diff --git a/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.Rmd b/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.Rmd
index 6d028c72..da9a08f4 100644
--- a/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.Rmd
+++ b/modules/Manipulating_Data_in_R/Manipulating_Data_in_R.Rmd
@@ -125,9 +125,9 @@ Long: **Easier for R to make plots & do analysis**
 ex_long
 ```
 
-## Pivoting using `tidyr` package
+## Pivoting using the `tidyr` package (part of `tidyverse`)
 
-`tidyr` allows you to "tidy" your data.  We will be talking about:
+We will be talking about:
 
 - `pivot_longer` - make multiple columns into variables, (wide to long)
 - `pivot_wider` - make a variable into multiple columns, (long to wide)
@@ -154,10 +154,20 @@ You might see old functions `gather` and `spread` when googling. These are older
 
 ```{r}
 ex_wide
-ex_long <- ex_wide %>% pivot_longer(cols = !ends_with("State"))
+ex_long <- ex_wide %>% pivot_longer(cols = ends_with("rate"))
 ex_long
 ```
 
+## GUT CHECK!
+
+What does `pivot_longer()` do?
+
+A. Summarize data
+
+B. Import data
+
+C. Reshape data
+
 ## Reshaping wide to long: Better column names {.codesmall}
 
 `pivot_longer()` - puts column data into rows (`tidyr` package)
@@ -169,29 +179,29 @@ ex_long
 <div class = "codeexample">
 ```{r, eval=FALSE}
 {long_data} <- {wide_data} %>% pivot_longer(cols = {columns to pivot},
-                                        names_to = {name for old columns},
-                                        values_to = {name for cell values})
+                                            names_to = {name for old columns},
+                                            values_to = {name for cell values})
 ```
 </div>
 
-## Reshaping data from **wide to long**
+## Reshaping wide to long: Better column names {.codesmall}
+
+Newly created column names ("Month" and "Rate") are enclosed in quotation marks. It helps us be more specific than "name" and "value".
 
 ```{r}
-ex_wide
-ex_long <- ex_wide %>% pivot_longer(cols = !ends_with("State"),
-                                        names_to = "Month",
-                                        values_to = "Rate")
+ex_long <- ex_wide %>% pivot_longer(cols = ends_with("rate"),
+                                    names_to = "Month",
+                                    values_to = "Rate")
 ex_long
 ```
 
-Newly created column names are enclosed in quotation marks.
-
-## Data used: Nitrate exposure
+## Data used: Nitrate exposure{.codesmall}
 
 Let's look at some data on levels of nitrate in water from Washington. This dataset reports the amount of people in Washington exposed to excess levels of nitrate in their water between 1999 and 2020.
 
 ```{r, message = FALSE}
-wide_nitrate <- read_csv(file = "https://daseh.org/data/Nitrate_Exposure_for_WA_Public_Water_Systems_byquarter_data.csv")
+wide_nitrate <- 
+  read_csv(file = "https://daseh.org/data/Nitrate_Exposure_for_WA_Public_Water_Systems_byquarter_data.csv")
 head(wide_nitrate)
 ```
 
@@ -219,9 +229,7 @@ wide_nitrate
 
 ```{r}
 long_nitrate <- wide_nitrate %>%
-  pivot_longer(!c(year, quarter, pop_on_sampled_PWS),
-               names_to = "conc_cat",
-               values_to = "conc_count")
+  pivot_longer(!c(year, quarter, pop_on_sampled_PWS))
 long_nitrate
 ```
 
@@ -239,7 +247,7 @@ Let's make the `conc_count` into a proportion.
 
 ```{r}
 long_nitrate <- long_nitrate %>%
-  mutate(conc_prop = conc_count / pop_on_sampled_PWS)
+  mutate(conc_prop = value / pop_on_sampled_PWS)
 long_nitrate
 ```
 
@@ -249,7 +257,7 @@ Now our data is more tidy, and we can take the averages easily!
 
 ```{r}
 long_nitrate %>% 
-  group_by(conc_cat) %>% 
+  group_by(name) %>% 
   summarize("avg_prop_exposedpop" = mean(conc_prop))
 ```
 
@@ -257,7 +265,6 @@ long_nitrate %>%
 
 There are many ways to **select** the columns we want. Check out https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html to look at more column selection options.
 
-
 # `pivot_wider`...
 
 ## Reshaping data from **long to wide**
@@ -281,6 +288,13 @@ We can use `pivot_wider` to convert long data to wide format. Let's try it with
 
 ```{r}
 ex_long
+```
+
+## Reshaping data from **long to wide**
+
+We can use `pivot_wider` to convert long data to wide format. Let's try it with the vaccine data from earlier.
+
+```{r}
 ex_wide2 <- ex_long %>% pivot_wider(names_from = "Month", 
                                        values_from = "Rate") 
 ex_wide2
@@ -288,7 +302,7 @@ ex_wide2
 
 ## Reshaping nitrate exposure data{.codesmall}
 
-Let's go back to the nitrate exposure dataset. What if we wanted to make a wide version of the data that displayed the number of people at each level of nitrate exposure for each quarter?
+Let's go back to the nitrate exposure dataset. What if we wanted to make a wide version of the data that displayed the proportion of people at each level of nitrate exposure, with each quarter as a column?
 
 ```{r}
 long_nitrate
@@ -296,9 +310,20 @@ long_nitrate
 
 ## Reshaping nitrate exposure data
 
+Drop some columns we don't need.
+
+```{r}
+long_nitrate <- long_nitrate %>%
+  select(!c(pop_on_sampled_PWS, value))
+long_nitrate
+```
+
+## Reshaping nitrate exposure data
+
+Pivot the data!
+
 ```{r}
 wide_nitrate <- long_nitrate %>%
-  select(!c(pop_on_sampled_PWS, conc_count)) %>%
   pivot_wider(names_from = "quarter", values_from = "conc_prop")
 wide_nitrate
 ```
@@ -337,7 +362,6 @@ knitr::include_graphics("images/joins.png")
 * `anti_join(x, y)` - all rows from `x` not in `y` keeping just columns from `x`.
 
 ## Merging: Simple Data
-Let's load in some datasets about vaccination rates by state. These data are saved in two different files.
 
 ```{r message=FALSE}
 data_As <- read_csv(
@@ -447,7 +471,7 @@ fj
 
 <IMG style="position:absolute;bottom:10.5%;left:85%;width:120px;"SRC="images/full.png">
 
-## Watch out for "`includes duplicates`"
+## "`includes duplicates`"
 
 
 ```{r message=FALSE}
@@ -462,7 +486,7 @@ data_As
 data_cold
 ```
 
-## Watch out for "`includes duplicates`"
+## "`includes duplicates`"
 
 ```{r}
 lj <- left_join(data_As, data_cold)
@@ -470,7 +494,7 @@ lj <- left_join(data_As, data_cold)
 
 <IMG style="position:absolute;bottom:10.5%;left:85%;width:120px;"SRC="images/left.png">
 
-## Watch out for "`includes duplicates`"
+## "`includes duplicates`"
 
 Data including the joining column ("State") has been duplicated.
 
@@ -484,7 +508,7 @@ Note that "Alaska willow ptarmigan" appears twice.
 
 <IMG style="position:absolute;bottom:10.5%;left:85%;width:120px;"SRC="images/left.png">
 
-## Watch out for "`includes duplicates`"
+## "`includes duplicates`"
 
 https://github.com/gadenbuie/tidyexplain/blob/main/images/left-join-extra.gif
 
@@ -538,6 +562,16 @@ anti_join(data_cold, data_As, by = "State") # order switched
 
 <IMG style="position:absolute;bottom:10.5%;left:85%;width:120px;"SRC="images/anti.png">
 
+## GUT CHECK!
+
+Why use `join` functions?
+
+A. Combine different data sources
+
+B. Connect Rmd to other files
+
+C. Using one data source is too easy and we want our analysis ~ fancy ~
+
 ## Summary
 
 * Merging/joining data sets together - assumes all column names that overlap
@@ -555,6 +589,12 @@ anti_join(data_cold, data_As, by = "State") # order switched
 
 💻 [Lab](https://daseh.org/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab.Rmd)
 
+📃 [Day 6 Cheatsheet](https://daseh.org/modules/cheatsheets/Day-6.pdf)
+
+📃 [Posit's `tidyr` Cheatsheet](https://rstudio.github.io/cheatsheets/tidyr.pdf)
+
+📃 [Posit's `dplyr` Cheatsheet](https://rstudio.github.io/cheatsheets/data-transformation.pdf)
+
 ```{r, fig.alt="The End", out.width = "50%", echo = FALSE, fig.align='center'}
 knitr::include_graphics(here::here("images/the-end-g23b994289_1280.jpg"))
 ```
diff --git a/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.Rmd b/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.Rmd
index 259f37a3..13355dcc 100644
--- a/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.Rmd
+++ b/modules/Manipulating_Data_in_R/lab/Manipulating_Data_in_R_Lab_Key.Rmd
@@ -9,139 +9,102 @@ editor_options:
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-Some data in this lab comes from the OCS "Exploring CO2 emissions across time" activity (https://www.opencasestudies.org/ocs-bp-co2-emissions/. This dataset is available in the `dasehr` package.
+Some data in this lab comes from the OCS "Exploring CO2 emissions across time" activity (https://www.opencasestudies.org/ocs-bp-co2-emissions/.
 
 Additional data about climate change disasters can be found at "https://daseh.org/data/Yearly_CC_Disasters.csv".
 
 ```{r message=FALSE}
 library(tidyverse)
-library(dasehr)
 ```
 
 # Part 1
 
 ### 1.1
 
-Open the `yearly_co2_emissions` dataset from the `dasehr` package and assign it to an object called `co2`. (You can also use `read_csv()` from the `readr` package and download the dataset directly from the daseh.org website: "https://daseh.org/data/Yearly_CO2_Emissions_1000_tonnes.csv")
+Open the `yearly_co2_emissions` dataset. Use `read_csv()` from the `tidyverse` / `readr` package. You can download the data or use this URL directly: https://daseh.org/data/Yearly_CO2_Emissions_1000_tonnes.csv
 
--  Remember to use `read_csv()` from the `readr` package.
--  Do NOT use `read.csv()`.
+Check out the data to understand the format.
 
 ```{r 1.1response}
-co2 <- yearly_co2_emissions
-
 co2 <- read_csv("https://daseh.org/data/Yearly_CO2_Emissions_1000_tonnes.csv")
+head(co2)
 ```
 
 ### 1.2
 
-Look at the column names using `colnames` - do you notice any patterns?
-
-```{r 1.2response}
-colnames(co2)
-# Most column names are years
-```
-
-### 1.3
-
-Let's rename the columns "co2" from this type of format: "2011" to this: "CO2_2011" using `rename`.
-Be sure to do this for all years 2012, 2013, and 2014. Make sure that you end up with the renamed columns in a data frame named `co2` here and in subsequent steps.
-
-Hint: If you run code to rename the columns and store back into a data frame of the same name like `co2` you will not be able to re-run the renaming code without error (the columns are already renamed so it won't be able to find the old name of the column anymore)
-
-```
-# General format
-new_data <- old_data %>% rename(newname = oldname)
-```
-
-```{r 1.3response}
-co2 <- co2 %>% rename(
-  CO2_2011 = `2011`,
-  CO2_2012 = `2012`,
-  CO2_2013 = `2013`,
-  CO2_2014 = `2014`
-)
-```
-
-### 1.4
-
-Select only the columns "country", and those that start with "CO2_". Use `select` and `starts_with("CO2_")`.
-
-```
-# General format
-new_data <- old_data %>% select(colname1, colname2, ...)
-```
-
-```{r 1.4response}
-co2 <- co2 %>% select(country, starts_with("CO2_"))
-
-```
-
-### 1.5
+Create a new dataset "co2_long" that does `pivot_longer()` on all columns except "country". Remember that `!country` means all columns except "country". 
 
-Create a new dataset "co2_long" that does `pivot_longer()` on all columns except "country". Remember that `!country` means all columns except "country".
+Reassign to co2_long.
 
 ```
 # General format
 new_data <- old_data %>% pivot_longer(cols = colname(s))
 ```
 
-```{r 1.5response}
+```{r 1.2response}
 co2_long <- co2 %>% pivot_longer(cols = !country)
 ```
 
-### 1.6
+### 1.3
 
-Using `co2_long`, filter the "country" column so it only includes values from Indonesia and Canada. **Hint**: use `filter` and `%in%`.
+Using `co2_long`, filter the "country" column so it only includes values from Indonesia and Canada. **Hint**: use `filter` and `%in%`. 
+
+Reassign to co2_long.
 
 ```
 # General format
 new_data <- old_data %>% filter(colname %in% c(...))
 ```
 
-```{r 1.6response}
+```{r 1.3response}
 co2_long <- co2_long %>%
   filter(country %in% c("Indonesia", "Canada"))
 ```
 
-### 1.7
+### 1.4
 
-Use `pivot_wider` to reshape "co2_long". Use "county" for the `names_from` argument. Use "value" for the `values_from` argument. Call this new data `co2_wide`. Look at the data. How do these years compare to one another?
+Use `pivot_wider` to reshape "co2_long". Use "county" for the `names_from` argument. Use "value" for the `values_from` argument. Call this new data `co2_wide`. 
 
 ```
 # General format
 new_data <- old_data %>% pivot_wider(names_from = column1, values_from = column2)
 ```
 
-```{r 1.7response}
+```{r 1.4response}
 co2_wide <- co2_long %>%
   pivot_wider(
     names_from = country,
     values_from = value
   )
-co2_wide
 ```
 
+### 1.5
+
+Using `co2_wide`, drop all NA values using `drop_na()`.
+
+Reassign to co2_wide. Compare the years - what conclusions can you draw?
+
+```{r 1.5response}
+co2_wide <- co2_wide %>% drop_na()
+```
+
+Tip: you can adjust scientific notation with `options(scipen=<n>)` 
+
 
 # Practice on Your Own!
 
 ### P.1
 
-Take the code from Questions 1.1 and 1.3-1.7. Chain all of this code together using the pipe ` %>% `. Call your data `co2_compare`.
+Take the code from Questions 1.1 - 1.5. Chain all of this code together using the pipe ` %>% `. Call your data `co2_compare`.
 
 ```{r P.1response}
 co2_compare <-
-  yearly_co2_emissions %>%
-  rename(
-    CO2_2011 = `2011`,
-    CO2_2012 = `2012`,
-    CO2_2013 = `2013`,
-    CO2_2014 = `2014`
-  ) %>% select(country, starts_with("CO2_")) %>%
+  read_csv("https://daseh.org/data/Yearly_CO2_Emissions_1000_tonnes.csv") %>%
   pivot_longer(cols = !country) %>%
   filter(country %in% c("Indonesia", "Canada")) %>%
-  pivot_wider(names_from = country, values_from = value)
-
+  pivot_wider(names_from = country, values_from = value) %>% 
+  drop_na()
+  
 co2_compare
 ```
 
@@ -149,26 +112,32 @@ co2_compare
 
 Modify the code from Question P.1:
 
--  Choose 4 different years to examine
 -  Select different countries to compare
 -  Call your data `co2_compare2`
 
 ```{r P.2response}
 co2_compare2 <-
-  yearly_co2_emissions %>%
-  rename(
-    CO2_1950 = `1950`,
-    CO2_1960 = `1960`,
-    CO2_1970 = `1970`,
-    CO2_1980 = `1980`
-  ) %>% select(country, starts_with("CO2_")) %>%
+  read_csv("https://daseh.org/data/Yearly_CO2_Emissions_1000_tonnes.csv") %>%
   pivot_longer(cols = !country) %>%
   filter(country %in% c("Brazil", "Mexico")) %>%
-  pivot_wider(names_from = country, values_from = value)
+  pivot_wider(names_from = country, values_from = value) %>% 
+  drop_na()
 
 co2_compare2
 ```
 
+### P.3
+
+Add on to Question P.1 to add a column. The column values for each line should be "TRUE" if Canada has greater emissions than Indonesia for that year.
+
+```{r P.3response}
+co2_compare <-
+  co2_compare %>% 
+  mutate(Canada_greater = Canada > Indonesia)
+
+co2_compare
+```
+
 
 # Part 2
 
@@ -178,7 +147,6 @@ Open the `Yearly_CC_Disasters` dataset using the url below. Save the dataset as
 
 "https://daseh.org/data/Yearly_CC_Disasters.csv"
 
-
 ```{r 2.1response}
 cc <- read_csv("https://daseh.org/data/Yearly_CC_Disasters.csv") %>%
   rename(country = Country)
@@ -232,42 +200,44 @@ anti_join(cc, co2, by = "country") %>% select(country) %>% distinct()
 
 # Practice on Your Own!
 
-### P.3
+### P.4
 
 Take the code from 2.2 and save the output as an object "co2_cc". Filter the dataset. Filter so that you only keep Indonesia and Canada.
 
-```{r P.3response}
+```{r P.4response}
 co2_cc <- full_join(co2, cc, by = "country") %>%
   filter(country %in% c("Indonesia", "Canada"))
 ```
 
 
-### P.4
+### P.5
 
 Select:
 
 * the "country" column
-* data from the years 2014 originally in BOTH DATASETS (columns "CO2_2014" and `2014`)
+* data from the years 2014 in BOTH DATASETS (columns "2014.x" and `2014.y`)
 * the "Indicator" column
 
 Rename:
 
-* emissions = CO2_2014
-* disasters = `2014`
+* emissions = `2014.x`
+* disasters = `2014.y`
 
 Reassign to "co2_cc".
 
-```{r P.4response}
-co2_cc <- co2_cc %>% select(country, CO2_2014, Indicator, `2014`) %>%
-  rename(emissions = CO2_2014, disasters = `2014`)
+```{r P.5response}
+co2_cc <- co2_cc %>% select(country, `2014.x`, Indicator, `2014.y`) %>%
+  rename(emissions = `2014.x`, disasters = `2014.y`)
 ```
 
 
-### P.5
+### P.6
 
 Use `stringr` to trim the piece of text, "Climate related disasters frequency, Number of Disasters: ", from the "Indicator" column. You will use the function `str_remove()` to do this. It works similarly to other `stringr` functions. Try to intuit how it works by using the documentation page (`?str_remove`).
 
-```{r P.5response}
+Reassign to "co2_cc".
+
+```{r P.6response}
 library(stringr)
 co2_cc <- co2_cc %>% mutate(Indicator = str_remove(
   Indicator,
@@ -276,11 +246,11 @@ co2_cc <- co2_cc %>% mutate(Indicator = str_remove(
 ```
 
 
-### P.6
+### P.7
 
 Pivot the dataset so that there are columns for country, emissions, and a column for each "Indicator".
 
-```{r P.6response}
+```{r P.7response}
 co2_cc %>% pivot_wider(
   names_from = Indicator,
   values_from = disasters