Importing Data

Importing data via buttons

The data import features can be accessed from the environment pane or from the tools menu. The importers are grouped into 3 categories: Text data, Excel data and statistical data. To access this feature, use the “Import Dataset” dropdown from the “Environment” pane:

Data import via environment. ©Javier Luraschi

Or through the “File” menu, followed by the “Import Dataset” submenu:

Data import via Filemenu. ©Javier Luraschi

Importing From Text (readr) files allows you to import csv files and in general, character delimited files using the readr package. This Text importer provides support to:

Import from the file system or an url
Change column data types
Skip or include-only columns
Rename the data set
Skip the first n rows
Use the header row for column names
Trim spaces in names
Change the column delimiter
Encoding selection
Select quote, escape, comment and NA identifiers

Importing data via code

Reading or writing tabulated data into or from a data frame is a quite common task in data analysis. The read.table or the read.csv functions for this.

df <- read.csv(file.csv) # or read.csv2 -> selection depends on the delimiter and seperator

df <- read.table(file.csv, header = FALSE, sep = “”,
                 dec = “.”, skip = 0, ...)

write.table(df, file = <file>, sep = “ “, dec = “.”, ...)

Reading data from csv files

Reading csv files is realized using the read.table function from R’s utils library. The function will return a data frame which contains the information of the csv file (example taken from here) .

df <- read.table(paste0(envrmt$path_data_csv, "/AI001_gebiet_flaeche.csv"),
                 skip = 4, header = TRUE, sep = ";", dec = ",")

As you can see, the read.table function gets several arguments (which is common for many functions). The first one gives the filename inclducing the path to the file.

skip = 4 tells the function to skip the first four lines (which are plain text lines in this case and not tabulated values)
header = TRUE tells the function, that the csv file has a header line which is used by read.table to name the columns of the returning data frame
sep = ";" defines the separator of the individual columns in the data frame
dec = "," defines the decimal separator used in the dataset

A note on the sequence of the arguments: the sequence of the arguments does not matter as long as you name them explicetly. If you do not use the argument identfier as it is the case for the first argument, the filename, in the example then the sequence matters. To get information on the default sequence and of course the general application of the each R function, type ?<function name> (e.g. ?read.table) in an R console.

After you executing the read.table function above, the content of the csv file is stored into a two dimensional data frame called df in the example above.

A quick way to check if everything is fine is to display the first few lines of the data file using the head function (without the 2, it will print 5 lines as a standard setting).

head(df,2)

##      X X.1                  X.2
## 1 1996  DG          Deutschland
## 2 1996  01   Schleswig-Holstein
##   Anteil.Siedlungs..und.Verkehrsfläche.an.Gesamtfl.
## 1                                              11,8
## 2                                              10,8
##   Anteil.Erholungsfläche.an.Gesamtfläche
## 1                                    0,7
## 2                                    0,7
##   Anteil.Landwirtschaftsfläche.an.Gesamtfläche
## 1                                         54,1
## 2                                         73,0
##   Anteil.Waldfläche.an.Gesamtfläche
## 1                              29,4
## 2                               9,3

Writing data to csv files

Writing data is as easy as reading it. Just use the write.table function.

write.table(df, file = paste0(envrmt$path_data_tmp, "new.csv"),
            sep = ",", dec = ".")

As you can see, you can define any column or decimal separator.

For more information have a look at e.g. the respective data importing and data exporting site at Quick R, have a look into the package documentation or search the web.

Alternative data I/O using RDS

Writing into CSV files is a good choice for data exchange into the non-R world. If you want to re-use the information stored in a data.frame or any other variable in R, writing the actual R object to a file connection will be of some benefit especially for more complex objects like model outputs or geo-datasets which do not represent the final information (because this would likely be shared to others using GeoTiff or other well recognized formats). To save an R object to a file connection, use the saveRDS function, to read it, use the readRDS function. The file extension .rds is generally used for that format.

If you stay within R for reading and writing R objects from and to data files, you could also use the serialization of readRDS and saveRDS.

saveRDS(df, file = paste0(envrmt$path_data_tmp, "new.rds"))

# Read data to different variable
df2 = readRDS(paste0(envrmt$path_data_tmp, "new.rds"))

saveRDS(df, file = <file>)

df = readRDS(<file>)

See the respective help pages for more details.

Reading or writing tabulated data into or from a data frame is a quite common task in data analysis. You could use the read.table function for this.

df <- read.table(<file>, header = FALSE, sep = “”,
                 dec = “.”, skip = 0, ...)

write.table(df, file = <file>, sep = “ “, dec = “.”, ...)

Writing into CSV files is a good choice for data exchange into the non-R world. If you want to re-use the information stored in a data.frame or any other variable in R, writing the actual R object to a file connection will be of some benefit especially for more complex objects like model outputs or geo-datasets which do not represent the final information (because this would likely be shared to others using GeoTiff or other well recognized formats). To save an R object to a file connection, use the saveRDS function, to read it, use the readRDS function. The file extension “rds” is generally used for that format.

saveRDS(df, file = <file>)

df = readRDS(<file>)