使用purrr :: map将多个参数应用于函数

Question

I have a data frame like this

   df <- data.frame(tiny = rep(letters[1:3], 20), 
                  block = rnorm(60), tray = runif(60, min=0.4, max=2),
                  indent = sample(0.5:2.0, 60, replace = TRUE))

I nested this data frame

nm <- df%>%
       group_by(tiny)%>%
       nest()

then wrote these functions

library(dplyr)
library(purrr)
library(tidyr)

model <- function(dfr, x, y){
             lm(y~x, data = dfr)
         }

model1 <- function(dfr){
           lm(block~tray, data = dfr)
          }

I want to run this model for all tiny classes, so I did

 nm%>%
   mutate(
     mod = data %>% map(model1)
   )

the above code works fine but if I want to supply the variables as arguments like I have in the model1 function, I get errors. This is what I do

 nm%>%
    mutate(mod = data %>% map(model(x=tray, y=block)))

I keep getting the error Error in mode(x = tray, y = block) : unused argument (y = block) .

Also I tried plotting these using ggplot2

plot <- function(dfr, i){
    dfr %>%
    ggplot(., aes(x=tray, y=block))+
geom_point()+
xlab("Soil Properties")+ylab("Slope Coefficient")+
ggtitle(nm$tiny[i])

nm%>%
 mutate(put = data %>% map(plot))

the idea is that I want ggplot to put titles a , b , and c for each of the plots that will be produced. Any help would be greatly appreciated. Thanks

Answer 1

use base function split to split data into list of groups.

library( purrr )
library( ggplot2 )
df %>% 
  split( .$tiny) %>%
  map(~ lm( block ~ tray, data = .))

df %>% 
  split( .$tiny) %>%
  map(~ ggplot( data = ., aes( x = tray, y = block ) ) +
        geom_point( ) +
        xlab("Soil Properties") + 
        ylab("Slope Coefficient") +
        ggtitle( as.character( unique(.$tiny) ) ) )

Using Functions:

lm_model <- function( data ) 
{
  return( lm( block ~ tray, data = data ) )
}

plot_fun <- function( data )
{
  p <- ggplot( data = data, aes( x = tray, y = block ) ) +
    geom_point( ) +
    xlab("Soil Properties") + 
    ylab("Slope Coefficient") +
    ggtitle( as.character( unique(data$tiny) ) )

  return( p )
}

df %>% 
  split( .$tiny) %>%
  map(~ lm_model( data = . ) )

df %>% 
  split( .$tiny) %>%
  map(~ plot_fun( data = . ) )

Creating formula inside function

lm_model <- function( data, x, y ) 
{
  form <- reformulate( y, x )

  return( lm( formula = form, data = data ) )
}

df %>% 
  split( .$tiny) %>%
  map(~ lm_model( data = ., x = 'tray', y = 'block' ) )

Your solution would have worked if you had your function formulated like below.

model <- function(dfr, x, y){
  lm( formula = eval(parse(text = paste('as.formula( ', y, ' ~ ', x, ')', sep = ''))),
      data = dfr)
}

Answer 2

If you want to use mutate with map , you'll need to also use tidyr for nest . You'll be using tibbles to store the output (or data frames with list-columns of data frames).

I used the functions from @Sathish's detailed answer (with some modifications).

library(purrr)
library(dplyr)
library(tidyr) 

df <- data.frame(tiny = rep(letters[1:3], 20), 
                 block = rnorm(60), tray = runif(60, min=0.4, max=2),
                 indent = sample(0.5:2.0, 60, replace = TRUE))

lm_model <- function( data ) 
{
  return( lm( block ~ tray, data = data ) )
}

# Altered function to include title parameter with purrr::map2
plot_fun <- function( data, title )
{
  p <- ggplot( data = data, aes( x = tray, y = block ) ) +
    geom_point( ) +
    xlab("Soil Properties") + 
    ylab("Slope Coefficient") +
    ggtitle( as.character( title ) )

  return( p )
}


results <- df %>% 
  group_by(tiny) %>% 
  nest() %>% 
  mutate(model = map(data, lm_model),
         plot = map2(data, tiny, plot_fun))

You end up with:

> results

# A tibble: 3 × 4
    tiny              data    model     plot
  <fctr>            <list>   <list>   <list>
1      a <tibble [20 × 3]> <S3: lm> <S3: gg>
2      b <tibble [20 × 3]> <S3: lm> <S3: gg>
3      c <tibble [20 × 3]> <S3: lm> <S3: gg>

And you can access what you need using unnest or via extraction ( [ and [[ )

> results$model[[1]]

Call:
lm(formula = block ~ tray, data = data)

Coefficients:
(Intercept)         tray  
    -0.3461       0.3998