Chapter_12.Rmd

---
title: "Chapter 12"
author: "Laura"
date: "11/25/2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)

library(tidyverse); library(skimr); library(nycflights13); library(GGally); library(ggstance); library(lvplot); library(hexbin); library(modelr); library(magrittr)

```

## Notes for Chapter 12 Tidy data

### 12.2 Tidy data

There are three interrelated rules which make a dataset tidy:

1. Each variable must have its own column.
2. Each observation must have its own row.
3. Each value must have its own cell.

#### 12.3.3 Exercises

```{r ex12333}

people <- tribble(
  ~name,             ~key,    ~value,
  #-----------------|--------|------
  "Phillip Woods",   "age",       45,
  "Phillip Woods",   "height",   186,
  "Phillip Woods",   "age1",       50,
  "Jessica Cordero", "age",       37,
  "Jessica Cordero", "height",   156
)
people

tidypeople <- people %>% 
  spread(key = key, value = value)
```

#### 12.4.1 Separate

```{r ch1241}

table3 %<>% 
  mutate(bla = rep("bla;", 6))

table3 %>% 
  separate(bla, into = c("newbla", "garbage")) %>%  #, remove = FALSE)) %>% 
  select(-garbage) %>% 
  rename(bla = newbla)

(tibble(x = c("a,b,c", "d,e,f,g", "h,i,j")))  %>% 
  separate(x, c("one", "two", "three", "four"))

tibble(x = c("a,b,c", "d,e", "f,g,i")) %>% 
  separate(x, c("one", "two", "three"), fill = "right")
```

### 12.6 Case Study

The WHO dataset: The data uses the original codes given by the World Health Organization. The column names for columns five through 60 are made by combining new_ to a code for method of diagnosis (rel = relapse, sn = negative pulmonary smear, sp = positive pulmonary smear, ep = extrapulmonary) to a code for gender (f = female, m = male) to a code for age group (014 = 0-14 yrs of age, 1524 = 15-24 years of age, 2534 = 25 to 34 years of age, 3544 = 35 to 44 years of age, 4554 = 45 to 54 years of age, 5564 = 55 to 64 years of age, 65 = 65 years of age or older).

```{r ch1261}
who

who_longer <- who %>% 
  pivot_longer(5:60, names_to = "key", 
               values_to = "case_count") %>% 
  separate(key, c("new", "rest"), sep =3) %>% 
  select(-new) %>% 
  separate(rest, c("diagnosis", "genderage"), sep = 3) %>% 
  separate(genderage, c("gender", "age"), sep = 2) %>% 
  mutate(diagnosis = case_when(diagnosis == "_sp" ~ "possmear",
                               diagnosis == "_np" ~ "negsmear",
                               diagnosis == "rel" ~ "relapse",
                               diagnosis == "_ep" ~ "extrapul"),
         gender = case_when(gender == "_f" ~ "female",
                            gender == "_m" ~ "male"))
         
# this plot is fu
who_longer %>% 
  group_by(country, year, gender) %>% 
  filter(case_count > 1 & year > 1990) %>% 
  ggplot(aes(year, case_count, color = gender)) +
  geom_line()

glimpse(who_longer)

```