Bas(e|ic) R

Lesson 2 with Ian Carroll

Why learn R?
The Editor
Data types
Multi-dimensional data structures
Load data into R
Parts of an Object
Functions
Flow control
Linear models
Review
Exercise solutions

Why learn R?

High-level programming language good for interactive statistical analysis
General purpose programming language for scripting entire data-processing pipelines
Large selection of add-on packages that extend the capabilities of “base R”
Large user community especially within statistics and ecology
Open source

The Editor

The console is for evaluating commands you don’t intend to keep or reuse. It’s useful for testing commands and poking around. The editor is where you compose scripts that will process data, perform analyses, code up visualizations, and even write reports.

These work together in RStudio, which has multiple ways to send parts of the script you are editing to the console for immediate evaluation. Alternatively you can “source” the entire script.

Open up “worksheet-2.R” in the editor, and follow along by replacing the ... placeholders with the code here. Then evalute just this line (Ctrl+Enter on Windows, ⌘+Enter on Mac OS).

vals <- seq(1, 100)

Let’s review the elements of this statement, from left to right:

vals is the name of a (new) variable
<- is an operator that assigns what’s named on the left to equal the result of the expression on the right
seq is the name of the function
( is the opening paren of the function call
1 and 100 are both arguments, or parameters, to the function
) is the closing paren of a function call

Question: Why call vals a “variable” and seq a “function”?
Answer: It is true they are both names of objects known to R, and could be called variables. But seq has the important distinguishing feature of being callable, which is indicated in documentation by writing the function name with empty parens, as in seq().

Our call to the function seq could have been much more explicit. We could give the arguments by the names that seq is expecting.

vals <- seq(from = 1,
            to = 100)

Run this code either line-by-line, or highlight the section to run (optionally with keyboard shortcut Ctrl-Return or ⌘ Return).

Question: What’s an advantage of naming arguments?
Answer: One advantage is that you can put them in any order. A related advantage is that you can then skip some arguments, which is fine to do if each skipped argument has a default value. A third advantage is code readability, which you should always be concious of while writing in the editor.

Readability

Code readability in the editor cuts both ways: sometimes verbosity is useful, sometimes it is cumbersome. The seq() function has an operator form available when only the from and to arguments are needed.

1:100

  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
 [18]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34
 [35]  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51
 [52]  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68
 [69]  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85
 [86]  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100

The : operator should be used whenever possible because it replaces a common, cumbersome function call with an brief, intuitive syntax. Likewise, the assign function duplicates the functionallity of the <- symbol, but is never used when the simpler operator will suffice.

Function documentation

How would you get to know these properties and the names of a function’s arguments?

?seq

How would you even know what function to call?

??sequence

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Data types

Type	Example
integer	-4, 0, 999
double	3.1, -4, Inf, NaN
character	‘a’, “4”, “👏”
logical	TRUE, FALSE
missing	NA

Data structures

Compound objects, built from one or more of these data types, or even other objects.

Common one-dimensional, array data structures:

Vectors
Lists
Factors

Vectors

Vectors are the basic data structure in R. They are a collection of data that are all of the same type. Create a vector by combining elements together using the function c(). Use the operator : for a sequence of numbers (forwards or backwards), otherwise separate elements with commas.

counts <- c(4, 3, 7, 5)

All elements of an vector must be the same type, so when you attempt to combine different types they will be coerced to the most flexible type.

c(1, 2, "c")

[1] "1" "2" "c"

Lists

Lists are like vectors but their elements can be of any data type or structure. You construct lists by using list() instead of c().

list(1, 2, "c")

[[1]]
[1] 1

[[2]]
[1] 2

[[3]]
[1] "c"

Lists can even include another list!

list(1, list(2, 3))

[[1]]
[1] 1

[[2]]
[[2]][[1]]
[1] 2

[[2]][[2]]
[1] 3

Exercise 1

Look at the outputs of list(1, list(2, 3)) and c(1, list(1, 2)). Store the output of each command as new variables, and then examine each variable’s structure with the str() function. What’s different about the structure of the two variables? Are both lists?

View solution

Factors

A factor is a vector that can contain only predefined values, and is used to store categorical data. Factors are built on top of integer vectors using two attributes: the class(), “factor”, which makes them behave differently from regular integer vectors, and their levels(), or the set of allowed values.

Use factor() to create a vector with predefined values, which are often characters or “strings”.

education <- factor(
    c("college", "highschool", "college", "middle"),
    levels = c("middle", "highschool", "college"))

str(education)

 Factor w/ 3 levels "middle","highschool",..: 3 2 3 1

A factor can be unorderd, as above, or ordered with each level somehow “less than” the next.

education <- factor(
    c("college", "highschool", "college", "middle"),
    levels = c("middle", "highschool", "college"),
    ordered = TRUE)

str(education)

 Ord.factor w/ 3 levels "middle"<"highschool"<..: 3 2 3 1

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Multi-dimensional data structures

Data can be stored in several types of data structures depending on its complexity.

Dimensions	Homogeneous	Heterogeneous
1d	c()	list()
2d	matrix()	data.frame()
nd	array()

Of these, the data frame is far and away the most used.

Data frames

Data frames are 2-dimensional and can contain heterogenous data like numbers in one column and a factor in another.

It is the data structure most similar to a spreadsheet, with two key differences:

Data frames columns are equal-length vectors.
As vectors, the columns are homogeneous and cannot hold values of the wrong type.

Creating a data frame from scratch can be done by combining vectors with the data.frame() function.

df <- data.frame(education, counts)

df

   education counts
1    college      4
2 highschool      3
3    college      7
4     middle      5

Some functions to get to know your data frame are:

Function	Output
`dim()`	dimensions
`nrow()`	number of rows
`ncol()`	number of columns
`names()`	(column) names
`str()`	structure
`summary()`	summary info
`head()`	shows beginning rows

names(df)

[1] "education" "counts"

Exercise 2

Create a data frame with two columns, one called “species” and another called “abund”. Store your data frame as a variable called data. You can do this with or without populating the data frame with values.

View solution

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Load data into R

We will use the function read.csv() that reads in a Comma-Separated-Values file by passing it the location of the file. The essential argument for the function to read in data is the path to the file, and optinal additional arguments specify additional ways of reading the data. Additional file types can be read in using read.table(); in fact, read.csv() is a simple wrapper for the read.table() function that specifies default values for some of the optional arguments.

Type a comma after read.csv( and then press tab to see what arguments that this function takes. Hovering over each item in the list will show a description of that argument from the help documentation about that function. Specify the values to use for an argument using the syntax name = value.

read.csv(file = "data/plots.csv", header = TRUE)

   id                 treatment
1   1         Spectab exclosure
2   2                   Control
3   3  Long-term Krat Exclosure
4   4                   Control
5   5          Rodent Exclosure
6   6 Short-term Krat Exclosure
7   7          Rodent Exclosure
8   8                   Control
9   9         Spectab exclosure
10 10          Rodent Exclosure
11 11                   Control
12 12                   Control
13 13 Short-term Krat Exclosure
14 14                   Control
15 15  Long-term Krat Exclosure
16 16          Rodent Exclosure
17 17                   Control
18 18 Short-term Krat Exclosure
19 19  Long-term Krat Exclosure
20 20 Short-term Krat Exclosure
21 21  Long-term Krat Exclosure
22 22                   Control
23 23          Rodent Exclosure
24 24          Rodent Exclosure

Question: Is the header argument necessary?
Answer: No. Look at ?read.csv to see that TRUE is the default value for this argument.

Use the assignment operator “<-“ to store that data in memory and work with it

plots <- read.csv(file = "data/plots.csv")
animals <- read.csv(file = "data/animals.csv")

You can specify what indicates missing data in the read.csv function using either na.strings = "NA" or na = "NA". You can also specify multiple things to be interpreted as missing values, such as na.strings = c("missing", "no data", "< 0.05 mg/L", "XX").

After reading in the “animals.csv” and “plots.csv” files, let’s explore what types of data are in each column and what kind of structure your data has.

str(plots)

'data.frame':	24 obs. of  2 variables:
 $ id       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ treatment: Factor w/ 5 levels "Control","Long-term Krat Exclosure",..: 5 1 2 1 3 4 3 1 5 3 ...

summary(plots)

       id                            treatment
 Min.   : 1.00   Control                  :8  
 1st Qu.: 6.75   Long-term Krat Exclosure :4  
 Median :12.50   Rodent Exclosure         :6  
 Mean   :12.50   Short-term Krat Exclosure:4  
 3rd Qu.:18.25   Spectab exclosure        :2  
 Max.   :24.00

Exercise 3

By default, all character data is read in to a data.frame as factors. Use the read.csv() argument stringsAsFactors to suppress this behavior, then subsequently modify the sex column in animals to make it a factor. Columns of a data.frame are identified to the R interpreter with the $ operator, e.g. animals$sex. We’ll see more on this below.

View solution

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Parts of an Object

Parts of objects are always accessible, either by their name or by their position, using square brackets: [ and ].

Position

counts[1]

[1] 4

counts[3]

[1] 7

Names

Parts of an object can usually also have a name. The names can be given when you are creating a vector or afterwards using the names() function.

df['education']

   education
1    college
2 highschool
3    college
4     middle

names(df) <- c('ed', 'ct')

df['ed']

          ed
1    college
2 highschool
3    college
4     middle

Question: This use of <- with names(x) on the left is a little odd. What’s going on?
Answer: We are overwriting an existing variable, but one that is accessed through the output of the function on the left rather than the global environment.

In a multi-dimensional array, you separate the dimension along which a part is requested with a comma.

df[3, 'ed']

[1] college
Levels: middle < highschool < college

It’s fine to mix names and indices when selecting parts of an object.

Subsetting ranges

There are multiple ways to simultaneously extract multiple parts of an object.

Use in brackets	Subset instructions
positive integers	elements at the specified positions
negative integers	omit elements at the specified positions
logical vectors	select elements where the corresponding value is TRUE
nothing	return the original vector (all)

days <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
weekdays <- days[2:6]
weekend <- days[c(1, 7)]

weekdays

[1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"

weekend

[1] "Sunday"   "Saturday"

Exercise 4

Get weekdays using negative integers.
Get M-W-F using a vector of postitions generated by seq() that uses the by argument (don’t forget to ?seq for help).

View solution

Subsetting data frames

The $ sign is an operator that makes for quick access to a single, named part of an object. It’s most useful when used interactively with “tab completion” on the columns of a data frame.

df$ed

[1] college    highschool college    middle    
Levels: middle < highschool < college

A logical test applied to a single column produces a vector of TRUE and FALSE values that’s the right length for subsetting the data.

df[df$ed == 'college', ]

       ed ct
1 college  4
3 college  7

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Functions

The purpose of R functions is to package up a batch of commands. There are several reasons to develop functions

reuse
readability
modularity
consistency

Writing functions to use multiple times within a project prevents you from duplicating code, a real time-saver when you want to update what the function does. If you see blocks of similar lines of code through your project, those are usually candidates for being moved into functions.

Anatomy of a function

Writing functions is also a great way to understand the terminology and workings of R. Like all programming languages, R has keywords that are reserved for import activities, like creating functions. Keywords are usually very intuitive, the one we need is function.

function(...) {
    ...
    return(...)
}

Three components:

arguments: control how you can call the function
body: the code inside the function
return value: controls what output the function gives

We’ll make a function to extract the first row of its argument, which we give a name to use inside the function:

function(dat) {
    result <- dat[1, ]
    return(result)
}

Note that dat doesn’t exist until we call the function, which merely contains the instructions for how any dat will be handled.

Finally, we need to give the function a name so we can use it like we used c() and seq() above.

first <- function(dat) {
    result <- dat[1, ]
    return(result)
}

first(df)

       ed ct
1 college  4

Question: Can you explain the result of entering first(counts) into the console?
Answer: The function caused an error, which prompted the interpreter to print a helpful error message. Never ignore an error message. (It’s okay to ignore a “warning”.)

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Flow control

The R interpreter’s “focus” flows through a script (or any section of code you run) line by line. Without additional instruction, every line is processed from the top to bottom. “Flow control” is the generic term for causing the interpreter to repeat or skip certain lines, using concepts like “for loops” and “if/else conditionals”.

Flow control happens within blocks of code isolated between curly braces { and }, known as “statements”.

if (...) {
    ...
} else {
    ...
}

The keyword if must be followed by a logical test which determines, at runtime, what to do next. The R interpreter goes to the first statement if the logical value is TRUE and to the second statement if it’s FALSE.

An if/else conditional would allow the first function to avoid the error thrown by calling first(counts).

first <- function(dat) {
    if (is.vector(dat)) {
        result <- dat[1]
    } else {
        result <- dat[1, ]
    }
    return(result)
}

first(df)

       ed ct
1 college  4

first(counts)

[1] 4

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Linear models

The formula requires a response variable left of a “~” and any number of predictors to its right.

Formula	Equivalent	Description
`y ~ a`	`y ~ 1 + a`	constant and one predictor
`y ~ -1 + a`	`y ~ 0 + a`	one predictor with no constant
`y ~ a + b`	`y ~ 1 + a + b`	constant and two predictors
`y ~ a:b`		constant and one predictor, the interaction of (at least) one factor and another variable
`y ~ a*b`	`y ~ 1 + a + b + a:b`	constant and three predictors
`y ~ a*b - a`	`y ~ 1 + b + a:b`	constant and two predictors
`y ~ (a + b + ... )^n`		constant and all combinations of predictors up to order `n`

Linear model

In addition, certain functions are allowed within the formula definition.

animals <- read.csv('data/animals.csv', stringsAsFactors = FALSE, na.strings = '')
fit <- lm(
  log(weight) ~ hindfoot_length,
  data = animals)

summary(fit)


Call:
lm(formula = log(weight) ~ hindfoot_length, data = animals)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.39077 -0.21749 -0.05046  0.15017  2.08463 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)     1.5604389  0.0072416   215.5   <2e-16 ***
hindfoot_length 0.0650048  0.0002357   275.8   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3943 on 30736 degrees of freedom
  (4811 observations deleted due to missingness)
Multiple R-squared:  0.7122,	Adjusted R-squared:  0.7122 
F-statistic: 7.607e+04 on 1 and 30736 DF,  p-value: < 2.2e-16

Exercise 5

Regress hindfoot_length against weight and species_id. Does it appear that the Chihuahuan Desert’s common kangaroo rat (DM) have inordinately large feet for their weight?

Pay attention to factors

Data type matters in statistical modelling. For the predictors in a linear model, the most important distinction is discrete versus continuous.

animals$species_id <- factor(animals$species_id)
fit <- lm(
  log(weight) ~ species_id,
  data = animals)

summary(fit)


Call:
lm(formula = log(weight) ~ species_id, data = animals)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.28157 -0.10063  0.02803  0.12574  1.48272 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   2.12857    0.03110  68.448  < 2e-16 ***
species_idDM  1.62159    0.03117  52.031  < 2e-16 ***
species_idDO  1.74519    0.03134  55.690  < 2e-16 ***
species_idDS  2.63791    0.03139  84.024  < 2e-16 ***
species_idNL  2.89645    0.03170  91.373  < 2e-16 ***
species_idOL  1.29724    0.03181  40.780  < 2e-16 ***
species_idOT  1.04031    0.03142  33.110  < 2e-16 ***
species_idOX  0.91176    0.09066  10.056  < 2e-16 ***
species_idPB  1.30426    0.03135  41.609  < 2e-16 ***
species_idPE  0.92374    0.03165  29.188  < 2e-16 ***
species_idPF -0.07717    0.03155  -2.446 0.014447 *  
species_idPH  1.28769    0.04869  26.446  < 2e-16 ***
species_idPI  0.82629    0.08004  10.323  < 2e-16 ***
species_idPL  0.79433    0.04665  17.029  < 2e-16 ***
species_idPM  0.90396    0.03189  28.349  < 2e-16 ***
species_idPP  0.69278    0.03133  22.114  < 2e-16 ***
species_idPX  0.81448    0.15075   5.403 6.61e-08 ***
species_idRF  0.45257    0.03934  11.505  < 2e-16 ***
species_idRM  0.20908    0.03137   6.665 2.70e-11 ***
species_idRO  0.18447    0.08004   2.305 0.021191 *  
species_idRX  0.56824    0.15075   3.769 0.000164 ***
species_idSF  1.87613    0.04504  41.656  < 2e-16 ***
species_idSH  2.10024    0.03572  58.803  < 2e-16 ***
species_idSO  1.80152    0.04504  40.000  < 2e-16 ***
species_idSS  2.32672    0.15075  15.434  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.2086 on 32258 degrees of freedom
  (3266 observations deleted due to missingness)
Multiple R-squared:  0.9216,	Adjusted R-squared:  0.9215 
F-statistic: 1.58e+04 on 24 and 32258 DF,  p-value: < 2.2e-16

The difference between 1 and 24 degrees of freedom between the last two models—with one fixed effect each—arises from the discreteness of species_id.

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Review

In this introduction to R, we briefly touched on several key principle of scientific programming.

Data types
Assignment
Reading data
Subsetting data
Functions (keyword function)
Statistics

Special characters in R

Perhaps more than most languages, an R script can appear like a jumble of archaic symbols. Here is a little table of characters to recognize as having special meaning

Symbol	Meaning
`?`	get help
`#`	comment
`:`	sequence
`::`, `:::`	access namespaces (advanced)
`<-`	assignment
`$`, `[ ]`, `[[ ]]`	subsetting
`% %`	infix operators, e.g. `%*%`
`{ }`	statements
`.`
`@`	slot (advanced)

Yes, the . in R has no fixed meaning and is often used as _ might be used to separate words in a variable name.

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Exercise solutions

Solution 1

x <- list(1, list(2, 3))
y <- c(1, list(2, 3))

str(x)

List of 2
 $ : num 1
 $ :List of 2
  ..$ : num 2
  ..$ : num 3

str(y)

List of 3
 $ : num 1
 $ : num 2
 $ : num 3

The variable x contains two elements, a number and a list. The variable y has concatenation of the two arguments, coerced to the more flexible of the two (a list is more flexible than a number). Both x and y are lists.

Return

Solution 2

species <- c()
abund <- c()
data <- data.frame(species, abund)

str(data)

'data.frame':	0 obs. of  0 variables

Return

Solution 3

animals <- read.csv('data/animals.csv', stringsAsFactors = FALSE, na.strings = '')
animals$sex <- factor(animals$sex)

str(animals)

'data.frame':	35549 obs. of  9 variables:
 $ id             : int  2 3 4 5 6 7 8 9 10 11 ...
 $ month          : int  7 7 7 7 7 7 7 7 7 7 ...
 $ day            : int  16 16 16 16 16 16 16 16 16 16 ...
 $ year           : int  1977 1977 1977 1977 1977 1977 1977 1977 1977 1977 ...
 $ plot_id        : int  3 2 7 3 1 2 1 1 6 5 ...
 $ species_id     : chr  "NL" "DM" "DM" "DM" ...
 $ sex            : Factor w/ 2 levels "F","M": 2 1 2 2 2 1 2 1 1 1 ...
 $ hindfoot_length: int  33 37 36 35 14 NA 37 34 20 53 ...
 $ weight         : int  NA NA NA NA NA NA NA NA NA NA ...

Return

Solution 4

sol5a <- days[c(-1, -7)]
sol5b <- days[seq(2, 7, 2)]

sol5a

[1] "Monday"    "Tuesday"   "Wednesday" "Thursday"  "Friday"

sol5b

[1] "Monday"    "Wednesday" "Friday"

Return

Solution 5

hl_model <- lm(hindfoot_length ~ log(weight) * species_id, data = animals)

summary(hl_model)


Call:
lm(formula = hindfoot_length ~ log(weight) * species_id, data = animals)

Residuals:
     Min       1Q   Median       3Q      Max 
-24.0566  -0.6536   0.0456   0.7234  29.7702 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)               12.7858     2.1240   6.020 1.77e-09 ***
log(weight)                0.1006     0.9932   0.101 0.919307    
species_idDM              11.9327     2.1438   5.566 2.63e-08 ***
species_idDO              14.3676     2.1935   6.550 5.84e-11 ***
species_idDS              17.7785     2.2183   8.015 1.14e-15 ***
species_idNL               7.8180     2.2269   3.511 0.000448 ***
species_idOL               3.8329     2.2205   1.726 0.084326 .  
species_idOT               3.9959     2.1768   1.836 0.066421 .  
species_idOX               2.8502    19.2255   0.148 0.882145    
species_idPB               7.7369     2.1607   3.581 0.000343 ***
species_idPE               2.7944     2.2052   1.267 0.205104    
species_idPF               0.1775     2.1608   0.082 0.934532    
species_idPH               0.3679     4.6568   0.079 0.937035    
species_idPI               6.7215    19.7938   0.340 0.734180    
species_idPL               5.5096     3.3721   1.634 0.102294    
species_idPM               3.3929     2.1991   1.543 0.122874    
species_idPP               4.3321     2.1504   2.015 0.043963 *  
species_idPX              34.6473    54.3637   0.637 0.523918    
species_idRF               2.7435     3.4223   0.802 0.422754    
species_idRM               1.5570     2.1465   0.725 0.468236    
species_idRO              11.3148     6.9792   1.621 0.104982    
species_idRX              -7.8187     9.0769  -0.861 0.389032    
species_idSF               3.0692     3.2602   0.941 0.346492    
species_idSH               0.8047     2.5495   0.316 0.752296    
species_idSO               6.0981     2.9040   2.100 0.035746 *  
log(weight):species_idDM   2.9055     0.9962   2.917 0.003541 ** 
log(weight):species_idDO   2.0768     1.0032   2.070 0.038436 *  
log(weight):species_idDS   3.9753     1.0022   3.967 7.31e-05 ***
log(weight):species_idNL   2.2186     1.0020   2.214 0.026832 *  
log(weight):species_idOL   1.0443     1.0110   1.033 0.301634    
log(weight):species_idOT   0.9991     1.0045   0.995 0.319923    
log(weight):species_idOX   1.4617     6.3414   0.231 0.817702    
log(weight):species_idPB   1.5264     0.9998   1.527 0.126857    
log(weight):species_idPE   1.4128     1.0119   1.396 0.162681    
log(weight):species_idPF   1.1727     1.0116   1.159 0.246392    
log(weight):species_idPH   3.5936     1.5661   2.295 0.021763 *  
log(weight):species_idPI   0.7853     6.7317   0.117 0.907135    
log(weight):species_idPL   0.4921     1.3354   0.368 0.712515    
log(weight):species_idPM   1.3015     1.0107   1.288 0.197849    
log(weight):species_idPP   1.5413     1.0003   1.541 0.123355    
log(weight):species_idPX  -9.5918    18.4815  -0.519 0.603767    
log(weight):species_idRF   0.6685     1.4342   0.466 0.641130    
log(weight):species_idRM   0.7976     1.0019   0.796 0.426004    
log(weight):species_idRO  -3.8730     3.0337  -1.277 0.201735    
log(weight):species_idRX   4.9175     3.4007   1.446 0.148179    
log(weight):species_idSF   2.6055     1.1681   2.231 0.025712 *  
log(weight):species_idSH   3.4480     1.0472   3.292 0.000994 ***
log(weight):species_idSO   1.6095     1.1125   1.447 0.147980    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.375 on 30690 degrees of freedom
  (4811 observations deleted due to missingness)
Multiple R-squared:  0.9793,	Adjusted R-squared:  0.9792 
F-statistic: 3.085e+04 on 47 and 30690 DF,  p-value: < 2.2e-16

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Top of Section

Bas(e|ic) R

Contents

Why learn R?

The Editor

Readability

Function documentation

Data types

Data structures

Vectors

Lists

Exercise 1

Factors

Multi-dimensional data structures

Data frames

Exercise 2

Load data into R

Exercise 3

Parts of an Object

Position

Names

Subsetting ranges

Exercise 4

Subsetting data frames

Functions

Anatomy of a function

Flow control

Linear models

Linear model

Exercise 5

Pay attention to factors

Review

Special characters in R

Exercise solutions

Solution 1

Solution 2

Solution 3

Solution 4

Solution 5