diff --git a/04-tidyr.md b/04-tidyr.md
index 5bebd0efc..77ebb91ba 100644
--- a/04-tidyr.md
+++ b/04-tidyr.md
@@ -118,16 +118,16 @@ interviews %>%
 # A tibble: 10 × 4
    key_ID village  interview_date      instanceID                               
     <dbl> <chr>    <dttm>              <chr>                                    
- 1     46 Chirodzo 2016-11-17 00:00:00 uuid:35f297e0-aa5d-4149-9b7b-4965004cfc37
- 2     43 Chirodzo 2016-11-17 00:00:00 uuid:b4dff49f-ef27-40e5-a9d1-acf287b47358
- 3     67 Chirodzo 2016-11-16 00:00:00 uuid:6c15d667-2860-47e3-a5e7-7f679271e419
- 4    199 Chirodzo 2017-06-04 00:00:00 uuid:ffc83162-ff24-4a87-8709-eff17abc0b3b
- 5      9 Chirodzo 2016-11-16 00:00:00 uuid:846103d2-b1db-4055-b502-9cd510bb7b37
- 6     56 Chirodzo 2016-11-16 00:00:00 uuid:973c4ac6-f887-48e7-aeaf-4476f2cfab76
- 7     54 Chirodzo 2016-11-16 00:00:00 uuid:273ab27f-9be3-4f3b-83c9-d3e1592de919
- 8     45 Chirodzo 2016-11-17 00:00:00 uuid:e3554d22-35b1-4fb9-b386-dd5866ad5792
- 9     58 Chirodzo 2016-11-16 00:00:00 uuid:a7a3451f-cd0d-4027-82d9-8dcd1234fcca
-10     57 Chirodzo 2016-11-16 00:00:00 uuid:a7184e55-0615-492d-9835-8f44f3b03a71
+ 1     64 Chirodzo 2016-11-16 00:00:00 uuid:28cfd718-bf62-4d90-8100-55fafbe45d06
+ 2     36 Chirodzo 2016-11-17 00:00:00 uuid:c90eade0-1148-4a12-8c0e-6387a36f45b1
+ 3     34 Chirodzo 2016-11-17 00:00:00 uuid:14c78c45-a7cc-4b2a-b765-17c82b43feb4
+ 4     21 Chirodzo 2016-11-16 00:00:00 uuid:cc7f75c5-d13e-43f3-97e5-4f4c03cb4b12
+ 5     46 Chirodzo 2016-11-17 00:00:00 uuid:35f297e0-aa5d-4149-9b7b-4965004cfc37
+ 6     54 Chirodzo 2016-11-16 00:00:00 uuid:273ab27f-9be3-4f3b-83c9-d3e1592de919
+ 7     69 Chirodzo 2016-11-16 00:00:00 uuid:f86933a5-12b8-4427-b821-43c5b039401d
+ 8     66 Chirodzo 2016-11-16 00:00:00 uuid:a457eab8-971b-4417-a971-2e55b8702816
+ 9     61 Chirodzo 2016-11-16 00:00:00 uuid:2401cf50-8859-44d9-bd14-1bf9128766f2
+10    200 Chirodzo 2017-06-04 00:00:00 uuid:aa77a0d7-7142-41c8-b494-483a5b68d8a7
 ```
 
 We notice that the layout or format of the `interviews` data is in a format that
@@ -367,13 +367,13 @@ other with "solar panel" in the `items_owned` column.
 separate_rows(items_owned, sep = ";") %>%
 ```
 
-You may notice that one of the columns is called `´NA´`. This is because some
-of the respondents did not own any of the items that was in the interviewer's
-list. We can use the `replace_na()` function to change these `NA` values to
-something more meaningful. The `replace_na()` function expects for you to give
-it a `list()` of columns that you would like to replace the `NA` values in,
-and the value that you would like to replace the `NA`s. This ends up looking
-like this:
+You may notice that the `items_owned` column contains `NA` values. 
+This is because some of the respondents did not own any of the items that was in
+the interviewer's list. We can use the `replace_na()` function to change these
+`NA` values to something more meaningful. The `replace_na()` function expects
+for you to give it a `list()` of columns that you would like to replace the `NA`
+values in, and the value that you would like to replace the `NA`s. This ends up
+looking like this:
 
 
 ```r
diff --git a/data-visualisation-handout.md b/data-visualisation-handout.md
new file mode 100644
index 000000000..d51c90a9b
--- /dev/null
+++ b/data-visualisation-handout.md
@@ -0,0 +1,293 @@
+---
+title: Code Handout - Data Visualisation with ggplot2
+output:
+  html_document:
+    df_print: paged
+    code_download: yes
+---
+
+This document contains all of the functions that we have covered thus far in the
+course. It will be updated every week, after we've added new skills. Each
+function is presented alongside an example of how it is used.
+
+All of the examples below are in the context of the Palmer Penguins, found
+[here (link)](https://allisonhorst.github.io/palmerpenguins/index.html).
+
+
+
+## Foundations of `ggplot()`
+
+- `ggplot()` -- a function to create the shell of a visualization, where
+  specific variables are mapped to different aspects of the plot
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species))
+```
+
+- `aes()` -- aesthetics that can be used when creating a `ggplot()`, where the
+  aesthetics can either be hard coded (e.g. `color = "blue"`) or associated with
+  a variable (e.g. `color = sex`).
+  
+  - The following are the aesthetic options for *most* plots:
+    - `x`
+    - `y`
+    - `alpha` -- changes transparency
+    - `color` -- produces colored outline
+    - `fill` -- fills with color
+    - `group` -- used with categorical variables, similar to color
+
+- **`+`** -- an important aspect creating a `ggplot()` is to note that the
+  `geom_XXX()` function is separated from the `ggplot()` function with a plus
+  sign, `+`.
+  
+  - `ggplot()` plots are constructed in series of layers, where the plus sign
+    separates these layers.
+  - Generally, the `+` sign can be thought of as the end of a line, so you
+    should always hit enter/return after it. While it is not mandatory to move
+    to the next line for each layer, doing so makes the code a lot easier to
+    organize and read.
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
+  geom_point()
+```
+
+## Geometric Objects to Visualize the Data
+
+- `geom_histogram( )` -- adds a histogram to the plot,
+  where the observations are binned into ranges of values and then frequencies
+  of observations are plotted on the y-axis
+  - You can specify the number of bins you want with the `bins` argument
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm)) + 
+  geom_histogram(bins = 20)
+```
+
+- `geom_boxplot( )` -- adds a boxplot to the plot, where observations are
+  aggregated (summarized), the min, Q1, median, Q3, and maximum are plotted as the
+  box and whiskers, and "outliers" are plotted as points.
+  - You can plot a vertical boxplot by specifying the `x` variable, or a
+    horizontal boxplot by specifying the `y` variable.
+  - Note: the min and max may not be included in the whiskers, if they are
+    deemed to be "outliers" based on the $1.5 \\times \\text{IQR}$ rule.
+
+
+```r
+## Horizontal boxplot
+penguins %>% 
+  ggplot(aes(x = bill_length_mm)) + 
+  geom_boxplot()
+
+## Vertical boxplot
+penguins %>% 
+  ggplot(aes(y = bill_length_mm)) + 
+  geom_boxplot()
+```
+
+- `geom_density()` -- adds a density curve to the plot, where the probability
+  density is plotted on the y-axis (so the density curve has a total area of one).
+  - By default this creates a density curve without shading. By specifying a
+    color in the `fill` argument, the density curve is shaded.
+  - Can be thought of as the "one group" violin plot!
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm)) + 
+  geom_density(fill = "tomato")
+```
+
+- `geom_violin()` -- plots violins for each level of a categorical variable
+  - Can be thought of as a hybrid mix of `geom_boxplot()` and `geom_density()`,
+    as the density is displayed, but it is reflected to provide a plot similar in
+    nature to a boxplot.
+  - To obtain violins stacked vertically, declare the categorical variable as `y`.
+    To obtain side-by-side violins, declare the categorical variable as `x`.
+
+
+```r
+## Stacked vertically
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = species)) + 
+  geom_violin()
+
+## Side-by-side
+penguins %>% 
+  ggplot(aes(y = bill_length_mm, x = species)) + 
+  geom_violin()
+```
+
+- `geom_bar()` -- creates a barchart of a categorical variable
+  - Can produce stacked barcharts by specifying a variable as the `fill`
+    aesthetic.
+  - Can change from stacked barchart to a side-by-side barchart by specifying
+    `position = "dodge"`.
+  - If your data are already in counts (e.g. output from `count()`), then you
+    can specify the `stat = "identity"` argument inside `geom_bar()`.
+
+
+```r
+## Stacked barchart
+penguins %>%
+    ggplot(aes(x = species)) +
+    geom_bar(aes(fill = sex))
+
+## Side-by-side barchart
+penguins %>%
+    ggplot(aes(x = species)) +
+    geom_bar(aes(fill = sex),
+             position = "dodge")
+
+## If data are raw counts
+penguins %>% 
+  count(species, sex) %>% 
+  ggplot(aes(x = species, y = n)) + 
+  geom_bar(aes(fill = sex),
+           stat = "identity",
+           position = "dodge")
+```
+
+- `geom_point()` -- plots each observation as an (x, y) point, used to create
+  scatterplots
+  - Can use `alpha` to increase the transparency of the points, to reduce
+    overplotting.
+  - Can specify `aes`thetics inside of `geom_point()` for local aesthetics (point
+    level) or inside of `ggplot()` for global aesthetics (plot level)
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 
+  geom_point(aes(color = species))
+```
+
+- `geom_jitter()` -- plots each observation as an (x, y) point and adds a small
+  amount of jitter around the point
+  - Useful so that we can see each point in the locations where there are
+    overlapping points.
+  - Can specify the `width` and `height` of the jittering using the optional
+    arguments.
+
+
+```r
+penguins %>% 
+  ggplot(aes(y = body_mass_g, x = species)) + 
+  geom_violin() + 
+  geom_jitter(aes(color = sex), width = 0.25, height = 0.25)
+```
+
+- `geom_smooth()` -- plots a line over a set of points, draws the readers eye
+  to a specific trend
+  - The methods we will use are "lm" for a linear model (straight line), and
+    "loess" for a wiggly line
+  - By default, the smoother gives you gray SE bars, to remove these add
+    `se = FALSE`
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
+  geom_point() + 
+  geom_smooth(method = "lm") 
+```
+
+- `facet_wrap()` -- creates subplots of your original plot, based on the levels
+  of the variable you input
+  - To facet by one variable, use `~variable`.
+  - To facet by two variables, use `variable1 ~ variable2`.
+  - If you prefer for your facets to be organized in rows or columns, use the
+    `nrow` and/or `ncol` arguments.
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
+  geom_point() + 
+  geom_smooth(method = "lm") + 
+  facet_wrap(~island, nrow = 1)
+```
+
+## Plot Characteristics
+
+- `labs()` -- specifies the plot labels, possible labels are: x, y, color, fill,
+  title, and subtitle
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
+  geom_point() + 
+  geom_smooth(method = "lm") +
+  labs(x = "Bill Length (mm)", 
+       y = "Bill Depth (mm)", 
+       color = "Penguin Species")
+```
+
+- `theme_bw()` -- changes the plotting background to the classic dark-on-light
+  ggplot2 theme.
+  - This theme may work better for presentations displayed with a projector.
+  - Other theme options are `theme_minimal()`, `theme_light()`, and `theme_void()`.
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
+  geom_point() + 
+  geom_smooth(method = "lm") +
+  labs(x = "Bill Length (mm)", 
+       y = "Bill Depth (mm)", 
+       color = "Penguin Species") + 
+  theme_bw()
+```
+
+- `theme()` --
+  - Possible options are:
+    - `panel.grid` -- controls the grid lines (`panel.grid = element_blank()`
+      removes grid lines)
+    - `text` -- specifies font size for the entire plot (e.g.
+      `text = element_text(size = 16)`
+    - `axis.text.x` -- specifies the font size for the x-axis text
+    - `axis.text.y` -- specifies the font size for the y-axis text
+    - `plot.title` -- specifies aspects of the plot title, can use
+      `plot.title = element_text(hjust = 0.5)` to centre the title
+
+
+```r
+penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
+  geom_point() + 
+  geom_smooth(method = "lm") +
+  labs(x = "Bill Length (mm)", 
+       y = "Bill Depth (mm)", 
+       color = "Penguin Species") + 
+  theme_bw() + 
+  theme(axis.text.x = element_text(size = 12), 
+        axis.text.y = element_text(size = 12))
+```
+
+## Exporting Plots
+
+- `ggsave()` -- convenient function for saving a plot
+  - Unless specified, defaults to the last plot that was made.
+  - Uses the size of the current graphics device to determine the size of the
+    plot.
+
+
+```r
+plot1 <- penguins %>% 
+  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species)) + 
+  geom_point() + 
+  geom_smooth(method = "lm") + 
+  facet_wrap(~island, nrow = 1)
+
+ggsave(path = "images/faceted_plot.png", plot = plot1)
+```
+
+
diff --git a/data-wrangling-handout.md b/data-wrangling-handout.md
new file mode 100644
index 000000000..533da1cd2
--- /dev/null
+++ b/data-wrangling-handout.md
@@ -0,0 +1,284 @@
+---
+title: Code Handout - Data Wrangling with dplyr & tidyr
+output:
+  html_document:
+    df_print: paged
+    code_download: yes
+---
+
+
+
+This document contains all of the functions that we have covered thus far in the
+course. It will be updated every week, after we've added new skills. Each
+function is presented alongside an example of how it is used.
+
+All of the examples below are in the context of the Palmer Penguins, found
+[here (link)](https://allisonhorst.github.io/palmerpenguins/index.html).
+
+## Packages
+
+- `library()` -- loads packages into your `R` session
+
+
+```r
+library(tidyverse)
+library(palmerpenguins)
+```
+
+## Inspecting Data
+
+- `glimpse()` -- shows a summary of the dataset, the number of rows and columns,
+  variable names, and the first 10 entries of each variable
+
+
+```r
+glimpse(penguins)
+```
+
+## Working with Data
+
+- `<-` -- "assignment arrow", assigns a value (vector, dataframe, single value)
+  to the name of a variable
+
+
+```r
+penguins_2007 <- penguins %>% 
+  filter(year == 2007)
+```
+
+- `c()` -- the "concatenate" function combines inputs to form a vector, the
+  values have to be the same data type.
+
+
+```r
+cat_variables <- c("Species", "Island", "Sex")
+```
+
+\\newpage
+
+## Verbs of Data Wrangling
+
+- `select()` -- selects variables (columns) from a dataframe
+
+
+```r
+penguins %>% 
+select(species)
+```
+
+- `filter()` -- filters observations (rows) out of / into a dataframe, where
+  the inputs (arguments) are the conditions to be satisfied in the data that are
+  kept
+
+
+```r
+## It's nice to have a new line for each condition, so your code is easier to read!
+penguins %>% 
+filter(species == "Adelie",
+       body_mass_g > 3000,
+       year == 2008)
+```
+
+**Logical operators:** Filtering for certain observations (e.g. flights from a
+particular airport) is often of interest in data frames where we might want to
+examine observations with certain characteristics separately from the rest of
+the data. To do so, you can use the `filter` function and a series of **logical
+operators**. The most commonly used logical operators for data analysis are as
+follows:
+
+- `==` means "equal to"
+
+- `!=` means "not equal to"
+
+- `>` or `<` means "greater than" or "less than"
+
+- `>=` or `<=` means "greater than or equal to" or "less than or equal to"
+
+- `mutate()` -- creates new variables or modifies existing variables
+
+
+```r
+penguins %>% 
+  filter(is.na(bill_length_mm) != TRUE, 
+         is.na(bill_depth_mm) != TRUE) %>% 
+  mutate(body_mass_kg = body_mass_g / 1000)
+```
+
+- `group_by()` -- groups the dataframe based on levels of a categorical variable,
+  usually used alongside `summarize()`
+
+
+```r
+penguins %>% 
+  group_by(island)
+```
+
+- summarize()`-- creates data summaries of variables in a dataframe, for grouped  summaries use alongside`group\_by()\`
+
+
+```r
+penguins %>% 
+  filter(is.na(body_mass_g) != TRUE) %>% 
+  group_by(island) %>% 
+  summarize(mean_mass = mean(body_mass_g))  
+```
+
+- `ungroup()` -- removes the grouping of a dataframe, typically used after group
+  summaries when additional ungrouped operations are required
+
+
+```r
+penguins %>% 
+  filter(is.na(body_mass_g) != TRUE) %>% 
+  group_by(island) %>% 
+  summarize(mean_mass = mean(body_mass_g)) %>% 
+  ungroup() 
+```
+
+- `arrange()` -- orders a dataframe based on the values of a numerical variable,
+  paired with `desc()` to order in descending order
+
+
+```r
+penguins %>% 
+  filter(is.na(body_mass_g) != TRUE) %>% 
+  group_by(island) %>% 
+  summarize(mean_mass = mean(body_mass_g)) %>% 
+  arrange(desc(mean_mass))
+```
+
+- `%>%` -- the "pipe" operator, joins sequences of data wrangling steps together,
+  works with any function that has `data = ` as the first argument
+
+
+```r
+penguins %>%
+  select(species, island, body_mass_g, sex, year) %>% 
+  filter(island ==   "Torgersen", 
+         is.na(body_mass_g) != TRUE) %>% 
+  group_by(species, year) %>% 
+  summarize(mean_mass = mean(body_mass_g),
+            median_mass = median(body_mass_g),
+            observations = n()) %>% 
+  arrange(desc(mean_mass))
+```
+
+## Other Data Wrangling Tools
+
+- `count()` -- counts the number of observations (rows) of the different levels
+  of a categorical variable
+  - can add `sort = TRUE` to sort the table in descending order (similar to
+    using `arrange(desc())` )
+
+
+```r
+penguins %>% 
+count(species)
+```
+
+- `mean()` -- finds the mean of a numerical variable, not resistant to `NA` values,
+  so either filter out prior or use `na.omit = TRUE` argument
+  
+  - Other summary functions include:
+    - `var()` -- find the variance of a numerical variable
+    - `sd()` -- finds the standard deviation of a numerical variable
+    - `IQR()` -- find the innerquartile range (Q3 - Q1) of a numerical variable
+    - `median()` -- finds the median of a numerical variable
+
+- `is.na()` -- returns a vector of `TRUE` and `FALSE` values corresponding to
+  whether a particular row of a variable was `NA` (missing)
+
+
+```r
+penguins %>% 
+  mutate(missing_weight = is.na(body_mass_g))
+```
+
+- `sample_n()` -- selects $n$ rows from the dataframe, based on the value of
+  `size` specified
+
+
+```r
+penguins %>% 
+  sample_n(size = 10)
+```
+
+- `replace_na()` -- replaces NA values with the value specified
+  - The values to be replaced must be passed to the function (input) as a
+    `list()` object.
+
+
+```r
+penguins %>% 
+  replace_na(list(bill_length_mm = "no_measurement", 
+                  bill_depth_mm = "no_measurement")) %>% 
+  glimpse()
+```
+
+- `separate_rows()` -- separates a variable with multiple values based on the
+  delimiter specified.
+  
+  - Variables whose entries are stored as a list with commas or semicolons are
+    great candidates for this function!
+
+- `rowSums()` -- forms row sums for numeric variables
+  
+  - Note: In the lesson `rowSums()` was used on a `logical` variable, because
+    logical values can be numerically represented as 0 (FALSE) and 1 (TRUE)
+
+
+```r
+x <- tibble(x1 = 3, x2 = c(4:1, 2:5))
+rowSums(x)
+```
+
+## Pivoting Dataframes
+
+- `pivot_wider()` -- transforms a dataframe from long to wide format
+  - takes three principal arguments:
+    1. the data
+    2. the *names\_from* column variable whose values will become new column names
+    3. the *values\_from* column variable whose values will fill the new column
+      variables.
+  - Further arguments include `values_fill` which, if set, fills in missing
+    values with the value provided.
+
+
+```r
+wide <- penguins %>% 
+  mutate(island_logical = TRUE) %>% 
+  pivot_wider(names_from = species, 
+              values_from = island_logical, 
+              values_fill = list(island_logical = FALSE))
+
+glimpse(wide)
+```
+
+- `pivot_longer()` -- transforms a dataframe from wide to long format
+  - takes four principal arguments:
+  1. the data
+  2. *cols* are the names of the columns we use to fill the a new values variable
+    (or to drop).
+  3. the *names\_to* column variable we wish to create from the *cols* provided.
+  4. the *values\_to* column variable we wish to create and fill with values
+    associated with the *cols* provided.
+
+
+```r
+wide %>% 
+  pivot_longer(cols = Adelie:Gentoo, 
+               names_to = "species", 
+               values_to = "island_logical")
+```
+
+## Extracting Data
+
+- `write_csv()` -- writes a dataframe to a csv file, output into the file path
+  specified
+
+
+```r
+write_csv(wide, path = "data/penguins_wide.csv")
+```
+
+
diff --git a/intro-R-handout.md b/intro-R-handout.md
new file mode 100644
index 000000000..43d28f907
--- /dev/null
+++ b/intro-R-handout.md
@@ -0,0 +1,212 @@
+---
+title: Code Handout - Introduction to R
+output: md_document
+---
+
+
+
+This document contains all of the functions that were covered in the
+*Introduction to R* workshop. Each function is presented alongside an example of
+how it can be used.
+
+## Creating Objects
+
+- `<-` -- "assignment arrow", assigns a value (vector, dataframe, single value)
+  to the name of a variable
+
+
+```r
+x <- 3
+y <- c(1, 2, 3)
+z <- x + y
+```
+
+- `c()` -- the "concatenate" function combines inputs to form a vector, the
+  values have to be the same data type.
+
+
+```r
+animals <- c("bird", "cat", "dog")
+numbers <- c(1, 14, 57, 89)
+logicals <- c(TRUE, FALSE, TRUE, TRUE)
+```
+
+## Inspecting Objects
+
+- `str()` -- compact display of the structure of an R object
+
+
+```r
+str(animals)
+```
+
+- `class()` -- returns the type of element of any R object
+
+
+```r
+class(logicals)
+```
+
+- `typeof()` -- returns the data type or storage mode of any R object
+
+
+```r
+typeof(numbers)
+```
+
+## Functions in R
+
+- `args()` -- returns the arguments of a function
+
+
+```r
+args(round)
+```
+
+- named arguments -- the name of the argument the function expects
+  - You can choose to not name your arguments, **if** you know the **exact**
+    order they should be in!
+  - However, we generally discourage this.
+
+
+```r
+## Either of these work, since the digits argument is named explicitly.
+round(3.14159, digits = 2)
+round(digits = 2, 3.14159)
+
+## This does not work, since the arguments are not named and in the incorrect order. 
+round(2, 3.14159)
+```
+
+## Functions to Summarize Data
+
+- `sqrt()` -- returns the square root of a numeric variable
+
+
+```r
+sqrt(numbers)
+```
+
+- `mean()` -- returns the mean of a numeric variable
+  - You can add the `na.rm` argument, to remove `NA` values before calculating
+    the mean.
+
+
+```r
+sqrt(numbers)
+```
+
+- `max()` -- returns the maximum of a numeric variable
+  - You can add the `na.rm` argument, to remove `NA` values before calculating
+    the max.
+
+
+```r
+sqrt(numbers)
+```
+
+- `sum()` -- returns the sum of a numeric variable
+  - You can add the `na.rm` argument, to remove `NA` values before calculating
+    the sum.
+
+
+```r
+sqrt(numbers)
+```
+
+- `length()` -- returns the length of a vector (of any datatype)
+
+
+```r
+length(animals)
+```
+
+## Subsetting Data
+
+- `[]` -- used to subset elements from a vector
+
+
+```r
+animals[3]
+## selects the third element
+
+animals[2:3]
+## selects the second and third element
+
+animals[c(1, 3)]
+## selects the first and third element
+```
+
+- relational operators -- return logical values indicating where a relation is
+  satisfied. The most commonly used logical operators for data analysis are as follows:
+  - `==` means "equal to"
+  - `!=` means "not equal to"
+  - `>` or `<` means "greater than" or "less than"
+  - `>=` or `<=` means "greater than or equal to" or "less than or equal to"
+
+
+```r
+animals == "dog"
+
+animals != "cat"
+
+numbers > 4
+
+numbers <= 12
+```
+
+- logical operators -- join subset criteria together
+  - `&` means "and" -- where two criteria must **both** be satisfied
+  - `|` means "or" -- where at least one criteria must be satisfied
+
+
+```r
+numbers > 4 & numbers < 20
+
+animals == "dog" | animals == "cat"
+```
+
+- `%in%` -- the "inclusion operator", allows you to test if any of the elements
+  of a search vector (on the left hand side) are found in the target vector (on
+  the right hand side).
+  - The levels of the target vector must be included in a vector (`c()`).
+
+
+```r
+possessions <- c("car", "bicycle", "radio", "television", "mobile_phone")
+
+possessions %in% c("car", "bicycle", "motorcycle")
+```
+
+## Missing Data
+
+- `is.na()` -- returns a vector of logical values indicating which elements of
+  a vector have `NA` values
+  - Often combined with `!`, where the `!` negates the previous statement (e.g.
+    `!TRUE` is equal to `FALSE`).
+
+
+```r
+missing <- c(1, 3, NA, 7, 12, NA)
+
+is.na(missing)
+
+!is.na(missing)
+```
+
+- `na.omit()` -- removes the observations with `NA` values
+
+
+```r
+na.omit(missing)
+```
+
+- `complete.cases()` -- returns a vector of logical values indicating which
+  elements of a vector **are not** missing (`NA`) values
+
+
+```r
+complete.cases(missing)
+```
+
+
diff --git a/md5sum.txt b/md5sum.txt
index 6618ddeba..28deb4b08 100644
--- a/md5sum.txt
+++ b/md5sum.txt
@@ -7,11 +7,15 @@
 "episodes/01-intro-to-r.Rmd" "23d7135e2cc8d6412e87ca12e4a22446" "site/built/01-intro-to-r.md" "2023-07-10"
 "episodes/02-starting-with-data.Rmd" "1cc16a683b4fdbfa5a5c2e85f3bb324b" "site/built/02-starting-with-data.md" "2023-07-10"
 "episodes/03-dplyr.Rmd" "1afb9c880a904ce10789ec6a607922a6" "site/built/03-dplyr.md" "2023-07-10"
-"episodes/04-tidyr.Rmd" "b630435fafa0871e975cee0cb0557594" "site/built/04-tidyr.md" "2023-07-10"
+"episodes/04-tidyr.Rmd" "b4fe0fcd6f363a72ddd98492fe8e77e3" "site/built/04-tidyr.md" "2023-07-25"
 "episodes/05-ggplot2.Rmd" "f5eab52ad1a54fec7d1abaab1ba53bee" "site/built/05-ggplot2.md" "2023-07-10"
 "episodes/06-rmarkdown.Rmd" "f547d49b8f79fbcc6fbe8a78e512df61" "site/built/06-rmarkdown.md" "2023-07-10"
 "episodes/07-json.Rmd" "e5aeddc239ecdb8549653c16e38823f9" "site/built/07-json.md" "2023-07-10"
+"instructors/data-visualisation-handout.Rmd" "af73107d4c87c96857e7d1a803f12013" "site/built/data-visualisation-handout.md" "2023-07-25"
+"instructors/data-wrangling-handout.Rmd" "03ecc7a97f4207b7bc80c78ecbcfcb69" "site/built/data-wrangling-handout.md" "2023-07-25"
 "instructors/instructor-notes.md" "10d2ddec20a1ef8f2537f4002342f6ac" "site/built/instructor-notes.md" "2023-07-10"
+"instructors/intro-R-handout.Rmd" "3648cf0b516f4c68c6729e188d8f3667" "site/built/intro-R-handout.md" "2023-07-25"
+"instructors/starting-with-data-handout.Rmd" "b7deeaa71b74f89ae011c8159e77b36a" "site/built/starting-with-data-handout.md" "2023-07-25"
 "learners/reference.md" "84ec59e74313499cccfab60a73fd0b13" "site/built/reference.md" "2023-07-10"
 "learners/R-handout.Rmd" "3eb70f6b3d7998e1109e7ad91c822fd8" "site/built/R-handout.md" "2023-07-11"
 "learners/setup.md" "b879b810b382d3df882dc7d521398454" "site/built/setup.md" "2023-07-10"
diff --git a/starting-with-data-handout.md b/starting-with-data-handout.md
new file mode 100644
index 000000000..8d2c98944
--- /dev/null
+++ b/starting-with-data-handout.md
@@ -0,0 +1,240 @@
+---
+title: Code Handout - Starting with Data
+output:
+  html_document:
+    df_print: paged
+    code_download: yes
+---
+
+
+
+This document contains all of the functions that were covered in the
+*Introduction to R* workshop. Each function is presented alongside an example of
+how it can be used.
+
+All of the examples below are in the context of the Palmer Penguins, found
+[here (link)](https://allisonhorst.github.io/palmerpenguins/index.html).
+
+## Packages
+
+- `library()` -- loads packages into your `R` session
+
+
+```r
+library(tidyverse)
+library(lubridate)
+```
+
+## Importing Data
+
+- `read_csv()` -- function to import a csv file.
+  - First argument is the path to the data, passed as a character
+    (inside quotations).
+  - You can specify what values should be considered missing, using the `na`
+    argument.
+
+
+```r
+penguins <- read_csv("data/penguins.csv")
+```
+
+## Inspecting Data
+
+- `dim()` - returns a vector with the number of rows as the first element,
+  and the number of columns as the second element (the **dim**ensions of
+  the object)
+
+
+```r
+dim(penguins)
+```
+
+- `nrow()` - returns the number of rows
+- `ncol()` - returns the number of columns
+
+
+```r
+nrow(penguins)
+ncol(penguins)
+```
+
+- `head()` - displays the first 6 rows of the dataframe
+- `tail()` - displays the last 6 rows of the dataframe
+
+
+```r
+head(penguins)
+tail(penguins)
+```
+
+- `names()` - returns the all of the names of an object (both row and column)
+- `colnames()` - returns column names for dataframes (without row names)
+
+
+```r
+names(penguins)
+colnames(penguins)
+```
+
+- `glimpse()` - provides a preview of the data, where column names are presented
+  with their associated data types, and the entries from each column are printed
+  in each row
+
+
+```r
+glimpse(penguins)
+```
+
+- `str()` - returns the structure of the object and information about the class,
+  the names and data types of each column, and a preview of the first entries of
+  each column
+
+
+```r
+str(penguins)
+```
+
+- `summary()` - provides summary statistics for each column
+  - Note: summary statistics for character variables are not meaningful, as they
+    simply state the number of observations (length) of the variable
+
+
+```r
+summary(penguins)
+```
+
+## Subsetting Data
+
+- `[]` -- selects rows and columns from a dataframe
+  - The first entry is the row number, the second entry is the column number(s),
+    and they are separated with a comma.
+
+
+```r
+## Selects the element in the first row, second column
+penguins[1, 2]
+
+## Selects every element in the fourth row
+penguins[4, ]
+
+## Selects every element in the third column
+penguins[, 3]
+```
+
+- `[[]]` -- selects a column from a dataframe
+  - Inside the brackets you can pass either the number of the column or the
+    name of the column (in quotations)
+
+
+```r
+penguins[[1]]
+
+penguins[["island"]]
+```
+
+- `$` -- selects a column from a dataframe, where the name of the dataframe is
+  on the left and the name of the column is on the right
+
+
+```r
+penguins$body_mass_g
+```
+
+## Working with Different Data Types
+
+- `factor()` -- creates a categorical variable from a character or numeric
+  variable, variable has a factor datatype
+  - the values (level) of the factor levels is specified in the `levels`
+    argument, where the levels must be specified in a vector (using `c()`)
+  - Note: the order you wish for the levels to appear is how you should list
+    them in the `levels` argument, you can also specify `ordered = TRUE` to
+    ensure the levels remain in this order
+
+
+```r
+penguins$year_fct <- factor(penguins$year, 
+                               levels = c("2007", "2008", "2009"), 
+                            ordered = TRUE)
+```
+
+- `as.factor()` -- creates a categorical variable from a character or numeric
+  variable, variable has a factor datatype
+  - does not allow for you to specify the order of the levels
+  - defaults to alphabetical ordering for factor levels
+
+
+```r
+penguins$year_fct <- as.factor(penguins$year)
+```
+
+- `levels()` -- returns the levels of a variable with a factor datatype, in the
+  order they were stored
+  - Note: this function will not work for character datatypes
+
+
+```r
+levels(penguins$year_fct)
+```
+
+- `nlevels()` -- returns the number of levels of a variable with a factor
+  datatype
+  - Note: this function will not work for character datatypes
+
+
+```r
+nlevels(penguins$year_fct)
+```
+
+- `as.character()` -- creates a character variable from a numeric or factor
+  variable
+
+
+```r
+penguins$species_chr <- as.character(penguins$species)
+```
+
+- `ymd()` -- transforms dates stored as character or numeric variables to dates
+  - Note: to use this function, dates must be stored in year-month-day format
+  - The function does well with heterogeneous formats (as seen below), but
+    formats where some of the entries are not in double digits may not be parsed
+    correctly.
+
+
+```r
+x <- c("2009-01-01", "2009-01-02", "2009-01-03")
+ymd(x)
+```
+
+- `day()` -- extracts the day (number) of a date variable
+
+
+```r
+day(x)
+```
+
+- `month()` -- extracts the month (number) of a date variable
+
+
+```r
+month(x)
+```
+
+- `year()` -- extracts the year of a date variable
+
+
+```r
+year(x)
+```
+
+## Visualizing Data
+
+- `plot()` -- a generic function for plotting R objects
+  - In this lesson `plot()` was used to create bargraphs of categorical
+    variables.
+
+
+```r
+plot(penguins$species)
+```
+
+