01-notes.Rmd

---
layout: topic
title: Intro to R (notes)
author: Data Carpentry contributors
minutes: 45
---

```{r, echo=FALSE}
knitr::opts_chunk$set(results='hide', fig.path='img/r-lesson-')
```

## R and RStudio

### R

- Free and open source general statistical programming language
- Broad statistical and graphics functions
- Huge community of add-on packages
- Not just for statistics, but also general purpose programming.

### RStudio

- Integrated Development Environment (IDE) for R
- Separate program, developed by a company but also free and open
  source.
- By far the best thing to use when learning R.

### RStudio orientation

Four panes (can be re-organized, but always these four panes)

- Scripts (top-left)
- R console (bottom-left)
- Environment/history (top-right)
- Files/plots/help/etc (bottom-right)

## R as a calculator

- `Ctrl-Shift-2` to focus only on the R console.
- `Ctrl-Shift-0` to get back.

- Explain `>` prompt and do some calculations

```{r add, results="markup"}
2016 - 1969
```

```{r calculations, eval=FALSE}
4*6
4/6
4^6
log(4)
log10(4)
```

- Explain that `log` is a function with two arguments.

## Getting help

- Pause for a moment after typing `log`
- Type `?log`

## Need for scripts

- Want to save what we're doing
- Really want to organize the scripts and data for a project

### Create a new R project

- File → New Project → Existing directory → Browse for folder
  → Create project

### Create an R script

- File → New File → R script
- Save it in the `Code/` directory, `data-carpentry-script.R`


## Typing into the script and pasting to console

- Type some stuff into the script

```{r rscript}
# R intro

2016 - 1969
```

- Paste to console with <kbd>`Ctrl`</kbd> + <kbd>`Enter`</kbd>

- Explain comment sign, `#`

## Assigment operator

```{r assign}
age <- 2016 - 1969
```

- Paste into console.
- Can also use `=`, but recommend `<-`.
- Can use <kbd>Alt</kbd> + <kbd>-</kbd>

### Using the stuff you assigned

```{r sqrt}
sqrt(age)
```

```{r more_assign}
sqrt_age <- sqrt(age)
round(sqrt_age)
round(sqrt_age, 2)
```

### Object names

- explicit but not too long
- can't start with a number
- there are some names not allowed (`if`, `for`, `else`)
- other names you can but shouldn't use (`c`, `data`, `mean`)
- Can use `.`, but best not to

- Reminder: The last round shows a function with 2 arguments and order matters.

### Named arguments

- Named arguments useful for commands that have lots of them as order can get confusing
- Can then switch them
- Generally the first argument or two will be entered positionally and then later ones w/name
- Names can be found using help methods from earlier

```{r round_w_named_args}
round(x = sqrt_age, digits = 2)
round(digits = 2,x = sqrt_age)
round(sqrt_age, digits = 2)
```

- We may occasionally used named arguments in these lessons

### Challenge

What is the value of `y` after doing the following?

```{r}
x <- 50
y <- x * 2
x <- 80
```

- Objects don't get linked together

## Plunge into Survey Data

```{r read_csv_from_web}
surveys <- read.csv("http://kbroman.org/datacarp/portal_data_joined.csv")
```


### Objects in workspace

- `ls()`
- Environment panel


## Data frames

Use `head()` to view the first few rows.

```{r head}
head(surveys)
```

Use `tail()` to view the last few rows.


```{r tail}
tail(surveys)
```

Use `str()` to look at the structure of the data.

```{r str}
str(surveys)
```

Talk about integers and factors; more on factors later.


### Challenge

Study the output of `str(surveys)`. How are the missing values being treated?

<!-- end challenge -->


### Another summary

```{r summary}
summary(surveys)
```

- six-number summary for the numeric columns
- table for the factor columns


## Inspecting data frames

- `dim()`
- `nrow()`
- `ncol()`
- `names()`
- `colnames()`
- `rownames()`


## Download and then load the data

We first download the file:

```{r download_file, eval=FALSE}
download.file("http://kbroman.org/datacarp/portal_data_joined.csv",
              "CleanData/portal_data_joined.csv")
```

We then load them into R:

```{r load_from_local_file, eval=FALSE}
surveys <- read.csv("CleanData/portal_data_joined.csv")
```

## Indexing, Sequences, and Subsetting

We can pull out parts of a data frame using square brackets.  We need
to provide two values: row and column, with a comma between them.

For example, to get the element in the 1st row, 1st column:

```{r one_one_element}
surveys[1,1]
```

To get the element in the 2nd row, 7th column:

```{r two_seven_element}
surveys[2,7]
```

To get the entire 2nd row, leave the column part blank:

```{r second_row}
surveys[2,]
```

And to get the entire 7th column, leave the row part blank:

```{r seventh_column}
sex <- surveys[,7]
```

You can also refer to columns by name, in multiple ways.

```{r grab_sex, eval = FALSE}
sex <- surveys[, "sex"]
sex <- surveys$sex
sex <- surveys[["sex"]]
```

When we pull out a single column, the result is a "vector". We can
again use square brackets to pull out individual values, but providing
a single number.

```{r grab_individual_sexes}
sex[1]
sex[10000]
```


### Slices

You can pull out larger slices from the vector by providing vectors of
indices.  You can use the `c()` function to create a vector.

```{r grab_some_sexes}
c(1,3,5)
sex[c(1,3,5)]
```

To pull out larger slices, it's
helpful to have ways of creating sequences of numbers.

First, the operator `:` gives you a sequence of consecutive values.

```{r}
1:10
10:1
5:8
```

`seq` is more flexible.

```{r}
seq(1, 10, by=2)
seq(5, 10, length.out=3)
seq(50, by=5, length.out=10)
seq(1, 8, by=3) # sequence stops to stay below upper limit
seq(10, 2, by=-2)  # can also go backwards
```


To get slices of our data frame, we can include a vector for the row
or column indexes (or both)

```{r}
surveys[1:3, 7]   # first three elements in the 7th column
surveys[1, 1:3]   # first three columns in the first row
surveys[2:4, 6:7] # rows 2-4, columns 6-7
```


### Challenge

The function `nrow()` on a `data.frame` returns the number of rows.

Use `nrow()`, in conjuction with `seq()` to create a new `data.frame` called
`surveys_by_10` that includes every 10th row of the survey data frame
starting at row 10 (10, 20, 30, ...)

<!-- end challenge -->



## Missing data

Missing data value, `NA`. Different from character string `"NA"`.

```{r create_vec_with_missing}
heights <- c(2, 4, 4, NA, 6)
```

Many functions will return `NA` if there is any missing data. Need to
use `na.rm=TRUE` to strip off missing values.

```{r na_rm}
mean(heights)
max(heights)
mean(heights, na.rm = TRUE)
max(heights, na.rm = TRUE)
```

If your data include missing values, you may want to become familiar with the
functions `is.na()` and `na.omit()`.


```{r extract_non_missing}
## Extract those elements which are not missing values.
heights[!is.na(heights)]
## shortcut to that
na.omit(heights)
```


### Treating blanks as missing

- blank character strings might be best treated as missing values.
- I prefer not to have blank fields, but it's difficult to choose a
universal missing value code.
- Use `na.strings` in `read.csv()` to indicate missing value codes.

```{r read.csv_blanks_missing, eval=FALSE}
surveys_noblanks <- read.csv("CleanData/portal_data_joined.csv", na.strings="")
```



## Factors

Factors represent categorical data. They can be useful, but they can
be a pain. And understanding them is necessary for statistical analysis and for
plotting.

Factors are stored as integers, and have labels associated with these unique
integers. While factors look (and often behave) like character vectors, they are
actually integers under the hood, and you need to be careful when treating them
like strings.

Once created, factors can only contain a pre-defined set of values, known as
*levels*. By default, R always sorts *levels* in alphabetical order. For
instance, if you use `factor()` to create a factor with 2 levels:

```{r create_sex_vector, purl=TRUE}
sex <- factor(c("male", "female", "female", "male"))
```

R will assign `1` to the level `"female"` and `2` to the level `"male"` (because
`f` comes before `m`, even though the first element in this vector is
`"male"`). You can check this by using the function `levels()`, and check the
number of levels using `nlevels()`:

```{r levels_of_sex}
levels(sex)
nlevels(sex)
```

Sometimes, the order of the factors does not matter, other times you might want
to specify a particular order.

```{r food_factor, purl=TRUE}
food <- factor(c("low", "high", "medium", "high", "low", "medium", "high"))
levels(food)
food <- factor(food, levels=c("low", "medium", "high"))
levels(food)
```


### Converting factors

If you need to convert a factor to a character vector, you use
`as.character(x)`.

Converting factors where the levels appear as numbers (such as concentration
levels) to a numeric vector is a little trickier.  One method is to convert
factors to characters and then numbers.
function. Compare:

```{r convert_factor, purl=TRUE}
f <- factor(c(1, 5, 10, 2))
as.numeric(f)               ## wrong! and there is no warning...
as.numeric(as.character(f)) ## works...
```

### Challenge

The function `table()` tabulates observations.

```{r table, eval=FALSE}
expt <- c("treat1", "treat2", "treat1", "treat3", "treat1",
          "control", "treat1", "treat2", "treat3")
expt <- factor(expt)
table(expt)
```

* In which order are the treatments listed?
* How can you recreate this table with "`control`" listed last instead
of first?

<!-- end challenge -->

<!---
```{r correct-order}
## Answers
##
## * The treatments are listed in alphabetical order because they are factors.
## * By redefining the order of the levels
expt <- c("treat1", "treat2", "treat1", "treat3", "treat1",
          "control", "treat1", "treat2", "treat3")
expt <- factor(expt, levels=c("treat1", "treat2", "treat3", "control"))
table(expt)
```
--->

### stringsAsFactors

The default when reading in data with `read.csv()`, columns with text
get turned into factors.

You can avoid this with the argument `stringsAsFactors=FALSE`.

```{r read_csv_stringsAsFactors, eval=FALSE}
surveys_chr <- read.csv("CleanData/portal_data_joined.csv", stringsAsFactors=FALSE)
```

Then when you look at the result of `str()`, you'll see that the
previously factor columns are now `chr`.

```{r str_on_that, eval=FALSE}
str(surveys_chr)
```




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