R Analysis - Revision history

Rb: /* Continuous variables */

2015-08-06T15:41:35Z

‎Continuous variables

Rb: /* Continuous variables */

2015-08-06T15:41:10Z

‎Continuous variables

Rb at 21:55, 4 August 2015

2015-08-04T21:55:15Z

Rb at 16:23, 4 August 2015

2015-08-04T16:23:18Z

Rb at 16:22, 4 August 2015

2015-08-04T16:22:06Z

Rb at 15:38, 4 August 2015

2015-08-04T15:38:03Z

Rb at 15:35, 4 August 2015

2015-08-04T15:35:08Z

Rb at 15:21, 4 August 2015

2015-08-04T15:21:43Z

Rb: /* Categorical Variables */

2015-08-04T15:07:23Z

‎Categorical Variables

Rb: /* Categorical Variables */

2015-08-04T15:06:34Z

‎Categorical Variables

@@ Line 252: / Line 252: @@
 A very nice feature of the <code>favstats</code> function is that you can calculate summary statistics easily for different subgroups. Let's say you want the summary statistics for wages but depending on how many kids younger than 6 a woman has. Just use the following command
-      favstats(~wage, , data=mydata)
+      favstats(~wage, groups = kidslt6, data=mydata)
 Check it out yourselves and in doubt check the help function to find out what the <code>groups = kidslt6</code> input does.

← Older revision		Revision as of 15:41, 6 August 2015
Line 249:		Line 249:

	this is really nice as it, by default disregarded the missing values. If you want these statistics for several variables, there are really two ways of doing this, either you merely replicate this like of code for all the variables for which you want the stats, or you use the code in the next subsection		this is really nice as it, by default disregarded the missing values. If you want these statistics for several variables, there are really two ways of doing this, either you merely replicate this like of code for all the variables for which you want the stats, or you use the code in the next subsection
		+
		+	A very nice feature of the <code>favstats</code> function is that you can calculate summary statistics easily for different subgroups. Let's say you want the summary statistics for wages but depending on how many kids younger than 6 a woman has. Just use the following command
		+
		+	favstats(~wage, , data=mydata)
		+
		+	Check it out yourselves and in doubt check the help function to find out what the <code>groups = kidslt6</code> input does.

	==== Advanced: using the dfapply function ====		==== Advanced: using the dfapply function ====

← Older revision		Revision as of 21:55, 4 August 2015
Line 1:		Line 1:
−	In this section we shall demonstrate how to do some basic data analysis on data in a dataframe. Here is an [~~http~~://youtu.be/~~szq0eZCpsgU~~?hd=1 online demonstration] of some of the material covered on this page.	+	In this section we shall demonstrate how to do some basic data analysis on data in a dataframe. Here is an [https://youtu.be/0pwRxhxG0tg?hd=1 online demonstration] of some of the material covered on this page.

	== Data Upload and Introduction ==		== Data Upload and Introduction ==

@@ Line 256: / Line 256: @@
        > mydata.sub2 <- subset(mydata,select=c("hours","husage","wage","huswage"))
        > dfapply(mydata.sub2,<span style="color:#ff0000">favstats</span>)
-       $hours
+       <source enclose=none>$</source>hours
         min Q1 median   Q3  max     mean       sd   n missing
   0    288 1516 4950 740.5764 871.3142 753       0
-       $husage
+       <source enclose=none>$</source>husage
         min Q1 median Q3 max     mean       sd   n missing
 38     46 52  60 45.12085 8.058793 753       0
-       $wage
+       <source enclose=none>$</source>wage
            min     Q1 median      Q3 max     mean       sd   n missing
 .1282 2.2626 3.4819 4.97075  25 4.177682 3.310282 428     325
-       $huswage
+       <source enclose=none>$</source>huswage
            min     Q1 median     Q3    max     mean       sd   n missing
 .4121 4.7883 6.9758 9.1667 40.509 7.482179 4.230559 753       0

@@ Line 228: / Line 228: @@
         Total 100.0000000 100.0000000
-You see that R delivered <span style="color:#00ff00">percentages</span> of women with certain numbers of children larger or equal to 6 years old, but it delivered two such columns of percentages, one for women who had <span style="color:#ff0000">at least 16 years </span>of education (equivalent to some postgrad degree) and another for those with <span style="color:#0000ff">fewer years</span> of education. The way in which we instructed R to condition on this was by using the following variable definition: <code>~kidsge6|educ>=16</code>; which implied that we wanted percentages for the kidsgt6 variable, but conditional on whether the womens' education was at least 16 years
+You see that R delivered <span style="color:#00ff00">percentages</span> of women with certain numbers of children larger or equal to 6 years old, but it delivered two such columns of percentages, one for women who had <span style="color:#ff0000">at least 16 years </span>of education (equivalent to some postgrad degree) and another for those with <span style="color:#0000ff">fewer years</span> of education. The way in which we instructed R to condition on this was by using the following variable definition: <code>~kidsge6|educ>=16</code>; which implied that we wanted percentages for the kidsge6 variable, but conditional on whether the womens' education was at least 16 years.
 === Continuous variables ===
@@ Line 234: / Line 234: @@
 There are a number of basic summary statistics that are part of every basic data toolbox. Being able to calculate means, medians and standard deviations for a set of data. Let's take a particular variable, the <source enclose=none>wage</source> variable. Try the following command:
-      mean(mydata<source enclose=none>$</source>wage, hours > 0)
+      mean(~wage, data=mydata)
-You could replace mean with median, sd, var, min or max (which all represent obvious sample summary statistics), the result is always that you will find an unpleasant <source enclose=none>NA</source>. Why is this? If you look again at the <source enclose=none>wage</source> data you will see that there are missing data in here (as we already discovered above). Now check the details of any of your function by, say, typing <source enclose=none>?mean</source> into the console. reading through the help function you will find that you will need to add the parameter <source enclose=none>na.rm=TRUE</source> to your function call. So:
+You could replace mean with median, sd, var, min or max (which all represent obvious sample summary statistics), the result is always that you will find an unpleasant <source enclose=none>NA</source>. Why is this? As we already discovered above, the wage variable has missing observations. Reading through the help function (<code>?mean</code> and then choosing the mosaic version) you will find that you will need to add an option to your function, <code>na.rm=TRUE</code> to your function call. So:
-      mean(mydata<source enclose=none>$</source>wage,na.rm=TRUE)
+      mean(~wage, data=mydata, na.rm=TRUE)
 will deliver the sample mean of <source enclose=none>4.177682</source>. This additional parameter essentially instructs the function mean to remove all NAs.
-While you could produce all sorts of summary statistics individually as just indicated, you could also obtain all in one go by using
+We already discussed how you can use the summary function to obtain a range of summary statistics, but annoyingly not the sample standard deviation. The good people who programmed the mosaic package got so annoyed by that that they have given us an alternative way to get a range of summary statistics which includes the standard deviation.
-      summary(mydata<source enclose=none>$</source>wage,na.rm=TRUE)
+      > favstats(~wage, data=mydata)
-which will return the mean, median, max, min, and quartiles (but annoyingly not the standard deviation).
+this is really nice as it, by default disregarded the missing values. If you want these statistics for several variables, there are really two ways of doing this, either you merely replicate this like of code for all the variables for which you want the stats, or you use the code in the next subsection
 == Re-classifying categorical/factor variables ==

@@ Line 212: / Line 212: @@
 The output is a contingency table that reports proportions (as we set <code>format = "prop"</code>). The important aspect here is how we indicated to R that it should be producing a cross table, <code><span style="color:#0000ff">~kidsge6+kidslt6</span></code>. The second variable was just added with a plus sign. Easy peazy, and you could actually add a 3rd variable and check out what sort of output you get (it get's quite unwieldy).
-You can also produce a cross tabulation by adding a second variable:
+     > tally(~kidsge6|educ>=16, data=mydata, margins = TRUE,<span style="color:#00ff00">format = "percent"</span>)
-     > table(mydata<source enclose=none>$</source>kidslt6,mydata<source enclose=none>$</source>kidsge6)
+You see that R delivered <span style="color:#00ff00">percentages</span> of women with certain numbers of children larger or equal to 6 years old, but it delivered two such columns of percentages, one for women who had <span style="color:#ff0000">at least 15 years </span>of education (equivalent to some postgrad degree) and another for those with <span style="color:#0000ff">fewer years</span> of education.
-which produces:
+=== Continuous variables ===
 There are a number of basic summary statistics that are part of every basic data toolbox. Being able to calculate means, medians and standard deviations for a set of data. Let's take a particular variable, the <source enclose=none>wage</source> variable. Try the following command:

@@ Line 190: / Line 190: @@
    118    26     3   753
-which tells you that there were 118 women with one child younger than 6 and that in total there were 753 responses. Let me go back to the command and explain what is happening. We called the function <code>tally</code> and we used three inputs<ref>As usual, you can check <code>?tally</code> to find out all about this function and how to use it.</ref>. Let's look at the second input first, <code>data=mydata</code>. This indicated to the function tally that it should be using data in the dataframe called mydata. The first input, <code>~kidslt6</code>, told the function to create a contingency table for the variable that counts the number of children under 6 years. The last input, <code>margins = TRUE</code>, ensured that we would get marginal counts, here the "Total". Some of these switches that are used to fine-tune how these functions work are operated with Boolean variables, <code>TRUE</code> meaning on and <code>FALSE</code> meaning off.
+which tells you that there were 118 women with one child younger than 6 and that in total there were 753 responses. Let me go back to the command and explain what is happening. We called the function <code>tally</code> and we used three inputs<ref>As usual, you can check <code>?tally</code> to find out all about this function and how to use it.</ref>. Let's look at the second input first, <code>data=mydata</code>. This indicated to the function tally that it should be using data in the dataframe called mydata. The first input, <code>~kidslt6</code>, told the function to create a contingency table for the variable that counts the number of children under 6 years. Don't worry about the use of the <code><span style="color:#0000ff">~</span></code> in front of the variable name. You don't need to understand why it is there, but it will make more sense after you learned about running regressions and producing graphs. The last input, <code>margins = TRUE</code>, ensured that we would get marginal counts, here the "Total". Some of these switches that are used to fine-tune how these functions work are operated with Boolean variables, <code>TRUE</code> meaning on and <code>FALSE</code> meaning off.
 You can also produce a cross tabulation by adding a second variable:

@@ Line 181: / Line 181: @@
       > tally(~kidslt6, data=mydata, margins = TRUE)
-If you get the following error message <code><span style="color:#0000ff">Error: could not find function "tally"</span></code> R is telling you that it has no idea what the "tally" function is. The most likely reason is that you havn't executed the <code>library(mosaic)</code> command yet. There us no better time to do it than now!
+If you get the following error message <code><span style="color:#ff0000">Error: could not find function "tally"</span></code> R is telling you that it has no idea what the "tally" function is. The most likely reason is that you havn't executed the <code>library(mosaic)</code> command yet. There us no better time to do it than now!
 The output for the tally function is