這幾天在優達Udacity學了用R做數據分析，以前也學過，不過沒有學得這么系統，把今天學的過程和作業貼在這里。有興趣的同學可以點擊鏈接去聽課

連續三天每天3個多小時，終于把核心內容學完了！現在對編R語言也有點信心了。其實工具都只是用來輔助自己的，最終數據分析的功夫還是在于基礎原理。就像一個人無論用Markdown、word、有道云、還是記事本寫文章，最終決定文章水平的還是寫作水平。所以學會這些軟件以后不能自我感動，還是多花時間提升底層的理論水平比較重要。工具畢竟是工具，最終決定能不能找到工作的還是對于業務的理解。

Lesson 5

Multivariate Data

Notes:

Moira Perceived Audience Size Colored by Age

Notes:

Third Qualitative Variable

Notes:

pf <- read.delim('pseudo_facebook.tsv')
library(ggplot2)
ggplot(aes(x = gender, y = age),
       data = subset(pf, !is.na(gender))) + 
  geom_boxplot()+
  stat_summary(fun.y=mean,geom="point",shape=4,color="blue")

image.png

ggplot(aes(x = age, y = friend_count),
       data = subset(pf, !is.na(gender))) + 
  geom_line(aes(color=gender),stat='summary',fun.y=median)

image.png

Plotting Conditional Summaries

Notes:

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#chain fuctions together %>%
pf.fc_by_age_gender <- pf%>%
  filter(!is.na(gender))%>%
  group_by(age,gender)%>%
  summarise(fri_mean=mean(friend_count),
            fri_median=median(friend_count))%>%
  arrange(age)

ggplot(aes(x=age,y=fri_median,color=gender),
       data=subset(pf.fc_by_age_gender,!is.na(gender)))+
  geom_line()

image.png

Thinking in Ratios

Notes:

Wide and Long Format

Notes:

Reshaping Data

Notes:

library(reshape2)
pf.fc_by_age_gender.wide <- dcast(pf.fc_by_age_gender,
                                 age ~ gender,
                                 value.var='fri_median')
head(pf.fc_by_age_gender.wide)

##   age female  male
## 1  13  148.0  55.0
## 2  14  224.0  92.5
## 3  15  276.0 106.5
## 4  16  258.5 136.0
## 5  17  245.5 125.0
## 6  18  243.0 122.0

Ratio Plot

Notes:

ggplot(aes(x=age,y=female/male),
       data=pf.fc_by_age_gender.wide)+
  geom_line()+
  geom_hline(yintercept=1,alpha=.3,linetype=2)

image.png

Third Quantitative Variable

Notes:

pf$year_joined <- floor(2014 -pf$tenure/365)
summary(pf$year_joined)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    2005    2012    2012    2012    2013    2014       2

table(pf$year_joined)

## 
##  2005  2006  2007  2008  2009  2010  2011  2012  2013  2014 
##     9    15   581  1507  4557  5448  9860 33366 43588    70

Cut a Variable

Notes:

pf$year_joined.bucket <- cut(pf$year_joined,
                             c(2004,2009,2011,2012,2014))

Plotting it All Together

Notes:

ggplot(aes(x = age, y = friend_count),
       data = subset(pf, !is.na(gender)&!is.na(year_joined.bucket))) + 
  geom_line(aes(color=year_joined.bucket),stat='summary',fun.y=median)

image.png

Plot the Grand Mean

Notes:

ggplot(aes(x = age, y = friend_count),
       data = subset(pf, !is.na(gender)&!is.na(year_joined.bucket))) + 
  geom_line(aes(color=year_joined.bucket),stat='summary',fun.y=mean)+
  geom_line(linetype=2,stat='summary',fun.y=mean)

image.png

Friending Rate

Notes:

with(subset(pf,tenure>=1),summary(friend_count/tenure))

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##   0.0000   0.0775   0.2205   0.6096   0.5658 417.0000

Friendships Initiated

Notes:

What is the median friend rate?

What is the maximum friend rate?

ggplot(aes(y=friendships_initiated/tenure,
           x=tenure),
       data=subset(pf,tenure>=1))+
  geom_line(aes(color=year_joined.bucket))

image.png

Bias-Variance Tradeoff Revisited

Notes:

q1 <- ggplot(aes(x = tenure, y = friendships_initiated / tenure),
       data = subset(pf, tenure >= 1)) +
  geom_line(aes(color = year_joined.bucket),
            stat = 'summary',
            fun.y = mean)+
  geom_smooth()

q2<- ggplot(aes(x = 7 * round(tenure / 7), y = friendships_initiated / tenure),
       data = subset(pf, tenure > 0)) +
  geom_line(aes(color = year_joined.bucket),
            stat = "summary",
            fun.y = mean)

q3<- ggplot(aes(x = 30 * round(tenure / 30), y = friendships_initiated / tenure),
       data = subset(pf, tenure > 0)) +
  geom_line(aes(color = year_joined.bucket),
            stat = "summary",
            fun.y = mean)

q4<- ggplot(aes(x = 90 * round(tenure / 90), y = friendships_initiated / tenure),
       data = subset(pf, tenure > 0)) +
  geom_line(aes(color = year_joined.bucket),
            stat = "summary",
            fun.y = mean)

library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

grid.arrange(q1,q2,q3,q4,ncol=1)

## `geom_smooth()` using method = 'gam'

image.png

Sean’s NFL Fan Sentiment Study

Notes:

Introducing the Yogurt Data Set

Notes:

Histograms Revisited

Notes:

yo<-read.csv('yogurt.csv')
str(yo)

## 'data.frame':    2380 obs. of  9 variables:
##  $ obs        : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ id         : int  2100081 2100081 2100081 2100081 2100081 2100081 2100081 2100081 2100081 2100081 ...
##  $ time       : int  9678 9697 9825 9999 10015 10029 10036 10042 10083 10091 ...
##  $ strawberry : int  0 0 0 0 1 1 0 0 0 0 ...
##  $ blueberry  : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ pina.colada: int  0 0 0 0 1 2 0 0 0 0 ...
##  $ plain      : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ mixed.berry: int  1 1 1 1 1 1 1 1 1 1 ...
##  $ price      : num  59 59 65 65 49 ...

yo$id<-factor(yo$id)
qplot(x=price,data=yo,fill=I('#F79420'))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

image.png

Number of Purchases

Notes:

summary(yo)

##       obs               id            time         strawberry     
##  Min.   :   1.0   2132290:  74   Min.   : 9662   Min.   : 0.0000  
##  1st Qu.: 696.5   2130583:  59   1st Qu.: 9843   1st Qu.: 0.0000  
##  Median :1369.5   2124073:  50   Median :10045   Median : 0.0000  
##  Mean   :1367.8   2149500:  50   Mean   :10050   Mean   : 0.6492  
##  3rd Qu.:2044.2   2101790:  47   3rd Qu.:10255   3rd Qu.: 1.0000  
##  Max.   :2743.0   2129528:  39   Max.   :10459   Max.   :11.0000  
##                   (Other):2061                                    
##    blueberry        pina.colada          plain         mixed.berry    
##  Min.   : 0.0000   Min.   : 0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median : 0.0000   Median : 0.0000   Median :0.0000   Median :0.0000  
##  Mean   : 0.3571   Mean   : 0.3584   Mean   :0.2176   Mean   :0.3887  
##  3rd Qu.: 0.0000   3rd Qu.: 0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
##  Max.   :12.0000   Max.   :10.0000   Max.   :6.0000   Max.   :8.0000  
##                                                                       
##      price      
##  Min.   :20.00  
##  1st Qu.:50.00  
##  Median :65.04  
##  Mean   :59.25  
##  3rd Qu.:68.96  
##  Max.   :68.96  
##

length(unique(yo$price))

## [1] 20

table(yo$price)

## 
##    20 24.96 33.04  33.2 33.28 33.36 33.52 39.04    44 45.04 48.96 49.52 
##     2    11    54     1     1    22     1   234    21    11    81     1 
##  49.6    50 55.04 58.96    62 63.04 65.04 68.96 
##     1   205     6   303    15     2   799   609

yo <- transform(yo,all.purchases = strawberry+blueberry+pina.colada+plain+mixed.berry)
summary(yo)

##       obs               id            time         strawberry     
##  Min.   :   1.0   2132290:  74   Min.   : 9662   Min.   : 0.0000  
##  1st Qu.: 696.5   2130583:  59   1st Qu.: 9843   1st Qu.: 0.0000  
##  Median :1369.5   2124073:  50   Median :10045   Median : 0.0000  
##  Mean   :1367.8   2149500:  50   Mean   :10050   Mean   : 0.6492  
##  3rd Qu.:2044.2   2101790:  47   3rd Qu.:10255   3rd Qu.: 1.0000  
##  Max.   :2743.0   2129528:  39   Max.   :10459   Max.   :11.0000  
##                   (Other):2061                                    
##    blueberry        pina.colada          plain         mixed.berry    
##  Min.   : 0.0000   Min.   : 0.0000   Min.   :0.0000   Min.   :0.0000  
##  1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.:0.0000   1st Qu.:0.0000  
##  Median : 0.0000   Median : 0.0000   Median :0.0000   Median :0.0000  
##  Mean   : 0.3571   Mean   : 0.3584   Mean   :0.2176   Mean   :0.3887  
##  3rd Qu.: 0.0000   3rd Qu.: 0.0000   3rd Qu.:0.0000   3rd Qu.:0.0000  
##  Max.   :12.0000   Max.   :10.0000   Max.   :6.0000   Max.   :8.0000  
##                                                                       
##      price       all.purchases   
##  Min.   :20.00   Min.   : 1.000  
##  1st Qu.:50.00   1st Qu.: 1.000  
##  Median :65.04   Median : 2.000  
##  Mean   :59.25   Mean   : 1.971  
##  3rd Qu.:68.96   3rd Qu.: 2.000  
##  Max.   :68.96   Max.   :21.000  
##

Prices over Time

Notes:

ggplot(aes(x=time,y=price),data=yo)+
  geom_jitter(alpha=1/5)

image.png

Sampling Observations

Notes:

Looking at Samples of Households

set.seed(4230)
sample.ids <- sample(levels(yo$id),16)

ggplot(aes(x=time,y=price),data=subset(yo,id %in% sample.ids))+
  facet_wrap(~ id) + 
  geom_line()+
  geom_point(aes(size=all.purchases),pch=1)

image.png

The Limits of Cross Sectional Data

Notes:

Many Variables

Notes:

Scatterplot Matrix

Notes:

Even More Variables

Notes:

Heat Maps

Notes:

nci <- read.table("nci.tsv")
colnames(nci) <- c(1:64)

nci.long.samp <- melt(as.matrix(nci[1:200,]))
names(nci.long.samp) <- c("gene", "case", "value")
head(nci.long.samp)

##   gene case  value
## 1    1    1  0.300
## 2    2    1  1.180
## 3    3    1  0.550
## 4    4    1  1.140
## 5    5    1 -0.265
## 6    6    1 -0.070

ggplot(aes(y = gene, x = case, fill = value),
  data = nci.long.samp) +
  geom_tile() +
  scale_fill_gradientn(colours = colorRampPalette(c("blue", "red"))(100))

image.png

Analyzing Three of More Variables

Reflection:

Click

KnitHTML

to see all of your hard work and to have an html page of this lesson, your answers, and your notes!

三个男躁一个女,国精产品一区一手机的秘密,麦子交换系列最经典十句话,欧美国产综合欧美视频

R語言學習記錄 - 多變量分析

R語言學習記錄 - 多變量分析

Lesson 5

Multivariate Data

Moira Perceived Audience Size Colored by Age

Third Qualitative Variable

Plotting Conditional Summaries

Thinking in Ratios

Wide and Long Format

Reshaping Data

Ratio Plot

Third Quantitative Variable

Cut a Variable

Plotting it All Together

Plot the Grand Mean

Friending Rate

Friendships Initiated

Bias-Variance Tradeoff Revisited

Sean’s NFL Fan Sentiment Study

Introducing the Yogurt Data Set

Histograms Revisited

Number of Purchases

Prices over Time

Sampling Observations

Looking at Samples of Households

The Limits of Cross Sectional Data

Many Variables

Scatterplot Matrix

Even More Variables

Heat Maps

Analyzing Three of More Variables

推薦閱讀更多精彩內容

三个男躁一个女,国精产品一区一手机的秘密,麦子交换系列最经典十句话,欧美 国产 综合 欧美 视频

R語言學習記錄 - 多變量分析

Lesson 5

Multivariate Data

Moira Perceived Audience Size Colored by Age

Third Qualitative Variable

Plotting Conditional Summaries

Thinking in Ratios

Wide and Long Format

Reshaping Data

Ratio Plot

Third Quantitative Variable

Cut a Variable

Plotting it All Together

Plot the Grand Mean

Friending Rate

Friendships Initiated

Bias-Variance Tradeoff Revisited

Sean’s NFL Fan Sentiment Study

Introducing the Yogurt Data Set

Histograms Revisited

Number of Purchases

Prices over Time

Sampling Observations

Looking at Samples of Households

The Limits of Cross Sectional Data

Many Variables

Scatterplot Matrix

Even More Variables

Heat Maps

Analyzing Three of More Variables

推薦閱讀更多精彩內容

三个男躁一个女,国精产品一区一手机的秘密,麦子交换系列最经典十句话,欧美国产综合欧美视频