Related
I have created a vector with the order of a dot plot mentioned but it doesn't plot in that order/ Thanks for the suggestions.
order <- sav %>%
filter(Subject == "Food") %>%
arrange(desc(Percentage)) %>%
select(Location) %>%
unlist() %>%
unname()
order <- replace(order, c(1, 8), order[c(8, 1)])
sav %>%
ggplot(aes(x = factor(Location, levels = order), y = Percentage,
color = Subject))+
geom_point(data = filter(sav, Location != "IRELAND"),
size = 4, position = position_dodge(0.5))+
geom_point(data = filter(sav, Location == "IRELAND"),
size = 6, position = position_dodge(1))+
geom_linerange(data = filter(sav, Location == "IRELAND"),
aes(ymin = 0, ymax = Percentage),
position = position_dodge(1),
linetype = "dotdash") +
geom_linerange(data = filter(sav, Location != "IRELAND"),
aes(ymin = 0, ymax = Percentage),
position = position_dodge(0.5), linetype = "dotdash") +
coord_flip()+
ggtitle(label = "Increase in inflation (by CPI) in Ireland compared to OECD and other countries in OECD")+
xlab("Countries --> ") +
ylab("Increase in CPI by % -->")+
scale_y_continuous(breaks = round(seq(0, 20, by = 1),1))+
scale_color_manual(name = "Type of Items",
labels = c("Food", "Total", "Excluding food and energy"),
values=c(unname(colorblind_colors[2]),
unname(colorblind_colors[3]),
unname(colorblind_colors[4])))+
theme(panel.grid.major.x = element_line(linewidth =.01, color="black"),
panel.grid.major.y = element_blank(),
legend.position = "top"
)
> dput(order)
c("IRELAND", "NETHERLANDS", "SPAIN", "OECD", "ITALY", "FRANCE",
"UNITED STATES", "GERMANY", "CANADA")
> dput(sav)
structure(list(Location = c("CANADA", "CANADA", "FRANCE", "FRANCE",
"GERMANY", "GERMANY", "IRELAND", "IRELAND", "ITALY", "ITALY",
"NETHERLANDS", "NETHERLANDS", "SPAIN", "SPAIN", "UNITED STATES",
"UNITED STATES", "OECD", "OECD", "CANADA", "ITALY", "SPAIN",
"FRANCE", "IRELAND", "UNITED STATES", "NETHERLANDS", "OECD",
"GERMANY"), Subject = c("Food", "Total", "Food", "Total", "Food",
"Total", "Food", "Total", "Food", "Total", "Food", "Total", "Food",
"Total", "Food", "Total", "Food", "Total", "Total_Minus_Food_Energy",
"Total_Minus_Food_Energy", "Total_Minus_Food_Energy", "Total_Minus_Food_Energy",
"Total_Minus_Food_Energy", "Total_Minus_Food_Energy", "Total_Minus_Food_Energy",
"Total_Minus_Food_Energy", "Total_Minus_Food_Energy"), Frequency = c("Monthly",
"Monthly", "Monthly", "Monthly", "Monthly", "Monthly", "Monthly",
"Monthly", "Monthly", "Monthly", "Monthly", "Monthly", "Monthly",
"Monthly", "Monthly", "Monthly", "Monthly", "Monthly", "Monthly",
"Monthly", "Monthly", "Monthly", "Monthly", "Monthly", "Monthly",
"Monthly", "Monthly"), Time = c("2022-12", "2022-12", "2022-12",
"2022-12", "2022-12", "2022-12", "2022-12", "2022-12", "2022-12",
"2022-12", "2022-12", "2022-12", "2022-12", "2022-12", "2022-12",
"2022-12", "2022-12", "2022-12", "2022-12", "2022-12", "2022-12",
"2022-12", "2022-12", "2022-12", "2022-12", "2022-12", "2022-12"
), Percentage = c(11.02015, 6.319445, 12.86678, 5.850718, 19.75631,
8.550855, 11.74636, 8.224299, 13.14815, 11.63227, 16.7983, 9.586879,
15.68565, 5.70769, 11.88275, 6.454401, 15.60381, 9.438622, 5.58275,
4.469475, 4.442303, 3.36004, 4.999758, 5.707835, 6.034873, 7.221961,
5.05511)), class = "data.frame", row.names = c(NA, -27L))
A couple of things:
The factors need to be in the data that ggplot sees at every geom, but while you're setting factor(Location,levels=order) in the first mapping, none of the data= arguments is using the same data.
For this, I generally prefer factorizing the data up-front, and using ~-style "functions" for data=.
Not sure exactly why this is the case, but it still doesn't work ... but if the first call to geom_point uses mutate and replaces IRELAND's percentage values with NA, the levels are retained. Weird.
### I still don't have these :-)
colorblind_colors <- 1:4
sav %>%
mutate(Location = factor(Location, levels = order)) %>%
ggplot(aes(x = Location, y = Percentage, color = Subject)) +
geom_point(data = ~ mutate(., Percentage = if_else(Location == "Ireland", Percentage[NA], Percentage)),
size = 4, position = position_dodge(0.5), na.rm = TRUE) +
geom_point(data = ~ filter(., Location == "IRELAND"),
size = 6, position = position_dodge(1)) +
geom_linerange(data = ~ filter(., Location == "IRELAND"),
aes(ymin = 0, ymax = Percentage),
position = position_dodge(1),
linetype = "dotdash") +
geom_linerange(data = ~ filter(., Location != "IRELAND"),
aes(ymin = 0, ymax = Percentage),
position = position_dodge(0.5), linetype = "dotdash") +
coord_flip()+
ggtitle(label = "Increase in inflation (by CPI) in Ireland compared to OECD and other countries in OECD")+
xlab("Countries --> ") +
ylab("Increase in CPI by % -->")+
scale_y_continuous(breaks = round(seq(0, 20, by = 1),1))+
scale_color_manual(name = "Type of Items",
labels = c("Food", "Total", "Excluding food and energy"),
values=c(unname(colorblind_colors[2]),
unname(colorblind_colors[3]),
unname(colorblind_colors[4])))+
theme(panel.grid.major.x = element_line(linewidth =.01, color="black"),
panel.grid.major.y = element_blank(),
legend.position = "top"
)
I am trying to replicate a plot using ggplot2 to show the performance between two soccer teams. I look for getting this plot:
I have the data df for that. This is the code I have been using:
library(ggplot2)
#Plot
ggplot(df,aes(x=type,y=value,fill=name))+
geom_bar(stat = 'identity')+
geom_text(aes(label=abs(value),hjust=1))+
coord_flip()
Which produces next result:
The result is far away from first plot.
The issues I face are next:
The scale is too large for some type values, so is not possible to see each measure with independent scale. I think a facet_grid() or facet_wrap() would solve this but it did not worked.
The values for type must go in the middle, but I do not know how to move the axis to middle.
The labels for each bar should be on top of each bar but when I adjust hjust one of them goes to the right place but the other is placed wrong.
Finally, in df there is column named logo which stores the flags of two teams, how I can add the flags for each team one at top right side and the other at top left side.
Many thanks. This is the data df.
#Data
df <- structure(list(type = c("Shots on Goal", "Shots off Goal", "Total Shots",
"Blocked Shots", "Shots insidebox", "Shots outsidebox", "Fouls",
"Corner Kicks", "Offsides", "Ball Possession % ", "Yellow Cards",
"Red Cards", "Goalkeeper Saves", "Total passes", "Passes accurate",
"Passes %", "Shots on Goal", "Shots off Goal", "Total Shots",
"Blocked Shots", "Shots insidebox", "Shots outsidebox", "Fouls",
"Corner Kicks", "Offsides", "Ball Possession % ", "Yellow Cards",
"Red Cards", "Goalkeeper Saves", "Total passes", "Passes accurate",
"Passes %"), value = c(7, 2, 13, 4, 10, 3, 9, 8, 2, 78, 0, 0,
1, 797, 716, 90, -3, -4, -8, -1, -6, -2, -14, 0, -2, -22, -2,
0, -1, -215, -142, -66), name = c("England", "England", "England",
"England", "England", "England", "England", "England", "England",
"England", "England", "England", "England", "England", "England",
"England", "Iran", "Iran", "Iran", "Iran", "Iran", "Iran", "Iran",
"Iran", "Iran", "Iran", "Iran", "Iran", "Iran", "Iran", "Iran",
"Iran"), logo = c("https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/10.png",
"https://media.api-sports.io/football/teams/10.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png", "https://media.api-sports.io/football/teams/22.png",
"https://media.api-sports.io/football/teams/22.png")), row.names = c(NA,
-32L), class = c("tbl_df", "tbl", "data.frame"))
Perhaps something like this?
library(tidyverse)
library(ggimage)
df %>%
group_by(type) %>%
mutate(tot = sum(abs(value)),
prop = value/tot) %>%
ggplot(aes(prop, type, color = name)) +
geom_linerange(aes(xmin = -1, xmax = 1), color = "gray95", linewidth = 3) +
geom_linerange(aes(xmin = prop, xmax = 0), linewidth = 3) +
scale_color_manual(values = c("#8ded05", "#00aaff")) +
geom_text(aes(label = type, x = 0), check_overlap = TRUE, nudge_y = 0.4,
color = "black") +
geom_text(aes(x = ifelse(name == "England", 1.05, -1.05),
label = abs(value)), color = "black") +
theme_void() +
scale_y_discrete(expand = c(0.1, 0)) +
annotate(geom = "text", x = c(-0.5, 0.5), y = c(17, 17),
label = c("Iran", "England"), size = 5) +
geom_image(data = data.frame(x = c(1, -1), y = 17,
image = unique(df$logo)),
aes(x, y, image = image), inherit.aes = FALSE)+
guides(color = guide_none())
I am looking to plot some data for APAC countries for a an upcoming presentation. I followed this tutorial however my output is different. Below is some sample data I would like to plot -
country_list <-
c("Australia", "India", "New Zealand", "Singapore", "Malasia", "China", "Indonesia",
"Hong Kong", "Hong Kong", "Japan", "Philipines", "Thailand", "Vietnam", "Korea", "Taiwan",
"Cambodia", "Mongolia, Myanmar", "Laos")
values <-
c(33260, 24586, 5468, 2698, 2547, 6248, 3654, 6589, 2545, 2548, 835, 536, 565, 665,
236, 548, 158, 152)
data <- cbind(country_list, values)
Then here is how I plot the map -
library(tidyverse)
library(mapdata)
countries <-
map_data("world",
region = country_list)
data2 <- merge(countries, data, by.x = "region", by.y = "country_list") %>%
mutate(values = values %>% as.numeric)
ggplot(data2, aes(long, lat, group = group, fill = values))+
geom_polygon()
And here is my output -
Clearly not fit for a presentation. How can I improve on this map, perhaps add some interactivity to it.
your code is not reproducible, I can just filter after 0 longitude to show well
ggplot(countries %>% filter(long>0), aes(long, lat, group = group))+
geom_polygon()
I am trying to build a web app in shiny that would allow for different user input and then plot graphs/output data tables accordingly. I am using WHO's data about suicide rates and there are two possible types of graphs: bar plot and line graph.
The user is given a choice between plotting the graph in which the x axis is either the age group (barplot) or year (line graph). They are also given the choice of plotting the graph separately for males and females and different countries as well.
The code below works fine for everything except when the user chooses x axis = year with gender = 'gender neutral'. The error says that the object rate is not found. However, the block of code which includes the object rate works perfectly fine in other places.
library(shiny)
library(dplyr)
library(ggplot2)
setwd("C:\\Users\\Lenovoi7\\Shrewsbury School\\IT\\Coursework")
who<-data.frame(read.csv("who.csv", stringsAsFactors = TRUE))
dput(head(who))
countries<-sort(unique(who$country))
countries<-union(countries, c("World"))
ui<-fluidPage(
titlePanel("Suicide statistics"),
sidebarLayout(
sidebarPanel(
selectInput(
inputId="x",
label="Please choose the x variable",
choices=c("",
"Age group"="age",
"Year"="year")),
conditionalPanel(
condition = "input.x == 'age' || input.x == 'year'",
selectInput(
inputId = "gender",
label = "Please specify the gender characteristics",
choices = c("", "Gender neutral" = "gender_neutral",
"Gender specific" = "gender_specific"),
selected = NULL),
#nested conditional panel
#only show this panel if the input is gender_specific
conditionalPanel(
condition = "input.gender == 'gender_specific'",
selectInput(
inputId = "country",
label = "Select a country:",
choices = countries,
selected = "Bosnia and Herzegovina")),
conditionalPanel(
condition = "input.gender == 'gender_neutral'",
selectInput(
inputId = "country",
label = "Select a country:",
choices = countries,
selected = "Bosnia and Herzegovina")))),
mainPanel(
plotOutput("graph")
)))
server <- function(input, output) {
x<-reactive({input$x})
gender<-reactive({input$gender})
country<-reactive({input$country})
output$graph <- renderPlot(
#x axis = age group
if (x()=="age"){
if (gender()=="gender_neutral"){
if (country()=="World"){
ggplot(data=who, aes(x=age)) + geom_bar(aes(weights=suicides_no), position="dodge")}
else {
#create a new subset of data that will be used??
who_subset<-subset(who, country == input$country)
ggplot(data=who_subset, aes(x=age)) + geom_bar(aes(weights=suicides_no))}}
else if (gender()=="gender_specific"){
if (country()=="World"){
ggplot(data=who, aes(x=age)) + geom_bar(aes(weights=suicides_no, fill=sex), position="dodge")}
else {
#create a new subset of data that will be used??
who_subset<-subset(who, country==input$country)
ggplot(data=who_subset, aes(x=age)) + geom_bar(aes(weights=suicides_no, fill=sex), position="dodge")}}}
else if (x()=="year"){
if (gender()=="gender_neutral"){
if (country()=="World"){
who_all <- who %>%
group_by(year) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no/population)
ggplot() +
geom_line(data = who_all, aes(year, rate))
}
else {
who_subset<-subset(who, country==input$country)
who_sub_sex <- who_subset %>%
group_by(year) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no/population)
ggplot() +
geom_line(data = who_subset, aes(year, rate))
}}
else if (gender()=="gender_specific"){
if (country()=="World"){
who_all <- who %>%
group_by(year) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no/population)
ggplot() +
geom_line(data = who_all, aes(year, rate))
}
else {
#create a new subset of data that will be used??
who_subset<-subset(who, country==input$country)
who_sub_sex <- who_subset %>%
group_by(year, sex) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no / population)
ggplot() +
geom_line(data = who_sub_sex, aes(year, rate, color = sex))}
}
}
)}
# Create a Shiny app object
shinyApp(ui = ui, server = server)
dput(head(who))
structure(list(country = structure(c(1L, 1L, 1L, 1L, 1L, 1L),
.Label = c("Albania",
"Anguilla", "Antigua and Barbuda", "Argentina", "Armenia", "Aruba",
"Australia", "Austria", "Azerbaijan", "Bahamas", "Bahrain", "Barbados",
"Belarus", "Belgium", "Belize", "Bermuda", "Bolivia",
"Bosnia and Herzegovina",
"Brazil", "British Virgin Islands", "Brunei Darussalam", "Bulgaria",
"Cabo Verde", "Canada", "Cayman Islands", "Chile", "Colombia",
"Costa Rica", "Croatia", "Cuba", "Cyprus", "Czech Republic",
"Denmark", "Dominica", "Dominican Republic", "Ecuador", "Egypt",
"El Salvador", "Estonia", "Falkland Islands (Malvinas)", "Fiji",
"Finland", "France", "French Guiana", "Georgia", "Germany", "Greece",
"Grenada", "Guadeloupe", "Guatemala", "Guyana", "Haiti", "Honduras",
"Hong Kong SAR", "Hungary", "Iceland", "Iran (Islamic Rep of)",
"Iraq", "Ireland", "Israel", "Italy", "Jamaica", "Japan", "Jordan",
"Kazakhstan", "Kiribati", "Kuwait", "Kyrgyzstan", "Latvia", "Lithuania",
"Luxembourg", "Macau", "Malaysia", "Maldives", "Malta", "Martinique",
"Mauritius", "Mayotte", "Mexico", "Monaco", "Mongolia", "Montenegro",
"Montserrat", "Morocco", "Netherlands", "Netherlands Antilles",
"New Zealand", "Nicaragua", "Norway", "Occupied Palestinian Territory",
"Oman", "Panama", "Paraguay", "Peru", "Philippines", "Poland",
"Portugal", "Puerto Rico", "Qatar", "Republic of Korea",
"Republic of Moldova",
"Reunion", "Rodrigues", "Romania", "Russian Federation",
"Saint Kitts and Nevis",
"Saint Lucia", "Saint Pierre and Miquelon",
"Saint Vincent and Grenadines",
"San Marino", "Sao Tome and Principe", "Saudi Arabia", "Serbia",
"Seychelles", "Singapore", "Slovakia", "Slovenia", "South Africa",
"Spain", "Sri Lanka", "Suriname", "Sweden", "Switzerland",
"Syrian Arab Republic",
"Tajikistan", "TFYR Macedonia", "Thailand", "Trinidad and Tobago",
"Tunisia", "Turkey", "Turkmenistan", "Turks and Caicos Islands",
"Ukraine", "United Arab Emirates", "United Kingdom",
"United States of America",
"Uruguay", "Uzbekistan", "Venezuela (Bolivarian Republic of)",
"Virgin Islands (USA)", "Zimbabwe"), class = "factor"),
year = c(1985L, 1985L, 1985L, 1985L, 1985L, 1985L),
sex = structure(c(1L, 1L, 1L, 1L, 1L, 1L),.
Label = c("female", "male"), class = "factor"),
age = structure(1:6, .Label = c("15-24 years", "25-34 years",
"35-54 years", "5-14 years", "55-74 years", "75+ years"),
class = "factor"),
suicides_no = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_, NA_integer_), population = c(277900L, 246800L,
267500L, 298300L, 138700L, 34200L)),
row.names = c(NA, 6L), class = "data.frame")
Is there any chance somebody knows a way out of this problem? Again I want the web app to output line graph when the user chooses x axis = year and gender = gender_neutral.
Try out with this server code.
The changes are already described in my comments. Since I dont have the who data.frame I could not test it.
server <- function(input, output) {
output$graph <- renderPlot({
if (input$x == "age") {
if (input$gender=="gender_neutral"){
if (input$country=="World"){
ggplot(data = who, aes(x = age)) + geom_bar(aes(weights = suicides_no), position="dodge")}
else {
#create a new subset of data that will be used??
who_subset <- subset(who, country == input$country)
ggplot(data=who_subset, aes(x=age)) + geom_bar(aes(weights=suicides_no))
}
} else if (input$gender=="gender_specific") {
if (input$country=="World"){
ggplot(data=who, aes(x=age)) + geom_bar(aes(weights=suicides_no, fill=sex), position="dodge")}
else {
#create a new subset of data that will be used??
who_subset <- subset(who, country==input$country)
ggplot(data = who_subset, aes(x=age)) + geom_bar(aes(weights=suicides_no, fill=sex), position="dodge")
}
}
} else if (input$x=="year"){
if (input$gender=="gender_neutral"){
if (input$country=="World"){
who_all <- who %>%
group_by(year) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no/population)
ggplot() +
geom_line(data = who_all, aes(year, rate))
} else {
who_subset <- subset(who, country==input$country)
who_sub_sex <- who_subset %>%
group_by(year) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no/population)
ggplot() +
geom_line(data = who_sub_sex, aes(year, rate))
}
} else if (input$gender=="gender_specific"){
if (input$country=="World"){
who_all <- who %>%
group_by(year) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no/population)
ggplot() +
geom_line(data = who_all, aes(year, rate))
} else {
#create a new subset of data that will be used??
who_subset <- subset(who, country==input$country)
who_sub_sex <- who_subset %>%
group_by(year, sex) %>%
summarize(suicides_no = sum(suicides_no),
population = sum(population)) %>%
mutate(rate = 100000 * suicides_no / population)
ggplot() +
geom_line(data = who_sub_sex, aes(year, rate, color = sex))}
}
}
})
}
I am looking for some help with the given sample data of countries on one column and count on another column. I am trying a build a geo maps using ggplot showing the count and name of the country in the respective places of the map when I hover above the country. Below is the sample data given. I tried with the ggmap with the lat and long position to identify the country but not able to show the count and name of the country on hovering.
structure(list(Countries = c("USA", "India", "Europe", "LATAM",
"Singapore", "Phillipines", "Australia", "EMEA", "Malaysia",
"Hongkong", "Philippines", "Thailand", "New Zealand"
), count = c(143002, 80316, 33513, 3736, 2180, 1905, 1816, 921,
707, 631, 207, 72, 49)), .Names = c("Countries", "count"), row.names = c(NA,
13L), class = "data.frame")
I tried the below code.
countries = geocode(Countryprofile$Countries)
Countryprofile = cbind(Countryprofile,countries)
mapWorld <- borders("world", colour="grey", fill="lightblue")
q<-ggplot(data = Countryprofile) + mapWorld + geom_point(aes(x=lon, y=lat) ,color="red", size=3)+
geom_text(data = Countryprofile,aes(x=lon,y=lat,label=Countries))
ggplotly(q)
You can change any attribute in the result from ggplotly. In this case you can set the text attribute of the 2nd trace (where you markers are defined).
plotly_map <- ggplotly(q)
plotly_map$x$data[[2]]$text <- paste(Countryprofile$Countries,
Countryprofile$count,
sep='<br />')
plotly_map
library(plotly)
library(ggmap)
Countryprofile <- structure(list(Countries = c("USA", "India", "Europe", "LATAM",
"Singapore", "Phillipines", "Australia", "EMEA", "Malaysia",
"Hongkong", "Philippines", "Thailand", "New Zealand"
), count = c(143002, 80316, 33513, 3736, 2180, 1905, 1816, 921,
707, 631, 207, 72, 49)), .Names = c("Countries", "count"), row.names = c(NA,
13L), class = "data.frame")
countries = geocode(Countryprofile$Countries)
Countryprofile = cbind(Countryprofile,countries)
mapWorld <- borders("world", colour="grey", fill="lightblue")
q<-ggplot(data = Countryprofile) + mapWorld + geom_point(aes(x=lon, y=lat) ,color="red", size=3)+
geom_text(data = Countryprofile,aes(x=lon,y=lat,label=Countries))
plotly_map <- ggplotly(q)
plotly_map$x$data[[2]]$text <- paste(Countryprofile$Countries, Countryprofile$count, sep='<br />')
plotly_map