I am working with ~30 excel workbooks containing data with different set of designs. However, excel sheets within workbook are well formatted.
# There is a pattern, wherein if we have string "System setting" at
# i.e.,
df[4,4] == "System setting" #(then it is single core setup)
# Similarly,
df[4,6] == "System setting" # (then it is double core setup)
df[4,9] == "System setting" # (then it is triple core setup)
Now, I want to classify all of workbooks based on this information; whether their system setting is of single core, double core or triple core.
I tried following.
# Creating list of file names within folder
file_list <- list.files(pattern = '*.xlsx')
file_list
# Extracting sheet of interest as list of dataframes
Setup <- sapply(file_list, function(i){
x = read_excel(i, sheet = "Setup")
x
}, simplify = F, USE.NAMES = T)
# Now looking for specific information at cell location, this is for specifically identify sheets with single core
Check_single_core <- for (i in Setup){
x = i[4,4]
x
}
There are 11 out of 30 files have string, "System setting" at this location (i.e., i[4,4]) However, output over here is null.
I also tried this with lapply
Check_single_core_2 <- lapply(Setup, function(j){
y = j[4,4]
y
})
However, this generates list of 30 tibbles. All of them are blank.
I would like to understand how I can resolve this and further classify all of them under correct category.
If the information is in a specific cell in your excel files, you could use the following approach :
You could also consider the following approach to read a specific cell :
Once you have the information you can use it to classify the excel files.