How to Combine Ggplot and Dplyr into a Function

How to combine ggplot and dplyr into a function?

ggplot does not yet support tidy eval syntax (you can't use the !!). You need to use more traditional standard evaluation calls. You can use aes_q in ggplot to help with this.

get_charts1 <- function(data, mygroup){

  quo_var <- enquo(mygroup)

  df_agg <- data %>% 
    group_by(!!quo_var) %>% 
    summarize(mean = mean(value, na.rm = TRUE),
              count = n()) %>% 
    ungroup()

  ggplot(df_agg, aes_q(x = quote(count), y = quote(mean), color = quo_var, group = quo_var)) + 
    geom_point() +
    geom_line() 
}

get_charts1(dataframe, group)

Programming with ggplot2 and dplyr

ggplot2 v3.0.0 released in July 2018 supports !! (bang bang), !!!, and :=.

facet_wrap() and facet_grid() support vars() inputs. The first two arguments of facet_grid() become rows and cols. facet_grid(vars(cyl), vars(am, vs)) is equivalent to facet_grid(cyl ~ am + vs) and facet_grid(cols = vars(am, vs)) is equivalent to facet_grid(. ~ am + vs).

So your example can be modified as follow:

library(rlang)
library(tidyverse)

foo <- function(df, y, gr, t=4) {
  y <- enquo(y)
  gr <- enquo(gr)

  df %>% 
    filter(!!y > t) %>% 
    ggplot(aes(!!y)) + 
    geom_histogram() +  
    facet_grid(cols = vars(!!gr))
}

foo(mtcars, y= cyl, gr= vs)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Created on 2018-04-04 by the reprex package (v0.2.0).

dplyr and ggplot in a function: use reorder in aes function

If you are going to use aes_string, then the whole value must be a string, not just partially a string. You can use paste() to help build the expression you want to use for x. For example

f <-  function(the_data, the_column){
       dat %>% group_by_(the_column) %>%  
       tally(sort = TRUE) %>% 
       ggplot(aes_string(x = paste0("reorder(",the_column,", n)"), y = 'n')) +
       geom_bar(stat = "identity")
}

Or you could use expressions rather than strings

f <-  function(the_data, the_column){
       dat %>% group_by_(the_column) %>%  
       tally(sort = TRUE) %>% 
       ggplot(aes_q(x = substitute(reorder(x, n),list(x=as.name(the_column))), y = quote(n))) +
       geom_bar(stat = "identity")
}

but the general idea is that you need to be careful when mixing strings and raw language elements (like names or expressions).

R: ggplot: Combine geom_points using dplyr with group averages

Is this what you want?

library(tidyverse)        

theme_set(theme_bw())

# Mean per Species
plt1 <- iris %>% 
  filter(Species %in% c("setosa", "virginica")) %>% 
  group_by(Species) %>% 
  mutate(mean_length = mean(Petal.Length, na.rm = TRUE),
         mean_width  = mean(Petal.Width,  na.rm = TRUE)) %>% 
  ungroup() %>% 
  ggplot(., aes(x=Petal.Width, y=Petal.Length, color=Species)) + 
  geom_point() +
  facet_grid(~ Species, scales = "free_x") +
  geom_hline(aes(yintercept = mean_length, linetype = c("Mean length"))) +
  geom_vline(aes(xintercept = mean_width,  linetype = c("Mean width")), 
             show.legend = FALSE) 

plt1 + scale_linetype_manual(NULL, 
                              values = c(5, 3),
                              labels = c("Mean length", "Mean width")) +
  guides(color = guide_legend(order = 1)) +
  # Move legends closer to each other 
  theme(
        legend.spacing.y = unit(0.05, "cm"),
        legend.margin = margin(0, 0, 0, 0),
        legend.box.margin = margin(0, 0, 0, 0))

# Mean for all Species
plt2 <- iris %>% 
  filter(Species %in% c("setosa", "virginica")) %>% 
  mutate(mean_length = mean(Petal.Length, na.rm = TRUE),
         mean_width  = mean(Petal.Width,  na.rm = TRUE)) %>% 
  ggplot(., aes(x=Petal.Width, y=Petal.Length, color=Species)) + 
  geom_point() +
  geom_hline(aes(yintercept = mean_length, linetype = c("Mean length"))) +
  geom_vline(aes(xintercept = mean_width,  linetype = c("Mean width")), 
             show.legend = FALSE) 

plt2 + scale_linetype_manual(NULL, 
                             values = c(1, 4),
                             labels = c("Mean length (all)", "Mean width (all)")) +
  guides(color = guide_legend(order = 1)) +
  theme(
    legend.spacing.y = unit(0.05, "cm"),
    legend.margin = margin(0, 0, 0, 0),
    legend.box.margin = margin(0, 0, 0, 0))

Created on 2018-05-23 by the reprex package (v0.2.0).

pass function arguments to both dplyr and ggplot

Tidy evaluation is now fully supported in ggplot2 v3.0.0 so it's not necessary to use aes_ or aes_string anymore.

library(rlang)
library(tidyverse)

diamond_plot <- function (data, group, metric) {
    quo_group  <- sym(group)
    quo_metric <- sym(metric)

    data %>%
        group_by(!! quo_group) %>%
        summarise(price = mean(!! quo_metric)) %>%
        ggplot(aes(x = !! quo_group, y = !! quo_metric)) +
        geom_col()
}

diamond_plot(diamonds, "clarity", "price")

Created on 2018-04-16 by the reprex package (v0.2.0).

how to create factor variables from quosures in functions using ggplot and dplyr?

Here is a possibility using other rlang functions.

get_charts1 <- function(data, mygroup){

  quo_var <- enquo(mygroup)

  df_agg <- data %>% 
    group_by(!!quo_var) %>% 
    summarize(mean = mean(value, na.rm = TRUE),
              count = n()) %>% 
    ungroup()

  cc <- rlang::expr(factor(!!(rlang::get_expr(quo_var))))
  # or just cc <- expr(factor(!!get_expr(quo_var))) if you include library(rlang)

  ggplot(df_agg, aes_q(x = quote(count), y = quote(mean), color = cc)) + 
    geom_point() +
    geom_line() 
}

We build the expression factor(group) using the expr() function. We use get_expr() to extract the symbol name "group" from the quosure quo_var. Once we've build the expression, we can pass it on to aes_q.

Hopefully ggplot will soon be tidy-eval-friendly and this will no longer be necessary.

dplyr do multiple plots in an anonymous function

I don't think you need the return value to be a frame. Try this:

plots <- df %>% 
    group_by(gene) %>%
    do(plot= {
        p <- ggplot(.,aes(position,score)) +
            geom_point()
        if (all(.$strand == "-")) p <- p + scale_y_reverse()
        p
    })
plots
# Source: local data frame [2 x 2]
# Groups: <by row>
# # A tibble: 2 x 2
#   gene  plot    
# * <fct> <list>  
# 1 alpha <S3: gg>
# 2 beta  <S3: gg>

I think one issue is that your conditional logic is fine but you did not name the block within do(...).

You can view one of them with:

plots$plot[[1]]

If you want to dump all plots (e.g., in a markdown document), just do plots$plot and they will be cycled through rather quickly (not as useful on the console).

Function with dplyr, tidyr and ggplot

The development version of tidyr, tidyr_0.6.3.9000, now uses tidyeval, so if you want to update to that you could use !! as you did in group_by.

plot_nice_chart <- function(df, param_col) {

     enq_param_col <- enquo(param_col)
     str_param_col <- deparse(substitute(param_col))
     str_param_col
     df %>%
          group_by(!!enq_param_col, date_col) %>%
          summarise(val_col = sum(val_col)) %>%
          ungroup() %>%
          complete(!!enq_param_col, date_col) %>%
          ggplot(aes_string("date_col", "val_col", color = str_param_col)) +
          geom_line()
}

Using the current version, you can use complete_ with variables as strings.

plot_nice_chart <- function(df, param_col) {

     enq_param_col <- enquo(param_col)
     str_param_col <- deparse(substitute(param_col))

     df %>%
          group_by(!!enq_param_col, date_col) %>%
          summarise(val_col = sum(val_col)) %>%
          ungroup() %>%
          complete_( c(str_param_col, "date_col") ) %>%
          ggplot(aes_string("date_col", "val_col", color = str_param_col)) +
          geom_line()
}

Combine with() and ggplot2

Your code doesn't work because the first argument of ggplot() is data. You need to specifically say that you want to use the argument mapping.

df <- data.frame(a= 1:10, b= 1:10)
with(df, ggplot(mapping = aes(a, b)) + geom_point())

Or you can do this

df <- data.frame(a= 1:10, b= 1:10)
with(df, ggplot() + geom_point(aes(a, b))

The second method works because the first argument for geom_* is mapping.

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