The document discusses various methods for importing and exporting data in R, focusing on functions such as read.table, read.csv, and other specialized functions for different file formats. It also covers how to interface with databases like PostgreSQL and handle spatial data using PostGIS. Furthermore, it includes details on saving data in various formats, emphasizing the importance of preserving data attributes.

1. Yusuf YIGINI, PhD - FAO, Land and Water Division (CBL)
GSP - Eurasian Soil
Partnership - Dijital
Toprak Haritalama ve
Modelleme Egitimi
Izmir, Turkiye
21-25 Agustos 2017

2. R - Import , Export

3. R- Import Data
The easiest way to create a data frame is to read in
data from a file—this is done using the function
read.table, which works with ASCII text files. Data
can be read in from other files as well, using
different functions, but read.table is the most
commonly used approach. R is very flexible in how
it reads in data from text files.

4. read.csv and read.csv2 are identical to read.table except for
the defaults. They are intended for reading ‘comma separated
value’ files (‘.csv’) or (read.csv2) the variant used in countries
that use a comma as decimal point and a semicolon as field
separator. Similarly, read.delim and read.delim2 are for
reading delimited files, defaulting to the TAB character for the
delimiter.
In various countries, as the comma “,” character
serves as the decimal point, the function
read.csv2 should be used instead!

5. R- Import Data
read.table("MASIS_SOC.csv", sep = ",")
read.csv("MASIS_SOC.csv", sep = ",")
read.csv2("MASIS_SOC.csv")
read.delim("MASIS_SOC.csv")
read.delim("MASIS_SOC.csv", sep = ",")

6. R- Import Data
read.table("MASIS_SOC.csv", sep = ",")
read.csv("MASIS_SOC.csv", sep = ",")
read.csv2("MASIS_SOC.csv")
read.delim("MASIS_SOC.csv")
read.delim("MASIS_SOC.csv", sep = ",")

7. R- Import Data
Unless you take any special action, read.table() reads all the
columns as character vectors and then tries to select a
suitable class for each variable in the data frame. It tries in
logical, integer, numeric and complex. If all of these fail, the
variable is converted to a factor.
More about Factors: https://www.stat.berkeley.edu/classes/s133/factors.html

8. R- Import Data
> is.na(SOC)
Id UpperDepth LowerDepth SOC Lambda tsme Region
[1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[3,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[5,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[6,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[7,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[8,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[9,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[10,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE
...

9. R- Import Data
> anyNA(SOC)
[1] TRUE
> sum(is.na(SOC$SOC))
[1] 1

10. R- Import Data
Importing from other statistical systems
install.packages("foreign")
library(foreign)
stata <- read.dta(“salary.dta”)
spss <- read.spss(“salary.sav”, to.data.frame=TRUE)
sasxport <- read.xport(“salary.xpt”)
epiinfo <- read.epiinfo(“salary.rec”) …
Note: The foreign package is in the standard distribution. It
handles import and export of data.

11. R- Import Data
Reading Data from Web
> read.table("http://www.cdc.noaa.gov/data/correlation/nao.data",skip=1,
nrow=70, na.strings="-99.90")
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
1 1948 NA NA NA NA NA NA NA NA NA NA NA NA
2 1949 NA NA NA NA NA NA NA NA NA NA NA NA
3 1950 0.56 0.01 -0.78 0.65 -0.50 0.25 -1.23 -0.19 0.39 1.43 -1.46 -1.03
4 1951 -0.42 0.35 -1.47 -0.38 -0.50 -1.35 1.39 -0.41 -1.18 2.54 -0.54 1.13
5 1952 0.57 -1.38 -1.97 0.95 -0.99 -0.10 -0.06 -0.49 -0.38 -0.28 -1.32 -0.49
6 1953 -0.12 -1.00 -0.45 -1.96 -0.56 1.41 0.43 -1.04 -0.19 1.95 0.96 -0.52
7 1954 -0.08 0.40 -1.27 1.31 -0.03 0.06 -0.57 -2.57 -0.28 1.16 0.29 0.55
8 1955 -2.65 -1.71 -0.96 -0.60 -0.26 -0.80 1.78 1.25 0.46 -1.09 -1.49 0.07
9 1956 -0.76 -1.71 -0.46 -1.30 2.10 0.41 -0.72 -1.89 0.38 1.47 0.40 0.00

12. R- Import Data
Reading Data from Web
> read.table("http://www.cdc.noaa.gov/data/correlation/nao.data",skip=1,
nrow=70)
V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13
1 1948 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90
2 1949 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90 -99.90
3 1950 0.56 0.01 -0.78 0.65 -0.50 0.25 -1.23 -0.19 0.39 1.43 -1.46 -1.03
4 1951 -0.42 0.35 -1.47 -0.38 -0.50 -1.35 1.39 -0.41 -1.18 2.54 -0.54 1.13
5 1952 0.57 -1.38 -1.97 0.95 -0.99 -0.10 -0.06 -0.49 -0.38 -0.28 -1.32 -0.49
6 1953 -0.12 -1.00 -0.45 -1.96 -0.56 1.41 0.43 -1.04 -0.19 1.95 0.96 -0.52
7 1954 -0.08 0.40 -1.27 1.31 -0.03 0.06 -0.57 -2.57 -0.28 1.16 0.29 0.55
8 1955 -2.65 -1.71 -0.96 -0.60 -0.26 -0.80 1.78 1.25 0.46 -1.09 -1.49 0.07
9 1956 -0.76 -1.71 -0.46 -1.30 2.10 0.41 -0.72 -1.89 0.38 1.47 0.40 0.00
10 1957 0.71 -0.32 -1.73 0.39 -0.68 -0.42 -1.16 -0.83 -1.47 1.95 0.63 0.02

13. R- Import Data
Reading Data from Web
> read.table("http://www.cdc.noaa.gov/data/correlation/nao.data")
Error in scan(file = file, what = what, sep = sep, quote = quote, dec =
dec, : line 1 did not have 13 elements

14. R- Import Data
PostGIS Spatial Databases
PostGIS is a spatial database extender for
PostgreSQL object-relational database. It adds
support for geographic objects allowing location
queries to be run in SQL.

15. R- Import Data
PostGIS Spatial Databases
PostGIS is a spatial database extender for
PostgreSQL object-relational database. It adds
support for geographic objects allowing location
queries to be run in SQL.

16. R- Import Data
To read data from PostgreSQL into R, postGIStools
provides the get_postgis_query function. Like the
dbGetQuery function in PostgreSQL, it requires a
connection object and a SQL statement, which in
this case must be a SELECT statement. In addition,
the user may identify a geometry and/or hstore field
by name.

17. R- Read-Import Data
library(RPostgreSQL)
library(postGIStools)
con <- dbConnect(PostgreSQL(), dbname = "gsp_db", user = "GSP",
host = "bla-bla.com",
password = "587vn34m98dhu")
countries <- get_postgis_query(con, "SELECT * FROM country
WHERE SOCStck> 102",
geom_name = "geom", hstore_name =
"translations")

18. R- Save Data
> write.csv(SOC,file = "SOCData.csv")
The easiest way to do this is to use write.csv(). By default, write.
csv() includes row names, but these are usually unnecessary and
may cause confusion.

19. R- Save Data
> write.csv(SOC,file = "SOCData.csv")
The easiest way to do this is to use write.csv(). By default, write.
csv() includes row names, but these are usually unnecessary and
may cause confusion.

20. Saving in R data format
# Save in a text format that can be easily loaded in R
> dump("data", "data.Rdmpd")
# Can save multiple objects:
> dump(c("data", "data1"), "data.Rdmpd")
# To load the data again:
source("data.Rdmpd")
# When loaded, the original data names will automatically be used.
write.csv() and write.table() are best for interoperability with other
data analysis programs. They will not, however, preserve special
attributes of the data structures, such as whether a column is a
character type or factor, or the order of levels in factors. In order to
do that, it should be written out in a special format for R.
More on: Rdmpd: http://www.cookbook-r.com/Data_input_and_output/Writing_data_to_a_file/

21. Saving in R data format
# Save in a text format that can be easily loaded in R
> dump("data", "data.Rdmpd")
# Can save multiple objects:
> dump(c("data", "data1"), "data.Rdmpd")
# To load the data again:
source("data.Rdmpd")
# When loaded, the original data names will automatically be used.
write.csv() and write.table() are best for interoperability with other
data analysis programs. They will not, however, preserve special
attributes of the data structures, such as whether a column is a
character type or factor, or the order of levels in factors. In order to
do that, it should be written out in a special format for R.
More on: Rdmpd: http://www.cookbook-r.com/Data_input_and_output/Writing_data_to_a_file/