R4DS
wj
2019,2,2
library(tidyverse)library(nycflights13)library(forcats)library(lubridate)library(modelr)
實際上就是讀取保存文件、數(shù)據(jù)庫或web Api中的數(shù)據(jù)，再加載到R的數(shù)據(jù)框中。
就是將數(shù)據(jù)保存為一致的形式，以滿足其所在數(shù)據(jù)集在語義上的要求。 簡而言之，如果數(shù)據(jù)是整潔的，那么每列都是一個變量，每行都是一個觀測值。
（3）轉換（dplyr）
包括選取感興趣的觀測（如居住在某個城市里的所有人，或者去年的所有數(shù)據(jù)）、使用現(xiàn)有變量創(chuàng)建新變量、以及計算一些摘要統(tǒng)計量（如均值或計數(shù)） 數(shù)據(jù)整理和數(shù)據(jù)轉化統(tǒng)稱為數(shù)據(jù)處理。
本質(zhì)上是人類活動。對數(shù)據(jù)提出新的問題。 ###（5）模型 如果將問題定義得足夠清晰，那么你就可以使用一個模型來回答問題。 模型本質(zhì)上是一種數(shù)學工具或計算工具。 每個模型都有前提假設，而且模型本身不會對自己的前提假設提出疑問。
數(shù)據(jù)分析可以分為兩類：假設生成和假設驗證（有時成為驗證性分析）。 無需掩飾，本書的重點就在于假設生成，或者說是數(shù)據(jù)探索。 經(jīng)常有人認為建模是用來進行假設驗證的工具，而可視化是用來進行假設生成的工具。這種簡單的二分法是錯誤的：模型經(jīng)常用于數(shù)據(jù)探索；只需稍作處理，可視化也可以用來進行假設驗證。核心區(qū)別在于你使用每個觀測的頻率：如果只用一次，那么就是假設驗證；如果多于一次，那么就是數(shù)據(jù)探索。
geom_point() 散點圖
facet_wrap() 單個變量分面
facet_grid() 多個變量分面
geom_smooth()直線圖
geom_bar()條形圖
geom_boxplot()箱線圖
coord_flip()交換x軸和y軸
coord_polar()設置極坐標
color=class在mpg數(shù)據(jù)集中按照class進行描繪顏色
alpha=class
shape
linetype線段形狀
se=FALSE平滑曲線上下無條狀帶
..prop..百分比
fill填充色
mpg
## # A
tibble: 234 x 11##??? manufacturer model displ? year??cyl trans drv???? cty?? hwy fl???class##??? ??????? ##? 1 audi????????a4????? 1.8? 1999????4 auto~ f??????? 18??? 29 p????comp~##? 2 audi????????a4????? 1.8? 1999????4 manu~ f??????? 21??? 29 p????comp~##? 3 audi????????a4????? 2??? 2008????4 manu~ f??????? 20??? 31 p????comp~##? 4 audi????????a4????? 2??? 2008????4 auto~ f??????? 21??? 30 p????comp~##? 5 audi????????a4????? 2.8? 1999????6 auto~ f??????? 16??? 26 p????comp~##? 6 audi????????a4????? 2.8? 1999????6 manu~ f??????? 18??? 26 p????comp~##? 7 audi????????a4????? 3.1? 2008????6 auto~ f??????? 18??? 27 p????comp~##? 8 audi????????a4 q~?? 1.8? 1999? ???4 manu~ 4??????? 18???26 p???? comp~##? 9 audi????????a4 q~?? 1.8? 1999????4 auto~ 4??????? 16??? 25 p????comp~## 10audi???????? a4 q~?? 2???2008???? 4 manu~ 4??????? 20???28 p???? comp~## # ...
with 224 more rows
displ:引擎大小，單位為升。
hwy：汽車在高速公路上行駛時的燃油效率，單位為英里/加侖。
繪制mpg的圖形，運行一下代碼將displ放在x軸，hwy放在y軸。
ggplot(data=mpg)+? geom_point(mapping=aes(x=displ,y=hwy))
[if !vml]
[endif]
ggplot（data=）+ (mapping=aes())
圖形屬性是圖中對象的可視化屬性，其中包括數(shù)據(jù)點的大小、形狀和顏色。
（1）顏色映射為class
ggplot(data=mpg)+? geom_point(mapping=aes(x=displ,y=hwy,color=class))
[if !vml]
[endif]
ggplot(data=mpg)+? geom_point(mapping=aes(x=displ,y=hwy))+? facet_wrap(~class,nrow=2)
[if !vml]
[endif]
ggplot(data=mpg)+? geom_smooth(mapping=aes(x=displ,y=hwy))
##
`geom_smooth()` using method = 'loess' and formula 'y ~ x'
[if !vml]
[endif]
這種堆疊是由position參數(shù)設定的位置調(diào)整自動完成。如果不想生成堆疊式條形圖，還可以使用一下3項之一：
position=identity
ggplot(? data=diamonds,? mapping=aes(x=cut,fill=clarity))+? geom_bar(alpha=1/5,position="identity")
[if !vml]
[endif]
ggplot(? data=diamonds,? mapping=aes(x=cut,color=clarity))+? geom_bar(fill=NA,position="identity")
[if !vml]
[endif]
position=“fill”的效果與堆疊相似，但每組堆疊條形具有同樣高度，因此這種條形圖可以非常輕松地比較各組間的比例：
ggplot(data=diamonds)+? geom_bar(??? mapping=aes(x=cut,fill=clarity),??? position="fill"? )
[if !vml]
[endif]
position=“dodge”將每組的條形依次并列放置
ggplot(data=diamonds)+? geom_bar(??? mapping=aes(x=cut,fill=clarity),??? position="dodge"? )
[if !vml]
[endif]
有些點會聚在一起，此時需要一個抖動，可以避免網(wǎng)格化排列。
ggplot(data=mpg)+? geom_point(??? mapping=aes(x=displ,y=hwy),??? position="jitter"? )
[if !vml]
[endif]
極坐標coord_polar()
bar <- ggplot(data=diamonds)+? geom_bar(??? mapping=aes(x=cut,fill=cut),??? show.legend=FALSE,??? width=1??? )+? theme(aspect.ratio=1)+? labs(x=NULL,y=NULL)bar+coord_flip()
[if !vml]
[endif]
bar+coord_flip()
[if !vml]
[endif]
ggplot(data=)+
(
?mapping=aes(),?stat=,?position=
)+
+
?你可以將任何圖形精確第描述為數(shù)據(jù)集、幾何對象、 映射集合、統(tǒng)計變換、位置調(diào)整、坐標系和分面模式 的一個組合
function_name(arg1=val1,arg2=val2,…)
seq()函數(shù)，可以生成規(guī)則的數(shù)值排列
x%in%y 選取出x是y中一個值時的所有行
is.na()判斷是否為缺失值
year:day選擇在year到day中的所有列
-（year：day）選擇不在year到day之間的所有列（不包括“year”和“day”）
starts_with(“abc”):匹配以“abc”開頭的名稱。
ends_with(“xyz”)匹配以xyz結尾的名稱
cotains（“ijk”）匹配包含ijk的名稱
matches（“（.）\1”）選擇匹配正則表達式的那些變量。這個正則表達式會匹配名稱中有重復字符的變量。
num_range(“x”,1:3)匹配x1，x2和x3
na.rm=TRUE為除去缺失值（sum（AB，na.rm=TRUE））
常用摘要函數(shù) mean（x）求x平均
median（x）求x的中位數(shù)
sd(x)均方誤差（又稱標準誤差，standard
deviation，sd）是分散程度的標準度量方式。
IQR（x）四分位距
mad（x）絕對中位差
秩的度量
min（x）
quantile（x，0.25）找出x中按從小到大順序大于前25%而小于75%的值
max（x）
first（x）=x[1]
nth(x,2)=x[2]
last(x)=x[length(x)]
sum(!is.na(x))計算非缺失值的數(shù)量
n_distinct（x）計算出唯一值的數(shù)量
wt=distance按照distance進行加權賦值
ALT+-：代碼的快速輸入
tab：選定需要的函數(shù)后再按一次Tab鍵，Rstudio可以自動添加括號（）
ctrl+shift創(chuàng)建腳本
ctrl+enter運行該段代碼
int整型
dbl雙精度浮點數(shù)型變量，或稱實數(shù)
chr表示字符向量，或稱字符串
dttm表示日期時間（日期+時間）型變量
lgl邏輯型
fctr表示因子，R是用來表示具有固定數(shù)目的值的分類變量
date表示日期型變量
向量化，使用所謂的“循環(huán)法則”
%/%整數(shù)除法
%%求余
log（）、log2（）、log10（）
lead（）和lag()函數(shù)可以返回一個序列的領先值和滯后值
5個dplyr核心函數(shù)
filter（）按值篩選觀測（選擇行，有邏輯表達式在后面）
arrange（）對行進行重新排序
mutate（）使用現(xiàn)有變量的函數(shù)創(chuàng)建新變量
summarize（）將多個值總結為一個摘要統(tǒng)計量
select（）選擇列
cumsum（）累加和
cumprod（）累加積
commin（）累加最小值
cummax（）累加最大值
cummean（）累加均值
filter（）篩選行
filter(flights,month==1,day==1)
## # A
tibble: 842 x 19##???? year month?? day dep_time sched_dep_time dep_delayarr_time##??? ??? ????????? ???? ??? ##? 1?2013???? 1???? 1?????517??????????? 515???????? 2?????830##? 2?2013???? 1???? 1?????533??????????? 529???????? 4?????850##? 3?2013???? 1???? 1?????542??????????? 540???????? 2?????923##? 4?2013???? 1???? 1?????544??????????? 545??????? -1????1004##? 5?2013???? 1???? 1?????554??????????? 600??????? -6?????812##? 6?2013???? 1???? 1?????554??????????? 558??????? -4?????740##? 7?2013???? 1???? 1?????555??????????? 600??????? -5?????913##? 8?2013???? 1???? 1?????557??????????? 600??????? -3?????709##? 9?2013???? 1???? 1?????557??????????? 600???????-3?????838## 10? 2013????1???? 1????? 558??????????? 600??????? -2?????753## # ...
with 832 more rows, and 12 more variables: sched_arr_time <int>,## #?? arr_delay , carrier ,flight , tailnum ,## #?? origin , dest ,air_time , distance , hour ,## #?? minute , time_hour
%in%
(nov_dec
<- filter(flights,month%in%c(11,12)))
## # A
tibble: 55,403 x 19##???? year month?? day dep_time sched_dep_time dep_delayarr_time##??? ??? ????????? ???? ??? ##? 1?2013??? 11???? 1???????5?????????? 2359???????? 6?????352##? 2?2013??? 11???? 1??????35?????????? 2250?????? 105?????123##? 3?2013??? 11???? 1?????455??????????? 500??????? -5?????641##? 4? 2013??? 11????1????? 539??????????? 545??????? -6?????856##? 5?2013??? 11???? 1?????542??????????? 545??????? -3?????831##? 6?2013??? 11???? 1?????549??????????? 600?????? -11?????912##? 7?2013??? 11???? 1?????550??????????? 600?????? -10?????705##? 8?2013??? 11???? 1?????554??????????? 600??????? -6?????659##? 9?2013??? 11???? 1?????554??????????? 600??????? -6?????826## 10? 2013???11???? 1????? 554??????????? 600??????? -6?????749## # ...
with 55,393 more rows, and 12 more variables: sched_arr_time <int>,## #?? arr_delay , carrier ,flight , tailnum ,## #?? origin , dest ,air_time , distance , hour ,## #?? minute , time_hour
filter()只能篩選出條件為TRUE的行；他會排除那些條件為FALSE和NA的行。
df <- tibble(x=c(1,NA,3))filter(df,x>1)
## # A
tibble: 1 x 1##?????? x##?? ## 1???? 3
filter(df,is.na(x)|x>1)
## # A
tibble: 2 x 1##?????? x##?? ## 1??? NA## 2???? 3
arrange()排列行
arrange(flights,year,month,day)
## # A
tibble: 336,776 x 19##???? year month?? day dep_time sched_dep_time dep_delayarr_time##??? ??? ????????? ???? ??? ##? 1?2013???? 1???? 1?????517??????????? 515???????? 2?????830##? 2?2013???? 1???? 1?????533??????????? 529??????? ?4?????850##? 3?2013???? 1???? 1?????542??????????? 540???????? 2?????923##? 4?2013???? 1???? 1?????544??????????? 545??????? -1????1004##? 5?2013???? 1???? 1?????554??????????? 600??????? -6?????812##? 6?2013???? 1???? 1?????554?????????? ?558???????-4????? 740##? 7?2013???? 1???? 1?????555??????????? 600??????? -5?????913##? 8?2013???? 1???? 1?????557??????????? 600??????? -3?????709##? 9?2013???? 1???? 1?????557??????????? 600??????? -3?????838## 10? 2013????1???? 1????? 558??????????? 600??????? -2?????753## # ...
with 336,766 more rows, and 12 more variables: sched_arr_time <int>,## #?? arr_delay , carrier ,flight , tailnum ,## #?? origin , dest ,air_time , distance , hour ,## #?? minute , time_hour
注意： filter是默認升序排列
使用desc（）可以按列進行降序排序：
arrange(flights,desc(arr_delay))
## # A
tibble: 336,776 x 19##???? year month?? day dep_time sched_dep_time dep_delayarr_time##??? ??? ????????? ? ?????? ##? 1?2013???? 1???? 9?????641??????????? 900????? 1301????1242##? 2?2013???? 6??? 15????1432?????????? 1935????? 1137????1607##? 3?2013???? 1??? 10????1121?????????? 1635????? 1126????1239##? 4?2013???? 9??? 20????1139???? ??????1845?????1014???? 1457##? 5?2013???? 7??? 22?????845?????????? 1600????? 1005????1044##? 6?2013???? 4??? 10????1100?????????? 1900?????? 960????1342##? 7?2013???? 3??? 17????2321??????????? 810?????? 911?????135##? 8?2013???? 7??? 22? ???2257??????????? 759?????? 898?????121##? 9?2013??? 12???? 5?????756?????????? 1700?????? 896????1058## 10? 2013????5???? 3???? 1133?????????? 2055?????? 878????1250## # ...
with 336,766 more rows, and 12 more variables: sched_arr_time <int>,## #?? arr_delay , carrier ,flight , tailnum ,## #?? origin , dest ,air_time , distance , hour ,## #?? minute , time_hour
缺失值總是排在最后
df <- tibble(x=c(5,2,NA))arrange(df,x)
## # A
tibble: 3 x 1##?????? x##?? ## 1???? 2## 2???? 5## 3??? NA
arrange(df,desc(x))
## # A
tibble: 3 x 1##?????? x##?? ## 1???? 5## 2???? 2## 3??? NA
使用select（）選擇列
select(flights,year,month,day)
## # A
tibble: 336,776 x 3##???? year month?? day##??? ##? 1?2013???? 1???? 1##? 2?2013???? 1???? 1##? 3?2013???? 1???? 1##? 4?2013???? 1???? 1##? 5?2013???? 1???? 1##? 6?2013???? 1???? 1##? 7?2013???? 1???? 1##? 8?2013???? 1???? 1##? 9?2013???? 1???? 1## 10? 2013????1???? 1## # ...
with 336,766 more rows
select(flights,year:day)
## # A
tibble: 336,776 x 3##???? year month?? day##??? ##? 1?2013???? 1???? 1##? 2?2013???? 1???? 1##? 3?2013???? 1???? 1##? 4?2013???? 1???? 1##? 5?2013???? 1???? 1##? 6?2013???? 1???? 1##? 7?2013???? 1???? 1##? 8?2013???? 1???? 1##? 9?2013???? 1???? 1## 10? 2013????1???? 1## # ...
with 336,766 more rows
選擇不再year到day之間的所有列（不包括year和day）
select(flights,-(year:day))
## # A
tibble: 336,776 x 16##??? dep_time sched_dep_time dep_delay arr_timesched_arr_time arr_delay##?????? ????????? ???? ??? ????????? ???? ##? 1?????517??????????? 515???????? 2?????830??????????? 819??????? 11##? 2?????533??????????? 529???????? 4?????850? ??????????830??????? 20##? 3?????542??????????? 540???????? 2?????923??????????? 850??????? 33##? 4?????544??????????? 545??????? -1????1004?????????? 1022?????? -18##? 5?????554??????????? 600??????? -6?????812??????????? 837?????? -25##? 6???? ?554???????????558??????? -4????? 740??????????? 728??????? 12##? 7?????555??????????? 600??????? -5?????913??????????? 854??????? 19##? 8?????557??????????? 600??????? -3?????709??????????? 723?????? -14##? 9?????557??????????? 600??????? -3?????838??????????? 846??????? -8## 10????? 558??????????? 600??????? -2?????753??????????? 745???????? 8## # ...
with 336,766 more rows, and 10 more variables: carrier <chr>,## #?? flight , tailnum ,origin , dest , air_time ,## #?? distance , hour ,minute , time_hour
將幾個變量移到數(shù)據(jù)框開頭
select(flights,time_hour,air_time,everything())
## # A
tibble: 336,776 x 19##??? time_hour?????????? air_time? year month??day dep_time sched_dep_time##??? ?????????????? ?? ??? ????????? ##? 1 2013-01-01 05:00:00????? 227?2013???? 1???? 1?????517??????????? 515##? 2 2013-01-01 05:00:00????? 227?2013???? 1???? 1?????533??????????? 529##? 3 2013-01-01 05:00:00????? 160?2013???? 1???? 1?????542??????????? 540##? 4 2013-01-01 05:00:00????? 183?2013???? 1???? 1?????544??????????? 545##? 5 2013-01-01 06:00:00????? 116?2013???? 1???? 1?????554??????????? 600##? 6 2013-01-01 05:00:00????? 150?2013???? 1???? 1?????554??????????? 558##? 7 2013-01-01 06:00:00????? 158?2013???? 1???? 1?????555??????????? 600##? 8 2013-01-01 06:00:00?????? 53?2013???? 1???? 1?????557??????????? 600##? 9 2013-01-01 06:00:00????? 140?2013???? 1???? 1?????557??????????? 600## 102013-01-01 06:00:00????? 138? 2013????1???? 1????? 558??????????? 600## # ...
with 336,766 more rows, and 12 more variables: dep_delay <dbl>,## #?? arr_time , sched_arr_time, arr_delay , carrier ,## #?? flight , tailnum ,origin , dest , distance ,## #?? hour , minute
使用mutate（）添加新變量
flights_sml
<- select(flights,? year:day,? ends_with("delay"),? distance,? air_time? )mutate(flights_sml,? gain=arr_delay-dep_delay,? speed=distance/air_time*60? )
## # A
tibble: 336,776 x 9##???? year month?? day dep_delay arr_delay distanceair_time? gain speed##??? ???? ???? ??? ??? ##? 1?2013???? 1???? 1????????2??????? 11???? 1400?????227???? 9? 370.##? 2?2013???? 1???? 1????????4??????? 20???? 1416?????227??? 16? 374.##? 3?2013???? 1???? 1????????2??????? 33???? 1089?????160??? 31? 408.##? 4?2013???? 1???? 1???????-1?????? -18???? 1576?????183?? -17? 517.##? 5?2013???? 1???? 1???????-6?????? -25?????762????? 116?? -19?394.##? 6?2013???? 1???? 1???????-4??????? 12????? 719?????150??? 16? 288.##? 7?2013???? 1???? 1???????-5??????? 19???? 1065?????158??? 24? 404.##? 8?2013???? 1???? 1???????-3?????? -14????? 229??????53?? -11? 259.##? 9?2013???? 1???? 1???????-3??????? -8????? 944?????140??? -5? 405.## 10? 2013????1???? 1??????? -2???????? 8?????733????? 138??? 10?319.## # ...
with 336,766 more rows
一旦創(chuàng)建，新列就可以立即使用：
mutate(flights_sml,? gain=arr_delay-dep_delay,? hours=air_time/60,? gain_per_hour=gain/hours? )
## # A
tibble: 336,776 x 10##???? year month?? day dep_delay arr_delay distanceair_time? gain hours##??? ???? ???? ??? ??? ##? 1?2013???? 1???? 1????????2??????? 11????1400????? 227???? 9 3.78##? 2?2013???? 1???? 1????????4??????? 20???? 1416?????227??? 16 3.78##? 3?2013???? 1???? 1????????2??????? 33???? 1089?????160??? 31 2.67##? 4?2013???? 1???? 1???????-1?????? -18???? 1576?????183?? -17 3.05##? 5?2013???? 1???? 1???????-6?????? -25????? 762?????116?? -19 1.93##? 6?2013???? 1???? 1???????-4??????? 12????? 719?????150??? 16 2.5? ##? 7?2013???? 1???? 1???????-5??????? 19???? 1065?????158??? 24 2.63##? 8?2013???? 1??? ?1???????-3?????? -14????? 229??????53?? -11 0.883##? 9?2013???? 1???? 1???????-3??????? -8????? 944?????140??? -5 2.33## 10? 2013????1???? 1??????? -2???????? 8?????733????? 138??? 10 2.3?## # ...
with 336,766 more rows, and 1 more variable: gain_per_hour <dbl>
如果只想保留新變量，可以使用transmute（）函數(shù)
transmute(flights,? gain=arr_delay-dep_delay,? hours=air_time/60,? gain_per_hour=gain/hours)
## # A
tibble: 336,776 x 3##???? gain hours gain_per_hour##??? ???????? ##? 1???? 93.78???? ??????2.38##? 2??? 163.78?????????? 4.23##? 3??? 312.67????????? 11.6##? 4?? -173.05????????? -5.57##? 5?? -191.93????????? -9.83##? 6??? 162.5??????????? 6.4##? 7??? 242.63?????????? 9.11##? 8?? -110.883??????? -12.5##? 9??? -52.33????????? -2.14## 10??? 10 2.3??????????? 4.35## # ...
with 336,766 more rows
lead（）和lag()函數(shù)可以返回一個序列的領先值和滯后值
(x <- 1:10)
##? [1]?1? 2? 3?4? 5? 6?7? 8? 9 10
lag(x)
##? [1] NA?1? 2? 3?4? 5? 6?7? 8? 9
lead(x)
##? [1]? 2?3? 4? 5? 6? 7?8? 9 10NA
使用summarize（）進行分組摘要 可以將數(shù)據(jù)框折疊成一行：
summarize(flights,delay=mean(dep_delay,na.rm=TRUE))
## # A
tibble: 1 x 1##?? delay##?? ## 1? 12.6
group_by()可以將分析單位從整個數(shù)據(jù)集更改為單個分組。接下來，在 分組后的數(shù)據(jù)框中使用dplyr函數(shù)時，會自動地應用到每個分組。
by_day = group_by(flights,year,month,day)summarize(by_day,delay=mean(dep_delay,na.rm=TRUE))
## # A
tibble: 365 x 4## #Groups:?? year, month [?]##???? year month?? day delay##??? ##? 1?2013???? 1???? 1 11.5##? 2?2013???? 1???? 2 13.9##? 3?2013????1???? 3 11.0##? 4?2013???? 1???? 4?8.95##? 5?2013???? 1???? 5?5.73##? 6?2013???? 1???? 6?7.15##? 7?2013???? 1???? 7?5.42##? 8?2013???? 1???? 8?2.55##? 9?2013???? 1???? 9?2.28## 10? 2013????1??? 10? 2.84## # ...
with 355 more rows
%>%管道最好讀作“然后”
x%>%f(y)會轉換為f（x，y）
x%>%f(y)%>%g(z)會轉換為g（f（x，y），z）
flights%>%? group_by(year,month,day)%>%? summarize(mean=mean(dep_delay))
## # A
tibble: 365 x 4## #Groups:?? year, month [?]##???? year month?? day? mean##??? ##? 1?2013???? 1???? 1???NA##? 2?2013???? 1???? 2???NA##? 3?2013???? 1???? 3???NA##? 4?2013???? 1???? 4???NA##? 5?2013???? 1???? 5???NA##? 6?2013???? 1???? 6???NA##? 7?2013???? 1???? 7???NA##? 8? 2013???? 1????8??? NA##? 9?2013???? 1???? 9???NA## 10? 2013????1??? 10??? NA## # ...
with 355 more rows
這樣會得到很多缺失值，這是因為聚合函數(shù)遵循缺失值的一般規(guī)則：如果輸入中有確實值，那么輸出也會是缺失值。
flights%>%? group_by(year,month,day)%>%? summarize(mean=mean(dep_delay,na.rm = TRUE))
## # A tibble:
365 x 4## #Groups:?? year, month [?]##???? year month?? day?mean##??? ##? 1?2013???? 1???? 1 11.5##? 2?2013???? 1???? 2 13.9##? 3?2013???? 1???? 3 11.0##? 4?2013???? 1???? 4?8.95##? 5?2013???? 1???? 5?5.73##? 6?2013???? 1???? 6?7.15##? 7?2013???? 1???? 7?5.42##? 8?2013???? 1???? 8?2.55##? 9?2013???? 1???? 9?2.28## 10? 2013????1??? 10? 2.84## # ...
with 355 more rows
缺失值表示取消的航班，我們也可以通過先去除取消的航班來解決缺失值問題。
not_cancelled
<- flights%>%? filter(!is.na(dep_delay),!is.na(arr_delay))#關鍵一步not_cancelled%>%? group_by(year,month,day)%>%? summarize(mean=mean(dep_delay))
## # A
tibble: 365 x 4## #Groups:?? year, month [?]##???? year month?? day?mean##??? ##? 1?2013???? 1???? 1 11.4##? 2?2013???? 1???? 2 13.7##? 3?2013???? 1???? 3 10.9##? 4?2013???? 1???? 4?8.97##? 5?2013???? 1???? 5?5.73##? 6?2013???? 1???? 6?7.15##? 7?2013???? 1???? 7?5.42##? 8?2013???? 1???? 8?2.56##? 9?2013???? 1???? 9?2.30## 10? 2013????1??? 10? 2.84## # ...
with 355 more rows
位置度量
not_cancelled%>%? group_by(year,month,day)%>%? summarize(??? avg_delay1=mean(arr_delay),??? avg_delay2=mean(arr_delay[arr_delay>0])#[arr_delay>0]為arr_delay向量中變量中的arr_delay大于0的元素。? )
## # A
tibble: 365 x 5## #Groups:?? year, month [?]##???? year month?? day avg_delay1 avg_delay2##??? ????? ????? ##? 1?2013???? 1???? 1????12.7???????? 32.5##? 2?2013???? 1???? 2????12.7???????? 32.0##? 3?2013???? 1????3?????5.73??????? 27.7##? 4?2013???? 1???? 4????-1.93??????? 28.3##? 5?2013???? 1???? 5????-1.53??????? 22.6##? 6?2013???? 1???? 6?????4.24??????? 24.4##? 7?2013???? 1???? 7????-4.95??????? 27.8##? 8?2013???? 1???? 8????-3.23??????? 20.8##? 9?2013???? 1???? 9????-0.264?????? 25.6## 10? 2013????1??? 10???? -5.90??????? 27.3## # ...
with 355 more rows
not_cancelled%>%? count(tailnum,wt=distance)
## # A
tibble: 4,037 x 2##??? tailnum?????n##??? ??? ##? 1 D942DN???3418##? 2 N0EGMQ?239143##? 3 N10156?109664##? 4 N102UW??25722##? 5 N103US??24619##? 6 N104UW??24616##? 7 N10575?139903##? 8 N105UW??23618##? 9 N107US??21677## 10N108UW?? 32070## # ...
with 4,027 more rows
邏輯計數(shù)和比例
sum（x>10）
true=1，false=0
not_cancelled%>%? group_by(year,month,day)%>%? summarize(n_early=sum(dep_time<500))
## # A
tibble: 365 x 4## #Groups:?? year, month [?]##???? year month?? day n_early##??? ?? ##? 1?2013???? 1???? 1??????0##? 2?2013???? 1?? ??2??????3##? 3?2013???? 1???? 3??????4##? 4?2013???? 1???? 4??????3##? 5?2013???? 1???? 5??????3##? 6?2013???? 1???? 6??????2##? 7?2013???? 1???? 7??????2##? 8?2013???? 1???? 8??????1##? 9?2013???? 1???? 9??????3## 10? 2013????1?? ?10??????3## # ...
with 355 more rows
如何使用可視化和數(shù)據(jù)轉換來系統(tǒng)化地探索數(shù)據(jù)，統(tǒng)計學家將這項任務稱為探索性數(shù)據(jù)分析（EDA）。EDA是一個可迭代的循環(huán)過程。
EDA工具：可視化，數(shù)據(jù)轉換和建模。
