便利だが普段はあまりつかわないため忘れがちな点についてメモしておく． 本テキストのどこかで扱う内容もメモする．
integrate(function(x) dnorm(x, 0, 1), lower = 0, upper = Inf)0.5 with absolute error < 4.7e-05integrate(function(x) dnorm(x, 0, 1), lower = -1.645, upper = 1.645)0.9000302 with absolute error < 7.4e-14integrate(function(x) dnorm(x, 0, 1), lower = -1.96, upper = 1.96)0.9500042 with absolute error < 1e-11integrate(function(x) dchisq(x, df = 5), lower = 0, upper = 5)0.5841198 with absolute error < 7.7e-05integrate(function(x) dchisq(x, df = 100), lower = 0, upper = 100)0.5188083 with absolute error < 1.2e-11library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(stringr)fruit [1] "apple" "apricot" "avocado"
[4] "banana" "bell pepper" "bilberry"
[7] "blackberry" "blackcurrant" "blood orange"
[10] "blueberry" "boysenberry" "breadfruit"
[13] "canary melon" "cantaloupe" "cherimoya"
[16] "cherry" "chili pepper" "clementine"
[19] "cloudberry" "coconut" "cranberry"
[22] "cucumber" "currant" "damson"
[25] "date" "dragonfruit" "durian"
[28] "eggplant" "elderberry" "feijoa"
[31] "fig" "goji berry" "gooseberry"
[34] "grape" "grapefruit" "guava"
[37] "honeydew" "huckleberry" "jackfruit"
[40] "jambul" "jujube" "kiwi fruit"
[43] "kumquat" "lemon" "lime"
[46] "loquat" "lychee" "mandarine"
[49] "mango" "mulberry" "nectarine"
[52] "nut" "olive" "orange"
[55] "pamelo" "papaya" "passionfruit"
[58] "peach" "pear" "persimmon"
[61] "physalis" "pineapple" "plum"
[64] "pomegranate" "pomelo" "purple mangosteen"
[67] "quince" "raisin" "rambutan"
[70] "raspberry" "redcurrant" "rock melon"
[73] "salal berry" "satsuma" "star fruit"
[76] "strawberry" "tamarillo" "tangerine"
[79] "ugli fruit" "watermelon" # 長さ
str_length(fruit) [1] 5 7 7 6 11 8 10 12 12 9 11 10 12 10 9 6 12 10 10 7 9 8 7 6 4
[26] 11 6 8 10 6 3 10 10 5 10 5 8 11 9 6 6 10 7 5 4 6 6 9 5 8
[51] 9 3 5 6 6 6 12 5 4 9 8 9 4 11 6 17 6 6 8 9 10 10 11 7 10
[76] 10 9 9 10 10# くっつける
str_c("fruit_", fruit) [1] "fruit_apple" "fruit_apricot"
[3] "fruit_avocado" "fruit_banana"
[5] "fruit_bell pepper" "fruit_bilberry"
[7] "fruit_blackberry" "fruit_blackcurrant"
[9] "fruit_blood orange" "fruit_blueberry"
[11] "fruit_boysenberry" "fruit_breadfruit"
[13] "fruit_canary melon" "fruit_cantaloupe"
[15] "fruit_cherimoya" "fruit_cherry"
[17] "fruit_chili pepper" "fruit_clementine"
[19] "fruit_cloudberry" "fruit_coconut"
[21] "fruit_cranberry" "fruit_cucumber"
[23] "fruit_currant" "fruit_damson"
[25] "fruit_date" "fruit_dragonfruit"
[27] "fruit_durian" "fruit_eggplant"
[29] "fruit_elderberry" "fruit_feijoa"
[31] "fruit_fig" "fruit_goji berry"
[33] "fruit_gooseberry" "fruit_grape"
[35] "fruit_grapefruit" "fruit_guava"
[37] "fruit_honeydew" "fruit_huckleberry"
[39] "fruit_jackfruit" "fruit_jambul"
[41] "fruit_jujube" "fruit_kiwi fruit"
[43] "fruit_kumquat" "fruit_lemon"
[45] "fruit_lime" "fruit_loquat"
[47] "fruit_lychee" "fruit_mandarine"
[49] "fruit_mango" "fruit_mulberry"
[51] "fruit_nectarine" "fruit_nut"
[53] "fruit_olive" "fruit_orange"
[55] "fruit_pamelo" "fruit_papaya"
[57] "fruit_passionfruit" "fruit_peach"
[59] "fruit_pear" "fruit_persimmon"
[61] "fruit_physalis" "fruit_pineapple"
[63] "fruit_plum" "fruit_pomegranate"
[65] "fruit_pomelo" "fruit_purple mangosteen"
[67] "fruit_quince" "fruit_raisin"
[69] "fruit_rambutan" "fruit_raspberry"
[71] "fruit_redcurrant" "fruit_rock melon"
[73] "fruit_salal berry" "fruit_satsuma"
[75] "fruit_star fruit" "fruit_strawberry"
[77] "fruit_tamarillo" "fruit_tangerine"
[79] "fruit_ugli fruit" "fruit_watermelon" # パターンの数
str_count(fruit, "[a-b]") [1] 1 1 2 4 1 2 3 3 2 2 2 2 2 2 1 0 0 0 1 0 2 1 1 1 1 1 1 1 1 1 0 1 1 1 1 2 0 1
[39] 1 2 1 0 1 0 0 1 0 2 1 1 1 0 0 1 1 3 1 1 1 0 1 1 0 2 0 1 0 1 3 2 1 0 3 2 1 2
[77] 2 1 0 1# パターンの発見
str_detect(fruit, "[a-b]") [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[13] TRUE TRUE TRUE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE TRUE
[25] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE
[37] FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
[49] TRUE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE FALSE
[61] TRUE TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE FALSE
[73] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE# 繰り返す
str_dup(letters, 5) [1] "aaaaa" "bbbbb" "ccccc" "ddddd" "eeeee" "fffff" "ggggg" "hhhhh" "iiiii"
[10] "jjjjj" "kkkkk" "lllll" "mmmmm" "nnnnn" "ooooo" "ppppp" "qqqqq" "rrrrr"
[19] "sssss" "ttttt" "uuuuu" "vvvvv" "wwwww" "xxxxx" "yyyyy" "zzzzz"# 終わる
fruit[str_ends(fruit, "a")][1] "banana" "cherimoya" "feijoa" "guava" "papaya" "satsuma" # 同じ
str_equal(fruit, "banana") [1] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE# エスケープ
str_escape("^")[1] "\\^"str_extract(fruit, "[a-c]") [1] "a" "a" "a" "b" "b" "b" "b" "b" "b" "b" "b" "b" "c" "c" "c" "c" "c" "c" "c"
[20] "c" "c" "c" "c" "a" "a" "a" "a" "a" "b" "a" NA "b" "b" "a" "a" "a" NA "c"
[39] "a" "a" "b" NA "a" NA NA "a" "c" "a" "a" "b" "c" NA NA "a" "a" "a" "a"
[58] "a" "a" NA "a" "a" NA "a" NA "a" "c" "a" "a" "a" "c" "c" "a" "a" "a" "a"
[77] "a" "a" NA "a"str_extract(fruit, "b[a-n]") [1] NA NA NA "ba" "be" "bi" "bl" "bl" "bl" "bl" "be" NA NA NA NA
[16] NA NA NA "be" NA "be" "be" NA NA NA NA NA NA "be" NA
[31] NA "be" "be" NA NA NA NA "be" NA NA "be" NA NA NA NA
[46] NA NA NA NA "be" NA NA NA NA NA NA NA NA NA NA
[61] NA NA NA NA NA NA NA NA NA "be" NA NA "be" NA NA
[76] "be" NA NA NA NA str_extract_all(fruit, "apple", simplify = TRUE) [,1]
[1,] "apple"
[2,] ""
[3,] ""
[4,] ""
[5,] ""
[6,] ""
[7,] ""
[8,] ""
[9,] ""
[10,] ""
[11,] ""
[12,] ""
[13,] ""
[14,] ""
[15,] ""
[16,] ""
[17,] ""
[18,] ""
[19,] ""
[20,] ""
[21,] ""
[22,] ""
[23,] ""
[24,] ""
[25,] ""
[26,] ""
[27,] ""
[28,] ""
[29,] ""
[30,] ""
[31,] ""
[32,] ""
[33,] ""
[34,] ""
[35,] ""
[36,] ""
[37,] ""
[38,] ""
[39,] ""
[40,] ""
[41,] ""
[42,] ""
[43,] ""
[44,] ""
[45,] ""
[46,] ""
[47,] ""
[48,] ""
[49,] ""
[50,] ""
[51,] ""
[52,] ""
[53,] ""
[54,] ""
[55,] ""
[56,] ""
[57,] ""
[58,] ""
[59,] ""
[60,] ""
[61,] ""
[62,] "apple"
[63,] ""
[64,] ""
[65,] ""
[66,] ""
[67,] ""
[68,] ""
[69,] ""
[70,] ""
[71,] ""
[72,] ""
[73,] ""
[74,] ""
[75,] ""
[76,] ""
[77,] ""
[78,] ""
[79,] ""
[80,] "" str_detect(c("a", "."), str_escape("."))[1] FALSE TRUEx <- "Hello, World!"
str_extract_all(x, boundary("character"), simplify = TRUE) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
[1,] "H" "e" "l" "l" "o" "," " " "W" "o" "r" "l" "d" "!" str_extract_all(x, boundary("line_break"), simplify = TRUE) [,1] [,2]
[1,] "Hello, " "World!"str_extract_all(x, boundary("sentence"), simplify = TRUE) [,1]
[1,] "Hello, World!"str_extract_all(x, boundary("word"), simplify = TRUE) [,1] [,2]
[1,] "Hello" "World"str_flatten(fruit)[1] "appleapricotavocadobananabell pepperbilberryblackberryblackcurrantblood orangeblueberryboysenberrybreadfruitcanary meloncantaloupecherimoyacherrychili pepperclementinecloudberrycoconutcranberrycucumbercurrantdamsondatedragonfruitdurianeggplantelderberryfeijoafiggoji berrygooseberrygrapegrapefruitguavahoneydewhuckleberryjackfruitjambuljujubekiwi fruitkumquatlemonlimeloquatlycheemandarinemangomulberrynectarinenutoliveorangepamelopapayapassionfruitpeachpearpersimmonphysalispineappleplumpomegranatepomelopurple mangosteenquinceraisinrambutanraspberryredcurrantrock melonsalal berrysatsumastar fruitstrawberrytamarillotangerineugli fruitwatermelon"str_flatten_comma(fruit)[1] "apple, apricot, avocado, banana, bell pepper, bilberry, blackberry, blackcurrant, blood orange, blueberry, boysenberry, breadfruit, canary melon, cantaloupe, cherimoya, cherry, chili pepper, clementine, cloudberry, coconut, cranberry, cucumber, currant, damson, date, dragonfruit, durian, eggplant, elderberry, feijoa, fig, goji berry, gooseberry, grape, grapefruit, guava, honeydew, huckleberry, jackfruit, jambul, jujube, kiwi fruit, kumquat, lemon, lime, loquat, lychee, mandarine, mango, mulberry, nectarine, nut, olive, orange, pamelo, papaya, passionfruit, peach, pear, persimmon, physalis, pineapple, plum, pomegranate, pomelo, purple mangosteen, quince, raisin, rambutan, raspberry, redcurrant, rock melon, salal berry, satsuma, star fruit, strawberry, tamarillo, tangerine, ugli fruit, watermelon"str_glue("I bought ", "{fruit}", " by {rnorm(length(fruit), mean = 100, sd = 10) |> format(digits = 3)} yen")I bought apple by 90.3 yen
I bought apricot by 101.2 yen
I bought avocado by 90.0 yen
I bought banana by 102.1 yen
I bought bell pepper by 81.2 yen
I bought bilberry by 113.2 yen
I bought blackberry by 94.6 yen
I bought blackcurrant by 103.7 yen
I bought blood orange by 95.5 yen
I bought blueberry by 110.6 yen
I bought boysenberry by 99.0 yen
I bought breadfruit by 93.1 yen
I bought canary melon by 106.1 yen
I bought cantaloupe by 95.7 yen
I bought cherimoya by 109.9 yen
I bought cherry by 92.7 yen
I bought chili pepper by 89.8 yen
I bought clementine by 95.4 yen
I bought cloudberry by 88.1 yen
I bought coconut by 80.9 yen
I bought cranberry by 108.0 yen
I bought cucumber by 91.2 yen
I bought currant by 89.8 yen
I bought damson by 100.6 yen
I bought date by 92.5 yen
I bought dragonfruit by 107.1 yen
I bought durian by 109.6 yen
I bought eggplant by 107.4 yen
I bought elderberry by 99.3 yen
I bought feijoa by 104.4 yen
I bought fig by 101.7 yen
I bought goji berry by 112.7 yen
I bought gooseberry by 107.2 yen
I bought grape by 82.8 yen
I bought grapefruit by 102.5 yen
I bought guava by 108.7 yen
I bought honeydew by 90.4 yen
I bought huckleberry by 102.3 yen
I bought jackfruit by 103.0 yen
I bought jambul by 91.4 yen
I bought jujube by 103.2 yen
I bought kiwi fruit by 94.5 yen
I bought kumquat by 97.1 yen
I bought lemon by 95.6 yen
I bought lime by 96.7 yen
I bought loquat by 89.7 yen
I bought lychee by 84.7 yen
I bought mandarine by 109.8 yen
I bought mango by 97.2 yen
I bought mulberry by 103.5 yen
I bought nectarine by 102.9 yen
I bought nut by 112.9 yen
I bought olive by 90.4 yen
I bought orange by 91.3 yen
I bought pamelo by 91.6 yen
I bought papaya by 90.1 yen
I bought passionfruit by 94.3 yen
I bought peach by 112.6 yen
I bought pear by 109.4 yen
I bought persimmon by 105.0 yen
I bought physalis by 105.7 yen
I bought pineapple by 105.9 yen
I bought plum by 104.2 yen
I bought pomegranate by 98.3 yen
I bought pomelo by 99.6 yen
I bought purple mangosteen by 100.7 yen
I bought quince by 106.3 yen
I bought raisin by 103.0 yen
I bought rambutan by 99.1 yen
I bought raspberry by 109.2 yen
I bought redcurrant by 80.2 yen
I bought rock melon by 107.5 yen
I bought salal berry by 107.2 yen
I bought satsuma by 88.1 yen
I bought star fruit by 96.4 yen
I bought strawberry by 110.6 yen
I bought tamarillo by 88.0 yen
I bought tangerine by 83.3 yen
I bought ugli fruit by 112.4 yen
I bought watermelon by 103.1 yenstarwars |> str_glue_data("Is {height} over 100? {ifelse(height >= 100, 'Yes','No')}.")Is 172 over 100? Yes.
Is 167 over 100? Yes.
Is 96 over 100? No.
Is 202 over 100? Yes.
Is 150 over 100? Yes.
Is 178 over 100? Yes.
Is 165 over 100? Yes.
Is 97 over 100? No.
Is 183 over 100? Yes.
Is 182 over 100? Yes.
Is 188 over 100? Yes.
Is 180 over 100? Yes.
Is 228 over 100? Yes.
Is 180 over 100? Yes.
Is 173 over 100? Yes.
Is 175 over 100? Yes.
Is 170 over 100? Yes.
Is 180 over 100? Yes.
Is 66 over 100? No.
Is 170 over 100? Yes.
Is 183 over 100? Yes.
Is 200 over 100? Yes.
Is 190 over 100? Yes.
Is 177 over 100? Yes.
Is 175 over 100? Yes.
Is 180 over 100? Yes.
Is 150 over 100? Yes.
Is NA over 100? NA.
Is 88 over 100? No.
Is 160 over 100? Yes.
Is 193 over 100? Yes.
Is 191 over 100? Yes.
Is 170 over 100? Yes.
Is 185 over 100? Yes.
Is 196 over 100? Yes.
Is 224 over 100? Yes.
Is 206 over 100? Yes.
Is 183 over 100? Yes.
Is 137 over 100? Yes.
Is 112 over 100? Yes.
Is 183 over 100? Yes.
Is 163 over 100? Yes.
Is 175 over 100? Yes.
Is 180 over 100? Yes.
Is 178 over 100? Yes.
Is 79 over 100? No.
Is 94 over 100? No.
Is 122 over 100? Yes.
Is 163 over 100? Yes.
Is 188 over 100? Yes.
Is 198 over 100? Yes.
Is 196 over 100? Yes.
Is 171 over 100? Yes.
Is 184 over 100? Yes.
Is 188 over 100? Yes.
Is 264 over 100? Yes.
Is 188 over 100? Yes.
Is 196 over 100? Yes.
Is 185 over 100? Yes.
Is 157 over 100? Yes.
Is 183 over 100? Yes.
Is 183 over 100? Yes.
Is 170 over 100? Yes.
Is 166 over 100? Yes.
Is 165 over 100? Yes.
Is 193 over 100? Yes.
Is 191 over 100? Yes.
Is 183 over 100? Yes.
Is 168 over 100? Yes.
Is 198 over 100? Yes.
Is 229 over 100? Yes.
Is 213 over 100? Yes.
Is 167 over 100? Yes.
Is 96 over 100? No.
Is 193 over 100? Yes.
Is 191 over 100? Yes.
Is 178 over 100? Yes.
Is 216 over 100? Yes.
Is 234 over 100? Yes.
Is 188 over 100? Yes.
Is 178 over 100? Yes.
Is 206 over 100? Yes.
Is NA over 100? NA.
Is NA over 100? NA.
Is NA over 100? NA.
Is NA over 100? NA.
Is NA over 100? NA.x <- c(NA,"a","b","c",NA)
x[1] NA "a" "b" "c" NA str_c("fruit_", x)[1] NA "fruit_a" "fruit_b" "fruit_c" NA str_c("fruit_", str_replace_na(x, replacement = "DKNA"))[1] "fruit_DKNA" "fruit_a" "fruit_b" "fruit_c" "fruit_DKNA"str_to_lower(LETTERS) [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"str_sub(fruit, 1, 1) <- str_to_upper(str_sub(fruit, 1, 1))
fruit [1] "Apple" "Apricot" "Avocado"
[4] "Banana" "Bell pepper" "Bilberry"
[7] "Blackberry" "Blackcurrant" "Blood orange"
[10] "Blueberry" "Boysenberry" "Breadfruit"
[13] "Canary melon" "Cantaloupe" "Cherimoya"
[16] "Cherry" "Chili pepper" "Clementine"
[19] "Cloudberry" "Coconut" "Cranberry"
[22] "Cucumber" "Currant" "Damson"
[25] "Date" "Dragonfruit" "Durian"
[28] "Eggplant" "Elderberry" "Feijoa"
[31] "Fig" "Goji berry" "Gooseberry"
[34] "Grape" "Grapefruit" "Guava"
[37] "Honeydew" "Huckleberry" "Jackfruit"
[40] "Jambul" "Jujube" "Kiwi fruit"
[43] "Kumquat" "Lemon" "Lime"
[46] "Loquat" "Lychee" "Mandarine"
[49] "Mango" "Mulberry" "Nectarine"
[52] "Nut" "Olive" "Orange"
[55] "Pamelo" "Papaya" "Passionfruit"
[58] "Peach" "Pear" "Persimmon"
[61] "Physalis" "Pineapple" "Plum"
[64] "Pomegranate" "Pomelo" "Purple mangosteen"
[67] "Quince" "Raisin" "Rambutan"
[70] "Raspberry" "Redcurrant" "Rock melon"
[73] "Salal berry" "Satsuma" "Star fruit"
[76] "Strawberry" "Tamarillo" "Tangerine"
[79] "Ugli fruit" "Watermelon" str_to_upper("gender")[1] "GENDER"str_to_upper("全角のａ")[1] "全角のＡ"str_to_lower("ESTSIMATE")[1] "estsimate"str_to_lower("Ａ")[1] "ａ"str_view(fruit, "a") [3] │ Avoc<a>do
[4] │ B<a>n<a>n<a>
[7] │ Bl<a>ckberry
[8] │ Bl<a>ckcurr<a>nt
[9] │ Blood or<a>nge
[12] │ Bre<a>dfruit
[13] │ C<a>n<a>ry melon
[14] │ C<a>nt<a>loupe
[15] │ Cherimoy<a>
[21] │ Cr<a>nberry
[23] │ Curr<a>nt
[24] │ D<a>mson
[25] │ D<a>te
[26] │ Dr<a>gonfruit
[27] │ Duri<a>n
[28] │ Eggpl<a>nt
[30] │ Feijo<a>
[34] │ Gr<a>pe
[35] │ Gr<a>pefruit
[36] │ Gu<a>v<a>
... and 28 morestr_view(fruit, "[ae]") [1] │ Appl<e>
[3] │ Avoc<a>do
[4] │ B<a>n<a>n<a>
[5] │ B<e>ll p<e>pp<e>r
[6] │ Bilb<e>rry
[7] │ Bl<a>ckb<e>rry
[8] │ Bl<a>ckcurr<a>nt
[9] │ Blood or<a>ng<e>
[10] │ Blu<e>b<e>rry
[11] │ Boys<e>nb<e>rry
[12] │ Br<e><a>dfruit
[13] │ C<a>n<a>ry m<e>lon
[14] │ C<a>nt<a>loup<e>
[15] │ Ch<e>rimoy<a>
[16] │ Ch<e>rry
[17] │ Chili p<e>pp<e>r
[18] │ Cl<e>m<e>ntin<e>
[19] │ Cloudb<e>rry
[21] │ Cr<a>nb<e>rry
[22] │ Cucumb<e>r
... and 53 morestr_view(fruit, ".a.") [3] │ Avo<cad>o
[4] │ <Ban>ana
[7] │ B<lac>kberry
[8] │ B<lac>kcur<ran>t
[9] │ Blood o<ran>ge
[12] │ Br<ead>fruit
[13] │ <Can>ary melon
[14] │ <Can><tal>oupe
[21] │ C<ran>berry
[23] │ Cur<ran>t
[24] │ <Dam>son
[25] │ <Dat>e
[26] │ D<rag>onfruit
[27] │ Dur<ian>
[28] │ Eggp<lan>t
[34] │ G<rap>e
[35] │ G<rap>efruit
[36] │ G<uav>a
[39] │ <Jac>kfruit
[40] │ <Jam>bul
... and 26 morestr_view(fruit, "^A")[1] │ <A>pple
[2] │ <A>pricot
[3] │ <A>vocadostr_view(fruit, "a$") [4] │ Banan<a>
[15] │ Cherimoy<a>
[30] │ Feijo<a>
[36] │ Guav<a>
[56] │ Papay<a>
[74] │ Satsum<a>str_view(fruit, ".a.|.b.") [3] │ Avo<cad>o
[4] │ <Ban>ana
[6] │ Bi<lbe>rry
[7] │ B<lac><kbe>rry
[8] │ B<lac>kcur<ran>t
[9] │ Blood o<ran>ge
[10] │ Blu<ebe>rry
[11] │ Boyse<nbe>rry
[12] │ Br<ead>fruit
[13] │ <Can>ary melon
[14] │ <Can><tal>oupe
[19] │ Clou<dbe>rry
[21] │ C<ran>berry
[22] │ Cucu<mbe>r
[23] │ Cur<ran>t
[24] │ <Dam>son
[25] │ <Dat>e
[26] │ D<rag>onfruit
[27] │ Dur<ian>
[28] │ Eggp<lan>t
... and 37 morestr_view(words, "^y")[975] │ <y>ear
[976] │ <y>es
[977] │ <y>esterday
[978] │ <y>et
[979] │ <y>ou
[980] │ <y>oungstr_view(words, "x$")[108] │ bo<x>
[747] │ se<x>
[772] │ si<x>
[841] │ ta<x>str_view(words, "^...$") [9] │ <act>
[12] │ <add>
[22] │ <age>
[24] │ <ago>
[26] │ <air>
[27] │ <all>
[38] │ <and>
[41] │ <any>
[51] │ <arm>
[54] │ <art>
[56] │ <ask>
[68] │ <bad>
[69] │ <bag>
[73] │ <bar>
[82] │ <bed>
[89] │ <bet>
[91] │ <big>
[94] │ <bit>
[108] │ <box>
[109] │ <boy>
... and 90 morestr_view(words, "^*.......*$") [4] │ <absolute>
[5] │ <accept>
[6] │ <account>
[7] │ <achieve>
[8] │ <across>
[10] │ <active>
[11] │ <actual>
[13] │ <address>
[15] │ <advertise>
[16] │ <affect>
[17] │ <afford>
[19] │ <afternoon>
[21] │ <against>
[29] │ <almost>
[31] │ <already>
[32] │ <alright>
[34] │ <although>
[35] │ <always>
[36] │ <america>
[37] │ <amount>
... and 368 morestr_view(words, "[^a-u]") [7] │ achie<v>e
[10] │ acti<v>e
[15] │ ad<v>ertise
[28] │ allo<w>
[31] │ alread<y>
[35] │ al<w>a<y>s
[40] │ ans<w>er
[41] │ an<y>
[45] │ appl<y>
[61] │ authorit<y>
[62] │ a<v>ailable
[63] │ a<w>are
[64] │ a<w>a<y>
[65] │ a<w>ful
[66] │ bab<y>
[79] │ beaut<y>
[86] │ belie<v>e
[90] │ bet<w>een
[98] │ blo<w>
[102] │ bod<y>
... and 229 morestr_view(words, "a|b") [1] │ <a>
[2] │ <a><b>le
[3] │ <a><b>out
[4] │ <a><b>solute
[5] │ <a>ccept
[6] │ <a>ccount
[7] │ <a>chieve
[8] │ <a>cross
[9] │ <a>ct
[10] │ <a>ctive
[11] │ <a>ctu<a>l
[12] │ <a>dd
[13] │ <a>ddress
[14] │ <a>dmit
[15] │ <a>dvertise
[16] │ <a>ffect
[17] │ <a>fford
[18] │ <a>fter
[19] │ <a>fternoon
[20] │ <a>g<a>in
... and 391 morestr_view(words, "(a|b)") [1] │ <a>
[2] │ <a><b>le
[3] │ <a><b>out
[4] │ <a><b>solute
[5] │ <a>ccept
[6] │ <a>ccount
[7] │ <a>chieve
[8] │ <a>cross
[9] │ <a>ct
[10] │ <a>ctive
[11] │ <a>ctu<a>l
[12] │ <a>dd
[13] │ <a>ddress
[14] │ <a>dmit
[15] │ <a>dvertise
[16] │ <a>ffect
[17] │ <a>fford
[18] │ <a>fter
[19] │ <a>fternoon
[20] │ <a>g<a>in
... and 391 moreメタキャラクター：. \ | ( ) [ { ^ $ * + ?
letters [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
[20] "t" "u" "v" "w" "x" "y" "z"grep("j|k", fruit)[1] 7 8 30 32 38 39 41 72grep("[jk]", fruit)[1] 7 8 30 32 38 39 41 72grepl("j|k", fruit) [1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
[37] FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEgrepl("[jk]", fruit) [1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE
[37] FALSE TRUE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
[73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEsub("[jk]","XXXXX", fruit) [1] "Apple" "Apricot" "Avocado"
[4] "Banana" "Bell pepper" "Bilberry"
[7] "BlacXXXXXberry" "BlacXXXXXcurrant" "Blood orange"
[10] "Blueberry" "Boysenberry" "Breadfruit"
[13] "Canary melon" "Cantaloupe" "Cherimoya"
[16] "Cherry" "Chili pepper" "Clementine"
[19] "Cloudberry" "Coconut" "Cranberry"
[22] "Cucumber" "Currant" "Damson"
[25] "Date" "Dragonfruit" "Durian"
[28] "Eggplant" "Elderberry" "FeiXXXXXoa"
[31] "Fig" "GoXXXXXi berry" "Gooseberry"
[34] "Grape" "Grapefruit" "Guava"
[37] "Honeydew" "HucXXXXXleberry" "JacXXXXXfruit"
[40] "Jambul" "JuXXXXXube" "Kiwi fruit"
[43] "Kumquat" "Lemon" "Lime"
[46] "Loquat" "Lychee" "Mandarine"
[49] "Mango" "Mulberry" "Nectarine"
[52] "Nut" "Olive" "Orange"
[55] "Pamelo" "Papaya" "Passionfruit"
[58] "Peach" "Pear" "Persimmon"
[61] "Physalis" "Pineapple" "Plum"
[64] "Pomegranate" "Pomelo" "Purple mangosteen"
[67] "Quince" "Raisin" "Rambutan"
[70] "Raspberry" "Redcurrant" "RocXXXXX melon"
[73] "Salal berry" "Satsuma" "Star fruit"
[76] "Strawberry" "Tamarillo" "Tangerine"
[79] "Ugli fruit" "Watermelon" gsub("[jk]","XXXXX", fruit) [1] "Apple" "Apricot" "Avocado"
[4] "Banana" "Bell pepper" "Bilberry"
[7] "BlacXXXXXberry" "BlacXXXXXcurrant" "Blood orange"
[10] "Blueberry" "Boysenberry" "Breadfruit"
[13] "Canary melon" "Cantaloupe" "Cherimoya"
[16] "Cherry" "Chili pepper" "Clementine"
[19] "Cloudberry" "Coconut" "Cranberry"
[22] "Cucumber" "Currant" "Damson"
[25] "Date" "Dragonfruit" "Durian"
[28] "Eggplant" "Elderberry" "FeiXXXXXoa"
[31] "Fig" "GoXXXXXi berry" "Gooseberry"
[34] "Grape" "Grapefruit" "Guava"
[37] "Honeydew" "HucXXXXXleberry" "JacXXXXXfruit"
[40] "Jambul" "JuXXXXXube" "Kiwi fruit"
[43] "Kumquat" "Lemon" "Lime"
[46] "Loquat" "Lychee" "Mandarine"
[49] "Mango" "Mulberry" "Nectarine"
[52] "Nut" "Olive" "Orange"
[55] "Pamelo" "Papaya" "Passionfruit"
[58] "Peach" "Pear" "Persimmon"
[61] "Physalis" "Pineapple" "Plum"
[64] "Pomegranate" "Pomelo" "Purple mangosteen"
[67] "Quince" "Raisin" "Rambutan"
[70] "Raspberry" "Redcurrant" "RocXXXXX melon"
[73] "Salal berry" "Satsuma" "Star fruit"
[76] "Strawberry" "Tamarillo" "Tangerine"
[79] "Ugli fruit" "Watermelon" x <- "Hello World!"
# 文頭
grepl("^e", x)[1] FALSEgrepl("^H", x)[1] TRUEgrepl("^h", x)[1] FALSE# 文末
grepl("d$", x)[1] FALSEgrepl("!$", x)[1] TRUE# いずれか
grepl("[!]", x)[1] TRUEgrepl("[a]", x)[1] FALSEgrepl("[A-Za-z0-9]", "!!!!!")[1] FALSEgrepl("[A-Za-z0-9]", "99999")[1] TRUEgrepl("[A-Za-z0-9]", "#####")[1] FALSEgrepl("[A-Za-z0-9]", "####1#")[1] TRUE# グルーピング
grepl("(e)", x)[1] TRUEgrepl("(e) +(d)", x)[1] FALSEgrepl("(e)+(w)", x)[1] FALSEgrepl("(H)(e)", x)[1] TRUEgrepl("(H)(l)", x)[1] FALSEgrep("[e]", x)[1] 1grepl("[e]", x)[1] TRUEgrepl("e", x)[1] TRUEgrepl("l|k", x)[1] TRUEgrepl("e", x)[1] TRUEgrepl("h", x)[1] FALSEgrepl(".", x)[1] TRUEgrep("[e]", letters)[1] 5grepl("[e]", letters) [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSEgrepl("e", letters) [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSEgrepl("l|k", letters) [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSEgrepl("e", letters) [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSEgrepl("h", letters) [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[25] FALSE FALSEgrepl(".", letters) [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUElibrary(tidyverse)
starwars$name[grepl("aa|[k]", starwars$name)] [1] "Luke Skywalker" "Biggs Darklighter" "Anakin Skywalker"
[4] "Wilhuff Tarkin" "Jek Tono Porkins" "Bossk"
[7] "Ackbar" "Wicket Systri Warrick" "Jar Jar Binks"
[10] "Quarsh Panaka" "Shmi Skywalker" "Dooku"
[13] "Shaak Ti" 変数名をオブジェクトとして用いるときに便利．
select_vars <- c("height", "mass")
starwars |> select(select_vars) Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
ℹ Please use `all_of()` or `any_of()` instead.
# Was:
data %>% select(select_vars)
# Now:
data %>% select(all_of(select_vars))
See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.# A tibble: 87 × 2
height mass
<int> <dbl>
1 172 77
2 167 75
3 96 32
4 202 136
5 150 49
6 178 120
7 165 75
8 97 32
9 183 84
10 182 77
# ℹ 77 more rowsstarwars |> select(any_of(select_vars))# A tibble: 87 × 2
height mass
<int> <dbl>
1 172 77
2 167 75
3 96 32
4 202 136
5 150 49
6 178 120
7 165 75
8 97 32
9 183 84
10 182 77
# ℹ 77 more rowslibrary(purrr)
x <- 1:10
sqrt(x) [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
[9] 3.000000 3.162278map(x, sqrt)[[1]]
[1] 1
[[2]]
[1] 1.414214
[[3]]
[1] 1.732051
[[4]]
[1] 2
[[5]]
[1] 2.236068
[[6]]
[1] 2.44949
[[7]]
[1] 2.645751
[[8]]
[1] 2.828427
[[9]]
[1] 3
[[10]]
[1] 3.162278map_vec(x, sqrt) [1] 1.000000 1.414214 1.732051 2.000000 2.236068 2.449490 2.645751 2.828427
[9] 3.000000 3.162278d <- d %>% mutate(gpa_std = (gpa_sum - mean(gpa_sum, na.rm = TRUE)) / sd(gpa_sum, na.rm = TRUE))
data.frame形式であれば$で変数名の部分一致が利用できるが，tibble形式ではできない．部分一致によってエラーが生じる可能性もあるので省略せずに入力するほうが良い．
library(tidyverse)
starwars# A tibble: 87 × 14
name height mass hair_color skin_color eye_color birth_year sex gender
<chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr>
1 Luke Sk… 172 77 blond fair blue 19 male mascu…
2 C-3PO 167 75 <NA> gold yellow 112 none mascu…
3 R2-D2 96 32 <NA> white, bl… red 33 none mascu…
4 Darth V… 202 136 none white yellow 41.9 male mascu…
5 Leia Or… 150 49 brown light brown 19 fema… femin…
6 Owen La… 178 120 brown, gr… light blue 52 male mascu…
7 Beru Wh… 165 75 brown light blue 47 fema… femin…
8 R5-D4 97 32 <NA> white, red red NA none mascu…
9 Biggs D… 183 84 black light brown 24 male mascu…
10 Obi-Wan… 182 77 auburn, w… fair blue-gray 57 male mascu…
# ℹ 77 more rows
# ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
# vehicles <list>, starships <list>starwars$heiWarning: Unknown or uninitialised column: `hei`.NULLstarwars$haWarning: Unknown or uninitialised column: `ha`.NULLstarwars_df <- as.data.frame(starwars)
starwars_df$hei [1] 172 167 96 202 150 178 165 97 183 182 188 180 228 180 173 175 170 180 66
[20] 170 183 200 190 177 175 180 150 NA 88 160 193 191 170 185 196 224 206 183
[39] 137 112 183 163 175 180 178 79 94 122 163 188 198 196 171 184 188 264 188
[58] 196 185 157 183 183 170 166 165 193 191 183 168 198 229 213 167 96 193 191
[77] 178 216 234 188 178 206 NA NA NA NA NAstarwars_df$ha [1] "blond" NA NA "none"
[5] "brown" "brown, grey" "brown" NA
[9] "black" "auburn, white" "blond" "auburn, grey"
[13] "brown" "brown" NA NA
[17] "brown" "brown" "white" "grey"
[21] "black" "none" "none" "black"
[25] "none" "none" "auburn" "brown"
[29] "brown" "none" "brown" "none"
[33] "blond" "brown" "none" "none"
[37] "none" "brown" "black" "none"
[41] "black" "black" "none" "none"
[45] "none" "none" "none" "none"
[49] "none" "none" "white" "none"
[53] "black" "none" "none" "none"
[57] "none" "none" "black" "brown"
[61] "brown" "none" "black" "black"
[65] "brown" "white" "black" "black"
[69] "blonde" "none" "none" "none"
[73] "white" "none" "none" "none"
[77] "none" "none" "brown" "brown"
[81] "none" "none" "black" "brown"
[85] "brown" "none" "none" x <- c(0,1,1,1,0)
y <- factor(x)
y[1] 0 1 1 1 0
Levels: 0 1str(y) Factor w/ 2 levels "0","1": 1 2 2 2 1attributes(y)$levels
[1] "0" "1"
$class
[1] "factor"levels(y)[1] "0" "1"as.numeric(y)[1] 1 2 2 2 1as.numeric(as.character(y))[1] 0 1 1 1 0# クロス表
occupationalStatus destination
origin 1 2 3 4 5 6 7 8
1 50 19 26 8 7 11 6 2
2 16 40 34 18 11 20 8 3
3 12 35 65 66 35 88 23 21
4 11 20 58 110 40 183 64 32
5 2 8 12 23 25 46 28 12
6 12 28 102 162 90 554 230 177
7 0 6 19 40 21 158 143 71
8 0 3 14 32 15 126 91 106# 集計データ
data.frame(occupationalStatus) origin destination Freq
1 1 1 50
2 2 1 16
3 3 1 12
4 4 1 11
5 5 1 2
6 6 1 12
7 7 1 0
8 8 1 0
9 1 2 19
10 2 2 40
11 3 2 35
12 4 2 20
13 5 2 8
14 6 2 28
15 7 2 6
16 8 2 3
17 1 3 26
18 2 3 34
19 3 3 65
20 4 3 58
21 5 3 12
22 6 3 102
23 7 3 19
24 8 3 14
25 1 4 8
26 2 4 18
27 3 4 66
28 4 4 110
29 5 4 23
30 6 4 162
31 7 4 40
32 8 4 32
33 1 5 7
34 2 5 11
35 3 5 35
36 4 5 40
37 5 5 25
38 6 5 90
39 7 5 21
40 8 5 15
41 1 6 11
42 2 6 20
43 3 6 88
44 4 6 183
45 5 6 46
46 6 6 554
47 7 6 158
48 8 6 126
49 1 7 6
50 2 7 8
51 3 7 23
52 4 7 64
53 5 7 28
54 6 7 230
55 7 7 143
56 8 7 91
57 1 8 2
58 2 8 3
59 3 8 21
60 4 8 32
61 5 8 12
62 6 8 177
63 7 8 71
64 8 8 106# 個票データ
df_occupationalStatus <- vcdExtra::expand.dft(data.frame(occupationalStatus), dreq = "Freq") |>
mutate(across(origin:destination, factor))
#
df_occupationalStatus |> summary() origin destination
6 :1355 6 :1186
4 : 518 7 : 593
7 : 458 4 : 459
8 : 387 8 : 424
3 : 345 3 : 330
5 : 156 5 : 244
(Other): 279 (Other): 262 mean()関数がどのようにして作成されているのかを確認したい場合，meanのように()をとって入力すればよい．しかし，UseMethod("mean")のように関数が表示され，中身を確認できない場合もある．
meanfunction (x, ...)
UseMethod("mean")
<bytecode: 0x1500fc4e8>
<environment: namespace:base>このような場合は，methods()関数を用いるとよい
methods(mean) [1] mean,ANY-method mean,denseMatrix-method mean,sparseMatrix-method
[4] mean,sparseVector-method mean.Date mean.default
[7] mean.difftime mean.POSIXct mean.POSIXlt
[10] mean.quosure* mean.vctrs_vctr* mean.yearmon*
[13] mean.yearqtr* mean.zoo*
see '?methods' for accessing help and source code様々な結果が示されるがここでは，2めの要素にあるmean.default*を確認しよう．*をとってmean.defaultと入力するだけでよい．
mean.defaultfunction (x, trim = 0, na.rm = FALSE, ...)
{
if (!is.numeric(x) && !is.complex(x) && !is.logical(x)) {
warning("argument is not numeric or logical: returning NA")
return(NA_real_)
}
if (isTRUE(na.rm))
x <- x[!is.na(x)]
if (!is.numeric(trim) || length(trim) != 1L)
stop("'trim' must be numeric of length one")
n <- length(x)
if (trim > 0 && n) {
if (is.complex(x))
stop("trimmed means are not defined for complex data")
if (anyNA(x))
return(NA_real_)
if (trim >= 0.5)
return(stats::median(x, na.rm = FALSE))
lo <- floor(n * trim) + 1
hi <- n + 1 - lo
x <- sort.int(x, partial = unique(c(lo, hi)))[lo:hi]
}
.Internal(mean(x))
}
<bytecode: 0x1330142b0>
<environment: namespace:base>getAnywhere(mean.default)A single object matching 'mean.default' was found
It was found in the following places
package:base
registered S3 method for mean from namespace base
namespace:base
with value
function (x, trim = 0, na.rm = FALSE, ...)
{
if (!is.numeric(x) && !is.complex(x) && !is.logical(x)) {
warning("argument is not numeric or logical: returning NA")
return(NA_real_)
}
if (isTRUE(na.rm))
x <- x[!is.na(x)]
if (!is.numeric(trim) || length(trim) != 1L)
stop("'trim' must be numeric of length one")
n <- length(x)
if (trim > 0 && n) {
if (is.complex(x))
stop("trimmed means are not defined for complex data")
if (anyNA(x))
return(NA_real_)
if (trim >= 0.5)
return(stats::median(x, na.rm = FALSE))
lo <- floor(n * trim) + 1
hi <- n + 1 - lo
x <- sort.int(x, partial = unique(c(lo, hi)))[lo:hi]
}
.Internal(mean(x))
}
<bytecode: 0x1330142b0>
<environment: namespace:base>library(DescTools)
OddsRatiofunction (x, conf.level = NULL, ...)
{
UseMethod("OddsRatio")
}
<bytecode: 0x14028e1a8>
<environment: namespace:DescTools>methods(OddsRatio)[1] OddsRatio.default* OddsRatio.glm* OddsRatio.multinom*
[4] OddsRatio.zeroinfl*
see '?methods' for accessing help and source codeDescTools:::OddsRatio.defaultfunction (x, conf.level = NULL, y = NULL, method = c("wald",
"mle", "midp"), interval = c(0, 1000), ...)
{
if (!is.null(y))
x <- table(x, y, ...)
if (is.null(conf.level))
conf.level <- NA
p <- (d <- dim(x))[1L]
if (!is.numeric(x) || length(d) != 2L || p != d[2L] || p !=
2L)
stop("'x' is not a 2x2 numeric matrix")
switch(match.arg(arg = method, choices = c("wald", "mle",
"midp")), wald = {
if (any(x == 0)) x <- x + 0.5
lx <- log(x)
or <- exp(lx[1, 1] + lx[2, 2] - lx[1, 2] - lx[2, 1])
if (is.na(conf.level)) {
res <- or
} else {
sigma2lor <- sum(1/x)
ci <- or * exp(c(1, -1) * qnorm((1 - conf.level)/2) *
sqrt(sigma2lor))
res <- c(`odds ratio` = or, lwr.ci = ci[1], upr.ci = ci[2])
}
}, mle = {
if (is.na(conf.level)) {
res <- unname(fisher.test(x, conf.int = FALSE)$estimate)
} else {
res <- fisher.test(x, conf.level = conf.level)
res <- c(res$estimate, lwr.ci = res$conf.int[1],
upr.ci = res$conf.int[2])
}
}, midp = {
a1 <- x[1, 1]
a0 <- x[1, 2]
b1 <- x[2, 1]
b0 <- x[2, 2]
or <- 1
mue <- function(a1, a0, b1, b0, or) {
mm <- matrix(c(a1, a0, b1, b0), 2, 2, byrow = TRUE)
fisher.test(mm, or = or, alternative = "l")$p - fisher.test(x = x,
or = or, alternative = "g")$p
}
midp <- function(a1, a0, b1, b0, or = 1) {
mm <- matrix(c(a1, a0, b1, b0), 2, 2, byrow = TRUE)
lteqtoa1 <- fisher.test(mm, or = or, alternative = "l")$p.val
gteqtoa1 <- fisher.test(mm, or = or, alternative = "g")$p.val
0.5 * (lteqtoa1 - gteqtoa1 + 1)
}
EST <- uniroot(function(or) {
mue(a1, a0, b1, b0, or)
}, interval = interval)$root
if (is.na(conf.level)) {
res <- EST
} else {
alpha <- 1 - conf.level
LCL <- uniroot(function(or) {
1 - midp(a1, a0, b1, b0, or) - alpha/2
}, interval = interval)$root
UCL <- 1/uniroot(function(or) {
midp(a1, a0, b1, b0, or = 1/or) - alpha/2
}, interval = interval)$root
res <- c(`odds ratio` = EST, lwr.ci = LCL, upr.ci = UCL)
}
})
return(res)
}
<bytecode: 0x1406baff8>
<environment: namespace:DescTools>getAnywhere(OddsRatio.default)A single object matching 'OddsRatio.default' was found
It was found in the following places
registered S3 method for OddsRatio from namespace DescTools
namespace:DescTools
with value
function (x, conf.level = NULL, y = NULL, method = c("wald",
"mle", "midp"), interval = c(0, 1000), ...)
{
if (!is.null(y))
x <- table(x, y, ...)
if (is.null(conf.level))
conf.level <- NA
p <- (d <- dim(x))[1L]
if (!is.numeric(x) || length(d) != 2L || p != d[2L] || p !=
2L)
stop("'x' is not a 2x2 numeric matrix")
switch(match.arg(arg = method, choices = c("wald", "mle",
"midp")), wald = {
if (any(x == 0)) x <- x + 0.5
lx <- log(x)
or <- exp(lx[1, 1] + lx[2, 2] - lx[1, 2] - lx[2, 1])
if (is.na(conf.level)) {
res <- or
} else {
sigma2lor <- sum(1/x)
ci <- or * exp(c(1, -1) * qnorm((1 - conf.level)/2) *
sqrt(sigma2lor))
res <- c(`odds ratio` = or, lwr.ci = ci[1], upr.ci = ci[2])
}
}, mle = {
if (is.na(conf.level)) {
res <- unname(fisher.test(x, conf.int = FALSE)$estimate)
} else {
res <- fisher.test(x, conf.level = conf.level)
res <- c(res$estimate, lwr.ci = res$conf.int[1],
upr.ci = res$conf.int[2])
}
}, midp = {
a1 <- x[1, 1]
a0 <- x[1, 2]
b1 <- x[2, 1]
b0 <- x[2, 2]
or <- 1
mue <- function(a1, a0, b1, b0, or) {
mm <- matrix(c(a1, a0, b1, b0), 2, 2, byrow = TRUE)
fisher.test(mm, or = or, alternative = "l")$p - fisher.test(x = x,
or = or, alternative = "g")$p
}
midp <- function(a1, a0, b1, b0, or = 1) {
mm <- matrix(c(a1, a0, b1, b0), 2, 2, byrow = TRUE)
lteqtoa1 <- fisher.test(mm, or = or, alternative = "l")$p.val
gteqtoa1 <- fisher.test(mm, or = or, alternative = "g")$p.val
0.5 * (lteqtoa1 - gteqtoa1 + 1)
}
EST <- uniroot(function(or) {
mue(a1, a0, b1, b0, or)
}, interval = interval)$root
if (is.na(conf.level)) {
res <- EST
} else {
alpha <- 1 - conf.level
LCL <- uniroot(function(or) {
1 - midp(a1, a0, b1, b0, or) - alpha/2
}, interval = interval)$root
UCL <- 1/uniroot(function(or) {
midp(a1, a0, b1, b0, or = 1/or) - alpha/2
}, interval = interval)$root
res <- c(`odds ratio` = EST, lwr.ci = LCL, upr.ci = UCL)
}
})
return(res)
}
<bytecode: 0x1406baff8>
<environment: namespace:DescTools>Cramer’s Vをchisq.test()の結果を利用して作成する． その際，unname()によってカイ2乗統計量の名前をとる．
occupationalStatus destination
origin 1 2 3 4 5 6 7 8
1 50 19 26 8 7 11 6 2
2 16 40 34 18 11 20 8 3
3 12 35 65 66 35 88 23 21
4 11 20 58 110 40 183 64 32
5 2 8 12 23 25 46 28 12
6 12 28 102 162 90 554 230 177
7 0 6 19 40 21 158 143 71
8 0 3 14 32 15 126 91 106chisq.test(occupationalStatus)Warning in chisq.test(occupationalStatus): Chi-squared approximation may be
incorrect
Pearson's Chi-squared test
data: occupationalStatus
X-squared = 1416, df = 49, p-value < 2.2e-16X2 <- chisq.test(occupationalStatus)$statistic |> unname()Warning in chisq.test(occupationalStatus): Chi-squared approximation may be
incorrectX2[1] 1416.04my_CramerV <- sqrt(X2/min(dim(occupationalStatus)-1)/sum(occupationalStatus))
my_CramerV[1] 0.2404799DescTools::CramerV(occupationalStatus)[1] 0.2404799