R Data - Revision history

Rb at 11:31, 30 October 2015

2015-10-30T11:31:26Z

Rb at 12:46, 27 August 2015

2015-08-27T12:46:53Z

Rb at 13:13, 9 July 2015

2015-07-09T13:13:28Z

Rb at 13:11, 9 July 2015

2015-07-09T13:11:56Z

Rb at 13:10, 9 July 2015

2015-07-09T13:10:46Z

Rb at 20:38, 22 June 2015

2015-06-22T20:38:54Z

Rb at 20:35, 22 June 2015

2015-06-22T20:35:41Z

Rb at 20:34, 22 June 2015

2015-06-22T20:34:54Z

Rb at 21:36, 14 January 2015

2015-01-14T21:36:16Z

Rb: /* Data Import from a R Data file */

2015-01-14T21:35:37Z

‎Data Import from a R Data file

@@ Line 96: / Line 96: @@
 * character, which are text variables. These are not used here, but they could be names.
 * NA, these indicate missing values in R.
 == Dealing with missing data ==

← Older revision		Revision as of 12:46, 27 August 2015
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	Confirm that all variables have the correct type using <source enclose=none>str(mydata)</source>.		Confirm that all variables have the correct type using <source enclose=none>str(mydata)</source>.
		+
		+	=== Removing NAs ===
		+
		+	Sometimes it may be appropriate to remove all observations with any missing data from any further analysis. But make sure you think carefully about this. Only if the missing values are missing at random this would be uncontroversial.
		+
		+	Once you decided that it is appropriate to do so, you can do this with the following command
		+
		+	<source>
		+	mydata <- na.omit(mydata) # Removes observations with missing values
		+	</source>
		+
		+	R will remove all entries that have any missing values.

← Older revision		Revision as of 13:13, 9 July 2015
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	It is the <source enclose=none>na.strings</source> option that tells R to immediatly assign a NA to all entries that are merely a decimal point. Let's say that there were some missing values that, in the original spreadsheet were labeled as "-999" and some as "." then the additional input would have been <source enclose=none>na.strings= c(".","-999")</source>, remembering that we could combine several names to a list using the <source enclose=none>na.strings= c( )</source> function.		It is the <source enclose=none>na.strings</source> option that tells R to immediatly assign a NA to all entries that are merely a decimal point. Let's say that there were some missing values that, in the original spreadsheet were labeled as "-999" and some as "." then the additional input would have been <source enclose=none>na.strings= c(".","-999")</source>, remembering that we could combine several names to a list using the <source enclose=none>na.strings= c( )</source> function.
	Return to the R [[R\|Start page]].		Return to the R [[R\|Start page]].
		+	Your script could now look a much simple:
		+
		+	<source>
		+	# This is my first R script!
		+	setwd("T:/ECLR/R/FirstSteps") # This sets the working directory
		+	mydata <- read.csv("mroz.csv",na.strings = ".") # Opens mroz.csv from working directory
		+	</source>
		+
		+	Confirm that all variables have the correct type using <source enclose=none>str(mydata)</source>.

@@ Line 146: / Line 146: @@
 === Converting to NAs during import ===
-The issue R had as it imported the csv file was that it saw entries with just decimal points and it decided (quite rightly) that these were not numbers and hence gave the variables a factor type. Fortunately there is an easier way to do that. We can let R know that when it is importing data it should immediately assign a NA to all values of the type ".". This is done by means of an additional and optional input into the <source>read.csv</source> function.
+The issue R had as it imported the csv file was that it saw entries with just decimal points and it decided (quite rightly) that these were not numbers and hence gave the variables a factor type. Fortunately there is an easier way to do that. We can let R know that when it is importing data it should immediately assign a NA to all values of the type ".". This is done by means of an additional and optional input into the <source enclose=none>read.csv</source> function.
       mydata <- read.csv("mroz.csv",na.strings = ".")
-It is the <source>na.strings</source> option that tells R to immediatly assign a NA to all entries that are merely a decimal point. Let's say that there were some missing values that, in the original spreadsheet were labeled as "-999" and some as "." then the additional input would have been <source>na.strings= c(".","-999")</source>, remembering that we could combine several names to a list using the <source>na.strings= c( )</source> function.
+It is the <source enclose=none>na.strings</source> option that tells R to immediatly assign a NA to all entries that are merely a decimal point. Let's say that there were some missing values that, in the original spreadsheet were labeled as "-999" and some as "." then the additional input would have been <source enclose=none>na.strings= c(".","-999")</source>, remembering that we could combine several names to a list using the <source enclose=none>na.strings= c( )</source> function.
 Return to the R [[R|Start page]].

@@ Line 96: / Line 96: @@
 * character, which are text variables. These are not used here, but they could be names.
 * NA, these indicate missing values in R.
 Let's try and explore why wage and lwage are treated as categorical variables. The 374 levels merely indicates that R found 374 different values in these variables. Something seems to be special about these variables. Go back to the spreadsheet view of mydata and scroll down and keep an eye out for the values in the wage column.
@@ Line 111: / Line 113: @@
 Try them both in your Console. We will soon learn that it is important to know how to address individual variables from a dataframe.
 As turns out, converting factors to num in R isn't the easiest of tasks. So, the easy solution is to go to your csv file and use Excel to replace all cells that have a full stop only in a cell, ".", with "NA". But be careful to choose the replacement option "Match entire cell contents"! In any case here is how you do it in R. And I shall describe it as it introduces you best helper: GOOGLE (other search engines exist!). Type "R how to convert factor to numeric" into your search engine and you will find the solution.
@@ Line 138: / Line 144: @@
 </source>
 Return to the R [[R|Start page]].

← Older revision		Revision as of 20:38, 22 June 2015
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	</source>		</source>

−	~~=== Re-classifying categorical/factor variables ===~~

−	When you have categorical data you may often want to re-classify your categories into new, usually broader categories. In the current data-set this isn't really an issue, but let's say we did have an ethnicity variable in our dataframe and for arguments sake assume that this variable these data are in <source enclose=none>mydata$Ethnicity</source> and let's assume that these data are encoded as a factor variable.
−
−	~~The reason for re-classifying (or re-coding) is that sometimes we will have too small categories. Too find out~~

	Return to the R [[R\|Start page]].		Return to the R [[R\|Start page]].

@@ Line 142: / Line 142: @@
 When you have categorical data you may often want to re-classify your categories into new, usually broader categories. In the current data-set this isn't really an issue, but let's say we did have an ethnicity variable in our dataframe and for arguments sake assume that this variable these data are in <source enclose=none>mydata$Ethnicity</source> and let's assume that these data are encoded as a factor variable.
-The reason for re-classifying (or re-coding) is that sometimes we will have too small categories
+The reason for re-classifying (or re-coding) is that sometimes we will have too small categories. Too find out
 Return to the R [[R|Start page]].

← Older revision		Revision as of 20:34, 22 June 2015
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	</source>		</source>

		+	=== Re-classifying categorical/factor variables ===
		+
		+	When you have categorical data you may often want to re-classify your categories into new, usually broader categories. In the current data-set this isn't really an issue, but let's say we did have an ethnicity variable in our dataframe and for arguments sake assume that this variable these data are in <source enclose=none>mydata$Ethnicity</source> and let's assume that these data are encoded as a factor variable.
		+
		+	The reason for re-classifying (or re-coding) is that sometimes we will have too small categories

	Return to the R [[R\|Start page]].		Return to the R [[R\|Start page]].

@@ Line 39: / Line 39: @@
 R has it's own way of saving data, its own data format. This means you don't always have to import a datafile from a csv file. If you are frequently using the same dataset you may want to save the data in this data format.
-In the above we created a R object, a dataframe, called <source>mydata</source>. The way in which you save this in a R datafile is by using the following command:
+In the above we created a R object, a dataframe, called <source enclose=none>mydata</source>. The way in which you save this in a R datafile is by using the following command:
       save(mydata, file = "test.RData")
-This will save the object mydata in the file "test.RData". If you had another object in your workspace, it could be another dataframe, or a variable, say called <source>testvar</source>, then you could save both by using:
+This will save the object mydata in the file "test.RData". If you had another object in your workspace, it could be another dataframe, or a variable, say called <source enclose=none>testvar</source>, then you could save both by using:
       save(mydata, testvar, file = "test.RData")

@@ Line 35: / Line 35: @@
       mydata <- read.csv("mroz.csv", header = FALSE)
-=== Data Import from a R Data file===
+=== Saving to and Importing from a R Data file===
-Let's say
+R has it's own way of saving data, its own data format. This means you don't always have to import a datafile from a csv file. If you are frequently using the same dataset you may want to save the data in this data format.
-     load("FILENAME.Rdata")   # assuming that the file is in your working directory
+In the above we created a R object, a dataframe, called <source>mydata</source>. The way in which you save this in a R datafile is by using the following command:
-This will load
+     save(mydata, file = "test.RData")
 == Data Types ==