R programming: ifelse on multiple tables [duplicate] - r

Question

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Merge dataframes of different sizes
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Update columns by joining more than one columns
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Closed 4 years ago.
I am just venturing into R programming and finding my way around.
Lets say I have a table as below:
Store | Product | Sales
X | A | 2
X | B | 1
X | C | 3
Y | A | 1
Y | B | 2
Y | C | 5
Z | A | 3
Z | B | 6
Z | C | 2
I need to change the sales values of certain products based on another table. Please find below:
Product | Sales
A | 10
B | 7
C | 15
My final table should be:
Store | Product | Sales
X | A | 10
X | B | 7
X | C | 15
Y | A | 10
Y | B | 7
Y | C | 15
Z | A | 10
Z | B | 7
Z | C | 15
I have 2 methods of doing this now:
1) Using joins
2) Using an if-else statement inside a for loop to subset the
Is there any other way to do this more effectively and in fewer steps?
Thanks in advance!
EDIT: I forgot to mention an exception earlier. What if my dataset is like below?
Store | Product | Sales
X | A | 2
X | B | 1
X | C | 3
X | D | 4
Y | A | 1
Y | B | 2
Y | C | 5
Y | D | 2
Z | A | 3
Z | B | 6
Z | C | 2
Z | D | 3
There's an extra product(D) with sales. I want to retain the value of sales for that product if it is not present in the 2nd table which is:
Product | Sales
A | 10
B | 7
C | 15

Answer 1

How about this join?
Since you want to change the Sales values of certain Products only so to illustrate this I have considered only two products in lookup_df
library(dplyr)
df %>%
left_join(lookup_df, by = "Product", suffix = c("_Original", "_New")) %>%
mutate(Sales_New = coalesce(Sales_New, Sales_Original))
Output is:
Store Product Sales_Original Sales_New
1 X A 2 10
2 X B 1 1
3 X C 3 15
4 Y A 1 10
5 Y B 2 2
6 Y C 5 15
7 Z A 3 10
8 Z B 6 6
9 Z C 2 15
Sample data:
df <- structure(list(Store = c("X", "X", "X", "Y", "Y", "Y", "Z", "Z",
"Z"), Product = c("A", "B", "C", "A", "B", "C", "A", "B", "C"
), Sales = c(2L, 1L, 3L, 1L, 2L, 5L, 3L, 6L, 2L)), .Names = c("Store",
"Product", "Sales"), class = "data.frame", row.names = c(NA,
-9L))
lookup_df <- structure(list(Product = c("A", "C"), Sales = c(10L, 15L)), .Names = c("Product", "Sales"), class = "data.frame", row.names = c(NA,
-2L))
# Product Sales
#1 A 10
#2 C 15

Answer 2

If you use a lookup-vector, it is relatively short:
d <- read.table(text = "
Store | Product | Sales
X | A | 2
X | B | 1
X | C | 3
Y | A | 1
Y | B | 2
Y | C | 5
Z | A | 3
Z | B | 6
Z | C | 2", sep = "|", header = T, stringsAsFactors = F)
lookup <- read.table(text = "Product | Sales
A | 10
B | 7
C | 15", sep = "|", header = T, stringsAsFactors = F)
lookup$Product <- gsub("^\\s+|\\s+$", "", lookup$Product) # remove spaces
lookup <- setNames(lookup$Sales, lookup$Product) # convert to vector
d$Product <- gsub("^\\s+|\\s+$", "", d$Product) # remove spaces
d$Sales <- lookup[d$Product] # main part
d