---
title: "Tidying data: extras"
---

## Packages

```{r tidy-extra-R-1}
library(tidyverse)
```

## The pig feed data again

```{r tidy-extra-R-2, message=F}
my_url <- "http://ritsokiguess.site/datafiles/pigs1.txt"
pigs <- read_table(my_url)
pigs
```

## Make longer (as before)

```{r tidy-extra-R-3}
pigs %>% pivot_longer(-pig, names_to="feed", 
                      values_to="weight") -> pigs_longer
pigs_longer
```

## Make wider two ways 1/2

`pivot_wider` is inverse of `pivot_longer`:

```{r tidy-extra-R-4}
pigs_longer %>% 
  pivot_wider(names_from=feed, values_from=weight)
```

we are back where we started.

## Make wider 2/2

Or

```{r tidy-extra-R-5}
pigs_longer %>% 
  pivot_wider(names_from=pig, values_from=weight)
```

## Disease presence and absence at two locations

Frequencies of plants observed with and without disease at two
locations:

```         
Species     Disease present         Disease absent
       Location X Location Y  Location X Location Y
A            44         12          38        10
B            28         22          20        18
```

This has two rows of headers, so I rewrote the data file:

```         
Species  present_x present_y    absent_x  absent_y
A            44         12          38        10
B            28         22          20        18
```

Read into data frame called `prevalence`.

```{r tidy-extra-R-6, echo=F, message=F}
my_url <- "http://ritsokiguess.site/STAC32/disease.txt"
prevalence <- read_table(my_url)
prevalence
```

## Lengthen and separate

```{r tidy-extra-R-7}
prevalence %>% 
  pivot_longer(-Species, names_to = "column", 
               values_to = "freq") %>% 
  separate_wider_delim(column, "_", 
                       names = c("disease", "location"))
```

## Making longer, the better way

```{r tidy-extra-R-8}
prevalence  %>% 
  pivot_longer(-Species, names_to=c("disease", "location"),
               names_sep="_", 
               values_to="frequency") -> prevalence_longer 
prevalence_longer
```

## Making wider, different ways

```{r tidy-extra-R-9}
prevalence_longer %>% 
  pivot_wider(names_from=c(Species, location), values_from=frequency)
```

```{r tidy-extra-R-10}
prevalence_longer %>% 
  pivot_wider(names_from=location, values_from=frequency)
```

## Interlude {.smaller}

```{r tidy-extra-R-11}
pigs_longer
pigs_longer %>% 
  group_by(feed) %>% 
  summarize(weight_mean=mean(weight))
```

## What if summary is more than one number?

eg. quartiles:

```{r tidy-extra-R-12, error=T}
pigs_longer %>% 
  group_by(feed) %>% 
  summarize(r=quantile(weight, c(0.25, 0.75)))
```

## Following the hint...

```{r}
pigs_longer %>% 
  group_by(feed) %>% 
  reframe(r=quantile(weight, c(0.25, 0.75)))
```

## this also works

```{r tidy-extra-R-13, error=T}
pigs_longer %>% 
  group_by(feed) %>% 
  summarize(r=quantile(weight, c(0.25, 0.75)))
pigs_longer %>% 
  group_by(feed) %>% 
  summarize(r=list(quantile(weight, c(0.25, 0.75)))) %>% 
  unnest(r)
```

## or, even better, use `enframe`:

```{r tidy-extra-R-14}
quantile(pigs_longer$weight, c(0.25, 0.75))
enframe(quantile(pigs_longer$weight, c(0.25, 0.75)))
```

## A nice look

Run this one line at a time to see how it works:

```{r tidy-extra-R-15, warning=FALSE}
pigs_longer %>% 
  group_by(feed) %>% 
  summarize(r=list(enframe(quantile(weight, c(0.25, 0.75))))) %>% 
  unnest(r) %>% 
  pivot_wider(names_from=name, values_from=value) -> d
d
d$`25%`
```

## A hairy one

18 people receive one of three treatments. At 3 different times (pre,
post, followup) two variables `y` and `z` are measured on each person:

```{r tidy-extra-R-16, echo=F, message=F}
my_url <- "http://ritsokiguess.site/STAC32/repmes.txt"
repmes0 <- read_table(my_url)
repmes0


repmes0 %>% mutate(id=str_c(treatment, ".", rep)) %>% 
  select(-rep) %>% 
  select(id, everything()) -> repmes
repmes
```

```{r}
repmes %>% pivot_longer(contains("_"),
                        names_to=c("time", ".value"),
                        names_sep="_"
                        ) 
```

## Attempt 1

```{r tidy-extra-R-17}
repmes %>% pivot_longer(contains("_"),
                        names_to=c("time", "var"),
                        names_sep="_",
                        values_to = "vvv"
                         )
```

This is *too* long! We wanted a column called `y` and a column called
`z`, but they have been pivoted-longer too.

## Attempt 2

```{r tidy-extra-R-18}
repmes %>% pivot_longer(contains("_"),
                        names_to=c("time", ".value"),
                        names_sep="_"
                        ) -> repmes3
repmes3
```

This has done what we wanted.

## make a graph

```{r tidy-extra-R-19}
ggplot(repmes3, aes(x=fct_inorder(time), y=y, 
                    colour=treatment, group=id)) +
  geom_point() + geom_line()
```

A so-called spaghetti plot. The three measurements for each person are
joined by lines, and the lines are coloured by treatment.

## or do the plot with means

```{r tidy-extra-R-20}
repmes3 %>% group_by(treatment, ftime=fct_inorder(time)) %>% 
  summarize(mean_y=mean(y)) %>% 
  ggplot(aes(x=ftime, y=mean_y, colour=treatment, 
             group=treatment)) + 
    geom_point() + geom_line()
```

On average, the two real treatments go up and level off, but the control
group is very different.