first commit

01-Fundamentals-exercises.Rmd

@@ -0,0 +1 @@
+# Fundamentals Exercises

01-Fundamentals.Rmd

@@ -0,0 +1,228 @@
+# Fundamentals
+
+
+```{r, echo=F, message=F, warning=F}
+library(tidyverse)
+```
+
+## Goals of this course
+
+-   Continue building *programming fundamentals*: how to make use of complex data structures, use custom functions built by other R users, and creating your own functions. How to iterate repeated tasks that scales naturally.
+
+-   Continue exploration of *data science fundamentals*: how to clean messy data to a Tidy form for analysis.
+
+-   Outcome: Conduct a full analysis in the data science workflow (minus model).
+
+    ![](https://r4ds.hadley.nz/diagrams/data-science/base.png){width="450"}
+
+## Data types in R
+
+-   Numeric: 18, -21, 65, 1.25
+
+-   Character: "ATCG", "Whatever", "948-293-0000"
+
+-   Logical: TRUE, FALSE
+
+-   Missing values: `NA`
+
+## Data structures
+
+-   Vector
+
+-   **Factor**
+
+-   **List**
+
+-   Dataframe
+
+## Vector
+
+We know what an **(atomic) vector** is: it can contains a data type, and all elements must be the same data type.
+
+![](https://d33wubrfki0l68.cloudfront.net/eb6730b841e32292d9ff36b33a590e24b6221f43/57192/diagrams/vectors/summary-tree-atomic.png){width="400"}
+
+-   We can test whether a vector is a certain type with `is.___()` functions, such as `is.character()`.
+
+    ```{r}
+    is.character(c("hello", "there"))
+    ```
+
+    For `NA`, the test will return a vector testing each element, because `NA` can be mixed into other values:
+
+    ```{r}
+    is.na(c(34, NA))
+    ```
+
+-   We can **coerce** vectors from one type to the other with `as.___()` functions, such as `as.numeric()`
+
+    ```{r}
+    as.numeric(c("23", "45"))
+    as.numeric(c(TRUE, FALSE))
+    ```
+
+-   It is common to have metadata **attributes,** such as **names,** attached to R data structures.
+
+    ```{r}
+    x = c(1, 2, 3)
+    names(x) = c("a", "b", "c")
+    x
+    ```
+
+    ![](https://d33wubrfki0l68.cloudfront.net/1140c34226b3b04438aec65c8fc6b28758d8c091/1748a/diagrams/vectors/attr-names-2.png)
+
+We can look for more general attributes via the `attributes()` function:
+
+```{r}
+attributes(x)
+```
+
+### Ways to subset a vector
+
+1.  Positive numeric vector
+2.  Negative numeric vector performs *exclusion*
+3.  Logical vector
+
+### Practice implicit subsetting
+
+1.  How do you subset the following vector so that it only has positive values?
+
+```{r}
+data = c(2, 4, -1, -3, 2, -1, 10)
+```
+
+```{r}
+data[data > 0]
+```
+
+2.  How do you subset the following vector so that it has doesn't have the character "temp"?
+
+```{r}
+chars = c("temp", "object", "temp", "wish", "bumblebee", "temp")
+```
+
+```{r}
+chars[chars != "temp"]
+```
+
+3.  How do you subset the following vector so that it has no `NA` values?
+
+```{r}
+vec_with_NA = c(2, 4, NA, NA, 3, NA)
+```
+
+```{r}
+vec_with_NA[!is.na(vec_with_NA)]
+```
+
+## Dataframes
+
+Usually, we load in a dataframe from a spreadsheet or a package, but we can create a new dataframe by using vectors of the same length via the `data.frame()` function:
+
+```{r}
+df = data.frame(x = 1:3, y = c("cup", "mug", "jar"))
+attributes(df)
+```
+
+```{r}
+library(palmerpenguins)
+attributes(penguins)
+```
+
+Why are row names [undesirable](https://adv-r.hadley.nz/vectors-chap.html#rownames)?
+
+Sometimes, data frames will be in a format called "tibble", as shown in the `penguins` class names as "tbl_df", and "tbl".
+
+### Subsetting dataframes
+
+*Getting one single column:*
+
+```{r}
+penguins$bill_length_mm
+```
+
+*I want to select columns `bill_length_mm`, `bill_depth_mm`, `species`, and filter for `species` that are "Gentoo":*
+
+```{r}
+penguins_select = select(penguins, bill_length_mm, bill_depth_mm, species)
+penguins_gentoo = filter(penguins_select, species == "Gentoo")
+```
+
+or
+
+```{r}
+penguins_select_2 = penguins[, c("bill_length_mm", "bill_depth_mm", "species")]
+penguins_gentoo_2 = penguins_select_2[penguins$species == "Gentoo" ,]
+```
+
+or
+
+```{r}
+penguins_gentoo_2 = penguins_select_2[penguins$species == "Gentoo", c("bill_length_mm", "bill_depth_mm", "species")]
+```
+
+*I want to filter out rows that have `NA`s in the column `bill_length_mm`:*
+
+```{r}
+penguins_clean = filter(penguins, !is.na(bill_length_mm))
+```
+
+or
+
+```{r}
+penguins_clean = penguins[!is.na(penguins$bill_depth_mm) ,]
+```
+
+## Lists
+
+Lists operate similarly as vectors as they group data into one dimension, but each element of a list can be any data type *or data structure*!
+
+```{r}
+l1 = list(
+  1:3, 
+  "a", 
+  c(TRUE, FALSE, TRUE), 
+  c(2.3, 5.9)
+)
+```
+
+![](https://d33wubrfki0l68.cloudfront.net/9628eed602df6fd55d9bced4fba0a5a85d93db8a/36c16/diagrams/vectors/list.png)
+
+Unlike vectors, you access the elements of a list via the double bracket `[[]]`. You access a smaller list with single bracket `[]`. (More discussion on the different uses of the bracket [here](https://stackoverflow.com/questions/1169456/the-difference-between-bracket-and-double-bracket-for-accessing-the-el).)
+
+Here's a [nice metaphor](https://adv-r.hadley.nz/subsetting.html#subset-single):
+
+> If list `x` is a train carrying objects, then `x[[5]]` is the object in car 5; `x[4:6]` is a train of cars 4-6.
+
+```{r}
+l1[[1]]
+l1[[1]][2]
+```
+
+Use `unlist()` to coerce a list into a vector. Notice all the automatic coersion that happened for the elements.
+
+```{r}
+unlist(l1)
+```
+
+We can give **names** to lists:
+
+```{r}
+l1 = list(
+  ranking = 1:3, 
+  name = "a", 
+  success =  c(TRUE, FALSE, TRUE), 
+  score = c(2.3, 5.9)
+)
+#or
+names(l1) = c("ranking", "name", "success", "score")
+```
+
+And access named elements of lists via the `$` operation:
+
+```{r}
+l1$score
+```
+
+Therefore, `l1$score` is the same as `l1[[4]]` and is the same as `l1[["score"]]`.
+
+A dataframe is just a named list of vectors of same length with **attributes** of (column) `names` and `row.names`.