Update a Ggplot Using a for Loop (R)

Update a ggplot using a for loop (R)

I was looking at some contributions that some people left me and there is one who solves it pretty efficiently and it is to use aes_() instead of aes(). The difference is that aes_() forces to evaluate and update the plot, while aes() only evaluates the indexes when the plot is drawn. Hence: it never updates while it is inside a for loop.

library(ggplot2)

x <- ggplot(mapping = aes(x = 1:100, y = 1:100)) +
  geom_line()

for(i in 1:6){
  x <- x + geom_vline(aes_(xintercept = i*5))
}

ggplot does not work if it is inside a for loop although it works outside of it

When in a for loop, you have to explicitly print your resulting ggplot object :

for (i in 1:5) { 
    print(ggplot(df,aes(x,y))+geom_point()) 
}

how to add layers in ggplot using a for-loop

One approach would be to reshape your data frame from wide format to long format using function melt() from library reshape2. In new data frame you will have x1 values, variable that determine from which column data came, and value that contains all original y values.

Now you can plot all data with one ggplot() and geom_line() call and use variable to have for example separate color for each line.

 library(reshape2)
 df.long<-melt(df,id.vars="x1")
 head(df.long)
  x1 variable value
1  1       y1   2.0
2  2       y1   5.4
3  3       y1   7.1
4  4       y1   4.6
5  5       y1   5.0
6  1       y2   0.4
 ggplot(df.long,aes(x1,value,color=variable))+geom_line()

If you really want to use for() loop (not the best way) then you should use names(df)[-1] instead of seq(). This will make vector of column names (except first column). Then inside geom_line() use aes_string(y=i) to select column by their name.

plotAllLayers<-function(df){
  p<-ggplot(data=df,aes(df[,1]))
  for(i in names(df)[-1]){ 
    p<-p+geom_line(aes_string(y=i))
  }
  return(p)
}

plotAllLayers(df)

How to generate multiple ggplots using a for loop

Try with this:

library(ggplot2)
#Function does not return graph
for (i in list){
  var <- sym(i)
  print(ggplot(data = test_df, aes(x= DateTime.lub, y = !!var))+
    geom_line(aes(colour = Step))+
    ggtitle(paste0('plot_',i)))
}

Update the data for ggplot2 line graph using the loop

Here is an example how to setup the desired behavior with shiny and plotly:

library(shiny)
library(plotly)
library(pingr) 

a simple ui with start button and plot area:

ui <- fluidPage(
  div(actionButton("button", "start")),
  div(plotlyOutput("plot"), id='graph')
)

server <- function(input, output, session) {
  p <- plot_ly(
    y = ping("8.8.8.8",count = 1),
    type = 'scatter',
    mode = 'lines') 
  output$plot <- renderPlotly(p)
  observeEvent(input$button, {
    while(TRUE){
      Sys.sleep(1)
      plotlyProxy("plot", session) %>%
        plotlyProxyInvoke("extendTraces", list(y=list(list(ping("8.8.8.8",count = 1)))), list(0))
    }
  })
}

shinyApp(ui, server)

after some time it looks like this:

EDIT: answer to the question in comment:
There are various ways to control the number of pings. Perhaps the simplest:

server <- function(input, output, session) {
  p <- plot_ly(
    y = ping("8.8.8.8",count = 1),
    type = 'scatter',
    mode = 'lines') 
  a = 1
  output$plot <- renderPlotly(p)
  observeEvent(input$button, {
    while(a <= 30){
      a <- a + 1
      Sys.sleep(1)
      plotlyProxy("plot", session) %>%
        plotlyProxyInvoke("extendTraces", list(y=list(list(ping("8.8.8.8",count = 1)))), list(0))
    }
  })
}

Here 30 pings are performed

To change the frequency of pings change Sys.sleep(1) to your linking.

Using for loop to create multiple graphs with ggplot in R

This makes a plot for each "Criteria"

library(tidyverse)

sample_df <- data.frame(
  stringsAsFactors = FALSE,
  check.names = FALSE,
          Criteria = c("A", "B", "C", "D", "E", "F"),
          `Person A` = c(10L, 50L, 10L, 15L, 40L, 12L),
          `Person B` = c(15L, 55L, 2L, 18L, 25L, 35L),
          `Person C` = c(12L, 40L, 5L, 22L, 18L, 10L),
          `Person D` = c(11L, 37L, 3L, 30L, 32L, 12L)
)

sample_df %>% 
    pivot_longer(cols = -Criteria,
                 names_to = "person",
                 names_prefix = "Person\\s",
                 values_to = "n") %>% 
    group_nest(Criteria) %>% 
    mutate(plot = map(data, ~ggplot(.x, aes(x = person, y = n)) + geom_col())) %>% 
    pull(plot)
#> [[1]]

#> 
#> [[2]]

#> 
#> [[3]]

#> 
#> [[4]]

#> 
#> [[5]]

#> 
#> [[6]]

^{Created on 2022-02-11 by the reprex package (v2.0.1)}

adding layers in ggplot2 with for loops

ggplot does a bit of lazy evaluation, so in your for loop example i isn't evaluated immediately. If we look at the layers, we can see the i is still there as i, not as 3 and 4 in the respective iterations. When you print the plot, that is when i is evaluated--at whatever value it takes when you print the plot. When can even change i after the loop to cause problems:

aux <- 3:4
p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()

for (i in 1:2) {
  p <-  p + geom_segment(aes(x = aux[i], y = 0, xend = aux[i], yend = 35), colour = "purple")
}
  
p$layers
# [[1]]
# geom_point: na.rm = FALSE
# stat_identity: na.rm = FALSE
# position_identity 
# 
# [[2]]
# mapping: x = ~aux[i], y = 0, xend = ~aux[i], yend = 35 
# geom_segment: arrow = NULL, arrow.fill = NULL, lineend = butt, linejoin = round, na.rm = FALSE
# stat_identity: na.rm = FALSE
# position_identity 
# 
# [[3]]
# mapping: x = ~aux[i], y = 0, xend = ~aux[i], yend = 35 
# geom_segment: arrow = NULL, arrow.fill = NULL, lineend = butt, linejoin = round, na.rm = FALSE
# stat_identity: na.rm = FALSE
# position_identity 

## changing `i` later can still cause problems:
i = 5
print(p)
# Warning messages:
# 1: Removed 32 rows containing missing values (geom_segment). 
# 2: Removed 32 rows containing missing values (geom_segment). 

So, no, you can't really use for loops quite like that. There are probably workarounds, but this feels like an XY problem - this isn't how ggplot is intended to be used, so using it this way will be difficult.

It's hard to know what your real use case is, but in this case we could put aux data in a data frame and do it like this (ggplot is made to work with data frames):

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_segment(
    data = data.frame(aux),
    aes(x = aux, xend = aux),
    y = 0, yend = 35, colour = "purple"
  )

Though, for the the special case of vertical lines, we can use geom_vline with the aux vector directly:

ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  geom_vline(xintercept = aux, colour = "purple")

using a custom ggplot function in a loop of variables from a list

Unless for some reason you want to be able to pass unquoted parameters, I would change the function so it only admits strings and use the .data[[]] pronoun which is the recommended way in the last versions of dplyr and ggplot. Its purpose is obvious to read when compared with quotes and bangs !! (Although someone may argue the opposite).

library(ggplot2)

gg_hist <- function(data, x){
  ggplot(data,
         aes(x = .data[[x]] )) + 
    geom_histogram()
}

gg_hist(mtcars, "hp") # Note now I use "hp" and not hp

vars <- c("mpg","hp")

for (variable in vars) {
  p <- gg_hist(mtcars, variable)
  print(p)
}

The main source for this would be the dplyr official documentation. It is true that is not a gpplot2 source but most of what is explained there is applicable. Other source for ggplot2 especifically is this rconf2020 slides. Hope this helps.

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