Homework01.Rmd

---
title: "Hw01"
author: "Dre"
date: "September 24, 2016"
output: html_document
---

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


```{r}
library(gapminder)
library(tidyverse)
```
# as_tibble() to view data as tibble if it wasn't made a tibble
```{r}
gapminder
str(gapminder)
glimpse(gapminder)
head(gapminder)
tail(gapminder)
```

# ctrl + alt + i to insert chunk

```{r}
(canada <- filter(gapminder, country == "canada"))
filter(gapminder, year>2000)
filter(gapminder, continent == "Europe", year == 2007)
filter(gapminder, country == "Bulgaria" | year == 2007)
filter(gapminder, country == "Bulgaria" | country == "Albania")
filter(gapminder, country %in% c("Bulgaria", "Albania"))
select(gapminder, year, lifeExp)
select(
  filter(gapminder, year, lifeExp, country), 
country =="Canada"
) 

```
```{r}
# cmd shift m gives %>%
# pipe always goes on the right of the command (end of the line)

gapminder %>% 
  filter(country == "Canada") %>% 
  select(year, lifeExp)

gapminder %>% 
  select(year, lifeExp, country) %>% 
  filter(country == "Canada") %>% 
  select(-country)

select(
  filter(gapminder, year, lifeExp, country), 
country =="Canada") 

y <- gapminder %>% 
  select(starts_with("co"))

```

Let's look at some functions to get to know a data frame.

```{r}
names(gapminder)
colnames(gapminder)
ncol(gapminder)
length(gapminder)
dim(gapminder)
nrow(gapminder)
```

Shift from describing the whole object to looking at the variables inside.

```{r}
summary(gapminder)
# main way to get one variable
gapminder$lifeExp

# Another way to get one variable
gapminder[["gdpPercap"]]



str(gapminder$lifeExp)
str(gapminder["lifeExp"])

```

Now we know how to get 1 variable. Let's explore single variables.

```{r}
summary(gapminder$continent)
table(gapminder$continent)
class(gapminder$continent)
levels(gapminder$continent)
nlevels(gapminder$continent)
barplot(table(gapminder$continent))
summary(gapminder$lifeExp)
quantile(gapminder$lifeExp)
mean(gapminder$lifeExp)
median(gapminder$lifeExp)
hist(gapminder$lifeExp)
```

Let's make a few figures.

```{r}
## library(ggplot2)

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) +
  geom_point()


## can also pipe data in:
gapminder %>% 
  #filter(country == "Canada") %>% 
  ggplot(aes(x = gdpPercap, y = lifeExp)) +
  geom_point()

p <- ggplot(gapminder, aes(x = gdpPercap, y = lifeExp))
## p + geom_point()
p + geom_point(aes(color = continent))
```