1 library(dplyr)
2
3 ### Intro
4 path2csv <- file.path('2014-07-08.csv')
5 df <- read.csv(path2csv, as.is = TRUE)
6 dim(df)
7 head(df)
8 cran <- tbl_df(df)
9 cran

 1 ##基本函数
 2 select(cran,ip_id,package,country)  #cran为数据集名称，ip_id,package,country为列名
 3 select(cran,r_arch:country)  #r_arch:country表示从r_arch列取到country列
 4 select(cran,country:r_arch)  #反过来排序
 5 select(cran,-time)  #不取time列
 6 select(cran,-(X:size))  #不取X到size的列
 7
 8 filter(cran,package=="swirl")  #cran为数据集，筛选package为”swirl“的所有行
 9 filter(cran,r_version=="3.1.1",country=="US")  #两个筛选条件
10 filter(cran,r_version<="3.0.2",country=="IN")
11 filter(cran,country=="US"|country=="IN")  #添加正则表达式
12 filter(cran,!is.na(r_version))  #筛选所有r_version非空的行
13
14 arrange(cran2,ip_id)  #按ip_id排升序（默认排序方式）
15 arrange(cran2,desc(ip_id))  #按ip_id排降序
16 arrange(cran2,package,ip_id)  #先按package列排序，再对ip_id排序
17
18 mutate(cran3,size_mb=size/2^20)  #利用已有列新建一列size_mb
19 mutate(cran3,size_mb=size/2^20,size_gb=size_mb/2^10)  #新建俩列
20
21 summarise(cran3,avg_bytes=mean(size))  #汇总，查看整体统计数据

 1 by_package<-group_by(cran,package)  #按package列进行分组统计
 2 summarise(by_package,mean(size))  #计算分组后每组记录的平均size
 3 pack_sum <- summarize(by_package,  #对分组数据by_package进行汇总统计
 4                       count =n(),  #n（）统计每组频数
 5                       unique = n_distinct(ip_id),  #统计每组有多少不同的ip_id
 6                       countries = n_distinct(country),
 7                       avg_bytes = mean(size))
 8
 9 quantile(pack_sum$unique, probs = 0.99)  #计算unique的99%分位数
10 top_unique<-filter(pack_sum,unique>465)  #筛选频数大于465的记录
11 top_unique_sorted<-arrange(top_unique,desc(unique))
12 top_countries <- filter(pack_sum, countries > 60)
13 result1 <- arrange(top_countries, desc(countries), avg_bytes)
14 print(result1)

 1 result2 <-
 2   arrange(
 3     filter(
 4       summarize(  #分组数据用summarize进行汇总
 5         group_by(cran,
 6                  package
 7         ),
 8         count = n(),
 9         unique = n_distinct(ip_id),
10         countries = n_distinct(country),
11         avg_bytes = mean(size)
12       ),
13       countries > 60
14     ),
15     desc(countries),
16     avg_bytes
17   )
18
19 print(result2)

 1 # you read it, you can pronounce the %>% operator as
 2 # the word 'then'.
 3 result3 <-
 4   cran %>%
 5   group_by(package) %>%
 6   summarize(count = n(),
 7             unique = n_distinct(ip_id),
 8             countries = n_distinct(country),
 9             avg_bytes = mean(size)
10   ) %>%
11   filter(countries > 60) %>%
12   arrange(desc(countries), avg_bytes)
13
14 # Print result to console
15 print(result3)

1 cran %>%
2   select(ip_id, country, package, size) %>%
3   mutate(size_mb = size / 2^20) %>%
4   filter(size_mb <= 0.5) %>%
5   arrange(desc(size_mb)) %>%
6   print

1 library(tidyr)
2 library(readr)
3 library(dplyr)
4
5 students2 %>%
6   gather( sex_class,count ,-grade ) %>%
7   separate( sex_class, c("sex", "class")) %>%
8   print

1 students3 %>%
2   gather(class, grade, class1:class5, na.rm = TRUE) %>%
3   #name未参与gather,因此照原格式输出
4   spread(test, grade) %>%
5   # spread:Spread a key-value pair across multiple columns.
6   mutate(class=parse_number(class)) %>%
7   # parse_numeric    Extract numeric component of variable.
8   print

 1 student_info <- students4 %>%
 2   select(id, name, sex) %>%
 3   unique() %>%  #去除重复记录
 4   print
 5
 6 gradebook <- students4 %>%
 7   select(id,class,midterm,final) %>%
 8   print
 9
10 merge.data.frame(gradebook,student_info,by="id")
11 #列合并不同行数的数据框
12
13 passed<-mutate(passed,status="passed")
14 failed<-mutate(failed,status="failed")
15 bind_rows(passed,failed)
16 #行合并俩个数据框

1 sat %>%
2   select(-contains("total")) %>%
3   gather(part_sex, count, -score_range) %>%
4   separate(part_sex, c("part", "sex")) %>%
5   group_by(part,sex) %>%
6   mutate( total = sum(count),  #统计每组频数
7           prop = count / total) %>%  #统计每组里面各分数段比例
8   print

1 library(lubridate)

1 ymd("1989-05-17")
2 ymd("1989 May 17")
3 mdy("March 12,1975")
4 dmy(25081985)
5 ymd("1920/1/2")
6
7 hms("03:22:14")
8 ymd_hms(c("2010-04-14-04-35-59", "2010-01-01 12:00:00"))
9 ymd_hms(now())