Course Description

Often in data science, you’ll encounter fascinating data that is spread across multiple tables. This course will teach you the skills you’ll need to join multiple tables together to analyze them in combination. You’ll practice your skills using a fun dataset about LEGOs from the Rebrickable website. The dataset contains information about the sets, parts, themes, and colors of LEGOs, but is spread across many tables. You’ll work with the data throughout the course as you learn a total of six different joins! You’ll learn four mutating joins: inner join, left join, right join, and full join, and two filtering joins: semi join and anti join. In the final chapter, you’ll apply your new skills to Stack Overflow data, containing each of the almost 300,000 Stack Oveflow questions that are tagged with R, including information about their answers, the date they were asked, and their score. Get ready to take your dplyr skills to the next level!

Joining Tables

Get started with your first joining verb: inner-join! You’ll learn to join tables together to answer questions about the LEGO dataset, which contains information across many tables about the sets, parts, themes, and colors of LEGOs over time.

The inner_join verb

What columns would you join on?

You’ll be joining together the parts and part_categories tables. You can first inspect them in the console. To join these two tables together using the inner_join verb, what columns would you join from each table?

Since you’ll be working with dplyr throughout the course, the package will be preloaded for you in each exercise.

  • c("id" = "part_num")
  • c("part_cat_id" = "id")
  • c("part_num" = "part_cat_id")
  • c("part_material_id" = "part_categories")

Joining parts and part categories

The inner_join is the key to bring tables together. To use it, you need to provide the two tables that must be joined and the columns on which they should be joined.

In this exercise, you’ll join a list of LEGO parts, available as parts, with these parts’ corresponding categories, available as part_categories. For example, the part Sticker Sheet 1 for Set 1650-1 is from the Stickers part category. You can join these tables to see all parts’ categories!

  • Add the correct joining verb, the name of the second table, and the joining column for the second table.
  • Now, use the suffix argument to add "_part" and "_category" suffixes to replace the name.x and name.y fields.
# edited/added
library(tidyverse)
parts <- readRDS('parts.rds')
part_categories <- readRDS('part_categories.rds')

# Add the correct verb, table, and joining column
parts %>% 
  inner_join(part_categories, by = c("part_cat_id" = "id"))
  
# Use the suffix argument to replace .x and .y suffixes
parts %>% 
  inner_join(part_categories, by = c("part_cat_id" = "id"), suffix = c("_part", "_category"))

Joining with a one-to-many relationship

Joining parts and inventories

The LEGO data has many tables that can be joined together. Often times, some of the things you care about may be a few tables away (we’ll get to that later in the course). For now, we know that parts is a list of all LEGO parts, and a new table, inventory_parts, has some additional information about those parts, such as the color_id of each part you would find in a specific LEGO kit.

Let’s join these two tables together to observe how joining parts with inventory_parts increases the size of your table because of the one-to-many relationship that exists between these two tables.

  • Connect the parts and inventory_parts tables by their part numbers using an inner join.
# edited/added
inventory_parts = readRDS('inventory_parts.rds')

# Combine the parts and inventory_parts tables
parts %>%
  inner_join(inventory_parts, by = "part_num")

Joining in either direction

An inner_join works the same way with either table in either position. The table that is specified first is arbitrary, since you will end up with the same information in the resulting table either way.

Let’s prove this by joining the same two tables from the last exercise in the opposite order!

  • Connect the inventory_parts and parts tables by their part numbers using an inner join.
# Combine the parts and inventory_parts tables
inventory_parts %>%
  inner_join(parts, by = "part_num")

Joining three or more tables

Joining three tables

You can string together multiple joins with inner_join and the pipe (%>%), both with which you are already very familiar!

We’ll now connect sets, a table that tells us about each LEGO kit, with inventories, a table that tells us the specific version of a given set, and finally to inventory_parts, a table which tells us how many of each part is available in each LEGO kit.

So if you were building a Batman LEGO set, sets would tell you the name of the set, inventories would give you IDs for each of the versions of the set, and inventory_parts would tell you how many of each part would be in each version.

  • Combine the inventories table with the sets table.
  • Next, join the inventory_parts table to the table you created in the previous join by the inventory IDs.
# edited/added
sets <- readRDS('sets.rds')
inventories <- readRDS('inventories.rds')

sets %>%
  # Add inventories using an inner join 
  inner_join(inventories, by = "set_num") %>%
  # Add inventory_parts using an inner join 
  inner_join(inventory_parts, by = c("id" = "inventory_id"))

What’s the most common color?

Now let’s join an additional table, colors, which will tell us the color of each part in each set, so that we can answer the question, “what is the most common color of a LEGO piece?”

  • Inner join the colors table using the color_id column from the previous join and the id column from colors; use the suffixes "_set" and "_color".
  • Count the name_color column and sort the results so the most prominent colors appear first.
# edited/added
colors = readRDS('colors.rds')

# Add an inner join for the colors table
sets %>%
  inner_join(inventories, by = "set_num") %>%
  inner_join(inventory_parts, by = c("id" = "inventory_id")) %>%
  inner_join(colors, by = c("color_id" = "id"), suffix = c("_set", "_color"))
  
# Count the number of colors and sort
sets %>%
  inner_join(inventories, by = "set_num") %>%
  inner_join(inventory_parts, by = c("id" = "inventory_id")) %>%
  inner_join(colors, by = c("color_id" = "id"), suffix = c("_set", "_color")) %>%
  count(name_color, sort = TRUE)

Left and Right Joins

Learn two more mutating joins, the left and right join, which are mirror images of each other! You’ll learn use cases for each type of join as you explore parts and colors of LEGO themes. Then, you’ll explore how to join tables to themselves to understand the hierarchy of LEGO themes in the data.

The left_join verb

Left joining two sets by part and color

In the video, you learned how to left join two LEGO sets. Now you’ll practice your ability to do this looking at two new sets: the Millennium Falcon and Star Destroyer sets. We’ve created these for you and they have been preloaded for you:

millennium_falcon <- inventory_parts_joined %>%
  filter(set_num == "7965-1")

star_destroyer <- inventory_parts_joined %>%
  filter(set_num == "75190-1")
  • Left join the star_destroyer and millennium_falcon tables on the part_num and color_id columns with the suffixes _falcon and _star_destroyer.
# edited/added
inventory_parts_joined <- sets %>%
  inner_join(inventories, by = "set_num") %>%
  inner_join(inventory_parts, by = c("id" = "inventory_id"))
millennium_falcon <- inventory_parts_joined %>%
  filter(set_num == "7965-1")
star_destroyer <- inventory_parts_joined %>%
  filter(set_num == "75190-1")
    
# Combine the star_destroyer and millennium_falcon tables
millennium_falcon %>%
  left_join(star_destroyer, by = c("part_num", "color_id"), suffix = c("_falcon", "_star_destroyer"))

Left joining two sets by color

In the videos and the last exercise, you joined two sets based on their part and color. What if you joined the datasets by color alone? As with the last exercise, the Millennium Falcon and Star Destroyer sets have been created and preloaded for you:

millennium_falcon <- inventory_parts_joined %>%
  filter(set_num == "7965-1")

star_destroyer <- inventory_parts_joined %>%
  filter(set_num == "75190-1")
  • Sum the quantity column by color_id in the Millennium Falcon dataset.
  • Now, sum the quantity column by color_id in the Star Destroyer dataset.
  • Left join the two datasets, millennium_falcon_colors and star_destroyer_colors, using the color_id column and the _falcon and _star_destroyer suffixes.
# Aggregate Millennium Falcon for the total quantity in each part
millennium_falcon_colors <- millennium_falcon %>%
  group_by(color_id) %>%
  summarize(total_quantity = sum(quantity))
  
# Aggregate Millennium Falcon for the total quantity in each part
millennium_falcon_colors <- millennium_falcon %>%
  group_by(color_id) %>%
  summarize(total_quantity = sum(quantity))

# Aggregate Star Destroyer for the total quantity in each part
star_destroyer_colors <- star_destroyer %>%
  group_by(color_id) %>%
  summarize(total_quantity = sum(quantity))
  
# Aggregate Millennium Falcon for the total quantity in each part
millennium_falcon_colors <- millennium_falcon %>%
  group_by(color_id) %>%
  summarize(total_quantity = sum(quantity))

# Aggregate Star Destroyer for the total quantity in each part
star_destroyer_colors <- star_destroyer %>%
  group_by(color_id) %>%
  summarize(total_quantity = sum(quantity))

# Left join the Millennium Falcon colors to the Star Destroyer colors
millennium_falcon_colors %>%
  left_join(star_destroyer_colors, by = "color_id", suffix = c("_falcon", "_star_destroyer"))

Finding an observation that doesn’t have a match

Left joins are really great for testing your assumptions about a data set and ensuring your data has integrity.

For example, the inventories table has a version column, for when a LEGO kit gets some kind of change or upgrade. It would be fair to assume that all sets (which joins well with inventories) would have at least a version 1. But let’s test this assumption out in the following exercise.

  • Use a left_join to join together sets and inventory_version_1 using their common column.
  • filter for where the version column is NA using is.na.
inventory_version_1 <- inventories %>%
    filter(version == 1)

# Join versions to sets
sets %>%
  left_join(inventory_version_1, by = "set_num") %>%
  # Filter for where version is na
  filter(is.na(version))

The right_join verb

Which join is best?

Now that you’ve learned three different types of joins, you will be able to identify situations in which each join should be used.

  • Match each definition with the type of join that is described.
inner

You want to keep only observations that match perfectly between tables.

left

You want to keep all observations in the first table, including matching observations in the second table.

Counting part colors

Sometimes you’ll want to do some processing before you do a join, and prioritize keeping the second (right) table’s rows instead. In this case, a right join is for you.

In this exercise, we’ll count the part_cat_id from parts, before using a right_join to join with part_categories. The reason we do this is because we don’t only want to know the count of part_cat_id in parts, but we also want to know if there are any part_cat_ids not present in parts.

  • Use the count verb to count each part_cat_id in the parts table.
  • Use a right_join to join part_categories. You’ll need to use the part_cat_id from the count and the id column from part_categories.
  • filter for where the column n is NA.
parts %>%
  # Count the part_cat_id
  count(part_cat_id) %>%
  # Right join part_categories
  right_join(part_categories, by = c("part_cat_id" = "id"))
  
parts %>%
  count(part_cat_id) %>%
  right_join(part_categories, by = c("part_cat_id" = "id")) %>%
  # Filter for NA
  filter(is.na(n))

Cleaning up your count

In both left and right joins, there is the opportunity for there to be NA values in the resulting table. Fortunately, the replace_na function can turn those NAs into meaningful values.

In the last exercise, we saw that the n column had NAs after the right_join. Let’s use the replace_na column, which takes a list of column names and the values with which NAs should be replaced, to clean up our table.

  • Use replace_na to replace NAs in the n column with the value 0.
parts %>%
  count(part_cat_id) %>%
  right_join(part_categories, by = c("part_cat_id" = "id")) %>%
  # Use replace_na to replace missing values in the n column
  replace_na(list(n = 0))

Joining tables to themselves

Joining themes to their children

Tables can be joined to themselves!

In the themes table, which is available for you to inspect in the console, you’ll notice there is both an id column and a parent_id column. Keeping that in mind, you can join the themes table to itself to determine the parent-child relationships that exist for different themes.

In the videos, you saw themes joined to their own parents. In this exercise, you’ll try a similar approach of joining themes to their own children, which is similar but reversed. Let’s try this out to discover what children the theme "Harry Potter" has.

  • Inner join themes to their own children, resulting in the suffixes "_parent" and "_child", respectively.
  • Filter this table to find the children of the "Harry Potter" theme.
# edited/added
themes <- readRDS('themes.rds')

themes %>% 
  # Inner join the themes table
  inner_join(themes, by = c("id" = "parent_id"), suffix = c("_parent", "_child")) %>% 
  # Filter for the "Harry Potter" parent name 
  filter(name_parent == "Harry Potter")

Joining themes to their grandchildren

We can go a step further than looking at themes and their children. Some themes actually have grandchildren: their children’s children.

Here, we can inner join themes to a filtered version of itself again to establish a connection between our last join’s children and their children.

  • Use another inner join to combine themes again with itself.
    • Be sure to use the suffixes "_parent" and "_grandchild" so the columns in the resulting table are clear.
    • Update the by argument to specify the correct columns to join on. If you’re unsure of what columns to join on, it might help to look at the result of the first join to get a feel for it.
# Join themes to itself again to find the grandchild relationships
themes %>% 
  inner_join(themes, by = c("id" = "parent_id"), suffix = c("_parent", "_child")) %>%
  inner_join(themes, by = c("id_child" = "parent_id"), suffix = c("_parent", "_grandchild"))

Left joining a table to itself

So far, you’ve been inner joining a table to itself in order to find the children of themes like "Harry Potter" or "The Lord of the Rings".

But some themes might not have any children at all, which means they won’t be included in the inner join. As you’ve learned in this chapter, you can identify those with a left_join and a filter().

  • Left join the themes table to its own children, with the suffixes _parent and _child respectively.
  • Filter the result of the join to find themes that have no children.
themes %>% 
  # Left join the themes table to its own children
  left_join(themes, by = c("id" = "parent_id"), suffix = c("_parent", "_child")) %>%
  # Filter for themes that have no child themes
  filter(is.na(name_child))

Full, Semi, and Anti Joins

In this chapter, you’ll cover three more joining verbs: full-join, semi-join, and anti-join. You’ll then use these verbs to answer questions about the similarities and differences between a variety of LEGO sets.

The full_join verb

Differences between Batman and Star Wars

In the video, you compared two sets. Now, you’ll compare two themes, each of which is made up of many sets.

First, you’ll need to join in the themes. Recall that doing so requires going through the sets first. You’ll use the inventory_parts_joined table from the video, which is already available to you in the console.

inventory_parts_joined <- inventories %>%
  inner_join(inventory_parts, by = c("id" = "inventory_id")) %>%
  arrange(desc(quantity)) %>%
  select(-id, -version)
  • In order to join in the themes, you’ll first need to combine the inventory_parts_joined and sets tables.
  • Then, combine the first join with the themes table, using the suffix argument to clarify which table each name came from ("_set" or "_theme").
# edited/added
inventory_parts_joined <- inventories %>%
  inner_join(inventory_parts, by = c("id" = "inventory_id")) %>%
  arrange(desc(quantity)) %>%
  select(-id, -version)

# Start with inventory_parts_joined table
inventory_parts_joined %>%
  # Combine with the sets table 
  inner_join(sets, by = "set_num") %>%
  # Combine with the themes table
  inner_join(themes, by = c("theme_id" = "id"), suffix = c("_set", "_theme"))

Aggregating each theme

Previously, you combined tables to compare themes. Before doing this comparison, you’ll want to aggregate the data to learn more about the pieces that are a part of each theme, as well as the colors of those pieces.

The table you created previously has been preloaded for you as inventory_sets_themes. It was filtered for each theme, and the objects have been saved as batman and star_wars.

inventory_sets_themes <- inventory_parts_joined %>%
  inner_join(sets, by = "set_num") %>%
  inner_join(themes, by = c("theme_id" = "id"), suffix = c("_set", "_theme"))

batman <- inventory_sets_themes %>%
  filter(name_theme == "Batman")

star_wars <- inventory_sets_themes %>%
  filter(name_theme == "Star Wars")
  • Count the part number and color id for the parts in Batman and Star Wars, weighted by quantity.
# edited/added
inventory_sets_themes <- inventory_parts_joined %>%
  inner_join(sets, by = "set_num") %>%
  inner_join(themes, by = c("theme_id" = "id"), suffix = c("_set", "_theme"))
batman <- inventory_sets_themes %>%
  filter(name_theme == "Batman")
star_wars <- inventory_sets_themes %>%
  filter(name_theme == "Star Wars")
    
# Count the part number and color id, weight by quantity
batman %>%
  count(part_num, color_id, wt = quantity)

star_wars %>%
  count(part_num, color_id, wt = quantity)

Full joining Batman and Star Wars LEGO parts

Now that you’ve got separate tables for the pieces in the batman and star_wars themes, you’ll want to be able to combine them to see any similarities or differences between the two themes. The aggregating from the last exercise has been saved as batman_parts and star_wars_parts, and is preloaded for you.

batman_parts <- batman %>%
  count(part_num, color_id, wt = quantity)

star_wars_parts <- star_wars %>%
  count(part_num, color_id, wt = quantity)
  • Combine the star_wars_parts table with the batman_parts table; use the suffix argument to include the "_batman" and "_star_wars" suffixes.
  • Replace all the NA values in the n_batman and n_star_wars columns with 0s.
# edited/added
batman_parts <- batman %>%
  count(part_num, color_id, wt = quantity)
star_wars_parts <- star_wars %>%
  count(part_num, color_id, wt = quantity)
    
batman_parts %>%
  # Combine the star_wars_parts table 
  full_join(star_wars_parts, by = c("part_num", "color_id"), suffix = c("_batman", "_star_wars")) %>%
  # Replace NAs with 0s in the n_batman and n_star_wars columns 
  replace_na(list(n_batman = 0, n_star_wars = 0))

Comparing Batman and Star Wars LEGO parts

The table you created in the last exercise includes the part number of each piece, the color id, and the number of each piece in the Star Wars and Batman themes. However, we have more information about each of these parts that we can gain by combining this table with some of the information we have in other tables. Before we compare the themes, let’s ensure that we have enough information to make our findings more interpretable. The table from the last exercise has been saved as parts_joined and is preloaded for you.

parts_joined <- batman_parts %>%
  full_join(star_wars_parts, by = c("part_num", "color_id"), suffix = c("_batman", "_star_wars")) %>%
  replace_na(list(n_batman = 0, n_star_wars = 0))
  • Sort the number of star wars pieces in the parts_joined table in descending order.
  • Inner join the colors table to the parts_joined table.
  • Combine the parts table to the previous join using an inner join; add "_color" and "_part" suffixes to specify whether or not the information came from the colors table or the parts table.
# edited/added
parts_joined <- batman_parts %>%
  full_join(star_wars_parts, by = c("part_num", "color_id"), suffix = c("_batman", "_star_wars")) %>%
  replace_na(list(n_batman = 0, n_star_wars = 0))

parts_joined %>%
  # Sort the number of star wars pieces in descending order 
  arrange(desc(n_star_wars)) %>%
  # Join the colors table to the parts_joined table
  inner_join(colors, by = c("color_id" = "id")) %>%
  # Join the parts table to the previous join 
  inner_join(parts, by = "part_num", suffix = c("_color", "_part"))

The semi_join and anti_join verbs

Select the join

Earlier in the course, you distinguished between inner, left, and right joins. Since then, you’ve learned three new types of joins! Can you match these joins to the type of join described here?

  • Match each definition with the type of join that is described.
full

Keep all observations from both tables.

semi

Filter the first table for observations that match the second.

anti

Filter the first table for observations that don’t match the second.

Something within one set but not another

In the videos, you learned how to filter using the semi- and anti join verbs to answer questions you have about your data. Let’s focus on the batwing dataset, and use our skills to determine which parts are in both the batwing and batmobile sets, and which sets are in one, but not the other. While answering these questions, we’ll also be determining whether or not the parts we’re looking at in both sets also have the same color in common.

The batmobile and batwing datasets have been preloaded for you.

batmobile <- inventory_parts_joined %>%
  filter(set_num == "7784-1") %>%
  select(-set_num)

batwing <- inventory_parts_joined %>%
  filter(set_num == "70916-1") %>%
  select(-set_num)
  • Filter the batwing set for parts that are also in the batmobile, whether or not they have the same color.
  • Filter the batwing set for parts that aren’t also in the batmobile, whether or not they have the same color.
# edited/added
batmobile <- inventory_parts_joined %>%
  filter(set_num == "7784-1") %>%
  select(-set_num)
batwing <- inventory_parts_joined %>%
  filter(set_num == "70916-1") %>%
  select(-set_num)

# Filter the batwing set for parts that are also in the batmobile set
batwing %>%
  semi_join(batmobile, by = c("part_num"))

# Filter the batwing set for parts that aren't in the batmobile set
batwing %>%
  anti_join(batmobile, by = c("part_num"))

What colors are included in at least one set?

Besides comparing two sets directly, you could also use a filtering join like semi_join to find out which colors ever appear in any inventory part. Some of the colors could be optional, meaning they aren’t included in any sets.

The inventory_parts and colors tables have been preloaded for you.

  • Use the inventory_parts table to find the colors that are included in at least one set.
# Use inventory_parts to find colors included in at least one set
colors %>%
  semi_join(inventory_parts, by = c("id" = "color_id"))

Which set is missing version 1?

Each set included in the LEGO data has an associated version number. We want to understand the version we are looking at to learn more about the parts that are included. Before doing that, we should confirm that there aren’t any sets that are missing a particular version.

Let’s start by looking at the first version of each set to see if there are any sets that don’t include a first version.

  • Use filter() to extract version 1 from the inventories table; save the filter to version_1_inventories.
  • Use anti_join to combine version_1_inventories with sets to determine which set is missing a version 1.
# Use filter() to extract version 1 
version_1_inventories <- inventories %>%
  filter(version == 1)

# Use anti_join() to find which set is missing a version 1
sets %>%
  anti_join(version_1_inventories, by = "set_num")

Visualizing set differences

Aggregating sets to look at their differences

To compare two individual sets, and the kinds of LEGO pieces that comprise them, we’ll need to aggregate the data into separate themes. Additionally, as we saw in the video, we’ll want to add a column so that we can understand the fractions of specific pieces that are part of each set, rather than looking at the numbers of pieces alone.

The inventory_parts_themes table has been preloaded for you.

inventory_parts_themes <- inventories %>%
  inner_join(inventory_parts, by = c("id" = "inventory_id")) %>%
  arrange(desc(quantity)) %>%
  select(-id, -version) %>%
  inner_join(sets, by = "set_num") %>%
  inner_join(themes, by = c("theme_id" = "id"), suffix = c("_set", "_theme"))
  • Add a filter for the "Batman" theme to create the batman_colors object.
  • Add a fraction column to batman_colors that displays the total divided by the sum of the total.
  • Repeat the steps to filter and aggregate the "Star Wars" set data to create the star_wars_colors object.
  • Add a fraction column to star_wars_colors to display the fraction of the total.
# edited/added
inventory_parts_themes <- inventories %>%
  inner_join(inventory_parts, by = c("id" = "inventory_id")) %>%
  arrange(desc(quantity)) %>%
  select(-id, -version) %>%
  inner_join(sets, by = "set_num") %>%
  inner_join(themes, by = c("theme_id" = "id"), suffix = c("_set", "_theme"))

batman_colors <- inventory_parts_themes %>%
  # Filter the inventory_parts_themes table for the Batman theme
  filter(name_theme == "Batman") %>%
  group_by(color_id) %>%
  summarize(total = sum(quantity)) %>%
  # Add a fraction column of the total divided by the sum of the total 
  mutate(fraction = total / sum(total))

# Filter and aggregate the Star Wars set data; add a fraction column
star_wars_colors <- inventory_parts_themes %>%
  filter(name_theme == "Star Wars") %>%
  group_by(color_id) %>%
  summarize(total = sum(quantity)) %>%
  mutate(fraction = total / sum(total))

Combining sets

The data you aggregated in the last exercise has been preloaded for you as batman_colors and star_wars_colors. Prior to visualizing the data, you’ll want to combine these tables to be able to directly compare the themes’ colors.

batman_colors <- inventory_parts_themes %>%
  filter(name_theme == "Batman") %>%
  group_by(color_id) %>%
  summarize(total = sum(quantity)) %>%
  mutate(fraction = total / sum(total))

star_wars_colors <- inventory_parts_themes %>%
  filter(name_theme == "Star Wars") %>%
  group_by(color_id) %>%
  summarize(total = sum(quantity)) %>%
  mutate(fraction = total / sum(total))
  • Join the batman_colors and star_wars_colors tables; be sure to include all observations from both tables.
  • Replace the NAs in the total_batman and total_star_wars columns.
  • Add a difference column which is the difference between fraction_batman and fraction_star_wars, and a total column, which is the sum of total_batman and total_star_wars.
  • Add a filter to select observations where total is at least 200.
batman_colors %>%
  # Join the Batman and Star Wars colors
  full_join(star_wars_colors, by = "color_id", suffix = c("_batman", "_star_wars")) %>%
  # Replace NAs in the total_batman and total_star_wars columns
  replace_na(list(total_batman = 0, total_star_wars = 0)) %>%
  inner_join(colors, by = c("color_id" = "id"))
  
batman_colors %>%
  full_join(star_wars_colors, by = "color_id", suffix = c("_batman", "_star_wars")) %>%
  replace_na(list(total_batman = 0, total_star_wars = 0)) %>%
  inner_join(colors, by = c("color_id" = "id")) %>%
  # Create the difference and total columns
  mutate(difference = fraction_batman - fraction_star_wars,
         total = total_batman + total_star_wars) %>%
  # Filter for totals greater than 200
  filter(total >= 200)

Visualizing the difference: Batman and Star Wars

In the last exercise, you created colors_joined. Now you’ll create a bar plot with one bar for each color (name), showing the difference in fractions.

Because factors and visualization are beyond the scope of this course, we’ve done some processing for you: here is the code that created the colors_joined table that will be used in the video.

colors_joined <- batman_colors %>%
  full_join(star_wars_colors, by = "color_id", suffix = c("_batman", "_star_wars")) %>%
  replace_na(list(total_batman = 0, total_star_wars = 0)) %>%
  inner_join(colors, by = c("color_id" = "id")) %>%
  mutate(difference = fraction_batman - fraction_star_wars,
         total = total_batman + total_star_wars) %>%
  filter(total >= 200) %>%
  mutate(name = fct_reorder(name, difference))
  • Create a bar plot using the colors_joined table to display the most prominent colors in the Batman and Star Wars themes, with the bars colored by their name.
# edited/added
colors_joined <- batman_colors %>%
  full_join(star_wars_colors, by = "color_id", suffix = c("_batman", "_star_wars")) %>%
  replace_na(list(total_batman = 0, total_star_wars = 0)) %>%
  inner_join(colors, by = c("color_id" = "id")) %>%
  mutate(difference = fraction_batman - fraction_star_wars,
         total = total_batman + total_star_wars) %>%
  filter(total >= 200) %>% 
  drop_na() %>% # edited/added
  mutate(name = fct_reorder(name, difference))
color_palette <- setNames(colors_joined$rgb, colors_joined$name)

# Create a bar plot using colors_joined and the name and difference columns
ggplot(colors_joined, aes(name, difference, fill = name)) +
  geom_col() +
  coord_flip() +
  scale_fill_manual(values = color_palette, guide = "none") +
  labs(y = "Difference: Batman - Star Wars")

Case Study: Joins on Stack Overflow Data

Put together all the types of join you learned in this course to analyze a new dataset: Stack Overflow questions, answers, and tags. This includes calculating and visualizing trends for some notable tags like dplyr and ggplot2. You’ll also master one more method for combining tables, the bind_rows verb, which stacks tables on top of each other.

Stack Overflow questions

Left joining questions and tags

Three of the Stack Overflow survey datasets are questions, question_tags, and tags:

  • questions: an ID and the score, or how many times the question has been upvoted; the data only includes R-based questions
  • question_tags: a tag ID for each question and the question’s id
  • tags: a tag id and the tag’s name, which can be used to identify the subject of each question, such as ggplot2 or dplyr

In this exercise, we’ll be stitching together these datasets and replacing NAs in important fields.

Note that we’ll be using left_joins in this exercise to ensure we keep all questions, even those without a corresponding tag. However, since we know the questions data is all R data, we’ll want to manually tag these as R questions with replace_na.

  • Join together questions and question_tags using the id and question_id columns, respectively.
  • Use another join to add in the tags table.
  • Use replace_na to change the NAs in the tag_name column to "only-r".
# edited/added
questions <- readRDS("questions.rds")
question_tags <- readRDS("question_tags.rds")
tags <- readRDS("tags.rds")

# Join the questions and question_tags tables
questions %>%
  left_join(question_tags, by = c("id" = "question_id"))
  
# Join in the tags table
questions %>%
  left_join(question_tags, by = c("id" = "question_id")) %>%
  left_join(tags, by = c("tag_id" = "id"))
  
# Replace the NAs in the tag_name column
questions %>%
  left_join(question_tags, by = c("id" = "question_id")) %>%
  left_join(tags, by = c("tag_id" = "id")) %>%
  replace_na(list(tag_name = "only-r"))

Comparing scores across tags

The complete dataset you created in the last exercise is available to you as questions_with_tags. Let’s do a quick bit of analysis on it! You’ll use familiar dplyr verbs like group_by, summarize, arrange, and n to find out the average score of the most asked questions.

  • Aggregate by the tag_name.
  • Summarize to get the mean score for each question, score, as well as the total number of questions, num_questions.
  • Arrange num_questions in descending order to sort the answers by the most asked questions.
# edited/added
questions_with_tags <- questions %>%
  left_join(question_tags, by = c("id" = "question_id")) %>%
  left_join(tags, by = c("tag_id" = "id")) %>%
  replace_na(list(tag_name = "only-r"))

questions_with_tags %>%
  # Group by tag_name
  group_by(tag_name) %>%
  # Get mean score and num_questions
  summarize(score = mean(score),
            num_questions = n()) %>%
  # Sort num_questions in descending order
  arrange(desc(num_questions))

What tags never appear on R questions?

The tags table includes all Stack Overflow tags, but some have nothing to do with R. How could you filter for just the tags that never appear on an R question? The tags and question_tags tables have been preloaded for you.

  • Use a join to determine which tags never appear on an R question.
# Using a join, filter for tags that are never on an R question
tags %>%
  anti_join(question_tags, by = c("id" = "tag_id"))

Joining questions and answers

Finding gaps between questions and answers

Now we’ll join together questions with answers so we can measure the time between questions and answers.

Make sure to explore the tables and columns in the console before starting the exercise. Can you tell how are questions identified in the questions table? How can you identify which answer corresponds to which question using the answers table?

  • Use an inner join to combine the questions and answers tables using the suffixes "_question" and "_answer", respectively.
  • Subtract creation_date_question from creation_date_answer within the as.integer() function to create the gap column.
# edited/added
answers <- readRDS("answers.rds")

questions %>%
  # Inner join questions and answers with proper suffixes
  inner_join(answers, by = c("id" = "question_id"), suffix = c("_question", "_answer")) %>%
  # Subtract creation_date_question from creation_date_answer to create gap
  mutate(gap = as.integer(creation_date_answer - creation_date_question))

Joining question and answer counts

We can also determine how many questions actually yield answers. If we count the number of answers for each question, we can then join the answers counts with the questions table.

  • Count and sort the question_id column in the answers table to create the answer_counts table.
  • Join the questions table with the answer_counts table and include all observations from the questions table.
  • Replace the NA values in the n column with 0s.
# Count and sort the question id column in the answers table
answer_counts <- answers %>%
  count(question_id, sort = TRUE)

# Combine the answer_counts and questions tables
questions %>%
  left_join(answer_counts, by = c("id" = "question_id")) %>%
  # Replace the NAs in the n column
  replace_na(list(n = 0))

Joining questions, answers, and tags

Let’s build on the last exercise by adding the tags table to our previous joins. This will allow us to do a better job of identifying which R topics get the most traction on Stack Overflow. The tables you created in the last exercise have been preloaded for you as answer_counts and question_answer_counts.

answer_counts <- answers %>%
    count(question_id, sort = TRUE)

question_answer_counts <- questions %>%
    left_join(answer_counts, by = c("id" = "question_id")) %>%
    replace_na(list(n = 0))
  • Combine the question_tags table with question_answer_counts using an inner_join.
  • Now, use another inner_join to add the tags table.
# edited/added
answer_counts <- answers %>%
  count(question_id, sort = TRUE)
question_answer_counts <- questions %>%
  left_join(answer_counts, by = c("id" = "question_id")) %>%
  replace_na(list(n = 0))
    
question_answer_counts %>%
  # Join the question_tags tables
  inner_join(question_tags, by = c("id" = "question_id")) %>%
  # Join the tags table
  inner_join(tags, by = c("tag_id" = "id"))

Average answers by question

The table you created in the last exercise has been preloaded for you as tagged_answers. You can use this table to determine, on average, how many answers each questions gets.

tagged_answers <- question_answer_counts %>%
    inner_join(question_tags, by = c("id" = "question_id")) %>%
    inner_join(tags, by = c("tag_id" = "id"))

Some of the important variables from this table include: n, the number of answers for each question, and tag_name, the name of each tag associated with each question.

Let’s use some of our favorite dplyr verbs to find out how many answers each question gets on average.

  • Aggregate the tagged_answers table by tag_name.
  • Summarize tagged_answers to get the count of questions and the average_answers.
  • Sort the resulting questions column in descending order.
# edited/added
tagged_answers <- question_answer_counts %>%
  inner_join(question_tags, by = c("id" = "question_id")) %>%
  inner_join(tags, by = c("tag_id" = "id"))

tagged_answers %>%
  # Aggregate by tag_name
  group_by(tag_name) %>%
  # Summarize questions and average_answers
  summarize(questions = n(),
            average_answers = mean(n)) %>%
  # Sort the questions in descending order
  arrange(desc(questions))

The bind_rows verb

Joining questions and answers with tags

To learn more about the questions and answers tables, you’ll want to use the question_tags table to understand the tags associated with each question that was asked, and each answer that was provided. You’ll be able to combine these tables using two inner joins on both the questions table and the answers table.

  • Use two inner joins to combine the question_tags and tags tables with the questions table.
  • Now, use two inner joins to combine the question_tags and tags tables with the answers table.
# Inner join the question_tags and tags tables with the questions table
questions %>%
  inner_join(question_tags, by = c("id" = "question_id")) %>%
  inner_join(tags, by = c("tag_id" = "id"))

# Inner join the question_tags and tags tables with the answers table
answers %>%
  inner_join(question_tags, by = "question_id") %>%
  inner_join(tags, by = c("tag_id" = "id"))

Binding and counting posts with tags

The tables you created in the previous exercise have been preloaded as questions_with_tags and answers_with_tags. First, you’ll want to combine these tables into a single table called posts_with_tags. Once the information is consolidated into a single table, you can add more information by creating a date variable using the lubridate package, which has been preloaded for you.

questions_with_tags <- questions %>%
  inner_join(question_tags, by = c("id" = "question_id")) %>%
  inner_join(tags, by = c("tag_id" = "id"))

answers_with_tags <- answers %>%
  inner_join(question_tags, by = "question_id") %>%
  inner_join(tags, by = c("tag_id" = "id"))
  • Combine the questions_with_tags and answers_with_tags tables into posts_with_tags.
  • Add a year column to the posts_with_tags table, then count posts by type, year, and tag_name.
# edited/added
questions_with_tags <- questions %>%
  inner_join(question_tags, by = c("id" = "question_id")) %>%
  inner_join(tags, by = c("tag_id" = "id"))
answers_with_tags <- answers %>%
  inner_join(question_tags, by = "question_id") %>%
  inner_join(tags, by = c("tag_id" = "id"))

# Combine the two tables into posts_with_tags
posts_with_tags <- bind_rows(questions_with_tags %>% mutate(type = "question"),
                              answers_with_tags %>% mutate(type = "answer"))

# Add a year column, then count by type, year, and tag_name
posts_with_tags %>%
  mutate(year = year(creation_date)) %>%
  count(type, year, tag_name)

Visualizing questions and answers in tags

In the last exercise, you modified the posts_with_tags table to add a year column, and aggregated by type, year, and tag_name. The modified table has been preloaded for you as by_type_year_tag, and has one observation for each type (question/answer), year, and tag. Let’s create a plot to examine the information that the table contains about questions and answers for the dplyr and ggplot2 tags. The ggplot2 package has been preloaded for you.

by_type_year_tag <- posts_with_tags %>%
  mutate(year = year(creation_date)) %>%
  count(type, year, tag_name)
  • Filter the by_type_year_tag table for the dplyr and ggplot2 tags.
  • Create a line plot with that filtered table that plots the frequency (n) over time, colored by question/answer and faceted by tag.
# edited/added
by_type_year_tag <- posts_with_tags %>%
  mutate(year = year(creation_date)) %>%
  count(type, year, tag_name)

# Filter for the dplyr and ggplot2 tag names 
by_type_year_tag_filtered <- by_type_year_tag %>%
  filter(tag_name %in% c("dplyr", "ggplot2"))

# Create a line plot faceted by the tag name 
ggplot(by_type_year_tag_filtered, aes(year, n, color = type)) +
  geom_line() +
  facet_wrap(~ tag_name)

Congratulations!

Congratulations!

Congratulations on making it through the course!

The joining verbs

To review, you learned about 6 different joins and how to use classic dplyr verbs to create meaningful insights from your joined data.

The mutating joins

You learned about the 4 mutating joins: inner join, which keeps only observations which match exactly between two tables, left joins, which keep all observations from the first table in your joins, right joins, which keep all observations from the second table in your joins, and full joins, which keep all observations from both tables.

The filtering joins

You also learned the 2 filtering joins; semi joins, which filter the first table for observations which also exist in the second table, and anti joins, which filter the first table for observations that do not exist in the second table.

Congratulations!

Pretty neat, huh? I hope you enjoyed the course, and feel confident that you can use these new tools in your future analyses. And can’t wait for you to JOIN us in future lessons!