Problem Publishing RShiny App Warning: Computation failed in `stat_summary_hex()`: - r

I am having a problem publishing my RShiny App. Whenever I publish it, everything works besides my stat_summary_hex() function within my ggplot. I am not really sure why it is not working, whenever I run it through RStudio it works perfectly fine, but not when I publish it. If I could get help, that would be much appreciated.
I am getting this message: Computation failed in stat_summary_hex():
Here's my code
library(shiny)
library(dplyr)
library(tidyverse)
library(ggplot2)
allStuff2 = read.csv("appData.csv")
allStuff2$player_name = iconv(allStuff2$player_name,from="UTF-8",to="ASCII//TRANSLIT")
allStuff2 = allStuff2 %>%
mutate(pitch_type = ifelse(pitch_type == "FF", "Fourseam Fastball", pitch_type),
pitch_type = ifelse(pitch_type == "SI", "Sinker", pitch_type),
pitch_type = ifelse(pitch_type == "FC", "Cutter", pitch_type),
pitch_type = ifelse(pitch_type == "SL", "Slider", pitch_type),
pitch_type = ifelse(pitch_type == "CU", "Curveball", pitch_type),
pitch_type = ifelse(pitch_type == "CH", "Changeup", pitch_type)) %>%
arrange(player_name)
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
titlePanel("2022 Stuff+ Dashboard"),
theme = shinythemes::shinytheme("journal"),
# Sidebar with a slider input for number of bins
sidebarLayout(
sidebarPanel(
selectInput('name',
'Select Pitcher:',
choices = unique(allStuff2$player_name)),
selectInput('pitch_type',
'Select Pitch Type:',
choices = c('Fourseam Fastball', 'Sinker', 'Cutter', 'Slider', 'Curveball', 'Changeup'),
selected = 'Fourseam Fastball')),
# Show a plot of the generated distribution
mainPanel(
plotOutput("stuffPlot"),
tableOutput("table")
)
)
)
# Define server logic required to draw a histogram
server <- function(input, output) {
pitcherAvg = reactive({
allStuff2 %>%
filter(player_name == input$name,
pitch_type == input$pitch_type) %>%
group_by(pitch_type) %>%
summarise(velo = mean(release_speed, na.rm = T),
pfx_x = -mean(pfx_x, na.rm = T),
pfx_z = mean(pfx_z, na.rm = T),
rp_x = mean(release_pos_x, na.rm = T),
rp_z = mean(release_pos_z, na.rm = T),
p_throws = p_throws[1],
Stuff. = mean(Stuff., na.rm = T))
})
output$stuffPlot = renderPlot({
ggplot(filter(allStuff2, pitch_type == input$pitch_type,
p_throws == pitcherAvg()$p_throws,
release_pos_x >= pitcherAvg()$rp_x - 0.5,
release_pos_x <= pitcherAvg()$rp_x + 0.5,
release_pos_z >= pitcherAvg()$rp_z - 0.5,
release_pos_z <= pitcherAvg()$rp_z + 0.5,
release_speed >= pitcherAvg()$velo - 1,
release_speed <= pitcherAvg()$velo + 1),
aes(x = -pfx_x, y = pfx_z, z = Stuff.)) +
stat_summary_hex() +
scale_fill_gradient2(midpoint = 100, low = "blue", mid = "white", high = "red") +
geom_point(aes(x=pitcherAvg()$pfx_x, y=pitcherAvg()$pfx_z), colour="black", size = 5) +
theme_bw() +
labs(x = 'Horizontal Movement', y = 'Vertical Movement', fill = 'Stuff+',
title = paste(strsplit(input$name, ", ")[[1]][1], "'s Stuff+ by Location for ", input$pitch_type, sep = ""),
subtitle = "Based off of Pitchers with Similar Release Point and Velocity (Pitcher's Point of View)",
caption = 'Email: fran1412#umn.edu') +
coord_fixed() +
geom_vline(xintercept = 0) +
geom_hline(yintercept = 0) +
xlim(-2.5,2.5) +
ylim(-2.5,2.5)
})
output$table = renderTable({
allStuff2 %>%
filter(player_name == input$name,
pitch_type == input$pitch_type) %>%
group_by(pitch_type) %>%
summarise(Velocity = mean(release_speed, na.rm = T),
`Horiz. Break` = -mean(pfx_x, na.rm = T),
`Vert. Break` = mean(pfx_z, na.rm = T),
`Horiz. Release Point` = -mean(release_pos_x, na.rm = T),
`Vert. Release Point` = mean(release_pos_z, na.rm = T),
`Stuff+` = as.integer(round(mean(Stuff., na.rm = T), 0))) %>%
select(-pitch_type)
})
}
# Run the application
shinyApp(ui = ui, server = server)
Here is my dput file:
structure(list(release_speed = c(85.9, 97.6, 100.5, 98.9, 87.3,
100.7, 100.6, 87.3, 95, 100.7, 101.2, 87.4, 102, 87.2, 103.2,
95.8, 94.3, 93.1, 89, 101, 87.2, 95.8, 101.5, 96.1, 96.2, 85.5,
88.7, 88.4, 87.7, 90.5, 88.2, 87.2, 101.2, 100.6, 96.1, 95, 85,
85.7, 102.3, 96.2, 89.3, 88.3, 91.4, 86.1, 96.5, 101.5, 96.4,
100.7, 84.5, 89.9, 101.1, 94.3, 98.8, 95.7, 95.4, 89.3, 94.7,
98.1, 94.7, 97, 95.7, 98.5, 98.9, 87.6, 90.4, 86.5, 88.4, 88.3,
101.9, 98.9, 100.7, 89, 95.4, 99.6, 102.4, 87.5, 87.2, 88, 87,
95.9, 99.1, 96.5, 91, 85.9, 87.2, 84.8, 87.1, 88, 98.3, 102.1,
86.3, 99.2, 101.9, 100, 84.7, 86.1, 98.2, 102.4, 99.8, 98.8),
pfx_x = c(0.5, -1.12, -0.79, -0.95, 0.52, -0.89, -1.44, 0.26,
-1.12, -1, -0.86, 0.2, -0.84, 0.32, -1.2, -1.09, -1.37, -0.54,
0.25, -1.12, 0.15, -0.77, -0.84, -0.99, -1.38, 0.26, 0.05,
0.32, 0.2, 0.36, 0.29, 0.01, -1.08, -1, -0.75, -0.67, 0,
0.27, -0.9, -1.48, 0.33, 0.11, 0.33, 0.22, -0.75, -1.23,
-1.25, -1.02, 0.27, -0.13, -1.1, -0.51, -0.91, -0.7, -0.84,
0.23, -1.64, -0.96, -0.76, -1.03, -1.53, -1.24, -1.41, 0.48,
0.39, 0.09, 0.56, 0.18, -1.08, -0.97, -1.21, 0.5, -1.21,
-0.89, -1.12, 0.13, 0.16, 0.39, 0.46, -1.1, -0.79, -1.19,
-0.65, 0.28, 0.23, 0.35, 0.1, 0.34, -0.35, -0.91, 0.02, -0.86,
-1.02, -0.75, 0.38, 0.08, -1.38, -1.08, -1.02, -1.15), pfx_z = c(-0.63,
0.06, 1.22, 0.13, -0.45, 1.3, 1.15, -1.16, 0.64, 1.18, 1.08,
-0.75, 1, -0.59, 1.04, 0.29, 0.25, 0.53, -0.62, 0.97, -0.61,
0.13, 1.16, 0.32, 0.05, -0.48, -0.4, -0.39, -0.9, -0.42,
-0.64, -0.59, 1.24, 1.01, 0.1, 0.25, -1.05, -0.51, 1.12,
0.35, -0.63, -0.52, -0.56, -0.69, 0.22, 0.8, 0.48, 1.18,
-0.43, -0.34, 1.17, -0.21, 1.07, 0.21, 0.32, -0.49, 0.16,
1.35, 0.12, 0.43, 0.57, 1.04, 0.42, -0.56, -0.58, -0.82,
-0.37, -0.48, 0.8, 1, 1.33, -0.7, 0.45, 0.95, 1.37, -0.32,
-0.57, -0.72, -0.85, 0.3, 1.32, 0.33, 0.51, -0.55, -0.68,
-0.58, -0.57, -0.49, 0.17, 1.3, -0.5, 1.21, 1.14, 1.28, -0.72,
-0.42, 0.33, 1.37, 1.24, 0.3), release_pos_x = c(-2.18, -1.97,
-2.11, -1.72, -1.4, -1.66, -2.19, -1.93, -2.09, -1.89, -1.85,
-1.95, -1.89, -2.16, -1.73, -2.2, -2.22, -2.45, -2.07, -2.11,
-2.18, -2.24, -1.85, -2.29, -2.23, -2.16, -2.06, -2.07, -1.77,
-1.81, -2, -1.85, -1.75, -2.13, -2.14, -2.49, -2.14, -2.05,
-1.89, -1.79, -1.71, -1.73, -1.82, -2.08, -2.32, -1.76, -2.03,
-1.93, -2.04, -2.13, -1.97, -2.27, -2.2, -2.1, -2.27, -1.94,
-1.95, -1.86, -2.27, -2.28, -1.84, -2.21, -2, -1.72, -1.94,
-1.73, -1.74, -2, -1.82, -1.89, -1.59, -1.83, -2.12, -2.12,
-1.84, -1.92, -2.13, -1.69, -1.9, -2.12, -2.15, -1.98, -2.63,
-2.24, -2.02, -1.83, -2.32, -1.96, -1.88, -2.02, -2.29, -2.2,
-2.15, -2.43, -1.68, -1.92, -2.05, -1.65, -2.12, -1.91),
release_pos_z = c(6.03, 6.29, 6.13, 6.04, 6.32, 6.24, 6.07,
6.18, 6.21, 6.07, 6.02, 6.14, 6.17, 6.16, 6.06, 5.95, 6.06,
5.8, 6, 6.02, 6.09, 6.14, 6.08, 5.93, 6.08, 6.26, 6.08, 6.12,
6.14, 6.04, 6.41, 6.23, 6.08, 6.11, 5.88, 5.95, 6.28, 6.05,
6.06, 6.29, 6.27, 6.2, 5.99, 6.23, 6.08, 6.07, 6.09, 6.18,
6.1, 6.19, 5.97, 5.95, 6.11, 5.99, 6.25, 5.97, 6.02, 6.15,
6.01, 6.04, 6.18, 6.06, 6.03, 6.2, 5.85, 6.25, 6.07, 6.25,
6.06, 5.99, 6.28, 6.18, 5.87, 6.15, 6.01, 6.12, 6.2, 6.09,
6.09, 5.94, 5.99, 6, 5.74, 6.36, 6.04, 6.16, 5.94, 6.2, 6,
5.96, 6.31, 5.99, 5.96, 6.13, 6.17, 6.21, 6.14, 6.04, 6.05,
6.21), player_name = c("Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan", "Duran, Jhoan",
"Duran, Jhoan"), p_throws = c("R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R", "R",
"R", "R", "R", "R", "R", "R", "R", "R", "R", "R"), pitch_type = c("CU",
"CH", "FF", "CH", "CU", "FF", "FF", "CU", "CH", "FF", "FF",
"CU", "FF", "CU", "FF", "CH", "CH", "SL", "CU", "FF", "CU",
"CH", "FF", "CH", "CH", "CU", "CU", "CU", "CU", "CU", "CU",
"CU", "FF", "FF", "CH", "CH", "CU", "CU", "FF", "CH", "CU",
"CU", "CU", "CU", "CH", "FF", "CH", "FF", "CU", "CU", "FF",
"CH", "FF", "CH", "CH", "CU", "CH", "FF", "CH", "CH", "CH",
"FF", "CH", "CU", "CU", "CU", "CU", "CU", "FF", "FF", "FF",
"CU", "CH", "FF", "FF", "CU", "CU", "CU", "CU", "CH", "FF",
"CH", "SL", "CU", "CU", "CU", "CU", "CU", "CH", "FF", "CU",
"FF", "FF", "FF", "CU", "CU", "CH", "FF", "FF", "CH"), Stuff. = c(110.783365414633,
83.9695389876577, 126.982883382714, 100.773492772016, 122.695393953566,
119.617766459935, 118.785623163818, 130.831514405023, 61.756262527081,
96.8279502585635, 99.8866109055696, 129.196132513915, 100.484491385581,
126.11256167432, 119.411360427872, 99.3219706412825, 114.233606464787,
85.7119791685036, 130.467610095058, 112.469680863445, 141.219595669374,
107.42847854836, 108.245236352338, 99.7562461898892, 125.500229256936,
122.603272894611, 140.858949561694, 127.283168181566, 124.003695368084,
127.799900621368, 111.05222857869, 123.422997655113, 123.389105069617,
110.298869169031, 111.024067639178, 94.9843404478164, 150.783474706561,
126.3180435826, 100.726570873835, 119.565181758901, 122.935086985841,
141.957127666565, 131.623575183857, 111.648889433675, 79.7759749825627,
86.5740112525524, 93.0267990053248, 111.314387796175, 121.148229426222,
146.59561919429, 113.787652486095, 110.791517820695, 84.8826511258123,
99.281267964766, 95.6569834353375, 139.898066773251, 116.718918712834,
98.5337807227119, 96.0645681960684, 63.3208562591806, 108.735190496635,
80.3261641922412, 188.974704795231, 117.766297421377, 129.541840071494,
134.2787846362, 130.058767183761, 123.568417985415, 84.8628879203198,
68.4933286167661, 116.547997910403, 123.002208002103, 94.3192200671762,
85.8888577862371, 141.693480608564, 125.100305847327, 121.625166716099,
109.272368981841, 107.346832906711, 100.57797732587, 108.425613182612,
78.6537787345208, 85.6841619149002, 121.150698692734, 131.428503129421,
113.792479159864, 135.83714795316, 112.501339659899, 88.7429637176584,
155.705150252501, 137.647519897197, 97.8102480290771, 135.083550736976,
140.716728877702, 98.6674738226451, 134.137954477671, 169.959982914227,
141.693480608564, 137.731456338842, 93.4183672266801)), class = "data.frame", row.names = c(NA,
-100L))

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Change proportion of zoom in facet_zoom [ggplot2]

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Here is my code
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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,
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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)

I want to grow a variable according to the weight of each country and the global changes (Creating a Bartik Instrument in R)

I am conducting some regression analysis and I need to first create a Bartik Instrument to use as an IV. Essentially, I have 10 decile groups of the income distribution. These are both at a global level and a country level (as there is an unbalanced panel of countries in the dataset). I want to grow each country's decile groups according to worldwide changes.
The image above represents the world and shows the percentage of people in each income decile on the left-hand side. On the right are the calculated percentage changes for each income decile between years. There are 10 columns all up for the 10 deciles.
The image below shows the country's decile groups. The starting year will be 1990 for each country (ie, the beginning decile proportion for each decile will be the year 1990 for each country. This serves as the "weight" in all of the statistics). Then, each decile will grow at the same percentage change as the global level.
For example, if dp1 is 1.92 in 1990 for the country Afghanistan, 1991 will be calculated from the global percentage change between 1990 and 1991. Because the global change was -2.857%, the predicted value of dp1 in 1991 for Afghanistan will be 1.865. This value will then be used in the calculation for predicting 1992.
The issue is, it needs to start at 1990 for each country and end in the final predicted year of 2019. I cannot just use a mutate function as it won't recognize that each country restarts in 1990.
Any guidance on this issue will be greatly appreciated. Please let me know if you need to see any more of the data as it is all open source and can therefore be freely shared.
Dput of the world data frame:
structure(list(Entity = c("World", "World", "World", "World",
"World", "World", "World", "World", "World", "World", "World",
"World", "World", "World", "World", "World", "World", "World",
"World", "World", "World", "World", "World", "World", "World",
"World", "World", "World", "World", "World", "World"), Year = c(1990,
1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001,
2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020), Code = c("WLD",
"WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD",
"WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD",
"WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD", "WLD",
"WLD", "WLD", "WLD"), gini = c("69.95", "70.18", "70.07", "69.88",
"69.79", "69.51", "69.16", "68.93", "68.98", "68.83", "68.76",
"68.43", "68.1", "67.68", "67.24", "66.79", "66.22", "65.57",
"64.9", "63.8", "63.28", "62.92", "62.54", "62.11", "61.68",
"61.47", "61.12", "60.92", "60.81", "60.65", "60.6"), palma = c(14.44,
14.74, 14.2, 13.7, 13.28, 12.95, 12.46, 12.31, 12.12, 12.04,
12.12, 11.74, 11.49, 11.18, 10.91, 10.55, 10.08, 9.67, 9.27,
8.53, 8.29, 8.12, 7.91, 7.74, 7.47, 7.36, 7.25, 7.11, 7.07, 6.95,
6.97), dp1 = c(0.35, 0.34, 0.35, 0.36, 0.36, 0.38, 0.38, 0.38,
0.39, 0.38, 0.38, 0.39, 0.38, 0.38, 0.39, 0.39, 0.4, 0.4, 0.4,
0.43, 0.43, 0.43, 0.43, 0.43, 0.45, 0.45, 0.44, 0.43, 0.44, 0.44,
0.43), dp2 = c(0.71, 0.72, 0.76, 0.78, 0.82, 0.82, 0.84, 0.86,
0.87, 0.9, 0.88, 0.89, 0.9, 0.92, 0.93, 0.93, 0.95, 0.98, 1.01,
1.07, 1.09, 1.1, 1.1, 1.14, 1.14, 1.14, 1.17, 1.19, 1.18, 1.19,
1.2), dp3 = c(1.09, 1.06, 1.1, 1.18, 1.19, 1.25, 1.29, 1.31,
1.3, 1.33, 1.31, 1.36, 1.38, 1.39, 1.43, 1.48, 1.5, 1.54, 1.59,
1.69, 1.72, 1.74, 1.79, 1.81, 1.83, 1.88, 1.89, 1.88, 1.94, 1.95,
1.94), dp4 = c(1.52, 1.5, 1.59, 1.64, 1.74, 1.75, 1.82, 1.82,
1.9, 1.89, 1.88, 1.93, 1.97, 2, 2.02, 2.07, 2.18, 2.24, 2.29,
2.44, 2.48, 2.51, 2.57, 2.58, 2.71, 2.72, 2.73, 2.82, 2.76, 2.81,
2.8), dp5 = c(2.11, 2.15, 2.27, 2.34, 2.42, 2.51, 2.53, 2.56,
2.6, 2.64, 2.7, 2.72, 2.74, 2.8, 2.92, 2.99, 3.05, 3.1, 3.2,
3.44, 3.52, 3.57, 3.65, 3.77, 3.76, 3.86, 3.93, 3.96, 3.95, 4.03,
4.04), dp6 = c(3.23, 3.18, 3.25, 3.38, 3.44, 3.52, 3.6, 3.64,
3.66, 3.74, 3.68, 3.87, 3.98, 4.08, 4.07, 4.14, 4.35, 4.54, 4.68,
4.85, 5.02, 5.11, 5.2, 5.29, 5.48, 5.43, 5.54, 5.56, 5.62, 5.6,
5.57), dp7 = c(5.49, 5.42, 5.43, 5.42, 5.37, 5.41, 5.5, 5.66,
5.49, 5.57, 5.67, 5.73, 5.86, 6.03, 6.23, 6.23, 6.49, 6.63, 6.91,
7.12, 7.37, 7.38, 7.59, 7.72, 7.84, 7.94, 7.92, 8.02, 8, 8.05,
8.13), dp8 = c(10.96, 10.76, 10.3, 10.04, 9.78, 9.73, 9.78, 9.82,
9.67, 9.61, 9.7, 9.75, 9.73, 10, 10.18, 10.5, 10.55, 10.88, 11.04,
11.32, 11.4, 11.63, 11.62, 11.72, 11.78, 11.82, 12.05, 11.85,
12.1, 12.08, 12.12), dp9 = c(21.51, 21.26, 20.81, 20.53, 20.22,
20.17, 20.15, 20.03, 19.9, 19.75, 19.77, 19.7, 19.88, 19.72,
19.74, 19.75, 19.69, 19.71, 19.75, 19.51, 19.48, 19.49, 19.39,
19.36, 19.23, 19.14, 19.05, 19.37, 19.25, 19.3, 19.38), dp10 = c(52.93,
53.51, 54.05, 54.24, 54.58, 54.39, 54.02, 53.85, 54.14, 54.13,
53.96, 53.6, 53.13, 52.61, 52.04, 51.45, 50.77, 49.93, 49.08,
48.07, 47.44, 46.98, 46.61, 46.14, 45.75, 45.56, 45.23, 44.9,
44.72, 44.52, 44.37), `dp1_PChangeFrom-1` = c(NA, -0.0285714285714284,
0.0294117647058822, 0.0285714285714286, 0, 0.0555555555555556,
0, 0, 0.0263157894736842, -0.0256410256410257, 0, 0.0263157894736842,
-0.0256410256410257, 0, 0.0263157894736842, 0, 0.0256410256410257,
0, 0, 0.0749999999999999, 0, 0, 0, 0, 0.0465116279069768, 0,
-0.0222222222222222, -0.0227272727272727, 0.0232558139534884,
0, -0.0227272727272727), `dp2_PChangeFrom-1` = c(NA, 0.0140845070422535,
0.0555555555555556, 0.0263157894736842, 0.0512820512820512, 0,
0.024390243902439, 0.0238095238095238, 0.0116279069767442, 0.0344827586206897,
-0.0222222222222222, 0.0113636363636364, 0.0112359550561798,
0.0222222222222222, 0.0108695652173913, 0, 0.0215053763440859,
0.0315789473684211, 0.0306122448979592, 0.0594059405940595, 0.0186915887850467,
0.00917431192660551, 0, 0.0363636363636362, 0, 0, 0.0263157894736842,
0.0170940170940171, -0.00840336134453782, 0.00847457627118645,
0.00840336134453782), `dp3_PChangeFrom-1` = c(NA, -0.0275229357798165,
0.0377358490566038, 0.0727272727272726, 0.00847457627118645,
0.0504201680672269, 0.032, 0.0155038759689923, -0.00763358778625955,
0.0230769230769231, -0.0150375939849624, 0.0381679389312977,
0.014705882352941, 0.00724637681159421, 0.0287769784172662, 0.034965034965035,
0.0135135135135135, 0.0266666666666667, 0.0324675324675325, 0.0628930817610062,
0.0177514792899408, 0.0116279069767442, 0.0287356321839081, 0.0111731843575419,
0.0110497237569061, 0.0273224043715846, 0.00531914893617022,
-0.0052910052910053, 0.0319148936170213, 0.00515463917525774,
-0.00512820512820513), `dp4_PChangeFrom-1` = c(NA, -0.0131578947368421,
0.0600000000000001, 0.031446540880503, 0.0609756097560976, 0.00574712643678161,
0.04, 0, 0.0439560439560439, -0.00526315789473685, -0.0052910052910053,
0.0265957446808511, 0.0207253886010363, 0.0152284263959391, 0.01,
0.0247524752475247, 0.0531400966183576, 0.0275229357798165, 0.0223214285714285,
0.0655021834061135, 0.0163934426229508, 0.0120967741935483, 0.0239043824701195,
0.00389105058365768, 0.0503875968992248, 0.00369003690036909,
0.00367647058823522, 0.0329670329670329, -0.0212765957446809,
0.0181159420289856, -0.00355871886121005), `dp5_PChangeFrom-1` = c(NA,
0.018957345971564, 0.0558139534883721, 0.0308370044052863, 0.0341880341880342,
0.037190082644628, 0.00796812749003985, 0.0118577075098815, 0.015625,
0.0153846153846154, 0.0227272727272727, 0.00740740740740741,
0.00735294117647059, 0.021897810218978, 0.0428571428571429, 0.0239726027397261,
0.0200668896321069, 0.0163934426229509, 0.0322580645161291, 0.0749999999999999,
0.0232558139534884, 0.0142045454545454, 0.0224089635854342, 0.0328767123287672,
-0.00265251989389927, 0.0265957446808511, 0.0181347150259068,
0.00763358778625949, -0.00252525252525247, 0.020253164556962,
0.00248138957816372), `dp6_PChangeFrom-1` = c(NA, -0.0154798761609907,
0.0220125786163522, 0.04, 0.0177514792899408, 0.0232558139534884,
0.0227272727272727, 0.0111111111111111, 0.0054945054945055, 0.0218579234972678,
-0.0160427807486631, 0.0516304347826087, 0.0284237726098191,
0.0251256281407035, -0.00245098039215681, 0.017199017199017,
0.0507246376811594, 0.0436781609195403, 0.0308370044052863, 0.0363247863247863,
0.0350515463917526, 0.0179282868525898, 0.0176125244618395, 0.0173076923076923,
0.0359168241965974, -0.00912408759124101, 0.0202578268876612,
0.00361010830324902, 0.0107913669064749, -0.00355871886121005,
-0.00535714285714274), `dp7_PChangeFrom-1` = c(NA, -0.0127504553734062,
0.00184501845018446, -0.00184162062615097, -0.00922509225092248,
0.00744878957169461, 0.0166358595194085, 0.0290909090909091,
-0.0300353356890459, 0.0145719489981785, 0.0179533213644524,
0.0105820105820107, 0.0226876090750436, 0.0290102389078498, 0.033167495854063,
0, 0.0417335473515248, 0.0215716486902927, 0.0422322775263952,
0.0303907380607815, 0.0351123595505618, 0.00135685210312073,
0.0284552845528455, 0.0171277997364954, 0.0155440414507772, 0.0127551020408164,
-0.00251889168765749, 0.0126262626262626, -0.00249376558603486,
0.00625000000000009, 0.00993788819875777), `dp8_PChangeFrom-1` = c(NA,
-0.0182481751824818, -0.0427509293680297, -0.0252427184466021,
-0.0258964143426295, -0.0051124744376277, 0.00513874614594028,
0.00408997955010234, -0.0152749490835031, -0.00620475698035165,
0.00936524453694067, 0.00515463917525781, -0.00205128205128201,
0.0277492291880781, 0.018, 0.031434184675835, 0.00476190476190483,
0.0312796208530806, 0.014705882352941, 0.0253623188405798, 0.00706713780918729,
0.0201754385964913, -0.00085984522785912, 0.00860585197934608,
0.00511945392491457, 0.00339558573853998, 0.0194585448392555,
-0.0165975103734441, 0.0210970464135021, -0.00165289256198344,
0.00331125827814562), `dp9_PChangeFrom-1` = c(NA, -0.0116225011622501,
-0.0211665098777047, -0.0134550696780393, -0.015099853872382,
-0.00247279920870411, -0.000991571641051221, -0.00595533498759293,
-0.00649026460309548, -0.00753768844221098, 0.00101265822784808,
-0.0035407182599899, 0.00913705583756344, -0.00804828973843059,
0.00101419878296144, 0.000506585612968671, -0.00303797468354424,
0.00101574403250379, 0.00202942668696089, -0.0121518987341771,
-0.00153767298821123, 0.000513347022587167, -0.00513083632632108,
-0.0015471892728211, -0.0067148760330578, -0.00468018720748829,
-0.00470219435736676, 0.0167979002624672, -0.0061951471347445,
0.00259740259740263, 0.00414507772020717), `dp10_PChangeFrom-1` = c(NA,
0.0109578688834309, 0.0100915716688469, 0.00351526364477345,
0.00626843657817102, -0.00348112861854155, -0.00680272108843533,
-0.00314698259903743, 0.00538532961931289, -0.000184706316956003,
-0.00314058747459822, -0.00667160859896218, -0.00876865671641789,
-0.00978731413514028, -0.0108344421212697, -0.011337432744043,
-0.0132167152575316, -0.0165452038605476, -0.0170238333667134,
-0.0205786471067644, -0.0131058872477637, -0.00969645868465432,
-0.00787569178373771, -0.0100836730315383, -0.00845253576072823,
-0.00415300546448082, -0.00724319578577712, -0.00729604244970149,
-0.00400890868596881, -0.00447227191413228, -0.00336927223719689
)), row.names = c(NA, -31L), class = "data.frame")
dput of the countries data frame::
structure(list(Year = numeric(0), Entity = character(0), Code = character(0),
gini = character(0), palma = numeric(0), dp1 = numeric(0),
dp2 = numeric(0), dp3 = numeric(0), dp4 = numeric(0), dp5 = numeric(0),
dp6 = numeric(0), dp7 = numeric(0), dp8 = numeric(0), dp9 = numeric(0),
dp10 = numeric(0)), class = c("grouped_df", "tbl_df", "tbl",
"data.frame"), row.names = integer(0), groups = structure(list(
Entity = character(0), Year = numeric(0), .rows = structure(list(), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), row.names = integer(0), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE))

Abline not showing for plots in loop

When I run abline(h=0) within a loop it will not add a line at the horizontal, for example:
library(glue)
library(magrittr)
par(mfrow=n2mfrow(4, 2))
for(i in 1:4){
plot(savings[, i], savings[,1], xlab = glue('predictor: {i}'), ylab = 'Dependent variable') %>%
with(abline(h=0))
}
Here's some sample data:
savings <- structure(list(sr = c(11.43, 12.07, 13.17, 5.75, 12.88, 8.79,
0.6, 11.9, 4.98, 10.78, 16.85, 3.59, 11.24, 12.64, 12.55), pop15 = c(29.35,
23.32, 23.8, 41.89, 42.19, 31.72, 39.74, 44.75, 46.64, 47.64,
24.42, 46.31, 27.84, 25.06, 23.31), pop75 = c(2.87, 4.41, 4.43,
1.67, 0.83, 2.85, 1.34, 0.67, 1.06, 1.14, 3.93, 1.19, 2.37, 4.7,
3.35), dpi = c(2329.68, 1507.99, 2108.47, 189.13, 728.47, 2982.88,
662.86, 289.52, 276.65, 471.24, 2496.53, 287.77, 1681.25, 2213.82,
2457.12), ddpi = c(2.87, 3.93, 3.82, 0.22, 4.56, 2.43, 2.67,
6.51, 3.08, 2.8, 3.99, 2.19, 4.32, 4.52, 3.44)), row.names = c("Australia",
"Austria", "Belgium", "Bolivia", "Brazil", "Canada", "Chile",
"China", "Colombia", "Costa Rica", "Denmark", "Ecuador", "Finland",
"France", "Germany"), class = "data.frame")

R plotly : Separate (with a blank) x-axis label Data

I have data with several data agregations (Year, Quarter, Month).
I try to leave a space between each Date aggregation in x-axis label.
I obtain this for the moment :
And I want to obtain :
Here my data :
My dataframe (dput) :
r8_plot = structure(list(DATE = c(2016L, 2017L, 2018L, 201701L, 201702L,
201703L, 201704L, 201801L, 201802L, 201803L, 201804L, 201801L,
201802L, 201803L, 201804L, 201805L, 201806L, 201807L, 201808L,
201809L, 201810L, 201811L, 201812L, 201844L, 201845L, 201846L,
201847L, 201848L, 201849L, 201850L), Var1 = c(6.64, 6.21, 6.53,
6.31, 6.01, 6.36, 6.17, 6.76, 6.37, 6.68, 6.27, 7.5, 6.49, 6.4,
6.54, 6.18, 6.37, 5.98, 6.37, 7.48, 6.6, 5.97, 6.25, 5.42, 6.18,
5.81, 6.46, 6.36, 6.05, 6.35), Var2 = c(2.38, 2.25, 2.36, 2.22,
2.52, 1.98, 2.27, 2.44, 2.31, 2.27, 2.41, 2.53, 2.25, 2.51, 2.35,
2.42, 2.19, 2.51, 1.91, 2.38, 2.34, 2.29, 2.68, 2.15, 1.89, 2.6,
2.52, 2.37, 2.97, 2.71), Var3 = c(4.26, 3.96, 4.17, 4.09, 3.5,
4.38, 3.9, 4.32, 4.06, 4.4, 3.86, 4.96, 4.23, 3.9, 4.19, 3.77,
4.18, 3.47, 4.46, 5.1, 4.26, 3.68, 3.57, 3.27, 4.29, 3.2, 3.95,
3.99, 3.09, 3.64), Var4 = c(35.84, 36.17, 36.08, 35.2, 41.86,
31.17, 36.76, 36.07, 36.27, 34.07, 38.43, 33.78, 34.76, 39.18,
35.95, 39.07, 34.35, 42.04, 29.91, 31.8, 35.48, 38.38, 42.86,
39.72, 30.53, 44.85, 38.94, 37.24, 48.98, 42.63), Var5 = c("Y",
"Y", "Y", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "M", "M", "M",
"M", "M", "M", "M", "M", "M", "M", "M", "M", "W", "W", "W", "W",
"W", "W", "W"), Var6 = structure(1:30, .Label = c("2016", "2017",
"2018", "Q1-2017", "Q2-2017", "Q3-2017", "Q4-2017", "Q1-2018",
"Q2-2018", "Q3-2018", "Q4-2018", "M01-2018", "M02-2018", "M03-2018",
"M04-2018", "M05-2018", "M06-2018", "M07-2018", "M08-2018", "M09-2018",
"M10-2018", "M11-2018", "M12-2018", "W44-2018", "W45-2018", "W46-2018",
"W47-2018", "W48-2018", "W49-2018", "W50-2018"), class = "factor"),
Var7 = c(7.1, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.7, 6.7, 6.6,
6.6, 6.7, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6,
6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6)), .Names = c("DATE",
"Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7"), row.names = c(NA,
30L), class = "data.frame")
r8_plot$Var6 = factor(r8_plot$Var6, labels = unique(r8_plot$Var6), levels=unique(r8_plot$Var6))
library(plotly)
r8_plot %>% plot_ly(x = ~Var6) %>%
add_bars(y = ~Var1,marker = list(color = '#00802b'),
name = "Var1") %>%
add_bars(y = ~Var2,marker = list(color = '#ff9933'),
name = "Var2")%>%
add_lines(y = ~Var4,
name = "Var4",
yaxis = "y2", line = list(color = '#1a1aff'))%>%
add_lines(y = ~Var7,
name = "Var7",
yaxis = "y1")%>%
layout(barmode = "stack",
yaxis2 = list(overlaying = "y",side = "right"),barmode = "stack",xaxis = list(title = 'DATE'), yaxis = list(title = 'All quantity'), title ="Chart") %>% layout(height = 750, width = 1000, hovermode = 'closest',margin = list(b = 115))
Thanks in advance

You can add NaN y-values to any data frame and Plotly will interrupt the plot at this position.
For example
library(plotly)
data = data.frame(list(x = c(1, 2, NaN, 3, 4),
y = c(1, 2, NaN, 3, 4)))
plot_ly(data, x = ~x) %>%
add_lines(y = ~y)
will give you
For more complex data, like in the question, a function introducing the empty rows might be easier than doing it manually.
split_by_date <- function(data) {
data_length <- length(data[,1])
index <- 0
new_data <- data
new_line <- list(replicate(length(data), NaN))
for (i in 2:length(data$DATE)) {
if (substr(toString(data$Var6[[i]]), 1, 1) != substr(toString(data$Var6[[i - 1]]), 1, 1)) {
new_data <- rbind.data.frame(new_data[1:i + index - 1,], new_line[[1]], data[i:data_length,])
new_data$Var6[[index + i]] <- paste(replicate(index + 1, " "), collapse = " ")
index <- index + 1
rownames(new_data) <- 1:as.integer(data_length + index)
}
}
return(new_data)
}
We just need to make sure that the x-value is always unique, i.e. simply concatenate an increasing number of spaces. Otherwise we'll just get a single break in the graph.
Also another row x is introduced to help with plotting the x-values in the correct order.
r8_plot = structure(list(DATE = c(2016L, 2017L, 2018L, 201701L, 201702L, 201703L, 201704L, 201801L, 201802L, 201803L, 201804L, 201801L, 201802L, 201803L, 201804L, 201805L, 201806L, 201807L, 201808L, 201809L, 201810L, 201811L, 201812L, 201844L, 201845L, 201846L, 201847L, 201848L, 201849L, 201850L),
Var1 = c(6.64, 6.21, 6.53, 6.31, 6.01, 6.36, 6.17, 6.76, 6.37, 6.68, 6.27, 7.5, 6.49, 6.4, 6.54, 6.18, 6.37, 5.98, 6.37, 7.48, 6.6, 5.97, 6.25, 5.42, 6.18, 5.81, 6.46, 6.36, 6.05, 6.35),
Var2 = c(2.38, 2.25, 2.36, 2.22, 2.52, 1.98, 2.27, 2.44, 2.31, 2.27, 2.41, 2.53, 2.25, 2.51, 2.35, 2.42, 2.19, 2.51, 1.91, 2.38, 2.34, 2.29, 2.68, 2.15, 1.89, 2.6, 2.52, 2.37, 2.97, 2.71),
Var3 = c(4.26, 3.96, 4.17, 4.09, 3.5, 4.38, 3.9, 4.32, 4.06, 4.4, 3.86, 4.96, 4.23, 3.9, 4.19, 3.77, 4.18, 3.47, 4.46, 5.1, 4.26, 3.68, 3.57, 3.27, 4.29, 3.2, 3.95, 3.99, 3.09, 3.64),
Var4 = c(35.84, 36.17, 36.08, 35.2, 41.86, 31.17, 36.76, 36.07, 36.27, 34.07, 38.43, 33.78, 34.76, 39.18, 35.95, 39.07, 34.35, 42.04, 29.91, 31.8, 35.48, 38.38, 42.86, 39.72, 30.53, 44.85, 38.94, 37.24, 48.98, 42.63),
Var5 = c("Y", "Y", "Y", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "Q", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "M", "W", "W", "W", "W", "W", "W", "W"),
Var6 = c("2016", "2017", "2018", "Q1-2017", "Q2-2017", "Q3-2017", "Q4-2017", "Q1-2018", "Q2-2018", "Q3-2018", "Q4-2018", "M01-2018", "M02-2018", "M03-2018","M04-2018", "M05-2018", "M06-2018", "M07-2018", "M08-2018", "M09-2018", "M10-2018", "M11-2018", "M12-2018", "W44-2018", "W45-2018", "W46-2018", "W47-2018", "W48-2018", "W49-2018", "W50-2018"),
Var7 = c(7.1, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.7, 6.7, 6.6, 6.6, 6.7, 6.7, 6.7, 6.7, 6.7, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6,6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6, 6.6)),
.Names = c("DATE", "Var1", "Var2", "Var3", "Var4", "Var5", "Var6", "Var7"), row.names = c(NA, 30L), class = "data.frame")
plot <- split_by_date(r8_plot)
plot$x <- structure(1:length(plot$Var6), .Label = plot$Var6, class = "factor")
plot %>% plot_ly(x = ~x, height = 750, width = 1000) %>%
add_bars(y = ~Var1,
marker = list(color = '#00802b'),
name = "Var1") %>%
add_bars(y = ~Var2,
marker = list(color = '#ff9933'),
name = "Var2") %>%
add_lines(y = ~Var4,
name = "Var4",
yaxis = "y2",
line = list(color = '#1a1aff')) %>%
add_lines(y = ~Var7,
name = "Var7",
yaxis = "y1") %>%
layout(barmode = "stack",
xaxis = list(title = 'DATE', range = c(-0.1, 10)),
yaxis = list(title = 'All quantity'),
yaxis2 = list(overlaying = "y",
side = "right"),
title ="Chart",
hovermode = 'closest')

ggplot density plot: Different x-axis for each group

I am trying to plot multiple density plots for some data.
I have the following code:
ggplot(data=stack) +
geom_density(aes(x=OfferPrice, group=Country, fill=Country),
alpha=0.5, adjust=2) +
facet_grid(~Country) +
theme_bw()
My problem arises with the x-axis on each of the density plots.
Looking a little closer at the data:
stack %>%
group_by(Country) %>%
summarise(min(OfferPrice),
mean(OfferPrice),
max(OfferPrice))
It looks like Country - JN has some very high numbers. Earlier I cut the top and bottom 5% of extreme values for each group so there shouldn´t necessarily be extreme values. What I think is wrong is the x-axis on the plots are using the maximum from the JN group. How is it possible to have different x-axis depending on each group?
Data:
stack <- structure(list(Country = c("US", "GB", "US", "HK", "JN", "US",
"CH", "CA", "US", "US", "CA", "JN", "GB", "AU", "US", "GB", "US",
"GB", "HK", "CH", "CA", "CA", "US", "GB", "TA", "JN", "CA", "CA",
"CA", "CA", "CH", "GB", "CA", "HK", "CA", "US", "US", "CA", "US",
"AU", "CA", "CA", "CA", "US", "GB", "GB", "AU", "US", "US", "AU",
"CA", "CA", "CA", "US", "CA", "GB", "CA", "US", "GB", "US", "AU",
"AU", "US", "CA", "US", "GB", "AU", "CH", "CA", "CA", "GB", "AU",
"AU", "CH", "CA", "AU", "CH", "US", "CH", "TA", "AU", "AU", "GB",
"CH", "HK", "AU", "AU", "CA", "US", "TA", "GB", "US", "AU", "US",
"CA", "CA", "US", "AU", "CA", "US", "CA", "US", "CA", "US", "CA",
"US", "US", "AU", "CA", "AU", "GB", "US", "HK", "AU", "US", "CA",
"JN", "JN", "GB", "JN", "CA", "CA", "AU", "GB", "GB", "US", "US",
"US", "AU", "GB", "CA", "CA", "US", "CH", "GB", "US", "US", "AU",
"GB", "CH", "JN", "CA", "AU", "CA", "US", "US", "AU", "AU", "CA",
"US", "GB", "GB", "US", "US", "CA", "US", "HK", "AU", "US", "GB",
"US", "GB", "GB", "US", "CA", "JN", "CA", "AU", "CA", "CA", "GB",
"CA", "HK", "HK", "US", "CH", "US", "AU", "TA", "US", "CH", "HK",
"AU", "US", "HK", "GB", "AU", "CH", "US", "AU", "US", "CH", "US",
"CH", "CA", "AU", "HK", "CA", "US", "CH", "GB", "CA", "CA", "CA",
"CA", "US", "CA", "CA", "US", "HK", "US", "HK", "AU", "GB", "AU",
"CH", "US", "AU", "CA", "CA", "US", "GB", "AU", "US", "CH", "CA",
"CA", "CA", "US", "AU", "GB", "GB", "CA", "AU", "CA", "AU", "US",
"HK", "AU", "US", "AU", "CA", "US", "US", "US", "CA", "GB", "CA",
"US", "CA", "US", "AU", "CA", "US", "AU", "CH", "GB", "CA", "CA",
"CA", "CA", "HK", "AU", "TA", "AU", "GB", "AU", "CA", "JN", "US",
"CA", "CA", "AU", "US", "US", "GB", "CA", "US", "GB", "US", "CA",
"CA", "CH", "US", "US", "US", "US", "US", "HK", "CH", "CA", "CA",
"CA", "AU", "GB", "CH", "CA", "GB", "CA", "AU"), EffectiveDate = structure(c(17617,
17500, 17556, 17596, 17618, 17667, 17786, 17728, 17569, 17760,
17585, 17613, 17806, 17847, 17786, 17665, 17702, 17683, 17574,
17725, 17723, 17658, 17563, 17847, 17584, 17578, 17842, 17522,
17697, 17521, 17680, 17794, 17813, 17697, 17773, 17578, 17556,
17595, 17669, 17501, 17655, 17562, 17668, 17589, 17582, 17780,
17812, 17667, 17611, 17758, 17751, 17617, 17505, 17505, 17725,
17506, 17513, 17541, 17644, 17702, 17828, 17688, 17696, 17519,
17850, 17746, 17779, 17547, 17806, 17701, 17638, 17759, 17786,
17770, 17835, 17750, 17841, 17653, 17788, 17553, 17794, 17654,
17724, 17675, 17802, 17638, 17625, 17514, 17709, 17758, 17681,
17507, 17708, 17758, 17800, 17569, 17821, 17493, 17834, 17848,
17639, 17549, 17590, 17575, 17630, 17639, 17760, 17724, 17701,
17725, 17756, 17794, 17511, 17494, 17822, 17758, 17709, 17492,
17605, 17709, 17602, 17644, 17814, 17696, 17760, 17603, 17730,
17675, 17590, 17724, 17743, 17680, 17690, 17711, 17560, 17570,
17702, 17512, 17626, 17618, 17576, 17731, 17527, 17709, 17729,
17672, 17515, 17527, 17641, 17597, 17812, 17540, 17602, 17743,
17662, 17709, 17505, 17737, 17603, 17515, 17568, 17843, 17738,
17508, 17816, 17578, 17590, 17812, 17743, 17528, 17616, 17687,
17646, 17515, 17816, 17557, 17507, 17564, 17777, 17802, 17511,
17842, 17584, 17556, 17547, 17844, 17590, 17674, 17759, 17583,
17836, 17721, 17724, 17801, 17578, 17808, 17682, 17849, 17708,
17515, 17746, 17633, 17759, 17591, 17802, 17540, 17560, 17588,
17800, 17787, 17821, 17724, 17645, 17527, 17722, 17556, 17704,
17844, 17619, 17792, 17577, 17637, 17843, 17765, 17688, 17562,
17834, 17738, 17653, 17645, 17718, 17676, 17637, 17570, 17490,
17534, 17646, 17625, 17766, 17808, 17675, 17786, 17808, 17555,
17739, 17802, 17617, 17619, 17667, 17634, 17662, 17711, 17806,
17513, 17627, 17673, 17574, 17647, 17609, 17619, 17521, 17543,
17686, 17807, 17613, 17543, 17543, 17528, 17694, 17576, 17584,
17521, 17605, 17618, 17723, 17641, 17683, 17823, 17634, 17844,
17836, 17816, 17539, 17583, 17618, 17687, 17589, 17602, 17717,
17535, 17718, 17625, 17822, 17651, 17521, 17751, 17617, 17563,
17578, 17772), class = "Date"), OfferPrice = c(44, 13, 33, 0.3,
3000, 23, 6.26, 0.35, 10, 6.25, 0.25, 7110, 109.5, 0.11, 16,
2, 5.5, 15, 0.5, 8.5, 0.5, 0.2, 5, 92.5, 22, 103740, 0.23, 0.75,
8.65, 1.23, 17.4, 1.5, 0.38, 0.84, 8.1, 27.5, 10, 1, 14, 0.42,
0.1, 1.82, 2, 39.8, 238, 340, 0.3, 4.5, 41.5, 0.2, 0.25, 0.27,
0.35, 5, 0.3, 115, 0.15, 5, 142.25, 14, 2.43, 0.02, 24, 0.115,
8.25, 25, 0.155, 9.6, 0.67, 6, 52.5, 0.2, 0.2, 1.37, 1.6, 0.65,
2.9, 4, 7, 72, 0.025, 0.14, 22.5, 6.75, 0.64, 0.8, 0.8, 0.4,
22, 94.8, 15, 10, 2.45, 34, 1, 9.3, 6.25, 0.018, 0.2, 24.5, 0.3,
2.9, 0.35, 2.05, 0.4, 29.5, 2.26, 0.36, 0.75, 0.027, 2.8, 16,
3.54, 0.018, 10, 0.15, 1780, 1602, 120, 3900, 0.25, 0.18, 2.32,
269, 175, 18, 18, 23, 0.2, 10, 0.1, 0.6, 4.8, 6, 164.5, 7, 26.42,
0.02, 190, 11, 992, 0.2, 1.42, 0.55, 23, 33.5, 0.35, 0.065, 1.16,
29.5, 65, 8, 27.5, 18.9, 0.15, 17, 0.63, 0.34, 26.25, 0.65, 6.9,
10, 6.75, 1.21, 0.95, 73125, 2.5961, 0.054, 1.2, 9.64, 251, 2.46,
0.18, 0.375, 9.97, 20.43, 25.5, 0.025, 60, 3, 1.55, 0.5, 0.2,
17, 0.443, 8, 0.05, 5.25, 1.15, 0.45, 7.155, 17, 24.5, 12.5,
2.1, 0.75, 0.35, 0.39, 38.2, 0.63, 16, 0.15, 0.1, 0.12, 2.32,
10, 0.3, 1.66, 17, 0.4, 1.3, 0.3, 1.08, 30, 0.8, 10.88, 0.9,
0.21, 0.17, 1.7, 2.25, 1, 0.08, 1.5, 14.75, 0.35, 0.44, 0.35,
17, 2, 37, 195, 0.165, 0.02, 0.2, 0.015, 25, 1.09, 0.45, 10,
0.145, 0.92, 36, 13.25, 4, 0.6, 101, 0.7, 15, 0.3, 0.7, 0.06,
0.25, 6.5, 1.1, 16.72, 1.25, 0.1, 0.12, 0.28, 1.18, 0.4, 0.02,
75, 0.08, 5, 0.02, 0.5, 2878, 8, 0.15, 0.33, 0.1, 21.25, 1, 12,
5.83, 4.25, 1.65, 3.15, 3, 0.15, 7.4, 31.25, 12, 24, 19.75, 41.5,
0.88, 13.65, 0.25, 0.15, 0.25, 2.35, 101, 1.26, 1.65, 10, 1.32,
0.5)), row.names = c(NA, -300L), class = c("tbl_df", "tbl", "data.frame"
))

You can specify whether you want free scales (x, y or both) in facet_grid.
ggplot(data=stack) +
geom_density(aes(x=OfferPrice, group=Country, fill=Country),
alpha=0.5, adjust=2) +
facet_grid(~Country, scales = "free_x") +
theme_bw()

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