Read comma separated value files with given column classes
Usage
safe_read_csv(
file,
header = TRUE,
sep = ",",
colClasses = NA,
skip = 0,
quote = "\"",
...,
stringsAsFactors = FALSE
)Details
Reading a comma separated value file using builtin function
read.csv might result in some unexpected
behavior. safe_read_csv does some preprocessing on the
format so that it take cares of the following cases.
1. If skip exceeds the maximum rows of the data, return
a blank data frame instead of raising error.
2. If row names are included in the file, colClasses
automatically skip that column and starts from the second column
3. If length of colClasses does not equal to the number of
columns, instead of cycling the class types, we set those columns
to be NA type and let read.csv decide
the default types.
4. stringsAsFactors is by default FALSE to be
consistent with R 4.0, if the function is called in R 3.x.
Examples
f <- tempfile()
x <- data.frame(a = letters[1:10], b = 1:10, c = 2:11)
# ------------------ Auto-detect row names ------------------
# Write with rownames
utils::write.csv(x, f, row.names = LETTERS[2:11])
# read csv with base library utils
table1 <- utils::read.csv(f, colClasses = c('character', 'character'))
# 4 columns including row names
str(table1)
#> 'data.frame': 10 obs. of 4 variables:
#> $ X: chr "B" "C" "D" "E" ...
#> $ a: chr "a" "b" "c" "d" ...
#> $ b: chr "1" "2" "3" "4" ...
#> $ c: chr "2" "3" "4" "5" ...
# read csv via safe_read_csv
table2 <- safe_read_csv(f, colClasses = c('character', 'character'))
# row names are automatically detected, hence 3 columns
# Only first columns are characters, the third column is auto
# detected as numeric
str(table2)
#> 'data.frame': 10 obs. of 3 variables:
#> $ a: chr "a" "b" "c" "d" ...
#> $ b: chr "1" "2" "3" "4" ...
#> $ c: int 2 3 4 5 6 7 8 9 10 11
# read table without row names
utils::write.csv(x, f, row.names = FALSE)
table2 <- safe_read_csv(f, colClasses = c('character', 'character'))
# still 3 columns, and row names are 1:nrow
str(table2)
#> 'data.frame': 10 obs. of 3 variables:
#> $ a: chr "a" "b" "c" "d" ...
#> $ b: chr "1" "2" "3" "4" ...
#> $ c: int 2 3 4 5 6 7 8 9 10 11
# --------------- Blank data frame when nrow too large ---------------
# instead of raising errors, return blank data frame
safe_read_csv(f, skip = 1000)
#> data frame with 0 columns and 0 rows