R-dplyr-如何更改行或行之间的分隔

Question

I found that dplyr is speedy and simple for aggregate and summarise data. But I can't find out how to solve the following problem with dplyr.

Given these data frames:

df_2017 <- data.frame(
  expand.grid(1:195,1:65,1:39), 
  value = sample(1:1000000,(195*65*39)),
  period = rep("2017",(195*65*39)),
   stringsAsFactors = F
)

df_2017 <- df_2017[sample(1:(195*65*39),450000),]

names(df_2017) <- c("company", "product", "acc_concept", "value", "period")

df_2017$company <- as.character(df_2017$company)
df_2017$product <- as.character(df_2017$product)
df_2017$acc_concept <- as.character(df_2017$acc_concept)
df_2017$value <- as.numeric(df_2017$value)

ratio_df <- data.frame(concept=c("numerator","numerator","numerator","denom", "denom", "denom","name"),
ratio1=c("1","","","4","","","Sales over Assets"),
ratio2=c("1","","","5","6","","Sales over Expenses A + B"), stringsAsFactors = F)

where the columns in df_2017 are:

• company = This is a categorical variable with companies from 1 to 195
• product = This is a categorical, with home apliance products from 1 to 65. For example, 1 could be equal to irons, 2 to television, etc
• acc_concept = This is a categorical variable with accounting concepts from 1 to 39. For example, 1 would be equal to "Sales", 2 to "Total Expenses", 3 to Returns", 4 to "Assets, etc
• value = This is a numeric variable, with USD from 1 to 100.000.000
• period = Categorical variable. Always 2017

As the expand.grid implies, the combinations of company - product - acc_concept are never duplicated, but, It could happen that certains subjects have not every company - product - acc_concept combinations. That's why the code line "df_2017 <- df_2017[sample(1:195*65*39),450000),]", and that's why the output could turn out into NA (see below).

And where the columns in ratio_df are:

• Concept = which acc_concept corresponds to numerator, which one to denominator, and which is name of the ratio
• ratio1 = acc_concept and name for ratio1
• ratio2 = acc_concept and name for ratio2

I want to calculate 2 ratios (ratio_df) between acc_concept, for each product within each company.

For example:

I take the first ratio "acc_concepts" and "name" from ratio_df:

num_acc_concept   <- ratio_df[ratio_df$concept == "numerator", 2]
denom_acc_concept <- ratio_df[ratio_df$concept == "denom", 2]
ratio_name        <- ratio_df[ratio_df$concept == "name", 2]

Then I calculate the ratio for one product of one company, just to show you want i want to do:

ratio1_value <- sum(df_2017[df_2017$company == 1 & df_2017$product == 1 & df_2017$acc_concept %in% num_acc_concept, 4]) / sum(df_2017[df_2017$company == 1 & df_2017$product == 1 & df_2017$acc_concept %in% denom_acc_concept, 4])

Output:

output <- data.frame(Company="1", Product="1", desc_ratio=ratio_name, ratio_value = ratio1_value, stringsAsFactors = F)

As i said before i want to do this for each product within each company

The output data.frame could be something like this (ratios aren't the true ones because i haven't done the calculations yet) :

company    product   desc_ratio              ratio_value
1           1        Sales over Assets           0.9303675
1           2        Sales over Assets           1.30
1           3        Sales over Assets           Nan
1           4        Sales over Assets           Inf
1           5        Sales over Assets           2.32
1           6        Sales over Assets           NA
.
.
.
1           1        Sales over Expenses A + B   3.25
.
.
.
2           1        Sales over Assets          0.256

and so on...

• NaN when ratio is 0 / 0
• Inf when ratio is number / 0
• NA when there is no data for certain company and product.

I hope i have made myself clear this time :)

Is there any way to solve this row problem with dplyr? Should I cast the df_2017 for mutating? In this case, which is the best way for casting?

Any help would be welcome!

Answer 1

This is one way of doing it. At the end I timed the code on all of your records.

First create a function to create all the ratios. Do note, this function is only useful inside the dplyr code.

ratio <- function(data){
  result <- data.frame(desc_ratio = rep(NA, ncol(ratio_df) -1), ratio_value = rep(NA, ncol(ratio_df) -1))

  for(i in 2:ncol(ratio_df)){
    num   <- ratio_df[ratio_df$concept == "numerator", i]
    denom <- ratio_df[ratio_df$concept == "denom", i]
    result$desc_ratio[i-1] <- ratio_df[ratio_df$concept == "name", i]
    result$ratio_value[i-1] <- sum(ifelse(data$acc_concept %in% num, data$value, 0)) / sum(ifelse(data$acc_concept %in% denom, data$value, 0))
  }
  return(result)
}

Using dplyr, tidyr and purrr to put everything together. First group by the data, nest the data needed for the function, run the function with a mutate on the nested data. Drop the not needed nested data and unnest to get your wanted output. I leave the sorting up to you.

library(dplyr)
library(purrr)
library(tidyr)
output <- df_2017 %>%
  group_by(company, product, period) %>% 
  nest() %>% 
  mutate(ratios = map(data, ratio)) %>% 
  select(-data) %>% 
  unnest

output

# A tibble: 25,350 x 5
   company product period desc_ratio                ratio_value
   <chr>   <chr>   <chr>  <chr>                           <dbl>
 1 103     2       2017   Sales over Assets               0.733
 2 103     2       2017   Sales over Expenses A + B       0.219
 3 26      26      2017   Sales over Assets               0.954
 4 26      26      2017   Sales over Expenses A + B       1.01 
 5 85      59      2017   Sales over Assets               4.14 
 6 85      59      2017   Sales over Expenses A + B       1.83 
 7 186     38      2017   Sales over Assets               7.85 
 8 186     38      2017   Sales over Expenses A + B       0.722
 9 51      25      2017   Sales over Assets               2.34 
10 51      25      2017   Sales over Expenses A + B       0.627
# ... with 25,340 more rows

Time it took to run this code on my machine measured with system.time :

   user  system elapsed 
   6.75    0.00    6.81