---
title: "When pivot-wider goes wrong"
---

## Packages

The inevitable:

```{r}
library(tidyverse)
```

## Some long data that should be wide

```{r wider-wrong-1, echo=FALSE}
d <- tribble(
  ~obs, ~time, ~y,
  1, "pre", 19,
  2, "post", 18,
  3, "pre", 17,
  4, "post", 16,
  5, "pre", 15,
  6, "post", 14
)
d
```

-   Six observations of variable `y`, but three measured before some
    treatment and three measured after.
-   Really matched pairs, so want column of `y`-values for `pre` and for
    `post`.
-   `pivot_wider`.

## What happens here?

```{r wider-wrong-2}
d %>% pivot_wider(names_from = time, values_from = y)
```

-   Should be *three* `pre` values and *three* `post`. Why did this
    happen?
-   `pivot_wider` needs to know which *row* to put each observation in.
-   Uses combo of columns *not* named in `pivot_wider`, here `obs`
    (only).

## The problem

```{r wider-wrong-3}
d %>% pivot_wider(names_from = time, values_from = y)
```

-   There are 6 different `obs` values, so 6 different rows.
-   No data for `obs` 2 and `pre`, so that cell missing (`NA`).
-   Not enough data (6 obs) to fill 12 ($= 2 \times 6$) cells.
-   `obs` needs to say which subject provided which *2* observations.

## Fixing it up

```{r wider-wrong-4, echo=FALSE}
d2 <- tribble(
  ~subject, ~time, ~y,
  1, "pre", 19,
  1, "post", 18,
  2, "pre", 17,
  2, "post", 16,
  3, "pre", 15,
  3, "post", 14
)
d2
```

-   column `subject` shows which subject provided each `pre` and `post`.
-   when we do `pivot_wider`, now only *3* rows, one per subject.

## Coming out right

```{r wider-wrong-5}
d2 %>% pivot_wider(names_from = time, values_from = y)
```

-   row each observation goes to determined by other column `subject`,
    and now a `pre` and `post` for each `subject`.
-   right layout for matched pairs $t$ or to make differences for sign
    test or normal quantile plot.
-   "spaghetti plot" needs data longer, as `d2`.

## Spaghetti plot

```{r wider-wrong-6}
d2 %>% mutate(time = fct_inorder(time)) %>% 
  ggplot(aes(x = time, y = y, group = subject)) +
    geom_point() + geom_line()
```

-   each subject's `y` decreases over time, with subject 1 highest
    overall.

## Another example

-   Two independent samples this time

```{r wider-wrong-8, echo=FALSE}
d3 <- tribble(
  ~group, ~y,
  "control", 8,
  "control", 11,
  "control", 13,
  "control", 14,
  "treatment", 12,
  "treatment", 15,
  "treatment", 16,
  "treatment", 17,
)
d3
```

-   These should be arranged like this
-   but what if we make them wider?

## Wider

```{r wider-wrong-9}
d3 %>% pivot_wider(names_from = group, values_from = y)
```

-   row determined by what not used for `pivot_wider`: nothing!
-   everything smooshed into *one* row!
-   this time, too *much* data for the layout.
-   Four data values squeezed into each of the two cells:
    "list-columns".

## Get the data out

-   To expand list-columns out into the data values they contain, can
    use `unnest`:

```{r wider-wrong-10}
d3 %>% pivot_wider(names_from = group, values_from = y) %>% 
  unnest(c(control, treatment))
```

-   in this case, wrong layout, because data values not paired.

## A proper use of list-columns

```{r wider-wrong-11}
d3 %>% nest_by(group) %>% 
  summarize(n = nrow(data), 
            mean_y = mean(data$y), 
            sd_y = sd(data$y))
```

-   another way to do `group_by` and `summarize` to find stats by group.
-   run this one piece at a time to see what it does.