---
title: "cm009 Exercises: tidy data"
output: 
  html_document:
    keep_md: true
    theme: paper
---

```{r, warning = FALSE, message = FALSE}
library(tidyverse)
lotr  <- read_csv("https://raw.githubusercontent.com/jennybc/lotr-tidy/master/data/lotr_tidy.csv")
guest <- read_csv("https://raw.githubusercontent.com/STAT545-UBC/Classroom/master/data/wedding/attend.csv")
email <- read_csv("https://raw.githubusercontent.com/STAT545-UBC/Classroom/master/data/wedding/emails.csv")
```

<!---The following chunk allows errors when knitting--->

```{r allow errors, echo = FALSE}
knitr::opts_chunk$set(error = TRUE, warning = FALSE)
```

## Exercise 1: Univariate Pivoting

Consider the Lord of the Rings data:

```{r}
lotr
```

1. Would you say this data is in tidy format?

2. Widen the data so that we see the words spoken by each race, by putting race as its own column.

```{r}
(lotr_wide <- lotr %>% 
  pivot_wider(FILL_THIS_IN = c(-Race, -Words), 
              FILL_THIS_IN = Race, 
              FILL_THIS_IN = Words))
```

3. Re-lengthen the wide LOTR data from Question 2 above.

```{r}
lotr_wide %>% 
  pivot_longer(FILL_THIS_IN = FILL_THIS_IN, 
               names_to  = FILL_THIS_IN, 
               values_to = FILL_THIS_IN)
```

## Exercise 2: Multivariate Pivoting

Congratulations, you're getting married! In addition to the wedding, you've decided to hold two other events: a day-of brunch and a day-before round of golf.  You've made a guestlist of attendance so far, along with food preference for the food events (wedding and brunch).

```{r}
guest %>% 
  DT::datatable(rownames = FALSE)
```

1. Put "meal" and "attendance" as their own columns, with the events living in a new column.

```{r}
(guest_long <- guest %>% 
  pivot_longer(cols      = FILL_THIS_IN, 
               names_to  = FILL_THIS_IN,
               FILL_THIS_IN))
```

2. Use `tidyr::separate()` to split the name into two columns: "first" and "last". Then, re-unite them with `tidyr::unite()`.

```{r}
guest_long %>% 
  separate(FILL_THIS_IN, into = FILL_THIS_IN)
  # unite(col = "name", FILL_THIS_IN, sep = FILL_THIS_IN)
```

3. Which parties still have a "PENDING" status for all members and all events?

```{r}
FILL_THIS_IN %>% 
  group_by(party) %>% 
  summarize(all_pending = all(attendance == "PENDING"))
```

4. Which parties still have a "PENDING" status for all members for the wedding?

```{r}
FILL_THIS_IN %>% 
  group_by(party) %>% 
  summarize(pending_wedding = all(FILL_THIS_IN == "PENDING"))
```


5. Put the data back to the way it was.

```{r}
guest_long %>% 
  pivot_wider(id_cols     = FILL_THIS_IN, 
              names_from  = FILL_THIS_IN, 
              names_sep   = "_", 
              values_from = FILL_THIS_IN)
```

6. You also have a list of emails for each party, in this worksheet under the variable `email`. Change this so that each person gets their own row. Use `tidyr::separate_rows()`

```{r}
email %>% 
  separate_rows(FILL_THIS_IN, sep = FILL_THIS_IN)
```


## Exercise 3: Making tibbles

1. Create a tibble that has the following columns:

- A `label` column with `"Sample A"` in its entries.
- 100 random observations drawn from the N(0,1) distribution in the column `x`
- `y` calculated as the `x` values + N(0,1) error. 

```{r}
n <- 100
FILL_THIS_IN(label = FILL_THIS_IN,
             FILL_THIS_IN = rnorm(n),
             FILL_THIS_IN)
```


2. Generate a Gaussian sample of size 100 for each combination of the following means (`mu`) and standard deviations (`sd`).

```{r}
n <- 100
mu <- c(-5, 0, 5)
sd <- c(1, 3, 10)
FILL_THIS_IN(mu = mu, sd = sd) %>% 
  group_by_all() %>% 
  mutate(z = list(rnorm(n, mu, sd))) %>% 
  FILL_THIS_IN
```

3. Fix the `experiment` tibble below (originally defined in the documentation of the `tidyr::expand()` function) so that all three repeats are displayed for each person, and the measurements are kept. The code is given, but needs one adjustment. What is it?

```{r}
experiment <- tibble(
  name = rep(c("Alex", "Robert", "Sam"), c(3, 2, 1)),
  trt  = rep(c("a", "b", "a"), c(3, 2, 1)),
  rep = c(1, 2, 3, 1, 2, 1),
  measurement_1 = runif(6),
  measurement_2 = runif(6)
)
experiment %>% expand(name, trt, rep)
```