Users, page views and sessions

Let’s start with some general numbers, such as the number of users, sessions and page views for the entire site. Note that for the present article, we use data over the past year, so from December 16, 2019 to December 15, 2020:

# set date range
start_date <- as.Date("2019-12-16")
end_date <- as.Date("2020-12-15")

# get Google Analytics (GA) data
gadata <- google_analytics(view_id,
  date_range = c(start_date, end_date),
  metrics = c("users", "sessions", "pageviews"),
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)
gadata
##    users sessions pageviews
## 1 321940   428217    560491

In its first year, Stats and R has attracted 321,940 users, who generated a total of 428,217 sessions and 560,491 page views.

For those unfamiliar with Google Analytics data and the difference between these metrics, remember that:

• a user is the number of new and returning people who visit your site during a set period of time
• a session is a group of user interactions with your website that take place within a given time frame
• a page view, as the name suggests, is defined as a view of a page on your site

So if person A reads three blog posts then leave the site and person B reads one blog post, your about page then leave the site, Google Analytics data will show 2 users, 2 sessions and 5 page views.

Sessions over time

In addition to the rather general metrics presented above, it is also interesting to illustrate the daily number of sessions over time in a scatterplot—together with a smoothed line—to analyze the evolution of the blog:

# get the Google Analytics (GA) data
gadata <- google_analytics(view_id,
  date_range = c(start_date, end_date),
  metrics = c("sessions"), # edit for other metrics
  dimensions = c("date"),
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)

# load required libraries
library(dplyr)
library(ggplot2)

# scatter plot with a trend line
gadata %>%
  ggplot(aes(x = date, y = sessions)) +
  geom_point(size = 1L, color = "steelblue") + # change size and color of points
  geom_smooth(color = "darkgrey", alpha = 0.25) + # change color of smoothed line and transparency of confidence interval
  theme_minimal() +
  labs(
    y = "Sessions",
    x = "",
    title = "Evolution of daily sessions",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com"
  ) +
  theme(plot.margin = unit(c(5.5, 15.5, 5.5, 5.5), "pt")) + # to avoid the plot being cut on the right edge
  scale_y_continuous(labels = scales::comma) # better y labels

(See how to draw plots with the {ggplot2} package, or with the {esquisse} addin if you are not familiar with the package.)

As you can see, there was a huge peak of traffic around end of April, with almost 12,000 users in a single day. Yes, you read it well and there is no bug. The blog post “A package to download free Springer books during Covid-19 quarantine” went viral and generated a massive traffic for a few days. The daily number of sessions returned to a more normal level after a couple of days. We also observe an upward trend in the last months (since end of August/beginning of September), which indicates that the blog is growing in terms of number of daily sessions.

Note that I decided to focus on the number of sessions and the number of page views in this section and the following ones, but you can always change to your preferred metrics by editing metrics = c("sessions") in the code. See all available metrics provided Google Analytics in this article.

Sessions per channel

Knowing how people come to your blog is a pretty important factor. Here is how to visualize the evolution of daily sessions per channel in a line plot:

# Get the data
trend_data <- google_analytics(view_id,
  date_range = c(start_date, end_date),
  dimensions = c("date"),
  metrics = "sessions",
  pivots = pivot_ga4("medium", "sessions"),
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)

# edit variable names
names(trend_data) <- c("Date", "Total", "Organic", "Referral", "Direct", "Email", "Social")

# Change the data into a long format
library(tidyr)
trend_long <- gather(trend_data, Channel, Sessions, -Date)

# Build up the line plot
ggplot(trend_long, aes(x = Date, y = Sessions, group = Channel)) +
  theme_minimal() +
  geom_line(aes(colour = Channel)) +
  labs(
    y = "Sessions",
    x = "",
    title = "Evolution of daily sessions per channel",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com"
  ) +
  scale_y_continuous(labels = scales::comma) # better y labels

We see that a large share of the traffic is from the organic channel, which indicates that most readers visit the blog after a query on search engines (mostly Google). In my case, where most of my posts are tutorials and which help people with specific problems, it is thus not a surprise that most of my traffic comes from organic search.

We also notice some small peaks of sessions generated from the referral and direct channels, which are probably happening on the date of publication of each article.

We also see that there seems to be a recurrent pattern of ups and downs in the number of daily sessions. Those are weekly cycles, with less readers during the weekend and which indicates that people are working on improving their statistical or R knowledge mostly during the week (which actually makes sense!).

Sessions per day of week

As shown above, traffic seems to be different depending on the day of week. To investigate this further, we draw a boxplot of the number of sessions for every day of the week:

# get data
gadata <- google_analytics(view_id,
  date_range = c(start_date, end_date),
  metrics = "sessions",
  dimensions = c("dayOfWeek", "date"),
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)

## Recoding gadata$dayOfWeek following GA naming conventions
gadata$dayOfWeek <- recode_factor(gadata$dayOfWeek,
  "0" = "Sunday",
  "1" = "Monday",
  "2" = "Tuesday",
  "3" = "Wednesday",
  "4" = "Thursday",
  "5" = "Friday",
  "6" = "Saturday"
)

## Reordering gadata$dayOfWeek to have Monday as first day of the week
gadata$dayOfWeek <- factor(gadata$dayOfWeek,
  levels = c(
    "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
    "Sunday"
  )
)

# Boxplot
gadata %>%
  ggplot(aes(x = dayOfWeek, y = sessions)) +
  geom_boxplot() +
  theme_minimal() +
  labs(
    y = "Sessions",
    x = "",
    title = "Sessions per day of week",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com"
  ) +
  scale_y_continuous(labels = scales::comma) # better y labels

As you can see there are some outliers, probably due (in part at least) to the article that went viral. For the sake of illustration, below the same plot after removing points considered as potential outliers according to the interquartile range (IQR) criterion (i.e., points above or below the whiskers), and after a couple of visual improvements:

# boxplot
gadata %>%
  filter(sessions <= 3000) %>% # filter out sessions > 3,000
  ggplot(aes(x = dayOfWeek, y = sessions, fill = dayOfWeek)) + # fill boxplot by dayOfWeek
  geom_boxplot(varwidth = TRUE) + # vary boxes width according to n obs.
  geom_jitter(alpha = 0.25, width = 0.2) + # adds random noise and limit its width
  theme_minimal() +
  labs(
    y = "Sessions",
    x = "",
    title = "Sessions per day of week",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com\nPoints > 3,000 excluded"
  ) +
  scale_y_continuous(labels = scales::comma) + # better y labels
  theme(legend.position = "none") # remove legend

After excluding data points above 3,000, it is now easier to see that the median number of sessions (represented by the horizontal bold line in the boxes) is the highest on Wednesdays, and lowest on Saturdays and Sundays.

The difference in sessions between weekdays is, however, not as large as expected. For the interested reader, if you want to test whether there is a significant difference between the days in terms of number of sessions, you could perform an ANOVA.

Sessions per day and time

We have seen the traffic per day of week. The example below shows a visualization of traffic, broken down this time by day of week and hour of day in a density plot. In this plot, the device type has also been added for more insights.

## Get data by deviceCategory, day of week and hour
weekly_data <- google_analytics(view_id,
  date_range = c(start_date, end_date),
  metrics = c("sessions"),
  dimensions = c("deviceCategory", "dayOfWeekName", "hour"),
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)

## Manipulation using dplyr
weekly_data_sessions <- weekly_data %>%
  group_by(deviceCategory, dayOfWeekName, hour)

## Reordering weekly_data_sessions$dayOfWeekName to have Monday as first day of the week
weekly_data_sessions$dayOfWeekName <- factor(weekly_data_sessions$dayOfWeekName,
  levels = c(
    "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",
    "Sunday"
  )
)

## Plotting using ggplot2
weekly_data_sessions %>%
  ggplot(aes(hour, sessions, fill = deviceCategory, group = deviceCategory)) +
  geom_area(position = "stack") +
  labs(
    title = "Sessions per day and time",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com",
    x = "Time",
    y = "Sessions",
    fill = "Device" # edit legend title
  ) +
  theme_minimal() +
  facet_wrap(~dayOfWeekName, ncol = 2, scales = "fixed") +
  theme(
    legend.position = "bottom", # move legend
    axis.text = element_text(size = 7) # change font of axis text
  ) +
  scale_y_continuous(labels = scales::comma) # better y labels

The above plot shows that:

• traffic increases in the afternoon then declines in the late evening
• traffic is the highest from Monday to Thursday, and lowest on Saturday and Sunday

In addition to that, thanks to the additional information on the device category, we also see that:

• the number of sessions on tablet is low (it is so low compared to desktop and mobile that it is not visible on the plot), and
• the number of sessions on mobile seems to be quite stable during the entire day,
• as opposed to sessions on desktop which are highest at the end of the day.

Sessions per month and year

In the following code, we create a barplot of the number of daily sessions per month and year:

# get data
df2 <- google_analytics(view_id,
  date_range = c(start_date, end_date),
  metrics = c("sessions"),
  dimensions = c("date"),
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)

# add in year month columns to dataframe
df2$month <- format(df2$date, "%m")
df2$year <- format(df2$date, "%Y")

# sessions by month by year using dplyr then graph using ggplot2 barplot
df2 %>%
  group_by(year, month) %>%
  summarize(sessions = sum(sessions)) %>%
  # print table steps by month by year
  # print(n = 100) %>%
  # graph data by month by year
  ggplot(aes(x = month, y = sessions, fill = year)) +
  geom_bar(position = "dodge", stat = "identity") +
  theme_minimal() +
  labs(
    y = "Sessions",
    x = "Month",
    title = "Sessions per month and year",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com"
  ) +
  scale_y_continuous(labels = scales::comma) # better y labels

This barplot allows to easily see the evolution of the number of sessions over the months, and compare this evolution across different years.

At the moment, since the blog is online only since December 2019, the year factor is not relevant. However, I still present the visualization for other users who work on older websites, and also to remind my future self to create this interesting barplot when there will be data for more than a year.

Top performing pages

Another important factor when measuring the performance of your blog or website is the number of page views for the different pages. The top performing pages in terms of page views over the year can easily be found in Google Analytics (you can access it via Behavior > Site Content > All pages).

For the interested reader, here is how to get the data in R (note that you can change n = 7 in the code to change the number of top performing pages to display):

## Make the request to GA
data_fetch <- google_analytics(view_id,
  date_range = c(start_date, end_date),
  metrics = c("pageviews"),
  dimensions = c("pageTitle"),
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)

## Create a table of the most viewed posts
library(lubridate)
library(reactable)
library(stringr)

most_viewed_posts <- data_fetch %>%
  mutate(Title = str_trunc(pageTitle, width = 40)) %>% # keep maximum 40 characters
  count(Title, wt = pageviews, sort = TRUE)
head(most_viewed_posts, n = 7) # edit n for more or less pages to display
##                                      Title     n
## 1 A package to download free Springer b... 85684
## 2 Variable types and examples - Stats a... 44951
## 3 Descriptive statistics in R - Stats a... 43621
## 4    Outliers detection in R - Stats and R 32560
## 5 The complete guide to clustering anal... 27184
## 6 Correlation coefficient and correlati... 21581
## 7                              Stats and R 16786

Here is how to visualize this table of top performing pages in a barplot:

# plot
top_n(most_viewed_posts, n = 7, n) %>% # edit n for more or less pages to display
  ggplot(., aes(x = reorder(Title, n), y = n)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  theme_minimal() +
  coord_flip() +
  labs(
    y = "Page views",
    x = "Title",
    title = "Top performing pages in terms of page views",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com"
  ) +
  scale_y_continuous(labels = scales::comma) # better y labels

This gives me a good first overview on how posts performed in terms of page views, so in some sense, what people find useful. For instance, I never thought that the post illustrating the different types of variables that exist in statistics (ranked #2) would be so appreciated when I wrote it.

This is something I learned with this blog: at the time of writing, there are some posts which I think no one will care about (and which I mostly write as a personal note for myself), and others which I think people will find very useful. However, after a couple of weeks after publication, sometimes I realize that it is actually exactly the opposite.

# figure out when a page actually launched by finding the
# first day where the page had at least 2 unique pageviews
first_day_pageviews_min <- 2

# exclude pages that have total traffic (daily unique pageviews) that are relatively low
total_unique_pageviews_cutoff <- 500

# set how many "days since publication" we want to include in our plot
days_live_range <- 180

# set number of top pages to display
n <- 7

# Create a dimension filter object
# You need to update the "expressions" value to be a regular expression that filters to
# the appropriate set of content on your site
page_filter_object <- dim_filter("pagePath",
  operator = "REGEXP",
  expressions = "/blog/.+"
)

# Now, put that filter object into a filter clause. The "operator" argument can be AND # or OR...but you have to have it be something, even though it doesn't do anything
# when there is only a single filter object.
page_filter <- filter_clause_ga4(list(page_filter_object),
  operator = "AND"
)

# Pull the GA data
ga_data <- google_analytics(
  viewId = view_id,
  date_range = c(start_date, end_date),
  metrics = "uniquePageviews",
  dimensions = c("date", "pagePath"),
  dim_filters = page_filter,
  anti_sample = TRUE # slows down the request but ensures data isn't sampled
)

# Find the first date for each post. This is actually a little tricky, so we're going to write a
# function that takes each page as an input, filters the data to just include those
# pages, finds the first page, and then puts a "from day 1" count on that data and
# returns it.
normalize_date_start <- function(page) {

  # Filter all the data to just be the page being processed
  ga_data_single_page <- ga_data %>% filter(pagePath == page)

  # Find the first value in the result that is greater than first_day_pageviews_min. In many
  # cases, this will be the first row, but, if there has been testing/previews before it
  # actually goes live, some noise may sneak in where the page may have been live, technically,
  # but wasn't actually being considered live.
  first_live_row <- min(which(ga_data_single_page$uniquePageviews > first_day_pageviews_min))

  # Filter the data to start with that page
  ga_data_single_page <- ga_data_single_page[first_live_row:nrow(ga_data_single_page), ]

  # As the content ages, there may be days that have ZERO traffic. Those days won't show up as
  # rows at all in our data. So, we actually need to create a data frame that includes
  # all dates in the range from the "publication" until the last day traffic was recorded. There's
  # a little trick here where we're going to make a column with a sequence of *dates* (date) and,
  # with a slightly different "seq," a "days_live" that corresponds with each date.
  normalized_results <- data.frame(
    date = seq.Date(
      from = min(ga_data_single_page$date),
      to = max(ga_data_single_page$date),
      by = "day"
    ),
    days_live = seq(min(ga_data_single_page$date):
    max(ga_data_single_page$date)),
    page = page
  ) %>%

    # Join back to the original data to get the uniquePageviews
    left_join(ga_data_single_page) %>%

    # Replace the "NAs" (days in the range with no uniquePageviews) with 0s (because
    # that's exactly what happened on those days!)
    mutate(uniquePageviews = ifelse(is.na(uniquePageviews), 0, uniquePageviews)) %>%

    # We're going to plot both the daily pageviews AND the cumulative total pageviews,
    # so let's add the cumulative total
    mutate(cumulative_uniquePageviews = cumsum(uniquePageviews)) %>%

    # Grab just the columns we need for our visualization!
    select(page, days_live, uniquePageviews, cumulative_uniquePageviews)
}

# We want to run the function above on each page in our dataset. So, we need to get a list
# of those pages. We don't want to include pages with low traffic overall, which we set
# earlier as the 'total_unique_pageviews_cutoff' value, so let's also filter our
# list to only include the ones that exceed that cutoff. We also select the top n pages
# in terms of page views to display in the visualization.
library(dplyr)
pages_list <- ga_data %>%
  group_by(pagePath) %>%
  summarise(total_traffic = sum(uniquePageviews)) %>%
  filter(total_traffic > total_unique_pageviews_cutoff) %>%
  top_n(n = n, total_traffic)

# The first little bit of magic can now occur. We'll run our normalize_date_start function on
# each value in our list of pages and get a data frame back that has our time-normalized
# traffic by page!
library(purrr)
ga_data_normalized <- map_dfr(pages_list$pagePath, normalize_date_start)

# We specified earlier -- in the `days_live_range` object -- how many "days since publication" we
# actually want to include, so let's do one final round of filtering to only include those
# rows.
ga_data_normalized <- ga_data_normalized %>% filter(days_live <= days_live_range)

# Create first plot
library(ggplot2)

gg <- ggplot(ga_data_normalized, mapping = aes(x = days_live, y = uniquePageviews, color = page)) +
  geom_line() + # The main "plot" operation
  scale_y_continuous(labels = scales::comma) + # Include commas in the y-axis numbers
  labs(
    title = "Page views by day since publication",
    x = "Days since publication",
    y = "Page views",
    subtitle = paste0(format(start_date, "%b %d, %Y"), " to ", format(end_date, "%b %d, %Y")),
    caption = "Data: Google Analytics data of statsandr.com"
  ) +
  theme_minimal() + # minimal theme
  theme(
    legend.position = "none", # remove legend
  )

# Output the plot, wrapped in ggplotly so we will get some interactivity in the plot
library(plotly)
ggplotly(gg, dynamicTicks = TRUE)