Introduction to R

<img src="img/IEU-logo-colour.png" width="50%">
]

---

## Things you can do with R

---

## Getting started
<hr>

what, why, downloading, opening, using, rstudio, help, errors

---

## Getting started: what

* R is a statistical programming language (based on S)
* R is open source - researchers develop packages to 
    implement new statistical methods, plots or applications
* R runs on Windows, MacOS and UNIX

John Chambers
]
.ii[
<img src="img/dirk-eddelbuettel.jpg" width="50%" >

Dirk Eddelbuettel
]]

---

## Getting started: why

... rather than S, STATA, SPSS, etc

* R is free!

* R is flexible

* R is good at handling large datasets and multiple objects

* R has good plotting tools and packages for statistical analysis

---

## Getting started: installing

* Visit https://www.r-project.org and click "Download R"

* Choose your nearest CRAN mirror (such as http://www.stats.bris.ac.uk/R)

* Choose "Download R for [Windows/Mac/Linux]"

* Choose "base" for Windows and click on "Download R 3.5.2 for Windows"

* Choose "R-3.5.2.pkg" for Mac

* Once the .exe (Windows) or .pkg (Mac) have downloaded, run and install

---

## Getting started: opening

* Click "Start" | "All Programs" | "R" | "R x64 3.6.0"

---

## Getting started: navigating

* The **R console** initially just shows information 
    on the version of R running, how to get help and how to quit

* At the bottom of the console is the **R command line** where 
    you can tell R what to do
]

---

## Getting started: menus

.ii[
* A **script** is sequence of R commands. 
Creating or opening a script will open
the script in a text editor.

* A **workspace** includes all the objects
which are in R’s memory at a given time.
These can include data or results. To see
what is in the current workspace type ls().

* The **history** includes all the commands
which are displayed during an R session
]

---

## Getting started: RStudio

* RStudio makes R easier to use
* It includes a code editor, debugging & visualization tools
* https://www.rstudio.com

---

## Getting started: RStudio script window

In the script window, you can **save a script** (disk image), **run** selected commands ("Run") and **run** the entire script ("Script").

---

## Getting started: help!

* Use the command `help(topic)` or `?topic`
* In R this will open a web browser
* In RStudio this will open the Help tab

---

## Getting started: `help(mean)`
 
--

**Description**

Generic function for the (trimmed) arithmetic mean.
]

`mean(x, ...)`

`## Default S3 method:` 
`mean(x, trim = 0, na.rm = FALSE, ...)`
]

|||
:-:|:-:
|`x`|An R object. Currently there are methods for numeric/logical vectors and date, date-time and time interval objects. Complex vectors are allowed for trim = 0, only.|
|`trim`|the fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed. Values of trim outside that range are taken as the nearest endpoint.|
|`na.rm`|a logical value indicating whether NA values should be stripped before the computation proceeds.|
|...|further arguments passed to or from other methods.
]

If `trim` is zero (the default), the arithmetic mean of the values in `x` is computed, as a numeric or complex vector of length one. If `x` is not `logical` (coerced to `numeric`), `numeric` (including `integer`) or `complex`, `NA_real_` is returned, with a warning.
]

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
]

`weighted.mean`, `mean.POSIXct`, `colMeans` for row and column means.
]

`x <- c(0:10, 50)` 
`xm <- mean(x)` 
`c(xm, mean(x, trim = 0.10))`
]

---

## Getting started: error messages

Suppose you type the following but variable 'a' has not been created.
```r
> 10*a
```
--

```r
Error: object 'a' not found
```

R provides informative error messages

---

## Getting started: www.r-project.org

Note:
* R Project | Search
* Help with R
* Manuals
* FAQs

---

## Getting started: forums

* Mailing list archive and forum http://r.789695.n4.nabble.com

* Stack Overflow https://stackoverflow.com

* Google!

---

## Things you can do with R
<hr>

*remember, calculate, store, generate, analyse, visualise, save, repeat, apply, decide, share*

---

## Remember data

A **variable** is a storage location for data with a name.

Recall data ...
```r
> x
[1] 3
> name 
[1] "abcdef"
> nchar(name)
[1] 6
> z
Error: object 'z' not found
```
]

> is.logical(name)
[1] FALSE
```

Change data type ...
```r
> as.character(x)
[1] "3"

> as.numeric("3.14")
[1] 3.14

> as.logical("abc")
[1] NA

> as.logical(x)
[1] TRUE

> as.logical(0)
[1] FALSE
```

]

---

## Calculate

```r
> x + y + 0.8
[1] 8
```
--
```r
> 4*x/2
[1] 6
```
--
```r
> log10(100)
[1] 2
> 2^x 
[1] 8
> sin(pi/2)
[1] 1
> ln(exp(1))
[1] 1
> sqrt(x^2)
[1] 3
```

---

## Store and manipulate sequences of values

A **vector** is a sequence of variables of the same type.

```r
> x <- c(11,12,13,14,15,16,17,18,19,20)
> x
```
--
```r
[1] 11 12 13 14 15 16 17 18 19 20
```
--
```r
> x[3] ## show the third
```
--
```r
[1] 13
```
--
```r
> length(x)
```
--
```r
[1] 10
```
---

## Store and manipulate sequences of values (continued)

Vectors can be subset and combined.

```r
> x[c(1, 5, 7)]
```
--
```r
[1] 11 15 17
```
--
```r
> x[-c(1, 5, 7)]
```
--
```r
[1] 12 13 14 16 18 19 20
```
--
```r
> x[x>13]
```
--
```r
[1] 14 15 16 17 18 19 20
```
--
```r
y <- c("Dog", "Cat")
z <- c(x[1:3], y)
z
```
--
```r
[1] "11" "12" "13" "Dog" "Cat"
```

---

## Calculate with vectors

```r
> x+1
```
--
```r
[1] 12 13 14 15 16 17 18 19 20 21
```
--
```r
> 3*x
```
--
```r
[1] 33 36 39 42 45 48 51 54 57 60
```
--
```r
> x/2
```
--
```r
[1] 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10
```
--
```r
> log10(x)
```
--
```r
[1] 1.041393 1.079181 1.113943 1.146128 1.176091 1.204120 1.230449 1.255273
[9] 1.278754 1.301030
```

---

## Calculate with vectors (continued)

```r
> y <- x + 1
> x + y
```
--
```r
[1] 23 25 27 29 31 33 35 37 39 41
```
--
```r
> x-y
```
--
```r
[1] -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
```
--
```r
> x*y
```
--
```r
[1] 132 156 182 210 240 272 306 342 380 420
```
--
```r
> x/(y-1)
```
--
```r
[1] 1 1 1 1 1 1 1 1 1 1
```

---

## Generate data: `seq()`

```r
> a <- seq(0, 100, by=5)
> a
```
--
```r
[1] 0 5 10 15 20 25 30 35 40 45 50
[12] 55 60 65 70 75 80 85 90 95 100
```
--
```r
> length(a)
```
--
```r
[1] 21
```
--
```r
> a <- seq(0, 100, length=5)
> a
```
--
```r
[1] 0 25 50 75 100
```
---

## Generate data: `rep()`

```r
> b <- c(1, 1, 1, 1, 1)
> b
```
--
```r
[1] 1 1 1 1 1
```
--
```r
> b <- rep(1, 5)
> b
```
--
```r
[1] 1 1 1 1 1
```
--
```r
> rep(c(1,2,3),4)
```
--
```r
[1] 1 2 3 1 2 3 1 2 3 1 2 3
```

---

## Store and manipulate matrices

A **matrix** is a set of elements laid out in rows and columns.

```r
> x <- rep(1, 5)
> y <- c("A","B","A","B","C")
> z <- matrix(c(x, y), nrow=5)
> z
```
--
```r
 [,1][,2]
[1,] "1" "A"
[2,] "1" "B"
[3,] "1" "A"
[4,] "1" "B"
[5,] "1" "C"
```
--
```r
> dim(z) ## dimensions
```
--
```r
[1] 5 2
```

---

## Store and manipulate matrices (continued)

```r
> t(z) ## transpose
```
--
```r
[,1] [,2] [,3] [,4] [,5]
[1,] "1" "1" "1" "1" "1"
[2,] "A" "B" "A" "B" "C"
```
--
```r
> cbind(0:4, z) 
```
--
```r
[1,] "0" "1" "A"
[2,] "1" "1" "B"
[3,] "2" "1" "A"
[4,] "3" "1" "B"
[5,] "4" "1" "C"
```
--
```r
> rbind(c("0","X"), z)
```
--
```r
[1,] "0" "X"
[2,] "1" "A"
[3,] "1" "B"
[4,] "1" "A"
[5,] "1" "B"
[6,] "1" "C"
```

---

## Store and manipulate matrices (continued)

```r
> z[,1]
```
--
```r
[1] "1" "1" "1" "1" "1"
```
--
```r
> z[1,2]
```
--
```r
[1] "A"
```
--
```r
> z[2,]
```
--
```r
[1] "1" "B"
```
--
```r
> z[c(1,3),2]
```
--
```r
[1] "A" "A"
```

---

## Store and manipulate matrices (continued)

```r
> colnames(z) <- c("x", "y")
> rownames(z) <- c("r1", "r2", "r3", "r4", "r5")
> z
```
--
```r
 x y 
r1 "1" "A"
r2 "1" "B"
r3 "1" "A"
r4 "1" "B"
r5 "1" "C"
```
--
```r
> z[1,2]
"A"
```
--
```r
> z['r2','y']
"B"
```
--
```r
> z[,'y']
```
--
```r
 r1 r2 r3 r4 r5 
"A" "B" "A" "B" "C" 
```

---

## Calculate with matrices

```r
> m <- rbind(c(1,2,3),
 c(3,2,1),
 c(5,6,4))
> m
[1,] 1 2 3
[2,] 3 2 1
[3,] 5 6 4
```
--
```r
> m + 1
[1,] 2 3 4
[2,] 4 3 2
[3,] 6 7 5
```
--
```r
> m %*% solve(m) ## multiple m by its inverse

[,1]          [,2]         [,3]
[1,]    1 -2.2e-16 2.2e-16
[2,]    0  1.0e+00 2.2e-16
[3,]    0  0.0e+00 1.0e+00
```

---

## Store and manipulate datasets: lists

A **list** is like a vector, but it can contain
elements of different types.

```r
> y <- diag(2)
> w <- list(x=1, y=y, z="abc")
```
--
```r
w$y
```
--
```r
 [,1] [,2]
[1,] 1 0
[2,] 0 1
```

```r
names(r)
```
--
```r
[1] "x" "y" "z"
```

---

## Store and manipulate datasets: data frames

A **data frame** is like a matrix but the columns can be different types.
You could think of it as a list of vectors each of the same length.

```r
> x <- rep(1, 5)
> y <- c("A","B","A","B","C")
> d <- data.frame(a=x, b=y)
```
--
```r
> d[2,]
 a b
2 1 B
```
--
```r
> d$a
[1] 1 1 1 1 1
```
--
```r
> class(d$a)
[1] "numeric" 
```
--
```r
> class(d$b)
[1] "character"
```

---

## `stop("Time for a break")`  
### We will resume after `Sys.sleep(10*60)`.

---

## Handle missing data

```r
> y <- c("A","B","A","B","C")
> y[2] <- 2
> y[3] <- NA
> y
[1] "A" "2" NA "B" "C"
```
--
```r
> is.na(y)
[1] FALSE FALSE TRUE FALSE FALSE
```
--
```r
> is.na(y[3])
[1] TRUE
```
--
```r
> na.omit(y)
[1] "A" "2" "B" "C"
```

---

## Save and load data: working directory

To open files, you will need to tell R where the files are. 
To do this, it helps to know where R will start looking.
This is called the 'working directory'.

```r
> getwd()
[1] "C:/"
```

You can change this using `setwd`.

```r
> setwd("O:/Documents")
```

The working directory can also be
chosen using the Session menu in RStudio.

---

## Save and load data: reading csv files

```r
"id","age","sex","diet","bmi"
1,32,"M",0,25.7957231474661
2,35,"M",0,28.8952139451377
3,41,"M",0,29.9258448186199
4,29,"M",0,27.3383500990741
5,33.5,"M",1,28.2469210985821
6,33.2,"M",1,27.0473176810354
7,32.9,"M",1,30.3031786852156
8,32.6,"F",1,28.5621419729205
9,32.3,"F",1,28.05344654591
10,32,"F",1,28.8864676350323
...
```
]

```r
> dat <- read.csv("bmi.csv")
```

```r
> dat[1:5,]
   id  age sex diet      bmi
1   1 32.0   M    0 25.79572
2   2 35.0   M    0 28.89521
3   3 41.0   M    0 29.92584
4   4 29.0   M    0 27.33835
5   5 33.5   M    1 28.24692
```

```r
> dat$bmi[1:5]
[1] 25.79572 28.89521 29.92584 27.33835 28.24692
> dat[5,]
  id  age sex diet      bmi
5  5 33.5   M    1 28.24692
```
]

---

## Save and load data: writing csv files

`write.csv` saves a data frame as a csv file.

```r
> dat.corrected <- dat
> dat.corrected$sex[5] <- "F" ## make correction
> write.csv(dat.corrected, "bmi-corrected.csv", row.names=F, quote=F)
```

`write.table` is similar but allows the user to change the column separator 
character.  Here we separate columns by a semicolon rather than a comma.

```r
> write.table(dat, "bmi-corrected.csv", row.names=F, quote=F, sep=";")
```

.box[
Note: There is similarly a function `read.table()` just like `read.csv()` 
but more flexible.
]

---
## Statistical analyses: basic summaries

```r
> mean(dat$bmi)                 ## mean
[1] 27.60761
```
--
```r
> median(dat$bmi)               ## median
[1] 27.58069
```
--
```r
> sd(dat$bmi)                   ## standard deviation
[1] 1.47353
```
--
```r
> min(dat$bmi)                  ## min
[1] 25.34356
```
--
```r
> quantile(dat$bmi, probs=0.25) ## first quartile
   25% 
26.60918
```
--
```r
> table(dat$sex)                ## frequencies
F  M
13 7
```

---
## Statistical analyses: dataset summaries

The `summary` function can be used to summarize single variables

```r
> summary(dat$bmi)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  25.34   26.61   27.58   27.61   28.64   30.30 
```
--
or entire datasets.
```r
> summary(dat)
       id             age            sex                 diet           bmi       
 Min.   : 1.00   Min.   :29.00   Length:20          Min.   :0.00   Min.   :25.34  
 1st Qu.: 5.75   1st Qu.:30.12   Class :character   1st Qu.:0.00   1st Qu.:26.61  
 Median :10.50   Median :31.55   Mode  :character   Median :1.00   Median :27.58  
 Mean   :10.50   Mean   :31.85                      Mean   :0.55   Mean   :27.61  
 3rd Qu.:15.25   3rd Qu.:32.67                      3rd Qu.:1.00   3rd Qu.:28.64  
 Max.   :20.00   Max.   :41.00                      Max.   :1.00   Max.   :30.30  
```

---

## Statistical analyses: evaluating associations

```r
> cor(dat$bmi, dat$age)
[1] 0.5705649
```

Here is the association.
```r
> fit <- lm(bmi~age, data=dat)
> fit
Call:
lm(formula = bmi ~ age, data = dat)

Coefficients:
(Intercept) age 
 17.6770 0.3118 
```
]
--
.ii[
<img src="img/bmi-and-age.png" width=90%>

The **regression line** in the plot runs from 
`(29, 17.6770 + 0.3118*29)=(29, 26.7192)`
to 
`(41, 17.6770 + 0.3118*41)=(41, 30.4608)`.
]

---

## Statistical analyses: summarizing regression model fits

```r
> summary(fit)

Call:
lm(formula = bmi ~ age, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9334 -0.7760  0.2152  0.5293  2.3682

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  17.6770     3.3805   5.229 5.67e-05 ***
age           0.3118     0.1058   2.948  0.00861 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.243 on 18 degrees of freedom
Multiple R-squared:  0.3255,  Adjusted R-squared:  0.2881 
F-statistic: 8.688 on 1 and 18 DF,  p-value: 0.008612
```

---

## Statistical analyses: fitting multiple variable models

```r
> fit <- lm(bmi ~ age + sex + diet, data=dat)
```
--

```r
> summary(fit)

Call:
lm(formula = bmi ~ age + sex + diet, data = dat)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2978 -0.6210  0.0106  0.8208  1.7891

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  18.4784     3.7464   4.932  0.00015 ***
age           0.2643     0.1228   2.152  0.04700 *  
sexM          0.3512     0.6866   0.512  0.61596    
diet          1.0671     0.5532   1.929  0.07168 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.188 on 16 degrees of freedom
Multiple R-squared:  0.4528,  Adjusted R-squared:  0.3502 
F-statistic: 4.413 on 3 and 16 DF,  p-value: 0.01921
```

---

## Statistical analyses: retrieving model fit elements

```r
> coef(fit)
(Intercept)         age        sexM        diet 
 18.4783563   0.2643460   0.3512052   1.0671192 
```
--
```r
> coef(summary(fit))
              Estimate Std. Error   t value     Pr(>|t|)
(Intercept) 18.4783563  3.7463529 4.9323587 0.0001500352
age          0.2643460  0.1228288 2.1521491 0.0469973143
sexM         0.3512052  0.6865686 0.5115369 0.6159621270
diet         1.0671192  0.5532367 1.9288657 0.0716810178
```
--
```r
> coef(summary(fit))["age","Estimate"]
[1] 0.264346
```
--
```r
> names(summary(fit))
 [1]  "call"          "terms"         "residuals"     
 [4]  "coefficients"  "aliased"       "sigma"
 [7]  "df"            "r.squared"     "adj.r.squared" 
 [10] "fstatistic"    "cov.unscaled" 
```
--
```r
> summary(fit)$adj.r.squared
[1] 0.3502004
```

---

## Visualise: scatterplot

```r
plot(dat$age, dat$bmi, 
     main="age vs BMI", 
     xlab="age", 
     ylab="BMI")
```
--

---

## Visualise: scatterplot with regression line

```r
plot(dat$age, dat$bmi, 
 main="age vs BMI", 
 xlab="age", 
 ylab="BMI")
fit <- lm(bmi ~ age, 
 data=dat)
abline(fit, col="red", lwd=3)
```

---

## Visualise: histogram

```r
hist(dat$bmi, main="BMI")
```

## Visualise: boxplot

```r
boxplot(bmi ~ diet, data=dat)
```

---

## Visualise: saving

## Visualise: saving *like a total maniac*

```r
> png(filename="boxplot.png", width=10, height=10, units="cm", res=500)
> boxplot(bmi~diet, data=dat)
> dev.off()
```

---

## Decisions: ask questions

```r
> x <- 11:15
```
--
```r
> x[3] == 13 ## 13 equals 13
[1] TRUE
```
--
```r
> x[3] != 12 ## 13 not equal to 12
```
--
```r
> x[3] < x[4] ## 13 less than 14
```
--
```r
> x[1] <= x[2] & !x[2] > x[3] ## 11 <= 12 and 12 not greater than 13
```
--
```r
> x[1] < x[2] | x[2] > x[3] ## 11 < 12 or 12 > 13
```
--
```r
> x < 12 
[1] TRUE FALSE FALSE FALSE FALSE
```
--
```r
> all(x >= 11) ## each value in x >= 11
[1] TRUE
```

---

## Decisions: alternatives

```r
if (score > 85) {
 grade <- "A"
} else {
 grade <- "F"
}
```
--
```r
grade <- ifelse(score > 85, "A", "F")
```
--
```r
if (score > 85) {
 grade <- "A"
} else if (score > 75)
 grade <- "B"
} else {
 grade <- "F"
}
```
--
```r
grade <- ifelse(score > 85, "A", ifelse(score > 75, "B", "F"))
```

---

## Recipes: create and apply

**Functions** consist of a sequence of commands applied to a set of variables 
that return some output.

R provides many functions such as `help`, `cor`, `median`.

Users can create their own functions for repetitive tasks, 
e.g. a function for the distance of a point from 0.

```r
euclidean.norm <- function(x) {
 res <- sqrt(sum(x^2))
 return(res)
}
```
--
```r
> euclidean.norm(c(3,4))
[1] 5

> sqrt(3*3 + 4*4)
[1] 5

> euclidean.norm(c(2,3,6))
[1] 7

> euclidean.norm(c(1,4,8))
[1] 9
```

---

## Recipes: repeat

.ii[
Given a dataset giving a scores for a group of people:
```r
> people <- c("bill", 
 "jane", 
 "bob", 
 "felicia", 
 "carl", 
 "apple")
> scores <- c(90, 80, 
 60, 95, 
 75, 99)
> stats <- data.frame(person=people, score=scores)
> stats
 person score
1 bill 90
2 jane 80
3 bob 60
4 felicia 95
5 carl 75
6 apple 99
```

Suppose we want to make a list of people who obtained high scores.
]

.ii[
**Step 1.** Create a function for determining a high score.
 
```r
is.high <- function(s) {
 ## something really complicated
 ## that takes into account grade 
 ## barriers and homework completed
 ## and the name of their first pet
 ## ... well okay, we'll just say 
 ## scores above 90 ...
 s > 90
}

> is.high(65)
[1] FALSE

> is.high(94)
[1] TRUE

> is.high(stats$score[2])
[1] FALSE
```
]

---

## Recipes: repeat (cont)

**Step 2.** Construct the list of people by applying `is.high()` to each score. Here are 3 equivalent ways:

```r
n <- nrow(stats)
has.high.score <- rep(F,n) 
i <- 1
while (i <= n) {
 has.high.score[i] <- is.high(stats$score[i])
 i <- i + 1
}
high.scorers <- stats$person[has.high.score]
```
]

```r
n <- nrow(stats)
has.high.score <- rep(F,n)

for (i in 1:n) {
 has.high.score[i] <- is.high(stats$score[i])
}

high.scorers <- stats$person[has.high.score]
```
]

```r
has.high.score <- sapply(stats$score, is.high)
high.scorers <- stats$person[has.high.score]
```
]

> high.scorers
[1] "felicia" "apple"

> stats[has.high.score,]
   person score
4 felicia    95
6   apple    99
```
]

---
## Share: loading packages

An **R package** is acollection of functions and/or data sets
created for use by other R users. 
A package can be loaded using `library`.
```r
> library(Hmisc)
Attaching package: ‘Hmisc’
```

After loading, functions and data provided by the package can be used.

```r
> describe(c(1,1,2,1,3,5,1,3,5,2,3,4))
       n  missing distinct     Info 
      12        0        5    0.944 
    Mean      Gmd 
   2.583    1.742

lowest : 1 2 3 4 5, highest: 1 2 3 4 5
                                  
Value          1     2     3     4
Frequency      4     2     3     1
Proportion 0.333 0.167 0.250 0.083
                
Value          5
Frequency      2
Proportion 0.167
```

---

## Share: installing packages

If the package has not been installed, you'll see an error message 
like this.

```r
> library(Hmisc)
Error in library(Hmisc) : there is no package called ‘Hmisc’
```

The package can be installed using `install.packages`.

```r
> install.packages("Hmisc")

Installing package into ‘C:/Users/dude/Documents/R/win-library/4.0’
(as ‘lib’ is unspecified)
also installing the dependencies ‘png’, ‘jpeg’, ‘Formula’, 
‘latticeExtra’, ‘gridExtra’, ‘htmlTable’, ‘viridis’

trying URL 'https://cran.rstudio.com/bin/windows/contrib/4.0/png_0.1-7.zip'
Content type 'application/zip' length 336667 bytes (328 KB)
downloaded 328 KB

...

package ‘Hmisc’ successfully unpacked and MD5 sums checked

The downloaded binary packages are in
  C:\Users\dude\AppData\Local\Temp\RtmpS221DR\downloaded_packages
```

---

## Share: package help

.center[
.ii[
The *help* function can be used to open the documentation for an R
package, e.g. `help(package="Hmisc")`

<img src="img/package-help.png" width="90%">
]
.gap[ ]
.ii[
Search the package list on CRAN 
https://cran.r-project.org/web/packages/Hmisc/

<img src="img/package-online.png" width="90%">
]
]
---
## Share: learn R with "swirl"

swirl (http://swirlstats.com/students.html)
allows you to learn R within R itself.

```r
> install.packages("swirl")
> library(swirl)
| Hi! Type swirl() when you are ready to begin.
```

---

## Share: creating packages for others

If you have a set of R functions and/or a dataset 
that you think others might like to use, 
create a package and put them in it!

Here is a good place to get started:

https://support.rstudio.com/hc/en-us/articles/200486488-Developing-Packages-with-RStudio

---

## Acknowledgements

Slides were based on work by these beautiful minds.

Harriet Mills

University of Bristol
]
.iii[
<img src="img/andrew-simpkin.jpg" width="70%">

Andrew Simpkin

National University of Ireland
]
.iii[
<img src="img/james-staley.jpg" width="90%">

James Staley

UCB
]
]