Computing in R

This is an R Markdown document for the course ‘Computing in R’. Using this document, you can easily run selected pieces of R code shown during the lectures from within RStudio.

Basics of R

2+7
## [1] 9
sqrt(2)
## [1] 1.414214
cos(pi)
## [1] -1
log10(10^3)
## [1] 3
x <- 2
print(x)
## [1] 2
x
## [1] 2
x^2
## [1] 4
x
## [1] 2
#y < - x 
help(sqrt)
library()
help(read.dta)
## No documentation for 'read.dta' in specified packages and libraries:
## you could try '??read.dta'
??read.dta
library(foreign)
help(read.dta)
help(package = stats)
help.start()
## If nothing happens, you should open
## 'http://127.0.0.1:25903/doc/html/index.html' yourself
help(mean)
# ? is a shorthand for help()
?mean

Syntax: data

Data structures

x <- c(10, 9, 8, 7, 6, 5, 4, 3, 2, 1)
x
##  [1] 10  9  8  7  6  5  4  3  2  1
x <- seq(from = 10, to = 1, by = -1)
x
##  [1] 10  9  8  7  6  5  4  3  2  1
x <- seq(10, 1)
x
##  [1] 10  9  8  7  6  5  4  3  2  1
x <- 10:1
x
##  [1] 10  9  8  7  6  5  4  3  2  1
x[5]
## [1] 6
x[10]
## [1] 1
x[5] + x[10]
## [1] 7
indx <- c(5,10)
indx
## [1]  5 10
x[indx]
## [1] 6 1
x[c(5,10)]
## [1] 6 1
c(-5, -10)
## [1]  -5 -10
x[c(-5, -10)]
## [1] 10  9  8  7  5  4  3  2
x[4] <- 12
x
##  [1] 10  9  8 12  6  5  4  3  2  1
mean(x)
## [1] 6
x + 1
##  [1] 11 10  9 13  7  6  5  4  3  2
2*x
##  [1] 20 18 16 24 12 10  8  6  4  2
help(matrix)
A <- matrix(data = 1:10, nrow = 2, ncol = 5)
A <- matrix(1:10, 2, 5)
A
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    3    5    7    9
## [2,]    2    4    6    8   10
A[2, 3]
## [1] 6
A[2,3] <- 12
A[1, ]
## [1] 1 3 5 7 9
A[1,1:5]
## [1] 1 3 5 7 9
A[, c(1, 5)]
##      [,1] [,2]
## [1,]    1    9
## [2,]    2   10
A[1:2, c(1,5)]
##      [,1] [,2]
## [1,]    1    9
## [2,]    2   10
dim(A[, c(1, 5)])
## [1] 2 2
#x[1,3]
x[c(1,3)]
## [1] 10  8
ls()
## [1] "A"    "indx" "r"    "x"
summary(x)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00    3.25    5.50    6.00    8.75   12.00
summary(A)
##        V1             V2             V3              V4             V5       
##  Min.   :1.00   Min.   :3.00   Min.   : 5.00   Min.   :7.00   Min.   : 9.00  
##  1st Qu.:1.25   1st Qu.:3.25   1st Qu.: 6.75   1st Qu.:7.25   1st Qu.: 9.25  
##  Median :1.50   Median :3.50   Median : 8.50   Median :7.50   Median : 9.50  
##  Mean   :1.50   Mean   :3.50   Mean   : 8.50   Mean   :7.50   Mean   : 9.50  
##  3rd Qu.:1.75   3rd Qu.:3.75   3rd Qu.:10.25   3rd Qu.:7.75   3rd Qu.: 9.75  
##  Max.   :2.00   Max.   :4.00   Max.   :12.00   Max.   :8.00   Max.   :10.00
a2 <- 1
#2a <- 1
c <- 1
c
## [1] 1
c(1,2)
## [1] 1 2
#else <- 1
c(1, 2, 3, 4)
## [1] 1 2 3 4
-2 < -2
## [1] FALSE
letters[1:3]
## [1] "a" "b" "c"
mode(x)
## [1] "numeric"
as.character(x)
##  [1] "10" "9"  "8"  "12" "6"  "5"  "4"  "3"  "2"  "1"
x
##  [1] 10  9  8 12  6  5  4  3  2  1
x[c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE,FALSE, TRUE, FALSE)]
## [1] 10  8  6  4  2
x>5
##  [1]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
x[x>5]
## [1] 10  9  8 12  6
length(x>5)
## [1] 10
length(x)
## [1] 10
x[x==3]   # == : test for equality
## [1] 3
x[x!=3]   # != : test for inequality
## [1] 10  9  8 12  6  5  4  2  1
x %in% c(3,8) # %in%: test which values are part of a set of specified values
##  [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
x[x %in% c(3,8)]
## [1] 8 3
x[x=3]    # = : assignment operator
## [1] 8
sum(x>3)
## [1] 7
TRUE & FALSE
## [1] FALSE
TRUE | FALSE
## [1] TRUE
x
##  [1] 10  9  8 12  6  5  4  3  2  1
x>5
##  [1]  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
x<8
##  [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
x>5 & x<8
##  [1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
sum(x>5 & x<8)
## [1] 1
x[x>5 & x<8]
## [1] 6
m <- c(1,2,3,4)
names(m) <- c("gene1","gene2","gene3","gene4")
m
## gene1 gene2 gene3 gene4 
##     1     2     3     4
rownames(A)
## NULL
rownames(A) <- c("gene1", "gene2")
colnames(A) <- c("sample1", "sample2", "sample3","sample4", "sample5")
A
##       sample1 sample2 sample3 sample4 sample5
## gene1       1       3       5       7       9
## gene2       2       4      12       8      10
m["gene3"]
## gene3 
##     3
A["gene1", ]
## sample1 sample2 sample3 sample4 sample5 
##       1       3       5       7       9
c("gene1", 5)
## [1] "gene1" "5"
list(gene = "gene1", number = 5)
## $gene
## [1] "gene1"
## 
## $number
## [1] 5
x <- list(gene = "gene1", number = 5)
x[1]
## $gene
## [1] "gene1"
x[[1]]
## [1] "gene1"
x$gene
## [1] "gene1"
pclass <- c("1st","2nd","1st")
survived <- c(1,1,0)
name <- c("Elisabeth Walton","Hudson Trevor","Helen Loraine")
age <- c(29.0,0.9167,2.0)
titanic <- data.frame(pclass,survived,name,age)
titanic
##   pclass survived             name     age
## 1    1st        1 Elisabeth Walton 29.0000
## 2    2nd        1    Hudson Trevor  0.9167
## 3    1st        0    Helen Loraine  2.0000
titanic[c(2,3),]
##   pclass survived          name    age
## 2    2nd        1 Hudson Trevor 0.9167
## 3    1st        0 Helen Loraine 2.0000
titanic[,c("name","age")]
##               name     age
## 1 Elisabeth Walton 29.0000
## 2    Hudson Trevor  0.9167
## 3    Helen Loraine  2.0000
titanic[c(2,3),c("name","age")]
##            name    age
## 2 Hudson Trevor 0.9167
## 3 Helen Loraine 2.0000
titanic$age
## [1] 29.0000  0.9167  2.0000
titanic[["age"]]
## [1] 29.0000  0.9167  2.0000
# You can also add a new variable to a data frame
titanic$status <- c("yes","yes","no")
titanic
##   pclass survived             name     age status
## 1    1st        1 Elisabeth Walton 29.0000    yes
## 2    2nd        1    Hudson Trevor  0.9167    yes
## 3    1st        0    Helen Loraine  2.0000     no

In the exercises, we use the Titanic data set. See here for a description of the data.

library(foreign)
# First download file from course website and then import
#titanic3 <- read.dta("titanic3.dta", convert.underscore=TRUE) # convert.underscore: Convert "_" in Stata variable names to "." in R names?
titanic3 <- read.dta("Exercises/titanic3.dta", convert.underscore=TRUE)
dim(titanic3)
## [1] 1309   17
head(titanic3[,1:4])
##   pclass survived                            name    sex
## 1    1st        1   Allen, Miss. Elisabeth Walton female
## 2    1st        1  Allison, Master. Hudson Trevor   male
## 3    1st        0    Allison, Miss. Helen Loraine female
## 4    1st        0 Allison, Mr. Hudson Joshua Crei   male
## 5    1st        0 Allison, Mrs. Hudson J C (Bessi female
## 6    1st        1             Anderson, Mr. Harry   male
tail(titanic3[,1:4])
##      pclass survived                      name    sex
## 1304    3rd        0     Yousseff, Mr. Gerious   male
## 1305    3rd        0      Zabour, Miss. Hileni female
## 1306    3rd        0     Zabour, Miss. Thamine female
## 1307    3rd        0 Zakarian, Mr. Mapriededer   male
## 1308    3rd        0       Zakarian, Mr. Ortin   male
## 1309    3rd        0        Zimmerman, Mr. Leo   male
str(titanic3[,1:4])
## 'data.frame':    1309 obs. of  4 variables:
##  $ pclass  : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ...
##  $ survived: int  1 1 0 0 0 1 1 0 1 0 ...
##  $ name    : chr  "Allen, Miss. Elisabeth Walton" "Allison, Master. Hudson Trevor" "Allison, Miss. Helen Loraine" "Allison, Mr. Hudson Joshua Crei" ...
##  $ sex     : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...

Data import

#In R, we can also import directly from a web site: #{r "Basic data import/export from other formats (I)"} #load(url("https://biostat.app.vumc.org/wiki/pub/Main/DataSets/titanic3.sav")) #ls() #

An example using the readxl package for MS Excel files. The data set can be obtained from here and from the course website. If the data set has been stored in a subfolder Exercises, it can be imported via

install.packages("readxl")
## package 'readxl' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(readxl)
titanic3 <- read_excel("Exercises/titanic3.xls")
## Explain warning message and repair by converting to number
## tibble
dim(titanic3)
## [1] 1309   14
head(titanic3[,1:4])
## # A tibble: 6 x 4
##   pclass survived name                                            sex   
##    <dbl>    <dbl> <chr>                                           <chr> 
## 1      1        1 Allen, Miss. Elisabeth Walton                   female
## 2      1        1 Allison, Master. Hudson Trevor                  male  
## 3      1        0 Allison, Miss. Helen Loraine                    female
## 4      1        0 Allison, Mr. Hudson Joshua Creighton            male  
## 5      1        0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female
## 6      1        1 Anderson, Mr. Harry                             male
tail(titanic3[,1:4])
## # A tibble: 6 x 4
##   pclass survived name                      sex   
##    <dbl>    <dbl> <chr>                     <chr> 
## 1      3        0 Yousseff, Mr. Gerious     male  
## 2      3        0 Zabour, Miss. Hileni      female
## 3      3        0 Zabour, Miss. Thamine     female
## 4      3        0 Zakarian, Mr. Mapriededer male  
## 5      3        0 Zakarian, Mr. Ortin       male  
## 6      3        0 Zimmerman, Mr. Leo        male

We run the code to import the STATA file, which is what we will do in the exercises as well.

library(foreign)
titanic3 <- read.dta("Exercises/titanic3.dta", convert.underscore=TRUE)

Functions; special data formats; selections

Functions

Using the logarithm as an example, we show the different ways to obtain the 10-log of 100 (which should give 2 as outcome).

## Basic structure
log(100)  # does not give 2
## [1] 4.60517
help(log)
log(100, 10) 
## [1] 2
log(10, 100) # does not give 2
## [1] 0.5
log(base=10, x=100)
## [1] 2
log(b=10,x=100)
## [1] 2
## Some functions have an ... argument
c
## [1] 1
c(3,6,8)
## [1] 3 6 8
paste
## function (..., sep = " ", collapse = NULL, recycle0 = FALSE) 
## .Internal(paste(list(...), sep, collapse, recycle0))
## <bytecode: 0x000000000b4175c0>
## <environment: namespace:base>
help(paste)
paste("Academic","Medical","Center")
## [1] "Academic Medical Center"
## Some functions have an alias that is closer to common use
## E.g. + is an alias for the "+" function
"+"(1,7)
## [1] 8
## To cite R in publications:
citation()
## 
## To cite R in publications use:
## 
##   R Core Team (2022). R: A language and environment for statistical
##   computing. R Foundation for Statistical Computing, Vienna, Austria.
##   URL https://www.R-project.org/.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {R: A Language and Environment for Statistical Computing},
##     author = {{R Core Team}},
##     organization = {R Foundation for Statistical Computing},
##     address = {Vienna, Austria},
##     year = {2022},
##     url = {https://www.R-project.org/},
##   }
## 
## We have invested a lot of time and effort in creating R, please cite it
## when using it for data analysis. See also 'citation("pkgname")' for
## citing R packages.

We can see the contents of a function by leaving out the parentheses. And we can write our own functions.

help 
## function (topic, package = NULL, lib.loc = NULL, verbose = getOption("verbose"), 
##     try.all.packages = getOption("help.try.all.packages"), help_type = getOption("help_type")) 
## {
##     types <- c("text", "html", "pdf")
##     help_type <- if (!length(help_type)) 
##         "text"
##     else match.arg(tolower(help_type), types)
##     if (!missing(package)) 
##         if (is.name(y <- substitute(package))) 
##             package <- as.character(y)
##     if (missing(topic)) {
##         if (!is.null(package)) {
##             if (interactive() && help_type == "html") {
##                 port <- tools::startDynamicHelp(NA)
##                 if (port <= 0L) 
##                   return(library(help = package, lib.loc = lib.loc, 
##                     character.only = TRUE))
##                 browser <- if (.Platform$GUI == "AQUA") {
##                   get("aqua.browser", envir = as.environment("tools:RGUI"))
##                 }
##                 else getOption("browser")
##                 browseURL(paste0("http://127.0.0.1:", port, "/library/", 
##                   package, "/html/00Index.html"), browser)
##                 return(invisible())
##             }
##             else return(library(help = package, lib.loc = lib.loc, 
##                 character.only = TRUE))
##         }
##         if (!is.null(lib.loc)) 
##             return(library(lib.loc = lib.loc))
##         topic <- "help"
##         package <- "utils"
##         lib.loc <- .Library
##     }
##     ischar <- tryCatch(is.character(topic) && length(topic) == 
##         1L, error = function(e) FALSE)
##     if (!ischar) {
##         reserved <- c("TRUE", "FALSE", "NULL", "Inf", "NaN", 
##             "NA", "NA_integer_", "NA_real_", "NA_complex_", "NA_character_")
##         stopic <- deparse1(substitute(topic))
##         if (!is.name(substitute(topic)) && !stopic %in% reserved) 
##             stop("'topic' should be a name, length-one character vector or reserved word")
##         topic <- stopic
##     }
##     paths <- index.search(topic, find.package(if (is.null(package)) 
##         loadedNamespaces()
##     else package, lib.loc, verbose = verbose))
##     try.all.packages <- !length(paths) && is.logical(try.all.packages) && 
##         !is.na(try.all.packages) && try.all.packages && is.null(package) && 
##         is.null(lib.loc)
##     if (try.all.packages) {
##         for (lib in .libPaths()) {
##             packages <- .packages(TRUE, lib)
##             packages <- packages[is.na(match(packages, .packages()))]
##             paths <- c(paths, index.search(topic, file.path(lib, 
##                 packages)))
##         }
##         paths <- paths[nzchar(paths)]
##     }
##     structure(unique(paths), call = match.call(), topic = topic, 
##         tried_all_packages = try.all.packages, type = help_type, 
##         class = "help_files_with_topic")
## }
## <bytecode: 0x000000000d43c150>
## <environment: namespace:utils>
good.morning <- function(work){ 
  if(work==TRUE) cat("wake up") else 
    cat("you can stay in bed")
}
good.morning
## function(work){ 
##   if(work==TRUE) cat("wake up") else 
##     cat("you can stay in bed")
## }
#good.morning()
good.morning(work=FALSE)
## you can stay in bed

Selections

install.packages("ISwR")
## package 'ISwR' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(ISwR)
help(juul)
summary(juul)
##       age            menarche          sex             igf1      
##  Min.   : 0.170   Min.   :1.000   Min.   :1.000   Min.   : 25.0  
##  1st Qu.: 9.053   1st Qu.:1.000   1st Qu.:1.000   1st Qu.:202.2  
##  Median :12.560   Median :1.000   Median :2.000   Median :313.5  
##  Mean   :15.095   Mean   :1.476   Mean   :1.534   Mean   :340.2  
##  3rd Qu.:16.855   3rd Qu.:2.000   3rd Qu.:2.000   3rd Qu.:462.8  
##  Max.   :83.000   Max.   :2.000   Max.   :2.000   Max.   :915.0  
##  NA's   :5        NA's   :635     NA's   :5       NA's   :321    
##      tanner        testvol      
##  Min.   :1.00   Min.   : 1.000  
##  1st Qu.:1.00   1st Qu.: 1.000  
##  Median :2.00   Median : 3.000  
##  Mean   :2.64   Mean   : 7.896  
##  3rd Qu.:5.00   3rd Qu.:15.000  
##  Max.   :5.00   Max.   :30.000  
##  NA's   :240    NA's   :859
juul$age[1:10] 
##  [1]   NA   NA   NA   NA   NA 0.17 0.17 0.17 0.17 0.17
juul[1:10,"age"]
##  [1]   NA   NA   NA   NA   NA 0.17 0.17 0.17 0.17 0.17
## head and tail functions
tail(juul[, c("menarche","sex")])
##      menarche sex
## 1334        2   2
## 1335        2   2
## 1336        2   2
## 1337        2   2
## 1338        2   2
## 1339        2   2
## in baby steps
tmp <- juul[, c("menarche","sex")] # all rows, two columns
tail(tmp) # last six rows
##      menarche sex
## 1334        2   2
## 1335        2   2
## 1336        2   2
## 1337        2   2
## 1338        2   2
## 1339        2   2
## or 
tail(juul)[, c("menarche","sex")]
##      menarche sex
## 1334        2   2
## 1335        2   2
## 1336        2   2
## 1337        2   2
## 1338        2   2
## 1339        2   2
## in baby steps 
tmp <- tail(juul) # last six rows, all columns
tmp[, c("menarche","sex")] # two columns
##      menarche sex
## 1334        2   2
## 1335        2   2
## 1336        2   2
## 1337        2   2
## 1338        2   2
## 1339        2   2
## some selections using subset function
subset(juul, tanner==2 & age<10)
##      age menarche sex igf1 tanner testvol
## 192 9.50       NA   1   NA      2       2
## 831 9.82        1   2   NA      2      NA
## 835 9.89        1   2  229      2      NA
subset(juul, tanner>=4 & age>45)
##        age menarche sex igf1 tanner testvol
## 1325 47.37        2   2  144      5      NA
## 1326 48.01        2   2  154      5      NA
## 1329 51.07        2   2  187      5      NA
## 1334 58.95        2   2  218      5      NA
## 1335 60.99        2   2  226      5      NA
subset(juul, tanner %in% c(1,5) & (age==0.25|age>50))
##        age menarche sex igf1 tanner testvol
## 13    0.25       NA   1   90      1      NA
## 14    0.25       NA   1  141      1      NA
## 628   0.25       NA   2   51      1      NA
## 1329 51.07        2   2  187      5      NA
## 1334 58.95        2   2  218      5      NA
## 1335 60.99        2   2  226      5      NA
with(juul, table(sex, tanner))
##    tanner
## sex   1   2   3   4   5
##   1 291  55  34  41 124
##   2 224  48  38  40 204
xtabs(~sex+tanner, data=juul)
##    tanner
## sex   1   2   3   4   5
##   1 291  55  34  41 124
##   2 224  48  38  40 204
xtabs(~sex+tanner, data=juul, subset=(menarche==1))
##    tanner
## sex   1   2   3   4   5
##   2 221  43  32  14   2
## also select columns using subset function
subset(juul, tanner==2 & age<10, select=c(menarche,sex))
##     menarche sex
## 192       NA   1
## 831        1   2
## 835        1   2

Some special data types

Missing data

When checking for missingness, we need to use the is.na function.

titanic3$age[1:20]
##  [1] 29.0000  0.9167  2.0000 30.0000 25.0000 48.0000 63.0000 39.0000 53.0000
## [10] 71.0000 47.0000 18.0000 24.0000 26.0000 80.0000      NA 24.0000 50.0000
## [19] 32.0000 36.0000
table(titanic3$age==NA)
## < table of extent 0 >
3==NA
## [1] NA
is.na(3)
## [1] FALSE
table(is.na(titanic3$age))
## 
## FALSE  TRUE 
##  1046   263
## select those with missing igf1 value
subset(juul, tanner %in% c(1,5) & is.na(igf1) & (age<5|age>18))
##        age menarche sex igf1 tanner testvol
## 630   2.64        1   2   NA      1      NA
## 1225 18.09        2   2   NA      5      NA
## 1239 18.44        2   2   NA      5      NA
#quantile(juul$age, prob=c(0.025,0.25,0.5,0.75,0.975)) # gives an error
quantile(juul$age, prob=c(0.025,0.25,0.5,0.75,0.975), na.rm=TRUE)
##     2.5%      25%      50%      75%    97.5% 
##  2.88525  9.05250 12.56000 16.85500 51.48175
with(juul, table(sex, menarche))
##    menarche
## sex   1   2
##   1   0   0
##   2 369 335
with(juul, table(sex, menarche, useNA="always"))
##       menarche
## sex      1   2 <NA>
##   1      0   0  621
##   2    369 335    9
##   <NA>   0   0    5

Factors

DiseaseState <- factor(c("Cancer", "Cancer", "Normal"))
DiseaseState
## [1] Cancer Cancer Normal
## Levels: Cancer Normal
str(DiseaseState)
##  Factor w/ 2 levels "Cancer","Normal": 1 1 2
levels(DiseaseState)
## [1] "Cancer" "Normal"
typeof(DiseaseState) # the internal coding is via integers
## [1] "integer"
is.factor(DiseaseState)
## [1] TRUE
help(factor)
## change labels
factor(c("Cancer", "Cancer", "Normal"), labels=c("Dis","Norm"))
## [1] Dis  Dis  Norm
## Levels: Dis Norm
## change ordering of levels
DiseaseState2 <- factor(c("Cancer", "Cancer", "Normal"),levels=c("Normal","Cancer"))
table(DiseaseState)
## DiseaseState
## Cancer Normal 
##      2      1
table(DiseaseState2)
## DiseaseState2
## Normal Cancer 
##      1      2
## can also use relevel function
relevel(DiseaseState, ref="Normal")
## [1] Cancer Cancer Normal
## Levels: Normal Cancer
## see what changes internally
as.numeric(DiseaseState)
## [1] 1 1 2
as.numeric(relevel(DiseaseState, ref="Normal"))
## [1] 2 2 1

Dates

as.Date("15 April 1912", "%d %b %Y")
## [1] "1912-04-15"
julian(as.Date("15 April 1912", "%d %b %Y"))
## [1] -21080
## attr(,"origin")
## [1] "1970-01-01"
titanic3$dob <- as.Date("15 April 1912", "%d %b %Y")+ (-titanic3$age*365.25)
as.Date("15041912", "%d%m%Y")
## [1] "1912-04-15"
as.Date("150412", "%d%m%y")
## [1] "2012-04-15"
format(as.Date("1912April15", "%Y%b%d"),"%A %B %d, %Y")
## [1] "Monday April 15, 1912"
install.packages("lubridate")
## package 'lubridate' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(lubridate)
wday(as.Date("1912April15", "%Y%b%d"),label=TRUE)
## [1] Mon
## Levels: Sun < Mon < Tue < Wed < Thu < Fri < Sat

Swirl

#install.packages("swirl")
#library(swirl)
#install_from_swirl("R Programming")
#swirl()

Graphics

Basic graphics

x <- (0:100)/10
plot(x, x^3 - 13 * x^2 + 39 * x)

plot(x, x^3 - 13 * x^2 + 39 * x,cex.axis=1.5,cex.lab=1.5)

plot(x,x^3-13*x^2+39*x,type="l",xlab="time (hours)",ylab="temperature",main="Enhanced plot",cex.axis=1.5,cex.lab=1.5)

colors()
##   [1] "white"                "aliceblue"            "antiquewhite"        
##   [4] "antiquewhite1"        "antiquewhite2"        "antiquewhite3"       
##   [7] "antiquewhite4"        "aquamarine"           "aquamarine1"         
##  [10] "aquamarine2"          "aquamarine3"          "aquamarine4"         
##  [13] "azure"                "azure1"               "azure2"              
##  [16] "azure3"               "azure4"               "beige"               
##  [19] "bisque"               "bisque1"              "bisque2"             
##  [22] "bisque3"              "bisque4"              "black"               
##  [25] "blanchedalmond"       "blue"                 "blue1"               
##  [28] "blue2"                "blue3"                "blue4"               
##  [31] "blueviolet"           "brown"                "brown1"              
##  [34] "brown2"               "brown3"               "brown4"              
##  [37] "burlywood"            "burlywood1"           "burlywood2"          
##  [40] "burlywood3"           "burlywood4"           "cadetblue"           
##  [43] "cadetblue1"           "cadetblue2"           "cadetblue3"          
##  [46] "cadetblue4"           "chartreuse"           "chartreuse1"         
##  [49] "chartreuse2"          "chartreuse3"          "chartreuse4"         
##  [52] "chocolate"            "chocolate1"           "chocolate2"          
##  [55] "chocolate3"           "chocolate4"           "coral"               
##  [58] "coral1"               "coral2"               "coral3"              
##  [61] "coral4"               "cornflowerblue"       "cornsilk"            
##  [64] "cornsilk1"            "cornsilk2"            "cornsilk3"           
##  [67] "cornsilk4"            "cyan"                 "cyan1"               
##  [70] "cyan2"                "cyan3"                "cyan4"               
##  [73] "darkblue"             "darkcyan"             "darkgoldenrod"       
##  [76] "darkgoldenrod1"       "darkgoldenrod2"       "darkgoldenrod3"      
##  [79] "darkgoldenrod4"       "darkgray"             "darkgreen"           
##  [82] "darkgrey"             "darkkhaki"            "darkmagenta"         
##  [85] "darkolivegreen"       "darkolivegreen1"      "darkolivegreen2"     
##  [88] "darkolivegreen3"      "darkolivegreen4"      "darkorange"          
##  [91] "darkorange1"          "darkorange2"          "darkorange3"         
##  [94] "darkorange4"          "darkorchid"           "darkorchid1"         
##  [97] "darkorchid2"          "darkorchid3"          "darkorchid4"         
## [100] "darkred"              "darksalmon"           "darkseagreen"        
## [103] "darkseagreen1"        "darkseagreen2"        "darkseagreen3"       
## [106] "darkseagreen4"        "darkslateblue"        "darkslategray"       
## [109] "darkslategray1"       "darkslategray2"       "darkslategray3"      
## [112] "darkslategray4"       "darkslategrey"        "darkturquoise"       
## [115] "darkviolet"           "deeppink"             "deeppink1"           
## [118] "deeppink2"            "deeppink3"            "deeppink4"           
## [121] "deepskyblue"          "deepskyblue1"         "deepskyblue2"        
## [124] "deepskyblue3"         "deepskyblue4"         "dimgray"             
## [127] "dimgrey"              "dodgerblue"           "dodgerblue1"         
## [130] "dodgerblue2"          "dodgerblue3"          "dodgerblue4"         
## [133] "firebrick"            "firebrick1"           "firebrick2"          
## [136] "firebrick3"           "firebrick4"           "floralwhite"         
## [139] "forestgreen"          "gainsboro"            "ghostwhite"          
## [142] "gold"                 "gold1"                "gold2"               
## [145] "gold3"                "gold4"                "goldenrod"           
## [148] "goldenrod1"           "goldenrod2"           "goldenrod3"          
## [151] "goldenrod4"           "gray"                 "gray0"               
## [154] "gray1"                "gray2"                "gray3"               
## [157] "gray4"                "gray5"                "gray6"               
## [160] "gray7"                "gray8"                "gray9"               
## [163] "gray10"               "gray11"               "gray12"              
## [166] "gray13"               "gray14"               "gray15"              
## [169] "gray16"               "gray17"               "gray18"              
## [172] "gray19"               "gray20"               "gray21"              
## [175] "gray22"               "gray23"               "gray24"              
## [178] "gray25"               "gray26"               "gray27"              
## [181] "gray28"               "gray29"               "gray30"              
## [184] "gray31"               "gray32"               "gray33"              
## [187] "gray34"               "gray35"               "gray36"              
## [190] "gray37"               "gray38"               "gray39"              
## [193] "gray40"               "gray41"               "gray42"              
## [196] "gray43"               "gray44"               "gray45"              
## [199] "gray46"               "gray47"               "gray48"              
## [202] "gray49"               "gray50"               "gray51"              
## [205] "gray52"               "gray53"               "gray54"              
## [208] "gray55"               "gray56"               "gray57"              
## [211] "gray58"               "gray59"               "gray60"              
## [214] "gray61"               "gray62"               "gray63"              
## [217] "gray64"               "gray65"               "gray66"              
## [220] "gray67"               "gray68"               "gray69"              
## [223] "gray70"               "gray71"               "gray72"              
## [226] "gray73"               "gray74"               "gray75"              
## [229] "gray76"               "gray77"               "gray78"              
## [232] "gray79"               "gray80"               "gray81"              
## [235] "gray82"               "gray83"               "gray84"              
## [238] "gray85"               "gray86"               "gray87"              
## [241] "gray88"               "gray89"               "gray90"              
## [244] "gray91"               "gray92"               "gray93"              
## [247] "gray94"               "gray95"               "gray96"              
## [250] "gray97"               "gray98"               "gray99"              
## [253] "gray100"              "green"                "green1"              
## [256] "green2"               "green3"               "green4"              
## [259] "greenyellow"          "grey"                 "grey0"               
## [262] "grey1"                "grey2"                "grey3"               
## [265] "grey4"                "grey5"                "grey6"               
## [268] "grey7"                "grey8"                "grey9"               
## [271] "grey10"               "grey11"               "grey12"              
## [274] "grey13"               "grey14"               "grey15"              
## [277] "grey16"               "grey17"               "grey18"              
## [280] "grey19"               "grey20"               "grey21"              
## [283] "grey22"               "grey23"               "grey24"              
## [286] "grey25"               "grey26"               "grey27"              
## [289] "grey28"               "grey29"               "grey30"              
## [292] "grey31"               "grey32"               "grey33"              
## [295] "grey34"               "grey35"               "grey36"              
## [298] "grey37"               "grey38"               "grey39"              
## [301] "grey40"               "grey41"               "grey42"              
## [304] "grey43"               "grey44"               "grey45"              
## [307] "grey46"               "grey47"               "grey48"              
## [310] "grey49"               "grey50"               "grey51"              
## [313] "grey52"               "grey53"               "grey54"              
## [316] "grey55"               "grey56"               "grey57"              
## [319] "grey58"               "grey59"               "grey60"              
## [322] "grey61"               "grey62"               "grey63"              
## [325] "grey64"               "grey65"               "grey66"              
## [328] "grey67"               "grey68"               "grey69"              
## [331] "grey70"               "grey71"               "grey72"              
## [334] "grey73"               "grey74"               "grey75"              
## [337] "grey76"               "grey77"               "grey78"              
## [340] "grey79"               "grey80"               "grey81"              
## [343] "grey82"               "grey83"               "grey84"              
## [346] "grey85"               "grey86"               "grey87"              
## [349] "grey88"               "grey89"               "grey90"              
## [352] "grey91"               "grey92"               "grey93"              
## [355] "grey94"               "grey95"               "grey96"              
## [358] "grey97"               "grey98"               "grey99"              
## [361] "grey100"              "honeydew"             "honeydew1"           
## [364] "honeydew2"            "honeydew3"            "honeydew4"           
## [367] "hotpink"              "hotpink1"             "hotpink2"            
## [370] "hotpink3"             "hotpink4"             "indianred"           
## [373] "indianred1"           "indianred2"           "indianred3"          
## [376] "indianred4"           "ivory"                "ivory1"              
## [379] "ivory2"               "ivory3"               "ivory4"              
## [382] "khaki"                "khaki1"               "khaki2"              
## [385] "khaki3"               "khaki4"               "lavender"            
## [388] "lavenderblush"        "lavenderblush1"       "lavenderblush2"      
## [391] "lavenderblush3"       "lavenderblush4"       "lawngreen"           
## [394] "lemonchiffon"         "lemonchiffon1"        "lemonchiffon2"       
## [397] "lemonchiffon3"        "lemonchiffon4"        "lightblue"           
## [400] "lightblue1"           "lightblue2"           "lightblue3"          
## [403] "lightblue4"           "lightcoral"           "lightcyan"           
## [406] "lightcyan1"           "lightcyan2"           "lightcyan3"          
## [409] "lightcyan4"           "lightgoldenrod"       "lightgoldenrod1"     
## [412] "lightgoldenrod2"      "lightgoldenrod3"      "lightgoldenrod4"     
## [415] "lightgoldenrodyellow" "lightgray"            "lightgreen"          
## [418] "lightgrey"            "lightpink"            "lightpink1"          
## [421] "lightpink2"           "lightpink3"           "lightpink4"          
## [424] "lightsalmon"          "lightsalmon1"         "lightsalmon2"        
## [427] "lightsalmon3"         "lightsalmon4"         "lightseagreen"       
## [430] "lightskyblue"         "lightskyblue1"        "lightskyblue2"       
## [433] "lightskyblue3"        "lightskyblue4"        "lightslateblue"      
## [436] "lightslategray"       "lightslategrey"       "lightsteelblue"      
## [439] "lightsteelblue1"      "lightsteelblue2"      "lightsteelblue3"     
## [442] "lightsteelblue4"      "lightyellow"          "lightyellow1"        
## [445] "lightyellow2"         "lightyellow3"         "lightyellow4"        
## [448] "limegreen"            "linen"                "magenta"             
## [451] "magenta1"             "magenta2"             "magenta3"            
## [454] "magenta4"             "maroon"               "maroon1"             
## [457] "maroon2"              "maroon3"              "maroon4"             
## [460] "mediumaquamarine"     "mediumblue"           "mediumorchid"        
## [463] "mediumorchid1"        "mediumorchid2"        "mediumorchid3"       
## [466] "mediumorchid4"        "mediumpurple"         "mediumpurple1"       
## [469] "mediumpurple2"        "mediumpurple3"        "mediumpurple4"       
## [472] "mediumseagreen"       "mediumslateblue"      "mediumspringgreen"   
## [475] "mediumturquoise"      "mediumvioletred"      "midnightblue"        
## [478] "mintcream"            "mistyrose"            "mistyrose1"          
## [481] "mistyrose2"           "mistyrose3"           "mistyrose4"          
## [484] "moccasin"             "navajowhite"          "navajowhite1"        
## [487] "navajowhite2"         "navajowhite3"         "navajowhite4"        
## [490] "navy"                 "navyblue"             "oldlace"             
## [493] "olivedrab"            "olivedrab1"           "olivedrab2"          
## [496] "olivedrab3"           "olivedrab4"           "orange"              
## [499] "orange1"              "orange2"              "orange3"             
## [502] "orange4"              "orangered"            "orangered1"          
## [505] "orangered2"           "orangered3"           "orangered4"          
## [508] "orchid"               "orchid1"              "orchid2"             
## [511] "orchid3"              "orchid4"              "palegoldenrod"       
## [514] "palegreen"            "palegreen1"           "palegreen2"          
## [517] "palegreen3"           "palegreen4"           "paleturquoise"       
## [520] "paleturquoise1"       "paleturquoise2"       "paleturquoise3"      
## [523] "paleturquoise4"       "palevioletred"        "palevioletred1"      
## [526] "palevioletred2"       "palevioletred3"       "palevioletred4"      
## [529] "papayawhip"           "peachpuff"            "peachpuff1"          
## [532] "peachpuff2"           "peachpuff3"           "peachpuff4"          
## [535] "peru"                 "pink"                 "pink1"               
## [538] "pink2"                "pink3"                "pink4"               
## [541] "plum"                 "plum1"                "plum2"               
## [544] "plum3"                "plum4"                "powderblue"          
## [547] "purple"               "purple1"              "purple2"             
## [550] "purple3"              "purple4"              "red"                 
## [553] "red1"                 "red2"                 "red3"                
## [556] "red4"                 "rosybrown"            "rosybrown1"          
## [559] "rosybrown2"           "rosybrown3"           "rosybrown4"          
## [562] "royalblue"            "royalblue1"           "royalblue2"          
## [565] "royalblue3"           "royalblue4"           "saddlebrown"         
## [568] "salmon"               "salmon1"              "salmon2"             
## [571] "salmon3"              "salmon4"              "sandybrown"          
## [574] "seagreen"             "seagreen1"            "seagreen2"           
## [577] "seagreen3"            "seagreen4"            "seashell"            
## [580] "seashell1"            "seashell2"            "seashell3"           
## [583] "seashell4"            "sienna"               "sienna1"             
## [586] "sienna2"              "sienna3"              "sienna4"             
## [589] "skyblue"              "skyblue1"             "skyblue2"            
## [592] "skyblue3"             "skyblue4"             "slateblue"           
## [595] "slateblue1"           "slateblue2"           "slateblue3"          
## [598] "slateblue4"           "slategray"            "slategray1"          
## [601] "slategray2"           "slategray3"           "slategray4"          
## [604] "slategrey"            "snow"                 "snow1"               
## [607] "snow2"                "snow3"                "snow4"               
## [610] "springgreen"          "springgreen1"         "springgreen2"        
## [613] "springgreen3"         "springgreen4"         "steelblue"           
## [616] "steelblue1"           "steelblue2"           "steelblue3"          
## [619] "steelblue4"           "tan"                  "tan1"                
## [622] "tan2"                 "tan3"                 "tan4"                
## [625] "thistle"              "thistle1"             "thistle2"            
## [628] "thistle3"             "thistle4"             "tomato"              
## [631] "tomato1"              "tomato2"              "tomato3"             
## [634] "tomato4"              "turquoise"            "turquoise1"          
## [637] "turquoise2"           "turquoise3"           "turquoise4"          
## [640] "violet"               "violetred"            "violetred1"          
## [643] "violetred2"           "violetred3"           "violetred4"          
## [646] "wheat"                "wheat1"               "wheat2"              
## [649] "wheat3"               "wheat4"               "whitesmoke"          
## [652] "yellow"               "yellow1"              "yellow2"             
## [655] "yellow3"              "yellow4"              "yellowgreen"
plot(x,x^3-13*x^2+39*x,pch=18,xlab="time (hours)",ylab="temperature",col="red",main="Enhanced plot",cex.axis=1.5,cex.lab=1.5)

palette()
## [1] "black"   "#DF536B" "#61D04F" "#2297E6" "#28E2E5" "#CD0BBC" "#F5C710"
## [8] "gray62"
plot(x,x^3-13*x^2+39*x,pch=18,xlab="time (hours)",ylab="temperature",col=3,main="Enhanced plot",cex.axis=1.5,cex.lab=1.5)

plot(1:25, pch=1:25,cex=2,bg="grey") 

x<-(0:100)/10
plot(x,x^3-13*x^2+39*x,type="l",xlab="time (hours)",ylab="temperature",cex.axis=1.5,cex.lab=1.5)
points(2,34,col="red",pch=16,cex=2)
arrows(4,50,2.2,34.5)
text(4.15,50,"local maximum",adj=0,col="blue",cex=1.5)
lines(x,30-50*sin(x/2),col="blue")
legend(x=0,y=80,legend=c("Sahara","Gobi"),lty=1,col=c("black","blue"),cex=1.5)

?par
par("bg")
## [1] "white"
par(bg="green")
par("bg")
## [1] "green"
# rerun the plot commands
x<-(0:100)/10
plot(x,x^3-13*x^2+39*x,type="l",xlab="time (hours)",ylab="temperature",cex.axis=1.5,cex.lab=1.5)
points(2,34,col="red",pch=16,cex=2)
arrows(4,50,2.2,34.5)
text(4.15,50,"local maximum",adj=0,col="blue",cex=1.5)
lines(x,30-50*sin(x/2),col="blue")
legend(x=0,y=80,legend=c("Sahara","Gobi"),lty=1,col=c("black","blue"),cex=1.5)

plot(1:10)
par(bg="white")
plot(1:10)

# save default graphical parameters to be able to restore them later on
par.defaults <- par(no.readonly=TRUE)
par(bg="green")
par("bg")
## [1] "green"
plot(1:10)
par(par.defaults)
par("bg")
## [1] "white"
plot(1:10)

x<-(0:100)/10
plot(x,x^3-13*x^2+39*x,type="l",xlab= "time (hours)",ylab="temperature",lwd=3,las=1,cex.axis=1.5,cex.lab=1.5)

hist(titanic3$age,breaks=15,freq=FALSE,cex.axis=1.5,cex.lab=1.5)

boxplot(titanic3$fare,ylim=c(0,300),ylab="fare",cex.axis=1.5,cex.lab=1.5)

boxplot(fare ~ pclass,data=titanic3,ylim=c(0,300),ylab="fare",cex.axis=1.5,cex.lab=1.5)
plot(fare ~ pclass,data=titanic3,ylim=c(0,300),ylab="fare",cex.axis=1.5,cex.lab=1.5)

Advanced graphics

# graphics
plot(fare~age,data=titanic3) # very skewed distribution

plot(log10(fare)~age,data=titanic3) # plot log10(y) vs x

plot(fare~age,data=titanic3,log="y") # original data, but log scale
min(titanic3$fare,na.rm=TRUE) # zero values!
## [1] 0
sum(titanic3$fare==0,na.rm=TRUE) # zero values!
## [1] 17
install.packages("ggplot2")
## package 'ggplot2' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(ggplot2)

qplot(age,fare,data=titanic3)

qplot(age,fare,data=titanic3) + scale_y_log10() # log scale for y values

qplot(age,fare,data=titanic3,facets=~pclass) # one plot per pclass

qplot(age,fare,data=titanic3,facets=~pclass,ylim=c(0,280)) # better for comparison

qplot(age,fare,data=titanic3,facets=sex~pclass) + scale_y_log10() # even better

qplot(age,fare,data=titanic3,facets=pclass~sex) + scale_y_log10() # better for comparing males and females per class

#install.packages("devtools")
#library(devtools)
#install_github("rCharts", "ramnathv", ref = "dev") 
#library(rCharts)
#titanic3$survived = factor(titanic3$survived, labels=c("no","yes"))
#rPlot(fare ~  age| sex + pclass, data = titanic3, type = "point", color = "survived", size = list(const = 2))

#dt <- dTable(titanic3,sPaginationType=  "full_numbers")
#dt
## Open the PDF device
pdf("titanic3_ggplot_v1.pdf")
qplot(age,fare,data=titanic3,facets=pclass~sex) + scale_y_log10()
## Close the device again to save the file
dev.off()
## png 
##   2
qplot(age,fare,data=titanic3,facets=pclass~sex) + scale_y_log10()

dev.print("titanic3_ggplot_v2.pdf",device=pdf)
## png 
##   2
## Change size, for pdf device width and height are specified in inches
pdf("titanic3_ggplot_v1_big.pdf", width=21, height=21)
qplot(age,fare,data=titanic3,facets=pclass~sex) + scale_y_log10()
## Close the device again to save the file
dev.off()
## png 
##   2

Structure of R

search()  
##  [1] ".GlobalEnv"        "package:ggplot2"   "package:lubridate"
##  [4] "package:ISwR"      "package:readxl"    "package:foreign"  
##  [7] "package:stats"     "package:graphics"  "package:grDevices"
## [10] "package:utils"     "package:datasets"  "package:methods"  
## [13] "Autoloads"         "package:base"
ls() # objects in Workspace
##  [1] "A"             "a2"            "age"           "c"            
##  [5] "DiseaseState"  "DiseaseState2" "good.morning"  "indx"         
##  [9] "m"             "name"          "par.defaults"  "pclass"       
## [13] "r"             "survived"      "titanic"       "titanic3"     
## [17] "tmp"           "x"
ls("package:stats")[1:20] # some objects in stats package
##  [1] "acf"                  "acf2AR"               "add.scope"           
##  [4] "add1"                 "addmargins"           "aggregate"           
##  [7] "aggregate.data.frame" "aggregate.ts"         "AIC"                 
## [10] "alias"                "anova"                "ansari.test"         
## [13] "aov"                  "approx"               "approxfun"           
## [16] "ar"                   "ar.burg"              "ar.mle"              
## [19] "ar.ols"               "ar.yw"
ls("package:base")[1:30] # some objects in base package
##  [1] "-"                 "-.Date"            "-.POSIXt"         
##  [4] "!"                 "!.hexmode"         "!.octmode"        
##  [7] "!="                "$"                 "$.DLLInfo"        
## [10] "$.package_version" "$<-"               "$<-.data.frame"   
## [13] "%%"                "%*%"               "%/%"              
## [16] "%in%"              "%o%"               "%x%"              
## [19] "&"                 "&&"                "&.hexmode"        
## [22] "&.octmode"         "("                 "*"                
## [25] "*.difftime"        "/"                 "/.difftime"       
## [28] ":"                 "::"                ":::"
## Hierarchical structure and duplicate names
data(quakes) # from package:datasets
## New quakes object in Workspace hides the other
quakes <- 1:10 
quakes
##  [1]  1  2  3  4  5  6  7  8  9 10
## Removing it makes the other visible again
rm(quakes)
quakes$mag # original quakes not overwritten!
##    [1] 4.8 4.2 5.4 4.1 4.0 4.0 4.8 4.4 4.7 4.3 4.4 4.6 4.4 4.4 6.1 4.3 6.0 4.5
##   [19] 4.4 4.4 4.5 4.2 4.4 4.7 5.4 4.0 4.6 5.2 4.5 4.4 4.6 4.7 4.8 4.0 4.5 4.3
##   [37] 4.5 4.6 4.1 4.4 4.7 4.6 4.4 4.3 4.6 4.9 4.5 4.4 4.3 5.1 4.2 4.0 4.6 4.3
##   [55] 4.2 4.4 4.5 4.0 4.4 4.3 4.7 4.1 5.0 4.6 4.9 4.7 4.1 5.0 4.5 5.5 4.0 4.5
##   [73] 4.3 5.2 4.4 4.3 4.1 4.5 4.2 5.3 5.2 4.5 4.6 4.3 4.0 4.3 4.7 4.5 4.2 4.3
##   [91] 5.1 4.7 5.2 4.2 4.2 4.0 4.5 5.2 5.1 4.7 4.1 4.6 4.7 4.7 4.6 4.2 4.4 4.6
##  [109] 5.7 5.0 4.5 4.2 4.0 4.8 4.4 4.2 5.3 4.7 4.8 4.2 4.8 4.3 4.7 4.5 4.4 5.1
##  [127] 4.2 5.0 4.8 4.3 4.5 4.2 4.5 4.6 4.3 4.7 5.1 4.6 4.9 4.2 4.6 4.0 5.0 4.4
##  [145] 4.2 4.2 4.4 4.9 5.3 4.0 5.7 6.4 4.3 4.2 4.7 4.7 4.2 4.3 4.9 4.6 4.1 4.8
##  [163] 4.6 4.6 4.8 5.0 5.6 5.3 4.7 4.5 4.3 4.6 4.1 4.1 4.1 5.7 5.0 4.5 4.1 4.6
##  [181] 4.5 4.3 4.4 4.2 4.1 4.9 4.3 4.9 4.6 4.6 5.3 4.7 4.6 4.1 4.6 4.2 4.6 4.5
##  [199] 4.3 5.2 4.3 4.0 4.7 4.5 4.5 4.3 5.2 4.5 4.7 4.2 4.7 4.3 4.1 5.4 4.3 4.3
##  [217] 4.2 4.6 4.4 4.2 4.7 4.6 4.9 4.2 4.5 4.9 4.2 4.5 5.0 5.0 4.7 4.3 4.4 4.9
##  [235] 4.5 4.0 4.4 5.0 4.7 4.8 4.5 4.1 5.3 4.8 5.0 4.6 4.3 4.7 5.3 4.2 4.7 4.5
##  [253] 5.1 4.9 4.4 4.2 4.6 4.7 4.2 4.9 5.1 4.7 4.4 4.4 4.2 4.9 4.6 4.4 4.6 4.5
##  [271] 4.4 4.9 4.1 4.2 5.7 4.6 5.0 4.3 4.5 5.2 4.4 4.2 4.6 4.0 4.4 4.7 4.2 4.7
##  [289] 4.5 5.0 5.0 4.8 4.6 4.6 5.0 5.0 5.6 4.0 4.0 4.6 4.4 4.8 4.7 4.6 4.3 4.7
##  [307] 4.1 4.9 4.6 4.8 4.9 5.1 5.4 4.5 4.9 4.4 4.1 5.3 4.5 4.9 4.8 5.2 4.4 4.4
##  [325] 4.9 4.4 4.2 4.6 4.6 5.3 5.3 4.5 4.4 5.0 5.1 4.5 4.5 5.4 4.5 4.7 4.2 4.8
##  [343] 4.5 4.3 4.3 4.3 4.6 4.5 5.0 4.6 4.6 4.9 4.1 5.5 4.7 5.0 5.1 5.5 4.6 4.7
##  [361] 4.2 4.0 5.4 4.3 4.4 4.6 5.1 4.5 4.2 4.1 5.1 5.4 5.1 5.1 4.4 5.7 4.4 5.1
##  [379] 4.6 5.5 5.1 4.4 5.0 5.0 5.1 5.1 4.2 4.5 4.0 4.9 4.3 4.8 4.4 4.2 4.9 4.2
##  [397] 5.3 5.0 5.7 5.3 4.7 4.6 4.3 5.4 4.4 4.3 4.3 4.7 4.4 4.2 4.5 4.8 4.8 4.3
##  [415] 4.4 5.1 4.4 4.4 4.3 4.8 4.3 4.5 4.1 5.1 4.7 4.6 4.7 4.6 4.4 4.2 4.1 4.7
##  [433] 4.0 4.8 4.4 4.3 4.4 4.5 4.5 4.7 4.3 4.7 4.8 4.6 5.1 4.7 4.3 5.1 5.5 4.5
##  [451] 4.3 4.3 4.7 4.3 4.3 4.6 4.5 4.5 5.4 4.6 4.4 5.0 5.2 4.4 5.1 4.4 4.4 4.8
##  [469] 4.4 4.7 4.7 4.4 4.3 5.0 4.5 4.6 5.4 4.6 4.5 4.4 4.5 4.1 4.0 4.9 4.1 5.2
##  [487] 4.4 4.1 4.9 4.6 4.5 4.8 4.2 4.2 4.1 5.5 4.4 4.8 4.2 4.8 4.9 4.6 4.5 4.4
##  [505] 4.6 4.2 4.7 4.4 4.5 4.9 4.7 5.5 4.7 4.6 4.1 4.7 4.6 4.3 4.4 4.7 4.3 4.1
##  [523] 4.7 4.5 5.5 4.5 4.6 5.1 4.4 4.7 5.5 4.6 4.0 4.6 4.8 4.7 4.7 4.2 5.4 4.6
##  [541] 5.5 4.2 4.5 4.5 4.8 4.6 5.4 4.6 5.0 4.3 4.3 4.8 4.7 4.7 4.8 4.2 4.2 5.9
##  [559] 4.6 4.5 4.9 4.7 4.9 5.3 4.2 4.2 4.5 5.2 4.5 5.6 5.2 4.8 4.5 5.0 4.4 4.5
##  [577] 4.6 4.5 5.2 5.1 4.8 4.3 5.1 4.7 4.6 4.8 4.6 4.4 4.6 5.1 4.3 4.4 4.7 4.8
##  [595] 4.1 4.6 4.9 4.0 4.7 4.7 5.2 4.6 4.6 4.9 5.7 4.7 4.4 4.9 4.8 4.6 4.4 4.8
##  [613] 4.7 4.2 5.1 4.8 4.9 5.1 4.3 4.4 4.7 4.7 5.3 5.1 4.9 4.5 4.7 4.2 5.1 4.8
##  [631] 4.2 4.8 4.4 4.5 4.3 5.6 4.0 5.0 4.2 4.6 4.6 4.5 5.0 4.7 4.6 4.8 4.6 4.4
##  [649] 5.6 4.2 5.4 4.8 5.6 4.4 4.7 4.6 5.1 4.6 4.5 4.2 4.8 4.9 5.5 5.0 4.2 5.2
##  [667] 4.8 4.4 4.8 4.4 4.1 4.9 4.6 4.5 5.3 4.8 4.6 4.8 4.7 4.9 5.3 4.4 4.6 4.3
##  [685] 4.4 4.2 4.1 4.2 5.0 4.1 4.3 5.2 4.2 4.5 4.4 4.4 5.0 4.0 4.8 5.0 4.2 5.2
##  [703] 5.2 4.5 4.3 4.5 4.6 5.1 4.4 4.3 4.7 5.6 4.9 5.1 4.6 4.4 4.8 4.2 4.7 4.1
##  [721] 4.7 4.0 4.9 5.0 4.5 4.4 4.0 4.5 4.7 4.4 4.7 4.7 4.0 4.2 4.5 4.7 4.5 4.1
##  [739] 4.6 4.3 4.6 5.2 4.6 4.7 5.0 5.2 4.4 4.6 4.5 4.0 4.2 5.3 5.9 4.9 4.5 4.4
##  [757] 5.4 5.3 5.1 4.1 4.3 4.5 4.1 5.1 5.3 4.5 4.1 4.4 4.7 4.0 5.1 4.0 4.3 4.3
##  [775] 4.0 4.1 4.4 4.3 4.2 4.0 4.5 4.7 5.0 4.3 5.1 4.7 5.3 5.0 4.5 5.0 4.1 4.9
##  [793] 4.1 4.0 4.4 4.5 4.4 4.5 4.3 4.3 5.0 4.5 4.5 4.4 4.5 4.1 4.9 4.7 4.3 4.6
##  [811] 4.6 5.2 4.5 4.3 4.6 4.0 4.5 4.8 4.6 4.1 4.9 4.7 4.1 4.3 4.4 4.0 4.8 4.4
##  [829] 4.4 4.6 4.2 4.1 4.2 4.0 4.1 4.1 4.7 4.8 5.1 5.0 4.8 4.4 5.0 5.2 4.2 4.1
##  [847] 4.8 4.5 5.0 5.1 4.1 4.7 5.2 4.8 4.5 4.0 4.7 4.1 4.3 4.3 4.0 4.7 4.1 4.2
##  [865] 4.8 4.8 4.2 4.2 5.7 6.0 4.2 4.5 4.9 4.4 4.0 4.3 4.2 4.5 4.8 4.4 4.2 4.2
##  [883] 5.0 4.2 5.2 4.5 4.6 5.0 5.0 5.4 4.8 4.2 5.5 4.3 4.1 4.4 4.9 4.5 4.9 4.0
##  [901] 4.5 5.0 4.9 4.5 4.2 4.2 4.2 5.2 4.3 5.1 4.6 4.6 4.4 4.4 4.9 5.1 4.2 4.7
##  [919] 4.0 5.6 5.3 5.0 4.3 4.1 5.1 4.5 4.9 5.4 4.7 4.8 4.5 4.2 4.6 4.6 5.6 5.4
##  [937] 4.0 5.2 4.2 4.7 4.3 4.8 4.6 5.4 4.8 4.6 4.2 5.5 4.7 4.7 4.5 5.5 4.5 4.1
##  [955] 4.0 4.3 4.7 4.9 4.5 4.7 4.5 4.8 4.3 4.1 5.4 4.1 4.8 4.2 4.8 5.4 4.4 5.2
##  [973] 4.7 4.9 4.9 4.2 4.4 4.4 4.3 4.9 5.0 4.7 4.9 4.3 4.5 4.2 5.2 4.8 4.0 4.7
##  [991] 4.3 4.3 4.9 4.0 4.2 4.4 4.7 4.5 4.5 6.0
find("quakes")
## [1] "package:datasets"
datasets::quakes
##         lat   long depth mag stations
## 1    -20.42 181.62   562 4.8       41
## 2    -20.62 181.03   650 4.2       15
## 3    -26.00 184.10    42 5.4       43
## 4    -17.97 181.66   626 4.1       19
## 5    -20.42 181.96   649 4.0       11
## 6    -19.68 184.31   195 4.0       12
## 7    -11.70 166.10    82 4.8       43
## 8    -28.11 181.93   194 4.4       15
## 9    -28.74 181.74   211 4.7       35
## 10   -17.47 179.59   622 4.3       19
## 11   -21.44 180.69   583 4.4       13
## 12   -12.26 167.00   249 4.6       16
## 13   -18.54 182.11   554 4.4       19
## 14   -21.00 181.66   600 4.4       10
## 15   -20.70 169.92   139 6.1       94
## 16   -15.94 184.95   306 4.3       11
## 17   -13.64 165.96    50 6.0       83
## 18   -17.83 181.50   590 4.5       21
## 19   -23.50 179.78   570 4.4       13
## 20   -22.63 180.31   598 4.4       18
## 21   -20.84 181.16   576 4.5       17
## 22   -10.98 166.32   211 4.2       12
## 23   -23.30 180.16   512 4.4       18
## 24   -30.20 182.00   125 4.7       22
## 25   -19.66 180.28   431 5.4       57
## 26   -17.94 181.49   537 4.0       15
## 27   -14.72 167.51   155 4.6       18
## 28   -16.46 180.79   498 5.2       79
## 29   -20.97 181.47   582 4.5       25
## 30   -19.84 182.37   328 4.4       17
## 31   -22.58 179.24   553 4.6       21
## 32   -16.32 166.74    50 4.7       30
## 33   -15.55 185.05   292 4.8       42
## 34   -23.55 180.80   349 4.0       10
## 35   -16.30 186.00    48 4.5       10
## 36   -25.82 179.33   600 4.3       13
## 37   -18.73 169.23   206 4.5       17
## 38   -17.64 181.28   574 4.6       17
## 39   -17.66 181.40   585 4.1       17
## 40   -18.82 169.33   230 4.4       11
## 41   -37.37 176.78   263 4.7       34
## 42   -15.31 186.10    96 4.6       32
## 43   -24.97 179.82   511 4.4       23
## 44   -15.49 186.04    94 4.3       26
## 45   -19.23 169.41   246 4.6       27
## 46   -30.10 182.30    56 4.9       34
## 47   -26.40 181.70   329 4.5       24
## 48   -11.77 166.32    70 4.4       18
## 49   -24.12 180.08   493 4.3       21
## 50   -18.97 185.25   129 5.1       73
## 51   -18.75 182.35   554 4.2       13
## 52   -19.26 184.42   223 4.0       15
## 53   -22.75 173.20    46 4.6       26
## 54   -21.37 180.67   593 4.3       13
## 55   -20.10 182.16   489 4.2       16
## 56   -19.85 182.13   562 4.4       31
## 57   -22.70 181.00   445 4.5       17
## 58   -22.06 180.60   584 4.0       11
## 59   -17.80 181.35   535 4.4       23
## 60   -24.20 179.20   530 4.3       12
## 61   -20.69 181.55   582 4.7       35
## 62   -21.16 182.40   260 4.1       12
## 63   -13.82 172.38   613 5.0       61
## 64   -11.49 166.22    84 4.6       32
## 65   -20.68 181.41   593 4.9       40
## 66   -17.10 184.93   286 4.7       25
## 67   -20.14 181.60   587 4.1       13
## 68   -21.96 179.62   627 5.0       45
## 69   -20.42 181.86   530 4.5       27
## 70   -15.46 187.81    40 5.5       91
## 71   -15.31 185.80   152 4.0       11
## 72   -19.86 184.35   201 4.5       30
## 73   -11.55 166.20    96 4.3       14
## 74   -23.74 179.99   506 5.2       75
## 75   -17.70 181.23   546 4.4       35
## 76   -23.54 180.04   564 4.3       15
## 77   -19.21 184.70   197 4.1       11
## 78   -12.11 167.06   265 4.5       23
## 79   -21.81 181.71   323 4.2       15
## 80   -28.98 181.11   304 5.3       60
## 81   -34.02 180.21    75 5.2       65
## 82   -23.84 180.99   367 4.5       27
## 83   -19.57 182.38   579 4.6       38
## 84   -20.12 183.40   284 4.3       15
## 85   -17.70 181.70   450 4.0       11
## 86   -19.66 184.31   170 4.3       15
## 87   -21.50 170.50   117 4.7       32
## 88   -23.64 179.96   538 4.5       26
## 89   -15.43 186.30   123 4.2       16
## 90   -15.41 186.44    69 4.3       42
## 91   -15.48 167.53   128 5.1       61
## 92   -13.36 167.06   236 4.7       22
## 93   -20.64 182.02   497 5.2       64
## 94   -19.72 169.71   271 4.2       14
## 95   -15.44 185.26   224 4.2       21
## 96   -19.73 182.40   375 4.0       18
## 97   -27.24 181.11   365 4.5       21
## 98   -18.16 183.41   306 5.2       54
## 99   -13.66 166.54    50 5.1       45
## 100  -24.57 179.92   484 4.7       33
## 101  -16.98 185.61   108 4.1       12
## 102  -26.20 178.41   583 4.6       25
## 103  -21.88 180.39   608 4.7       30
## 104  -33.00 181.60    72 4.7       22
## 105  -21.33 180.69   636 4.6       29
## 106  -19.44 183.50   293 4.2       15
## 107  -34.89 180.60    42 4.4       25
## 108  -20.24 169.49   100 4.6       22
## 109  -22.55 185.90    42 5.7       76
## 110  -36.95 177.81   146 5.0       35
## 111  -15.75 185.23   280 4.5       28
## 112  -16.85 182.31   388 4.2       14
## 113  -19.06 182.45   477 4.0       16
## 114  -26.11 178.30   617 4.8       39
## 115  -26.20 178.35   606 4.4       21
## 116  -26.13 178.31   609 4.2       25
## 117  -13.66 172.23    46 5.3       67
## 118  -13.47 172.29    64 4.7       14
## 119  -14.60 167.40   178 4.8       52
## 120  -18.96 169.48   248 4.2       13
## 121  -14.65 166.97    82 4.8       28
## 122  -19.90 178.90    81 4.3       11
## 123  -22.05 180.40   606 4.7       27
## 124  -19.22 182.43   571 4.5       23
## 125  -31.24 180.60   328 4.4       18
## 126  -17.93 167.89    49 5.1       43
## 127  -19.30 183.84   517 4.2       21
## 128  -26.53 178.57   600 5.0       69
## 129  -27.72 181.70    94 4.8       59
## 130  -19.19 183.51   307 4.3       19
## 131  -17.43 185.43   189 4.5       22
## 132  -17.05 181.22   527 4.2       24
## 133  -19.52 168.98    63 4.5       21
## 134  -23.71 180.30   510 4.6       30
## 135  -21.30 180.82   624 4.3       14
## 136  -16.24 168.02    53 4.7       12
## 137  -16.14 187.32    42 5.1       68
## 138  -23.95 182.80   199 4.6       14
## 139  -25.20 182.60   149 4.9       31
## 140  -18.84 184.16   210 4.2       17
## 141  -12.66 169.46   658 4.6       43
## 142  -20.65 181.40   582 4.0       14
## 143  -13.23 167.10   220 5.0       46
## 144  -29.91 181.43   205 4.4       34
## 145  -14.31 173.50   614 4.2       23
## 146  -20.10 184.40   186 4.2       10
## 147  -17.80 185.17    97 4.4       22
## 148  -21.27 173.49    48 4.9       42
## 149  -23.58 180.17   462 5.3       63
## 150  -17.90 181.50   573 4.0       19
## 151  -23.34 184.50    56 5.7      106
## 152  -15.56 167.62   127 6.4      122
## 153  -23.83 182.56   229 4.3       24
## 154  -11.80 165.80   112 4.2       20
## 155  -15.54 167.68   140 4.7       16
## 156  -20.65 181.32   597 4.7       39
## 157  -11.75 166.07    69 4.2       14
## 158  -24.81 180.00   452 4.3       19
## 159  -20.90 169.84    93 4.9       31
## 160  -11.34 166.24   103 4.6       30
## 161  -17.98 180.50   626 4.1       19
## 162  -24.34 179.52   504 4.8       34
## 163  -13.86 167.16   202 4.6       30
## 164  -35.56 180.20    42 4.6       32
## 165  -35.48 179.90    59 4.8       35
## 166  -34.20 179.43    40 5.0       37
## 167  -26.00 182.12   205 5.6       98
## 168  -19.89 183.84   244 5.3       73
## 169  -23.43 180.00   553 4.7       41
## 170  -18.89 169.42   239 4.5       27
## 171  -17.82 181.83   640 4.3       24
## 172  -25.68 180.34   434 4.6       41
## 173  -20.20 180.90   627 4.1       11
## 174  -15.20 184.68    99 4.1       14
## 175  -15.03 182.29   399 4.1       10
## 176  -32.22 180.20   216 5.7       90
## 177  -22.64 180.64   544 5.0       50
## 178  -17.42 185.16   206 4.5       22
## 179  -17.84 181.48   542 4.1       20
## 180  -15.02 184.24   339 4.6       27
## 181  -18.04 181.75   640 4.5       47
## 182  -24.60 183.50    67 4.3       25
## 183  -19.88 184.30   161 4.4       17
## 184  -20.30 183.00   375 4.2       15
## 185  -20.45 181.85   534 4.1       14
## 186  -17.67 187.09    45 4.9       62
## 187  -22.30 181.90   309 4.3       11
## 188  -19.85 181.85   576 4.9       54
## 189  -24.27 179.88   523 4.6       24
## 190  -15.85 185.13   290 4.6       29
## 191  -20.02 184.09   234 5.3       71
## 192  -18.56 169.31   223 4.7       35
## 193  -17.87 182.00   569 4.6       12
## 194  -24.08 179.50   605 4.1       21
## 195  -32.20 179.61   422 4.6       41
## 196  -20.36 181.19   637 4.2       23
## 197  -23.85 182.53   204 4.6       27
## 198  -24.00 182.75   175 4.5       14
## 199  -20.41 181.74   538 4.3       31
## 200  -17.72 180.30   595 5.2       74
## 201  -19.67 182.18   360 4.3       23
## 202  -17.70 182.20   445 4.0       12
## 203  -16.23 183.59   367 4.7       35
## 204  -26.72 183.35   190 4.5       36
## 205  -12.95 169.09   629 4.5       19
## 206  -21.97 182.32   261 4.3       13
## 207  -21.96 180.54   603 5.2       66
## 208  -20.32 181.69   508 4.5       14
## 209  -30.28 180.62   350 4.7       32
## 210  -20.20 182.30   533 4.2       11
## 211  -30.66 180.13   411 4.7       42
## 212  -16.17 184.10   338 4.3       13
## 213  -28.25 181.71   226 4.1       19
## 214  -20.47 185.68    93 5.4       85
## 215  -23.55 180.27   535 4.3       22
## 216  -20.94 181.58   573 4.3       21
## 217  -26.67 182.40   186 4.2       17
## 218  -18.13 181.52   618 4.6       41
## 219  -20.21 183.83   242 4.4       29
## 220  -18.31 182.39   342 4.2       14
## 221  -16.52 185.70    90 4.7       30
## 222  -22.36 171.65   130 4.6       39
## 223  -22.43 184.48    65 4.9       48
## 224  -20.37 182.10   397 4.2       22
## 225  -23.77 180.16   505 4.5       26
## 226  -13.65 166.66    71 4.9       52
## 227  -21.55 182.90   207 4.2       18
## 228  -16.24 185.75   154 4.5       22
## 229  -23.73 182.53   232 5.0       55
## 230  -22.34 171.52   106 5.0       43
## 231  -19.40 180.94   664 4.7       34
## 232  -24.64 180.81   397 4.3       24
## 233  -16.00 182.82   431 4.4       16
## 234  -19.62 185.35    57 4.9       31
## 235  -23.84 180.13   525 4.5       15
## 236  -23.54 179.93   574 4.0       12
## 237  -28.23 182.68    74 4.4       20
## 238  -21.68 180.63   617 5.0       63
## 239  -13.44 166.53    44 4.7       27
## 240  -24.96 180.22   470 4.8       41
## 241  -20.08 182.74   298 4.5       33
## 242  -24.36 182.84   148 4.1       16
## 243  -14.70 166.00    48 5.3       16
## 244  -18.20 183.68   107 4.8       52
## 245  -16.65 185.51   218 5.0       52
## 246  -18.11 181.67   597 4.6       28
## 247  -17.95 181.65   619 4.3       26
## 248  -15.50 186.90    46 4.7       18
## 249  -23.36 180.01   553 5.3       61
## 250  -19.15 169.50   150 4.2       12
## 251  -10.97 166.26   180 4.7       26
## 252  -14.85 167.24    97 4.5       26
## 253  -17.80 181.38   587 5.1       47
## 254  -22.50 170.40   106 4.9       38
## 255  -29.10 182.10   179 4.4       19
## 256  -20.32 180.88   680 4.2       22
## 257  -16.09 184.89   304 4.6       34
## 258  -19.18 169.33   254 4.7       35
## 259  -23.81 179.36   521 4.2       23
## 260  -23.79 179.89   526 4.9       43
## 261  -19.02 184.23   270 5.1       72
## 262  -20.90 181.51   548 4.7       32
## 263  -19.06 169.01   158 4.4       10
## 264  -17.88 181.47   562 4.4       27
## 265  -19.41 183.05   300 4.2       16
## 266  -26.17 184.20    65 4.9       37
## 267  -14.95 167.24   130 4.6       16
## 268  -18.73 168.80    82 4.4       14
## 269  -20.21 182.37   482 4.6       37
## 270  -21.29 180.85   607 4.5       23
## 271  -19.76 181.41   105 4.4       15
## 272  -22.09 180.38   590 4.9       35
## 273  -23.80 179.90   498 4.1       12
## 274  -20.16 181.99   504 4.2       11
## 275  -22.13 180.38   577 5.7      104
## 276  -17.44 181.40   529 4.6       25
## 277  -23.33 180.18   528 5.0       59
## 278  -24.78 179.22   492 4.3       16
## 279  -22.00 180.52   561 4.5       19
## 280  -19.13 182.51   579 5.2       56
## 281  -30.72 180.10   413 4.4       22
## 282  -22.32 180.54   565 4.2       12
## 283  -16.45 177.77   138 4.6       17
## 284  -17.70 185.00   383 4.0       10
## 285  -17.95 184.68   260 4.4       21
## 286  -24.40 179.85   522 4.7       29
## 287  -19.30 180.60   671 4.2       16
## 288  -21.13 185.32   123 4.7       36
## 289  -18.07 181.57   572 4.5       26
## 290  -20.60 182.28   529 5.0       50
## 291  -18.48 181.49   641 5.0       49
## 292  -13.34 166.20    67 4.8       18
## 293  -20.92 181.50   546 4.6       31
## 294  -25.31 179.69   507 4.6       35
## 295  -15.24 186.21   158 5.0       57
## 296  -16.40 185.86   148 5.0       47
## 297  -24.57 178.40   562 5.6       80
## 298  -17.94 181.51   601 4.0       16
## 299  -30.64 181.20   175 4.0       16
## 300  -18.64 169.32   260 4.6       23
## 301  -13.09 169.28   654 4.4       22
## 302  -19.68 184.14   242 4.8       40
## 303  -16.44 185.74   126 4.7       30
## 304  -21.09 181.38   555 4.6       15
## 305  -14.99 171.39   637 4.3       21
## 306  -23.30 179.70   500 4.7       29
## 307  -17.68 181.36   515 4.1       19
## 308  -22.00 180.53   583 4.9       20
## 309  -21.38 181.39   501 4.6       36
## 310  -32.62 181.50    55 4.8       26
## 311  -13.05 169.58   644 4.9       68
## 312  -12.93 169.63   641 5.1       57
## 313  -18.60 181.91   442 5.4       82
## 314  -21.34 181.41   464 4.5       21
## 315  -21.48 183.78   200 4.9       54
## 316  -17.40 181.02   479 4.4       14
## 317  -17.32 181.03   497 4.1       13
## 318  -18.77 169.24   218 5.3       53
## 319  -26.16 179.50   492 4.5       25
## 320  -12.59 167.10   325 4.9       26
## 321  -14.82 167.32   123 4.8       28
## 322  -21.79 183.48   210 5.2       69
## 323  -19.83 182.04   575 4.4       23
## 324  -29.50 182.31   129 4.4       14
## 325  -12.49 166.36    74 4.9       55
## 326  -26.10 182.30    49 4.4       11
## 327  -21.04 181.20   483 4.2       10
## 328  -10.78 165.77    93 4.6       20
## 329  -20.76 185.77   118 4.6       15
## 330  -11.41 166.24    83 5.3       55
## 331  -19.10 183.87    61 5.3       42
## 332  -23.91 180.00   534 4.5       11
## 333  -27.33 182.60    42 4.4       11
## 334  -12.25 166.60   219 5.0       28
## 335  -23.49 179.07   544 5.1       58
## 336  -27.18 182.18    56 4.5       14
## 337  -25.80 182.10    68 4.5       26
## 338  -27.19 182.18    69 5.4       68
## 339  -27.27 182.38    45 4.5       16
## 340  -27.10 182.18    43 4.7       17
## 341  -27.22 182.28    65 4.2       14
## 342  -27.38 181.70    80 4.8       13
## 343  -27.27 182.50    51 4.5       13
## 344  -27.54 182.50    68 4.3       12
## 345  -27.20 182.39    69 4.3       14
## 346  -27.71 182.47   103 4.3       11
## 347  -27.60 182.40    61 4.6       11
## 348  -27.38 182.39    69 4.5       12
## 349  -21.54 185.48    51 5.0       29
## 350  -27.21 182.43    55 4.6       10
## 351  -28.96 182.61    54 4.6       15
## 352  -12.01 166.29    59 4.9       27
## 353  -17.46 181.32   573 4.1       17
## 354  -30.17 182.02    56 5.5       68
## 355  -27.27 182.36    65 4.7       21
## 356  -17.79 181.32   587 5.0       49
## 357  -22.19 171.40   150 5.1       49
## 358  -17.10 182.68   403 5.5       82
## 359  -27.18 182.53    60 4.6       21
## 360  -11.64 166.47   130 4.7       19
## 361  -17.98 181.58   590 4.2       14
## 362  -16.90 185.72   135 4.0       22
## 363  -21.98 179.60   583 5.4       67
## 364  -32.14 179.90   406 4.3       19
## 365  -18.80 169.21   221 4.4       16
## 366  -26.78 183.61    40 4.6       22
## 367  -20.43 182.37   502 5.1       48
## 368  -18.30 183.20   103 4.5       14
## 369  -15.83 182.51   423 4.2       21
## 370  -23.44 182.93   158 4.1       20
## 371  -23.73 179.99   527 5.1       49
## 372  -19.89 184.08   219 5.4      105
## 373  -17.59 181.09   536 5.1       61
## 374  -19.77 181.40   630 5.1       54
## 375  -20.31 184.06   249 4.4       21
## 376  -15.33 186.75    48 5.7      123
## 377  -18.20 181.60   553 4.4       14
## 378  -15.36 186.66   112 5.1       57
## 379  -15.29 186.42   153 4.6       31
## 380  -15.36 186.71   130 5.5       95
## 381  -16.24 167.95   188 5.1       68
## 382  -13.47 167.14   226 4.4       26
## 383  -25.50 182.82   124 5.0       25
## 384  -14.32 167.33   204 5.0       49
## 385  -20.04 182.01   605 5.1       49
## 386  -28.83 181.66   221 5.1       63
## 387  -17.82 181.49   573 4.2       14
## 388  -27.23 180.98   401 4.5       39
## 389  -10.72 165.99   195 4.0       14
## 390  -27.00 183.88    56 4.9       36
## 391  -20.36 186.16   102 4.3       21
## 392  -27.17 183.68    44 4.8       27
## 393  -20.94 181.26   556 4.4       21
## 394  -17.46 181.90   417 4.2       14
## 395  -21.04 181.20   591 4.9       45
## 396  -23.70 179.60   646 4.2       21
## 397  -17.72 181.42   565 5.3       89
## 398  -15.87 188.13    52 5.0       30
## 399  -17.84 181.30   535 5.7      112
## 400  -13.45 170.30   641 5.3       93
## 401  -30.80 182.16    41 4.7       24
## 402  -11.63 166.14   109 4.6       36
## 403  -30.40 181.40    40 4.3       17
## 404  -26.18 178.59   548 5.4       65
## 405  -15.70 184.50   118 4.4       30
## 406  -17.95 181.50   593 4.3       16
## 407  -20.51 182.30   492 4.3       23
## 408  -15.36 167.51   123 4.7       28
## 409  -23.61 180.23   475 4.4       26
## 410  -33.20 181.60   153 4.2       21
## 411  -17.68 186.80   112 4.5       35
## 412  -22.24 184.56    99 4.8       57
## 413  -20.07 169.14    66 4.8       37
## 414  -25.04 180.10   481 4.3       15
## 415  -21.50 185.20   139 4.4       15
## 416  -14.28 167.26   211 5.1       51
## 417  -14.43 167.26   151 4.4       17
## 418  -32.70 181.70   211 4.4       40
## 419  -34.10 181.80   246 4.3       23
## 420  -19.70 186.20    47 4.8       19
## 421  -24.19 180.38   484 4.3       27
## 422  -26.60 182.77   119 4.5       29
## 423  -17.04 186.80    70 4.1       22
## 424  -22.10 179.71   579 5.1       58
## 425  -32.60 180.90    57 4.7       44
## 426  -33.00 182.40   176 4.6       28
## 427  -20.58 181.24   602 4.7       44
## 428  -20.61 182.60   488 4.6       12
## 429  -19.47 169.15   149 4.4       15
## 430  -17.47 180.96   546 4.2       23
## 431  -18.40 183.40   343 4.1       10
## 432  -23.33 180.26   530 4.7       22
## 433  -18.55 182.23   563 4.0       17
## 434  -26.16 178.47   537 4.8       33
## 435  -21.80 183.20   325 4.4       19
## 436  -27.63 182.93    80 4.3       14
## 437  -18.89 169.48   259 4.4       21
## 438  -20.30 182.30   476 4.5       10
## 439  -20.56 182.04   499 4.5       29
## 440  -16.10 185.32   257 4.7       30
## 441  -12.66 166.37   165 4.3       18
## 442  -21.05 184.68   136 4.7       29
## 443  -17.97 168.52   146 4.8       33
## 444  -19.83 182.54   524 4.6       14
## 445  -22.55 183.81    82 5.1       68
## 446  -22.28 183.52    90 4.7       19
## 447  -15.72 185.64   138 4.3       21
## 448  -20.85 181.59   499 5.1       91
## 449  -21.11 181.50   538 5.5      104
## 450  -25.31 180.15   467 4.5       25
## 451  -26.46 182.50   184 4.3       11
## 452  -24.09 179.68   538 4.3       21
## 453  -16.96 167.70    45 4.7       23
## 454  -23.19 182.80   237 4.3       18
## 455  -20.81 184.70   162 4.3       20
## 456  -15.03 167.32   136 4.6       20
## 457  -18.06 181.59   604 4.5       23
## 458  -19.00 185.60   107 4.5       15
## 459  -23.53 179.99   538 5.4       87
## 460  -18.18 180.63   639 4.6       39
## 461  -15.66 186.80    45 4.4       11
## 462  -18.00 180.62   636 5.0      100
## 463  -18.08 180.70   628 5.2       72
## 464  -18.05 180.86   632 4.4       15
## 465  -29.90 181.16   215 5.1       51
## 466  -20.90 181.90   556 4.4       17
## 467  -15.61 167.50   135 4.4       21
## 468  -16.03 185.43   297 4.8       25
## 469  -17.68 181.11   568 4.4       22
## 470  -31.94 180.57   168 4.7       39
## 471  -19.14 184.36   269 4.7       31
## 472  -18.00 185.48   143 4.4       29
## 473  -16.95 185.94    95 4.3       12
## 474  -10.79 166.06   142 5.0       40
## 475  -20.83 185.90   104 4.5       19
## 476  -32.90 181.60   169 4.6       27
## 477  -37.93 177.47    65 5.4       65
## 478  -29.09 183.20    54 4.6       23
## 479  -23.56 180.23   474 4.5       13
## 480  -19.60 185.20   125 4.4       13
## 481  -21.39 180.68   617 4.5       18
## 482  -14.85 184.87   294 4.1       10
## 483  -22.70 183.30   180 4.0       13
## 484  -32.42 181.21    47 4.9       39
## 485  -17.90 181.30   593 4.1       13
## 486  -23.58 183.40    94 5.2       79
## 487  -34.40 180.50   201 4.4       41
## 488  -17.61 181.20   537 4.1       11
## 489  -21.07 181.13   594 4.9       43
## 490  -13.84 170.62   638 4.6       20
## 491  -30.24 181.63    80 4.5       17
## 492  -18.49 169.04   211 4.8       30
## 493  -23.45 180.23   520 4.2       19
## 494  -16.04 183.54   384 4.2       23
## 495  -17.14 185.31   223 4.1       15
## 496  -22.54 172.91    54 5.5       71
## 497  -15.90 185.30    57 4.4       19
## 498  -30.04 181.20    49 4.8       20
## 499  -24.03 180.22   508 4.2       23
## 500  -18.89 184.46   242 4.8       36
## 501  -16.51 187.10    62 4.9       46
## 502  -20.10 186.30    63 4.6       19
## 503  -21.06 183.81   203 4.5       34
## 504  -13.07 166.87   132 4.4       24
## 505  -23.46 180.09   543 4.6       28
## 506  -19.41 182.30   589 4.2       19
## 507  -11.81 165.98    51 4.7       28
## 508  -11.76 165.96    45 4.4       51
## 509  -12.08 165.76    63 4.5       51
## 510  -25.59 180.02   485 4.9       48
## 511  -26.54 183.63    66 4.7       34
## 512  -20.90 184.28    58 5.5       92
## 513  -16.99 187.00    70 4.7       30
## 514  -23.46 180.17   541 4.6       32
## 515  -17.81 181.82   598 4.1       14
## 516  -15.17 187.20    50 4.7       28
## 517  -11.67 166.02   102 4.6       21
## 518  -20.75 184.52   144 4.3       25
## 519  -19.50 186.90    58 4.4       20
## 520  -26.18 179.79   460 4.7       44
## 521  -20.66 185.77    69 4.3       25
## 522  -19.22 182.54   570 4.1       22
## 523  -24.68 183.33    70 4.7       30
## 524  -15.43 167.38   137 4.5       16
## 525  -32.45 181.15    41 5.5       81
## 526  -21.31 180.84   586 4.5       17
## 527  -15.44 167.18   140 4.6       44
## 528  -13.26 167.01   213 5.1       70
## 529  -15.26 183.13   393 4.4       28
## 530  -33.57 180.80    51 4.7       35
## 531  -15.77 167.01    64 5.5       73
## 532  -15.79 166.83    45 4.6       39
## 533  -21.00 183.20   296 4.0       16
## 534  -16.28 166.94    50 4.6       24
## 535  -23.28 184.60    44 4.8       34
## 536  -16.10 167.25    68 4.7       36
## 537  -17.70 181.31   549 4.7       33
## 538  -15.96 166.69   150 4.2       20
## 539  -15.95 167.34    47 5.4       87
## 540  -17.56 181.59   543 4.6       34
## 541  -15.90 167.42    40 5.5       86
## 542  -15.29 166.90   100 4.2       15
## 543  -15.86 166.85    85 4.5       22
## 544  -16.20 166.80    98 4.5       21
## 545  -15.71 166.91    58 4.8       20
## 546  -16.45 167.54   125 4.6       18
## 547  -11.54 166.18    89 5.4       80
## 548  -19.61 181.91   590 4.6       34
## 549  -15.61 187.15    49 5.0       30
## 550  -21.16 181.41   543 4.3       17
## 551  -20.65 182.22   506 4.3       24
## 552  -20.33 168.71    40 4.8       38
## 553  -15.08 166.62    42 4.7       23
## 554  -23.28 184.61    76 4.7       36
## 555  -23.44 184.60    63 4.8       27
## 556  -23.12 184.42   104 4.2       17
## 557  -23.65 184.46    93 4.2       16
## 558  -22.91 183.95    64 5.9      118
## 559  -22.06 180.47   587 4.6       28
## 560  -13.56 166.49    83 4.5       25
## 561  -17.99 181.57   579 4.9       49
## 562  -23.92 184.47    40 4.7       17
## 563  -30.69 182.10    62 4.9       25
## 564  -21.92 182.80   273 5.3       78
## 565  -25.04 180.97   393 4.2       21
## 566  -19.92 183.91   264 4.2       23
## 567  -27.75 182.26   174 4.5       18
## 568  -17.71 181.18   574 5.2       67
## 569  -19.60 183.84   309 4.5       23
## 570  -34.68 179.82    75 5.6       79
## 571  -14.46 167.26   195 5.2       87
## 572  -18.85 187.55    44 4.8       35
## 573  -17.02 182.41   420 4.5       29
## 574  -20.41 186.51    63 5.0       28
## 575  -18.18 182.04   609 4.4       26
## 576  -16.49 187.80    40 4.5       18
## 577  -17.74 181.31   575 4.6       42
## 578  -20.49 181.69   559 4.5       24
## 579  -18.51 182.64   405 5.2       74
## 580  -27.28 183.40    70 5.1       54
## 581  -15.90 167.16    41 4.8       42
## 582  -20.57 181.33   605 4.3       18
## 583  -11.25 166.36   130 5.1       55
## 584  -20.04 181.87   577 4.7       19
## 585  -20.89 181.25   599 4.6       20
## 586  -16.62 186.74    82 4.8       51
## 587  -20.09 168.75    50 4.6       23
## 588  -24.96 179.87   480 4.4       25
## 589  -20.95 181.42   559 4.6       27
## 590  -23.31 179.27   566 5.1       49
## 591  -20.95 181.06   611 4.3       20
## 592  -21.58 181.90   409 4.4       19
## 593  -13.62 167.15   209 4.7       30
## 594  -12.72 166.28    70 4.8       47
## 595  -21.79 185.00    74 4.1       15
## 596  -20.48 169.76   134 4.6       33
## 597  -12.84 166.78   150 4.9       35
## 598  -17.02 182.93   406 4.0       17
## 599  -23.89 182.39   243 4.7       32
## 600  -23.07 184.03    89 4.7       32
## 601  -27.98 181.96    53 5.2       89
## 602  -28.10 182.25    68 4.6       18
## 603  -21.24 180.81   605 4.6       34
## 604  -21.24 180.86   615 4.9       23
## 605  -19.89 174.46   546 5.7       99
## 606  -32.82 179.80   176 4.7       26
## 607  -22.00 185.50    52 4.4       18
## 608  -21.57 185.62    66 4.9       38
## 609  -24.50 180.92   377 4.8       43
## 610  -33.03 180.20   186 4.6       27
## 611  -30.09 182.40    51 4.4       18
## 612  -22.75 170.99    67 4.8       35
## 613  -17.99 168.98   234 4.7       28
## 614  -19.60 181.87   597 4.2       18
## 615  -15.65 186.26    64 5.1       54
## 616  -17.78 181.53   511 4.8       56
## 617  -22.04 184.91    47 4.9       47
## 618  -20.06 168.69    49 5.1       49
## 619  -18.07 181.54   546 4.3       28
## 620  -12.85 165.67    75 4.4       30
## 621  -33.29 181.30    60 4.7       33
## 622  -34.63 179.10   278 4.7       24
## 623  -24.18 179.02   550 5.3       86
## 624  -23.78 180.31   518 5.1       71
## 625  -22.37 171.50   116 4.9       38
## 626  -23.97 179.91   518 4.5       23
## 627  -34.12 181.75    75 4.7       41
## 628  -25.25 179.86   491 4.2       23
## 629  -22.87 172.65    56 5.1       50
## 630  -18.48 182.37   376 4.8       57
## 631  -21.46 181.02   584 4.2       18
## 632  -28.56 183.47    48 4.8       56
## 633  -28.56 183.59    53 4.4       20
## 634  -21.30 180.92   617 4.5       26
## 635  -20.08 183.22   294 4.3       18
## 636  -18.82 182.21   417 5.6      129
## 637  -19.51 183.97   280 4.0       16
## 638  -12.05 167.39   332 5.0       36
## 639  -17.40 186.54    85 4.2       28
## 640  -23.93 180.18   525 4.6       31
## 641  -21.23 181.09   613 4.6       18
## 642  -16.23 167.91   182 4.5       28
## 643  -28.15 183.40    57 5.0       32
## 644  -20.81 185.01    79 4.7       42
## 645  -20.72 181.41   595 4.6       36
## 646  -23.29 184.00   164 4.8       50
## 647  -38.46 176.03   148 4.6       44
## 648  -15.48 186.73    82 4.4       17
## 649  -37.03 177.52   153 5.6       87
## 650  -20.48 181.38   556 4.2       13
## 651  -18.12 181.88   649 5.4       88
## 652  -18.17 181.98   651 4.8       43
## 653  -11.40 166.07    93 5.6       94
## 654  -23.10 180.12   533 4.4       27
## 655  -14.28 170.34   642 4.7       29
## 656  -22.87 171.72    47 4.6       27
## 657  -17.59 180.98   548 5.1       79
## 658  -27.60 182.10   154 4.6       22
## 659  -17.94 180.60   627 4.5       29
## 660  -17.88 180.58   622 4.2       23
## 661  -30.01 180.80   286 4.8       43
## 662  -19.19 182.30   390 4.9       48
## 663  -18.14 180.87   624 5.5      105
## 664  -23.46 180.11   539 5.0       41
## 665  -18.44 181.04   624 4.2       21
## 666  -18.21 180.87   631 5.2       69
## 667  -18.26 180.98   631 4.8       36
## 668  -15.85 184.83   299 4.4       30
## 669  -23.82 180.09   498 4.8       40
## 670  -18.60 184.28   255 4.4       31
## 671  -17.80 181.32   539 4.1       12
## 672  -10.78 166.10   195 4.9       45
## 673  -18.12 181.71   594 4.6       24
## 674  -19.34 182.62   573 4.5       32
## 675  -15.34 167.10   128 5.3       18
## 676  -24.97 182.85   137 4.8       40
## 677  -15.97 186.08   143 4.6       41
## 678  -23.47 180.24   511 4.8       37
## 679  -23.11 179.15   564 4.7       17
## 680  -20.54 181.66   559 4.9       50
## 681  -18.92 169.37   248 5.3       60
## 682  -20.16 184.27   210 4.4       27
## 683  -25.48 180.94   390 4.6       33
## 684  -18.19 181.74   616 4.3       17
## 685  -15.35 186.40    98 4.4       17
## 686  -18.69 169.10   218 4.2       27
## 687  -18.89 181.24   655 4.1       14
## 688  -17.61 183.32   356 4.2       15
## 689  -20.93 181.54   564 5.0       64
## 690  -17.60 181.50   548 4.1       10
## 691  -17.96 181.40   655 4.3       20
## 692  -18.80 182.41   385 5.2       67
## 693  -20.61 182.44   518 4.2       10
## 694  -20.74 181.53   598 4.5       36
## 695  -25.23 179.86   476 4.4       29
## 696  -23.90 179.90   579 4.4       16
## 697  -18.07 181.58   603 5.0       65
## 698  -15.43 185.19   249 4.0       11
## 699  -14.30 167.32   208 4.8       25
## 700  -18.04 181.57   587 5.0       51
## 701  -13.90 167.18   221 4.2       21
## 702  -17.64 177.01   545 5.2       91
## 703  -17.98 181.51   586 5.2       68
## 704  -25.00 180.00   488 4.5       10
## 705  -19.45 184.48   246 4.3       15
## 706  -16.11 187.48    61 4.5       19
## 707  -23.73 179.98   524 4.6       11
## 708  -17.74 186.78   104 5.1       71
## 709  -21.56 183.23   271 4.4       36
## 710  -20.97 181.72   487 4.3       16
## 711  -15.45 186.73    83 4.7       37
## 712  -15.93 167.91   183 5.6      109
## 713  -21.47 185.86    55 4.9       46
## 714  -21.44 170.45   166 5.1       22
## 715  -22.16 180.49   586 4.6       13
## 716  -13.36 172.76   618 4.4       18
## 717  -21.22 181.51   524 4.8       49
## 718  -26.10 182.50   133 4.2       17
## 719  -18.35 185.27   201 4.7       57
## 720  -17.20 182.90   383 4.1       11
## 721  -22.42 171.40    86 4.7       33
## 722  -17.91 181.48   555 4.0       17
## 723  -26.53 178.30   605 4.9       43
## 724  -26.50 178.29   609 5.0       50
## 725  -16.31 168.08   204 4.5       16
## 726  -18.76 169.71   287 4.4       23
## 727  -17.10 182.80   390 4.0       14
## 728  -19.28 182.78   348 4.5       30
## 729  -23.50 180.00   550 4.7       23
## 730  -21.26 181.69   487 4.4       20
## 731  -17.97 181.48   578 4.7       43
## 732  -26.02 181.20   361 4.7       32
## 733  -30.30 180.80   275 4.0       14
## 734  -24.89 179.67   498 4.2       14
## 735  -14.57 167.24   162 4.5       18
## 736  -15.40 186.87    78 4.7       44
## 737  -22.06 183.95   134 4.5       17
## 738  -25.14 178.42   554 4.1       15
## 739  -20.30 181.40   608 4.6       13
## 740  -25.28 181.17   367 4.3       25
## 741  -20.63 181.61   599 4.6       30
## 742  -19.02 186.83    45 5.2       65
## 743  -22.10 185.30    50 4.6       22
## 744  -38.59 175.70   162 4.7       36
## 745  -19.30 183.00   302 5.0       65
## 746  -31.03 181.59    57 5.2       49
## 747  -30.51 181.30   203 4.4       20
## 748  -22.55 183.34    66 4.6       18
## 749  -22.14 180.64   591 4.5       18
## 750  -25.60 180.30   440 4.0       12
## 751  -18.04 181.84   611 4.2       20
## 752  -21.29 185.77    57 5.3       69
## 753  -21.08 180.85   627 5.9      119
## 754  -20.64 169.66    89 4.9       42
## 755  -24.41 180.03   500 4.5       34
## 756  -12.16 167.03   264 4.4       14
## 757  -17.10 185.90   127 5.4       75
## 758  -21.13 185.60    85 5.3       86
## 759  -12.34 167.43    50 5.1       47
## 760  -16.43 186.73    75 4.1       20
## 761  -20.70 184.30   182 4.3       17
## 762  -21.18 180.92   619 4.5       18
## 763  -17.78 185.33   223 4.1       10
## 764  -21.57 183.86   156 5.1       70
## 765  -13.70 166.75    46 5.3       71
## 766  -12.27 167.41    50 4.5       29
## 767  -19.10 184.52   230 4.1       16
## 768  -19.85 184.51   184 4.4       26
## 769  -11.37 166.55   188 4.7       24
## 770  -20.70 186.30    80 4.0       10
## 771  -20.24 185.10    86 5.1       61
## 772  -16.40 182.73   391 4.0       16
## 773  -19.60 184.53   199 4.3       21
## 774  -21.63 180.77   592 4.3       21
## 775  -21.60 180.50   595 4.0       22
## 776  -21.77 181.00   618 4.1       10
## 777  -21.80 183.60   213 4.4       17
## 778  -21.05 180.90   616 4.3       10
## 779  -10.80 165.80   175 4.2       12
## 780  -17.90 181.50   589 4.0       12
## 781  -22.26 171.44    83 4.5       25
## 782  -22.33 171.46   119 4.7       32
## 783  -24.04 184.85    70 5.0       48
## 784  -20.40 186.10    74 4.3       22
## 785  -15.00 184.62    40 5.1       54
## 786  -27.87 183.40    87 4.7       34
## 787  -14.12 166.64    63 5.3       69
## 788  -23.61 180.27   537 5.0       63
## 789  -21.56 185.50    47 4.5       29
## 790  -21.19 181.58   490 5.0       77
## 791  -18.07 181.65   593 4.1       16
## 792  -26.00 178.43   644 4.9       27
## 793  -20.21 181.90   576 4.1       16
## 794  -28.00 182.00   199 4.0       16
## 795  -20.74 180.70   589 4.4       27
## 796  -31.80 180.60   178 4.5       19
## 797  -18.91 169.46   248 4.4       33
## 798  -20.45 182.10   500 4.5       37
## 799  -22.90 183.80    71 4.3       19
## 800  -18.11 181.63   568 4.3       36
## 801  -23.80 184.70    42 5.0       36
## 802  -23.42 180.21   510 4.5       37
## 803  -23.20 184.80    97 4.5       13
## 804  -12.93 169.52   663 4.4       30
## 805  -21.14 181.06   625 4.5       35
## 806  -19.13 184.97   210 4.1       22
## 807  -21.08 181.30   557 4.9       78
## 808  -20.07 181.75   582 4.7       27
## 809  -20.90 182.02   402 4.3       18
## 810  -25.04 179.84   474 4.6       32
## 811  -21.85 180.89   577 4.6       43
## 812  -19.34 186.59    56 5.2       49
## 813  -15.83 167.10    43 4.5       19
## 814  -23.73 183.00   118 4.3       11
## 815  -18.10 181.72   544 4.6       52
## 816  -22.12 180.49   532 4.0       14
## 817  -15.39 185.10   237 4.5       39
## 818  -16.21 186.52   111 4.8       30
## 819  -21.75 180.67   595 4.6       30
## 820  -22.10 180.40   603 4.1       11
## 821  -24.97 179.54   505 4.9       50
## 822  -19.36 186.36   100 4.7       40
## 823  -22.14 179.62   587 4.1       23
## 824  -21.48 182.44   364 4.3       20
## 825  -18.54 168.93   100 4.4       17
## 826  -21.62 182.40   350 4.0       12
## 827  -13.40 166.90   228 4.8       15
## 828  -15.50 185.30    93 4.4       25
## 829  -15.67 185.23    66 4.4       34
## 830  -21.78 183.11   225 4.6       21
## 831  -30.63 180.90   334 4.2       28
## 832  -15.70 185.10    70 4.1       15
## 833  -19.20 184.37   220 4.2       18
## 834  -19.70 182.44   397 4.0       12
## 835  -19.40 182.29   326 4.1       15
## 836  -15.85 185.90   121 4.1       17
## 837  -17.38 168.63   209 4.7       29
## 838  -24.33 179.97   510 4.8       44
## 839  -20.89 185.26    54 5.1       44
## 840  -18.97 169.44   242 5.0       41
## 841  -17.99 181.62   574 4.8       38
## 842  -15.80 185.25    82 4.4       39
## 843  -25.42 182.65   102 5.0       36
## 844  -21.60 169.90    43 5.2       56
## 845  -26.06 180.05   432 4.2       19
## 846  -17.56 181.23   580 4.1       16
## 847  -25.63 180.26   464 4.8       60
## 848  -25.46 179.98   479 4.5       27
## 849  -22.23 180.48   581 5.0       54
## 850  -21.55 181.39   513 5.1       81
## 851  -15.18 185.93    77 4.1       16
## 852  -13.79 166.56    68 4.7       41
## 853  -15.18 167.23    71 5.2       59
## 854  -18.78 186.72    68 4.8       48
## 855  -17.90 181.41   586 4.5       33
## 856  -18.50 185.40   243 4.0       11
## 857  -14.82 171.17   658 4.7       49
## 858  -15.65 185.17   315 4.1       15
## 859  -30.01 181.15   210 4.3       17
## 860  -13.16 167.24   278 4.3       17
## 861  -21.03 180.78   638 4.0       14
## 862  -21.40 180.78   615 4.7       51
## 863  -17.93 181.89   567 4.1       27
## 864  -20.87 181.70   560 4.2       13
## 865  -12.01 166.66    99 4.8       36
## 866  -19.10 169.63   266 4.8       31
## 867  -22.85 181.37   397 4.2       15
## 868  -17.08 185.96   180 4.2       29
## 869  -21.14 174.21    40 5.7       78
## 870  -12.23 167.02   242 6.0      132
## 871  -20.91 181.57   530 4.2       20
## 872  -11.38 167.05   133 4.5       32
## 873  -11.02 167.01    62 4.9       36
## 874  -22.09 180.58   580 4.4       22
## 875  -17.80 181.20   530 4.0       15
## 876  -18.94 182.43   566 4.3       20
## 877  -18.85 182.20   501 4.2       23
## 878  -21.91 181.28   548 4.5       30
## 879  -22.03 179.77   587 4.8       31
## 880  -18.10 181.63   592 4.4       28
## 881  -18.40 184.84   221 4.2       18
## 882  -21.20 181.40   560 4.2       12
## 883  -12.00 166.20    94 5.0       31
## 884  -11.70 166.30   139 4.2       15
## 885  -26.72 182.69   162 5.2       64
## 886  -24.39 178.98   562 4.5       30
## 887  -19.64 169.50   204 4.6       35
## 888  -21.35 170.04    56 5.0       22
## 889  -22.82 184.52    49 5.0       52
## 890  -38.28 177.10   100 5.4       71
## 891  -12.57 167.11   231 4.8       28
## 892  -22.24 180.28   601 4.2       21
## 893  -13.80 166.53    42 5.5       70
## 894  -21.07 183.78   180 4.3       25
## 895  -17.74 181.25   559 4.1       16
## 896  -23.87 180.15   524 4.4       22
## 897  -21.29 185.80    69 4.9       74
## 898  -22.20 180.58   594 4.5       45
## 899  -15.24 185.11   262 4.9       56
## 900  -17.82 181.27   538 4.0       33
## 901  -32.14 180.00   331 4.5       27
## 902  -19.30 185.86    48 5.0       40
## 903  -33.09 180.94    47 4.9       47
## 904  -20.18 181.62   558 4.5       31
## 905  -17.46 181.42   524 4.2       16
## 906  -17.44 181.33   545 4.2       37
## 907  -24.71 179.85   477 4.2       34
## 908  -21.53 170.52   129 5.2       30
## 909  -19.17 169.53   268 4.3       21
## 910  -28.05 182.39   117 5.1       43
## 911  -23.39 179.97   541 4.6       50
## 912  -22.33 171.51   112 4.6       14
## 913  -15.28 185.98   162 4.4       36
## 914  -20.27 181.51   609 4.4       32
## 915  -10.96 165.97    76 4.9       64
## 916  -21.52 169.75    61 5.1       40
## 917  -19.57 184.47   202 4.2       28
## 918  -23.08 183.45    90 4.7       30
## 919  -25.06 182.80   133 4.0       14
## 920  -17.85 181.44   589 5.6      115
## 921  -15.99 167.95   190 5.3       81
## 922  -20.56 184.41   138 5.0       82
## 923  -17.98 181.61   598 4.3       27
## 924  -18.40 181.77   600 4.1       11
## 925  -27.64 182.22   162 5.1       67
## 926  -20.99 181.02   626 4.5       36
## 927  -14.86 167.32   137 4.9       22
## 928  -29.33 182.72    57 5.4       61
## 929  -25.81 182.54   201 4.7       40
## 930  -14.10 166.01    69 4.8       29
## 931  -17.63 185.13   219 4.5       28
## 932  -23.47 180.21   553 4.2       23
## 933  -23.92 180.21   524 4.6       50
## 934  -20.88 185.18    51 4.6       28
## 935  -20.25 184.75   107 5.6      121
## 936  -19.33 186.16    44 5.4      110
## 937  -18.14 181.71   574 4.0       20
## 938  -22.41 183.99   128 5.2       72
## 939  -20.77 181.16   568 4.2       12
## 940  -17.95 181.73   583 4.7       57
## 941  -20.83 181.01   622 4.3       15
## 942  -27.84 182.10   193 4.8       27
## 943  -19.94 182.39   544 4.6       30
## 944  -23.60 183.99   118 5.4       88
## 945  -23.70 184.13    51 4.8       27
## 946  -30.39 182.40    63 4.6       22
## 947  -18.98 182.32   442 4.2       22
## 948  -27.89 182.92    87 5.5       67
## 949  -23.50 184.90    61 4.7       16
## 950  -23.73 184.49    60 4.7       35
## 951  -17.93 181.62   561 4.5       32
## 952  -35.94 178.52   138 5.5       78
## 953  -18.68 184.50   174 4.5       34
## 954  -23.47 179.95   543 4.1       21
## 955  -23.49 180.06   530 4.0       23
## 956  -23.85 180.26   497 4.3       32
## 957  -27.08 183.44    63 4.7       27
## 958  -20.88 184.95    82 4.9       50
## 959  -20.97 181.20   605 4.5       31
## 960  -21.71 183.58   234 4.7       55
## 961  -23.90 184.60    41 4.5       22
## 962  -15.78 167.44    40 4.8       42
## 963  -12.57 166.72   137 4.3       20
## 964  -19.69 184.23   223 4.1       23
## 965  -22.04 183.95   109 5.4       61
## 966  -17.99 181.59   595 4.1       26
## 967  -23.50 180.13   512 4.8       40
## 968  -21.40 180.74   613 4.2       20
## 969  -15.86 166.98    60 4.8       25
## 970  -23.95 184.64    43 5.4       45
## 971  -25.79 182.38   172 4.4       14
## 972  -23.75 184.50    54 5.2       74
## 973  -24.10 184.50    68 4.7       23
## 974  -18.56 169.05   217 4.9       35
## 975  -23.30 184.68   102 4.9       27
## 976  -17.03 185.74   178 4.2       32
## 977  -20.77 183.71   251 4.4       47
## 978  -28.10 183.50    42 4.4       17
## 979  -18.83 182.26   575 4.3       11
## 980  -23.00 170.70    43 4.9       20
## 981  -20.82 181.67   577 5.0       67
## 982  -22.95 170.56    42 4.7       21
## 983  -28.22 183.60    75 4.9       49
## 984  -27.99 183.50    71 4.3       22
## 985  -15.54 187.15    60 4.5       17
## 986  -12.37 166.93   291 4.2       16
## 987  -22.33 171.66   125 5.2       51
## 988  -22.70 170.30    69 4.8       27
## 989  -17.86 181.30   614 4.0       12
## 990  -16.00 184.53   108 4.7       33
## 991  -20.73 181.42   575 4.3       18
## 992  -15.45 181.42   409 4.3       27
## 993  -20.05 183.86   243 4.9       65
## 994  -17.95 181.37   642 4.0       17
## 995  -17.70 188.10    45 4.2       10
## 996  -25.93 179.54   470 4.4       22
## 997  -12.28 167.06   248 4.7       35
## 998  -20.13 184.20   244 4.5       34
## 999  -17.40 187.80    40 4.5       14
## 1000 -21.59 170.56   165 6.0      119
## This also holds for functions
install.packages("dplyr")
## package 'vctrs' successfully unpacked and MD5 sums checked
## package 'dplyr' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(dplyr)
## The function filter occurs in two packages 
?filter
x <- 1:100
## Using example code from the function in the stats package throws an error
## because it is hidden by the function in the dplyr package
#filter(x, rep(1, 3))
## You need to indicate which package use with ::
stats::filter(x, rep(1, 3))
## Time Series:
## Start = 1 
## End = 100 
## Frequency = 1 
##   [1]  NA   6   9  12  15  18  21  24  27  30  33  36  39  42  45  48  51  54
##  [19]  57  60  63  66  69  72  75  78  81  84  87  90  93  96  99 102 105 108
##  [37] 111 114 117 120 123 126 129 132 135 138 141 144 147 150 153 156 159 162
##  [55] 165 168 171 174 177 180 183 186 189 192 195 198 201 204 207 210 213 216
##  [73] 219 222 225 228 231 234 237 240 243 246 249 252 255 258 261 264 267 270
##  [91] 273 276 279 282 285 288 291 294 297  NA
save(titanic3, file="Titanic.RData")
rm(titanic3)
load("Titanic.RData")
getwd()
## [1] "C:/Users/pdmoerland/Dropbox/Education/Computing in R"
#setwd("c:/AMC/Teaching/RCursus") # just an example, note that a forward slash is used

Data management

## sort
#install.packages("dplyr")
library(dplyr)
titanic.SortedAge <- arrange(titanic3, age)
head(titanic.SortedAge)
##      pclass survived                              name    sex    age sibsp
## 764     3rd        1 Dean, Miss. Elizabeth Gladys \\"M female 0.1667     1
## 748     3rd        0   Danbom, Master. Gilbert Sigvard   male 0.3333     0
## 1241    3rd        1   Thomas, Master. Assad Alexander   male 0.4167     0
## 428     2nd        1         Hamalainen, Master. Viljo   male 0.6667     1
## 658     3rd        1            Baclini, Miss. Eugenie female 0.7500     2
## 659     3rd        1     Baclini, Miss. Helene Barbara female 0.7500     2
##      parch    ticket    fare cabin    embarked boat body
## 764      2 C.A. 2315 20.5750       Southampton   10   NA
## 748      2    347080 14.4000       Southampton        NA
## 1241     1      2625  8.5167         Cherbourg   16   NA
## 428      1    250649 14.5000       Southampton    4   NA
## 658      1      2666 19.2583         Cherbourg    C   NA
## 659      1      2666 19.2583         Cherbourg    C   NA
##                       home.dest        dob family agecat
## 764  Devon, England Wichita, KS 1912-02-14    yes (0,18]
## 748                 Stanton, IA 1911-12-15    yes (0,18]
## 1241                            1911-11-14    yes (0,18]
## 428                 Detroit, MI 1911-08-15    yes (0,18]
## 658          Syria New York, NY 1911-07-16    yes (0,18]
## 659          Syria New York, NY 1911-07-16    yes (0,18]
## base R
x <- c(5,8,1)
sort(x)
## [1] 1 5 8
order(x)
## [1] 3 1 2
x[order(x)]
## [1] 1 5 8
z <- data.frame(x=c(5,8,1), y=c("A","M","C"))
z
##   x y
## 1 5 A
## 2 8 M
## 3 1 C
z[sort(z$x),]
##       x    y
## 1     5    A
## NA   NA <NA>
## NA.1 NA <NA>
z[order(z$x),]
##   x y
## 3 1 C
## 1 5 A
## 2 8 M
## merge
emb <- data.frame(embarked=c("Cherbourg","Queenstown","Southampton"), country=c("France","Ireland","United Kingdom"))
emb
##      embarked        country
## 1   Cherbourg         France
## 2  Queenstown        Ireland
## 3 Southampton United Kingdom
titanic3.merged <- left_join(titanic3, emb, by="embarked") # all rows from titanic3, all columns from both
head(titanic3.merged)
##   pclass survived                            name    sex     age sibsp parch
## 1    1st        1   Allen, Miss. Elisabeth Walton female 29.0000     0     0
## 2    1st        1  Allison, Master. Hudson Trevor   male  0.9167     1     2
## 3    1st        0    Allison, Miss. Helen Loraine female  2.0000     1     2
## 4    1st        0 Allison, Mr. Hudson Joshua Crei   male 30.0000     1     2
## 5    1st        0 Allison, Mrs. Hudson J C (Bessi female 25.0000     1     2
## 6    1st        1             Anderson, Mr. Harry   male 48.0000     0     0
##   ticket     fare   cabin    embarked boat body                       home.dest
## 1  24160 211.3375      B5 Southampton    2   NA                    St Louis, MO
## 2 113781 151.5500 C22 C26 Southampton   11   NA Montreal, PQ / Chesterville, ON
## 3 113781 151.5500 C22 C26 Southampton        NA Montreal, PQ / Chesterville, ON
## 4 113781 151.5500 C22 C26 Southampton       135 Montreal, PQ / Chesterville, ON
## 5 113781 151.5500 C22 C26 Southampton        NA Montreal, PQ / Chesterville, ON
## 6  19952  26.5500     E12 Southampton    3   NA                    New York, NY
##          dob family  agecat        country
## 1 1883-04-14     no (18,30] United Kingdom
## 2 1911-05-16    yes  (0,18] United Kingdom
## 3 1910-04-15    yes  (0,18] United Kingdom
## 4 1882-04-14    yes (18,30] United Kingdom
## 5 1887-04-14    yes (18,30] United Kingdom
## 6 1864-04-14     no (40,80] United Kingdom
## base R
titanic3.merged <- merge(titanic3, emb)

## wide <-> long
install.packages("tidyr")
## package 'tidyr' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(tidyr)
df1 <- data.frame(c("a", "a", "a", "a", "b", "b", "b", "b"), c(1,2,3,4,1,2,3,4), c(0.1, 0.2, 0.3, 0.4, 2.1, 2.2, 2.3, 2.4))
colnames(df1) <-  c("Name", "Time", "Value")
df1
##   Name Time Value
## 1    a    1   0.1
## 2    a    2   0.2
## 3    a    3   0.3
## 4    a    4   0.4
## 5    b    1   2.1
## 6    b    2   2.2
## 7    b    3   2.3
## 8    b    4   2.4
df1.wide <- pivot_wider(df1, names_from = Time, values_from = Value)
df1.wide
## # A tibble: 2 x 5
##   Name    `1`   `2`   `3`   `4`
##   <chr> <dbl> <dbl> <dbl> <dbl>
## 1 a       0.1   0.2   0.3   0.4
## 2 b       2.1   2.2   2.3   2.4
df1.long <- pivot_longer(df1.wide, cols = 2:5, names_to = "Time", values_to = "Value")
df1.long
## # A tibble: 8 x 3
##   Name  Time  Value
##   <chr> <chr> <dbl>
## 1 a     1       0.1
## 2 a     2       0.2
## 3 a     3       0.3
## 4 a     4       0.4
## 5 b     1       2.1
## 6 b     2       2.2
## 7 b     3       2.3
## 8 b     4       2.4
arrange(df1.long, Name, Time)
## # A tibble: 8 x 3
##   Name  Time  Value
##   <chr> <chr> <dbl>
## 1 a     1       0.1
## 2 a     2       0.2
## 3 a     3       0.3
## 4 a     4       0.4
## 5 b     1       2.1
## 6 b     2       2.2
## 7 b     3       2.3
## 8 b     4       2.4
## base R
df1.wide <- reshape(df1, timevar="Time", idvar="Name", direction="wide", v.names="Value")
df1.wide
##   Name Value.1 Value.2 Value.3 Value.4
## 1    a     0.1     0.2     0.3     0.4
## 5    b     2.1     2.2     2.3     2.4
df1.long <- reshape(df1.wide, varying=2:5, direction="long")
df1.long
##     Name time Value id
## 1.1    a    1   0.1  1
## 2.1    b    1   2.1  2
## 1.2    a    2   0.2  1
## 2.2    b    2   2.2  2
## 1.3    a    3   0.3  1
## 2.3    b    3   2.3  2
## 1.4    a    4   0.4  1
## 2.4    b    4   2.4  2
df1.long[order(df1.long$Name,df1.long$time),]
##     Name time Value id
## 1.1    a    1   0.1  1
## 1.2    a    2   0.2  1
## 1.3    a    3   0.3  1
## 1.4    a    4   0.4  1
## 2.1    b    1   2.1  2
## 2.2    b    2   2.2  2
## 2.3    b    3   2.3  2
## 2.4    b    4   2.4  2
## transformation using within
titanic3 <- within(titanic3, {
  agecat <- cut(age, breaks=c(0,18,30,40,80))
  family <- factor(ifelse(parch==0 & sibsp==0, "no", "yes"))
})
summary(titanic3[,c("age","agecat","family")])
##       age              agecat    family   
##  Min.   : 0.1667   (0,18] :193   no :790  
##  1st Qu.:21.0000   (18,30]:416   yes:519  
##  Median :28.0000   (30,40]:210            
##  Mean   :29.8811   (40,80]:227            
##  3rd Qu.:39.0000   NA's   :263            
##  Max.   :80.0000                          
##  NA's   :263
## transformation using transform
titanic3 <- transform(titanic3,
  agecat = cut(age, breaks=c(0,18,30,40,80)),
  family = factor(ifelse(parch==0 & sibsp==0, "no", "yes"))
)
summary(titanic3[,c("age","agecat","family")])
##       age              agecat    family   
##  Min.   : 0.1667   (0,18] :193   no :790  
##  1st Qu.:21.0000   (18,30]:416   yes:519  
##  Median :28.0000   (30,40]:210            
##  Mean   :29.8811   (40,80]:227            
##  3rd Qu.:39.0000   NA's   :263            
##  Max.   :80.0000                          
##  NA's   :263
# crosstable using descr package
install.packages("descr")
## package 'descr' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(descr)
descr(titanic3$age)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.1667 21.0000 28.0000 29.8811 39.0000 80.0000     263
with(titanic3, CrossTable(pclass,sex,format="SPSS"))
##    Cell Contents 
## |-------------------------|
## |                   Count | 
## | Chi-square contribution | 
## |             Row Percent | 
## |          Column Percent | 
## |           Total Percent | 
## |-------------------------|
## 
## ================================
##           sex
## pclass    female    male   Total
## --------------------------------
## 1st         144     179     323 
##           7.320   4.047         
##            44.6%   55.4%   24.7%
##            30.9%   21.2%        
##            11.0%   13.7%        
## --------------------------------
## 2nd         106     171     277 
##           0.554   0.306         
##            38.3%   61.7%   21.2%
##            22.7%   20.3%        
##             8.1%   13.1%        
## --------------------------------
## 3rd         216     493     709 
##           5.250   2.902         
##            30.5%   69.5%   54.2%
##            46.4%   58.5%        
##            16.5%   37.7%        
## --------------------------------
## Total       466     843    1309 
##            35.6%   64.4%        
## ================================
## summary by subgroup, all produce basically the same output
## basic R functions
aggregate(age~pclass, data=titanic3, FUN=summary)
##   pclass age.Min. age.1st Qu. age.Median age.Mean age.3rd Qu. age.Max.
## 1    1st  0.91670    28.00000   39.00000 39.15992    50.00000 80.00000
## 2    2nd  0.66670    22.00000   29.00000 29.50670    36.00000 70.00000
## 3    3rd  0.16670    18.00000   24.00000 24.81637    32.00000 74.00000
by(titanic3,titanic3$pclass,FUN=function(x) summary(x$age))
## titanic3$pclass: 1st
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.9167 28.0000 39.0000 39.1599 50.0000 80.0000      39 
## ------------------------------------------------------------ 
## titanic3$pclass: 2nd
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.6667 22.0000 29.0000 29.5067 36.0000 70.0000      16 
## ------------------------------------------------------------ 
## titanic3$pclass: 3rd
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.1667 18.0000 24.0000 24.8164 32.0000 74.0000     208
with(titanic3, tapply(age,pclass,FUN=summary))
## $`1st`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.9167 28.0000 39.0000 39.1599 50.0000 80.0000      39 
## 
## $`2nd`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.6667 22.0000 29.0000 29.5067 36.0000 70.0000      16 
## 
## $`3rd`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##  0.1667 18.0000 24.0000 24.8164 32.0000 74.0000     208
## using doBy package
install.packages("doBy")
## 
##   There is a binary version available but the source version is later:
##      binary source needs_compilation
## doBy 4.6.20 4.6.21             FALSE
## 
## package 'modelr' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(doBy)
summaryBy(age~pclass, data=titanic3, FUN=summary)
##   pclass age.Min. age.1st Qu. age.Median age.Mean age.3rd Qu. age.Max. age.NA's
## 1    1st   0.9167          28         39 39.15992          50       80       39
## 2    2nd   0.6667          22         29 29.50670          36       70       16
## 3    3rd   0.1667          18         24 24.81637          32       74      208
## Nice graphical summary using summarytools package
install.packages("summarytools")
## package 'summarytools' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\pdmoerland\AppData\Local\Temp\RtmpOo6hhH\downloaded_packages
library(summarytools)
view(dfSummary(titanic3))

Documentation and help

help(summary)
fit.age <- lm(age ~ pclass, data=titanic3)
summary(fit.age)
## 
## Call:
## lm(formula = age ~ pclass, data = titanic3)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.243  -7.816  -0.816   7.840  49.184 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  39.1599     0.7790  50.269   <2e-16 ***
## pclass2nd    -9.6532     1.1257  -8.575   <2e-16 ***
## pclass3rd   -14.3436     0.9751 -14.709   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.13 on 1043 degrees of freedom
##   (263 observations deleted due to missingness)
## Multiple R-squared:  0.172,  Adjusted R-squared:  0.1704 
## F-statistic: 108.3 on 2 and 1043 DF,  p-value: < 2.2e-16
class(fit.age)
## [1] "lm"
help(summary.lm)

help(descriptive)
## No documentation for 'descriptive' in specified packages and libraries:
## you could try '??descriptive'
#help.search("descriptive statistics") # same as ??descriptive
#RSiteSearch("{descriptive statistics}") # braces to indicate that words are to be searched as one entity
#install.packages("sos")
#library(sos)
#findFn("{descriptive statistics}")

Model fitting; formulas

help.search("Student")
help(t.test)
with(titanic3,t.test(age[sex=="male"],age[sex=="female"]))
## 
##  Welch Two Sample t-test
## 
## data:  age[sex == "male"] and age[sex == "female"]
## t = 2.0497, df = 798.36, p-value = 0.04072
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.08036699 3.71595773
## sample estimates:
## mean of x mean of y 
##  30.58523  28.68707
t.test(subset(titanic3,sex=="male")$age,subset(titanic3,sex=="female")$age)
## 
##  Welch Two Sample t-test
## 
## data:  subset(titanic3, sex == "male")$age and subset(titanic3, sex == "female")$age
## t = 2.0497, df = 798.36, p-value = 0.04072
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.08036699 3.71595773
## sample estimates:
## mean of x mean of y 
##  30.58523  28.68707
test.age <- t.test(age ~ sex, data = titanic3)
test.age
## 
##  Welch Two Sample t-test
## 
## data:  age by sex
## t = -2.0497, df = 798.36, p-value = 0.04072
## alternative hypothesis: true difference in means between group female and group male is not equal to 0
## 95 percent confidence interval:
##  -3.71595773 -0.08036699
## sample estimates:
## mean in group female   mean in group male 
##             28.68707             30.58523
fit.age <- lm(age ~ pclass, data=titanic3)
fit.age
## 
## Call:
## lm(formula = age ~ pclass, data = titanic3)
## 
## Coefficients:
## (Intercept)    pclass2nd    pclass3rd  
##      39.160       -9.653      -14.344
summary(fit.age)
## 
## Call:
## lm(formula = age ~ pclass, data = titanic3)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -38.243  -7.816  -0.816   7.840  49.184 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  39.1599     0.7790  50.269   <2e-16 ***
## pclass2nd    -9.6532     1.1257  -8.575   <2e-16 ***
## pclass3rd   -14.3436     0.9751 -14.709   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 13.13 on 1043 degrees of freedom
##   (263 observations deleted due to missingness)
## Multiple R-squared:  0.172,  Adjusted R-squared:  0.1704 
## F-statistic: 108.3 on 2 and 1043 DF,  p-value: < 2.2e-16
## output of model is a list
str(fit.age)
## List of 14
##  $ coefficients : Named num [1:3] 39.16 -9.65 -14.34
##   ..- attr(*, "names")= chr [1:3] "(Intercept)" "pclass2nd" "pclass3rd"
##  $ residuals    : Named num [1:1046] -10.16 -38.24 -37.16 -9.16 -14.16 ...
##   ..- attr(*, "names")= chr [1:1046] "1" "2" "3" "4" ...
##  $ effects      : Named num [1:1046] -966.41 -6.98 -193.11 -6.64 -11.64 ...
##   ..- attr(*, "names")= chr [1:1046] "(Intercept)" "pclass2nd" "pclass3rd" "" ...
##  $ rank         : int 3
##  $ fitted.values: Named num [1:1046] 39.2 39.2 39.2 39.2 39.2 ...
##   ..- attr(*, "names")= chr [1:1046] "1" "2" "3" "4" ...
##  $ assign       : int [1:3] 0 1 1
##  $ qr           :List of 5
##   ..$ qr   : num [1:1046, 1:3] -32.3419 0.0309 0.0309 0.0309 0.0309 ...
##   .. ..- attr(*, "dimnames")=List of 2
##   .. .. ..$ : chr [1:1046] "1" "2" "3" "4" ...
##   .. .. ..$ : chr [1:3] "(Intercept)" "pclass2nd" "pclass3rd"
##   .. ..- attr(*, "assign")= int [1:3] 0 1 1
##   .. ..- attr(*, "contrasts")=List of 1
##   .. .. ..$ pclass: chr "contr.treatment"
##   ..$ qraux: num [1:3] 1.03 1.02 1.05
##   ..$ pivot: int [1:3] 1 2 3
##   ..$ tol  : num 1e-07
##   ..$ rank : int 3
##   ..- attr(*, "class")= chr "qr"
##  $ df.residual  : int 1043
##  $ na.action    : 'omit' Named int [1:263] 16 38 41 47 60 70 71 75 81 107 ...
##   ..- attr(*, "names")= chr [1:263] "16" "38" "41" "47" ...
##  $ contrasts    :List of 1
##   ..$ pclass: chr "contr.treatment"
##  $ xlevels      :List of 1
##   ..$ pclass: chr [1:3] "1st" "2nd" "3rd"
##  $ call         : language lm(formula = age ~ pclass, data = titanic3)
##  $ terms        :Classes 'terms', 'formula'  language age ~ pclass
##   .. ..- attr(*, "variables")= language list(age, pclass)
##   .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. ..$ : chr [1:2] "age" "pclass"
##   .. .. .. ..$ : chr "pclass"
##   .. ..- attr(*, "term.labels")= chr "pclass"
##   .. ..- attr(*, "order")= int 1
##   .. ..- attr(*, "intercept")= int 1
##   .. ..- attr(*, "response")= int 1
##   .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. ..- attr(*, "predvars")= language list(age, pclass)
##   .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "factor"
##   .. .. ..- attr(*, "names")= chr [1:2] "age" "pclass"
##  $ model        :'data.frame':   1046 obs. of  2 variables:
##   ..$ age   : num [1:1046] 29 0.917 2 30 25 ...
##   ..$ pclass: Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ...
##   ..- attr(*, "terms")=Classes 'terms', 'formula'  language age ~ pclass
##   .. .. ..- attr(*, "variables")= language list(age, pclass)
##   .. .. ..- attr(*, "factors")= int [1:2, 1] 0 1
##   .. .. .. ..- attr(*, "dimnames")=List of 2
##   .. .. .. .. ..$ : chr [1:2] "age" "pclass"
##   .. .. .. .. ..$ : chr "pclass"
##   .. .. ..- attr(*, "term.labels")= chr "pclass"
##   .. .. ..- attr(*, "order")= int 1
##   .. .. ..- attr(*, "intercept")= int 1
##   .. .. ..- attr(*, "response")= int 1
##   .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
##   .. .. ..- attr(*, "predvars")= language list(age, pclass)
##   .. .. ..- attr(*, "dataClasses")= Named chr [1:2] "numeric" "factor"
##   .. .. .. ..- attr(*, "names")= chr [1:2] "age" "pclass"
##   ..- attr(*, "na.action")= 'omit' Named int [1:263] 16 38 41 47 60 70 71 75 81 107 ...
##   .. ..- attr(*, "names")= chr [1:263] "16" "38" "41" "47" ...
##  - attr(*, "class")= chr "lm"
names(fit.age)
##  [1] "coefficients"  "residuals"     "effects"       "rank"         
##  [5] "fitted.values" "assign"        "qr"            "df.residual"  
##  [9] "na.action"     "contrasts"     "xlevels"       "call"         
## [13] "terms"         "model"
summary(fit.age$residuals)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -38.2432  -7.8164  -0.8164   0.0000   7.8401  49.1836
## or
summary(resid(fit.age))
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## -38.2432  -7.8164  -0.8164   0.0000   7.8401  49.1836
hist(resid(fit.age))

coef(fit.age)
## (Intercept)   pclass2nd   pclass3rd 
##   39.159918   -9.653213  -14.343551
predict(fit.age, newdata=data.frame(pclass=factor(levels(titanic3$pclass))))
##        1        2        3 
## 39.15992 29.50670 24.81637
update(fit.age,. ~ .+sex)
## 
## Call:
## lm(formula = age ~ pclass + sex, data = titanic3)
## 
## Coefficients:
## (Intercept)    pclass2nd    pclass3rd      sexmale  
##      37.182       -9.927      -14.957        3.720
## the output has a specific class, in this case lm
class(fit.age)
## [1] "lm"
## which functions have been written for this class?
help(methods)
methods(class="lm")
##  [1] add1           alias          anova          case.names     coerce        
##  [6] confint        cooks.distance deviance       dfbeta         dfbetas       
## [11] drop1          dummy.coef     effects        esticon        extractAIC    
## [16] family         formula        fortify        hatvalues      influence     
## [21] initialize     kappa          labels         linest         logLik        
## [26] model.frame    model.matrix   nobs           plot           predict       
## [31] print          proj           qr             residuals      rstandard     
## [36] rstudent       show           simulate       slotsFromS3    summary       
## [41] variable.names vcov          
## see '?methods' for accessing help and source code
## we want to know what the summary function does with an object of class lm 
help(summary) # does not give the desired help file
## we need to consult the help file for summary.lm
methods(summary)
##  [1] summary,ANY-method                  summary,diagonalMatrix-method      
##  [3] summary,sparseMatrix-method         summary.aov                        
##  [5] summary.aovlist*                    summary.aspell*                    
##  [7] summary.check_packages_in_dir*      summary.connection                 
##  [9] summary.data.frame                  summary.Date                       
## [11] summary.default                     summary.Duration*                  
## [13] summary.ecdf*                       summary.esticon_class*             
## [15] summary.factor                      summary.ggplot*                    
## [17] summary.glm                         summary.hcl_palettes*              
## [19] summary.infl*                       summary.Interval*                  
## [21] summary.linest_class*               summary.linest_matrix_class*       
## [23] summary.lm                          summary.lmBy*                      
## [25] summary.loess*                      summary.loglm*                     
## [27] summary.manova                      summary.matrix                     
## [29] summary.microbenchmark*             summary.mlm*                       
## [31] summary.model_stability_glm_class*  summary.negbin*                    
## [33] summary.nls*                        summary.packageStatus*             
## [35] summary.Period*                     summary.polr*                      
## [37] summary.POSIXct                     summary.POSIXlt                    
## [39] summary.ppr*                        summary.prcomp*                    
## [41] summary.princomp*                   summary.proc_time                  
## [43] summary.rlang:::list_of_conditions* summary.rlang_error*               
## [45] summary.rlang_message*              summary.rlang_trace*               
## [47] summary.rlang_warning*              summary.rlm*                       
## [49] summary.section_function*           summary.shingle*                   
## [51] summary.srcfile                     summary.srcref                     
## [53] summary.stepfun                     summary.stl*                       
## [55] summary.table                       summary.trellis*                   
## [57] summary.tukeysmooth*                summary.vctrs_sclr*                
## [59] summary.vctrs_vctr*                 summary.warnings                   
## see '?methods' for accessing help and source code
help(summary.lm)
plot(fit.age)

plot(age ~ fare, data=titanic3)

## the type of plot is automatically adapted to the type of variable
plot(age~pclass, data=titanic3)

Programming and ply functions

x <- 10
z <- if (x < 2) 4 else 3
z
## [1] 3
# Or more interesting from the function that we wrote earlier
work <- FALSE
if(work==TRUE){
  cat("wake up")
  } else{ 
    cat("you can stay in bed")
  }
## you can stay in bed
A <- matrix(1:10, 2, 5)
rownames(A) <- c("gene1", "gene2")
colnames(A) <- c("sample1", "sample2", "sample3","sample4", "sample5")
results <- numeric(2)
results
## [1] 0 0
for (i in 1:2) {
  results[i] <- mean(A[i, ])
}
results
## [1] 5 6
apply(A, 1, mean)
## gene1 gene2 
##     5     6
titanic3 <- read.dta("Exercises/titanic3.dta", convert.underscore=TRUE)
head(titanic3[,c("fare","pclass")])
##       fare pclass
## 1 211.3375    1st
## 2 151.5500    1st
## 3 151.5500    1st
## 4 151.5500    1st
## 5 151.5500    1st
## 6  26.5500    1st
with(titanic3,tapply(fare, pclass, mean,na.rm=T))
##      1st      2nd      3rd 
## 87.50899 21.17920 13.30289
# Solution using the dplyr package
#install.packages("dplyr")
library(dplyr)
summarise(group_by(titanic3, pclass), mean(fare,na.rm=TRUE))
## # A tibble: 3 x 2
##   pclass `mean(fare, na.rm = TRUE)`
##   <fct>                       <dbl>
## 1 1st                          87.5
## 2 2nd                          21.2
## 3 3rd                          13.3