Complex Data Frame calculations in R - r

Question

I currently am importing two tables (in the most basic form) that appear as such
Table 1
State Month Account Value
NY Jan Expected Sales 1.04
NY Jan Expected Expenses 1.02
Table 2
State Month Account Value
NY Jan Sales 1,000
NY Jan Customers 500
NY Jan F Expenses 1,000
NY Jan V Expenses 100
And my end goal is to create a 3rd data frame that includes the values of the first two rows and calculates a 4th column based off of functions
NextYearExpenses = (t2 F Expenses + t2 V Expenses)* t1 Expected Expenses
NextYearSales = (t2 sales) * t1 Expected Sales
So my desired output is as followed
State Month New Account Value
NY Jan Sales 1,040
NY Jan Expenses 1,122
I am relatively new to R and I think ifelse statements might be my best bet. I have tried merging the tables and calculating with simple column functions but with no real progress.
Any suggestions?

Answer 1

You may need to do some data wrangling but nothing out of the ordinary
require(dplyr)
Table1<-tibble(State=c("NY","NY"), Month=c("Jan","Jan"), Account=c("Expected Sales", "Expected Expenses"), Value=c(1.04,1.02))
Table2<-tibble(State=c("NY","NY","NY","NY"), Month=c("Jan","Jan","Jan","Jan"), Account=c("Sales", "Customers", "F Expenses","V Expenses"), Value=c(1000,500,1000,100))
First thing I do is rename the accounts to have a common name, i.e. expenses, this is going to help me to merge later on to Table1
Table2$Account[Table2$Account=="F Expenses"]<-"Expenses"
Table2$Account[Table2$Account=="V Expenses"]<-"Expenses"
then I use the group_by function and group by State, Month and Account and do the sum
Table2 <- Table2 %>% group_by(State, Month,Account) %>%
summarise(Tot_Value=sum(Value)) %>% ungroup()
head(Table2)
## State Month Account Tot_Value
## <chr> <chr> <chr> <dbl>
## 1 NY Jan Customers 500
## 2 NY Jan Expenses 1100
## 3 NY Jan Sales 1000
then something similar with the renaming for the accounts in table 1
Table1$Account[Table1$Account=="Expected Sales"]<-"Sales"
Table1$Account[Table1$Account=="Expected Expenses"]<-"Expenses"
Merge into a third table, Table 3
Table3<- left_join(Table1,Table2)
use mutate to do the needed operation
Table3 <- Table3 %>% mutate(Value2=Value*Tot_Value)
head(Table3)
## # A tibble: 2 x 6
## State Month Account Value Tot_Value Value2
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 NY Jan Sales 1.04 1000 1040
## 2 NY Jan Expenses 1.02 1100 1122

Answer 2

Here's what I did with dplyr and tidyr.
First I combined your initial tables with rbind into a single long format table. Since you have unique identifiers for each of the Account values, these don't need to be separate tables. Next I group_by State and Month to group these assuming eventually you'll have a variety of states/months. Next I summarise based on the values of Account that you specified and created two new columns. Finally to get it into the long format that you want I used gather from tidyr to go from wide format to long format. You can separate these commands into smaller chunks by deleting after the %>% to get a better idea of what each step does.
library(dplyr)
library(tidyr)
rbind(df,df2) %>%
group_by(State,Month) %>%
summarise(Expenses = (Value[which(Account == "F Expenses")] + Value[which(Account == "V Expenses")]) * Value[which(Account == "Expected Expenses")],
Sales = Value[which(Account == "Sales")] * Value[which(Account == "Expected Sales")]) %>%
gather(New_Account,Value, c(Expenses,Sales))
# A tibble: 2 x 4
# Groups: State [1]
# State Month New_Account Value
# <chr> <chr> <chr> <dbl>
#1 NY Jan Expenses 1122
#2 NY Jan Sales 1040

Answer 3

I'd recommend checking out the concept of "tidy data", as there are some real challenges with working on data with the structure you currently have. E.g. creating t3 should only take 2-3 lines of code, all of this is just to work around your data architecture:
library(tidyverse)
t1 <- data.frame(State = rep("NY", 2),
Month = rep(as.Date("2018-01-01"), 2),
Account = c("Expected Sales", "Expected Expenses"),
Value = c(1.04, 1.02),
stringsAsFactors = FALSE)
t2 <- data.frame(State = rep("NY", 4),
Month = rep(as.Date("2018-01-01"), 4),
Account = c("Sales", "Customers", "F Expenses", "V Expenses"),
Value = c(1000, 500, 1000, 100),
stringsAsFactors = FALSE)
t3 <- t2 %>%
spread(Account, Value) %>%
inner_join({
t1 %>%
spread(Account, Value)
}, by = c("State" = "State", "Month" = "Month")) %>%
mutate(NewExpenses = (`F Expenses` + `V Expenses`) * `Expected Expenses`,
NewSales = Sales * `Expected Sales`) %>%
select(State, Month, Sales = NewSales, Expenses = NewExpenses) %>%
gather(Sales, Expenses, key = `New Account`, value = Value)