Shiny and Rcharts y-axis marks? - r

I have the following code working:
library(rCharts)
library(shiny)
X <- data.frame(Var1 = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L),
Var2 = structure(c(1L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control","treatment1", "treatment2"), class = "factor"),
Freq = c(0L,0L, 3L, 2L, 6L, 9L, 13L, 36L, 50L, 497L, 0L, 2L, 1L, 3L, 6L, 4L, 11L, 29L, 50L, 499L, 1L, 2L, 0L, 2L, 5L, 6L, 12L, 22L, 63L,490L)
)
runApp(
list(ui = fluidPage(
titlePanel("Quiz 3 grades distribution"),
fluidRow(
column(3,
#helpText("Select grade in Quiz 1 before the treatment:"),
selectInput("select", label = h3("Grade Quiz 1 before the treatment:"),
choices = list("All" = 0, "Not Perfect" = 1, "Perfect" = 2),
selected = 0)
),
column(9, div(showOutput("histogram","nvd3")), style = 'align:center;')
, tags$head(tags$style(HTML(".nv-axislabel {font: 22px Arial;}"))) # to style labels
)
),
server = shinyServer(
function(input, output, session) {
output$histogram <- renderChart2({
n2 <- nPlot(Freq ~ Var1, group = 'Var2', data = X, type = 'multiBarChart')
n2$params$width <- 500
n2$params$height <- 400
n2$xAxis(axisLabel = "my x axis label")
n2$yAxis(axisLabel = "my y axis label", width = 50)
n2
})
}
)
)
)
How can I change the marks in the y axis to appear, for example, every 25 instead of every 50 counts?
Thanks a lot!

Use tickValues passed as an option to yAxis
library(rCharts)
library(shiny)
X <- data.frame(Var1 = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L),
Var2 = structure(c(1L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control","treatment1", "treatment2"), class = "factor"),
Freq = c(0L,0L, 3L, 2L, 6L, 9L, 13L, 36L, 50L, 497L, 0L, 2L, 1L, 3L, 6L, 4L, 11L, 29L, 50L, 499L, 1L, 2L, 0L, 2L, 5L, 6L, 12L, 22L, 63L,490L)
)
runApp(
list(ui = fluidPage(
titlePanel("Quiz 3 grades distribution"),
fluidRow(
column(3,
#helpText("Select grade in Quiz 1 before the treatment:"),
selectInput("select", label = h3("Grade Quiz 1 before the treatment:"),
choices = list("All" = 0, "Not Perfect" = 1, "Perfect" = 2),
selected = 0)
),
column(9, div(showOutput("histogram","nvd3")), style = 'align:center;')
,tags$head(tags$style(HTML(".nv-axislabel {font: 22px Arial;}")))
)
),
server = shinyServer(
function(input, output, session) {
output$histogram <- renderChart2({
n2 <- nPlot(Freq ~ Var1, group = 'Var2', data = X, type = 'multiBarChart')
n2$params$width <- 500
n2$params$height <- 400
n2$xAxis(axisLabel = "my x axis label")
n2$yAxis(axisLabel = "my y axis label", width = 50)
n2$yAxis(tickValues = do.call(seq, c(as.list(range(X$Freq)), 25)))
n2
})
}
)
)
)

Related

Linking values of stacked barplot with a table in R Shiny

I have created a stacked barplot in the shiny app in R:
library(shiny)
library(ggplot2)
ui = shinyUI(fluidPage(
titlePanel("Competency"),
fluidRow(
column(6,
plotOutput("Competency.Name", click = "plot1_click")
),
column(5,
br(), br(), br(),
htmlOutput("x_value"),
verbatimTextOutput("selected_rows"))),
))
server <- function(input, output) {
report <- structure(list(Competency.Official.Rating = structure(c(1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L
), .Label = c("0", "1", "100", "2", "3"), class = "factor"),
Competency.Name = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L), .Label = c("Agile",
"Co-creating the future", "Collaboration", "Entrepreneurship",
"Feedback", "Impact", "One company", "One voice", "Responsibility",
"Simplification"), class = "factor"), Freq = c(2L, 9L, 308L,
221L, 95L, 7L, 76L, 310L, 191L, 51L, 2L, 12L, 308L, 193L,
120L, 2L, 43L, 310L, 220L, 60L, 2L, 49L, 311L, 211L, 62L,
3L, 58L, 310L, 208L, 56L, 4L, 22L, 312L, 182L, 115L, 3L,
11L, 310L, 196L, 115L, 2L, 9L, 309L, 161L, 154L, 3L, 38L,
309L, 226L, 59L)), class = "data.frame", row.names = c(NA,
-50L))
output$Competency.Name <- renderPlot({
ggplot(report, aes(x = Competency.Name, y = Freq, fill = Competency.Official.Rating, label = Freq)) +
geom_bar(stat = "identity") + # position = fill will give the %; stack will give #of people
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
# Print the name of the x value
output$x_value <- renderText({
if (is.null(input$plot1_click$x)) return("")
else {
lvls <- levels(report$Competency.Name)
name <- lvls[round(input$plot1_click$x)]
HTML("You've selected <code>", name, "</code>",
"<br><br>Here are the first 10 rows that ",
"match that category:")
}
})
# Print the rows of the data frame which match the x value
output$selected_rows <- renderPrint({
if (is.null(input$plot1_click$x)) return()
else {
keeprows <- round(input$plot1_click$x) == as.numeric(report$Competency.Name)
head(report[keeprows, ], 10)
}
})
}
shinyApp(ui, server)
In the app, when I select a column on my barplot it shows the table for the whole bar (the fact that it is a stacked barplot with different values is not taken in account by my code). In the table I would like to see values only for a selected stack. I know in this example it does not male sense but I have a bigger table with more variables and I could use this modification there.
Thank you!
You need to calculate the cumulative sum of your input and then you can compare it to input$plot1_click$y like this:
library(shiny)
library(ggplot2)
library(dplyr)
report <- structure(
list(Competency.Official.Rating =
structure(c(1L, 2L,
3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L,
4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L,
5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L
), .Label = c("0", "1", "100", "2", "3"), class = "factor"),
Competency.Name =
structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L,
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L),
.Label =
c("Agile",
"Co-creating the future", "Collaboration", "Entrepreneurship",
"Feedback", "Impact", "One company", "One voice",
"Responsibility", "Simplification"), class = "factor"),
Freq = c(2L, 9L, 308L,
221L, 95L, 7L, 76L, 310L, 191L, 51L, 2L, 12L, 308L, 193L,
120L, 2L, 43L, 310L, 220L, 60L, 2L, 49L, 311L, 211L, 62L,
3L, 58L, 310L, 208L, 56L, 4L, 22L, 312L, 182L, 115L, 3L,
11L, 310L, 196L, 115L, 2L, 9L, 309L, 161L, 154L, 3L, 38L,
309L, 226L, 59L)), class = "data.frame",
row.names = c(NA,
-50L))
report_stats <- report %>%
arrange(Competency.Name, desc(Competency.Official.Rating)) %>%
group_by(Competency.Name) %>%
mutate(cumsum = cumsum(Freq))
ui = shinyUI(fluidPage(
titlePanel("Competency"),
fluidRow(
column(6,
plotOutput("Competency.Name", click = "plot1_click")
),
column(5,
br(), br(), br(),
htmlOutput("x_value"),
verbatimTextOutput("selected_rows"))),
))
server <- function(input, output) {
x_val <- reactive({
x <- req(input$plot1_click$x)
lvls <- levels(report$Competency.Name)
lvls[round(input$plot1_click$x)]
})
y_val <- reactive({
x <- req(x_val())
y <- req(input$plot1_click$y)
report_stats %>%
filter(Competency.Name == x,
y <= cumsum) %>%
slice(1L) %>%
pull(Competency.Official.Rating)
})
output$Competency.Name <- renderPlot({
ggplot(report, aes(x = Competency.Name, y = Freq,
fill = Competency.Official.Rating, label = Freq)) +
geom_bar(stat = "identity") +
geom_text(size = 3, position = position_stack(vjust = 0.5))
})
# Print the name of the x value
output$x_value <- renderText({
HTML("You've selected <code>", req(x_val()), "</code>",
"<br><br>Here are the first 10 rows that ",
"match that category:")
})
# Print the rows of the data frame which match the x value
output$selected_rows <- renderPrint({
x <- req(x_val())
y <- req(y_val())
head(report[report$Competency.Name == x & report$Competency.Official.Rating == y, ], 10)
})
}
shinyApp(ui, server)

ggarrange() function overvrites the color of my boxplots

I am making two boxplots and want to arrange them beside each other. I have made each of them look like I want when displaying them separately but when I use ggarrange() the colors disappear. This is my code for the plots:
BOX1_data <- read.table(file = "clipboard",
sep = "\t", header=TRUE)
BOX1_data$Diagnosis <- as.factor(BOX1_data$Diagnosis)
BOX1plot <- ggplot(BOX1_data, aes(x=Diagnosis, y=No.Variants, fill= Diagnosis)) + geom_boxplot() +
scale_fill_brewer(palette = "Dark2") +
scale_x_discrete(labels = c("AC\nN=38", "SqCC\nN=15", "SCLC\nN=8", "BL disease\nN=16"))
BOX2_data <- read.table(file = "clipboard",
sep = "\t", header=TRUE)
BOX2_data$Stage <- as.factor(BOX2_data$Stage)
BOX2plot <- ggplot(BOX2_data, aes(x=Stage, y=No.Variants, fill = Stage)) + geom_boxplot(width = 0.4) +
scale_fill_brewer(palette = "Dark2") +
scale_x_discrete(labels = c("Stage I-III\nN=24", "Stage IV\nN=37"))
To arrange the plots I then write:
BOX_list <- list(BOX1plot, BOX2plot)
ggarrange(plotlist = BOX_list, labels = c('A', 'B'), ncol = 2)
The easiest way of getting rid of gridlines etc I thought was by using theme_set() and I think that this might be my problem.
My code is:
theme_set(theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), panel.background = element_blank(),
axis.line = element_line(colour = "grey")))
I realize that theme_bw() overwrites my colors in the boxes. But I have tried removing it, switching it for theme_transparent() (this removes all my labels) and neither works. I have searched for a way of just adding a transparency to my boxes in the theme so that my colors will shine through. I am also suspicious that maybe the palette that I chose might give me the same colors in the two plots which I also do not want. To add, if it matters, I have 4 groups in the first plot and 2 in the second.
dput(BOX1_data)
structure(list(Diagnosis = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L), .Label = c("1", "2", "3", "4"), class = "factor"),
No.Variants = c(3L, 4L, 6L, 14L, 3L, 3L, 4L, 3L, 3L, 3L,
8L, 6L, 22L, 10L, 6L, 9L, 1L, 9L, 3L, 4L, 8L, 2L, 13L, 3L,
11L, 19L, 5L, 5L, 3L, 12L, 4L, 2L, 4L, 18L, 8L, 7L, 7L, 12L,
4L, 1L, 6L, 3L, 2L, 8L, 10L, 3L, 15L, 9L, 13L, 13L, 15L,
10L, 10L, 12L, 6L, 3L, 12L, 9L, 15L, 10L, 18L, 3L, 6L, 3L,
6L, 1L, 3L, 3L, 7L, 1L, 2L, 10L, 7L, 7L, 1L, 0L, 2L)), row.names = c(NA,
-77L), class = "data.frame")
dput(BOX2_data)
structure(list(No.Variants = c(3L, 4L, 6L, 14L, 3L, 3L, 4L, 3L,
3L, 3L, 8L, 6L, 22L, 10L, 6L, 9L, 1L, 9L, 3L, 4L, 8L, 2L, 13L,
3L, 11L, 19L, 5L, 5L, 3L, 12L, 4L, 2L, 4L, 18L, 8L, 7L, 7L, 12L,
4L, 1L, 6L, 3L, 2L, 8L, 10L, 3L, 15L, 9L, 13L, 13L, 15L, 10L,
10L, 12L, 6L, 3L, 12L, 9L, 15L, 10L, 18L), Stage = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("1",
"2"), class = "factor")), row.names = c(NA, -61L), class = "data.frame")
Grateful for any tips!
As already pointed out, it seems the OP's issue with theme_set() removing the fill colors set in your two plots was solved by updating to a new version of ggplot2. Herein, I have a solution for the second part of OP's question (that was clarified in the comments). Represented here for convenience:
Now it is just the problem that I want the palette to continue on the second plot's boxes and not restart so that I will get different colors on all boxes.
In order to do this, one has to realize that there are 4 fill colors for the first plot BOX1plot, and 2 fill colors for BOX2plot. For BOX1plot, we want the color palette to begin at the first color, but for BOX2plot, we want the palette to start on the 5th color sequence in the palette. There's no way to do this through the scale_*_brewer() functions, so the approach here will be to access the Brewer palette from RcolorBrewer::brewer.pal(), and then assign where to begin and end in that sequence based on the number of levels of each factor using scale_fill_manual() to just set the color values from the extracted Brewer color palette.
You can just "know" that you need to "use colors 1-4" for BOX1plot and "use color 5 and 6" for BOX2plot; however, it is much more elegant to just calculate this automatically based on the number of levels (in case you want to run this again). The code below does this:
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
# ... read in your data as before
# create factors (as OP did before)
BOX1_data$Diagnosis <- as.factor(BOX1_data$Diagnosis)
BOX2_data$Stage <- as.factor(BOX2_data$Stage)
# make color palette based on Brewer "Dark2" palette
lev_diag <- length(levels(BOX1_data$Diagnosis))
lev_stage <- length(levels(BOX2_data$Stage))
lev_total <- lev_diag + lev_stage
my_colors <- brewer.pal(lev_total, "Dark2")
BOX1plot <- ggplot(BOX1_data, aes(x=Diagnosis, y=No.Variants, fill= Diagnosis)) + geom_boxplot() +
scale_fill_manual(values=my_colors[1:lev_diag]) +
scale_x_discrete(labels = c("AC\nN=38", "SqCC\nN=15", "SCLC\nN=8", "BL disease\nN=16"))
BOX2plot <- ggplot(BOX2_data, aes(x=Stage, y=No.Variants, fill = Stage)) + geom_boxplot(width = 0.4) +
scale_fill_manual(values = my_colors[(lev_diag+1):lev_total]) +
scale_x_discrete(labels = c("Stage I-III\nN=24", "Stage IV\nN=37"))
BOX_list <- list(BOX1plot, BOX2plot)
ggarrange(plotlist = BOX_list, labels = c('A', 'B'), ncol = 2)
If you have issues with ggarrange() I would suggest next approach using patchwork:
library(ggplot2)
library(patchwork)
#Data format
BOX1_data$Diagnosis <- as.factor(BOX1_data$Diagnosis)
#Plot 1
BOX1plot <- ggplot(BOX1_data, aes(x=Diagnosis, y=No.Variants, fill= Diagnosis)) + geom_boxplot() +
scale_fill_brewer(palette = "Dark2") +
scale_x_discrete(labels = c("AC\nN=38", "SqCC\nN=15", "SCLC\nN=8", "BL disease\nN=16"))
#Data format
BOX2_data$Stage <- as.factor(BOX2_data$Stage)
#Plot 2
BOX2plot <- ggplot(BOX2_data, aes(x=Stage, y=No.Variants, fill = Stage)) + geom_boxplot(width = 0.4) +
scale_fill_brewer(palette = "Dark2") +
scale_x_discrete(labels = c("Stage I-III\nN=24", "Stage IV\nN=37"))
#Arrange plots
BOX1plot+BOX2plot+plot_annotation(tag_levels = 'A')
The output:

Merge and edit multiple legends when facets and geom_line are plotted separately

Following this guide I have plotted the following graph using the following code. I did split my dataset into one that contains the data that goes in all plots 'control', and the rest 'dfnocontrol'.
ggplot(dfnocontrol,aes(y=value,x=year)) + geom_line(data=dfnocontrol,
aes(color=survivorship),size=1.5) + facet_wrap(~density,nrow=2) +
geom_line(data=dfcontrol,aes(linetype=simulname),color='grey',size=1.5)
I have tried many ways to have only one legend, or to edit the existing two legend but nothing seems to work. scale_fill_manual() seems to be ignored, even though I don't get any error message. I was forced to use linetype to make the 'control' appear in the legend. How can I merge these two legends?
edit: these are the data for control
structure(list(year = 1:2, psize = structure(c(6L, 6L), .Label = c("all plants",
"all plants no-seedl", "seedlings", "SmallerT10", "SmallerT10 no-seedl",
"LargerT10", "10-30", "30-50", "50+"), class = "factor"), value = c(392.884450281975,
392.76842677951), simulname = structure(c(1L, 1L), .Label = c("control",
"d02s70", "d02s80", "d02s90", "d05s70", "d05s80", "d05s90", "d07s70",
"d07s80", "d07s90", "d1s70", "d1s80", "d1s90", "d2s70", "d2s80",
"d2s90", "d3s70", "d3s80", "d3s90", "d4s70", "d4s80", "d4s90",
"d5s70", "d5s80", "d5s90"), class = "factor"), survivorship = structure(c(1L,
1L), .Label = c("control", "s70", "s80", "s90"), class = "factor")), .Names = c("year",
"psize", "value", "simulname", "survivorship"), row.names = 2501:2502, class = "data.frame")
and data for the rest
structure(list(year = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L), psize = structure(c(6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("all plants",
"all plants no-seedl", "seedlings", "SmallerT10", "SmallerT10 no-seedl",
"LargerT10", "10-30", "30-50", "50+"), class = "factor"), value = c(391.933827876557,
390.784233661738, 391.931768654094, 390.777949423224, 391.930831801103,
390.775125884957, 391.904131913644, 390.671681105517, 391.903377880798,
390.669377819171, 391.902842713777, 390.667498067697, 391.874743014214,
390.557893743236, 391.874006362415, 390.555639401299, 391.8735511448,
390.554149478021, 391.84367266143, 390.443618794749, 391.843064602404,
390.442149462261, 391.842594963982, 390.440725187945, 391.72267802326,
388.998242801555, 391.722309813432, 388.996838950063, 391.721745089041,
388.995715149179, 384.967818982887, 383.215849576989, 384.967407490871,
383.214728664341, 384.96689031843, 383.213390281481, 391.897592532656,
389.445606459513, 391.897234485415, 389.444632515097, 391.89681267375,
389.443358475326, 391.402389493961, 388.987279260992, 391.401979078947,
388.985920091544, 391.401583421483, 388.984891027315), simulname = structure(c(2L,
2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L,
10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L,
17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L,
23L, 24L, 24L, 25L, 25L), .Label = c("control", "d02s70", "d02s80",
"d02s90", "d05s70", "d05s80", "d05s90", "d07s70", "d07s80", "d07s90",
"d1s70", "d1s80", "d1s90", "d2s70", "d2s80", "d2s90", "d3s70",
"d3s80", "d3s90", "d4s70", "d4s80", "d4s90", "d5s70", "d5s80",
"d5s90"), class = "factor"), density = structure(c(2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("control",
"d02", "d05", "d07", "d1", "d2", "d3", "d4", "d5"), class = "factor"),
survivorship = structure(c(2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L,
3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L,
4L, 2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L,
3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L, 4L), .Label = c("control",
"s70", "s80", "s90"), class = "factor")), .Names = c("year",
"psize", "value", "simulname", "density", "survivorship"), row.names = c(6081L,
6082L, 9845L, 9846L, 14345L, 14346L, 17985L, 17986L, 21797L,
21798L, 26297L, 26298L, 30567L, 30568L, 34528L, 34529L, 38744L,
38745L, 43144L, 43145L, 47519L, 47520L, 51983L, 51984L, 56483L,
56484L, 60983L, 60984L, 65483L, 65484L, 69983L, 69984L, 74483L,
74484L, 78983L, 78984L, 83483L, 83484L, 87983L, 87984L, 92483L,
92484L, 96983L, 96984L, 101483L, 101484L, 105983L, 105984L), class = "data.frame")
Since you provided no data, I will give you an example using the economics data set.
library(wesanderson) # for the colours
library(tidyverse)
data("economics")
We will need two data sets for this task. Variable unemploy will serve as our 'control' (6th column). All variables will be scaled.
First data set:
economics_gathered <- economics[, 1:5] %>% # exclude unemploy
modify_if(is.numeric, scale) %>%
gather(key, value, -date)
Second data set:
economics_control <- economics[, c(1, 6)] %>%
dplyr::rename(control = unemploy) %>%
gather(some_other_key, value, 2) %>%
mutate(value = scale(value))
Now we can plot:
ggplot() +
geom_line(data = economics_control, aes(x = date, y = value, col = some_other_key)) +
geom_line(data = economics_gathered, aes(date, value, col = key)) +
scale_colour_manual(values = c("grey", wes_palette("GrandBudapest"))) +
facet_wrap(~key, scales = "free_y")
To which the result is the plot below.
EDIT
With the data provided by the OP the following code
ggplot() +
geom_line(data = dfcontrol, aes(year, value, col = survivorship), size = 1.5) +
geom_line(data = dfnocontrol, aes(year, value, col = survivorship), size = 1.5) +
facet_wrap( ~ density, nrow = 2) +
scale_colour_manual(values = c("grey", "forestgreen", "red", "blue"))
gives this plot:
DATA
1)
dfcontrol <- structure(list(year = 1:2, psize = structure(c(6L, 6L), .Label = c("all plants",
"all plants no-seedl", "seedlings", "SmallerT10", "SmallerT10 no-seedl",
"LargerT10", "10-30", "30-50", "50+"), class = "factor"), value = c(392.884450281975,
392.76842677951), simulname = structure(c(1L, 1L), .Label = c("control",
"d02s70", "d02s80", "d02s90", "d05s70", "d05s80", "d05s90", "d07s70",
"d07s80", "d07s90", "d1s70", "d1s80", "d1s90", "d2s70", "d2s80",
"d2s90", "d3s70", "d3s80", "d3s90", "d4s70", "d4s80", "d4s90",
"d5s70", "d5s80", "d5s90"), class = "factor"), survivorship = structure(c(1L,
1L), .Label = c("control", "s70", "s80", "s90"), class = "factor")), .Names = c("year",
"psize", "value", "simulname", "survivorship"), row.names = 2501:2502, class = "data.frame")
2)
dfnocontrol <- structure(list(year = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L), psize = structure(c(6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L), .Label = c("all plants",
"all plants no-seedl", "seedlings", "SmallerT10", "SmallerT10 no-seedl",
"LargerT10", "10-30", "30-50", "50+"), class = "factor"), value = c(391.933827876557,
390.784233661738, 391.931768654094, 390.777949423224, 391.930831801103,
390.775125884957, 391.904131913644, 390.671681105517, 391.903377880798,
390.669377819171, 391.902842713777, 390.667498067697, 391.874743014214,
390.557893743236, 391.874006362415, 390.555639401299, 391.8735511448,
390.554149478021, 391.84367266143, 390.443618794749, 391.843064602404,
390.442149462261, 391.842594963982, 390.440725187945, 391.72267802326,
388.998242801555, 391.722309813432, 388.996838950063, 391.721745089041,
388.995715149179, 384.967818982887, 383.215849576989, 384.967407490871,
383.214728664341, 384.96689031843, 383.213390281481, 391.897592532656,
389.445606459513, 391.897234485415, 389.444632515097, 391.89681267375,
389.443358475326, 391.402389493961, 388.987279260992, 391.401979078947,
388.985920091544, 391.401583421483, 388.984891027315), simulname = structure(c(2L,
2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L,
10L, 11L, 11L, 12L, 12L, 13L, 13L, 14L, 14L, 15L, 15L, 16L, 16L,
17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L,
23L, 24L, 24L, 25L, 25L), .Label = c("control", "d02s70", "d02s80",
"d02s90", "d05s70", "d05s80", "d05s90", "d07s70", "d07s80", "d07s90",
"d1s70", "d1s80", "d1s90", "d2s70", "d2s80", "d2s90", "d3s70",
"d3s80", "d3s90", "d4s70", "d4s80", "d4s90", "d5s70", "d5s80",
"d5s90"), class = "factor"), density = structure(c(2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L), .Label = c("control",
"d02", "d05", "d07", "d1", "d2", "d3", "d4", "d5"), class = "factor"),
survivorship = structure(c(2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L,
3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L,
4L, 2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L, 4L, 2L, 2L,
3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 4L, 4L), .Label = c("control",
"s70", "s80", "s90"), class = "factor")), .Names = c("year",
"psize", "value", "simulname", "density", "survivorship"), row.names = c(6081L,
6082L, 9845L, 9846L, 14345L, 14346L, 17985L, 17986L, 21797L,
21798L, 26297L, 26298L, 30567L, 30568L, 34528L, 34529L, 38744L,
38745L, 43144L, 43145L, 47519L, 47520L, 51983L, 51984L, 56483L,
56484L, 60983L, 60984L, 65483L, 65484L, 69983L, 69984L, 74483L,
74484L, 78983L, 78984L, 83483L, 83484L, 87983L, 87984L, 92483L,
92484L, 96983L, 96984L, 101483L, 101484L, 105983L, 105984L), class = "data.frame")

R shiny app with rCharts

I'm able to create this graph with rCharts:
library(rCharts)
X <- structure(list(Var1 = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10"), class = "factor"), Var2 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control",
"treatment1", "treatment2"), class = "factor"), Freq = c(0L,
0L, 3L, 2L, 6L, 9L, 13L, 36L, 50L, 497L, 0L, 2L, 1L, 3L, 6L,
4L, 11L, 29L, 50L, 499L, 1L, 2L, 0L, 2L, 5L, 6L, 12L, 22L, 63L,
490L)), .Names = c("Var1", "Var2", "Freq"), row.names = c(NA,
-30L), class = "data.frame")
n1<-nPlot(Freq ~ Var1, group = 'Var2', data = X, type = 'multiBarChart')
print(n1)
Now I'm trying to embeded in a Shiny app. I can do a shiny app with ggplot2, but I'm not sure how to print the rCharts graph.
This is the shiny code that I have right now:
#server.R
library(rCharts)
X <- structure(list(Var1 = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), .Label = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10"), class = "factor"), Var2 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control",
"treatment1", "treatment2"), class = "factor"), Freq = c(0L,
0L, 3L, 2L, 6L, 9L, 13L, 36L, 50L, 497L, 0L, 2L, 1L, 3L, 6L,
4L, 11L, 29L, 50L, 499L, 1L, 2L, 0L, 2L, 5L, 6L, 12L, 22L, 63L,
490L)), .Names = c("Var1", "Var2", "Freq"), row.names = c(NA,
-30L), class = "data.frame")
shinyServer(
function(input, output) {
output$histogram <- renderPlot({
# You can access the value of the widget with input$select, e.g.
output$value <- renderPrint({ input$select })
n2 <- nPlot(Freq ~ Var1, group = 'Var2', data = X, type = 'multiBarChart')
n2$set(dom = "histogram")
return(n2)
})
}
)
#ui.R
shinyUI(fluidPage(
titlePanel("Quiz 3 grades distribution"),
sidebarLayout(
sidebarPanel(
helpText("Quiz 3 grade distribution by treatment group"),
selectInput("select", label = h3("Select box"),
choices = list("All" = 0, "Not Perfect" = 1, "Perfect" = 2),
selected = 0)
),
mainPanel(plotOutput("histogram"))
)
))
What am I doing wrong? Thanks!
Use renderChart2 and showOutput to display nvd3 plots in shiny. Using renderChart2 doesn't require the using $set(dom = ....
library(rCharts)
library(shiny)
X <- data.frame(Var1 = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L),
Var2 = structure(c(1L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control","treatment1", "treatment2"), class = "factor"),
Freq = c(0L,0L, 3L, 2L, 6L, 9L, 13L, 36L, 50L, 497L, 0L, 2L, 1L, 3L, 6L, 4L, 11L, 29L, 50L, 499L, 1L, 2L, 0L, 2L, 5L, 6L, 12L, 22L, 63L,490L)
)
runApp(
list(ui = fluidPage(
titlePanel("Quiz 3 grades distribution"),
sidebarLayout(
sidebarPanel(
helpText("Quiz 3 grade distribution by treatment group"),
selectInput("select", label = h3("Select box"),
choices = list("All" = 0, "Not Perfect" = 1, "Perfect" = 2),
selected = 0)
),
mainPanel(
showOutput("histogram","Nvd3")
)
)
),
server = shinyServer(
function(input, output, session) {
output$histogram <- renderChart2({
n2 <- nPlot(Freq ~ Var1, group = 'Var2', data = X, type = 'multiBarChart')
n2
})
}
)
)
)

x and y labels with shiny and rCharts

I'm almost done creating the shiny app that I want.
How can I add labels to the x and y axis of my graph?
This is what I have right now:
library(rCharts)
library(shiny)
X <- data.frame(Var1 = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L),
Var2 = structure(c(1L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control","treatment1", "treatment2"), class = "factor"),
Freq = c(0L,0L, 3L, 2L, 6L, 9L, 13L, 36L, 50L, 497L, 0L, 2L, 1L, 3L, 6L, 4L, 11L, 29L, 50L, 499L, 1L, 2L, 0L, 2L, 5L, 6L, 12L, 22L, 63L,490L)
)
runApp(
list(ui = fluidPage(
titlePanel("Quiz 3 grades distribution"),
fluidRow(
column(3,
#helpText("Select grade in Quiz 1 before the treatment:"),
selectInput("select", label = h3("Grade Quiz 1 before the treatment:"),
choices = list("All" = 0, "Not Perfect" = 1, "Perfect" = 2),
selected = 0)
),
column(9, div(showOutput("histogram","nvd3")), style = 'align:center;')
)
),
server = shinyServer(
function(input, output, session) {
output$histogram <- renderChart2({
n2 <- nPlot(Freq ~ Var1, group = 'Var2', data = X, type = 'multiBarChart')
n2$params$width <- 500
n2$params$height <- 400
n2
})
}
)
)
)
Thanks!
The nPlot object has a xAxis and yAxis method which take an option axisLabel. You may need to adjust the width on the yaxis.
library(rCharts)
library(shiny)
X <- data.frame(Var1 = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L,3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L),
Var2 = structure(c(1L,1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("control","treatment1", "treatment2"), class = "factor"),
Freq = c(0L,0L, 3L, 2L, 6L, 9L, 13L, 36L, 50L, 497L, 0L, 2L, 1L, 3L, 6L, 4L, 11L, 29L, 50L, 499L, 1L, 2L, 0L, 2L, 5L, 6L, 12L, 22L, 63L,490L)
)
runApp(
list(ui = fluidPage(
titlePanel("Quiz 3 grades distribution"),
fluidRow(
column(3,
#helpText("Select grade in Quiz 1 before the treatment:"),
selectInput("select", label = h3("Grade Quiz 1 before the treatment:"),
choices = list("All" = 0, "Not Perfect" = 1, "Perfect" = 2),
selected = 0)
),
column(9, div(showOutput("histogram","nvd3")), style = 'align:center;')
, tags$head(tags$style(HTML(".nv-axislabel {font: 22px Arial;}"))) # to style labels
)
),
server = shinyServer(
function(input, output, session) {
output$histogram <- renderChart2({
n2 <- nPlot(Freq ~ Var1, group = 'Var2', data = X, type = 'multiBarChart')
n2$params$width <- 500
n2$params$height <- 400
n2$xAxis(axisLabel = "my x axis label")
n2$yAxis(axisLabel = "my y axis label", width = 50)
n2
})
}
)
)
)

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