntax. My skepticism, however, changed to optimism once I began working on a rather large dataset a few weeks later. This dataset (a 43M row set of email opens and clickthroughs), took something like 30-40 minutes to read into R using the base read.csv function. Instead, I tried using the fread function in data.table. Low and behold, what took 30-40 minutes using base R took about 5 minutes using fread. Here’s timing data for a 3M row text file:

fread for r — 85% improvement in median performance using fread

The speed improvements are not just limited to reading data. Manipulations are also faster using data.table. Here are 2 functions written to group and count rows using the world cities population dataset.

library(data.table)
library(dplyr)
library(microbenchmark)

wcp <- fread(file="worldcitiespop.csv")

df_func <- function(x) {
  x %>%
    group_by(Country) %>%
    summarize(
      Count = n()
    ) %>%
    arrange(desc(Count))
}

dt_func <- function(x) {
  x[,.(Count = .N), Country][order(-Count)]
}

microbenchmark(df_func(wcp), dt_func(wcp),times = 10)

library(data.table)

library(dplyr)

library(microbenchmark)

wcp <- fread(file="worldcitiespop.csv")

df_func <- function(x) {

x %>%

group_by(Country) %>%

summarize(

Count = n()

) %>%

arrange(desc(Count))

}

dt_func <- function(x) {

x[,.(Count = .N), Country][order(-Count)]

}

microbenchmark(df_func(wcp), dt_func(wcp),times = 10)

data.table performance — 83% increase in median performance using data.table

Lastly, as you can see in the functions written above, the data.table function (dt_func) is less verbose than the dplyr function (df_func). One of the reasons for this is that dplyr is meant to be easily expressed from one programmer to another, however, some programmers will not need to share their code from one user to another. Nevertheless, once I learned the data.table syntax, I preferred using it over the dplyr syntax. This seems to be the case for many programmers with a previous foundation in SQL.

datacamp data.table SQL similarity — Datacamp’s data.table tutorial explains the data.table – SQL similarity

While learning syntax can be a tough task, I have to admit that the extra work of learning data.table syntax is worth it.

If you haven’t had a chance to read my last post on using R with Google Analytics data, please take a look. Also, if you have any comments, questions or if simply want to call me crazy, drop a comment below.

3 Ways To Analyze Google Analytics Data in R with RGA and ggplot2

In my opinion, Google Analytics is the single most influential development in marketing analytics ever. Quantcast estimates that 70% of its top 10,000 website have GA installed. Google has shown a relentless drive to improve the product over the years and it’s free price tag insures access to most anyone that runs a website. With that said, Google Analytics is a service and no service (great or lacking) is without flaws. One of the hidden advantages GA possesses is a robust API and this advantage allows users to build some of the features that are missing from the standard interface. I wanted to cover some of the ways a user could use R to deal with some of the features not available in GA.

In order to use any of these techniques, you will have to install R as well as the rga package and dplyr package which available on CRAN. Other packages used include ggplot2 for visualization, scales, lubridate and zoo. Use the script below to install.

install.packages("RGA")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("scales")
install.packages("lubridate")
install.packages("zoo")

install.packages("RGA")

install.packages("dplyr")

install.packages("ggplot2")

install.packages("scales")

install.packages("lubridate")

install.packages("zoo")

Event Conversion Rate Script

One of my gripes with Google Analytics is that the Top Events report includes total event counts but does not include a conversion metric. If you are using the Google Tag Manger click listening technique to add events to your site by listening for click elements, a you could add a bit of custom Javascript to pass an impression for the same element, however, in many cases, just a simple total event count over the pageview count would suffice. Here’s a script that grabs that simple metric:

library(RGA)
library(dplyr)

#Authorization for RGA
authorize()

##Enter your view ID here
profile <- XXXXXXXX

#Enter you start and end date here
start.date = "2017-05-01"
end.date = "2017-05-01"

#RGA script pulls event parameters, and event page
gaevents <- get_ga(profile, start.date, end.date, 
             metrics = "ga:totalEvents",
             dimensions = "ga:eventCategory,ga:eventAction,ga:eventLabel,ga:pagePath")

#pull pageview counts as well as content group for easy grouping if content groups are used
gapages <- get_ga(profile, start.date, end.date, 
                   metrics = "ga:pageviews",
                   dimensions = "ga:pagePath,ga:contentGroup1")

#join event and page data
gaevents <- inner_join(gaevents, gapages, by = "pagePath")

#create conversion metric
gaevents$pageconv <- round(gaevents$totalEvents/gaevents$pageviews,4)

library(RGA)

library(dplyr)

#Authorization for RGA

authorize()

##Enter your view ID here

profile <- XXXXXXXX

#Enter you start and end date here

start.date = "2017-05-01"

end.date = "2017-05-01"

#RGA script pulls event parameters, and event page

gaevents <- get_ga(profile, start.date, end.date,

metrics = "ga:totalEvents",

dimensions = "ga:eventCategory,ga:eventAction,ga:eventLabel,ga:pagePath")

#pull pageview counts as well as content group for easy grouping if content groups are used

gapages <- get_ga(profile, start.date, end.date,

metrics = "ga:pageviews",

dimensions = "ga:pagePath,ga:contentGroup1")

#join event and page data

gaevents <- inner_join(gaevents, gapages, by = "pagePath")

#create conversion metric

gaevents$pageconv <- round(gaevents$totalEvents/gaevents$pageviews,4)

eventLabel	pagePath	totalEvents	contentGroup1	pageviews	pageconv
Social Link	/	3	Homepage	9	.3333

This gives all event parameters (Category, Action and Label) as well as the page URL and content group 1, allowing the user to easily aggregate pages if they are passing content groups. I strongly encourage using content groupings.

Analyze Acquisition Mediums with ggplot2

Google Analytics has some good embedded graphs for analyzing traffic mediums and the advent of Google Data Studio gives users even more flexibility, however, sites with high numbers of marketing mediums (10+) will pose issues for these tools. Using ggplot2 in R allows a user to create what analysts call “small multiples” or a series of similar graphs or charts using the same scale and axes, allowing them to be easily compared. Below is a script that returns small multiples for a year over year comparison of marketing mediums.

library(RGA)
library(ggplot2)
library(scales)
library(lubridate)
library(zoo)

#Authorization for RGA
authorize()

##Enter your view ID here
profile <- XXXXXXXX


##Enter your start and end date here
start.date = "2016-01-01"
end.date = "2017-05-31"

##RGA script
ga <- get_ga(profile, start.date, end.date, metrics = "ga:sessions",
             dimensions = "ga:yearMonth,ga:year,ga:medium")

##Zoo script converts YYYYMM format date to YYYY-MM-DD
ga$yearMonth <- as.Date(as.yearmon(ga$yearMonth, "%Y%m"))

##Lubridate script converts month to monthname for X axis of graphs
ga$monthname <- month(ga$yearMonth, label = TRUE)

##GGplot script creates line graph
ggplot(ga, aes(monthname, sessions, color = year, group = year)) + geom_line(size = 1) +
  
  ##converts any integer Y axis to continuous scale and adds a comma for easy reading   
  scale_y_continuous(labels = comma)  +
  
  ##creates a facet for multiple graphs by medium and forces Y axis to independent scales  
  facet_wrap(~ medium, ncol=4, scales = "free_y")

library(RGA)

library(ggplot2)

library(scales)

library(lubridate)

library(zoo)

#Authorization for RGA

authorize()

##Enter your view ID here

profile <- XXXXXXXX

##Enter your start and end date here

start.date = "2016-01-01"

end.date = "2017-05-31"

##RGA script

ga <- get_ga(profile, start.date, end.date, metrics = "ga:sessions",

dimensions = "ga:yearMonth,ga:year,ga:medium")

##Zoo script converts YYYYMM format date to YYYY-MM-DD

ga$yearMonth <- as.Date(as.yearmon(ga$yearMonth, "%Y%m"))

##Lubridate script converts month to monthname for X axis of graphs

ga$monthname <- month(ga$yearMonth, label = TRUE)

##GGplot script creates line graph

ggplot(ga, aes(monthname, sessions, color = year, group = year)) + geom_line(size = 1) +

##converts any integer Y axis to continuous scale and adds a comma for easy reading

scale_y_continuous(labels = comma) +

##creates a facet for multiple graphs by medium and forces Y axis to independent scales

facet_wrap(~ medium, ncol=4, scales = "free_y")

small multiples using ggplot2 and r — Small Multiples using ggplot2 and R

Analyze Product Performance, Content Groups or Other Categories with ggplot2

A user could also use the previous script for small multiples to learn about other categorical data like revenue by product:

library(RGA)
library(ggplot2)
library(scales)
library(lubridate)
library(zoo)

#Authorization for RGA
authorize()

##Enter your view ID here
profile <- XXXXXXXX


##Enter your start and end date here
start.date = "2016-01-01"
end.date = "2017-05-31"

##RGA script
ga <- get_ga(profile, start.date, end.date, metrics = "ga:itemRevenue",
             dimensions = "ga:yearMonth,ga:year,ga:productName")

##Zoo script converts YYYYMM format date to YYYY-MM-DD
ga$yearMonth <- as.Date(as.yearmon(ga$yearMonth, "%Y%m"))

##Lubridate script converts month to monthname for X axis of graphs
ga$monthname <- month(ga$yearMonth, label = TRUE)

##GGplot script creates line graph
ggplot(ga, aes(monthname, itemRevenue, color = year, group = year)) + geom_line(size = 1) +
  
  ##converts any integer Y axis to continuous scale and adds a comma for easy reading   
  scale_y_continuous(labels = comma)  +
  
  ##creates a facet for multiple graphs by product and forces Y axis to independent scales  
  facet_wrap(~ productName, ncol=4, scales = "free_y")

library(RGA)

library(ggplot2)

library(scales)

library(lubridate)

library(zoo)

#Authorization for RGA

authorize()

##Enter your view ID here

profile <- XXXXXXXX

##Enter your start and end date here

start.date = "2016-01-01"

end.date = "2017-05-31"

##RGA script

ga <- get_ga(profile, start.date, end.date, metrics = "ga:itemRevenue",

dimensions = "ga:yearMonth,ga:year,ga:productName")

##Zoo script converts YYYYMM format date to YYYY-MM-DD

ga$yearMonth <- as.Date(as.yearmon(ga$yearMonth, "%Y%m"))

##Lubridate script converts month to monthname for X axis of graphs

ga$monthname <- month(ga$yearMonth, label = TRUE)

##GGplot script creates line graph

ggplot(ga, aes(monthname, itemRevenue, color = year, group = year)) + geom_line(size = 1) +

##converts any integer Y axis to continuous scale and adds a comma for easy reading

scale_y_continuous(labels = comma) +

##creates a facet for multiple graphs by product and forces Y axis to independent scales

facet_wrap(~ productName, ncol=4, scales = "free_y")

If you haven’t had a chance yet, please read my post on why an analyst should learn R.

Have any questions or comments??? Let me know in the comments section.

6 Reasons Why An Analyst Should Learn R: Why Learn R for Data Analysis???

I had some very specific reasons for deciding to learn R for data analysis. One of my original reasons was to be able to work directly with APIs, and while that reason is valid, in hindsight, I have uncovered a number of other reasons to learn R. I wanted to write about my 6 best reasons for learning R. I’m not writing this post to pontificate on the differences between R and Python. I think both languages have distinct advantages. As R was written with statistical analysis and data visualization in mind, it was the language I (an analyst) decided to learn first. Also, please keep in mind that these are reasons surrounding how to best manipulate, analyze and learn from data, not to simply “get a job.” So, here they are:

1. Reproducible Analysis

Download a set of data from a data source, open file in excel, manipulate data in Excel…etc. This was the normal flow of analysis I had when I used Excel for my main analysis tool. R simplifies or simply removes most of this workflow. R analyses can be written, saved and re-run in the future. A single well written script can handle the tasks of pulling data from a local file source or an API, munging the data, producing an analysis, displaying a visualization and exporting a file. A well written script can also be passed from one analyst to another with little accompanying assistance. Packages such as Rmarkdown can also aid in the process of creating reproducible reports by allowing users to create documents such as html files for the presentation of their analysis.

rmarkdown for data analysis — Data Analysis in R Markdown

2. R doesn’t have the 1M row limit Excel has

When I began my career as an analyst, Excel 2003 was the spreadsheet tool of choice for data analysis. Back then, Excel had a row limit of 65K rows. Excel 2007 extended the row limit to 1M rows, however, this limit is still a bit modest for many analysts. With R, row limits are based only on the limitation of the data you have and the hardware you use. The largest dataset I have loaded into memory and manipulated was around 43M rows (Windows 10 desktop, i5 processor, 8GB of ram), however, I am sure I could load much larger datasets. Keep in mind, as the dataset grows so does the load on your resources. With this in mind, certain packages in R help deal with large datasets whether locally or via cloud computing (data.table, bigmemory, sparklyr, etc.).

10M rows for data analysis in R — 10M rows of random numbers

3. Large Community of Users

There is an estimated 2 million users of R around the world
CRAN or the Comprehensive R Archive Network currently lists over 10K packages for R
stackoverflow.com lists over 180K questions currently tagged for R

As you can see, there are a number of quantitative measures proving the proliferation of R and a vast amount of support.

4. R is a language written specifically for statistical analysis and data visualization.

The R language is widely used among statisticians and data miners for developing statistical software as well as creating data visualizations.

Data Analysis in Shiny for R — New York Short Term Rental Data in Shiny for R

5. RStudio is an excellent IDE.

Many other programming languages have scores of Integrated Development Environments or IDEs (PyCharm for Python, Spyder for Python, Visual Studio for .NET, PHPStorm for PHP, etc.). Some languages have 10 or 20 IDEs. Making a choice among a number of IDEs can be problematic as some IDEs contain features that others do not. Some IDEs are not open source and require payment for maintenance. Much like R, RStudio was built with a focus on statistical computing. Instead of most IDEs, which are designed with general programming in mind, RStudio’s workflow is meant for the analyst or statistician. RStudio is one of the only IDEs for R therefore, there are loads of resources available on using RStudio. RStudio is also open source.

6. Because R was written specifically for statistical analysis, a number of machine learning and data science algorithms are also available for R.

This makes R a good springboard for analysts wanting to get started in machine learning and data science.

Bonus! – R is open source.

With some ingenuity and research, R can do many of the same things as Excel, SAS and Tableau or it can be a great compliment to these tools. Also, with R being open source, it is completely extensible, can be distributed and changed to suit a user’s needs.