RworkshopII.Rmd

---
title: "Hello, R!"
author: "Yue Hu's R Workshop Series I"
output:
  ioslides_presentation:
    incremental: true
    logo: image/logo.gif
    self_contained: yes
    slidy_presentation: null
    transition: faster
    widescreen: yes
---

# Preface
## What are covered

*Fall*

* A overview of R
* Data manipulation (input/output, row/column selections, etc.)
* Quantitative Analysis
* Basic Data Visualization


# Data Input and Manipulation
## Input default data types{.build}

* Default data types: .Rds, .Rdata(.Rda)

```{r eval=FALSE}
load("<FileName>.RData")

df_txt <- read.table("<FileName>.txt")
df_csv <- read.csv("<FileName>.csv")

```

* Some data are already embedded in R. To call them, use `data()`, e.g.

```{r eval=FALSE}
data(mtcars)
```


## Input data with packages
```{r eval=FALSE}
# SPSS, Stata, SAS
library(haven)
df_spss <- read_spss("<FileName>.sav")
df_stata <- read_dta("<FileName>.dta") 
df_sas <- read_sas("<FileName>.sas7bdat")  

# Excel sheets
library(readxl)
df_excel <- read_excel("<FileName>.xls");read_excel("<FileName>.xlsx") 

# JavaScript Object Notation 
library(rjson)
df_json <- fromJSON(file = "<FileName>.json" )

# XML/Html
df_xml <- xmlTreeParse("<url>")
df_html <- readHTMLTable(url, which=3)

```


## Output data{.build}

* Save in a R dataset (`.RData`) 

```{r eval = F}
save(object, file = "./Data/mydata.Rdata")
```

* Save as `.csv`

```{r eval = F}
write.csv(object, file = "mydata.csv")
```

* Save as `.feather` (<span style="color:purple">Tip</span>)

```{r eval=F}
feather::write_feather(mydata, "mydata.feather")
```

<div class="notes">
Feather is a fast, lightweight, and easy-to-use binary file format for storing data frames, which can be read by both R and Python.
See more details in [Feather](https://blog.rstudio.org/2016/03/29/feather/).
</div>


## Manipulate the data{.build}
* let's call a dataset first,

```{r}
data(mtcars)
```

* Variable numbers and Observations

```{r}
ncol(mtcars);names(mtcars)
nrow(mtcars)
```


## Have a glimpse.
```{r}
dplyr::glimpse(mtcars)
```

----

```{r}
head(mtcars) # show the first six lines of mtcars
```

## Let's zoom in!{.build}
* locate a specific row, column, or cell of data: `data[row#, col#]` or `data["rowName","colName"]`. 

```{r}
mtcars[1:2,3:4] # show first and the second rows of the third and fourth columns
```

```{r eval=FALSE}
mtcars[ ,"mpg"] # show the column "mpg"
mtcars[ ,"mpg"][3]
```

----

Select with special conditions

```{r}
mtcars[mtcars$mpg < 20,][1,] # show the first rows which mpg are below 5.
```

Create new rows/columns

```{r}
mtcars$id <- seq(1:nrow(mtcars))
```


## Let's generalize!{.build}
* Summarise vector in categories

```{r}
unique(mtcars$cyl)
table(mtcars$cyl)
```

----

For a dataset or a numeric vector

```{r}
summary(mtcars$cyl)
```

One can use `mean`, `sd`, `max`, `min`, etc. to extract specific descriptive statistics.

```{r}
mean(mtcars$cyl)
```

## Let's create!{.build}
* Create a variable into the dataset (<span style="color:purple">Tip</span>)

```{r}
mtcars$newvar <- c(1:nrow(mtcars)) # create an "ID" variable
mtcars$newvar
```

<div class="notes">
Obviously, variables can be immediately overwrite without any specific setting. 

It is convenient but also <span style="color:red">risky</span>.
</div>

* Remove a variable from the dataset

```{r}
mtcars$newvar <- NULL
mtcars$newvar
```

----

Remove variable, result, function, or data from the environment

```{r eval=FALSE}
rm(x)
```

Recode a variable: e.g., numeric to binary, mpg > mean, 1, otherwise 0

```{r eval=FALSE}
# Method I
mtcars$newvar[mtcars$mpg > mean(mtcars$mpg)] <- 1
mtcars$newvar[mtcars$mpg <= mean(mtcars$mpg)] <- 0

# Method II
mtcars$newvar <- ifelse(mtcars$mpg > mean(mtcars$mpg), 1, 0) # overwrite the NAs
```


## Wrap Up 
* Input/output: `load()`/`read.`series and `save()`/`write.`series
* A glimpse of data: `head()` or `dplyr::glimpse`
* Description: `summary()`, `table()`
    + More specific: `mean`, `sd`, `max`, `min`, etc.
* Manipulation: 
    + create: `mtcars$newvar <- c(1:nrow(mtcars))`
    + Remove: `mtcars$newvar <- NULL`; `rm()`
    + Recode: `recodevar[<condition>] <- <new value>`
* There are also [`apply` family](http://www.r-bloggers.com/r-tutorial-on-the-apply-family-of-functions/) functions for with batching management of data.


## Next lecture: Hypothsis test

<div class="centered">
![](http://mathsupport.mas.ncl.ac.uk/images/d/d0/95contint.gif)
</div>


## See you then ~

<div class = "center">
![](http://rescuethepresent.net/tomandjerry/files/2016/05/16-thanks.gif)
</div>


## External Sources
* My email: [yue-hu-1@uiowa.edu](mailto: yue-hu-1@uiowa.edu)

* Workshops: http://ppc.uiowa.edu/node/3608
* Consulting service: http://ppc.uiowa.edu/node/3385/
* Q&A Blogs: 
    + http://stackoverflow.com/questions/tagged/r
    + https://stat.ethz.ch/mailman/listinfo/r-help

* Blog for new stuffs: http://www.r-bloggers.com/

* Graph Blogs:
    + http://www.cookbook-r.com/Graphs/
    + http://shiny.stat.ubc.ca/r-graph-catalog/