diff --git a/materials/sections/census-data.qmd b/materials/sections/census-data.qmd
index decd32e7..a21ae667 100644
--- a/materials/sections/census-data.qmd
+++ b/materials/sections/census-data.qmd
@@ -237,10 +237,11 @@ Tables available in the 2020 Census PL file:
 
 | Table Name | Description                                    |
 |------------|------------------------------------------------|
-| H1         | Occupancy status by household                  |
+| H1         | Occupancy status (housing) |
 | P1         | Race by Hispanic origin                        |
+| P2         | Hispanic or Latino, and not Hispanic or Latino by Race |
 | P3         | Race for the population 18+                    |
-| P4         | Race by Hispanic origin for the population 18+ |
+| P4         | Hispanic or Latino, and not Hispanic or Latino by Race for the Population 18 Years and Over |
 | P5         | Group quarters status                          |
 
 Note: "Group quarters are places where people live or stay, in a group living arrangement, that is owned or managed by an entity or organization providing housing and/or services for the residents." ([US Census Bureau Glossary](https://www.census.gov/glossary/?term=Group+quarters+population))
@@ -265,10 +266,13 @@ The idea behind `load_variables()` is for you to be able to search for the varia
 
 Now that we've talked about variables let's talk a little bit about geography and how `tidycensus` makes it easy to query data within census geographies. Census data is tabulated in enumeration units. These units are specific geographies including legal entities such as states and counties, and statistical entities that are not official jurisdictions but used to standardize data. The graphic below, provided by [census.gov](https://www.census.gov/programs-surveys/geography/guidance/hierarchy.html) shows the standard hierarchy of census geographic entities.
 
-![](images/census_geos.png) The parameter `geography =` in `get_acs()` and `get_decennial()` allows us to request data from common enumeration units. This mean we can name the specific geography we want data from. For example, let's get data for Hispanic population the 6 counties around the Delta.
+![](images/census_geos.png) 
+
+The parameter `geography =` in `get_acs()` and `get_decennial()` allows us to request data from common enumeration units. This mean we can name the specific geography we want data from. For example, let's get data for Native population the different counties in Alaska.
 
 ```{r}
 #| eval: false
+#| echo: false
 
 delta_hispanic <- get_decennial(
   geography = "county",
@@ -279,6 +283,21 @@ delta_hispanic <- get_decennial(
 
 ```
 
+
+```{r}
+#| eval: false
+
+alaska_native <- get_decennial(
+  geography = "county",
+  state = "AK",
+  county = c("Anchorage", "Bristol Bay", "Juneau", "Bethel"),
+  variables = "P2_007N",
+  year = 2020)
+
+```
+
+
+
 To learn more about the arguments for geography for each core function of `tidycensus`, check out the documentation [here](https://walker-data.com/tidycensus/articles/basic-usage.html#geography-in-tidycensus).
 
 #### Quering for multiple variables
@@ -297,10 +316,10 @@ race_vars <- c(
   Asian = "P2_008N",
   HIPI = "P2_009N") ## Native Hawaiian and other Pacific Islander
 
-delta_race <- get_decennial(
+alaska_race <- get_decennial(
   geography = "county",
-  state = "CA",
-  county = c("Alameda", "Contra Costa", "Sacramento", "San Joaquin", "Solano", "Yolo"),
+  state = "AK",
+  county = c("Anchorage", "Bristol Bay", "Juneau", "Bethel"),
   variables = race_vars,
   summary_var = "P2_001N",
   year = 2020)
@@ -316,18 +335,18 @@ In every table you can generally find a variable that is an appropriate denomina
 
 Once we access the data we want, we can apply our data wrangling skills to get the data in the format that we want.
 
-Let's demonstrate this with an example. Let's compare the distribution of percentage White population and percentage Hispanic population by census track vary among the Delta Counties.
+Let's demonstrate this with an example. Let's compare the distribution of percentage White population and percentage Native population by census track infour Alaska counties.
 
 The first step is to get the data.
 
 ::: callout-note
 ## Exercise 1: `get_decennial()`
 
-1. Make a query to get White and Hispanic population data for Delta counties **by tracks** from the 2020 Decennial Census. Include the total population summary variable (`summary_var = "P2_001N"`).
+1. Make a query to get White and Native population data for 3 Alaska counties **by tract** from the 2020 Decennial Census. Include the total population summary variable (`summary_var = "P2_001N"`).
 
 Hint: variable codes are:
 
--   Total Hispanic population = P2_002N
+-   Total Native population = P2_007N
 -   Total White population = P2_005N
 
 
@@ -337,28 +356,28 @@ Hint: variable codes are:
 #| code-fold: true
 #| code-summary: "Answer"
 
-delta_track_hw <- get_decennial(
+alaska_tract_nw <- get_decennial(
   geography = "tract",
-  variables = c(hispanic = "P2_002N",
+  variables = c(native = "P2_007N",
                 white = "P2_005N"),
   summary_var = "P2_001N",
-  state = "CA",
-  county = c("Alameda", "Contra Costa", "Sacramento", "San Joaquin", "Solano", "Yolo"),
+  state = "AK",
+  county = c("Anchorage", "Bristol Bay", "Juneau", "Bethel"),
   year = 2020)
 
 ```
 
 
-We can check our data by calling the `View(delta_track_hw)` function in the console.
+We can check our data by calling the `View(alaska_tract_nw)` function in the console.
 
-2. Now that we have our data, next thing we will do is calculate the percentage of White and Hispanic population in each track. Given that we have the summary variable within our data set we can easily add a new column with the percentage. And then, we will also clean the `NAMES` column and separate track, county and state into it's own column (hint: `tidyr::separate()`).
+2. Now that we have our data, next thing we will do is calculate the percentage of White and Native population in each track. Given that we have the summary variable within our data set we can easily add a new column with the percentage. And then, we will also clean the `NAMES` column and separate track, county and state into it's own column (hint: `tidyr::separate()`).
 
 ```{r}
 #| eval: false
 #| code-fold: true
 #| code-summary: "Answer"
 
-delta_track_clean <- delta_track_hw %>% 
+alaska_tract_nw_clean <- alaska_tract_nw %>% 
     mutate(percent = 100 * (value / summary_value)) %>% 
     separate(NAME, into = c("tract", "county", "state"),
            sep = ", ")
@@ -369,18 +388,39 @@ delta_track_clean <- delta_track_hw %>%
 Note that we can apply all other `dplyr` functions we have learned to this dataset depending on what we want to achieve. One of the main goals of `tidycensus` is to make the output data frames compatible with `tidyverse` functions.
 
 
-3. Now that we have or "clean" data, with all the variables we need. Let's plot this data to **compare the distribution of percentage** White population and percentage Hispanic population by census track vary among the Delta Counties (hint: `geom_density()`).
+3. Now that we have or "clean" data, with all the variables we need. Let's plot this data to **compare the distribution of percentage** White population and percentage Native population by census tract vary among  Counties in Alaska. 
 
 ```{r}
 #| eval: false
 #| code-fold: true
 #| code-summary: "Answer"
 
-ggplot(delta_track_hw_cl, 
+ggplot(alaska_tract_nw_clean,
+       aes(x = county, y = value, fill = variable)) +
+    geom_bar(position = "fill", stat = "identity") +
+    scale_y_continuous(labels = scales::percent) +
+    scale_fill_manual(guide = guide_legend(reverse = TRUE),
+                      values = c("lightblue2", "gold2")) +
+    labs(
+        title = "Native/White Population",
+        subtitle = "Subset of 4 Alaska Counties",
+        fill = "Race",
+        caption = "Decennial Census 2020 | tidycensus R package",
+        x = "",
+        y = ""
+    ) +
+    theme_minimal() +
+    coord_flip() +
+    theme(legend.position = "top")
+ 
+    
+## Another geom to check out (note: Bristol Bay has only one tract therefore is not plotted)
+ggplot(alaska_tract_nw_clean, 
        aes(x = percent, fill = county)) + 
-  geom_density(alpha = 0.3)+
+    geom_density(alpha = 0.5)+
     facet_wrap(~variable)+
     theme_light()
+    
 ```
 
 
@@ -400,7 +440,7 @@ Applying all what we learned earlier this week, we are going to use `ggplot2` to
 -   The two required arguments are `geography` and `variables`. The function defaults to the 2017-2021 5-year ACS
 -   1-year ACS data are more current, but are only available for geographies of population 65,000 and greater
 -   Access 1-year ACS data with the argument `survey = "acs1"`; defaults to "acs5"
--   Example code to get median income for California by county
+-   Example code to get median income for Alaska by county
 
 ```{r}
 #| eval: false
@@ -409,7 +449,7 @@ Applying all what we learned earlier this week, we are going to use `ggplot2` to
 median_income_1yr <- get_acs(
   geography = "county",
   variables = "B19013_001",
-  state = "CA",
+  state = "AK",
   year = 2021,
   survey = "acs1")
 
@@ -417,7 +457,7 @@ median_income_1yr <- get_acs(
 median_income_5yr <- get_acs(
   geography = "county",
   variables = "B19013_001",
-  state = "CA")
+  state = "AK")
 
 ```
 
@@ -447,7 +487,7 @@ vars_acs5_21 <- load_variables(2021, "acs5")
 2. Find code for total median gross rent.
 
 
-3. Get acs data for median gross rent by county in California
+3. Get acs data for median gross rent by county in Alaska.
 
 ```{r}
 #| eval: false
@@ -455,10 +495,10 @@ vars_acs5_21 <- load_variables(2021, "acs5")
 #| code-summary: "Answer"
 
 
-ca_rent <- get_acs(
+ak_rent <- get_acs(
   geography = "county",
   variables = "B25031_001",
-  state = "CA",
+  state = "AK",
   year = 2021)
 
 ```
@@ -470,7 +510,7 @@ ca_rent <- get_acs(
 #| code-fold: true
 #| code-summary: "Answer"
 
-ggplot(ca_rent, aes(x = estimate, y = reorder(NAME, estimate))) + 
+ggplot(ak_rent, aes(x = estimate, y = reorder(NAME, estimate))) + 
   geom_point()
 ```
 
@@ -487,10 +527,18 @@ geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe),
 
 scale_x_continuous(labels = label_dollar()) 
 
+```
+
+
+```{r}
+#| eval: false
+#| echo: false
+
 
 scale_y_discrete(labels = function(x) str_remove(x, " County, California|, California"))
 ```
 
+
 6. Enhance you plot adding a theme_*, changing the color of the points, renaming the labels, adding a title, or any other modification you want to make.
 
 
diff --git a/materials/sections/intro-tidy-text-data.qmd b/materials/sections/intro-tidy-text-data.qmd
index 1a725e86..6f6ef082 100644
--- a/materials/sections/intro-tidy-text-data.qmd
+++ b/materials/sections/intro-tidy-text-data.qmd
@@ -6,7 +6,7 @@ bibliography: book.bib
 
 -   Describe principles of tidy text data
 -   Employ strategies to wrangle unstructured text data into a tidy text format using the `tidytext` package
--   Become familiar non-tidy text formats and how to convert between tidy text and non-tidy text formats
+-   Describe non-tidy text formats and how to convert between tidy text and non-tidy text formats
 -   Become familiar with text analysis (or text mining) methods and when to use them
 
 ::: callout-note
@@ -124,7 +124,7 @@ library(ggplot2) # plot data
 ```{r}
 #| eval: false
 # Group A
-gutenberg_works(title == "Dracula") # dracula text
+gutenberg_works(title == "The Phantom of the Opera") # phantom text
 
 # Group B
 gutenberg_works(title == "Frankenstein; Or, The Modern Prometheus") # frankenstein text
@@ -136,7 +136,7 @@ gutenberg_works(title == "The Strange Case of Dr. Jekyll and Mr. Hyde") # jekyll
 
 ### Questions
 
-The answers in the code chunks are using the text of *The Phantom of the Opera*.
+The answers in the code chunks are using the text of *The Great Gatsby*.
 
 ::: callout-note
 #### Question 1
@@ -150,10 +150,10 @@ Get the id number from the `gutenberg_works()` function so that you can download
 #| eval: false
 
 # get id number
-gutenberg_works(title == "The Phantom of the Opera")
+gutenberg_works(title == "The Great Gatsby")
 
 # access text data using id number from `gutenberg_works()`
-phantom_corp <- gutenberg_download(175)
+gatsby_corp <- gutenberg_download(64317)
 ```
 
 ::: callout-note
@@ -168,7 +168,7 @@ Tokenize the corpus data using `unnest_tokens()`. Take a look at the data - do w
 #| eval: false
 
 # tidy text data - unnest and remove stop words
-tidy_phantom <- phantom_corp %>% 
+gatsby_tidy <- gatsby_corp %>% 
     unnest_tokens(word, text)
 ```
 
@@ -186,7 +186,8 @@ Take a look at the data - are you satisfied with your data? We won't conduct any
 #| eval: false
 
 # remove stop words
-tidy_phantom <- tidy_phantom %>% dplyr::anti_join(stop_words, by = "word")
+gatsby_tidy <- gatsby_tidy %>% 
+    dplyr::anti_join(stop_words, by = "word")
 ```
 
 ::: callout-note
@@ -201,7 +202,7 @@ Calculate the top 10 most frequent words using the functions `count()` and `slic
 #| eval: false
 
 # calculate top 10 most frequent words
-count_phantom <- tidy_phantom %>%
+gatsby_count <- gatsby_tidy %>%
     count(word) %>% 
     slice_max(n = 10, order_by = n)
 ```
@@ -220,19 +221,31 @@ We recommend creating either a bar plot using `geom_col()` or a lollipop plot us
 #| eval: false
 
 # bar plot
-ggplot(data = count_phantom, aes(n, reorder(word, n))) +
+ggplot(data = gatsby_count, aes(n, reorder(word, n))) +
   geom_col() +
     labs(x = "Count",
          y = "Token")
 ```
 
-<details>
 
-<summary>Step 8 Bar Plot</summary>
+```{r}
+#| code-fold: true
+#| code-summary: Base Lollipop Plot Code
+#| eval: false
+
+# initial lollipop plot
+ggplot(data = gatsby_count, aes(x=word, y=n)) +
+    geom_point() +
+    geom_segment(aes(x=word, xend=word, y=0, yend=n)) +
+    coord_flip() +
+    labs(x = "Token",
+         y = "Count")
+
+```
+
+
 
-![](images/tidytext-barplot.png)
 
-</details>
 
 ### Bonus Question
 
@@ -244,19 +257,11 @@ Consider elements in `theme()` and improve your plot.
 
 ```{r}
 #| code-fold: true
-#| code-summary: Lollipop Plot Code
+#| code-summary: Custom Lollipop Plot Code
 #| eval: false
 
-# initial lollipop plot
-ggplot(data = count_phantom, aes(x=word, y=n)) +
-    geom_point() +
-    geom_segment(aes(x=word, xend=word, y=0, yend=n)) +
-    coord_flip() +
-    labs(x = "Token",
-         y = "Count")
-
 # ascending order pretty lollipop plot
-ggplot(data = count_phantom, aes(x=reorder(word, n), y=n)) +
+ggplot(data = gatsby_count, aes(x=reorder(word, n), y=n)) +
     geom_point(color="cyan4") +
     geom_segment(aes(x=word, xend=word, y=0, yend=n), color="cyan4") +
     coord_flip() +
@@ -269,13 +274,6 @@ ggplot(data = count_phantom, aes(x=reorder(word, n), y=n)) +
     )
 ```
 
-<details>
-
-<summary>Step 9 Lollipop Plot</summary>
-
-![](images/tidytext-lollipopplot.png)
-
-</details>
 
 ## Tidy Text to Non-tidy Text Workflows
 
@@ -287,9 +285,12 @@ Many text analysis methods, in particular NLP techniques (e.g. topic models) req
 
 Silge and Robinson kept this in mind as they built the `tidytext` package, and included helpful `cast()` functions to turn a tidy text object (again a table with one-token-per-row) into a matrix.
 
-### Use `cast()` to Convert to a Matrix (Non-tidy) Format
 
-In these examples, we'll be using multiple books as our text: *The Phantom of the Opera*, *The Strange Case of Dr. Jekyll and Mr. Hyde*, *Frankenstein; Or, The Modern Prometheus*, and *Dracula*.
+<!---
+
+### Use `cast()` to Convert tidy-text to a Matrix (Non-tidy) Format
+
+In these examples, we'll be using multiple books as our text: *The Phantom of the Opera*, *The Strange Case of Dr. Jekyll and Mr. Hyde*, and *Frankenstein; Or, The Modern Prometheus*.
 
 We'll access these texts using `gutenbergr`'s `gutenberg_download()` function, and we'll make sure to include the titles in the download.
 
@@ -299,8 +300,7 @@ We'll access these texts using `gutenbergr`'s `gutenberg_download()` function, a
 # download corpus
 all_books_corp <- gutenberg_download(c(175, # phantom of the opera
                                        42, # jekyll & hyde
-                                       84, # frankenstein
-                                       345), # dracula
+                                       41445), # frankenstein
                                      meta_fields = c("title"))
 ```
 
@@ -310,7 +310,7 @@ This code chunk is formatting our corpus into the tidy text format, and it is co
 #| eval: false
 
 # turn corpus into tidy text format
-tidy_all_books <- all_books_corp %>% 
+all_books_tidy <- all_books_corp %>% 
     unnest_tokens(output = word, # output col created 
                   input = text # input col that is split
                   ) %>% 
@@ -324,7 +324,7 @@ This code chunk is converting our corpus of all books into a sparse matrix.
 
 # convert tidy text table to sparse matrix from `Matrix` package
 # requires `Matrix` to be installed
-all_books_sparse <- tidy_all_books %>% 
+all_books_sparse <- all_books_tidy %>% 
     cast_sparse(row = title,
                 column = word,
                 value = n)
@@ -337,7 +337,7 @@ This code chunk is converting our corpus of all books into a document-term matri
 
 # convert tidy text table to DTM object from `tm` package
 # requires `tm` to be installed
-all_books_dtm <- tidy_all_books %>% 
+all_books_dtm <- all_books_tidy %>% 
     cast_dtm(term = word,
              document = title,
              value = n)
@@ -350,14 +350,19 @@ This code chunk is converting our corpus of all books into a document-feature ma
 
 # convert tidy text table to DFM object from `quanteda` package
 # requires `quanteda` to be installed
-all_books_dfm <- tidy_all_books %>% 
+all_books_dfm <- all_books_tidy %>% 
     cast_dfm(term = word, 
              document = title, 
              value = n)
 ```
 
+-->
+
+
 ## Exercise: Explore Unstructured Text Data from a PDF
 
+Frequently the text data we want to analyzed is in PDF format. In the next exercise we walk through how to read in a PDF file into R to be able to programmatically analyze the text. 
+
 ::: callout-tip
 ### Setup
 
@@ -409,6 +414,7 @@ dp_ch6 <- pdftools::pdf_text(path_df)
 
 # ch 8 is used for demonstration and testing
 path_df <- "data/text/dsc-plan-ch8.pdf"
+path_df <- here("materials/data/text/dsc-plan-ch8.pdf")
 dp_ch8 <- pdftools::pdf_text(path_df)
 ```
 
@@ -423,14 +429,14 @@ dp_ch8 <- pdftools::pdf_text(path_df)
 ```{r}
 #| eval: false
 
-corpus_dp_ch <- quanteda::corpus(dp_ch)
+dp_ch_corpus <- quanteda::corpus(dp_ch)
 ```
 
 ```{r}
 #| include: false
 
 # ch 8 is used for demonstration and testing
-corpus_dp_ch8 <- quanteda::corpus(dp_ch8)
+dp_ch8_corpus <- quanteda::corpus(dp_ch8)
 ```
 
 5.  Using `tidy()` from `tidytext`, make the corpus a tidy object.
@@ -443,14 +449,14 @@ corpus_dp_ch8 <- quanteda::corpus(dp_ch8)
 ```{r}
 #| eval: false
 
-tidy_dp_ch <- tidytext::tidy(corpus_dp_ch)
+dp_ch_tidy <- tidytext::tidy(dp_ch_corpus)
 ```
 
 ```{r}
 #| include: false
 
 # ch 8 is used for demonstration and testing
-tidy_dp_ch8 <- tidy(corpus_dp_ch8)
+dp_ch8_tidy <- tidy(dp_ch8_corpus)
 ```
 :::
 
@@ -467,9 +473,9 @@ Tokenize the tidy text data using `unnest_tokens()`
 ```{r}
 #| code-fold: true
 #| code-summary: Answer
-unnest_dp_ch8 <- tidy_dp_ch8 %>% 
+dp_ch8_unnest <- dp_ch8_tidy %>% 
     unnest_tokens(output = word,
-                  input = text) 
+                  input = text)
 ```
 
 ::: callout-note
@@ -482,7 +488,7 @@ Remove stop words using `anti_join()` and the `stop_words` data frame from `tidy
 #| code-fold: true
 #| code-summary: Answer
 #| message: false
-words_dp_ch8 <- unnest_dp_ch8 %>% 
+dp_ch8_words <- dp_ch8_unnest %>% 
     dplyr::anti_join(stop_words)
 ```
 
@@ -495,7 +501,7 @@ Calculate the top 10 most frequently occurring words. Consider using `count()` a
 ```{r}
 #| code-fold: true
 #| code-summary: Answer
-count_dp_ch8 <- words_dp_ch8 %>%
+dp_ch8_count <- dp_ch8_words %>%
     count(word) %>%
     slice_max(n = 10, order_by = n)
 ```
@@ -510,7 +516,7 @@ Visualize the results using a plot of your choice (e.g. bar plot, lollipop plot,
 #| code-fold: true
 #| code-summary: Plot Code
 # bar plot
-ggplot(count_dp_ch8, aes(x = reorder(word, n), y = n)) +
+ggplot(dp_ch8_count, aes(x = reorder(word, n), y = n)) +
     geom_col() +
     coord_flip() +
     labs(title = "Top 10 Most Frequently Occurring Words in Chapter 8 of the Delta Plan",
@@ -523,7 +529,7 @@ ggplot(count_dp_ch8, aes(x = reorder(word, n), y = n)) +
 #| code-fold: true
 #| code-summary: Plot Code
 # lollipop plot
-ggplot(data = count_dp_ch8, aes(x=reorder(word, n), y=n)) +
+ggplot(data = dp_ch8_count, aes(x=reorder(word, n), y=n)) +
     geom_point() +
     geom_segment(aes(x=word, xend=word, y=0, yend=n)) +
     coord_flip() +
@@ -537,8 +543,8 @@ ggplot(data = count_dp_ch8, aes(x=reorder(word, n), y=n)) +
 #| code-fold: true
 #| code-summary: Plot Code
 # wordcloud
-wordcloud(words = count_dp_ch8$word,
-          freq = count_dp_ch8$n)
+wordcloud(words = dp_ch8_count$word,
+          freq = dp_ch8_count$n)
 ```
 
 ### Bonus Question
diff --git a/materials/session_13.qmd b/materials/session_13.qmd
index 6b9c5370..0577feb0 100644
--- a/materials/session_13.qmd
+++ b/materials/session_13.qmd
@@ -11,5 +11,4 @@ format:
     code-overflow: wrap
 ---
 
-
 {{< include /sections/intro-tidy-text-data.qmd >}}
diff --git a/materials/session_18.qmd b/materials/session_18.qmd
index 49659370..95cae438 100644
--- a/materials/session_18.qmd
+++ b/materials/session_18.qmd
@@ -3,5 +3,4 @@ title: "U.S Census Data in R"
 title-block-banner: true
 ---
 
-
 {{< include /sections/census-data.qmd >}}
\ No newline at end of file