---
title: "Lab 1.1: Introduction to _R_"
output:
  BiocStyle::html_document:
    toc: true
vignette: >
  % \VignetteIndexEntry{Lab 1.1: Introduction to R}
  % \VignetteEngine{knitr::rmarkdown}
---

```{r style, echo = FALSE, results = 'asis'}
BiocStyle::markdown()
```

```{r setup, echo=FALSE}
knitr::opts_chunk$set(
    eval=as.logical(Sys.getenv("KNITR_EVAL", "TRUE")),
    cache=as.logical(Sys.getenv("KNITR_CACHE", "TRUE"))
)
```

Original Authors: Martin Morgan, Sonali Arora<br />
Presenting Authors: [Martin Morgan][], [Lori Shepherd][]</br >
Date: 22 July, 2019</br >
Back: [Monday labs](lab-1-intro-to-r-bioc.html)

[Martin Morgan]: mailto: Martin.Morgan@RoswellPark.org
[Lori Shepherd]: mailto: Lori.Shepherd@RoswellPark.org

**Objective**: Gain confidence working with base R commands and data
  structures.
  
**Lessons learned**:

- Basic data input
- Working with data.frames
- Subsetting, including working with NA values and factors
- Summary statistics
- Visualization using base R and [ggplot2][]

# R

## Language and environment for statistical computing and graphics

- Full-featured programming language
- Interactive and *interpretted* -- convenient and forgiving
- Coherent, extensive documentation
- Statistical, e.g. `factor()`, `NA`
- Extensible -- CRAN, Bioconductor, github, ...

## Vector, class, object

- Efficient _vectorized_ calculations on 'atomic' vectors `logical`,
  `integer`, `numeric`, `complex`, `character`, `byte`
- Atomic vectors are building blocks for more complicated _objects_
  - `matrix` -- atomic vector with 'dim' attribute
  - `data.frame` -- list of equal length atomic vectors
- Formal _classes_ represent complicated combinations of vectors,
  e.g., the return value of `lm()`, below

## Function, generic, method

- Functions transform inputs to outputs, perhaps with side effects,
  e.g., `rnorm(1000)`
  - Argument matching first by name, then by position
  - Functions may define (some) arguments to have default values
- _Generic_ functions dispatch to specific _methods_ based on class of
  argument(s), e.g., `print()`.
- Methods are functions that implement specific generics, e.g.,
  `print.factor`; methods are invoked _indirectly_, via the generic.
- Many but not all functions able to manipulate a particular class are
  methods, e.g., `abline()` used below is a plain-old-funciton.

## Programming

Iteration:

- `lapply()`

    ```{r lapply-args}
    args(lapply)
    ```

   - Meaning: for a vector `X` (typically a `list()`), apply a
     function `FUN` to each vector element, returning the result as
     a **l**ist. `...` are additional arguments to `FUN`.
   - `FUN` can be built-in, or a user-defined function

    ```{r lapply-eg}
    lst <- list(a=1:2, b=2:4)
    lapply(lst, log)      # 'base' argument default; natural log
    lapply(lst, log, 10)  # '10' is second argument to 'log()', i.e., log base 10
    ```

   - `sapply()` -- like `lapply()`, but simplify the result to a
     vector, matrix, or array, if possible.
   - `vapply()` -- like `sapply()`, but requires that the return
     type of `FUN` is specified; this can be safer -- an error when
     the result is of an unexpected type.

- `mapply()` (also `Map()`)

    ```{r}
    args(mapply)
    ```

  - `...` are one or more vectors, recycled to be of the same
    length. `FUN` is a function that takes as many arguments as
    there are components of `...`. `mapply` returns the result of
    applying `FUN` to the elements of the vectors in `...`.

    ```{r mapply-eg}
    mapply(seq, 1:3, 4:6, SIMPLIFY=FALSE) # seq(1, 4); seq(2, 5); seq(3, 6)
    ```

  - `apply()`

    ```{r apply}
    args(apply)
    ```

  - For a matrix or array `X`, apply `FUN` to each `MARGIN`
    (dimension, e.g., `MARGIN=1` means apply `FUN` to each row,
    `MARGIN=2` means apply `FUN` to each column)

- Traditional iteration programming constructs `repeat {}`, `for () {}`

  - Almost always more error-prone, less efficient, and harder to 
    understand than `lapply()` !

Conditional

```{r, eval=FALSE}
if (test) {
    ## code if TEST == TRUE
} else {
    ## code if TEST == FALSE
}
```

Functions (see table below for a few favorites)

- Easy to define your own functions

```{r myfun}
fun <- function(x) {
    length(unique(x))
}
## list of length 5, each containsing a sample (with replacement) of letters
lets <- replicate(5, sample(letters, 50, TRUE), simplify=FALSE)
sapply(lets, fun)
```

## Introspection & Help

Introspection

- General properties, e.g., `class()`, `str()`
- Class-specific properties, e.g., `dim()`

Help

- `?"print"`: help on the generic print
- `?"print.data.frame"`: help on print method for objects of class
    data.frame.
- `help(package="GenomeInfoDb")`
- `browseVignettes("GenomicRanges")`
- `methods("plot")`
- `methods(class="lm")`

## Examples

_R_ vectors, vectorized operations, `data.frame()`, formulas,
functions, objects, class and method discovery (introspection).

```{r}
x <- rnorm(1000)                     # atomic vectors
y <- x + rnorm(1000, sd=.5)
df <- data.frame(x=x, y=y)           # object of class 'data.frame'
plot(y ~ x, df)                      # generic plot, method plot.formula
fit <- lm(y ~x, df)                  # object of class 'lm'
methods(class=class(fit))            # introspection
anova(fit)
plot(y ~ x, df)                      # methods(plot); ?plot.formula
abline(fit, col="red", lwd=3, lty=2) # a function, not generic.method
```

Programming example -- group 1000 SYMBOLs into GO identifiers

<!--
```{r lapply-setup, echo=FALSE}
fl <- "symgo.csv"
```
-->
```{r lapply-user-setup, eval=FALSE}
## example data
fl <- file.choose()      ## symgo.csv
```
```{r lapply}
symgo <- read.csv(fl, row.names=1, stringsAsFactors=FALSE)
head(symgo)
dim(symgo)
length(unique(symgo$SYMBOL))
## split-sapply
go2sym <- split(symgo$SYMBOL, symgo$GO)
len1 <- sapply(go2sym, length)          # compare with lapply, vapply
## built-in functions for common actions
len2 <- lengths(go2sym)
identical(len1, len2)
## smarter built-in functions, e.g., omiting NAs
len3 <- aggregate(SYMBOL ~ GO, symgo, length)
head(len3)
## more fun with aggregate()
head(aggregate(GO ~ SYMBOL, symgo, length))
head(aggregate(SYMBOL ~ GO, symgo, c))
## your own function -- unique, lower-case identifiers
uidfun  <- function(x) {
    unique(tolower(x))
}
head(aggregate(SYMBOL ~ GO , symgo, uidfun))
## as an 'anonymous' function
head(aggregate(SYMBOL ~ GO, symgo, function(x) {
    unique(tolower(x))
}))
```

# Case studies

These case studies serve as refreshers on _R_ input and manipulation
of data.

## ALL phenotypic data

Input a file that contains ALL (acute lymphoblastic leukemia) patient
information

```{r echo=TRUE, eval=FALSE}
fname <- file.choose()   ## "ALLphenoData.tsv"
stopifnot(file.exists(fname))
pdata <- read.delim(fname)
```
<!--
```{r echo=FALSE}
fname <- "ALLphenoData.tsv"
stopifnot(file.exists(fname))
pdata <- read.delim(fname)
```
-->
Check out the help page `?read.delim` for input options, and explore
basic properties of the object you've created, for instance...

```{r ALL-properties}
class(pdata)
colnames(pdata)
dim(pdata)
head(pdata)
summary(pdata$sex)
summary(pdata$cyto.normal)
```

Remind yourselves about various ways to subset and access columns of a
data.frame

```{r ALL-subset}
pdata[1:5, 3:4]
pdata[1:5, ]
head(pdata[, 3:5])
tail(pdata[, 3:5], 3)
head(pdata$age)
head(pdata$sex)
head(pdata[pdata$age > 21,])
```

It seems from below that there are 17 females over 40 in the data set,
but when sub-setting `pdata` to contain just those individuals 19 rows
are selected. Why? What can we do to correct this?

```{r ALL-subset-NA}
idx <- pdata$sex == "F" & pdata$age > 40
table(idx)
dim(pdata[idx,])
```

Use the `mol.biol` column to subset the data to contain just
individuals with 'BCR/ABL' or 'NEG', e.g.,

```{r ALL-BCR/ABL-subset}
bcrabl <- pdata[pdata$mol.biol %in% c("BCR/ABL", "NEG"),]
```

The `mol.biol` column is a factor, and retains all levels even after
subsetting. How might you drop the unused factor levels?

```{r ALL-BCR/ABL-drop-unused}
bcrabl$mol.biol <- factor(bcrabl$mol.biol)
```

The `BT` column is a factor describing B- and T-cell subtypes

```{r ALL-BT}
levels(bcrabl$BT)
```

How might one collapse B1, B2, ... to a single type B, and likewise for T1, T2, ..., so there are only two subtypes, B and T

```{r ALL-BT-recode}
table(bcrabl$BT)
levels(bcrabl$BT) <- substring(levels(bcrabl$BT), 1, 1)
table(bcrabl$BT)
```

Use `xtabs()` (cross-tabulation) to count the number of samples with
B- and T-cell types in each of the BCR/ABL and NEG groups

```{r ALL-BCR/ABL-BT}
xtabs(~ BT + mol.biol, bcrabl)
```

Use `aggregate()` to calculate the average age of males and females in
the BCR/ABL and NEG treatment groups.

```{r ALL-aggregate}
aggregate(age ~ mol.biol + sex, bcrabl, mean)
```

Use `t.test()` to compare the age of individuals in the BCR/ABL versus
NEG groups; visualize the results using `boxplot()`. In both cases,
use the `formula` interface. Consult the help page `?t.test` and re-do
the test assuming that variance of ages in the two groups is
identical. What parts of the test output change?

```{r ALL-age}
t.test(age ~ mol.biol, bcrabl)
boxplot(age ~ mol.biol, bcrabl)
```

## Weighty matters

This case study is a second walk through basic data manipulation and
visualization skills.  We use data from the US Center for Disease
Control's Behavioral Risk Factor Surveillance System ([BRFSS][])
annual survey. Check out the web page for a little more
information. We are using a small subset of this data, including a
random sample of 10000 observations from each of 1990 and 2010.

Input the data using `read.csv()`, creating a variable `brfss` to hold
it.  Use `file.choose()` to locate the data file BRFSS-subset.csv

```{r echo=TRUE, eval=FALSE}
fname <- file.choose()   ## BRFSS-subset.csv
stopifnot(file.exists(fname))
brfss <- read.csv(fname)
```
<!--
```{r echo=FALSE}
fname <- "BRFSS-subset.csv"
stopifnot(file.exists(fname))
brfss <- read.csv(fname)
```
-->
**Base plotting functions**

1. Explore the data using `class()`, `dim()`, `head()`, `summary()`,
   etc. Use `xtabs()` to summarize the number of males and females in
   the study, in each of the two years.

2. Use `aggregate()` to summarize the average weight in each sex and
   year.

3. Create a scatterplot showing the relationship between the square
   root of weight and height, using the `plot()` function and the
   `main` argument to annotate the plot. Note the transformed
   Y-axis. Experiment with different plotting symbols (try the command
   `example(points)` to view different points).

    ```{r brfss-simple-plot}
    plot(sqrt(Weight) ~ Height, brfss, main="All Years, Both Sexes")
    ```

4. Color the female and male points differently. To do this, use the
   `col` argument to `plot()`. Provide as a value to that argument a
   vector of colors, subset by `brfss$Sex`.

5. Create a subset of the data containing only observations from
   2010.

    ```{r brfss-subset}
    brfss2010 <- brfss[brfss$Year == "2010", ]
    ```

6. Create the figure below (two panels in a single figure). Do this by
   using the `par()` function with the `mfcol` argument before calling
   `plot()`. You'll need to create two more subsets of data, perhaps
   when you are providing the data to the function `plot`.

    ```{r brfss-pair-plot}
    opar <- par(mfcol=c(1, 2))
    plot(sqrt(Weight) ~ Height, brfss2010[brfss2010$Sex == "Female", ],
         main="2010, Female")
    plot(sqrt(Weight) ~ Height, brfss2010[brfss2010$Sex == "Male", ],
         main="2010, Male")
    par(opar)                           # reset 'par' to original value
    ```

7. Plotting large numbers of points means that they are often
   over-plotted, potentially obscuring important patterns. Experiment
   with arguments to `plot()` to address over-plotting, e.g.,
   `pch='.'` or `alpha=.4`. Try using the `smoothScatter()` function
   (the data have to be presented as `x` and `y`, rather than as a
   formula). Try adding the [hexbin][] library to your R session
   (using `library()`) and creating a `hexbinplot()`.

**ggplot2 graphics**

1. Create a scatterplot showing the relationship between the square
   root of weight and height, using the `r CRANpkg("ggplot2")`
   library, and the annotate the plot. Two equivalent ways to create
   the plot are show in the solution.
    ```{r ggplot2-brfss-simple-plot}
    library(ggplot2)

    ## 'quick' plot
    qplot(Height, sqrt(Weight), data=brfss)

    ## specify the data set and 'aesthetics', then how to plot
    ggplot(brfss, aes(x=Height, y=sqrt(Weight))) +
        geom_point()
    ```
   `qplot()` gives us a warning which states that it has removed rows
   containing missing values. This is actually very helpful because we
   find out that our dataset contains `NA`'s and we can take a design
   decision here about what we'd like to do these `NA`'s. We can find
   the indicies of the rows containing `NA` using `is.na()`, and count
   the number of rows with `NA` values using `sum()`:
    ```{r ggplot2-na-in-dataset}
    sum(is.na(brfss$Height))
    sum(is.na(brfss$Weight))
    drop <- is.na(brfss$Height) | is.na(brfss$Weight)
    sum(drop)
    ```
   Remove the rows which contain `NA`'s in Height and Weight.
    ```{r ggplot2-remove-na}
    brfss <- brfss[!drop,]
    ```
   Plot is annotated with
    ```{r ggplot2-annotate}
    qplot(Height, sqrt(Weight), data=brfss) +
        ylab("Square root of Weight") + 
            ggtitle("All Years, Both Sexes")
    ```

2. Color the female and male points differently.

    ```{r ggplot2-color}
    ggplot(brfss, aes(x=Height, y=sqrt(Weight), color=Sex)) + 
        geom_point()
    ```
   One can also change the shape of the points for the female and male
   groups

    ```{r ggplot2-shape}
    ggplot(brfss, aes(x=Height, y = sqrt(Weight), color=Sex, shape=Sex)) + 
        geom_point()
    ```
   or plot Male and Female in different panels using `facet_grid()`
    ```{r ggplot2-shape-facet}
    ggplot(brfss, aes(x=Height, y = sqrt(Weight), color=Sex)) + 
        geom_point() +
            facet_grid(Sex ~ .)
    ```

3. Create a subset of the data containing only observations from 2010
   and make density curves for male and female groups. Use the `fill`
   aesthetic to indicate that each sex is to be calculated separately,
   and `geom_density()` for the density plot.

    ```{r ggplot2-subset-facet}
    brfss2010 <- brfss[brfss$Year == "2010", ]
    ggplot(brfss2010, aes(x=sqrt(Weight), fill=Sex)) +
        geom_density(alpha=.25)
    ```

4. Plotting large numbers of points means that they are often
   over-plotted, potentially obscuring important patterns. Make the
   points semi-transparent using alpha. Here we make them 60%
   transparent. The solution illustrates a nice feature of ggplot2 --
   a partially specified plot can be assigned to a variable, and the
   variable modified at a later point.

    ```{r ggplot2-transparent}
    sp <- ggplot(brfss, aes(x=Height, y=sqrt(Weight)))
    sp + geom_point(alpha=.4)
    ```

5. Add a fitted regression model to the scatter plot.

    ```{r ggplot2-regression}
    sp + geom_point() + stat_smooth(method=lm)
    ```
   By default, `stat_smooth()` also adds a 95% confidence region for
   the regression fit. The confidence interval can be changed by
   setting level, or it can be disabled with `se=FALSE`.

    ```{r ggplot2-regression-2, eval=FALSE}
    sp + geom_point() + stat_smooth(method=lm + level=0.95)
    sp + geom_point() + stat_smooth(method=lm, se=FALSE)
    ```

6. How do you fit a linear regression line for each group? First we'll
   make the base plot object sps, then we'll add the linear regression
   lines to it.

    ```{r ggplot2-regression-bygroup}
    sps <- ggplot(brfss, aes(x=Height, y=sqrt(Weight), colour=Sex)) +
        geom_point() +
            scale_colour_brewer(palette="Set1")
    sps + geom_smooth(method="lm")
    ```

[BRFSS]: http://www.cdc.gov/brfss/

[biocViews]: http://bioconductor.org/packages/BiocViews.html#___Software
[AnnotationData]: http://bioconductor.org/packages/BiocViews.html#___AnnotationData

[aprof]: http://cran.r-project.org/web/packages/aprof
[hexbin]: http://cran.r-project.org/web/packages/hexbin
[lineprof]: https://github.com/hadley/lineprof
[microbenchmark]: http://cran.r-project.org/web/packages/microbenchmark
[ggplot2]: http://cran.r-project.org/web/packages/ggplot2

[AnnotationDbi]: http://bioconductor.org/packages/AnnotationDbi
[BSgenome]: http://bioconductor.org/packages/BSgenome
[Biostrings]: http://bioconductor.org/packages/Biostrings
[CNTools]: http://bioconductor.org/packages/CNTools
[ChIPQC]: http://bioconductor.org/packages/ChIPQC
[ChIPpeakAnno]: http://bioconductor.org/packages/ChIPpeakAnno
[DESeq2]: http://bioconductor.org/packages/DESeq2
[DiffBind]: http://bioconductor.org/packages/DiffBind
[GenomicAlignments]: http://bioconductor.org/packages/GenomicAlignments
[GenomicRanges]: http://bioconductor.org/packages/GenomicRanges
[IRanges]: http://bioconductor.org/packages/IRanges
[KEGGREST]: http://bioconductor.org/packages/KEGGREST
[PSICQUIC]: http://bioconductor.org/packages/PSICQUIC
[Rsamtools]: http://bioconductor.org/packages/Rsamtools
[ShortRead]: http://bioconductor.org/packages/ShortRead
[VariantAnnotation]: http://bioconductor.org/packages/VariantAnnotation
[VariantFiltering]: http://bioconductor.org/packages/VariantFiltering
[VariantTools]: http://bioconductor.org/packages/VariantTools
[biomaRt]: http://bioconductor.org/packages/biomaRt
[cn.mops]: http://bioconductor.org/packages/cn.mops
[h5vc]: http://bioconductor.org/packages/h5vc
[edgeR]: http://bioconductor.org/packages/edgeR
[ensemblVEP]: http://bioconductor.org/packages/ensemblVEP
[limma]: http://bioconductor.org/packages/limma
[metagenomeSeq]: http://bioconductor.org/packages/metagenomeSeq
[phyloseq]: http://bioconductor.org/packages/phyloseq
[snpStats]: http://bioconductor.org/packages/snpStats

[org.Hs.eg.db]: http://bioconductor.org/packages/org.Hs.eg.db
[TxDb.Hsapiens.UCSC.hg38.knownGene]: http://bioconductor.org/packages/TxDb.Hsapiens.UCSC.hg38.knownGene
[BSgenome.Hsapiens.UCSC.hg38]: http://bioconductor.org/packages/BSgenome.Hsapiens.UCSC.hg38

# End matter

## Session Info

```{r}
sessionInfo()
```

## Acknowledgements

Research reported in this tutorial was supported by the National Human
Genome Research Institute and the National Cancer Institute of the
National Institutes of Health under award numbers U41HG004059 and
U24CA180996.

This project has received funding from the European Research Council
(ERC) under the European Union's Horizon 2020 research and innovation
programme (grant agreement number 633974)