CODE:
Get the code used for this section here
Data frames, data import, and data export
As described above, a data frame is a type of data structure in R with
rows and columns, where different columns contain data with different
modes. A data frame is probably the most common data structure that you
will use for storing soil information data sets. Recall from before the
data frame that we created.
dat<- data.frame(profile_id= c("Chromosol","Vertosol","Sodosol"),
FID=c("a1","a10","a11"), easting=c(337859, 344059,347034),
northing=c(6372415,6376715,6372740), visted=c(TRUE, FALSE, TRUE))
dat
## profile_id FID easting northing visted
## 1 Chromosol a1 337859 6372415 TRUE
## 2 Vertosol a10 344059 6376715 FALSE
## 3 Sodosol a11 347034 6372740 TRUE
We can quickly assess the different modes of a data frame (or any other
object for that matter) by using the str (structure) function.
str(dat)
## 'data.frame': 3 obs. of 5 variables:
## $ profile_id: Factor w/ 3 levels "Chromosol","Sodosol",..: 1 3 2
## $ FID : Factor w/ 3 levels "a1","a10","a11": 1 2 3
## $ easting : num 337859 344059 347034
## $ northing : num 6372415 6376715 6372740
## $ visted : logi TRUE FALSE TRUE
The str is probably one of the most used R functions. It is great
for exploring the format and contents of any object created or imported.
reading data from files
The easiest way to create a data frame is to read in data from a file.
This is done using the function , which works with ASCII text files. The
function read.csv is also a utility function for reading in data files
with the stipulation that the data columns have a comma separation. 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.
Note that the column labels in the header have to be compatible with
R’s variable naming convention, or else R will make some changes as
they are read in (or will not read in the data correctly). So lets
import some real soil data. These soil data are some chemical and
physical properties from a collection of soil profiles sampled at
various locations in New South Wales, Australia.
## This will throw an error
soil.data<- read.table("USYD_soil1.txt", header=TRUE, sep=",")
str(soil.data)
head(soil.data)
However, you may find that an error occurs, saying something like that
the file does not exist. This is true as it has not been provided to
you. Rather, to use this data you will need to load up the previously
installed ithir package. See installation instructions if you have not installed already.
library(ithir)
data(USYD_soil1)
soil.data<- USYD_soil1
str(soil.data)
## 'data.frame': 166 obs. of 16 variables:
## $ PROFILE : int 1 1 1 1 1 1 2 2 2 2 ...
## $ Landclass : Factor w/ 4 levels "Cropping","Forest",..: 4 4 4 4 4 4 3 3 3 3 ...
## $ Upper.Depth : num 0 0.02 0.05 0.1 0.2 0.7 0 0.02 0.05 0.1 ...
## $ Lower.Depth : num 0.02 0.05 0.1 0.2 0.3 0.8 0.02 0.05 0.1 0.2 ...
## $ clay : int 8 8 8 8 NA 57 9 9 9 NA ...
## $ silt : int 9 9 10 10 10 8 10 10 10 10 ...
## $ sand : int 83 83 82 83 79 36 81 80 80 81 ...
## $ pH_CaCl2 : num 6.35 6.34 4.76 4.51 4.64 6.49 5.91 5.94 5.63 4.22 ...
## $ Total_Carbon: num 1.07 0.98 0.73 0.39 0.23 0.35 1.14 1.14 1.01 0.48 ...
## $ EC : num 0.168 0.137 0.072 0.034 NA 0.059 0.123 0.101 0.026 0.042 ...
## $ ESP : num 0.3 0.5 0.9 0.2 0.9 0.3 0.3 0.6 NA NA ...
## $ ExchNa : num 0.01 0.02 0.02 0 0.02 0.04 0.01 0.02 NA NA ...
## $ ExchK : num 0.71 0.47 0.52 0.38 0.43 0.46 0.7 0.56 NA NA ...
## $ ExchCa : num 3.17 3.5 1.34 1.03 1.5 9.13 2.92 3.2 NA NA ...
## $ ExchMg : num 0.59 0.6 0.22 0.22 0.5 5.02 0.51 0.5 NA NA ...
## $ CEC : num 5.29 3.7 2.86 2.92 2.6 ...
head(soil.data)
## PROFILE Landclass Upper.Depth Lower.Depth clay silt sand pH_CaCl2
## 1 1 native pasture 0.00 0.02 8 9 83 6.35
## 2 1 native pasture 0.02 0.05 8 9 83 6.34
## 3 1 native pasture 0.05 0.10 8 10 82 4.76
## 4 1 native pasture 0.10 0.20 8 10 83 4.51
## 5 1 native pasture 0.20 0.30 NA 10 79 4.64
## 6 1 native pasture 0.70 0.80 57 8 36 6.49
## Total_Carbon EC ESP ExchNa ExchK ExchCa ExchMg CEC
## 1 1.07 0.168 0.3 0.01 0.71 3.17 0.59 5.29
## 2 0.98 0.137 0.5 0.02 0.47 3.50 0.60 3.70
## 3 0.73 0.072 0.9 0.02 0.52 1.34 0.22 2.86
## 4 0.39 0.034 0.2 0.00 0.38 1.03 0.22 2.92
## 5 0.23 NA 0.9 0.02 0.43 1.50 0.50 2.60
## 6 0.35 0.059 0.3 0.04 0.46 9.13 5.02 14.96
When we import a file into R, it is good habit to look at its
structure (str) to ensure the data is as it should be. As can be seen,
this data set (frame) has 166 observations, and 15 columns. The head
function is also a useful exploratory function, which simply allows us
to print out the data frame, but only the first 6 rows of it (good for
checking data frame integrity). Note that you must specify
header=TRUE, or else R will interpret the row of labels as data. If
the file you are loading is not in the directory that R is working in
(the working directory, which can be checked with getwd() and changed
with setwd(file = ``filename'')). When setting the working
directory (setwd()), you can include the file path, but note that the
path should have forward, not backward slashes (or double backward
slashes, if you prefer).
The column separator function sep (an argument of read.table) lets
you tell R, where the column breaks or delimiters occur. In the
soil.data object, we specify that the data is comma separated. If you
do not specify a field separator, R assumes that any spaces or tabs
separate the data in your text file. However, any character data that
contain spaces must be surrounded by quotes (otherwise, R interprets
the data on either side of the white spaces as different elements).
Besides comma separators, tab separators are also common sep= "\"}, so
is sep="."} (full stop separator). Two consecutive separators will be
interpreted as a missing value. Conversely, with the default options,
you need to explicitly identify missing values in your data file with
NA (or any other character, as long as you tell R what it is with
the na.strings argument).
For some field separators, there are alternate functions that can be
used with the default arguments, e.g. read.csv, which is identical to
read.table, except default arguments differ. Also, R does not care
what the name of your file is, or what its extension is, as long as it
is an ASCII text file. A few other handy bits of information for using
read.table follow. You can include comments at the end of rows in your
data file—just precede them with a #. Also R will recognize NaN,
and Inf, and -Inf in input files.
Probably the easiest approach to handling missing values is to indicate
their presence with NA in the text file. R will automatically
recognize these as missing values. Alternatively, just leave the missing
values as they are (blank spaces), and let R do the rest.
which(is.na(soil.data$CEC))
## [1] 9 10 45 63 115
soil.data[8:11,]
## PROFILE Landclass Upper.Depth Lower.Depth clay silt sand pH_CaCl2
## 8 2 improved pasture 0.02 0.05 9 10 80 5.94
## 9 2 improved pasture 0.05 0.10 9 10 80 5.63
## 10 2 improved pasture 0.10 0.20 NA 10 81 4.22
## 11 2 improved pasture 0.38 0.50 36 8 56 6.48
## Total_Carbon EC ESP ExchNa ExchK ExchCa ExchMg CEC
## 8 1.14 0.101 0.6 0.02 0.56 3.2 0.5 4.0
## 9 1.01 0.026 NA NA NA NA NA NA
## 10 0.48 0.042 NA NA NA NA NA NA
## 11 0.18 0.053 0.4 0.04 0.49 6.0 2.3 8.6
In most cases, it makes sense to put your data into a text file for
reading into R. This can be done in various ways. Data download from
the internet are often in text files to begin with. Data can be entered
directly into a text file using a text editor. For data that are in
spreadsheet program such as Excel or JMP, there are facilities available
for saving these tabular frames to text files for reading into R.
This all may seem confusing, but it is really not that bad. Your best
bet is to play around with the different options, find one that you
like, and stick with it. Lastly, data frames can also be edited
interactively in R using the edit function. This is really only
useful for small data sets, and the function is not supported by RStudio
(you could try with using Tinn-R instead if you want to explore using
this function).
soil.data<- edit(soil.data)
Creating data frames manually
Data frames can be made manually using the data.frame function:
soil<- c("Chromosol", "Vertosol", "Organosol", "Anthroposol")
carbon<- c(2.1, 2.9, 5.5, 0.2)
dat<- data.frame(soil.type=soil, soil.OC=carbon)
dat
## soil.type soil.OC
## 1 Chromosol 2.1
## 2 Vertosol 2.9
## 3 Organosol 5.5
## 4 Anthroposol 0.2
While this approach is not an efficient way to enter data that could be
read in directly, it can be very handy for some applications, such as
the creation of customized summary tables. Note that column names are
specified using an equal sign. It is also possible to specify (or
change, or check) column names for an existing data frame using the
function names.
names(dat)<- c("soil","SOC")
dat
## soil SOC
## 1 Chromosol 2.1
## 2 Vertosol 2.9
## 3 Organosol 5.5
## 4 Anthroposol 0.2
Row names can be specified in the data.frame function with the
row.names argument.
dat<- data.frame(soil.type=soil, soil.OC=carbon,
row.names=c("Ch","Ve","Or","An"))
dat
## soil.type soil.OC
## Ch Chromosol 2.1
## Ve Vertosol 2.9
## Or Organosol 5.5
## An Anthroposol 0.2
Specifying row names can be useful if you want to index data, which will
be covered later. Row names can also be specified for an existing data
frame with the rownames function (not to be confused with the
row.names argument).
Working with data frames
So what do you do with data in R once it is in a data frame? Commonly,
the data in a data frame will be used in some type of analysis or
plotting procedure. It is usually necessary to be able to select and
identify specific columns (i.e. vectors) within data frames. There are
two ways to specify a given column of data within a data frame. The
first is to use the $ notation. To see what the column names are, we
can use the names function. Using our soil.data set:
names(soil.data)
## [1] "PROFILE" "Landclass" "Upper.Depth" "Lower.Depth" "clay"
## [6] "silt" "sand" "pH_CaCl2" "Total_Carbon" "EC"
## [11] "ESP" "ExchNa" "ExchK" "ExchCa" "ExchMg"
## [16] "CEC"
The $ just uses a $ between the data frame name and column name to
specify a particular column. Say we want to look at the ESP column,
which is the acronym for exchangeable sodium percentage.
soil.data$ESP
## [1] 0.3 0.5 0.9 0.2 0.9 0.3 0.3 0.6 NA NA 0.4 0.9 0.2 0.1 NA
## [16] 0.4 0.5 0.7 0.2 0.1 NA 0.2 0.3 NA 0.8 0.6 0.8 0.9 0.9 1.1
## [31] 0.5 0.6 1.1 0.2 0.6 1.1 0.4 0.3 0.5 1.0 2.2 0.1 0.1 0.1 NA
## [46] 0.1 0.1 0.4 NA 0.2 0.1 0.3 0.4 0.1 0.4 0.1 0.1 NA 0.1 0.3
## [61] NA 0.1 NA 0.2 1.8 2.6 0.2 13.0 0.0 0.1 0.3 0.1 0.1 0.3 0.0
## [76] 0.3 0.6 0.9 0.4 NA NA 2.4 0.2 0.3 0.2 0.0 0.1 0.4 NA 0.3
## [91] 0.3 0.2 0.3 0.6 0.3 0.2 0.7 0.3 0.4 1.0 7.9 6.1 5.7 5.2 4.7
## [106] 2.9 5.8 7.2 9.6 NA 17.4 NA 11.1 6.4 NA 4.0 12.1 21.2 2.2 1.9
## [121] NA 4.0 13.2 0.9 0.8 0.5 0.2 0.4 NA 1.2 0.6 0.2 1.0 0.4 0.6
## [136] 0.1 0.4 NA 0.7 0.5 0.7 0.9 4.8 3.8 4.9 6.2 10.4 16.4 2.7 NA
## [151] 1.2 0.5 1.9 2.0 2.1 1.9 1.8 3.5 7.7 2.7 1.8 0.8 0.5 0.5 0.3
## [166] 0.9
Although it is handy to think of data frame columns to have a vertical
orientation, this orientation is not present when they are printed
individually—instead, elements are printed from left to right, and then
top to bottom. The expression soil.data$ESP could be used just as you
would for any other vector. For example:
mean(soil.data$ESP)
## [1] NA
R can not calculate the mean because of the NA values in the vector.
Lets remove them first using the na.omit function.
mean(na.omit(soil.data$ESP))
## [1] 1.99863
The second option for working with individual columns within a data
frame is to use the commands attach and detach. Both of these
functions take a data frame as an argument. attaching a data frame
puts all the columns within the that data frame into R’s search path,
and they can be called by using their names alone without the $
notation.
attach(soil.data)
ESP
## [1] 0.3 0.5 0.9 0.2 0.9 0.3 0.3 0.6 NA NA 0.4 0.9 0.2 0.1 NA
## [16] 0.4 0.5 0.7 0.2 0.1 NA 0.2 0.3 NA 0.8 0.6 0.8 0.9 0.9 1.1
## [31] 0.5 0.6 1.1 0.2 0.6 1.1 0.4 0.3 0.5 1.0 2.2 0.1 0.1 0.1 NA
## [46] 0.1 0.1 0.4 NA 0.2 0.1 0.3 0.4 0.1 0.4 0.1 0.1 NA 0.1 0.3
## [61] NA 0.1 NA 0.2 1.8 2.6 0.2 13.0 0.0 0.1 0.3 0.1 0.1 0.3 0.0
## [76] 0.3 0.6 0.9 0.4 NA NA 2.4 0.2 0.3 0.2 0.0 0.1 0.4 NA 0.3
## [91] 0.3 0.2 0.3 0.6 0.3 0.2 0.7 0.3 0.4 1.0 7.9 6.1 5.7 5.2 4.7
## [106] 2.9 5.8 7.2 9.6 NA 17.4 NA 11.1 6.4 NA 4.0 12.1 21.2 2.2 1.9
## [121] NA 4.0 13.2 0.9 0.8 0.5 0.2 0.4 NA 1.2 0.6 0.2 1.0 0.4 0.6
## [136] 0.1 0.4 NA 0.7 0.5 0.7 0.9 4.8 3.8 4.9 6.2 10.4 16.4 2.7 NA
## [151] 1.2 0.5 1.9 2.0 2.1 1.9 1.8 3.5 7.7 2.7 1.8 0.8 0.5 0.5 0.3
## [166] 0.9
Note that when you are done using the individual columns, it is good
practice to detach your data frame. Once the data frame is detached,
R will no longer know what you mean when you specify the name of the
column alone:
## This will throw an error
detach(soil.data)
ESP
If you modify a variable that is part of an attached data frame, the data within the data frame remain unchanged; you are actually working with a copy of the data frame.
Another option (for selecting particular columns) is to use the square
braces [] to specify the column you want. Using the square braces to
select the ESP column from our data set you would use:
soil.data[,10]
## [1] 0.168 0.137 0.072 0.034 NA 0.059 0.123 0.101 0.026 0.042 0.053 0.072
## [13] 0.293 0.092 0.082 0.055 0.048 0.088 0.162 0.166 0.088 0.093 0.075 0.175
## [25] 0.269 0.083 0.085 0.048 0.044 0.295 0.039 0.036 0.049 0.040 0.021 0.025
## [37] 0.118 0.061 0.059 0.043 0.063 0.159 0.161 0.126 0.049 0.037 0.031 0.033
## [49] 0.152 0.126 0.106 0.068 0.047 0.029 0.041 0.122 0.098 0.076 0.039 0.033
## [61] 0.583 0.183 NA 0.089 0.031 0.042 0.718 0.499 0.081 0.063 0.042 0.265
## [73] 0.194 0.094 0.051 0.079 0.304 0.511 0.118 0.031 0.042 0.028 0.152 0.116
## [85] 0.076 0.035 0.036 0.048 0.950 0.511 0.209 0.068 0.048 0.044 0.946 0.510
## [97] 0.175 0.175 0.115 0.207 0.167 0.156 0.125 0.960 0.910 0.121 0.087 0.175
## [109] 0.117 0.069 0.223 1.271 0.301 0.180 0.090 0.033 0.214 0.292 0.105 0.109
## [121] 0.077 0.034 0.073 0.493 0.246 0.119 0.057 0.056 0.191 0.243 0.165 0.052
## [133] 0.064 0.228 0.125 0.056 0.039 0.175 0.417 0.434 0.352 0.115 0.125 0.235
## [145] 0.170 0.105 0.134 0.820 0.245 0.163 0.116 0.050 0.142 0.618 0.347 0.146
## [157] 0.079 0.052 0.139 0.268 0.146 0.061 0.035 0.038 0.044 0.060
Here you are specifying the column in the tenth position, which as you
should check is the ESP column. To use the square braces the row
position precedes to comma, and the column position proceeds to comma.
By leaving a blank space in front of the comma, we are essentially
instruction R to print out the whole column. You may be able to
surmise that it is also possible to subset a selection of columns quite
efficiently with this square brace method. We will use the square braces
more a little later on.
The $ notation can also be used to add columns to a data frame. For
example, if we want to express our Upper.Depth and Lower.Depth
columns in cm rather than m we could do the following.
soil.data$Upper<- soil.data$Upper.Depth*100
soil.data$Lower<- soil.data$Lower.Depth*100
head(soil.data)
## PROFILE Landclass Upper.Depth Lower.Depth clay silt sand pH_CaCl2
## 1 1 native pasture 0.00 0.02 8 9 83 6.35
## 2 1 native pasture 0.02 0.05 8 9 83 6.34
## 3 1 native pasture 0.05 0.10 8 10 82 4.76
## 4 1 native pasture 0.10 0.20 8 10 83 4.51
## 5 1 native pasture 0.20 0.30 NA 10 79 4.64
## 6 1 native pasture 0.70 0.80 57 8 36 6.49
## Total_Carbon EC ESP ExchNa ExchK ExchCa ExchMg CEC Upper Lower
## 1 1.07 0.168 0.3 0.01 0.71 3.17 0.59 5.29 0 2
## 2 0.98 0.137 0.5 0.02 0.47 3.50 0.60 3.70 2 5
## 3 0.73 0.072 0.9 0.02 0.52 1.34 0.22 2.86 5 10
## 4 0.39 0.034 0.2 0.00 0.38 1.03 0.22 2.92 10 20
## 5 0.23 NA 0.9 0.02 0.43 1.50 0.50 2.60 20 30
## 6 0.35 0.059 0.3 0.04 0.46 9.13 5.02 14.96 70 80
Many data frames that contain real data will have some missing
observations. R has several tools for working with these observations.
For starters, the na.omit function can be used for removing NAs from
a vector. Working again with the ESP column of our soil.data set:
soil.data$ESP
## [1] 0.3 0.5 0.9 0.2 0.9 0.3 0.3 0.6 NA NA 0.4 0.9 0.2 0.1 NA
## [16] 0.4 0.5 0.7 0.2 0.1 NA 0.2 0.3 NA 0.8 0.6 0.8 0.9 0.9 1.1
## [31] 0.5 0.6 1.1 0.2 0.6 1.1 0.4 0.3 0.5 1.0 2.2 0.1 0.1 0.1 NA
## [46] 0.1 0.1 0.4 NA 0.2 0.1 0.3 0.4 0.1 0.4 0.1 0.1 NA 0.1 0.3
## [61] NA 0.1 NA 0.2 1.8 2.6 0.2 13.0 0.0 0.1 0.3 0.1 0.1 0.3 0.0
## [76] 0.3 0.6 0.9 0.4 NA NA 2.4 0.2 0.3 0.2 0.0 0.1 0.4 NA 0.3
## [91] 0.3 0.2 0.3 0.6 0.3 0.2 0.7 0.3 0.4 1.0 7.9 6.1 5.7 5.2 4.7
## [106] 2.9 5.8 7.2 9.6 NA 17.4 NA 11.1 6.4 NA 4.0 12.1 21.2 2.2 1.9
## [121] NA 4.0 13.2 0.9 0.8 0.5 0.2 0.4 NA 1.2 0.6 0.2 1.0 0.4 0.6
## [136] 0.1 0.4 NA 0.7 0.5 0.7 0.9 4.8 3.8 4.9 6.2 10.4 16.4 2.7 NA
## [151] 1.2 0.5 1.9 2.0 2.1 1.9 1.8 3.5 7.7 2.7 1.8 0.8 0.5 0.5 0.3
## [166] 0.9
na.omit(soil.data$ESP)
## [1] 0.3 0.5 0.9 0.2 0.9 0.3 0.3 0.6 0.4 0.9 0.2 0.1 0.4 0.5 0.7
## [16] 0.2 0.1 0.2 0.3 0.8 0.6 0.8 0.9 0.9 1.1 0.5 0.6 1.1 0.2 0.6
## [31] 1.1 0.4 0.3 0.5 1.0 2.2 0.1 0.1 0.1 0.1 0.1 0.4 0.2 0.1 0.3
## [46] 0.4 0.1 0.4 0.1 0.1 0.1 0.3 0.1 0.2 1.8 2.6 0.2 13.0 0.0 0.1
## [61] 0.3 0.1 0.1 0.3 0.0 0.3 0.6 0.9 0.4 2.4 0.2 0.3 0.2 0.0 0.1
## [76] 0.4 0.3 0.3 0.2 0.3 0.6 0.3 0.2 0.7 0.3 0.4 1.0 7.9 6.1 5.7
## [91] 5.2 4.7 2.9 5.8 7.2 9.6 17.4 11.1 6.4 4.0 12.1 21.2 2.2 1.9 4.0
## [106] 13.2 0.9 0.8 0.5 0.2 0.4 1.2 0.6 0.2 1.0 0.4 0.6 0.1 0.4 0.7
## [121] 0.5 0.7 0.9 4.8 3.8 4.9 6.2 10.4 16.4 2.7 1.2 0.5 1.9 2.0 2.1
## [136] 1.9 1.8 3.5 7.7 2.7 1.8 0.8 0.5 0.5 0.3 0.9
## attr(,"na.action")
## [1] 9 10 15 21 24 45 49 58 61 63 80 81 89 110 112 115 121 129 138
## [20] 150
## attr(,"class")
## [1] "omit"
Although the result does contain more than just the non-NA values,
only the non-NA values will be used in subsequent operations. Note
that the result of na.omit contains more information than just the
non-NA values. This function can also be applied to complete data
frames. In this case, any row with an NA is removed (so be careful
with its usage).
soil.data.cleaned<- na.omit(soil.data)
It is often necessary to identify NAs present in a data structure. The
is.na function can be used for this. It can also be negated using the
``!’’ character.
is.na(soil.data$ESP)
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE
## [13] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [49] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [61] TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [121] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
Writing data to files
With R, it is easy to write data to files. The function write.table
is usually the best function for this purpose. Given only a data frame
and a file name, this function will write the data contained in the data
frame to a text file. There are a number of arguments that can be
controlled with this function as shown below (also look at the help
file).
`write.table(x, file = "", append = FALSE, quote = TRUE, sep = " ", eol = "\", na = "NA", dec = ".", row.names = TRUE, col.names = TRUE, qmethod = c("escape", "double"), fileEncoding = "")`
The important ones (or most frequently used) are the column separator
sep argument, and whether of not you want to keep the column and row
names (col.names and row.names respectively). For example, if we
want to write soil.data to text file (``file name.txt’’), retaining
the column names, not retaining row names, and having a tab delimited
column separator, we would use:
write.table(soil.data, file= "file name.txt",
col.names=TRUE, row.names=FALSE, sep="\t")
Setting the append argument to TRUE lets you add data to a file that
already exists.
The write.table function can not be used with all data structures in
R (like lists for example). However, it can be used for such things as
vectors and matrices.
Exercises
-
Using the
soil.dataobject, determine the minimum and maximum soil pH (PH_CaCl2) in the data frame. Next add a new column to the data frame that contains the log10 of soil carbonTotal_Carbon. -
Create a new data frame that contains the mean SOC, pH, and clay of the data set. Write out the summary to a new file using the default options. Finally, try changing the separator to a tab and write to a new file.
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There are a number of
NAvalues in the data set. We want to remove them. Could this be done in one step i.e. delete every row that contains anNA? Is this appropriate? How would you go about ensuring that no data is lost? Can you do this? or perhaps—do this!