naomitague.Rmd

---
title: "ntague"
author: "Naomi Tague"
date: "January 15, 2016"
output:
  html_document:
    theme: united
    highlight: tango
    toc: true
    toc_depth: 2
---

## Content
        
Provide some information on art, environment

![first diea](images/sculpture1.jpg)
        
## Techniques
        
Key techniques for visualization 
        
## Data
        
Data for visualization
  
Here is some data from [Water data](http://waterdata.usgs.gov/nwis/rt):
  
```{r}

pcp = read.csv('data/pcp.txt', header=T)
summary(pcp)
```

## Data Wrangling

```{r, eval=FALSE}
# present working directory
getwd()

# change working directory
setwd('.')

# list files
list.files()

# list files that end in '.jpg'
list.files(pattern=glob2rx('*.jpg'))

# file exists
file.exists('test.png')
```

# Install Packages

```{r, eval=FALSE}
# Run this chunk only once in your Console
# Do not evaluate when knitting Rmarkdown

# list of packages

```


## utils::read.csv

Traditionally, you would read a CSV like so:

```{r}
d = read.csv('../data/r-ecology/species.csv')
d
head(d)
summary(d)
```

## readr::read_csv

Better yet, try read_csv:

```{r}
library(readr)

d = read_csv('../data/r-ecology/species.csv')
d
head(d)
summary(d)
```


## dplry::tbl_df

Now convert to a dplyr table:

```{r}
library(readr)
library(dplyr)

d = read_csv('../data/r-ecology/species.csv')
d = tbl_df(d)

d = read_csv('../data/r-ecology/species.csv') %>%
  tbl_df()

d = tbl_df(read_csv('../data/r-ecology/species.csv'))

head(d)
summary(d)
glimpse(d)

```

## dplyr loosely

### What year does species 'NL' show up in the surveys.csv?


```{r}
library(readr)
library(dplyr)

read_csv('../data/r-ecology/surveys.csv') %>%
  select(species_id, year) %>%
  #filter(species_id == 'NL') %>%
  group_by(species_id, year) %>%
  summarize(count = n())

d = read_csv('../data/r-ecology/species.csv') %>%
  tbl_df()

d = tbl_df(read_csv('../data/r-ecology/species.csv'))
d
head(d)
summary(d)
glimpse(d)

```