I am trying to convert ggplot code which is repetitive into function i have done almost but only concern is i need to pass the application as a variable

Here is the plot code where i used column called Application inside the summarise and mutate. The challenge is same column has been used for y axis

    ggplot_common_function <- function(data,x,y,z){
  Removestring(ggplotly(
   data %>%
      group_by(m_year,status) %>%
      summarise(Applications = sum(Applications)) %>%
      mutate(total_sum = sum(Applications))%>% ggplot(mapping = aes({{x}},{{y}},text = paste(total_sum)))  
      geom_col(aes(fill = {{z}})) theme_classic() 
      theme(axis.line.y = element_blank(),axis.ticks = element_blank(),legend.position = "bottom") 
      labs(x="", y="Agreements Values (In Lakhs)", fill="") 
      theme(axis.title.y =element_text(size=8)) 
      scale_fill_manual(values=c("#1F7A3F","#70B821")) 
      scale_y_continuous(labels = function(x) format(x, scientific = FALSE),expand = expansion(mult = c(0,.3)),breaks = integer_breaks()),tooltip = c("text"))%>% 
      layout(legend = list(orientation = "h", x = 0.1, y = -0.2,font=list( family='Arial', size=10, color='black')),xaxis = x_labels,yaxis = y_labels)%>%
      config(displaylogo = FALSE,modeBarButtonsToRemove = list('sendDataToCloud', 'autoScale2d', 'resetScale2d', 'toggleSpikelines','hoverClosestCartesian', 'hoverCompareCartesian','zoom2d','pan2d','select2d','lasso2d','zoomIn2d','zoomOut2d')))
}
                  

ggplot_common_function(data,m_year,Applications,status)

To run the above code there is some pre function

integer_breaks <- function(n = 5, ...) {
  fxn <- function(x) {
    breaks <- floor(pretty(x, n, ...))
    names(breaks) <- attr(breaks, "labels")
    breaks
  }
  return(fxn)
}

Removestring = function(d){
  for (i in 1:length(d$x$data)){
    if (!is.null(d$x$data[[i]]$name)){
      d$x$data[[i]]$name =  gsub("\\(","",str_split(d$x$data[[i]]$name,",")[[1]][1])
    }
  }
  return(d)
}

Plan <- "#288D55"
multicolor = c("#135391","#0098DB","#828388","#231F20","#C41330","#698714","#162FBF","#F36717","#BD00FF")
x_labels = list(tickangle = -45,tickfont = list(family = "Arial",size = 10,color = "black",face="bold"))
y_labels = list(tickfont = list(family = "Arial",size = 10,color = "black",face="bold"))

Any suggestions would be appreciated

CodePudding user response：

This is basically a question related to programming in dplyr. To achieve your desired result and get rid of hardcoding the column names in your function and use x, y, z instead you could make use of the {{ curly-curly operator as you did in the ggplot code and the special assignment operator :=. Additionally instead of wrapping all your code inside ggplotly you proceed in steps. Do the data wrangling, make your ggplot and finally pass it to ggplotly:

library(plotly)
library(dplyr)
library(stringr)

ggplot_common_function <- function(data, x, y, z) {
  data <- data %>%
    group_by({{ x }}, {{ z }}) %>%
    summarise({{ y }} := sum({{ y }})) %>%
    mutate(total_sum = sum({{ y }}))

  p <- ggplot(data, mapping = aes({{ x }}, {{ y }}, text = paste(total_sum)))  
    geom_col(aes(fill = {{ z }}))  
    theme_classic()  
    theme(axis.line.y = element_blank(), axis.ticks = element_blank(), legend.position = "bottom")  
    labs(x = "", y = "Agreements Values (In Lakhs)", fill = "")  
    theme(axis.title.y = element_text(size = 8))  
    scale_fill_manual(values = c("#1F7A3F", "#70B821"))  
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE), expand = expansion(mult = c(0, .3)), breaks = integer_breaks())

  ggp <- ggplotly(p, tooltip = c("text")) %>%
    layout(legend = list(orientation = "h", x = 0.1, y = -0.2, font = list(family = "Arial", size = 10, color = "black")), xaxis = x_labels, yaxis = y_labels) %>%
    config(displaylogo = FALSE, modeBarButtonsToRemove = list("sendDataToCloud", "autoScale2d", "resetScale2d", "toggleSpikelines", "hoverClosestCartesian", "hoverCompareCartesian", "zoom2d", "pan2d", "select2d", "lasso2d", "zoomIn2d", "zoomOut2d"))

  Removestring(ggp)
}

ggplot_common_function(data, m_year, Applications, status)
#> `summarise()` has grouped output by 'm_year'. You can override using the
#> `.groups` argument.