R - Combine data frames to a table, separating values with a slash ("/") - r

I am working with the data frames shown below:
tbl45 <- structure(list(`2010's` = c(0.48, 1.45, 33.33, 25.6, 32.37, 6.76
), `2020's` = c(0.48, 0.97, 31.88, 36.71, 28.5, 1.45), `2030's` = c(0.48,
1.93, 27.54, 34.3, 33.33, 2.42), `2040's` = c(0.48, 1.93, 33.33,
26.57, 28.5, 9.18), `2050's` = c(0.48, 1.93, 33.33, 26.09, 32.85,
5.31), `2060's` = c(0.48, 3.38, 25.6, 32.37, 36.23, 1.93), `2070's` = c(0.48,
1.93, 33.82, 28.99, 31.4, 3.38), `2080's` = c(0.48, 2.42, 34.3,
31.4, 28.99, 2.42), `2090's` = c(0.48, 2.42, 31.4, 33.33, 29.95,
2.42)), .Names = c("2010's", "2020's", "2030's", "2040's", "2050's",
"2060's", "2070's", "2080's", "2090's"), row.names = c("[0,100]",
"(100,200]", "(200,300]", "(300,400]", "(400,500]", "(500,600]"
), class = "data.frame")
tbl85 <- structure(list(`2010's` = c(0.48, 1.45, 31.4, 30.43, 34.78, 1.45
), `2020's` = c(0.48, 1.45, 36.23, 29.95, 30.43, 1.45), `2030's` = c(0.48,
1.93, 32.37, 28.02, 34.3, 2.9), `2040's` = c(0.48, 2.9, 30.43,
33.33, 31.4, 1.45), `2050's` = c(0.48, 2.9, 32.85, 30.43, 29.47,
3.86), `2060's` = c(0.48, 4.83, 33.33, 30.43, 26.57, 4.35), `2070's` = c(0.48,
5.8, 31.88, 36.23, 24.15, 1.45), `2080's` = c(0.48, 5.8, 35.27,
33.82, 23.19, 1.45), `2090's` = c(1.45, 8.21, 38.16, 32.85, 17.87,
1.45)), .Names = c("2010's", "2020's", "2030's", "2040's", "2050's",
"2060's", "2070's", "2080's", "2090's"), row.names = c("[0,100]",
"(100,200]", "(200,300]", "(300,400]", "(400,500]", "(500,600]"
), class = "data.frame")
and I would like to combine them in one single table (or data frame), with the values separated by a slash ("/") or parenthesis. Then I will save it as a .xls file and copy the table to word.
The final result would be something like this (I am showing only the first column for the simplicity sake):
2010's
[0,100] 0.48 / 0.48
(100,200] 1.45 / 1.45
(200,300] 33.33 / 31.40
(300,400] 25.60 / 30.43
(400,500] 32.37 / 34.78
(500,600] 6.76 / 1.45
How can I achieve that using R?


Try this:
res <- mapply(function(x,y) paste(x,y, sep = "/"), tbl45, tbl85)
rownames(res) <- rownames(tbl45)
res
2010's 2020's 2030's 2040's 2050's 2060's
[0,100] "0.48/0.48" "0.48/0.48" "0.48/0.48" "0.48/0.48" "0.48/0.48" "0.48/0.48"
(100,200] "1.45/1.45" "0.97/1.45" "1.93/1.93" "1.93/2.9" "1.93/2.9" "3.38/4.83"
(200,300] "33.33/31.4" "31.88/36.23" "27.54/32.37" "33.33/30.43" "33.33/32.85" "25.6/33.33"
(300,400] "25.6/30.43" "36.71/29.95" "34.3/28.02" "26.57/33.33" "26.09/30.43" "32.37/30.43"
(400,500] "32.37/34.78" "28.5/30.43" "33.33/34.3" "28.5/31.4" "32.85/29.47" "36.23/26.57"
(500,600] "6.76/1.45" "1.45/1.45" "2.42/2.9" "9.18/1.45" "5.31/3.86" "1.93/4.35"
2070's 2080's 2090's
[0,100] "0.48/0.48" "0.48/0.48" "0.48/1.45"
(100,200] "1.93/5.8" "2.42/5.8" "2.42/8.21"
(200,300] "33.82/31.88" "34.3/35.27" "31.4/38.16"
(300,400] "28.99/36.23" "31.4/33.82" "33.33/32.85"
(400,500] "31.4/24.15" "28.99/23.19" "29.95/17.87"
(500,600] "3.38/1.45" "2.42/1.45" "2.42/1.45"


We could do this by unlisting both the datasets and then paste
res <- tbl45
res[] <- paste(unlist(tbl45), unlist(tbl85), sep='/')
res
# 2010's 2020's 2030's 2040's 2050's
#[0,100] 0.48/0.48 0.48/0.48 0.48/0.48 0.48/0.48 0.48/0.48
#(100,200] 1.45/1.45 0.97/1.45 1.93/1.93 1.93/2.9 1.93/2.9
#(200,300] 33.33/31.4 31.88/36.23 27.54/32.37 33.33/30.43 33.33/32.85
#(300,400] 25.6/30.43 36.71/29.95 34.3/28.02 26.57/33.33 26.09/30.43
#(400,500] 32.37/34.78 28.5/30.43 33.33/34.3 28.5/31.4 32.85/29.47
#(500,600] 6.76/1.45 1.45/1.45 2.42/2.9 9.18/1.45 5.31/3.86
# 2060's 2070's 2080's 2090's
#[0,100] 0.48/0.48 0.48/0.48 0.48/0.48 0.48/1.45
#(100,200] 3.38/4.83 1.93/5.8 2.42/5.8 2.42/8.21
#(200,300] 25.6/33.33 33.82/31.88 34.3/35.27 31.4/38.16
#(300,400] 32.37/30.43 28.99/36.23 31.4/33.82 33.33/32.85
#(400,500] 36.23/26.57 31.4/24.15 28.99/23.19 29.95/17.87
#(500,600] 1.93/4.35 3.38/1.45 2.42/1.45 2.42/1.45

Related

Change proportion of zoom in facet_zoom [ggplot2]

I would like to change the proportion of the graph covered by the "zoom". If we look at the following image we are at a ratio "graph:zoom" around 1:2 I would like instead a ratio 2:1. In other words, what I want is to reduce the height of the zoom. How should I proceed?
Here is my code
require(dplyr)
require(tidyverse)
require(ggforce)
HY <- DebitH %>%
ggplot(aes(x = Date, y = Débit_horaire)) +
geom_point(alpha = 6/10, size = 0.3, color = "blue") +
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labs(x = "Date", y = "Débit Horaire (m3/s)")+
theme_bw() +
theme(panel.grid.major.y = element_line(color = "grey",
size = 0.25,
linetype = 2),
legend.position = "none") +
facet_zoom(xlim = as.POSIXct(c("2021-01-15 00:00:00","2021-02-15 00:00:00")))
And here you will find a sample of the data
DebitH <-
structure(list(Date = structure(c(1610233200, 1610236800, 1610240400,
1610244000, 1610247600, 1610251200, 1610254800, 1610258400, 1610262000,
1610265600, 1610269200, 1610272800, 1610276400, 1610280000, 1610283600,
1610287200, 1610290800, 1610294400, 1610298000, 1610301600, 1610305200,
1610308800, 1610312400, 1610316000, 1610319600, 1610323200, 1610326800,
1610330400, 1610334000, 1610337600, 1610341200, 1610344800, 1610348400,
1610352000, 1610355600, 1610359200, 1610362800, 1610366400, 1610370000,
1610373600, 1610377200, 1610380800, 1610384400, 1610388000, 1610391600,
1610395200, 1610398800, 1610402400, 1610406000, 1610409600, 1610413200,
1610416800, 1610420400, 1610424000, 1610427600, 1610431200, 1610434800,
1610438400, 1610442000, 1610445600, 1610449200, 1610452800, 1610456400,
1610460000, 1610463600, 1610467200, 1610470800, 1610474400, 1610478000,
1610481600, 1610485200, 1610488800, 1610492400, 1610496000, 1610499600,
1610503200, 1610506800, 1610510400, 1610514000, 1610517600, 1610521200,
1610524800, 1610528400, 1610532000, 1610535600, 1610539200, 1610542800,
1610546400, 1610550000, 1610553600, 1610557200, 1610560800, 1610564400,
1610568000, 1610571600, 1610575200, 1610578800, 1610582400, 1610586000,
1610589600, 1610593200, 1610596800, 1610600400, 1610604000, 1610607600,
1610611200, 1610614800, 1610618400, 1610622000, 1610625600, 1610629200,
1610632800, 1610636400, 1610640000, 1610643600, 1610647200, 1610650800,
1610654400, 1610658000, 1610661600, 1610665200, 1610668800, 1610672400,
1610676000, 1610679600, 1610683200, 1610686800, 1610690400, 1610694000,
1610697600, 1610701200, 1610704800, 1610708400, 1610712000, 1610715600,
1610719200, 1610722800, 1610726400, 1610730000, 1610733600, 1610737200,
1610740800, 1610744400, 1610748000, 1610751600, 1610755200, 1610758800,
1610762400, 1610766000, 1610769600, 1610773200, 1610776800, 1610780400,
1610784000, 1610787600, 1610791200, 1610794800, 1610798400, 1610802000,
1610805600, 1610809200, 1610812800, 1610816400, 1610820000, 1610823600,
1610827200, 1610830800, 1610834400, 1610838000, 1610841600, 1610845200,
1610848800, 1610852400, 1610856000, 1610859600, 1610863200, 1610866800,
1610870400, 1610874000, 1610877600, 1610881200, 1610884800, 1610888400,
1610892000, 1610895600, 1610899200, 1610902800, 1610906400, 1610910000,
1610913600, 1610917200, 1610920800, 1610924400, 1610928000, 1610931600,
1610935200, 1610938800, 1610942400, 1610946000, 1610949600, 1610953200,
1610956800, 1610960400, 1610964000, 1610967600, 1610971200, 1610974800,
1610978400, 1610982000, 1610985600, 1610989200, 1610992800, 1610996400,
1.611e+09, 1611003600, 1611007200, 1611010800, 1611014400, 1611018000,
1611021600, 1611025200, 1611028800, 1611032400, 1611036000, 1611039600,
1611043200, 1611046800, 1611050400, 1611054000, 1611057600, 1611061200,
1611064800, 1611068400, 1611072000, 1611075600, 1611079200, 1611082800,
1611086400, 1611090000, 1611093600, 1611097200, 1611100800, 1611104400,
1611108000, 1611111600, 1611115200, 1611118800, 1611122400, 1611126000,
1611129600, 1611133200, 1611136800, 1611140400, 1611144000, 1611147600,
1611151200, 1611154800, 1611158400, 1611162000, 1611165600, 1611169200,
1611172800, 1611176400, 1611180000, 1611183600, 1611187200, 1611190800,
1611194400, 1611198000, 1611201600, 1611205200, 1611208800, 1611212400,
1611216000, 1611219600, 1611223200, 1611226800, 1611230400, 1611234000,
1611237600, 1611241200, 1611244800, 1611248400, 1611252000, 1611255600,
1611259200, 1611262800, 1611266400, 1611270000, 1611273600, 1611277200,
1611280800, 1611284400, 1611288000, 1611291600, 1611295200, 1611298800,
1611302400, 1611306000, 1611309600, 1611313200, 1611316800, 1611320400,
1611324000, 1611327600, 1611331200, 1611334800, 1611338400, 1611342000,
1611345600, 1611349200, 1611352800, 1611356400, 1611360000, 1611363600,
1611367200, 1611370800, 1611374400, 1611378000, 1611381600, 1611385200,
1611388800, 1611392400, 1611396000, 1611399600, 1611403200, 1611406800,
1611410400, 1611414000, 1611417600, 1611421200, 1611424800, 1611428400,
1611432000, 1611435600, 1611439200, 1611442800, 1611446400, 1611450000,
1611453600, 1611457200, 1611460800, 1611464400, 1611468000, 1611471600,
1611475200, 1611478800, 1611482400, 1611486000, 1611489600, 1611493200,
1611496800, 1611500400, 1611504000, 1611507600, 1611511200, 1611514800,
1611518400, 1611522000, 1611525600, 1611529200, 1611532800, 1611536400,
1611540000, 1611543600, 1611547200, 1611550800, 1611554400, 1611558000,
1611561600, 1611565200, 1611568800, 1611572400, 1611576000, 1611579600,
1611583200, 1611586800, 1611590400, 1611594000, 1611597600, 1611601200,
1611604800, 1611608400, 1611612000, 1611615600, 1611619200, 1611622800,
1611626400, 1611630000, 1611633600, 1611637200, 1611640800, 1611644400,
1611648000, 1611651600, 1611655200, 1611658800, 1611662400, 1611666000,
1611669600, 1611673200, 1611676800, 1611680400, 1611684000, 1611687600,
1611691200, 1611694800, 1611698400, 1611702000, 1611705600, 1611709200,
1611712800, 1611716400, 1611720000, 1611723600, 1611727200, 1611730800,
1611734400, 1611738000, 1611741600, 1611745200, 1611748800, 1611752400,
1611756000, 1611759600, 1611763200, 1611766800, 1611770400, 1611774000,
1611777600, 1611781200, 1611784800, 1611788400, 1611792000, 1611795600,
1611799200, 1611802800, 1611806400, 1611810000, 1611813600, 1611817200,
1611820800, 1611824400, 1611828000, 1611831600, 1611835200, 1611838800,
1611842400, 1611846000, 1611849600, 1611853200, 1611856800, 1611860400,
1611864000, 1611867600, 1611871200, 1611874800, 1611878400, 1611882000,
1611885600, 1611889200, 1611892800, 1611896400, 1611900000, 1611903600,
1611907200, 1611910800, 1611914400, 1611918000, 1611921600, 1611925200,
1611928800, 1611932400, 1611936000, 1611939600, 1611943200, 1611946800,
1611950400, 1611954000, 1611957600, 1611961200, 1611964800, 1611968400,
1611972000, 1611975600, 1611979200, 1611982800, 1611986400, 1611990000,
1611993600, 1611997200, 1612000800, 1612004400, 1612008000, 1612011600,
1612015200, 1612018800, 1612022400, 1612026000, 1612029600, 1612033200,
1612036800, 1612040400, 1612044000, 1612047600, 1612051200, 1612054800,
1612058400, 1612062000, 1612065600, 1612069200, 1612072800, 1612076400,
1612080000, 1612083600, 1612087200, 1612090800, 1612094400, 1612098000,
1612101600, 1612105200, 1612108800, 1612112400, 1612116000, 1612119600,
1612123200, 1612126800, 1612130400, 1612134000, 1612137600, 1612141200,
1612144800, 1612148400, 1612152000, 1612155600, 1612159200, 1612162800,
1612166400, 1612170000, 1612173600, 1612177200, 1612180800, 1612184400,
1612188000, 1612191600, 1612195200, 1612198800, 1612202400, 1612206000,
1612209600, 1612213200, 1612216800, 1612220400, 1612224000, 1612227600,
1612231200, 1612234800, 1612238400, 1612242000, 1612245600, 1612249200,
1612252800, 1612256400, 1612260000, 1612263600, 1612267200, 1612270800,
1612274400, 1612278000, 1612281600, 1612285200, 1612288800, 1612292400,
1612296000, 1612299600, 1612303200, 1612306800, 1612310400, 1612314000,
1612317600, 1612321200, 1612324800, 1612328400, 1612332000, 1612335600,
1612339200, 1612342800, 1612346400, 1612350000, 1612353600, 1612357200,
1612360800, 1612364400, 1612368000, 1612371600, 1612375200, 1612378800,
1612382400, 1612386000, 1612389600, 1612393200, 1612396800, 1612400400,
1612404000, 1612407600, 1612411200, 1612414800, 1612418400, 1612422000,
1612425600, 1612429200, 1612432800, 1612436400, 1612440000, 1612443600,
1612447200, 1612450800, 1612454400, 1612458000, 1612461600, 1612465200,
1612468800, 1612472400, 1612476000, 1612479600, 1612483200, 1612486800,
1612490400, 1612494000, 1612497600, 1612501200, 1612504800, 1612508400,
1612512000, 1612515600, 1612519200, 1612522800, 1612526400, 1612530000,
1612533600, 1612537200, 1612540800, 1612544400, 1612548000, 1612551600,
1612555200, 1612558800, 1612562400, 1612566000, 1612569600, 1612573200,
1612576800, 1612580400, 1612584000, 1612587600, 1612591200, 1612594800,
1612598400, 1612602000, 1612605600, 1612609200, 1612612800, 1612616400,
1612620000, 1612623600, 1612627200, 1612630800, 1612634400, 1612638000,
1612641600, 1612645200, 1612648800, 1612652400, 1612656000, 1612659600,
1612663200, 1612666800, 1612670400, 1612674000, 1612677600, 1612681200,
1612684800, 1612688400, 1612692000, 1612695600, 1612699200, 1612702800,
1612706400, 1612710000, 1612713600, 1612717200, 1612720800, 1612724400,
1612728000, 1612731600, 1612735200, 1612738800, 1612742400, 1612746000,
1612749600, 1612753200, 1612756800, 1612760400, 1612764000, 1612767600,
1612771200, 1612774800, 1612778400, 1612782000, 1612785600, 1612789200,
1612792800, 1612796400, 1612800000, 1612803600, 1612807200, 1612810800,
1612814400, 1612818000, 1612821600, 1612825200, 1612828800, 1612832400,
1612836000, 1612839600, 1612843200, 1612846800, 1612850400, 1612854000,
1612857600, 1612861200, 1612864800, 1612868400, 1612872000, 1612875600,
1612879200, 1612882800, 1612886400, 1612890000, 1612893600, 1612897200,
1612900800, 1612904400, 1612908000, 1612911600, 1612915200, 1612918800,
1612922400, 1612926000, 1612929600, 1612933200, 1612936800, 1612940400,
1612944000, 1612947600, 1612951200, 1612954800, 1612958400, 1612962000,
1612965600, 1612969200, 1612972800, 1612976400, 1612980000, 1612983600,
1612987200, 1612990800, 1612994400, 1612998000, 1613001600, 1613005200,
1613008800, 1613012400, 1613016000, 1613019600, 1613023200, 1613026800,
1613030400, 1613034000, 1613037600, 1613041200, 1613044800, 1613048400,
1613052000, 1613055600, 1613059200, 1613062800, 1613066400, 1613070000,
1613073600, 1613077200, 1613080800, 1613084400, 1613088000, 1613091600,
1613095200, 1613098800, 1613102400, 1613106000, 1613109600, 1613113200,
1613116800, 1613120400, 1613124000, 1613127600, 1613131200, 1613134800,
1613138400, 1613142000, 1613145600, 1613149200, 1613152800, 1613156400,
1613160000, 1613163600, 1613167200, 1613170800, 1613174400, 1613178000,
1613181600, 1613185200, 1613188800, 1613192400, 1613196000, 1613199600,
1613203200, 1613206800, 1613210400, 1613214000, 1613217600, 1613221200,
1613224800, 1613228400, 1613232000, 1613235600, 1613239200, 1613242800,
1613246400, 1613250000, 1613253600, 1613257200, 1613260800, 1613264400,
1613268000, 1613271600, 1613275200, 1613278800, 1613282400, 1613286000,
1613289600, 1613293200, 1613296800, 1613300400, 1613304000, 1613307600,
1613311200, 1613314800, 1613318400, 1613322000, 1613325600, 1613329200,
1613332800, 1613336400, 1613340000, 1613343600, 1613347200, 1613350800,
1613354400, 1613358000, 1613361600, 1613365200, 1613368800, 1613372400,
1613376000, 1613379600, 1613383200, 1613386800, 1613390400, 1613394000,
1613397600, 1613401200, 1613404800, 1613408400, 1613412000, 1613415600,
1613419200, 1613422800, 1613426400, 1613430000, 1613433600, 1613437200,
1613440800, 1613444400, 1613448000, 1613451600, 1613455200, 1613458800,
1613462400, 1613466000, 1613469600, 1613473200, 1613476800, 1613480400,
1613484000, 1613487600, 1613491200, 1613494800, 1613498400, 1613502000,
1613505600, 1613509200, 1613512800, 1613516400, 1613520000, 1613523600,
1613527200, 1613530800, 1613534400, 1613538000, 1613541600, 1613545200,
1613548800, 1613552400, 1613556000, 1613559600, 1613563200, 1613566800,
1613570400, 1613574000, 1613577600, 1613581200, 1613584800, 1613588400,
1613592000, 1613595600, 1613599200, 1613602800, 1613606400, 1613610000,
1613613600, 1613617200, 1613620800, 1613624400, 1613628000, 1613631600,
1613635200, 1613638800, 1613642400, 1613646000, 1613649600, 1613653200,
1613656800, 1613660400, 1613664000, 1613667600, 1613671200, 1613674800,
1613678400, 1613682000, 1613685600, 1613689200, 1613692800, 1613696400,
1613700000, 1613703600, 1613707200, 1613710800, 1613714400, 1613718000,
1613721600, 1613725200, 1613728800, 1613732400, 1613736000, 1613739600,
1613743200, 1613746800, 1613750400, 1613754000, 1613757600, 1613761200,
1613764800, 1613768400, 1613772000, 1613775600, 1613779200, 1613782800,
1613786400, 1613790000, 1613793600, 1613797200, 1613800800, 1613804400,
1613808000, 1613811600, 1613815200, 1613818800, 1613822400, 1613826000,
1613829600, 1613833200, 1613836800, 1613840400, 1613844000, 1613847600,
1613851200, 1613854800, 1613858400, 1613862000, 1613865600, 1613869200,
1613872800, 1613876400, 1613880000, 1613883600, 1613887200, 1613890800,
1613894400, 1613898000, 1613901600, 1613905200, 1613908800, 1613912400,
1613916000, 1613919600, 1613923200, 1613926800, 1613930400, 1613934000,
1613937600, 1613941200, 1613944800, 1613948400, 1613952000, 1613955600,
1613959200, 1613962800, 1613966400, 1613970000, 1613973600, 1613977200,
1613980800, 1613984400, 1613988000, 1613991600, 1613995200, 1613998800,
1614002400, 1614006000, 1614009600, 1614013200, 1614016800, 1614020400,
1614024000, 1614027600, 1614031200, 1614034800, 1614038400, 1614042000,
1614045600, 1614049200, 1614052800, 1614056400, 1614060000, 1614063600,
1614067200, 1614070800, 1614074400, 1614078000, 1614081600, 1614085200,
1614088800, 1614092400, 1614096000, 1614099600, 1614103200, 1614106800,
1614110400, 1614114000, 1614117600, 1614121200, 1614124800, 1614128400,
1614132000, 1614135600, 1614139200, 1614142800, 1614146400, 1614150000,
1614153600, 1614157200, 1614160800, 1614164400, 1614168000, 1614171600,
1614175200, 1614178800, 1614182400, 1614186000, 1614189600, 1614193200,
1614196800, 1614200400, 1614204000, 1614207600, 1614211200, 1614214800,
1614218400, 1614222000, 1614225600, 1614229200, 1614232800, 1614236400,
1614240000, 1614243600, 1614247200, 1614250800, 1614254400, 1614258000,
1614261600, 1614265200, 1614268800, 1614272400, 1614276000, 1614279600,
1614283200, 1614286800, 1614290400, 1614294000, 1614297600, 1614301200,
1614304800, 1614308400, 1614312000, 1614315600, 1614319200, 1614322800,
1614326400, 1614330000, 1614333600, 1614337200, 1614340800, 1614344400,
1614348000, 1614351600, 1614355200, 1614358800, 1614362400, 1614366000,
1614369600, 1614373200, 1614376800, 1614380400, 1614384000, 1614387600,
1614391200, 1614394800, 1614398400, 1614402000, 1614405600, 1614409200,
1614412800, 1614416400, 1614420000, 1614423600, 1614427200, 1614430800,
1614434400, 1614438000, 1614441600, 1614445200, 1614448800, 1614452400,
1614456000, 1614459600, 1614463200, 1614466800, 1614470400, 1614474000,
1614477600, 1614481200, 1614484800, 1614488400, 1614492000, 1614495600,
1614499200, 1614502800, 1614506400, 1614510000, 1614513600, 1614517200,
1614520800, 1614524400, 1614528000, 1614531600, 1614535200, 1614538800,
1614542400, 1614546000, 1614549600, 1614553200, 1614556800, 1614560400,
1614564000, 1614567600, 1614571200, 1614574800, 1614578400, 1614582000,
1614585600, 1614589200, 1614592800, 1614596400, 1614600000, 1614603600,
1614607200, 1614610800, 1614614400, 1614618000, 1614621600, 1614625200,
1614628800, 1614632400, 1614636000, 1614639600, 1614643200, 1614646800,
1614650400, 1614654000, 1614657600, 1614661200, 1614664800, 1614668400,
1614672000, 1614675600, 1614679200, 1614682800, 1614686400, 1614690000,
1614693600, 1614697200, 1614700800, 1614704400, 1614708000, 1614711600,
1614715200, 1614718800, 1614722400, 1614726000, 1614729600, 1614733200,
1614736800, 1614740400, 1614744000, 1614747600, 1614751200, 1614754800,
1614758400, 1614762000, 1614765600, 1614769200, 1614772800, 1614776400,
1614780000, 1614783600, 1614787200, 1614790800, 1614794400, 1614798000,
1614801600, 1614805200, 1614808800, 1614812400, 1614816000, 1614819600,
1614823200, 1614826800, 1614830400, 1614834000, 1614837600, 1614841200,
1614844800, 1614848400, 1614852000, 1614855600, 1614859200, 1614862800,
1614866400, 1614870000, 1614873600, 1614877200, 1614880800, 1614884400,
1614888000, 1614891600, 1614895200, 1614898800), tzone = "UTC", class = c("POSIXct",
"POSIXt")), Débit_horaire = c(5.052, 5.036, 5.009, 4.982, 4.958,
4.937, 4.917, 4.885, 4.858, 4.834, 4.804, 4.786, 4.769, 4.753,
4.741, 4.722, 4.713, 4.702, 4.689, 4.68, 4.669, 4.664, 4.658,
4.645, 4.623, 4.595, 4.567, 4.539, 4.51, 4.48, 4.443, 4.413,
4.381, 4.352, 4.327, 4.318, 4.291, 4.27, 4.268, 4.268, 4.265,
4.257, 4.254, 4.258, 4.27, 4.283, 4.295, 4.299, 4.303, 4.307,
4.305, 4.308, 4.32, 4.334, 4.355, 4.396, 4.457, 4.531, 4.624,
4.738, 4.879, 5.057, 5.28, 5.558, 5.863, 6.186, 6.562, 6.958,
7.327, 7.555, 7.638, 7.638, 7.577, 7.452, 7.291, 7.089, 6.867,
6.646, 6.469, 6.301, 6.161, 6.043, 5.946, 5.872, 5.807, 5.752,
5.702, 5.67, 5.658, 5.65, 5.647, 5.656, 5.692, 5.74, 5.786, 5.839,
5.91, 6.003, 6.124, 6.256, 6.374, 6.489, 6.596, 6.686, 6.72,
6.725, 6.706, 6.684, 6.646, 6.61, 6.578, 6.568, 6.554, 6.54,
6.529, 6.535, 6.538, 6.542, 6.553, 6.531, 6.547, 6.54, 6.534,
6.51, 6.504, 6.486, 6.462, 6.444, 6.429, 6.412, 6.395, 6.373,
6.365, 6.354, 6.344, 6.334, 6.326, 6.315, 6.303, 6.295, 6.295,
6.298, 6.296, 6.298, 6.289, 6.273, 6.258, 6.238, 6.225, 6.219,
6.21, 6.213, 6.216, 6.216, 6.22, 6.222, 6.229, 6.233, 6.236,
6.239, 6.238, 6.248, 6.258, 6.271, 6.292, 6.311, 6.33, 6.342,
6.348, 6.358, 6.36, 6.365, 6.366, 6.359, 6.353, 6.342, 6.333,
6.327, 6.33, 6.327, 6.319, 6.326, 6.332, 6.324, 6.34, 6.356,
6.373, 6.402, 6.443, 6.487, 6.537, 6.571, 6.582, 6.582, 6.563,
6.54, 6.515, 6.478, 6.442, 6.41, 6.372, 6.331, 6.304, 6.277,
6.246, 6.218, 6.205, 6.199, 6.193, 6.186, 6.187, 6.203, 6.233,
6.274, 6.315, 6.362, 6.4, 6.432, 6.459, 6.484, 6.512, 6.539,
6.544, 6.548, 6.543, 6.529, 6.525, 6.593, 6.638, 6.713, 6.831,
6.968, 7.142, 7.37, 7.656, 7.924, 8.117, 8.181, 8.262, 8.273,
8.25, 8.243, 8.183, 8.13, 8.073, 8.021, 7.953, 7.888, 7.819,
7.765, 7.733, 7.703, 7.675, 7.663, 7.661, 7.673, 7.701, 7.739,
7.8, 7.843, 7.902, 7.973, 8.06, 8.136, 8.216, 8.342, 8.429, 8.585,
8.694, 8.838, 9.026, 9.38, 9.815, 10.294, 10.786, 11.347, 11.859,
12.233, 12.637, 12.966, 13.283, 13.685, 13.908, 14.334, 14.721,
15.149, 15.581, 16.521, 17.244, 17.842, 18.501, 19.313, 20.18,
20.831, 21.707, 22.559, 23.352, 24.222, 25.223, 25.933, 26.78,
27.336, 28.231, 28.845, 29.255, 29.805, 30.559, 30.458, 30.79,
30.01, 30.098, 29.579, 29.405, 29.149, 28.992, 28.879, 28.596,
28.513, 27.866, 27.839, 27.721, 27.259, 27.385, 26.951, 26.759,
26.467, 26.188, 25.971, 25.564, 25.572, 25.392, 25.243, 25.029,
24.91, 24.723, 24.44, 24.286, 24.039, 23.907, 23.594, 23.454,
23.31, 23.152, 22.881, 22.773, 22.481, 22.202, 21.956, 21.718,
21.385, 21.212, 20.804, 20.526, 20.251, 19.879, 19.599, 19.357,
19.033, 18.914, 18.647, 18.546, 18.301, 18.163, 17.975, 17.763,
17.563, 17.43, 17.19, 17.061, 16.821, 16.673, 16.518, 16.312,
16.137, 16.007, 15.833, 15.698, 15.528, 15.349, 15.195, 15.025,
14.867, 14.718, 14.57, 14.357, 14.224, 14.021, 13.917, 13.79,
13.584, 13.405, 13.267, 13.115, 13, 12.925, 12.786, 12.678, 12.518,
12.44, 12.342, 12.221, 12.138, 12.006, 11.919, 11.84, 11.776,
11.701, 11.605, 11.54, 11.484, 11.371, 11.321, 11.252, 11.177,
11.123, 11.066, 11.028, 10.961, 10.917, 10.842, 10.808, 10.706,
10.66, 10.588, 10.534, 10.496, 10.439, 10.434, 10.407, 10.393,
10.326, 10.289, 10.243, 10.188, 10.134, 10.09, 10.07, 10.039,
10.089, 10.19, 10.407, 10.695, 11.179, 11.769, 12.402, 13.126,
13.95, 14.598, 15.244, 15.748, 16.186, 16.655, 16.938, 17.177,
17.321, 17.365, 17.337, 17.256, 17.256, 17.163, 17.1, 17.143,
17.363, 17.495, 17.883, 18.542, 19.075, 19.628, 20.129, 20.61,
20.944, 21.508, 22.023, 22.557, 23.16, 23.827, 24.4, 24.849,
25.24, 25.387, 25.282, 25.309, 25.296, 25.562, 25.898, 26, 26.274,
26.225, 26.295, 26.394, 26.274, 26.071, 26.072, 26.035, 26.211,
26.383, 26.303, 26.501, 26.474, 26.513, 26.728, 26.825, 27.036,
27.121, 27.443, 27.65, 27.775, 27.689, 27.79, 28.197, 27.82,
28.144, 27.998, 27.789, 27.686, 26.934, 26.954, 26.612, 26.222,
26.192, 25.722, 25.616, 25.531, 25.292, 25.189, 24.797, 24.832,
24.664, 24.525, 24.343, 24.419, 24.284, 24.044, 24.054, 23.818,
23.651, 23.619, 23.587, 23.589, 23.397, 23.314, 23.144, 22.834,
22.846, 22.571, 22.437, 22.204, 22.036, 21.734, 21.588, 21.283,
21.018, 20.798, 20.394, 20.138, 19.926, 19.679, 19.477, 19.19,
18.914, 18.683, 18.514, 18.243, 18.11, 17.981, 17.873, 17.723,
17.655, 17.56, 17.496, 17.444, 17.353, 17.342, 17.289, 17.221,
17.182, 17.05, 16.966, 16.87, 16.649, 16.514, 16.457, 16.305,
16.172, 16.121, 16.223, 16.198, 16.412, 16.695, 16.984, 17.443,
17.808, 18.403, 19.015, 19.509, 19.919, 20.47, 20.922, 21.258,
21.561, 21.636, 21.557, 21.321, 21.223, 20.979, 20.834, 20.559,
20.501, 20.434, 20.312, 20.226, 20.08, 19.971, 19.846, 19.8,
19.712, 19.652, 19.59, 19.502, 19.442, 19.378, 19.261, 19.197,
19.193, 19.159, 19.119, 19.053, 18.966, 18.916, 18.877, 18.774,
18.71, 18.623, 18.501, 18.422, 18.277, 18.145, 18.057, 17.947,
17.828, 17.73, 17.56, 17.472, 17.347, 17.187, 17.019, 16.896,
16.784, 16.676, 16.496, 16.395, 16.255, 16.154, 16.125, 16.037,
15.981, 15.889, 15.897, 15.811, 15.786, 15.723, 15.584, 15.535,
15.433, 15.333, 15.291, 15.213, 15.091, 15.02, 14.928, 14.845,
14.804, 14.916, 15.406, 15.627, 16, 16.414, 16.809, 17.147, 17.457,
17.723, 17.973, 18.175, 18.371, 18.551, 18.613, 18.581, 18.389,
18.036, 17.58, 17.179, 16.751, 16.425, 16.129, 15.928, 15.791,
15.61, 15.487, 15.389, 15.363, 15.355, 15.312, 15.326, 15.301,
15.324, 15.291, 15.266, 15.184, 15.102, 15.056, 15.008, 14.947,
14.899, 14.84, 14.82, 14.819, 14.781, 14.766, 14.768, 14.715,
14.696, 14.631, 14.54, 14.455, 14.344, 14.209, 14.103, 14.027,
13.931, 13.848, 13.769, 13.662, 13.611, 13.493, 13.424, 13.358,
13.286, 13.232, 13.132, 13.034, 12.98, 12.937, 12.86, 12.76,
12.694, 12.604, 12.561, 12.466, 12.361, 12.257, 12.138, 12.025,
11.95, 11.863, 11.767, 11.675, 11.554, 11.473, 11.38, 11.287,
11.198, 11.125, 11.074, 11.007, 10.96, 10.906, 10.85, 10.768,
10.693, 10.62, 10.513, 10.435, 10.33, 10.226, 10.147, 10.045,
9.956, 9.844, 9.758, 9.669, 9.596, 9.542, 9.488, 9.453, 9.423,
9.359, 9.331, 9.332, 9.307, 9.279, 9.242, 9.204, 9.147, 9.079,
8.982, 8.892, 8.814, 8.74, 8.659, 8.602, 8.531, 8.45, 8.412,
8.35, 8.284, 8.243, 8.202, 8.168, 8.133, 8.111, 8.08, 8.062,
8.044, 8.024, 8.007, 7.975, 7.924, 7.867, 7.782, 7.71, 7.624,
7.544, 7.463, 7.373, 7.297, 7.233, 7.166, 7.103, 7.052, 7.024,
6.985, 6.938, 6.917, 6.914, 6.909, 6.893, 6.9, 6.903, 6.917,
6.891, 6.853, 6.803, 6.749, 6.694, 6.639, 6.565, 6.483, 6.42,
6.37, 6.32, 6.282, 6.252, 6.226, 6.204, 6.214, 6.215, 6.166,
6.155, 6.134, 6.133, 6.158, 6.175, 6.181, 6.175, 6.167, 6.148,
6.116, 6.082, 6.046, 6.019, 5.983, 5.962, 5.933, 5.912, 5.891,
5.864, 5.85, 5.831, 5.822, 5.806, 5.806, 5.813, 5.828, 5.856,
5.876, 5.907, 5.931, 5.97, 6.023, 6.055, 6.072, 6.069, 6.06,
6.052, 6.028, 6.015, 5.987, 5.965, 5.937, 5.902, 5.869, 5.836,
5.798, 5.77, 5.756, 5.767, 5.812, 5.869, 5.935, 6.029, 6.119,
6.188, 6.235, 6.292, 6.332, 6.383, 6.455, 6.575, 6.634, 6.617,
6.543, 6.441, 6.336, 6.241, 6.162, 6.08, 6.011, 5.942, 5.885,
5.83, 5.785, 5.756, 5.729, 5.691, 5.666, 5.628, 5.581, 5.531,
5.478, 5.431, 5.383, 5.327, 5.278, 5.23, 5.193, 5.159, 5.119,
5.086, 5.057, 5.034, 5.016, 4.995, 4.912, 4.963, 4.987, 5.019,
5.099, 5.21, 5.313, 5.476, 5.692, 5.883, 6.152, 6.457, 6.633,
6.623, 6.445, 6.218, 5.996, 5.813, 5.667, 5.548, 5.461, 5.379,
5.303, 5.24, 5.168, 5.122, 5.078, 5.044, 5.004, 4.973, 4.943,
4.911, 4.875, 4.848, 4.823, 4.794, 4.772, 4.75, 4.727, 4.711,
4.693, 4.668, 4.652, 4.639, 4.627, 4.617, 4.608, 4.593, 4.573,
4.56, 4.549, 4.544, 4.541, 4.532, 4.512, 4.504, 4.493, 4.474,
4.465, 4.45, 4.438, 4.417, 4.407, 4.396, 4.387, 4.381, 4.375,
4.361, 4.351, 4.337, 4.334, 4.33, 4.325, 4.32, 4.311, 4.312,
4.305, 4.291, 4.274, 4.257, 4.248, 4.233, 4.23, 4.222, 4.216,
4.215, 4.2, 4.19, 4.184, 4.177, 4.172, 4.161, 4.152, 4.145, 4.141,
4.136, 4.133, 4.126, 4.114, 4.099, 4.087, 4.078, 4.064, 4.05,
4.043, 4.035, 4.025, 4.013, 4.002, 3.989, 3.979, 3.97, 3.966,
3.958, 3.943, 3.94, 3.932, 3.928, 3.918, 3.91, 3.901, 3.88, 3.874,
3.866, 3.849, 3.831, 3.818, 3.804, 3.795, 3.787, 3.781, 3.779,
3.773, 3.767, 3.763, 3.757, 3.754, 3.751, 3.745, 3.738, 3.729,
3.731, 3.724, 3.723, 3.72, 3.72, 3.709, 3.707, 3.709, 3.707,
3.685, 3.668, 3.667, 3.682, 3.671, 3.652, 3.644, 3.635, 3.632,
3.626, 3.617, 3.614, 3.604, 3.598, 3.591, 3.586, 3.58, 3.578,
3.57, 3.564, 3.558, 3.547, 3.534, 3.524, 3.514, 3.499, 3.481,
3.476, 3.459, 3.449, 3.44, 3.43, 3.414, 3.41, 3.411, 3.417, 3.431,
3.455, 3.496, 3.53, 3.568, 3.604, 3.645, 3.685, 3.717, 3.728,
3.727, 3.69, 3.635, 3.592, 3.558, 3.52, 3.498, 3.472, 3.44, 3.413,
3.393, 3.37, 3.353, 3.321, 3.287, 3.261, 3.24, 3.237, 3.23, 3.227,
3.225, 3.223, 3.233, 3.242, 3.247, 3.234, 3.208, 3.181, 3.154,
3.134, 3.123, 3.109, 3.097, 3.091, 3.091, 3.085, 3.08, 3.074,
3.073, 3.07, 3.046, 3.052, 3.053, 3.049, 3.046, 3.029, 3.028,
3.018, 3.003, 2.997, 2.981, 2.971, 2.957, 2.946, 2.937, 2.931,
2.926, 2.916, 2.907, 2.904, 2.893, 2.887, 2.876, 2.866, 2.861,
2.854, 2.851, 2.85, 2.855, 2.865, 2.86, 2.859, 2.855, 2.843,
2.838, 2.828, 2.806, 2.793, 2.782, 2.775, 2.77, 2.759, 2.752,
2.744, 2.737, 2.729, 2.723, 2.719, 2.711, 2.703, 2.693, 2.697,
2.7, 2.699, 2.695, 2.693, 2.693, 2.692, 2.69, 2.681, 2.67, 2.658,
2.642, 2.637, 2.63, 2.623, 2.614, 2.611, 2.604, 2.604, 2.598,
2.591, 2.593, 2.594, 2.591, 2.585, 2.602, 2.601, 2.597, 2.59,
2.583, 2.582, 2.569, 2.562, 2.551, 2.544, 2.536, 2.537, 2.523,
2.516, 2.513, 2.515, 2.516, 2.516, 2.519, 2.518, 2.517, 2.516,
2.52, 2.534, 2.566, 2.607, 2.625, 2.675, 2.703, 2.742, 2.782,
2.827, 2.956)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-1297L))

You could set the size of the zoom panel relative to the main plot via the argument zoom.size which defaults to 2, i.e. the zoom panel is twice the size of the main panel. Hence, to achieve your desired result set zoom.size=.5:
require(dplyr)
require(tidyverse)
require(ggforce)
DebitH %>%
ggplot(aes(x = Date, y = Débit_horaire)) +
geom_point(alpha = 6 / 10, size = 0.3, color = "blue") +
geom_line(alpha = 4 / 10, size = 0.5, color = "blue") +
labs(x = "Date", y = "Débit Horaire (m3/s)") +
theme_bw() +
theme(
panel.grid.major.y = element_line(
color = "grey",
size = 0.25,
linetype = 2
),
legend.position = "none"
) +
facet_zoom(xlim = as.POSIXct(c("2021-01-15 00:00:00", "2021-02-15 00:00:00")), zoom.size = .5)

Run Forecasting model with multiple Dependent and Independent variables in R

I have a data set with 7 features including the date column where my dependent variables are NORTH and YORKSANDTHEHUMBER and the rest are independent variables. I want to automate the process where I take my first dependent feature NORTH and run it against all the independent variables in a univariate manner so that the first model gives me the result for NORTH and x1, second for NORTH and x2 and so on via using for loop but I couldn't make the sense. Can anyone please guide me in this?
Data:
structure(list(Date = structure(c(289094400, 297043200, 304992000,
312854400, 320716800, 328665600, 336614400, 344476800, 352252800,
360201600, 368150400, 376012800, 383788800, 391737600, 399686400,
407548800, 415324800, 423273600, 431222400, 439084800, 446947200,
454896000, 462844800, 470707200, 478483200, 486432000, 494380800,
502243200, 510019200, 517968000, 525916800, 533779200, 541555200,
549504000, 557452800, 565315200, 573177600, 581126400, 589075200,
596937600, 604713600, 612662400, 620611200, 628473600, 636249600,
644198400, 652147200, 660009600, 667785600, 675734400, 683683200,
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1212278400, 1220227200, 1228089600, 1235865600, 1243814400, 1251763200,
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1448928000, 1456790400, 1464739200, 1472688000, 1480550400, 1488326400,
1496275200, 1504224000, 1512086400, 1519862400, 1527811200, 1535760000,
1543622400, 1551398400, 1559347200, 1567296000, 1575158400, 1583020800,
1590969600, 1598918400, 1606780800, 1614556800, 1622505600, 1630454400,
1638316800), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
Industrialproduction = c(8.2, 8.79, 0.94, 1.53, -3.18, -8.66,
-8.96, -11.93, -8.14, -4.5, 1.53, 2.06, 2.39, 2.02, 2.01,
1.68, 2.16, 2.15, 3.77, 5.95, 3.58, 0.81, -1.58, -1.72, 3.62,
9.78, 8.51, 3.49, 1.97, -1.02, 1.92, 6.13, 3.87, 3.54, 2.76,
4.19, 4.73, 4.84, 6.64, 3.88, 2.05, 1.36, 0.53, 1.47, 1.61,
3.22, -1.45, -2.76, -3.83, -5.06, -4.01, -1.76, -0.27, -0.82,
2.23, 0.69, 1.38, 2.07, 2.32, 4.1, 4.61, 5.68, 6.13, 5.91,
2.85, 1.66, 1, 0.37, 2.52, 1.26, 1.24, 1.48, 0.37, 2.24,
2.7, 4.38, 7.6, 3.89, 0.84, -0.82, -0.46, 5.61, 9.48, 5.06,
1.95, 2.1, 1.08, 6.27, 1.46, 2.28, 3.21, 3.37, 12.94, -1.06,
-2.07, -6.22, -5.19, 6.65, 6.78, 4.35, -2.69, -1.31, -2.08,
3.44, -3.08, -0.92, -1.62, -0.91, 8.32, 2.57, 4.33, 2.44,
1.52, -1.3, -4.94, -3.97, -3.59, -1.83, 1.77, -1.86, -4.86,
-5.07, -7.55, -5.37, -0.33, -1.2, -0.11, -1.11, -8.39, -5.4,
-5.52, -4.16, 0.12, -0.7, -0.58, -0.59, 0.48, 3.87, 5.29,
7.91, 7.21, -0.45, -2.23, -1.86, 4.19, 5.9, 5.94, 2.45, 0,
-0.75, -1.08, 1.63, -3.28, -0.22, 3.49, 1.07, 1.53, 5.3,
4.21, 6.14, 10.24, 2.26, 0.71, -1.3, -8.9, -12.36, -5.02,
-2.83, 3.76, 9.86, 1.9, 0.94), Householdconsumption = c(30.09,
32.53, 33.35, 35.23, 37.18, 37.59, 38.89, 39.82, 41.56, 42.7,
43.74, 45.03, 46.19, 46.95, 48.29, 49.84, 51.26, 52.15, 53.5,
54.36, 55.4, 56.7, 57.05, 58.88, 60.09, 61.44, 63.27, 64.74,
66.63, 68.35, 69.55, 70.81, 72.3, 74.29, 76.65, 78.82, 81.51,
83.81, 86.53, 88.4, 90.29, 92.46, 93.95, 95.99, 97.85, 100.83,
102.42, 104.05, 106.08, 107.79, 109.33, 110.63, 111.71, 113.52,
114.9, 116.02, 118.31, 119.4, 122.27, 124.05, 125.13, 125.99,
127.59, 129.19, 130.16, 132.29, 135.06, 136.61, 139.34, 142.14,
144.59, 146.95, 149.43, 151.71, 155.34, 156.37, 158.39, 160.69,
164.47, 164.41, 167.54, 169.48, 170.09, 172.51, 176.26, 177.61,
179.44, 180.28, 182.96, 184.01, 186.83, 186.34, 188.79, 190.18,
191.94, 194.56, 196.46, 198.86, 201.75, 203.09, 205.24, 208.26,
210.84, 213.9, 216.18, 217.54, 220.61, 222.9, 223.67, 227.66,
230.62, 232.57, 234.8, 237.82, 241.91, 244.47, 248.84, 248.63,
248.14, 243.9, 241.46, 239.04, 240.72, 243.03, 241.87, 248,
249.95, 251.91, 254.92, 254.81, 257.17, 261.11, 262.28, 265.29,
266.74, 271.42, 274.28, 277.61, 282.48, 282.94, 285.76, 290.21,
292.88, 294.9, 296.07, 299.14, 302.58, 302.82, 309.63, 313.2,
318.64, 320.87, 323.41, 325.57, 326.56, 329.67, 335.95, 337.61,
341.08, 345.09, 346.16, 350.18, 350.23, 347.89, 339.85, 270.86,
325.65, 320.28, 311.3, 341.24, 354.61, 361.47), Investmentgrowth = c(17.3,
22.73, 25.8, 29.99, 21.59, 15.49, 11.11, 6.04, 4.23, 4.42,
4.28, 3.51, 6.53, 8.81, 10.52, 12.63, 14.6, 8.04, 7.42, 10.72,
11.15, 16.11, 15.45, 11.36, 18.41, 8.32, 8.99, 8.18, 0.86,
5.04, 9.07, 14.27, 11.11, 19.61, 23.14, 19.47, 27.16, 24.6,
17.45, 16.17, 20.57, 17.01, 17.76, 15.36, 8.28, 7.05, 2.92,
2.83, -3.08, -4.32, -7.48, -6.69, -3.71, -4.64, -3.87, -4.88,
-1.72, -0.38, 1.97, 4.65, 2.84, 2.98, 3.68, 2.88, 0.69, 3.5,
4.91, 5.66, 11.3, 13.85, 10.87, 4.01, -5.63, -8.06, -3.81,
3.94, 10.74, 9.14, 3.83, 3.36, 3.29, 3.24, 7.59, 3.43, 7.05,
13.14, 1.12, 7.68, 4.22, 1.34, 9.27, 0.78, 0.66, -1.52, 4.17,
12.34, 11.74, 5.2, 1.89, -1.56, 2.26, 5.89, 5.79, 4.84, 3.44,
7.15, 7.27, 7.31, 6.11, 5.7, 8.15, 6.96, 7.79, 10.05, 2.71,
9.61, 4.63, 2.72, 1.13, -6.1, -8.98, -14.36, -9.8, -11.41,
-3.13, 1.28, 3.81, 9.18, 1.62, 2.05, 2.14, 2.03, 7.32, 3.88,
0.09, 3.44, -1.27, 6.8, 10.41, 5.73, 12.93, 7.89, 6.8, 7.92,
8.2, 9.32, 6.18, 7.39, 5.22, 6.07, 9.44, 5.64, 6.8, 7.2,
4.77, 6.83, 3.74, 1.63, 2.59, 1.17, 4.39, 3.28, 3.78, 2.18,
-1.93, -19.78, -7.51, -2.54, -0.99, 23.33, 6.54, 4.25), ConsumerPriceIndex = c(24.88,
25.94, 27.55, 28.28, 29.79, 31.39, 31.92, 32.55, 33.55, 34.94,
35.55, 36.48, 37.02, 38.14, 38.14, 38.45, 38.73, 39.54, 40.1,
40.49, 40.76, 41.57, 41.99, 42.35, 43.25, 44.46, 44.46, 44.74,
45.06, 45.58, 45.81, 46.42, 46.88, 47.49, 47.72, 48.14, 48.44,
49.43, 49.83, 50.33, 50.82, 52.02, 52.42, 53.11, 53.91, 55.6,
56.69, 57.09, 57.59, 60.27, 60.67, 61.27, 61.67, 62.56, 62.56,
62.86, 63.16, 64.05, 64.45, 64.35, 64.55, 65.35, 65.45, 65.64,
66.24, 67.04, 67.34, 67.63, 68.03, 68.63, 68.93, 69.13, 69.13,
69.82, 70.22, 70.32, 70.7, 71.3, 71.5, 71.8, 71.9, 72.3,
72.4, 72.6, 72.3, 72.9, 73.1, 73.2, 73, 74.1, 74.1, 74, 74.1,
74.6, 74.8, 75.2, 75.3, 75.4, 75.9, 76.2, 76.1, 76.6, 76.7,
77.4, 77.5, 78.1, 78.6, 78.9, 78.9, 80.1, 80.5, 81.3, 81.4,
82, 81.9, 83, 83.4, 85.2, 86.1, 85.5, 85.8, 86.7, 87.1, 88,
88.7, 89.5, 89.8, 91.2, 92.2, 93.3, 94.4, 95.1, 95.4, 95.5,
96.5, 97.6, 98.1, 98.3, 99.1, 99.6, 99.7, 100.2, 100.3, 100.1,
99.7, 100.2, 100.2, 100.3, 100.2, 100.6, 101.1, 101.9, 102.7,
103.5, 104.3, 105, 105.1, 105.9, 106.6, 107.1, 107, 107.9,
108.4, 108.5, 108.6, 108.8, 109.2, 109.4, 109.7, 111.4, 112.4,
114.7), NORTH = c(4.06976744186047, 5.51675977653633, 7.2799470549305,
4.75015422578655, 4.59363957597172, 3.15315315315317, 1.2008733624454,
-0.377562028047452, -0.108283703302655, 0.650406504065032,
0.969305331179318, 0.106666666666688, 3.09003729355352, 2.11886304909562,
2.32793522267207, 5.68743818001977, -1.46934955545156, 3.95611702127658,
5.19438987619354, -0.0912012507600199, 2.81677896109541,
3.97412590369087, 1.30118326353028, 3.31553807249226, 1.32872294960955,
2.93700394923507, 0.908853875665812, 1.81241002546971, -1.3414545718222,
4.81772747317361, -3.4743890895067, 4.63823913990992, 0.857370960463727,
1.78620594713658, 0.527472527472524, -4.05973562947765, -0.136726966764838,
3.16657890117607, 5.95161125667812, 8.01002055498458, 10.5501040737437,
13.4138468987035, 2.93371279497212, 8.84291046495554, -6.87764606265876,
2.90741287990725, 3.71548486856639, 1.23317430567388, -1.1153443739474,
4.31313207880924, -1.64273763383666, 0.751373343751978, -3.21877014345816,
1.16314882913623, -3.59065232516701, -4.65283582701413, 4.98489115166134,
3.18459755147199, -3.72875180849018, 2.20137289784552, -4.22488416879167,
-0.706371260732776, -2.33320725244584, -2.77596063540517,
9.48636128308308, -2.15172116987927, -5.71766285746257, 1.92271571537407,
0.655934629757954, 4.01517293049256, -2.89270965830984, 3.910032505864,
-1.31616434600239, 1.51533020314829, 3.09793915477058, 1.00146317751519,
-0.516295759142123, 4.36356154298765, -0.254418667464494,
-1.38015492270122, -0.375369475589906, 3.79511767246943,
1.67693295616696, 0.197127124553074, -1.01758464617007, 5.70477696100394,
-1.37564670926045, 1.39335708665185, 2.29473337483174, -1.40489357721877,
10.7514355294201, -0.403985348024547, -0.0106181613732362,
10.6504339189417, 7.72602065226992, 6.66622841015428, 7.3618861388054,
7.20852539277177, 7.17954849482943, 5.47999408979134, 9.96115783870405,
6.960515961579, 4.82626274289161, -0.428385428540776, 1.6283388103162,
2.07440844957785, -0.707412409361252, -4.9247119657169, 4.3311229522328,
2.53158682305453, -0.8800288960527, 2.40275362264064, 0.67520264383003,
3.97711266595697, 0.00749650524863867, -0.990038901876062,
-0.63991866618197, -2.00199671222057, -5.15098853828302,
-3.65317386916235, -4.67277715297035, -0.564594703469009,
3.29526766976492, 0.0888482310529472, -0.524228981506815,
3.04012050839788, -1.53185447929528, -0.338917708381546,
-2.5450727924491, 3.36238295093309, -0.918735392055365, -0.766840492430499,
-0.767135363240273, 0.0468961039030733, 1.51618073336643,
-2.02356670927575, -1.11584500803018, 2.45568937824186, 0.989863990072745,
-0.4214032191629, 2.8219393653178, 4.51474479784726, -2.49049271581373,
-0.41346860604498, 3.13864420514751, -0.0877964623534655,
-0.674347043417658, -0.143267961613368, -0.243406512930108,
0.0402054219496719, 0.12912750657269, 0.168664845016241,
-0.713623226415894, 1.49163339466038, 1.57747101133233, -2.10536689354583,
3.12980292320487, -0.90833324273064, -1.71375697178543, 0.582188469928239,
2.89692448021907, 0.0768238907010953, -1.53392147948349,
1.23622644511851, -0.0506227154778281, 0.327869614383542,
2.62019966395382, 3.48629495563575, 0.593740862165774, 4.09560684327741,
2.32207959691005, 0.506809670097958), YORKSANDTHEHUMBER = c(4.0121120363361,
5.45851528384282, 9.52380952380951, 6.04914933837431, 3.03030303030299,
5.42099192618225, 2.78993435448577, -0.53219797764768, 1.97966827180309,
1.15424973767052, 0.466804979253115, -1.96179659266907, 2.42232754081095,
0.719794344473031, -0.306278713629415, 3.37941628264209,
2.74393263992076, 3.91920555341303, 1.91585099967527, 0.892125625853447,
2.91888477848958, 3.78293078507868, 0.109815847271484, 6.83486625601216,
0.722691730511011, 3.56008625759656, -0.227160867754524,
2.69419041475355, -1.17134094520194, 2.78546324684064, 1.01487759630426,
1.54843356139717, 4.15602836879435, 4.43619773934357, -0.309698451507728,
-1.45519947678222, -1.09839057574248, 9.08267346664877, 11.8913598474363,
13.9511229623114, 9.71243848306475, 7.66524473371739, 6.46801731884651,
-2.26736490763654, -4.35729847494552, -2.93870179974964,
-7.72353426221536, -7.01127302722023, 2.02543627323513, 2.51245245873873,
0.712134856164617, -2.74951902189779, 3.20525370229387, -2.17225212432703,
0.304311135936791, -5.21962007478405, -1.22771231792975,
5.62676205566459, -0.0988236572110239, 0.865912760888606,
-3.71050647202427, 1.5475703474865, -3.43233328040058, -2.86288061069106,
-0.551968808874026, 2.05442655433966, 0.388675938226524,
-2.60493926554792, -2.23312255163324, 5.04817095211292, 1.43656632546456,
2.53687507970646, -2.37376845704496, 4.95419269721737, 2.5486061891899,
-0.64046817419928, 1.75846231104579, 0.542834308795226, -0.322606591645488,
-2.67961743436791, 3.57498650723638, 2.89743475977992, 1.28567849851333,
1.828392232888, -0.335580970541442, 5.34860062451308, -2.98213938289875,
3.55468980520775, 2.76514398982056, 3.45832186518539, 1.32470422187813,
2.79428923624948, 3.8093136923264, 9.02544568216825, 7.65854560247412,
11.0775256253873, -0.658987130155868, 10.726463566155, 5.35747018223358,
4.66387144397987, 5.14763674355188, 10.581371911713, 3.46926043870116,
-0.000369065205607915, 0.924675325682334, 3.681119585314,
-0.0731638011738147, 0.690177922935143, 1.33427941484383,
2.65734876034112, 1.62515008951355, 1.48038293242949, 0.494192527588077,
2.39510739408179, 0.818557817036399, -1.1083492547105, -1.89465779498896,
-3.74953204588813, -3.7238074999174, -4.9788025925358, -4.65464963206228,
3.34588197167384, 2.20886725349025, 1.99954661835316, -0.777545762347822,
3.58681336123701, -2.96757202302368, -3.36310924643208, 2.01483012871867,
2.4154475314586, -0.642314624781054, -2.0920093049768, -1.73904001349183,
1.69071701857513, 0.201962934561265, -2.66472457335063, 0.323680874793625,
1.37879437405697, 3.26467995053582, 2.21645486418079, -0.646736928898328,
2.06516965491332, 1.8250141624007, -1.68545096699093, -0.818973277015041,
4.05215303886115, -1.16233786449552, -1.56747999678074, 0.67708495662531,
2.92754908797974, 1.50505329502891, -1.12667258046976, -0.765034978617734,
2.67854615526131, -0.306294171526678, 0.175047038539941,
1.56451236885344, 0.618844724791642, 3.34585295985361, -1.76420421213768,
-0.079420811764984, 1.56942028744185, 0.407910173531572,
-0.268243129544691, 2.57107118459526, -0.758721256899304,
3.03713057699041, 2.68699850192726, 1.88666482868311, 4.78697689266296,
2.43248653386118, 1.27252711337855)), row.names = c(NA, -172L
), class = "data.frame")
Code:
library(tseries)
library(dplyr)
# ARDL MODELING AND FORECASTING
in_sampleARDL <- data %>%
dplyr::filter(Date < '2020-03-01')
out_sampleARDL <-data %>%
dplyr::filter(Date >= '2020-03-01')
auto_ardl(NORTH~Householdconsumption,
data = in_sampleARDL, max_order = 4, selection = 'BIC')
pred1 <-forecast(ardlDlm(formula = NORTH ~ diff(Householdconsumption),
data = in_sampleARDL, p =3)
, x =out_sampleARDL$NORTH, h = 4)
error1 = out_sampleARDL$NORTH[1:2]- pred1[["forecasts"]]
mean(error1^2)
auto_ardl(NORTH~Industrialproduction,
data = in_sampleARDL, max_order = 4, selection = 'BIC')
pred2 <-forecast(ardlDlm(formula = NORTH ~ Industrialproduction,
data = in_sampleARDL, p =3)
, x =out_sampleARDL$NORTH, h = 4)
error2 = out_sampleARDL$NORTH[1:4]- pred2[["forecasts"]]
mean(error2^2)

Getting the distance matrix back from already clustered data

I have used hclust in the TSclust package to do agglomerative hierarchical clustering. My question is, Can I get the dissimlarity (distance) matrix back from hclust? I wanted the values of the distance to rank which variable is closer to a single variable in the group of variables.
example: If (x1,x2, x3,x4,x5,x6,x7,x8,x9,x10) are the variables used to form the distance matrix, then what I wanted is the distance between x3 and the rest of variables (x3x1,x3x2,x3x4,x3x5, and so on). Can we do that? Here is the code and reproducible data.
Data:
structure(list(x1 = c(186.41, 100.18, 12.3, 14.38, 25.97, 0.06,
0, 6.17, 244.06, 19.26, 256.18, 255.69, 121.88, 75, 121.45, 11.34,
34.68, 3.09, 34.3, 26.13, 111.31), x2 = c(327.2, 8.05, 4.23,
6.7, 3.12, 1.91, 37.03, 39.17, 140.06, 83.72, 263.29, 261.22,
202.48, 23.27, 2.87, 7.17, 14.48, 3.41, 5.95, 70.56, 91.58),
x3 = c(220.18, 126.14, 98.59, 8.56, 0.5, 0.9, 17.45, 191.1,
164.64, 224.36, 262.86, 237.75, 254.88, 42.05, 9.12, 0.04,
12.22, 0.61, 61.86, 114.08, 78.94), x4 = c(90.74, 26.11,
47.86, 10.86, 3.74, 23.69, 61.79, 68.12, 87.92, 171.76, 260.98,
266.62, 96.27, 57.15, 78.89, 16.73, 6.59, 49.44, 57.21, 202.2,
67.17), x5 = c(134.09, 27.06, 7.44, 4.53, 17, 47.66, 95.96,
129.53, 40.23, 157.37, 172.61, 248.56, 160.84, 421.94, 109.93,
22.77, 2.11, 49.18, 64.13, 52.61, 180.87), x6 = c(173.17,
46.68, 6.54, 3.05, 0.35, 0.12, 5.09, 72.46, 58.19, 112.31,
233.77, 215.82, 100.63, 65.84, 2.69, 0.01, 3.63, 12.93, 66.55,
28, 61.74), x7 = c(157.22, 141.81, 19.98, 116.18, 16.55,
122.3, 62.67, 141.84, 78.3, 227.27, 340.22, 351.38, 147.73,
0.3, 56.12, 33.2, 5.51, 54.4, 82.98, 152.66, 218.26), x8 = c(274.08,
51.92, 54.86, 15.37, 0.31, 0.05, 36.3, 162.04, 171.78, 181.39,
310.73, 261.55, 237.99, 123.99, 1.92, 0.74, 0.23, 18.51,
7.68, 65.55, 171.33), x9 = c(262.71, 192.34, 2.75, 21.68,
1.69, 3.92, 0.09, 9.33, 120.36, 282.92, 236.7, 161.59, 255.44,
126.44, 7.63, 2.04, 1.02, 0.12, 5.87, 146.25, 134.11), x10 = c(82.71,
44.09, 1.52, 2.63, 4.38, 28.64, 168.43, 80.62, 20.36, 39.29,
302.31, 247.52, 165.73, 18.27, 2.67, 1.77, 23.13, 53.47,
53.14, 46.61, 86.29)), class = "data.frame", row.names = c(NA,
-21L))
Code:
as.ts(cdata)
library(dplyr) # data wrangling
library(ggplot2) # grammar of graphics
library(ggdendro) # dendrograms
library(TSclust) # cluster time series
cluster analysis
dist_ts <- TSclust::diss(SERIES = t(cdata), METHOD = "INT.PER") # note the data frame must be transposed
hc <- stats::hclust(dist_ts, method="complete") # method can be also "average" or diana (for DIvisive ANAlysis Clustering)
hcdata <- ggdendro::dendro_data(hc)
names_order <- hcdata$labels$label
# Use the following to remove labels from dendogram so not doubling up - but good for checking hcdata$labels$label <- ""
hcdata%>%ggdendro::ggdendrogram(., rotate=FALSE, leaf_labels=FALSE)

I believe the object you are looking for is stored in the variable dist_ts:
dist_ts <- TSclust::diss(SERIES = t(cdata), METHOD = "INT.PER")
print(dist_ts)

How to make hierarchical cluster pheatmap in r?

I have use this code to make hierarchical cluster heatmap but no color is coming
library(tidyverse)
Mydata <- structure(list(Location = c("Karnaphuli River", "Sangu River", "Kutubdia Channel", "Moheshkhali Channel", "Bakkhali River", "Naf River", "St. Martin's Island", "Mean "), Cr = c(114.92, 2.75, 18.88, 27.6, 39.5, 12.8, 17.45, 33.41), Pb = c(31.29, 26.42, 52.3, 59.45, 34.65, 12.8, 9.5, 32.34), Cu = c(9.48, 54.39, 52.4, 73.28, 76.26, 19.48, 8.94, 42.03), Zn = c(66.2, 71.17, 98.7, 95.3, 127.84, 27.76, 21.78, 72.67), As = c(89.67, 9.85, 8.82, 18.54, 15.38, 7.55, 16.45, 23.75), Cd = c(1.06, 0, 0.96, 2.78, 3.12, 0.79, 0.45, 1.53)), class = "data.frame", row.names = c(NA, -8L))
library(pheatmap)
Mydata %>% column_to_rownames(var = "Location") %>%
as.matrix() %>% pheatmap(Mydata, cutree_cols = 6)

You don't need to pass data again when using pipes. Try :
library(pheatmap)
Mydata %>%
column_to_rownames(var = "Location") %>%
as.matrix() %>% pheatmap(cutree_cols = 6)

problem with calculating mean absolute percentage error on a weekly basis in R

I have a large time series of actual and forecasted electricity generation values for each quarter of the hour, that looks like this:
df<-structure(list(DATETIME = structure(c(1604188800, 1604189700,
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1604784600, 1604785500, 1604786400, 1604787300, 1604788200, 1604789100,
1604790000, 1604790900, 1604791800, 1604792700), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), Actual = c(5.718, 5.844, 5.971, 5.925,
5.414, 5.432, 5.521, 5.513, 5.524, 6.182, 6.947, 7.133, 7.746,
7.058, 6.615, 9.665, 12.015, 12.029, 11.957, 12.981, 16.03, 16.821,
16.897, 15.383, 14.029, 14.642, 10.721, 9.187, 8.466, 6.89, 5.916,
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7.077, 6.514, 5.942, 6.502, 5.731, 5.276, 7.513, 7.141, 6.74,
6.36, 7.061, 8.619, 8.845, 9.808, 9.702, 10.079, 8.703, 7.287,
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5.892, 5.148, 4.533, 4.223, 3.89, 3.498, 2.941, 3.06, 3.244,
3.521, 3.703, 3.314, 3.507, 3.705, 3.073, 2.472, 2.344, 2.695,
2.729, 2.652, 2.582, 2.731, 2.759, 2.866, 3.283, 3.138, 3.009,
3.126, 3.389, 3.205, 3.39, 3.942, 4.029, 4.186, 4.282, 4.335,
4.026, 3.863, 3.772, 3.25, 3.282, 3.332, 3.18, 2.929, 3.054,
3.579, 3.886, 3.145, 2.984, 3.305, 3.535, 3.59, 4.079, 4.141,
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1.907, 1.911, 1.901, 1.96, 1.854, 1.88, 2.25, 2.252, 2.154, 2.11,
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54.747, 53.934, 55.108, 55.669, 57.554, 57.824, 56.961, 54.605,
54.705, 54.599, 54.044, 53.627), Forecasted = c(4.843, 4.843,
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32.805, 32.805, 32.805, 28.593, 28.593, 28.593, 28.593, 22.283,
22.283, 22.283, 22.283, 19.164, 19.164, 19.164, 19.164, 17.839,
17.839, 17.839, 17.839, 15.454, 15.454, 15.454, 15.454, 12.195,
12.195, 12.195, 12.195, 8.568, 8.568, 8.568, 8.568, 7.477, 7.477,
7.477, 7.477, 6.314, 6.314, 6.314, 6.314, 5.763, 5.763, 5.763,
5.763, 4.615, 4.615, 4.615, 4.615, 3.57, 3.57, 3.57, 3.57, 3.438,
3.438, 3.438, 3.438, 3.618, 3.618, 3.618, 3.618, 2.983, 2.983,
2.983, 2.983, 2.513, 2.513, 2.513, 2.513, 2.075, 2.075, 2.075,
2.075, 2.015, 2.015, 2.015, 2.015, 2.315, 2.315, 2.315, 2.315,
2.325, 2.325, 2.325, 2.325, 2.363, 2.363, 2.363, 2.363, 2.058,
2.058, 2.058, 2.058, 1.455, 1.455, 1.455, 1.455, 1.878, 1.878,
1.878, 1.878, 2.165, 2.165, 2.165, 2.165, 2.633, 2.633, 2.633,
2.633, 3.279, 3.279, 3.279, 3.279, 2.935, 2.935, 2.935, 2.935,
2.833, 2.833, 2.833, 2.833, 2.89, 2.89, 2.89, 2.89, 3.952, 3.952,
3.952, 3.952, 4.037, 4.037, 4.037, 4.037, 4.311, 4.311, 4.311,
4.311, 4.242, 4.242, 4.242, 4.242, 2.478, 2.478, 2.478, 2.478,
6.285, 6.285, 6.285, 6.285, 6.803, 6.803, 6.803, 6.803, 7.54,
7.54, 7.54, 7.54, 8.383, 8.383, 8.383, 8.383, 8.86, 8.86, 8.86,
8.86, 8.65, 8.65, 8.65, 8.65, 8.11, 8.11, 8.11, 8.11, 7.783,
7.783, 7.783, 7.783, 6.995, 6.995, 6.995, 6.995, 5.98, 5.98,
5.98, 5.98, 5.22, 5.22, 5.22, 5.22, 5.023, 5.023, 5.023, 5.023,
7.343, 7.343, 7.343, 7.343, 7.29, 7.29, 7.29, 7.29, 7.315, 7.315,
7.315, 7.315, 7.63, 7.63, 7.63, 7.63, 7.875, 7.875, 7.875, 7.875,
7.993, 7.993, 7.993, 7.993, 5.315, 5.315, 5.315, 5.315, 5.787,
5.787, 5.787, 5.787, 5.097, 5.097, 5.097, 5.097, 6.878, 6.878,
6.878, 6.878, 6.337, 6.337, 6.337, 6.337, 4.025, 4.025, 4.025,
4.025, 2.665, 2.665, 2.665, 2.665, 4.573, 4.573, 4.573, 4.573,
6.248, 6.248, 6.248, 6.248, 5.534, 5.534, 5.534, 5.534, 6.268,
6.268, 6.268, 6.268, 5.964, 5.964, 5.964, 5.964, 5.23, 5.23,
5.23, 5.23, 7.155, 7.155, 7.155, 7.155, 9.775, 9.775, 9.775,
9.775, 12.565, 12.565, 12.565, 12.565, 11.868, 11.868, 11.868,
11.868, 11.473, 11.473, 11.473, 11.473, 10.698, 10.698, 10.698,
10.698, 12.933, 12.933, 12.933, 12.933, 13.148, 13.148, 13.148,
13.148, 12.845, 12.845, 12.845, 12.845, 11.588, 11.588, 11.588,
11.588, 16.374, 16.374, 16.374, 16.374, 19.543, 19.543, 19.543,
19.543, 21.938, 21.938, 21.938, 21.938, 22.425, 22.425, 22.425,
22.425, 22.738, 22.738, 22.738, 22.738, 28.695, 28.695, 28.695,
28.695, 29.352, 29.352, 29.352, 29.352, 34.76, 34.76, 34.76,
34.76, 31.81, 31.81, 31.81, 31.81, 33.791, 33.791, 33.791, 33.791,
35.23, 35.23, 35.23, 35.23, 35.308, 35.308, 35.308, 35.308, 35.13,
35.13, 35.13, 35.13, 37.11, 37.11, 37.11, 37.11, 37.073, 37.073,
37.073, 37.073, 40.596, 40.596, 40.596, 40.596, 40.7, 40.7, 40.7,
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59.98, 59.98, 59.98, 59.98, 59.233, 59.233, 59.233, 59.233, 56.633,
56.633, 56.633, 56.633, 57.81, 57.81, 57.81, 57.81, 63.66, 63.66,
63.66, 63.66, 67.34, 67.34, 67.34, 67.34, 67.59, 67.59, 67.59,
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61.142, 51.96, 51.96, 51.96, 51.96, 48.465, 48.465, 48.465, 48.465,
51.932, 51.932, 51.932, 51.932, 54.498, 54.498, 54.498, 54.498,
56.787, 56.787, 56.787, 56.787, 58.053, 58.053, 58.053, 58.053,
57.649, 57.649, 57.649, 57.649, 54.156, 54.156, 54.156, 54.156,
39.908, 39.908, 39.908, 39.908, 40.138, 40.138, 40.138, 40.138,
41.303, 41.303, 41.303, 41.303, 41.175, 41.175, 41.175, 41.175,
39.023, 39.023, 39.023, 39.023, 39.09, 39.09, 39.09, 39.09, 39.238,
39.238, 39.238, 39.238, 44.308, 44.308, 44.308, 44.308, 41.595,
41.595, 41.595, 41.595, 45.503, 45.503, 45.503, 45.503, 45.773,
45.773, 45.773, 45.773, 46.703, 46.703, 46.703, 46.703, 51.37,
51.37, 51.37, 51.37, 50.908, 50.908, 50.908, 50.908, 46.036,
46.036, 46.036, 46.036, 43.526, 43.526, 43.526, 43.526, 38.05,
38.05, 38.05, 38.05, 34.29, 34.29, 34.29, 34.29, 34.306, 34.306,
34.306, 34.306, 37.668, 37.668, 37.668, 37.668, 41.563, 41.563,
41.563, 41.563, 41.171, 41.171, 41.171, 41.171, 42.225, 42.225,
42.225, 42.225, 43.463, 43.463, 43.463, 43.463)), row.names = c(NA,
672L), class = "data.frame")
I want to calculate the mean absolute percentage error for each (calendar) week of the dataset and I have tried several ways, such as the function apply.weekly, but none of them seems to work.
apply.weekly(df, function(x) mean(abs((df$Actual-df$Forecasted)/df$Actual)))
I keep getting as outcome the same value for each quarter of the dataset, and not an outcome for each week.
Do you have any ideas on how to implement this calculation?
Thank you in advance for your help.

Perhaps like so:
library(lubridate)
library(magrittr)
df %>% group_by( isoweek( DATETIME ), year( DATETIME ) ) %>%
summarise( DateCount = n_distinct(date(DATETIME)), MeanError = mean( abs(Forecasted-Actual)/Actual ) )
It outputs:
`isoweek(DATETIME)` `year(DATETIME)` DateCount MeanError
<dbl> <dbl> <int> <dbl>
1 44 2020 1 0.858
2 45 2020 6 0.357
With the updated data source:
df <- read.delim( "https://raw.githubusercontent.com/Argiro1983/data-set/main/test1", sep=";" ) %>%
as_tibble
df %<>% mutate( DATETIME = dmy_hm(DATETIME) )
df %>% group_by( year( DATETIME ), isoweek( DATETIME ) ) %>%
summarise( DateCount = n_distinct(date(DATETIME)), MeanError = mean( abs(Forecasted-Actual)/Actual ) )
Output:
`year(DATETIME)` `isoweek(DATETIME)` DateCount MeanError
<dbl> <dbl> <int> <dbl>
1 2020 44 1 0.858
2 2020 45 7 0.321
3 2020 46 7 0.505
4 2020 47 7 0.439
5 2020 48 7 0.309
6 2020 49 7 0.545
7 2020 50 7 0.342
8 2020 51 7 0.357
9 2020 52 7 0.204
10 2020 53 4 0.120
# … with 14 more rows
Dates should work fine if you set them up correctly.

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