importing-cleaning-data-in-r-case-studies

导入数据

sales<-read_csv（"sales.csv"）

查看数据结构

> # View dimensions of sales

> dim(sales)

[1] 5000   46

>

> # Inspect first 6 rows of sales

> head(sales)

  X             event_id       primary_act_id     secondary_act_id

1 1 abcaf1adb99a935fc661 43f0436b905bfa7c2eec b85143bf51323b72e53c

2 2 6c56d7f08c95f2aa453c 1a3e9aecd0617706a794 f53529c5679ea6ca5a48

3 3 c7ab4524a121f9d687d2 4b677c3f5bec71eec8d1 b85143bf51323b72e53c

4 4 394cb493f893be9b9ed1 b1ccea01ad6ef8522796 b85143bf51323b72e53c

5 5 55b5f67e618557929f48 91c03a34b562436efa3c b85143bf51323b72e53c

6 6 4f10fd8b9f550352bd56 ac4b847b3fde66f2117e 63814f3d63317f1b56c4

   purch_party_lkup_id

1 7dfa56dd7d5956b17587

2 4f9e6fc637eaf7b736c2

3 6c2545703bd527a7144d

4 527d6b1eaffc69ddd882

5 8bd62c394a35213bdf52

6 3b3a628f83135acd0676

                                                      event_name

1 Xfinity Center Mansfield Premier Parking: Florida Georgia Line

2                  Gorge Camping - dave matthews band - sept 3-7

3                    Dodge Theatre Adams Street Parking - benise

4   Gexa Energy Pavilion Vip Parking : kid rock with sheryl crow

5                                  Premier Parking - motley crue

6                                      Fast Lane Access: Journey

                          primary_act_name secondary_act_name major_cat_name

1 XFINITY Center Mansfield Premier Parking               NULL           MISC

2                            Gorge Camping Dave Matthews Band           MISC

3                            Parking Event               NULL           MISC

4         Gexa Energy Pavilion VIP Parking               NULL           MISC

5 White River Amphitheatre Premier Parking               NULL           MISC

6                         Fast Lane Access            Journey           MISC

          minor_cat_name la_event_type_cat

1                PARKING           PARKING

2                CAMPING           INVALID

3                PARKING           PARKING

4                PARKING           PARKING

5                PARKING           PARKING

6 SPECIAL ENTRY (UPSELL)            UPSELL

                                                 event_disp_name

1 Xfinity Center Mansfield Premier Parking: Florida Georgia Line

2                  Gorge Camping - dave matthews band - sept 3-7

3                    Dodge Theatre Adams Street Parking - benise

4   Gexa Energy Pavilion Vip Parking : kid rock with sheryl crow

5                                  Premier Parking - motley crue

6                                      Fast Lane Access: Journey

                                                                                                                                                   ticket_text

1    THIS TICKET IS VALID        FOR PARKING ONLY         GOOD THIS DAY ONLY       PREMIER PARKING PASS    XFINITY CENTER,LOTS 4 PM  SAT SEP 12 2015 7:30 PM

2                                                                %OVERNIGHT C A M P I N G%* * * * * *%GORGE CAMPGROUND%* GOOD THIS DATE ONLY *%SEP 3 - 6, 2009

3                               ADAMS STREET GARAGE%PARKING FOR 4/21/06 ONLY%DODGE THEATRE PARKING PASS%ENTRANCE ON ADAMS STREET%BENISE%GARAGE OPENS AT 6:00PM

4    THIS TICKET IS VALID        FOR PARKING ONLY      GOOD FOR THIS DATE ONLY       VIP PARKING PASS        GEXA ENERGY PAVILION    FRI SEP 02 2011 7:00 PM

5                              THIS TICKET IS VALID%FOR PARKING ONLY%GOOD THIS DATE ONLY%PREMIER PARKING PASS%WHITE RIVER AMPHITHEATRE%SAT JUL 30, 2005 6:00PM

6         FAST LANE                  JOURNEY               FAST LANE EVENT         THIS IS NOT A TICKET    SAN MANUEL AMPHITHEATER   SAT JUL 21 2012 7:00 PM

  tickets_purchased_qty trans_face_val_amt delivery_type_cd     event_date_time

1                     1                 45          eTicket 2015-09-12 23:30:00

2                     1                 75       TicketFast 2009-09-05 01:00:00

3                     1                  5       TicketFast 2006-04-22 01:30:00

4                     1                 20             Mail 2011-09-03 00:00:00

5                     1                 20             Mail 2005-07-31 01:00:00

6                     2                 10       TicketFast 2012-07-22 02:00:00

    event_dt presale_dt  onsale_dt sales_ord_create_dttm sales_ord_tran_dt

1 2015-09-12       NULL 2015-05-15   2015-09-11 18:17:45        2015-09-11

2 2009-09-04       NULL 2009-03-13   2009-07-06 00:00:00        2009-07-05

3 2006-04-21       NULL 2006-02-25   2006-04-05 00:00:00        2006-04-05

4 2011-09-02       NULL 2011-04-22   2011-07-01 17:38:50        2011-07-01

5 2005-07-30 2005-03-02 2005-03-04   2005-06-18 00:00:00        2005-06-18

6 2012-07-21       NULL 2012-04-11   2012-07-21 17:20:18        2012-07-21

    print_dt timezn_nm     venue_city   venue_state venue_postal_cd_sgmt_1

1 2015-09-12       EST      MANSFIELD MASSACHUSETTS                  02048

2 2009-09-01       PST         QUINCY    WASHINGTON                  98848

3 2006-04-05       MST        PHOENIX       ARIZONA                  85003

4 2011-07-06       CST         DALLAS         TEXAS                  75210

5 2005-06-28       PST         AUBURN    WASHINGTON                  98092

6 2012-07-21       PST SAN BERNARDINO    CALIFORNIA                  92407

            sales_platform_cd print_flg la_valid_tkt_event_flg  fin_mkt_nm

1 www.concerts.livenation.com        T                      N       Boston

2                        NULL        T                      N      Seattle

3                        NULL        T                      N      Arizona

4                        NULL        T                      N       Dallas

5                        NULL        T                      N      Seattle

6          www.livenation.com        T                      N  Los Angeles

  web_session_cookie_val gndr_cd age_yr income_amt edu_val edu_1st_indv_val

1   7dfa56dd7d5956b17587    <NA>   <NA>       <NA>    <NA>             <NA>

2   4f9e6fc637eaf7b736c2    <NA>   <NA>       <NA>    <NA>             <NA>

3   6c2545703bd527a7144d    <NA>   <NA>       <NA>    <NA>             <NA>

4   527d6b1eaffc69ddd882    <NA>   <NA>       <NA>    <NA>             <NA>

5   8bd62c394a35213bdf52    <NA>   <NA>       <NA>    <NA>             <NA>

6   3b3a628f83135acd0676    <NA>   <NA>       <NA>    <NA>             <NA>

  edu_2nd_indv_val adults_in_hh_num married_ind child_present_ind

1             <NA>             <NA>        <NA>              <NA>

2             <NA>             <NA>        <NA>              <NA>

3             <NA>             <NA>        <NA>              <NA>

4             <NA>             <NA>        <NA>              <NA>

5             <NA>             <NA>        <NA>              <NA>

6             <NA>             <NA>        <NA>              <NA>

  home_owner_ind occpn_val occpn_1st_val occpn_2nd_val dist_to_ven

1           <NA>      <NA>          <NA>          <NA>          NA

2           <NA>      <NA>          <NA>          <NA>          59

3           <NA>      <NA>          <NA>          <NA>          NA

4           <NA>      <NA>          <NA>          <NA>          NA

5           <NA>      <NA>          <NA>          <NA>          NA

6           <NA>      <NA>          <NA>          <NA>          NA

>

> # View column names of sales

> names(sales)

 [1] "X"                      "event_id"               "primary_act_id"

 [4] "secondary_act_id"       "purch_party_lkup_id"    "event_name"

 [7] "primary_act_name"       "secondary_act_name"     "major_cat_name"

[10] "minor_cat_name"         "la_event_type_cat"      "event_disp_name"

[13] "ticket_text"            "tickets_purchased_qty"  "trans_face_val_amt"

[16] "delivery_type_cd"       "event_date_time"        "event_dt"

[19] "presale_dt"             "onsale_dt"              "sales_ord_create_dttm"

[22] "sales_ord_tran_dt"      "print_dt"               "timezn_nm"

[25] "venue_city"             "venue_state"            "venue_postal_cd_sgmt_1"

[28] "sales_platform_cd"      "print_flg"              "la_valid_tkt_event_flg"

[31] "fin_mkt_nm"             "web_session_cookie_val" "gndr_cd"

[34] "age_yr"                 "income_amt"             "edu_val"

[37] "edu_1st_indv_val"       "edu_2nd_indv_val"       "adults_in_hh_num"

[40] "married_ind"            "child_present_ind"      "home_owner_ind"

[43] "occpn_val"              "occpn_1st_val"          "occpn_2nd_val"

[46] "dist_to_ven"

下面的一些都是查数据结构的

# Look at structure of sales

str(sales)

# View a summary of sales

summary(sales)

# Load dplyr

require(dplyr)

# Get a glimpse of sales

glimpse(sales)

删除指定列

# Remove the first column of sales: sales2

两种写法是一样的

sales2 <- sales[, 2:ncol(sales)]

sales2<-sales[,-1]

Create a vector called keep that contains the indices of the columns you want to save. Remember: you want to keep everything besides the first 4 and last 15 columns of sales2.

# Define a vector of column indices: keep

keep <- 5:(ncol(sales2) - 15)

# Subset sales2 using keep: sales3

sales3 <- sales2[, keep]

separate 拆分单元格

可以参考separate帮助文档



# Load tidyr

require(tidyr)

# Split event_date_time: sales4

sales4 <- separate(sales3, event_date_time,

                  c("event_dt","event_time"), sep = " ")

# Split sales_ord_create_dttm: sales5

sales5<-separate(sales4,sales_ord_create_dttm,c("ord_create_dt" , "ord_create_time"),sep=" ")

# Split month column into month and year: mbta6

mbta6 <- separate(mbta5, month, c("year", "month"))

读取指定位置的数据

# Define an issues vector

issues<-c(2516, 3863, 4082, 4183)

# Print values of sales_ord_create_dttm at these indices

print(sales3$sales_ord_create_dttm[issues])

# Print a well-behaved value of sales_ord_create_dttm

print(sales3$sales_ord_create_dttm[2517])

stringr 包学习

参考stringr

str_detect()检查字符串匹配

# Load stringr

require(stringr)

# Find columns of sales5 containing "dt": date_cols

date_cols<-str_detect(names(sales5),"dt")

# Load lubridate

require(lubridate)

# Coerce date columns into Date objects

sales5[, date_cols] <- lapply(sales5[, date_cols] , ymd)

查看缺失值的个数

# Find date columns (don't change)

date_cols <- str_detect(names(sales5), "dt")

# Create logical vectors indicating missing values (don't change)

missing <- lapply(sales5[, date_cols], is.na)

# Create a numerical vector that counts missing values: num_missing

num_missing<-sapply(missing,sum)

# Print num_missing

num_missing

unite（）

# Combine the venue_city and venue_state columns

sales6 <-unite(sales5,venue_city_state,venue_city , venue_state,sep=", ")

# View the head of sales6

head(sales6)

从excel中读入数据，并且跳过第一行

关键是skip这个参数

# Load readxl

library(readxl)

# Import mbta.xlsx and skip first row: mbta

mbta<-read_excel("mbta.xlsx",skip=1)

有一种很简单的删除行列的方式

# Remove rows 1, 7, and 11 of mbta: mbta2

mbta2<-mbta[c(-1,-7,-11),]

# Remove the first column of mbta2: mbta3

mbta3<-mbta2[,-1]

gather()合并单元格

# Load tidyr

require(tidyr)

# Gather columns of mbta3: mbta4

mbta4<-gather(mbta3,month,thou_riders,-mode)

# View the head of mbta4

head(mbta4)

fread()

# Import food.csv as a data frame: food

food <-fread("food.csv")

读取xls文件

# Load the gdata package

library(gdata)

# Import the spreadsheet: att

att <- read.xls("attendance.xls")

Reference

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