Basic R for Exploratory Data Analysis"
Based on Andy’s Week 6 Hands On Lab (week6lab.R)
Uploaded by: Mauricio Alarcon rmalarc@msn.com
-> Everything below is as-is in Andy’s Rmd project. This is kind of FUN!!! <-
- Load the diamonds dataset, which is included with the ggplot2 package. Identify which variables in the diamond set are numeric, and which are categorical (factors).
#install.packages("ggplot2")
require(ggplot2)## Loading required package: ggplot2head(diamonds)## carat cut color clarity depth table price x y z
## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43
## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31
## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31
## 4 0.29 Premium I VS2 62.4 58 334 4.20 4.23 2.63
## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75
## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48# Identify which variables in the diamonds dataset are numeric, and which
# variables are categorical
str(diamonds)## 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...Generate Descriptive Statistics
- Generate summary level descriptive statistics: Show the mean, median, 25th and 75th quartiles, min, and max for each of the applicable variables in diamonds.
See also [http://www.statmethods.net/stats/frequencies.html]
summary(diamonds)## carat cut color clarity
## Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065
## 1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258
## Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194
## Mean :0.7979 Premium :13791 G:11292 VS1 : 8171
## 3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066
## Max. :5.0100 I: 5422 VVS1 : 3655
## J: 2808 (Other): 2531
## depth table price x
## Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000
## 1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710
## Median :61.80 Median :57.00 Median : 2401 Median : 5.700
## Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731
## 3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540
## Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740
##
## y z
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 4.720 1st Qu.: 2.910
## Median : 5.710 Median : 3.530
## Mean : 5.735 Mean : 3.539
## 3rd Qu.: 6.540 3rd Qu.: 4.040
## Max. :58.900 Max. :31.800
## - Determine the frequency for each of the diamond colors. Run the table() function against a single categorical variable See also: http://www.statmethods.net/stats/frequencies.html
table(diamonds$color)##
## D E F G H I J
## 6775 9797 9542 11292 8304 5422 2808- Determine the frequency for each of the diamond cuts, by color.
# Run the table() function against two categorical variables.
table(diamonds$color, diamonds$cut)##
## Fair Good Very Good Premium Ideal
## D 163 662 1513 1603 2834
## E 224 933 2400 2337 3903
## F 312 909 2164 2331 3826
## G 314 871 2299 2924 4884
## H 303 702 1824 2360 3115
## I 175 522 1204 1428 2093
## J 119 307 678 808 896R Graphics
- Create a graph for a single numeric variable.
First, in base R.
boxplot(diamonds$price)
hist(diamonds$price)
Look at the same single numeric variable, in ggplot2.
# ggplot2:
qplot(price, data=diamonds)## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
- Create a scatterplot of two numeric variables.
First, in base R:
plot(diamonds$price ~ diamonds$carat)
Look at the same scatterplot in ggplot2. Note that the order of carat and price are reversed.
qplot(carat, price, data=diamonds)