R/f - Read data from file.R
In pieterov/generaltoolbox: General toolbox by Pieter Overdevest
Defines functions
f_read_data_from_file
Documented in
f_read_data_from_file
#' @title Read data from file
#'
#' @description Reads data from file.
#'
#' @author Pieter Overdevest
#'
#' @param v.file.string String to search for in the filename. In case of Google Sheet this can be kept NULL (default: NULL).
#' @param c.file.type Options: 'xls', 'csv', 'tsv', 'txt', 'delim', 'rds', 'fst', 'xml', 'parquet', 'sqlite', 'shp', 'dbf', 'gs'
#' @param c.path Path where file should be searched for (default: NULL).
#' @param b.exact.match Exact match or not (default: FALSE).
#' @param c.file.string.exclude Means to filter out files by providing a string that should not exist in the filename
#' (default: NULL).
#' @param b.show.info Should we show info? (default: FALSE).
#' @param c.sheet.name In case of xls (default: NULL).
#' @param n.skip.rows In case of xls (default: 0).
#' @param c.delim In case of txt, csv, delim (default: NULL).
#' @param c.decimal.mark In case of txt, csv, delim (default: '.').
#' @param b.col.names Do the data contain header names? (default: TRUE).
#' @param l.col.type List with column types, see also Details (default: NULL).
#' @param n.guess.max Number of rows to check on data type (default: 1000).
#' @param c.table.name In case of SQLITE (default: NULL).
#' @param c.show.report Options: "none", "minimal", "all" (default: "all").
#' @param b.add.mod.date.path.file Should we add auxiliry features: mod.date, path.file? (default: FALSE).
#' @param b.clean.up.header.names Should we clean up the header names? (default: TRUE).
#'
#' @returns Data frame containing the data read from the file.
#'
#' @details In case of Excel file, l.col.type should consist of list of: "skip", "guess", "logical", "numeric", "date",
#' "text" or "list". In case of delimited file, l.col.type should consist of list of: c = character, i = integer,
#' n = number, d = double, l = logical, D = date, T = date time, t = time, ? = guess, or _/- to skip the column.
#'
#' @export
#'
#' @examples
#' f_read_data_from_file(
#'
#' v.file.string = "bord.allocation",
#' c.file.type = "xls",
#' c.path = path.data,
#' b.exact.match = FALSE,
#' c.file.string.exclude = NULL,
#' b.show.info = FALSE,
#' c.sheet.name = NULL,
#' n.skip.rows = 0,
#' c.delim = NULL,
#' c.decimal.mark = ".",
#' b.col.names = TRUE,
#' l.col.type = NULL,
#' n.guess.max = 1000,
#' c.table.name = NULL,
#' c.show.report = "all",
#' b.add.mod.date.path.file = FALSE,
#' b.clean.up.header.names = TRUE
#' )
#################################################################################
# FUNCTION.
#################################################################################
f_read_data_from_file <- function(
# String to search for in the filename. In case of Google Sheet this can be kept NULL.
v.file.string = NULL,
# Options: 'xls', 'csv', 'tsv', 'txt', 'delim', 'rds', 'fst', 'xml', 'parquet', 'sqlite', 'shp', 'dbf', 'gs'.
c.file.type,
# Path where file should be searched for.
c.path = NULL,
# Exact match or not.
b.exact.match = FALSE,
c.file.string.exclude = NULL,
# Show info
b.show.info = FALSE,
# In case of Excel file.
c.sheet.name = NULL, # was "Sheet1"
n.skip.rows = 0,
# Set to NULL, so we know when set by user.
c.delim = NULL,
# Set standard decimal mark.
c.decimal.mark = ".",
# Do the data contain header names?
b.col.names = TRUE,
# In case of Excel file, l.col.type should consist of list of:
# "skip", "guess", "logical", "numeric", "date", "text" or "list".
# In case of delimited file, l.col.type should consist of list of:
# c = character, i = integer, n = number, d = double,
# l = logical, D = date, T = date time, t = time,
# ? = guess, or _/- to skip the column.
# In the past I used 'cols(.default = "c")' (no longer needed).
# Or put everything on character.
l.col.type = NULL,
n.guess.max = 1000,
# In case of SQLite file.
c.table.name = NULL,
# Options: 'none', 'minimal', 'all'
c.show.report = "all",
# Add auxiliry fields (mod.date, path.file)
b.add.mod.date.path.file = FALSE,
# Clean up header names?
b.clean.up.header.names = TRUE
) {
##############################################################################
# TEST ONLY!!
##############################################################################
# DEFAULT WAARDEN - HOUDEN!!
# v.file.string = NULL
# c.path = NULL
# b.exact.match = FALSE
# c.file.string.exclude = NULL
# b.show.info = FALSE
# c.sheet.name = NULL
# n.skip.rows = 0
# c.delim = NULL
# b.col.names = TRUE
# l.col.type = NULL
# n.guess.max = 1000
# c.table.name = NULL
# c.show.report = "all"
# b.add.mod.date.path.file = FALSE
# b.clean.up.header.names = TRUE
# BLC
# v.file.string = "DPCOMP with sort test multivariate v1 case 4"
# c.file.type = "xls"
# c.path = path.data
# c.sheet.name = "model"
# v.file.string = "Binary Mixtures - Parts I II III - FINAL"
# c.file.type = "xls"
# c.path = path.deliverables
# c.sheet.name = "Solids"
# n.guess.max = 20000
# c.path = paste0(path.data, "AI")
# v.file.string = paste0(c.filename, "_", c.type)
# c.file.type = "sqlite"
# c.table.name = "ts"
# SQLite / AI Infrasolutions
# v.file.string = paste0(c.filename, "_", c.type)
# c.file.type = "sqlite"
# c.table.name = "bord"
# c.path = paste0(path.data, "AI")
##############################################################################
# ERRROR CHECK
##############################################################################
# Does c.file.type have a valid value?
if(!c.file.type %in% c("xls", "csv", "tsv", "txt", "rds", "fst", "xml", "parquet", "sqlite", "shp", "dbf", "gs")) {
stop(paste0(
"Note, the value for c.file.type ('", c.file.type, "') is invalid. It must be one of, ",
"'xls', 'csv', 'tsv', 'txt', 'rds', 'fst', 'xml', 'parquet', 'sqlite', 'shp', 'dbf', or 'gs'!"
))
}
##############################################################################
# INITIALIZE
##############################################################################
# Define c.file.category and c.delim in case of c.file.type to be 'csv' or 'txt'.
if(c.file.type %in% c("csv", "tsv", "txt")) {
c.file.category <- "delim"
c.delim <- ifelse(
is.null(c.delim),
case_when(
c.file.type == "csv" ~ ",",
c.file.type == "tsv" ~ "\t",
c.file.type == "txt" ~ " "
),
c.delim
)
} else {
c.file.category <- c.file.type
}
# Update c.path when it is not NULL.
if(!is.null(c.path)) {
# Append '/' if not there and it is not an url.
if(!grepl("/$", c.path) & !grepl("^http", c.path)) {
c.path <- paste0(c.path, "/")
}
# Get latest file in case the file is not read by URL (like Channable).
if(!grepl("^http", c.path)) {
l.path.file <- lapply(v.file.string, function(c.file.string) {# c.file.string <- v.file.string[1]
f_get_latest_file(
c.file.string = c.file.string,
c.file.type = c.file.type,
c.path = c.path,
b.exact.match = b.exact.match,
c.file.string.exclude = c.file.string.exclude,
c.sheet.name = c.sheet.name,
c.show.report = c.show.report
)
})
} else {
l.path.file <- list(c.path)
}
}
########################################################################################################
# Functions
########################################################################################################
f_post_processing <- function(df.input, c.path.file, c.delim, b.add.mod.date.path.file, b.show.info) {
# Add column with path name and date of modification.
if(b.add.mod.date.path.file) {
df.input <- df.input %>%
mutate(
path.file = c.path.file,
mod.date = file.mtime(c.path.file)
)
}
# Show header names, if needed.
if (b.show.info) {
cat(paste0("\nFile name: '", basename(c.path.file), "'"), "\n")
cat(paste0("Column names: ", f_paste(names(df.input), b.quotation = TRUE)), "\n")
if(!is.null(c.delim)) {
cat(paste0("Delimiter: '", c.delim, "'"))
}
}
# Return updated input
return(df.input)
}
##############################################################################
# Read data based on file type.
##############################################################################
# Get data.
switch(c.file.category,
"xls" = {
# Read data from Excel file. Suppress warnings for not being able to match the right format.
suppressWarnings(
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
# Get all sheet names in the workbook.
v.sheet.name <- readxl::excel_sheets(c.path.file)
if(is.null(c.sheet.name)) {
c.sheet.name <- v.sheet.name[1]
} else {
c.sheet.name <- v.sheet.name[grepl(c.sheet.name, v.sheet.name)]
}
df.temp <-
readxl::read_xlsx(
path = c.path.file,
sheet = c.sheet.name,
skip = n.skip.rows,
col_names = b.col.names,
col_types = l.col.type,
guess_max = n.guess.max
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
# Check whether one or more of the columns is empty. If so,
# advice to increase 'guess_max'
walk(names(df.temp), function(x) {
if(all(is.na(df.temp[x]))) {
cat(paste0(
"\nAll cells in column '", x, "' are empty. ",
"Consider increasing 'n.guess.max' to a ",
"number close to the number of rows in the ",
"data frame (",
format(nrow(df.temp), big.mark = ","), ").\n"
))
}
})
return(df.temp)
})
)
},
"delim" = {
# Read data from delimited file. Suppress warnings for not being able to match the right format.
suppressWarnings(
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
readr::read_delim(
file = c.path.file,
delim = c.delim,
locale = locale(decimal_mark = c.decimal.mark),
trim_ws = TRUE,
col_names = b.col.names,
col_types = l.col.type
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
})
)
},
"rds" = {
# Read data from RDS file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
readRDS(
file = c.path.file
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"fst" = {
# Read data from FST file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
read_fst(
path = c.path.file
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"xml" = {
# Read data from XML file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
xmlParse(
file = c.path.file
) %>%
xmlToDataFrame() %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"parquet" = {
# Read data from PARQUET file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
read_parquet(
file = c.path.file
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"sqlite" = {
# Check whether a table name is provided.
if (is.null(c.table.name)) stop(paste0("Missing c.table.name in function input."))
l.data.object <- lapply(
l.path.file,
function(c.path.file) {
# c.path.file <- l.path.file[[2]]
#c.table.name <- "borden"
#c.path.file <- l.path.file[[1]]
#print(c.path.file)
# db_path <- file.path(c.rootfolder.read, "SQLITE", df.temp$file.folder[1], df.temp$file.name[1])
# con <- dbConnect(SQLite(), dbname = db_path)
# db_tables <- dbListTables(con)
# print(db_tables)
# dbDisconnect(con)
# Connect to sqlite file.
con <- tryCatch(
dbConnect(
drv = RSQLite::SQLite(),
dbname = c.path.file
),
error = function(e) {
cat(paste0("\n\nError: ", c.path.file, " results in: '", e$message, "'"))
},
warning = function(w) {
cat(paste0("\n\nWarning: ", c.path.file, " results in: '", w$message, "'"))
}
)
# Check whether c.table.name is in the list of tables.
if (!c.table.name %in% dbListTables(con)) {
# Give warning to user.
warning(
paste0(
c.path.file," does not contain table '",
c.table.name, "', The SQLite does contain the ",
"following tables: ", f_paste(dbListTables(con))
)
)
# Disconnect from database.
dbDisconnect(con)
df.temp <- NULL
} else {
# Extract table c.table.name and add column with path name and date
# of modification.
df.temp <-
dbGetQuery(
con,
paste("select * from", c.table.name)
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
# Disconnect from database.
dbDisconnect(con)
}
return(df.temp)
}
)
},
"shp" = {
# Read data from SHAPE file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
# Open shapefile.
sf.temp <- readOGR(
dsn = dirname(c.path.file),
layer = gsub("\\.shp$", "", basename(c.path.file))
)
# Open shapefile.
df.temp <-
sf.temp %>%
# Converteer shapefile in dataframe.
as.data.frame(.) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"dbf" = {
# Read data from DBF file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
# Open dbf file.
df.temp <-
read.dbf(
file = c.path.file,
as.is = TRUE
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"gs" = {
# Read data from Google Sheet.
l.data.object <- lapply(v.file.string,
function(c.file.string) { # c.file.string <- v.file.string[[1]]
# Open dbf file.
df.temp <-
read_sheet(
ss = c.file.string,
sheet = c.sheet.name,
col_names = b.col.names,
col_types = l.col.type
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
})
# Bind list of data frames in one data frame.
df.data.object <- suppressWarnings(bind_rows(l.data.object))
# Clean up header names when requested.
if(b.clean.up.header.names) {
df.data.object <- df.data.object %>%
# Clean-up header names.
f_clean_up_header_names()
}
# Convert to tibble.
# df.data.object <- as_tibble(df.data.object)
# Add break lines.
cat("\n\n")
# Return data.object.
return(df.data.object)
}
pieterov/generaltoolbox documentation built on Jan. 25, 2025, 10:32 a.m.
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Defines functions f_read_data_from_file
Documented in f_read_data_from_file
#' @title Read data from file
#'
#' @description Reads data from file.
#'
#' @author Pieter Overdevest
#'
#' @param v.file.string String to search for in the filename. In case of Google Sheet this can be kept NULL (default: NULL).
#' @param c.file.type Options: 'xls', 'csv', 'tsv', 'txt', 'delim', 'rds', 'fst', 'xml', 'parquet', 'sqlite', 'shp', 'dbf', 'gs'
#' @param c.path Path where file should be searched for (default: NULL).
#' @param b.exact.match Exact match or not (default: FALSE).
#' @param c.file.string.exclude Means to filter out files by providing a string that should not exist in the filename
#' (default: NULL).
#' @param b.show.info Should we show info? (default: FALSE).
#' @param c.sheet.name In case of xls (default: NULL).
#' @param n.skip.rows In case of xls (default: 0).
#' @param c.delim In case of txt, csv, delim (default: NULL).
#' @param c.decimal.mark In case of txt, csv, delim (default: '.').
#' @param b.col.names Do the data contain header names? (default: TRUE).
#' @param l.col.type List with column types, see also Details (default: NULL).
#' @param n.guess.max Number of rows to check on data type (default: 1000).
#' @param c.table.name In case of SQLITE (default: NULL).
#' @param c.show.report Options: "none", "minimal", "all" (default: "all").
#' @param b.add.mod.date.path.file Should we add auxiliry features: mod.date, path.file? (default: FALSE).
#' @param b.clean.up.header.names Should we clean up the header names? (default: TRUE).
#'
#' @returns Data frame containing the data read from the file.
#'
#' @details In case of Excel file, l.col.type should consist of list of: "skip", "guess", "logical", "numeric", "date",
#' "text" or "list". In case of delimited file, l.col.type should consist of list of: c = character, i = integer,
#' n = number, d = double, l = logical, D = date, T = date time, t = time, ? = guess, or _/- to skip the column.
#'
#' @export
#'
#' @examples
#' f_read_data_from_file(
#'
#' v.file.string = "bord.allocation",
#' c.file.type = "xls",
#' c.path = path.data,
#' b.exact.match = FALSE,
#' c.file.string.exclude = NULL,
#' b.show.info = FALSE,
#' c.sheet.name = NULL,
#' n.skip.rows = 0,
#' c.delim = NULL,
#' c.decimal.mark = ".",
#' b.col.names = TRUE,
#' l.col.type = NULL,
#' n.guess.max = 1000,
#' c.table.name = NULL,
#' c.show.report = "all",
#' b.add.mod.date.path.file = FALSE,
#' b.clean.up.header.names = TRUE
#' )
#################################################################################
# FUNCTION.
#################################################################################
f_read_data_from_file <- function(
# String to search for in the filename. In case of Google Sheet this can be kept NULL.
v.file.string = NULL,
# Options: 'xls', 'csv', 'tsv', 'txt', 'delim', 'rds', 'fst', 'xml', 'parquet', 'sqlite', 'shp', 'dbf', 'gs'.
c.file.type,
# Path where file should be searched for.
c.path = NULL,
# Exact match or not.
b.exact.match = FALSE,
c.file.string.exclude = NULL,
# Show info
b.show.info = FALSE,
# In case of Excel file.
c.sheet.name = NULL, # was "Sheet1"
n.skip.rows = 0,
# Set to NULL, so we know when set by user.
c.delim = NULL,
# Set standard decimal mark.
c.decimal.mark = ".",
# Do the data contain header names?
b.col.names = TRUE,
# In case of Excel file, l.col.type should consist of list of:
# "skip", "guess", "logical", "numeric", "date", "text" or "list".
# In case of delimited file, l.col.type should consist of list of:
# c = character, i = integer, n = number, d = double,
# l = logical, D = date, T = date time, t = time,
# ? = guess, or _/- to skip the column.
# In the past I used 'cols(.default = "c")' (no longer needed).
# Or put everything on character.
l.col.type = NULL,
n.guess.max = 1000,
# In case of SQLite file.
c.table.name = NULL,
# Options: 'none', 'minimal', 'all'
c.show.report = "all",
# Add auxiliry fields (mod.date, path.file)
b.add.mod.date.path.file = FALSE,
# Clean up header names?
b.clean.up.header.names = TRUE
) {
##############################################################################
# TEST ONLY!!
##############################################################################
# DEFAULT WAARDEN - HOUDEN!!
# v.file.string = NULL
# c.path = NULL
# b.exact.match = FALSE
# c.file.string.exclude = NULL
# b.show.info = FALSE
# c.sheet.name = NULL
# n.skip.rows = 0
# c.delim = NULL
# b.col.names = TRUE
# l.col.type = NULL
# n.guess.max = 1000
# c.table.name = NULL
# c.show.report = "all"
# b.add.mod.date.path.file = FALSE
# b.clean.up.header.names = TRUE
# BLC
# v.file.string = "DPCOMP with sort test multivariate v1 case 4"
# c.file.type = "xls"
# c.path = path.data
# c.sheet.name = "model"
# v.file.string = "Binary Mixtures - Parts I II III - FINAL"
# c.file.type = "xls"
# c.path = path.deliverables
# c.sheet.name = "Solids"
# n.guess.max = 20000
# c.path = paste0(path.data, "AI")
# v.file.string = paste0(c.filename, "_", c.type)
# c.file.type = "sqlite"
# c.table.name = "ts"
# SQLite / AI Infrasolutions
# v.file.string = paste0(c.filename, "_", c.type)
# c.file.type = "sqlite"
# c.table.name = "bord"
# c.path = paste0(path.data, "AI")
##############################################################################
# ERRROR CHECK
##############################################################################
# Does c.file.type have a valid value?
if(!c.file.type %in% c("xls", "csv", "tsv", "txt", "rds", "fst", "xml", "parquet", "sqlite", "shp", "dbf", "gs")) {
stop(paste0(
"Note, the value for c.file.type ('", c.file.type, "') is invalid. It must be one of, ",
"'xls', 'csv', 'tsv', 'txt', 'rds', 'fst', 'xml', 'parquet', 'sqlite', 'shp', 'dbf', or 'gs'!"
))
}
##############################################################################
# INITIALIZE
##############################################################################
# Define c.file.category and c.delim in case of c.file.type to be 'csv' or 'txt'.
if(c.file.type %in% c("csv", "tsv", "txt")) {
c.file.category <- "delim"
c.delim <- ifelse(
is.null(c.delim),
case_when(
c.file.type == "csv" ~ ",",
c.file.type == "tsv" ~ "\t",
c.file.type == "txt" ~ " "
),
c.delim
)
} else {
c.file.category <- c.file.type
}
# Update c.path when it is not NULL.
if(!is.null(c.path)) {
# Append '/' if not there and it is not an url.
if(!grepl("/$", c.path) & !grepl("^http", c.path)) {
c.path <- paste0(c.path, "/")
}
# Get latest file in case the file is not read by URL (like Channable).
if(!grepl("^http", c.path)) {
l.path.file <- lapply(v.file.string, function(c.file.string) {# c.file.string <- v.file.string[1]
f_get_latest_file(
c.file.string = c.file.string,
c.file.type = c.file.type,
c.path = c.path,
b.exact.match = b.exact.match,
c.file.string.exclude = c.file.string.exclude,
c.sheet.name = c.sheet.name,
c.show.report = c.show.report
)
})
} else {
l.path.file <- list(c.path)
}
}
########################################################################################################
# Functions
########################################################################################################
f_post_processing <- function(df.input, c.path.file, c.delim, b.add.mod.date.path.file, b.show.info) {
# Add column with path name and date of modification.
if(b.add.mod.date.path.file) {
df.input <- df.input %>%
mutate(
path.file = c.path.file,
mod.date = file.mtime(c.path.file)
)
}
# Show header names, if needed.
if (b.show.info) {
cat(paste0("\nFile name: '", basename(c.path.file), "'"), "\n")
cat(paste0("Column names: ", f_paste(names(df.input), b.quotation = TRUE)), "\n")
if(!is.null(c.delim)) {
cat(paste0("Delimiter: '", c.delim, "'"))
}
}
# Return updated input
return(df.input)
}
##############################################################################
# Read data based on file type.
##############################################################################
# Get data.
switch(c.file.category,
"xls" = {
# Read data from Excel file. Suppress warnings for not being able to match the right format.
suppressWarnings(
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
# Get all sheet names in the workbook.
v.sheet.name <- readxl::excel_sheets(c.path.file)
if(is.null(c.sheet.name)) {
c.sheet.name <- v.sheet.name[1]
} else {
c.sheet.name <- v.sheet.name[grepl(c.sheet.name, v.sheet.name)]
}
df.temp <-
readxl::read_xlsx(
path = c.path.file,
sheet = c.sheet.name,
skip = n.skip.rows,
col_names = b.col.names,
col_types = l.col.type,
guess_max = n.guess.max
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
# Check whether one or more of the columns is empty. If so,
# advice to increase 'guess_max'
walk(names(df.temp), function(x) {
if(all(is.na(df.temp[x]))) {
cat(paste0(
"\nAll cells in column '", x, "' are empty. ",
"Consider increasing 'n.guess.max' to a ",
"number close to the number of rows in the ",
"data frame (",
format(nrow(df.temp), big.mark = ","), ").\n"
))
}
})
return(df.temp)
})
)
},
"delim" = {
# Read data from delimited file. Suppress warnings for not being able to match the right format.
suppressWarnings(
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
readr::read_delim(
file = c.path.file,
delim = c.delim,
locale = locale(decimal_mark = c.decimal.mark),
trim_ws = TRUE,
col_names = b.col.names,
col_types = l.col.type
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
})
)
},
"rds" = {
# Read data from RDS file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
readRDS(
file = c.path.file
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"fst" = {
# Read data from FST file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
read_fst(
path = c.path.file
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"xml" = {
# Read data from XML file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
xmlParse(
file = c.path.file
) %>%
xmlToDataFrame() %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"parquet" = {
# Read data from PARQUET file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
df.temp <-
read_parquet(
file = c.path.file
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"sqlite" = {
# Check whether a table name is provided.
if (is.null(c.table.name)) stop(paste0("Missing c.table.name in function input."))
l.data.object <- lapply(
l.path.file,
function(c.path.file) {
# c.path.file <- l.path.file[[2]]
#c.table.name <- "borden"
#c.path.file <- l.path.file[[1]]
#print(c.path.file)
# db_path <- file.path(c.rootfolder.read, "SQLITE", df.temp$file.folder[1], df.temp$file.name[1])
# con <- dbConnect(SQLite(), dbname = db_path)
# db_tables <- dbListTables(con)
# print(db_tables)
# dbDisconnect(con)
# Connect to sqlite file.
con <- tryCatch(
dbConnect(
drv = RSQLite::SQLite(),
dbname = c.path.file
),
error = function(e) {
cat(paste0("\n\nError: ", c.path.file, " results in: '", e$message, "'"))
},
warning = function(w) {
cat(paste0("\n\nWarning: ", c.path.file, " results in: '", w$message, "'"))
}
)
# Check whether c.table.name is in the list of tables.
if (!c.table.name %in% dbListTables(con)) {
# Give warning to user.
warning(
paste0(
c.path.file," does not contain table '",
c.table.name, "', The SQLite does contain the ",
"following tables: ", f_paste(dbListTables(con))
)
)
# Disconnect from database.
dbDisconnect(con)
df.temp <- NULL
} else {
# Extract table c.table.name and add column with path name and date
# of modification.
df.temp <-
dbGetQuery(
con,
paste("select * from", c.table.name)
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
# Disconnect from database.
dbDisconnect(con)
}
return(df.temp)
}
)
},
"shp" = {
# Read data from SHAPE file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
# Open shapefile.
sf.temp <- readOGR(
dsn = dirname(c.path.file),
layer = gsub("\\.shp$", "", basename(c.path.file))
)
# Open shapefile.
df.temp <-
sf.temp %>%
# Converteer shapefile in dataframe.
as.data.frame(.) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"dbf" = {
# Read data from DBF file.
l.data.object <- lapply(l.path.file,
function(c.path.file) { # c.path.file <- l.path.file[[1]]
# Open dbf file.
df.temp <-
read.dbf(
file = c.path.file,
as.is = TRUE
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
},
"gs" = {
# Read data from Google Sheet.
l.data.object <- lapply(v.file.string,
function(c.file.string) { # c.file.string <- v.file.string[[1]]
# Open dbf file.
df.temp <-
read_sheet(
ss = c.file.string,
sheet = c.sheet.name,
col_names = b.col.names,
col_types = l.col.type
) %>%
f_post_processing(
c.path.file,
c.delim,
b.add.mod.date.path.file,
b.show.info
)
return(df.temp)
}
)
})
# Bind list of data frames in one data frame.
df.data.object <- suppressWarnings(bind_rows(l.data.object))
# Clean up header names when requested.
if(b.clean.up.header.names) {
df.data.object <- df.data.object %>%
# Clean-up header names.
f_clean_up_header_names()
}
# Convert to tibble.
# df.data.object <- as_tibble(df.data.object)
# Add break lines.
cat("\n\n")
# Return data.object.
return(df.data.object)
}
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