Intrinsics
The following is in response to our 2nd week’s assignment. Using the provided list of functions I will define each and demonstrate the function in use.
General Bits
- help(topic) – opens the help menu to the R Documentation on topic written in the parantheses
help(plot)- ?topic – similar to help(topic), opens R Documentation
?distribution- ls() – shows defined variables
ls()## character(0)- dir() – shows files in directory
dir()## [1] "_site.yml" "docs"
## [3] "images" "index.Rmd"
## [5] "Intrinsics_cache" "Intrinsics.Rmd"
## [7] "Journal_cache" "Journal_files"
## [9] "Journal.Rmd" "LabJournalWebsite.Rproj"
## [11] "Links.Rmd" "README.md"- list.files() – lists the files in a directory/folder
list.files()## [1] "_site.yml" "docs"
## [3] "images" "index.Rmd"
## [5] "Intrinsics_cache" "Intrinsics.Rmd"
## [7] "Journal_cache" "Journal_files"
## [9] "Journal.Rmd" "LabJournalWebsite.Rproj"
## [11] "Links.Rmd" "README.md"Input and Output
- save() – function to save data created in R in a specific file
red<- seq(1,20,2)
save(red,file="seq.rmd")- load() – reloads objects or datasets that were configured with save()
load("seq.rmd")- data() – lists the data sets currently known or saved, loads specificed data sets
data("iris")- library() – lists the packages in R’s library and can load specified packages in library
library(ggplot2)- read.table() – opens a document or file as a table
read.table(file="int_prac.rtf",sep=",")## V1
## 1 {\\rtf1\\ansi\\ansicpg1252\\cocoartf1671\\cocoasubrtf200
## 2 {\\fonttbl\\f0\\fswiss\\fcharset0 Helvetica;}
## 3 {\\colortbl;\\red255\\green255\\blue255;}
## 4 {\\*\\expandedcolortbl;;}
## 5 \\margl1440\\margr1440\\vieww10800\\viewh8400\\viewkind0
## 6 \\pard\\tx720\\tx1440\\tx2160\\tx2880\\tx3600\\tx4320\\tx5040\\tx5760\\tx6480\\tx7200\\tx7920\\tx8640\\pardirnatural\\partightenfactor0
## 7 \\f0\\fs24 \\cf0 Name
## 8 Age\\
## 9 \\pard\\tx720\\tx1440\\tx2160\\tx2880\\tx3600\\tx4320\\tx5040\\tx5760\\tx6480\\tx7200\\tx7920\\tx8640\\pardirnatural\\partightenfactor0
## 10 \\cf0 Ana
## 11 23\\
## 12 Fred
## 13 34\\
## 14 Dan
## 15 62\\
## 16 David
## 17 12}– did not execute properly, don’t know what wrong.
- read.csv() – function to read a csv file and convert to R
read.csv(file="iris.csv", header=TRUE, sep="")## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa
## 51 7.0 3.2 4.7 1.4 versicolor
## 52 6.4 3.2 4.5 1.5 versicolor
## 53 6.9 3.1 4.9 1.5 versicolor
## 54 5.5 2.3 4.0 1.3 versicolor
## 55 6.5 2.8 4.6 1.5 versicolor
## 56 5.7 2.8 4.5 1.3 versicolor
## 57 6.3 3.3 4.7 1.6 versicolor
## 58 4.9 2.4 3.3 1.0 versicolor
## 59 6.6 2.9 4.6 1.3 versicolor
## 60 5.2 2.7 3.9 1.4 versicolor
## 61 5.0 2.0 3.5 1.0 versicolor
## 62 5.9 3.0 4.2 1.5 versicolor
## 63 6.0 2.2 4.0 1.0 versicolor
## 64 6.1 2.9 4.7 1.4 versicolor
## 65 5.6 2.9 3.6 1.3 versicolor
## 66 6.7 3.1 4.4 1.4 versicolor
## 67 5.6 3.0 4.5 1.5 versicolor
## 68 5.8 2.7 4.1 1.0 versicolor
## 69 6.2 2.2 4.5 1.5 versicolor
## 70 5.6 2.5 3.9 1.1 versicolor
## 71 5.9 3.2 4.8 1.8 versicolor
## 72 6.1 2.8 4.0 1.3 versicolor
## 73 6.3 2.5 4.9 1.5 versicolor
## 74 6.1 2.8 4.7 1.2 versicolor
## 75 6.4 2.9 4.3 1.3 versicolor
## 76 6.6 3.0 4.4 1.4 versicolor
## 77 6.8 2.8 4.8 1.4 versicolor
## 78 6.7 3.0 5.0 1.7 versicolor
## 79 6.0 2.9 4.5 1.5 versicolor
## 80 5.7 2.6 3.5 1.0 versicolor
## 81 5.5 2.4 3.8 1.1 versicolor
## 82 5.5 2.4 3.7 1.0 versicolor
## 83 5.8 2.7 3.9 1.2 versicolor
## 84 6.0 2.7 5.1 1.6 versicolor
## 85 5.4 3.0 4.5 1.5 versicolor
## 86 6.0 3.4 4.5 1.6 versicolor
## 87 6.7 3.1 4.7 1.5 versicolor
## 88 6.3 2.3 4.4 1.3 versicolor
## 89 5.6 3.0 4.1 1.3 versicolor
## 90 5.5 2.5 4.0 1.3 versicolor
## 91 5.5 2.6 4.4 1.2 versicolor
## 92 6.1 3.0 4.6 1.4 versicolor
## 93 5.8 2.6 4.0 1.2 versicolor
## 94 5.0 2.3 3.3 1.0 versicolor
## 95 5.6 2.7 4.2 1.3 versicolor
## 96 5.7 3.0 4.2 1.2 versicolor
## 97 5.7 2.9 4.2 1.3 versicolor
## 98 6.2 2.9 4.3 1.3 versicolor
## 99 5.1 2.5 3.0 1.1 versicolor
## 100 5.7 2.8 4.1 1.3 versicolor
## 101 6.3 3.3 6.0 2.5 virginica
## 102 5.8 2.7 5.1 1.9 virginica
## 103 7.1 3.0 5.9 2.1 virginica
## 104 6.3 2.9 5.6 1.8 virginica
## 105 6.5 3.0 5.8 2.2 virginica
## 106 7.6 3.0 6.6 2.1 virginica
## 107 4.9 2.5 4.5 1.7 virginica
## 108 7.3 2.9 6.3 1.8 virginica
## 109 6.7 2.5 5.8 1.8 virginica
## 110 7.2 3.6 6.1 2.5 virginica
## 111 6.5 3.2 5.1 2.0 virginica
## 112 6.4 2.7 5.3 1.9 virginica
## 113 6.8 3.0 5.5 2.1 virginica
## 114 5.7 2.5 5.0 2.0 virginica
## 115 5.8 2.8 5.1 2.4 virginica
## 116 6.4 3.2 5.3 2.3 virginica
## 117 6.5 3.0 5.5 1.8 virginica
## 118 7.7 3.8 6.7 2.2 virginica
## 119 7.7 2.6 6.9 2.3 virginica
## 120 6.0 2.2 5.0 1.5 virginica
## 121 6.9 3.2 5.7 2.3 virginica
## 122 5.6 2.8 4.9 2.0 virginica
## 123 7.7 2.8 6.7 2.0 virginica
## 124 6.3 2.7 4.9 1.8 virginica
## 125 6.7 3.3 5.7 2.1 virginica
## 126 7.2 3.2 6.0 1.8 virginica
## 127 6.2 2.8 4.8 1.8 virginica
## 128 6.1 3.0 4.9 1.8 virginica
## 129 6.4 2.8 5.6 2.1 virginica
## 130 7.2 3.0 5.8 1.6 virginica
## 131 7.4 2.8 6.1 1.9 virginica
## 132 7.9 3.8 6.4 2.0 virginica
## 133 6.4 2.8 5.6 2.2 virginica
## 134 6.3 2.8 5.1 1.5 virginica
## 135 6.1 2.6 5.6 1.4 virginica
## 136 7.7 3.0 6.1 2.3 virginica
## 137 6.3 3.4 5.6 2.4 virginica
## 138 6.4 3.1 5.5 1.8 virginica
## 139 6.0 3.0 4.8 1.8 virginica
## 140 6.9 3.1 5.4 2.1 virginica
## 141 6.7 3.1 5.6 2.4 virginica
## 142 6.9 3.1 5.1 2.3 virginica
## 143 5.8 2.7 5.1 1.9 virginica
## 144 6.8 3.2 5.9 2.3 virginica
## 145 6.7 3.3 5.7 2.5 virginica
## 146 6.7 3.0 5.2 2.3 virginica
## 147 6.3 2.5 5.0 1.9 virginica
## 148 6.5 3.0 5.2 2.0 virginica
## 149 6.2 3.4 5.4 2.3 virginica
## 150 5.9 3.0 5.1 1.8 virginica- scan() – scans a file (??)
scan(file = "iris.csv", what = list(""))## [[1]]
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
## [5] "Species" "1" "5.1" "3.5"
## [9] "1.4" "0.2" "setosa" "2"
## [13] "4.9" "3" "1.4" "0.2"
## [17] "setosa" "3" "4.7" "3.2"
## [21] "1.3" "0.2" "setosa" "4"
## [25] "4.6" "3.1" "1.5" "0.2"
## [29] "setosa" "5" "5" "3.6"
## [33] "1.4" "0.2" "setosa" "6"
## [37] "5.4" "3.9" "1.7" "0.4"
## [41] "setosa" "7" "4.6" "3.4"
## [45] "1.4" "0.3" "setosa" "8"
## [49] "5" "3.4" "1.5" "0.2"
## [53] "setosa" "9" "4.4" "2.9"
## [57] "1.4" "0.2" "setosa" "10"
## [61] "4.9" "3.1" "1.5" "0.1"
## [65] "setosa" "11" "5.4" "3.7"
## [69] "1.5" "0.2" "setosa" "12"
## [73] "4.8" "3.4" "1.6" "0.2"
## [77] "setosa" "13" "4.8" "3"
## [81] "1.4" "0.1" "setosa" "14"
## [85] "4.3" "3" "1.1" "0.1"
## [89] "setosa" "15" "5.8" "4"
## [93] "1.2" "0.2" "setosa" "16"
## [97] "5.7" "4.4" "1.5" "0.4"
## [101] "setosa" "17" "5.4" "3.9"
## [105] "1.3" "0.4" "setosa" "18"
## [109] "5.1" "3.5" "1.4" "0.3"
## [113] "setosa" "19" "5.7" "3.8"
## [117] "1.7" "0.3" "setosa" "20"
## [121] "5.1" "3.8" "1.5" "0.3"
## [125] "setosa" "21" "5.4" "3.4"
## [129] "1.7" "0.2" "setosa" "22"
## [133] "5.1" "3.7" "1.5" "0.4"
## [137] "setosa" "23" "4.6" "3.6"
## [141] "1" "0.2" "setosa" "24"
## [145] "5.1" "3.3" "1.7" "0.5"
## [149] "setosa" "25" "4.8" "3.4"
## [153] "1.9" "0.2" "setosa" "26"
## [157] "5" "3" "1.6" "0.2"
## [161] "setosa" "27" "5" "3.4"
## [165] "1.6" "0.4" "setosa" "28"
## [169] "5.2" "3.5" "1.5" "0.2"
## [173] "setosa" "29" "5.2" "3.4"
## [177] "1.4" "0.2" "setosa" "30"
## [181] "4.7" "3.2" "1.6" "0.2"
## [185] "setosa" "31" "4.8" "3.1"
## [189] "1.6" "0.2" "setosa" "32"
## [193] "5.4" "3.4" "1.5" "0.4"
## [197] "setosa" "33" "5.2" "4.1"
## [201] "1.5" "0.1" "setosa" "34"
## [205] "5.5" "4.2" "1.4" "0.2"
## [209] "setosa" "35" "4.9" "3.1"
## [213] "1.5" "0.2" "setosa" "36"
## [217] "5" "3.2" "1.2" "0.2"
## [221] "setosa" "37" "5.5" "3.5"
## [225] "1.3" "0.2" "setosa" "38"
## [229] "4.9" "3.6" "1.4" "0.1"
## [233] "setosa" "39" "4.4" "3"
## [237] "1.3" "0.2" "setosa" "40"
## [241] "5.1" "3.4" "1.5" "0.2"
## [245] "setosa" "41" "5" "3.5"
## [249] "1.3" "0.3" "setosa" "42"
## [253] "4.5" "2.3" "1.3" "0.3"
## [257] "setosa" "43" "4.4" "3.2"
## [261] "1.3" "0.2" "setosa" "44"
## [265] "5" "3.5" "1.6" "0.6"
## [269] "setosa" "45" "5.1" "3.8"
## [273] "1.9" "0.4" "setosa" "46"
## [277] "4.8" "3" "1.4" "0.3"
## [281] "setosa" "47" "5.1" "3.8"
## [285] "1.6" "0.2" "setosa" "48"
## [289] "4.6" "3.2" "1.4" "0.2"
## [293] "setosa" "49" "5.3" "3.7"
## [297] "1.5" "0.2" "setosa" "50"
## [301] "5" "3.3" "1.4" "0.2"
## [305] "setosa" "51" "7" "3.2"
## [309] "4.7" "1.4" "versicolor" "52"
## [313] "6.4" "3.2" "4.5" "1.5"
## [317] "versicolor" "53" "6.9" "3.1"
## [321] "4.9" "1.5" "versicolor" "54"
## [325] "5.5" "2.3" "4" "1.3"
## [329] "versicolor" "55" "6.5" "2.8"
## [333] "4.6" "1.5" "versicolor" "56"
## [337] "5.7" "2.8" "4.5" "1.3"
## [341] "versicolor" "57" "6.3" "3.3"
## [345] "4.7" "1.6" "versicolor" "58"
## [349] "4.9" "2.4" "3.3" "1"
## [353] "versicolor" "59" "6.6" "2.9"
## [357] "4.6" "1.3" "versicolor" "60"
## [361] "5.2" "2.7" "3.9" "1.4"
## [365] "versicolor" "61" "5" "2"
## [369] "3.5" "1" "versicolor" "62"
## [373] "5.9" "3" "4.2" "1.5"
## [377] "versicolor" "63" "6" "2.2"
## [381] "4" "1" "versicolor" "64"
## [385] "6.1" "2.9" "4.7" "1.4"
## [389] "versicolor" "65" "5.6" "2.9"
## [393] "3.6" "1.3" "versicolor" "66"
## [397] "6.7" "3.1" "4.4" "1.4"
## [401] "versicolor" "67" "5.6" "3"
## [405] "4.5" "1.5" "versicolor" "68"
## [409] "5.8" "2.7" "4.1" "1"
## [413] "versicolor" "69" "6.2" "2.2"
## [417] "4.5" "1.5" "versicolor" "70"
## [421] "5.6" "2.5" "3.9" "1.1"
## [425] "versicolor" "71" "5.9" "3.2"
## [429] "4.8" "1.8" "versicolor" "72"
## [433] "6.1" "2.8" "4" "1.3"
## [437] "versicolor" "73" "6.3" "2.5"
## [441] "4.9" "1.5" "versicolor" "74"
## [445] "6.1" "2.8" "4.7" "1.2"
## [449] "versicolor" "75" "6.4" "2.9"
## [453] "4.3" "1.3" "versicolor" "76"
## [457] "6.6" "3" "4.4" "1.4"
## [461] "versicolor" "77" "6.8" "2.8"
## [465] "4.8" "1.4" "versicolor" "78"
## [469] "6.7" "3" "5" "1.7"
## [473] "versicolor" "79" "6" "2.9"
## [477] "4.5" "1.5" "versicolor" "80"
## [481] "5.7" "2.6" "3.5" "1"
## [485] "versicolor" "81" "5.5" "2.4"
## [489] "3.8" "1.1" "versicolor" "82"
## [493] "5.5" "2.4" "3.7" "1"
## [497] "versicolor" "83" "5.8" "2.7"
## [501] "3.9" "1.2" "versicolor" "84"
## [505] "6" "2.7" "5.1" "1.6"
## [509] "versicolor" "85" "5.4" "3"
## [513] "4.5" "1.5" "versicolor" "86"
## [517] "6" "3.4" "4.5" "1.6"
## [521] "versicolor" "87" "6.7" "3.1"
## [525] "4.7" "1.5" "versicolor" "88"
## [529] "6.3" "2.3" "4.4" "1.3"
## [533] "versicolor" "89" "5.6" "3"
## [537] "4.1" "1.3" "versicolor" "90"
## [541] "5.5" "2.5" "4" "1.3"
## [545] "versicolor" "91" "5.5" "2.6"
## [549] "4.4" "1.2" "versicolor" "92"
## [553] "6.1" "3" "4.6" "1.4"
## [557] "versicolor" "93" "5.8" "2.6"
## [561] "4" "1.2" "versicolor" "94"
## [565] "5" "2.3" "3.3" "1"
## [569] "versicolor" "95" "5.6" "2.7"
## [573] "4.2" "1.3" "versicolor" "96"
## [577] "5.7" "3" "4.2" "1.2"
## [581] "versicolor" "97" "5.7" "2.9"
## [585] "4.2" "1.3" "versicolor" "98"
## [589] "6.2" "2.9" "4.3" "1.3"
## [593] "versicolor" "99" "5.1" "2.5"
## [597] "3" "1.1" "versicolor" "100"
## [601] "5.7" "2.8" "4.1" "1.3"
## [605] "versicolor" "101" "6.3" "3.3"
## [609] "6" "2.5" "virginica" "102"
## [613] "5.8" "2.7" "5.1" "1.9"
## [617] "virginica" "103" "7.1" "3"
## [621] "5.9" "2.1" "virginica" "104"
## [625] "6.3" "2.9" "5.6" "1.8"
## [629] "virginica" "105" "6.5" "3"
## [633] "5.8" "2.2" "virginica" "106"
## [637] "7.6" "3" "6.6" "2.1"
## [641] "virginica" "107" "4.9" "2.5"
## [645] "4.5" "1.7" "virginica" "108"
## [649] "7.3" "2.9" "6.3" "1.8"
## [653] "virginica" "109" "6.7" "2.5"
## [657] "5.8" "1.8" "virginica" "110"
## [661] "7.2" "3.6" "6.1" "2.5"
## [665] "virginica" "111" "6.5" "3.2"
## [669] "5.1" "2" "virginica" "112"
## [673] "6.4" "2.7" "5.3" "1.9"
## [677] "virginica" "113" "6.8" "3"
## [681] "5.5" "2.1" "virginica" "114"
## [685] "5.7" "2.5" "5" "2"
## [689] "virginica" "115" "5.8" "2.8"
## [693] "5.1" "2.4" "virginica" "116"
## [697] "6.4" "3.2" "5.3" "2.3"
## [701] "virginica" "117" "6.5" "3"
## [705] "5.5" "1.8" "virginica" "118"
## [709] "7.7" "3.8" "6.7" "2.2"
## [713] "virginica" "119" "7.7" "2.6"
## [717] "6.9" "2.3" "virginica" "120"
## [721] "6" "2.2" "5" "1.5"
## [725] "virginica" "121" "6.9" "3.2"
## [729] "5.7" "2.3" "virginica" "122"
## [733] "5.6" "2.8" "4.9" "2"
## [737] "virginica" "123" "7.7" "2.8"
## [741] "6.7" "2" "virginica" "124"
## [745] "6.3" "2.7" "4.9" "1.8"
## [749] "virginica" "125" "6.7" "3.3"
## [753] "5.7" "2.1" "virginica" "126"
## [757] "7.2" "3.2" "6" "1.8"
## [761] "virginica" "127" "6.2" "2.8"
## [765] "4.8" "1.8" "virginica" "128"
## [769] "6.1" "3" "4.9" "1.8"
## [773] "virginica" "129" "6.4" "2.8"
## [777] "5.6" "2.1" "virginica" "130"
## [781] "7.2" "3" "5.8" "1.6"
## [785] "virginica" "131" "7.4" "2.8"
## [789] "6.1" "1.9" "virginica" "132"
## [793] "7.9" "3.8" "6.4" "2"
## [797] "virginica" "133" "6.4" "2.8"
## [801] "5.6" "2.2" "virginica" "134"
## [805] "6.3" "2.8" "5.1" "1.5"
## [809] "virginica" "135" "6.1" "2.6"
## [813] "5.6" "1.4" "virginica" "136"
## [817] "7.7" "3" "6.1" "2.3"
## [821] "virginica" "137" "6.3" "3.4"
## [825] "5.6" "2.4" "virginica" "138"
## [829] "6.4" "3.1" "5.5" "1.8"
## [833] "virginica" "139" "6" "3"
## [837] "4.8" "1.8" "virginica" "140"
## [841] "6.9" "3.1" "5.4" "2.1"
## [845] "virginica" "141" "6.7" "3.1"
## [849] "5.6" "2.4" "virginica" "142"
## [853] "6.9" "3.1" "5.1" "2.3"
## [857] "virginica" "143" "5.8" "2.7"
## [861] "5.1" "1.9" "virginica" "144"
## [865] "6.8" "3.2" "5.9" "2.3"
## [869] "virginica" "145" "6.7" "3.3"
## [873] "5.7" "2.5" "virginica" "146"
## [877] "6.7" "3" "5.2" "2.3"
## [881] "virginica" "147" "6.3" "2.5"
## [885] "5" "1.9" "virginica" "148"
## [889] "6.5" "3" "5.2" "2"
## [893] "virginica" "149" "6.2" "3.4"
## [897] "5.4" "2.3" "virginica" "150"
## [901] "5.9" "3" "5.1" "1.8"
## [905] "virginica"- print() – prints the content of specificed variable onto screen
ans<- 5*8
print(ans)## [1] 40- cat() – concatenates two separate strings
AL <-cat("ana",".","lakshin")## ana . lakshin- write.table() – exports R data as a readable and downloadable file
write.table(iris,file="iris.csv",row.names = TRUE)Data Creation
- c() – combines a vector so that a single variable could be assigned a set of numbers
abs<- c(2,6,9,12)- from:to (where from and to are replaced with numbers, e.g. 1:10) – create a sequence of numbers
bc <- 12:4
cd <- bc + 2
2:5 *2## [1] 4 6 8 10- seq() – function to create a sequence of numbers
sam<- seq(4,18,.25)
print(sam)## [1] 4.00 4.25 4.50 4.75 5.00 5.25 5.50 5.75 6.00 6.25 6.50
## [12] 6.75 7.00 7.25 7.50 7.75 8.00 8.25 8.50 8.75 9.00 9.25
## [23] 9.50 9.75 10.00 10.25 10.50 10.75 11.00 11.25 11.50 11.75 12.00
## [34] 12.25 12.50 12.75 13.00 13.25 13.50 13.75 14.00 14.25 14.50 14.75
## [45] 15.00 15.25 15.50 15.75 16.00 16.25 16.50 16.75 17.00 17.25 17.50
## [56] 17.75 18.00- rep() – replicates values in the specified variable
rep(6:9, times=2)## [1] 6 7 8 9 6 7 8 9- data.frame() – converts matrices into data frames, creates data frames
yan <- matrix(seq(1,24,1), nrow=6, ncol = 4)
data.frame(yan)## X1 X2 X3 X4
## 1 1 7 13 19
## 2 2 8 14 20
## 3 3 9 15 21
## 4 4 10 16 22
## 5 5 11 17 23
## 6 6 12 18 24- list() – creates a list
kin<- list("a"=2.5, "b"= TRUE, "c"= 2:5)
#howdo you do this with names?- matrix() – creates a martix with specified rows and columns
yan <- matrix(seq(1,24,1), nrow=6, ncol = 4)
yan2 <- matrix(seq(25,50,1), nrow=6, ncol = 4)- factor() – converts vector into factor, finds unique characters/strings & sorts alphabetically
STEM<- c("science","math", "technology", "engineering","math")
STEM_factor <- factor(STEM)
print(STEM_factor)## [1] science math technology engineering math
## Levels: engineering math science technology- rbind() – combines two separate data matrices by row, has to be of same dataset or else combination meaningless
rbind(yan,yan2)## [,1] [,2] [,3] [,4]
## [1,] 1 7 13 19
## [2,] 2 8 14 20
## [3,] 3 9 15 21
## [4,] 4 10 16 22
## [5,] 5 11 17 23
## [6,] 6 12 18 24
## [7,] 25 31 37 43
## [8,] 26 32 38 44
## [9,] 27 33 39 45
## [10,] 28 34 40 46
## [11,] 29 35 41 47
## [12,] 30 36 42 48- cbind() – combines two separate data matrices by column, has to be of same dataset or else combination meaningless
cbind(yan, yan2)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 7 13 19 25 31 37 43
## [2,] 2 8 14 20 26 32 38 44
## [3,] 3 9 15 21 27 33 39 45
## [4,] 4 10 16 22 28 34 40 46
## [5,] 5 11 17 23 29 35 41 47
## [6,] 6 12 18 24 30 36 42 48Slicing and Extracting Data
Indexing vectors
- x[n] nth element – indexes the number or character by its position within the vector or matrix as specified by number in bracket
g <- matrix(1:20, nrow = 2, ncol = 10)
g[2]## [1] 2h <- c(4,8,21,34,51,83)
h [4]## [1] 34- x[-n] all but nth element – indexes entire vector or matrix except the specified element
g[-7]## [1] 1 2 3 4 5 6 8 9 10 11 12 13 14 15 16 17 18 19 20- x[1:n] first n elements – indexes the sequence of numbers specified within the brackets from a vector or matrix
g[2:4]## [1] 2 3 4h[1:3]## [1] 4 8 21- x[-(1:n)] elements from n+1 to the end – indexes all elements of a vector or matrix except those specified within the parentheses.
h[-(2:4)]## [1] 4 51 83g[-(5:10)]## [1] 1 2 3 4 11 12 13 14 15 16 17 18 19 20- x[c(1,4,2)] specific elements – indexes specific (combinations of) numbers from a vector or matrix
h[c(1,5)]## [1] 4 51g[c(2,6,20)]## [1] 2 6 20- x[“name”] elements named “name”
j <- c("john", "emily", "ana", "frank", "cam")
j["ana"]## [1] NAclass(j)## [1] "character"beb<- table(j)
b<- rep(beb,5) # ???? not replicating?!
names(b)## [1] "ana" "cam" "emily" "frank" "john" "ana" "cam" "emily"
## [9] "frank" "john" "ana" "cam" "emily" "frank" "john" "ana"
## [17] "cam" "emily" "frank" "john" "ana" "cam" "emily" "frank"
## [25] "john"b["john"]## john
## 1# wont work as a vector. make it a table first
#or names function- x[x>3] all elements greater than 3 – indexes numbers either greater than or less than the specified number
g[g>12]## [1] 13 14 15 16 17 18 19 20h[h<50]## [1] 4 8 21 34- x[x > 3 & x < 5] all elements between 3 and 5 – indexes numbers with logical statements
g[g > 6 & g< 12]## [1] 7 8 9 10 11h[h > 20 & h < 40]## [1] 21 34- x[x %in% c(“a”,“and”,“the”)] all elements in given set
"ana" %in% j## [1] TRUE"john" %in% b == FALSE## [1] TRUEIndexing lists
song<- list("Hotel California", 378, 4)
names(song) <- c("title", "duration", "track")
similar_song <- list (title = "Misery Business", duration= 190, track = 5)
song<- list (title = "Hotel California", duration = 378, track = 4, similar = similar_song)
str(song)## List of 4
## $ title : chr "Hotel California"
## $ duration: num 378
## $ track : num 4
## $ similar :List of 3
## ..$ title : chr "Misery Business"
## ..$ duration: num 190
## ..$ track : num 5#https://www.youtube.com/watch?v=Px9VNWHja4M- x[n] list with elements n – indexes main list’s nth item
song[2]## $duration
## [1] 378- x[[n]] nt h element of the list
song[[4]][1]## $title
## [1] "Misery Business"#2nd song- x[[“name”]] element of the list named “name”
song[["similar"]]["duration"]## $duration
## [1] 190- x$name id.
song$similar$duration## [1] 190#how do i get to the 2nd "song" Indexing matrices
as<- c(31,123,67,1123,987)
ss<- c(122,56,897,32,45)
sb<- list(as,ss)
sbb<-matrix(data=as & ss, nrow=5, ncol = 2, byrow=FALSE, dimnames = NULL)
#why doesnt a list work in a matrix if they're both numeric- x[i,j] element at row i, column j
sbb[1,2]## [1] TRUE- x[i,] row i
sbb[4]## [1] TRUE- x[,j] column j
sbb[,2]## [1] TRUE TRUE TRUE TRUE TRUE- x[,c(1,3)] columns 1 and 3
sbb[c(2,3)]## [1] TRUE TRUE- x[“name”,] row named “name”
first<-matrix(1:9, nrow=3)
rownames(first)<-(c("a","b","c"))
first["c",]## [1] 3 6 9Indexing data frames (matrix indexing plus the following)
name<- c("ana","pete", "dave")
age<- c(22,32,45)
pass<-c(TRUE,FALSE,FALSE)
nap<-data.frame(name,age,pass)
nap## name age pass
## 1 ana 22 TRUE
## 2 pete 32 FALSE
## 3 dave 45 FALSE- x[[“name”]] column named “name” – indexes variables within specified column
nap[["age"]]## [1] 22 32 45- x$nameid. – indexes variables within specified column
nap$pass## [1] TRUE FALSE FALSEVariable Conversion
- as.data.frame(x) – converts r objects into data frame format
as.data.frame(name)## name
## 1 ana
## 2 pete
## 3 dave- as.numeric(x) – change the composition of a vector into numbers (if possible)
dod <- c(5,"4",7)
as.numeric(dod)## [1] 5 4 7- as.logical(x) – converts all non-zero numbers to True and all zeros as false
ll<- c(0,4,7,2,0,1)
as.logical(ll)## [1] FALSE TRUE TRUE TRUE FALSE TRUE- as.character(x) – converts numbers into characters
cob<- 5.692
cob<- as.character(cob)Variable Information
- is.na(x) – determines if any values are missing or not available (??)
na <- c(4,7,NA,21)
is.na(na)## [1] FALSE FALSE TRUE FALSE- is.null(x) – checks if there is a null variable
is.null(age)## [1] FALSE- is.data.frame(x) – checks if object is in a data frame format
is.data.frame(nap)## [1] TRUE- is.numeric(x) – checks if variable/object is a number (returns logical)
is.numeric(cob)## [1] FALSE- is.character(x) – checks if variable/item is a character class
is.character(cob)## [1] TRUE- length(x) – returns length of vector
length(a)## [1] 4- dim(x) – returns dimension of object(vector/list/matrix etc)
dim(nap)## [1] 3 3- dimnames(x) – lists dimensions/columns and their names
dimnames(nap)## [[1]]
## [1] "1" "2" "3"
##
## [[2]]
## [1] "name" "age" "pass"- nrow(x) – returns how many rows are in a matrix/dataframe & sets number of rows
nrow(nap)## [1] 3- ncol(x) – returns number of columns in a matrix/dataframe & sets number of columns
ncol(nap)## [1] 3- class() – states a variable’s class i.e. char, num, integer
class(ans)## [1] "numeric"- attributes() – displays attributs of object
attributes(yan)## $dim
## [1] 6 4Data Selection and Manipulation
- which.max() – returns position of the highest value
which.max(a)## [1] 4- which.min() – returns position of the lowest value
which.min(a)## [1] 2- which() – returns position of which numbers fit whatever criteria you provide
v <- c(6,3,7,1,8)
which(v %% 2 ==0)## [1] 1 5- sort() – sorts a vector from lowest to highest
sort(v)## [1] 1 3 6 7 8- unique() – returns the unique values within a vector/list/matrix
w <- c(1,1,3,6,6,3,4,4)
unique(w)## [1] 1 3 6 4- table() – creates a table that keeps a count of the frequency of unique characters
vef<-c("sd","asf","ag","sd","rg","asf")
table(vef)## vef
## ag asf rg sd
## 1 2 1 2- sample() – takes a sample of numbers, random numbers under specified restrictions.
sample(100, 10, replace=FALSE)## [1] 65 32 40 83 55 50 16 43 58 86Math
- max() – returns the maximum number of a set or vector
felix <- seq(3,56,1.2)
max(felix)## [1] 55.8- min() – returns the minimum number of a set or vector
min(felix)## [1] 3- range() – returns the minimum and maximum of a set or vector
range(felix)## [1] 52.8- sum() – calculates the sum of a number set or vector
s_u <-sum(felix)
s_u## [1] 1323- mean() – function to find the mean of a number set or vector
m_ <-mean(c(3,7,12,99,23,54))
m_## [1] 33- median() – finds the median of a number set or vector
nad<- seq(1,50,3)
z<- median(nad)
z## [1] 25- var() – computes variance
ka <- c(1,5,3)
an <- c(4,5,8)
var(ka)## [1] 4- sd() – computes standard deviation
sd(an)## [1] 2.081666- cor() – computes correlation
cor(ka,an)## [1] 0.2401922- round() – rounds the value of input
round(9/2)## [1] 4- abs() – returns absolute value
abs(-5)## [1] 5Matrices
gre<-matrix(2:20, nrow=5)- t() – transposes matrix
t(first)## a b c
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9- diag() – returns numbers within the matrix that are in diagonal positions
diag(first)## [1] 1 5 9- rowSums() – returns sums of the rows
rowSums(gre)## [1] 38 42 46 50 35- colSums() – returns sums of the columns
colSums(gre)## [1] 20 45 70 76- rowMeans() – returns means of the rows
rowMeans(gre)## [1] 9.50 10.50 11.50 12.50 8.75- colMeans() – returns means of the columns
colMeans(gre)## [1] 4.0 9.0 14.0 15.2Advanced Data processing
- apply() – applies specified functions on matrices. 2nd number is margin 1=rows, 2=columns
gre## [,1] [,2] [,3] [,4]
## [1,] 2 7 12 17
## [2,] 3 8 13 18
## [3,] 4 9 14 19
## [4,] 5 10 15 20
## [5,] 6 11 16 2apply(gre,2,sum)## [1] 20 45 70 76- aggregate() –?
aggregate(gre)
#dont understand the functionStrings
- paste() – similar to concatenate, has separators
rat1 <- "17R1"
paste(rat1, "box", sep = " is in ")## [1] "17R1 is in box"strsplit() –
tolower() –
alpha<- LETTERS
tolower(alpha)## [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q"
## [18] "r" "s" "t" "u" "v" "w" "x" "y" "z"- toupper –
alphab<- letters
toupper(alphab)## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q"
## [18] "R" "S" "T" "U" "V" "W" "X" "Y" "Z"Plotting
- hist() - function for creating a histogram
uk <- rnorm(50,mean=20,sd=1)
hist(uk)
- plot() – function for plotting a graph
plot(rnorm(100,mean=38,sd=2))
Distributions
- rnorm()
q<- rnorm(20, mean = 5, sd = 2)
plot(q)
- runif() – generates random numbers (how many, min, max)
runif(10,5,50)## [1] 44.092221 25.278331 21.340110 14.973972 40.459667 10.419702 35.577748
## [8] 7.832178 13.582619 7.840849Programming
- Show that you can define a function
mult4<- function(m)
return(m*4)
mult4(10)## [1] 40- Show that you can write a for loop
d<- c(81,21,52,36,19,22)
for(s in 1:4)
d[s]<-d[s]+1
print(d)## [1] 82 22 53 37 19 22- Show that you can write a while loop
dud<- c("hello","R")
ded<- 4:12
while(ded>10){
print(dud)
ded=ded+1
}- Show that you can write an if else statement
a <- c(6,1,4,9)
ifelse(a%%2 == 0, "even","odd")## [1] "even" "odd" "even" "odd"- Explain what return() does inside a function, show you can use it Return is used within functions to assign a number, value, r object to be brought back after the function completes.
add2<-function(x){
return(x+2)
}
add2(5)## [1] 7- Explain what break() does, show you can use it Break is a function that controls the flow of a loop, it stops the iterations and flows out. Inside nested loops the flow exists the innermost loop.
f<- c(3,1,5,32,11,42)
f_num<-1:length(f)
for(g in f_num){
if(f[g]<30){
f[g]<- f[g]+3
}
if(f[g]>30){
break
}
}
print(f[g])## [1] 32