Ticket Sales
Ex1: Importing the data
# Import sales.csv: sales
download.file("http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/sales.csv",
"sales.csv")
sales <- read.csv("sales.csv", stringsAsFactors = FALSE)
# stringsAsFactors argument to FALSE
# character strings are preserved
# alternatively,
url_sales <- 'http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/sales.csv'
sales <- read.csv(url_sales, stringsAsFactors=F)
# stringsAsFactors argument to FALSE
# character strings are preservedEx2: Examining the data
# View dimensions of sales
dim(sales)
## [1] 5000 46# Inspect first 6 rows of sales
head(sales, 6)
## 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
## 1 XFINITY Center Mansfield Premier Parking NULL
## 2 Gorge Camping Dave Matthews Band
## 3 Parking Event NULL
## 4 Gexa Energy Pavilion VIP Parking NULL
## 5 White River Amphitheatre Premier Parking NULL
## 6 Fast Lane Access Journey
## major_cat_name minor_cat_name la_event_type_cat
## 1 MISC PARKING PARKING
## 2 MISC CAMPING INVALID
## 3 MISC PARKING PARKING
## 4 MISC PARKING PARKING
## 5 MISC PARKING PARKING
## 6 MISC 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
## 1 1 45 eTicket
## 2 1 75 TicketFast
## 3 1 5 TicketFast
## 4 1 20 Mail
## 5 1 20 Mail
## 6 2 10 TicketFast
## event_date_time event_dt presale_dt onsale_dt
## 1 2015-09-12 23:30:00 2015-09-12 NULL 2015-05-15
## 2 2009-09-05 01:00:00 2009-09-04 NULL 2009-03-13
## 3 2006-04-22 01:30:00 2006-04-21 NULL 2006-02-25
## 4 2011-09-03 00:00:00 2011-09-02 NULL 2011-04-22
## 5 2005-07-31 01:00:00 2005-07-30 2005-03-02 2005-03-04
## 6 2012-07-22 02:00:00 2012-07-21 NULL 2012-04-11
## sales_ord_create_dttm sales_ord_tran_dt print_dt timezn_nm
## 1 2015-09-11 18:17:45 2015-09-11 2015-09-12 EST
## 2 2009-07-06 00:00:00 2009-07-05 2009-09-01 PST
## 3 2006-04-05 00:00:00 2006-04-05 2006-04-05 MST
## 4 2011-07-01 17:38:50 2011-07-01 2011-07-06 CST
## 5 2005-06-18 00:00:00 2005-06-18 2005-06-28 PST
## 6 2012-07-21 17:20:18 2012-07-21 2012-07-21 PST
## venue_city venue_state venue_postal_cd_sgmt_1
## 1 MANSFIELD MASSACHUSETTS 02048
## 2 QUINCY WASHINGTON 98848
## 3 PHOENIX ARIZONA 85003
## 4 DALLAS TEXAS 75210
## 5 AUBURN WASHINGTON 98092
## 6 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
## 1 7dfa56dd7d5956b17587 <NA> <NA> <NA> <NA>
## 2 4f9e6fc637eaf7b736c2 <NA> <NA> <NA> <NA>
## 3 6c2545703bd527a7144d <NA> <NA> <NA> <NA>
## 4 527d6b1eaffc69ddd882 <NA> <NA> <NA> <NA>
## 5 8bd62c394a35213bdf52 <NA> <NA> <NA> <NA>
## 6 3b3a628f83135acd0676 <NA> <NA> <NA> <NA>
## edu_1st_indv_val edu_2nd_indv_val adults_in_hh_num married_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>
## child_present_ind home_owner_ind occpn_val occpn_1st_val occpn_2nd_val
## 1 <NA> <NA> <NA> <NA> <NA>
## 2 <NA> <NA> <NA> <NA> <NA>
## 3 <NA> <NA> <NA> <NA> <NA>
## 4 <NA> <NA> <NA> <NA> <NA>
## 5 <NA> <NA> <NA> <NA> <NA>
## 6 <NA> <NA> <NA> <NA> <NA>
## dist_to_ven
## 1 NA
## 2 59
## 3 NA
## 4 NA
## 5 NA
## 6 NA# View column names of sales
names(sales)
## [1] "X" "event_id"
## [3] "primary_act_id" "secondary_act_id"
## [5] "purch_party_lkup_id" "event_name"
## [7] "primary_act_name" "secondary_act_name"
## [9] "major_cat_name" "minor_cat_name"
## [11] "la_event_type_cat" "event_disp_name"
## [13] "ticket_text" "tickets_purchased_qty"
## [15] "trans_face_val_amt" "delivery_type_cd"
## [17] "event_date_time" "event_dt"
## [19] "presale_dt" "onsale_dt"
## [21] "sales_ord_create_dttm" "sales_ord_tran_dt"
## [23] "print_dt" "timezn_nm"
## [25] "venue_city" "venue_state"
## [27] "venue_postal_cd_sgmt_1" "sales_platform_cd"
## [29] "print_flg" "la_valid_tkt_event_flg"
## [31] "fin_mkt_nm" "web_session_cookie_val"
## [33] "gndr_cd" "age_yr"
## [35] "income_amt" "edu_val"
## [37] "edu_1st_indv_val" "edu_2nd_indv_val"
## [39] "adults_in_hh_num" "married_ind"
## [41] "child_present_ind" "home_owner_ind"
## [43] "occpn_val" "occpn_1st_val"
## [45] "occpn_2nd_val" "dist_to_ven"NB
- rows appear to represent individual purchases
- columns contain different pieces of information about each purchase.
- each row represents an observation
- each column a variable, or piece of information about that observation.
Ex3: Summarizing the data
# Look at structure of sales
str(sales)
## 'data.frame': 5000 obs. of 46 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ event_id : chr "abcaf1adb99a935fc661" "6c56d7f08c95f2aa453c" "c7ab4524a121f9d687d2" "394cb493f893be9b9ed1" ...
## $ primary_act_id : chr "43f0436b905bfa7c2eec" "1a3e9aecd0617706a794" "4b677c3f5bec71eec8d1" "b1ccea01ad6ef8522796" ...
## $ secondary_act_id : chr "b85143bf51323b72e53c" "f53529c5679ea6ca5a48" "b85143bf51323b72e53c" "b85143bf51323b72e53c" ...
## $ purch_party_lkup_id : chr "7dfa56dd7d5956b17587" "4f9e6fc637eaf7b736c2" "6c2545703bd527a7144d" "527d6b1eaffc69ddd882" ...
## $ event_name : chr "Xfinity Center Mansfield Premier Parking: Florida Georgia Line" "Gorge Camping - dave matthews band - sept 3-7" "Dodge Theatre Adams Street Parking - benise" "Gexa Energy Pavilion Vip Parking : kid rock with sheryl crow" ...
## $ primary_act_name : chr "XFINITY Center Mansfield Premier Parking" "Gorge Camping" "Parking Event" "Gexa Energy Pavilion VIP Parking" ...
## $ secondary_act_name : chr "NULL" "Dave Matthews Band" "NULL" "NULL" ...
## $ major_cat_name : chr "MISC" "MISC" "MISC" "MISC" ...
## $ minor_cat_name : chr "PARKING" "CAMPING" "PARKING" "PARKING" ...
## $ la_event_type_cat : chr "PARKING" "INVALID" "PARKING" "PARKING" ...
## $ event_disp_name : chr "Xfinity Center Mansfield Premier Parking: Florida Georgia Line" "Gorge Camping - dave matthews band - sept 3-7" "Dodge Theatre Adams Street Parking - benise" "Gexa Energy Pavilion Vip Parking : kid rock with sheryl crow" ...
## $ ticket_text : chr " THIS TICKET IS VALID FOR PARKING ONLY GOOD THIS DAY ONLY PREMIER PARKING PASS XFINIT"| __truncated__ "%OVERNIGHT C A M P I N G%* * * * * *%GORGE CAMPGROUND%* GOOD THIS DATE ONLY *%SEP 3 - 6, 2009" "ADAMS STREET GARAGE%PARKING FOR 4/21/06 ONLY%DODGE THEATRE PARKING PASS%ENTRANCE ON ADAMS STREET%BENISE%GARAGE OPENS AT 6:00PM" " THIS TICKET IS VALID FOR PARKING ONLY GOOD FOR THIS DATE ONLY VIP PARKING PASS GEXA"| __truncated__ ...
## $ tickets_purchased_qty : int 1 1 1 1 1 2 1 1 1 1 ...
## $ trans_face_val_amt : num 45 75 5 20 20 10 30 28 20 25 ...
## $ delivery_type_cd : chr "eTicket" "TicketFast" "TicketFast" "Mail" ...
## $ event_date_time : chr "2015-09-12 23:30:00" "2009-09-05 01:00:00" "2006-04-22 01:30:00" "2011-09-03 00:00:00" ...
## $ event_dt : chr "2015-09-12" "2009-09-04" "2006-04-21" "2011-09-02" ...
## $ presale_dt : chr "NULL" "NULL" "NULL" "NULL" ...
## $ onsale_dt : chr "2015-05-15" "2009-03-13" "2006-02-25" "2011-04-22" ...
## $ sales_ord_create_dttm : chr "2015-09-11 18:17:45" "2009-07-06 00:00:00" "2006-04-05 00:00:00" "2011-07-01 17:38:50" ...
## $ sales_ord_tran_dt : chr "2015-09-11" "2009-07-05" "2006-04-05" "2011-07-01" ...
## $ print_dt : chr "2015-09-12" "2009-09-01" "2006-04-05" "2011-07-06" ...
## $ timezn_nm : chr "EST" "PST" "MST" "CST" ...
## $ venue_city : chr "MANSFIELD" "QUINCY" "PHOENIX" "DALLAS" ...
## $ venue_state : chr "MASSACHUSETTS" "WASHINGTON" "ARIZONA" "TEXAS" ...
## $ venue_postal_cd_sgmt_1: chr "02048" "98848" "85003" "75210" ...
## $ sales_platform_cd : chr "www.concerts.livenation.com" "NULL" "NULL" "NULL" ...
## $ print_flg : chr "T " "T " "T " "T " ...
## $ la_valid_tkt_event_flg: chr "N " "N " "N " "N " ...
## $ fin_mkt_nm : chr "Boston" "Seattle" "Arizona" "Dallas" ...
## $ web_session_cookie_val: chr "7dfa56dd7d5956b17587" "4f9e6fc637eaf7b736c2" "6c2545703bd527a7144d" "527d6b1eaffc69ddd882" ...
## $ gndr_cd : chr NA NA NA NA ...
## $ age_yr : chr NA NA NA NA ...
## $ income_amt : chr NA NA NA NA ...
## $ edu_val : chr NA NA NA NA ...
## $ edu_1st_indv_val : chr NA NA NA NA ...
## $ edu_2nd_indv_val : chr NA NA NA NA ...
## $ adults_in_hh_num : chr NA NA NA NA ...
## $ married_ind : chr NA NA NA NA ...
## $ child_present_ind : chr NA NA NA NA ...
## $ home_owner_ind : chr NA NA NA NA ...
## $ occpn_val : chr NA NA NA NA ...
## $ occpn_1st_val : chr NA NA NA NA ...
## $ occpn_2nd_val : chr NA NA NA NA ...
## $ dist_to_ven : int NA 59 NA NA NA NA NA NA NA NA ...# View a summary of sales
summary(sales)
## X event_id primary_act_id secondary_act_id
## Min. : 1 Length:5000 Length:5000 Length:5000
## 1st Qu.:1251 Class :character Class :character Class :character
## Median :2500 Mode :character Mode :character Mode :character
## Mean :2500
## 3rd Qu.:3750
## Max. :5000
##
## purch_party_lkup_id event_name primary_act_name
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## secondary_act_name major_cat_name minor_cat_name
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## la_event_type_cat event_disp_name ticket_text
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## tickets_purchased_qty trans_face_val_amt delivery_type_cd
## Min. :1.000 Min. : 1.00 Length:5000
## 1st Qu.:1.000 1st Qu.: 20.00 Class :character
## Median :1.000 Median : 30.00 Mode :character
## Mean :1.639 Mean : 77.08
## 3rd Qu.:2.000 3rd Qu.: 85.00
## Max. :8.000 Max. :1520.88
##
## event_date_time event_dt presale_dt
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## onsale_dt sales_ord_create_dttm sales_ord_tran_dt
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## print_dt timezn_nm venue_city
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## venue_state venue_postal_cd_sgmt_1 sales_platform_cd
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## print_flg la_valid_tkt_event_flg fin_mkt_nm
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## web_session_cookie_val gndr_cd age_yr
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## income_amt edu_val edu_1st_indv_val
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## edu_2nd_indv_val adults_in_hh_num married_ind
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## child_present_ind home_owner_ind occpn_val
## Length:5000 Length:5000 Length:5000
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## occpn_1st_val occpn_2nd_val dist_to_ven
## Length:5000 Length:5000 Min. : 0.0
## Class :character Class :character 1st Qu.: 12.0
## Mode :character Mode :character Median : 26.0
## Mean : 158.2
## 3rd Qu.: 77.5
## Max. :2548.0
## NA's :4677# Load dplyr
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
# Get a glimpse of sales
glimpse(sales)
## Observations: 5,000
## Variables: 46
## $ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, ...
## $ event_id <chr> "abcaf1adb99a935fc661", "6c56d7f08c95f2...
## $ primary_act_id <chr> "43f0436b905bfa7c2eec", "1a3e9aecd06177...
## $ secondary_act_id <chr> "b85143bf51323b72e53c", "f53529c5679ea6...
## $ purch_party_lkup_id <chr> "7dfa56dd7d5956b17587", "4f9e6fc637eaf7...
## $ event_name <chr> "Xfinity Center Mansfield Premier Parki...
## $ primary_act_name <chr> "XFINITY Center Mansfield Premier Parki...
## $ secondary_act_name <chr> "NULL", "Dave Matthews Band", "NULL", "...
## $ major_cat_name <chr> "MISC", "MISC", "MISC", "MISC", "MISC",...
## $ minor_cat_name <chr> "PARKING", "CAMPING", "PARKING", "PARKI...
## $ la_event_type_cat <chr> "PARKING", "INVALID", "PARKING", "PARKI...
## $ event_disp_name <chr> "Xfinity Center Mansfield Premier Parki...
## $ ticket_text <chr> " THIS TICKET IS VALID FOR PAR...
## $ tickets_purchased_qty <int> 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 4, ...
## $ trans_face_val_amt <dbl> 45, 75, 5, 20, 20, 10, 30, 28, 20, 25, ...
## $ delivery_type_cd <chr> "eTicket", "TicketFast", "TicketFast", ...
## $ event_date_time <chr> "2015-09-12 23:30:00", "2009-09-05 01:0...
## $ event_dt <chr> "2015-09-12", "2009-09-04", "2006-04-21...
## $ presale_dt <chr> "NULL", "NULL", "NULL", "NULL", "2005-0...
## $ onsale_dt <chr> "2015-05-15", "2009-03-13", "2006-02-25...
## $ sales_ord_create_dttm <chr> "2015-09-11 18:17:45", "2009-07-06 00:0...
## $ sales_ord_tran_dt <chr> "2015-09-11", "2009-07-05", "2006-04-05...
## $ print_dt <chr> "2015-09-12", "2009-09-01", "2006-04-05...
## $ timezn_nm <chr> "EST", "PST", "MST", "CST", "PST", "PST...
## $ venue_city <chr> "MANSFIELD", "QUINCY", "PHOENIX", "DALL...
## $ venue_state <chr> "MASSACHUSETTS", "WASHINGTON", "ARIZONA...
## $ venue_postal_cd_sgmt_1 <chr> "02048", "98848", "85003", "75210", "98...
## $ sales_platform_cd <chr> "www.concerts.livenation.com", "NULL", ...
## $ print_flg <chr> "T ", "T ", "T ", "T ", "T ", "T ", "T ...
## $ la_valid_tkt_event_flg <chr> "N ", "N ", "N ", "N ", "N ", "N ", "N ...
## $ fin_mkt_nm <chr> "Boston", "Seattle", "Arizona", "Dallas...
## $ web_session_cookie_val <chr> "7dfa56dd7d5956b17587", "4f9e6fc637eaf7...
## $ gndr_cd <chr> NA, NA, NA, NA, NA, NA, "M", NA, NA, NA...
## $ age_yr <chr> NA, NA, NA, NA, NA, NA, "28", NA, NA, N...
## $ income_amt <chr> NA, NA, NA, NA, NA, NA, "112500", NA, N...
## $ edu_val <chr> NA, NA, NA, NA, NA, NA, "High School", ...
## $ edu_1st_indv_val <chr> NA, NA, NA, NA, NA, NA, "High School", ...
## $ edu_2nd_indv_val <chr> NA, NA, NA, NA, NA, NA, "NULL", NA, NA,...
## $ adults_in_hh_num <chr> NA, NA, NA, NA, NA, NA, "4", NA, NA, NA...
## $ married_ind <chr> NA, NA, NA, NA, NA, NA, "0", NA, NA, NA...
## $ child_present_ind <chr> NA, NA, NA, NA, NA, NA, "1", NA, NA, NA...
## $ home_owner_ind <chr> NA, NA, NA, NA, NA, NA, "0", NA, NA, NA...
## $ occpn_val <chr> NA, NA, NA, NA, NA, NA, "NULL", NA, NA,...
## $ occpn_1st_val <chr> NA, NA, NA, NA, NA, NA, "Craftsman Blue...
## $ occpn_2nd_val <chr> NA, NA, NA, NA, NA, NA, "NULL", NA, NA,...
## $ dist_to_ven <int> NA, 59, NA, NA, NA, NA, NA, NA, NA, NA,...Ex4: Removing redundant info
first column = X = counting = duplication of the row numbers
nrow(sales)
## [1] 5000
ncol(sales)
## [1] 46Remove the first column of sales
method 1: subset
# ```{r ... results='hide'}
# subset a data frame
sales[,4]
sales2 <- sales[,2:ncol(sales)]method 2: negative indices
# ```{r ... results='hide'}
# remove rows and columns using negative indices
sales[-(1:5), ] # Omit first 5 rows of sales
sales[, -4] # Omit fourth column of sales
sales2 <- sales[,-1] # Omit first row of salesVerify
# verify col X removed
str(sales[,1:5])
## 'data.frame': 5000 obs. of 5 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ event_id : chr "abcaf1adb99a935fc661" "6c56d7f08c95f2aa453c" "c7ab4524a121f9d687d2" "394cb493f893be9b9ed1" ...
## $ primary_act_id : chr "43f0436b905bfa7c2eec" "1a3e9aecd0617706a794" "4b677c3f5bec71eec8d1" "b1ccea01ad6ef8522796" ...
## $ secondary_act_id : chr "b85143bf51323b72e53c" "f53529c5679ea6ca5a48" "b85143bf51323b72e53c" "b85143bf51323b72e53c" ...
## $ purch_party_lkup_id: chr "7dfa56dd7d5956b17587" "4f9e6fc637eaf7b736c2" "6c2545703bd527a7144d" "527d6b1eaffc69ddd882" ...
str(sales2[,1:5])
## 'data.frame': 5000 obs. of 5 variables:
## $ event_id : chr "abcaf1adb99a935fc661" "6c56d7f08c95f2aa453c" "c7ab4524a121f9d687d2" "394cb493f893be9b9ed1" ...
## $ primary_act_id : chr "43f0436b905bfa7c2eec" "1a3e9aecd0617706a794" "4b677c3f5bec71eec8d1" "b1ccea01ad6ef8522796" ...
## $ secondary_act_id : chr "b85143bf51323b72e53c" "f53529c5679ea6ca5a48" "b85143bf51323b72e53c" "b85143bf51323b72e53c" ...
## $ purch_party_lkup_id: chr "7dfa56dd7d5956b17587" "4f9e6fc637eaf7b736c2" "6c2545703bd527a7144d" "527d6b1eaffc69ddd882" ...
## $ event_name : chr "Xfinity Center Mansfield Premier Parking: Florida Georgia Line" "Gorge Camping - dave matthews band - sept 3-7" "Dodge Theatre Adams Street Parking - benise" "Gexa Energy Pavilion Vip Parking : kid rock with sheryl crow" ...Ex5: Information not worth keeping
- first four columns contain internal codes representing particular events.
- last fifteen columns - too many missing values.
- create a vector containing the column indices you want to keep
# Define a vector of column indices: keep
# keep everything besides the first 4 and last 15
keep <- 5:(ncol(sales2) - 15)- subset the data based on that vector using single bracket subsetting.
# Subset sales2 using keep: sales3
sales3 <- sales2[,keep]Ex6: Separating columns
separate a column into two: one for date one for time
event_date_time
# Load tidyr
library(tidyr)
# look at event_date_time col
head(sales3$event_date_time)
## [1] "2015-09-12 23:30:00" "2009-09-05 01:00:00" "2006-04-22 01:30:00"
## [4] "2011-09-03 00:00:00" "2005-07-31 01:00:00" "2012-07-22 02:00:00"
#date and time are separated by a space
# => sep = " "
# Split event_date_time: sales4
sales4 <- separate(sales3, event_date_time,
c("event_dt", "event_time"), sep = " ")
# required c()
# required ""
## separate() should have four arguments:
## data frame
## column name without quotes
## character vector with names of new columns (in quotes)
## character string showing where to separate the original column (in quotes)sales_ord_create_dttm
# what sep needed?
head(sales4$sales_ord_create_dttm)
## [1] "2015-09-11 18:17:45" "2009-07-06 00:00:00" "2006-04-05 00:00:00"
## [4] "2011-07-01 17:38:50" "2005-06-18 00:00:00" "2012-07-21 17:20:18"
# Split sales_ord_create_dttm: sales5
sales5 <- separate(sales4, sales_ord_create_dttm,
c("ord_create_dt", "ord_create_time"), sep = " ")
## Warning: Expected 2 pieces. Missing pieces filled with `NA` in 4 rows
## [2516, 3863, 4082, 4183].Ex7: warnings?
“Too few values at 4 locations” locations (i.e. rows) given in the warning: 2516, 3863, 4082, and 4183
# Define an issues vector
issues <- c(2516, 3863, 4082, 4183)
# Print values of sales_ord_create_dttm at these indices
sales3$sales_ord_create_dttm[issues]
## [1] "NULL" "NULL" "NULL" "NULL"
# phew.. warning was just because of four missing valueslet’s see a well-behaved value
# Print a well-behaved value of sales_ord_create_dttm
sales3$sales_ord_create_dttm[2517]
## [1] "2013-08-04 23:07:19"Ex8: Identifying dates
all of the date columns in this dataset have the substring “dt” in their name
library(stringr)
# Find columns of sales5 containing "dt": date_cols
date_cols <- str_detect(names(sales5), "dt")
library(lubridate)
##
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
##
## date
# function names combine the letters y, m, d, h, m, and s
# Coerce date columns into Date objects
# lapply(my_data_frame[, cols], function_name)
sales5[, date_cols] <- lapply(sales5[,date_cols], ymd)
## Warning: 2892 failed to parse.
## Warning: 101 failed to parse.
## Warning: 4 failed to parse.
## Warning: 424 failed to parse.Ex9: warning: failure to parse
Warning message: 2892 failed to parse. Warning message: 101 failed to parse. Warning message: 4 failed to parse. Warning message: 424 failed to parse.
# 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)
## Each vector indicates the presence (or absence)
## of missing values in the corresponding column of sales5
## CHECK if:
## number of missing values in each column
## =
## number of rows that failed to parse
# Create a numerical vector that counts missing values: num_missing
num_missing <- sapply(missing, sum)
num_missing
## event_dt presale_dt onsale_dt ord_create_dt
## 0 2892 101 4
## sales_ord_tran_dt print_dt
## 0 424Ex10: Combining columns
unite separated by a comma and a space For example, “PORTLAND” “MAINE” should become “PORTLAND, MAINE”.
# unite() should have five arguments:
## data frame
## The name of the new column to create (no quotes)
## The name of the first column to merge (no quotes)
## The name of the second column to merge (no quotes)
## A character string containing the separator (with quotes)
# Combine the venue_city and venue_state columns
sales6 <- unite(sales5, venue_city_state, venue_city, venue_state,
sep = ", ") # the word "sep" was required
# View the head of sales6
head(sales6)
## 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
## 1 XFINITY Center Mansfield Premier Parking NULL
## 2 Gorge Camping Dave Matthews Band
## 3 Parking Event NULL
## 4 Gexa Energy Pavilion VIP Parking NULL
## 5 White River Amphitheatre Premier Parking NULL
## 6 Fast Lane Access Journey
## major_cat_name minor_cat_name la_event_type_cat
## 1 MISC PARKING PARKING
## 2 MISC CAMPING INVALID
## 3 MISC PARKING PARKING
## 4 MISC PARKING PARKING
## 5 MISC PARKING PARKING
## 6 MISC 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_dt
## 1 1 45 eTicket 2015-09-12
## 2 1 75 TicketFast 2009-09-05
## 3 1 5 TicketFast 2006-04-22
## 4 1 20 Mail 2011-09-03
## 5 1 20 Mail 2005-07-31
## 6 2 10 TicketFast 2012-07-22
## event_time presale_dt onsale_dt ord_create_dt ord_create_time
## 1 23:30:00 <NA> 2015-05-15 2015-09-11 18:17:45
## 2 01:00:00 <NA> 2009-03-13 2009-07-06 00:00:00
## 3 01:30:00 <NA> 2006-02-25 2006-04-05 00:00:00
## 4 00:00:00 <NA> 2011-04-22 2011-07-01 17:38:50
## 5 01:00:00 2005-03-02 2005-03-04 2005-06-18 00:00:00
## 6 02:00:00 <NA> 2012-04-11 2012-07-21 17:20:18
## sales_ord_tran_dt print_dt timezn_nm venue_city_state
## 1 2015-09-11 2015-09-12 EST MANSFIELD, MASSACHUSETTS
## 2 2009-07-05 2009-09-01 PST QUINCY, WASHINGTON
## 3 2006-04-05 2006-04-05 MST PHOENIX, ARIZONA
## 4 2011-07-01 2011-07-06 CST DALLAS, TEXAS
## 5 2005-06-18 2005-06-28 PST AUBURN, WASHINGTON
## 6 2012-07-21 2012-07-21 PST SAN BERNARDINO, CALIFORNIA
## venue_postal_cd_sgmt_1 sales_platform_cd print_flg
## 1 02048 www.concerts.livenation.com T
## 2 98848 NULL T
## 3 85003 NULL T
## 4 75210 NULL T
## 5 98092 NULL T
## 6 92407 www.livenation.com T
## la_valid_tkt_event_flg fin_mkt_nm
## 1 N Boston
## 2 N Seattle
## 3 N Arizona
## 4 N Dallas
## 5 N Seattle
## 6 N Los Angeles
head(sales6$venue_city_state)
## [1] "MANSFIELD, MASSACHUSETTS" "QUINCY, WASHINGTON"
## [3] "PHOENIX, ARIZONA" "DALLAS, TEXAS"
## [5] "AUBURN, WASHINGTON" "SAN BERNARDINO, CALIFORNIA"MBTA Ridership
MBTA = Massachusetts Bay Transportation Authority = “the T” for short * manage: * America’s oldest subway * Greater Boston’s * commuter rail * ferry * bus systems
TASK: average ridership through time => need to clean the data
Ex1: readxl
Import mbta.xlsx and skip first row
# url_mbta <- "http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/mbta.xlsx"
# download.file(url_mbta, "mbta.xlsx") # downloaded file did NOT open
# !!QUICK FIX!!
# went to link
# opened file
# saved file in Temp folder
# Load Hadley Wickham's readxl package
library(readxl)
# Import mbta.xlsx and skip first row: mbta
# we know: first row is a title
mbta <- read_excel("C:/Temp/mbta.xlsx", skip = 1)# ```{r ... eval=FALSE} as the following returns the error message below, which indicates "file not found".
mbta <- read_excel("http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/mbta.xlsx", skip = 1)
# Error in read_fun(path = path, sheet = sheet, limits = limits, shim = shim, :
# Evaluation error: zip file 'http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/mbta.xlsx' cannot be opened
Ex2: Examining the data
Do you notice anything strange about how the rows and columns are organized?
str(mbta)
## Classes 'tbl_df', 'tbl' and 'data.frame': 11 obs. of 60 variables:
## $ X__1 : num 1 2 3 4 5 6 7 8 9 10 ...
## $ mode : chr "All Modes by Qtr" "Boat" "Bus" "Commuter Rail" ...
## $ 2007-01: chr "NA" "4" "335.81900000000002" "142.19999999999999" ...
## $ 2007-02: chr "NA" "3.6" "338.67500000000001" "138.5" ...
## $ 2007-03: num 1188 40 340 138 459 ...
## $ 2007-04: chr "NA" "4.3" "352.16199999999998" "139.5" ...
## $ 2007-05: chr "NA" "4.9000000000000004" "354.36700000000002" "139" ...
## $ 2007-06: num 1246 5.8 350.5 143 477 ...
## $ 2007-07: chr "NA" "6.5209999999999999" "357.51900000000001" "142.39099999999999" ...
## $ 2007-08: chr "NA" "6.5720000000000001" "355.47899999999998" "142.364" ...
## $ 2007-09: num 1256.57 5.47 372.6 143.05 499.57 ...
## $ 2007-10: chr "NA" "5.1449999999999996" "368.84699999999998" "146.542" ...
## $ 2007-11: chr "NA" "3.7629999999999999" "330.82600000000002" "145.089" ...
## $ 2007-12: num 1216.89 2.98 312.92 141.59 448.27 ...
## $ 2008-01: chr "NA" "3.1749999999999998" "340.32400000000001" "142.14500000000001" ...
## $ 2008-02: chr "NA" "3.1110000000000002" "352.90499999999997" "142.607" ...
## $ 2008-03: num 1253.52 3.51 361.15 137.45 494.05 ...
## $ 2008-04: chr "NA" "4.1639999999999997" "368.18900000000002" "140.38900000000001" ...
## $ 2008-05: chr "NA" "4.0149999999999997" "363.90300000000002" "142.58500000000001" ...
## $ 2008-06: num 1314.82 5.19 362.96 142.06 518.35 ...
## $ 2008-07: chr "NA" "6.016" "370.92099999999999" "145.73099999999999" ...
## $ 2008-08: chr "NA" "5.8" "361.05700000000002" "144.565" ...
## $ 2008-09: num 1307.04 4.59 389.54 141.91 517.32 ...
## $ 2008-10: chr "NA" "4.2850000000000001" "357.97399999999999" "151.95699999999999" ...
## $ 2008-11: chr "NA" "3.488" "345.423" "152.952" ...
## $ 2008-12: num 1232.65 3.01 325.77 140.81 446.74 ...
## $ 2009-01: chr "NA" "3.0139999999999998" "338.53199999999998" "141.44800000000001" ...
## $ 2009-02: chr "NA" "3.1960000000000002" "360.41199999999998" "143.529" ...
## $ 2009-03: num 1209.79 3.33 353.69 142.89 467.22 ...
## $ 2009-04: chr "NA" "4.0490000000000004" "359.38" "142.34" ...
## $ 2009-05: chr "NA" "4.1189999999999998" "354.75" "144.22499999999999" ...
## $ 2009-06: num 1233.1 4.9 347.9 142 473.1 ...
## $ 2009-07: chr "NA" "6.444" "339.47699999999998" "137.691" ...
## $ 2009-08: chr "NA" "5.9029999999999996" "332.661" "139.15799999999999" ...
## $ 2009-09: num 1230.5 4.7 374.3 139.1 500.4 ...
## $ 2009-10: chr "NA" "4.2119999999999997" "385.86799999999999" "137.10400000000001" ...
## $ 2009-11: chr "NA" "3.5760000000000001" "366.98" "129.34299999999999" ...
## $ 2009-12: num 1207.85 3.11 332.39 126.07 440.93 ...
## $ 2010-01: chr "NA" "3.2069999999999999" "362.226" "130.91" ...
## $ 2010-02: chr "NA" "3.1949999999999998" "361.13799999999998" "131.91800000000001" ...
## $ 2010-03: num 1208.86 3.48 373.44 131.25 483.4 ...
## $ 2010-04: chr "NA" "4.452" "378.61099999999999" "131.72200000000001" ...
## $ 2010-05: chr "NA" "4.415" "380.17099999999999" "128.80000000000001" ...
## $ 2010-06: num 1244.41 5.41 363.27 129.14 490.26 ...
## $ 2010-07: chr "NA" "6.5129999999999999" "353.04" "122.935" ...
## $ 2010-08: chr "NA" "6.2690000000000001" "343.68799999999999" "129.732" ...
## $ 2010-09: num 1225.5 4.7 381.6 132.9 521.1 ...
## $ 2010-10: chr "NA" "4.4020000000000001" "384.98700000000002" "131.03299999999999" ...
## $ 2010-11: chr "NA" "3.7309999999999999" "367.95499999999998" "130.88900000000001" ...
## $ 2010-12: num 1216.26 3.16 326.34 121.42 450.43 ...
## $ 2011-01: chr "NA" "3.14" "334.95800000000003" "128.39599999999999" ...
## $ 2011-02: chr "NA" "3.2839999999999998" "346.23399999999998" "125.46299999999999" ...
## $ 2011-03: num 1223.45 3.67 380.4 134.37 516.73 ...
## $ 2011-04: chr "NA" "4.2510000000000003" "380.44600000000003" "134.16900000000001" ...
## $ 2011-05: chr "NA" "4.431" "385.28899999999999" "136.13999999999999" ...
## $ 2011-06: num 1302.41 5.47 376.32 135.58 529.53 ...
## $ 2011-07: chr "NA" "6.5810000000000004" "361.58499999999998" "132.41" ...
## $ 2011-08: chr "NA" "6.7329999999999997" "353.79300000000001" "130.61600000000001" ...
## $ 2011-09: num 1291 5 388 137 550 ...
## $ 2011-10: chr "NA" "4.484" "398.45600000000002" "128.72" ...
head(mbta, 6)
## # A tibble: 6 x 60
## X__1 mode `2007-01` `2007-02` `2007-03` `2007-04` `2007-05` `2007-06`
## <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl>
## 1 1 All M~ NA NA 1188. NA NA 1246.
## 2 2 Boat 4 3.6 40 4.3 4.900000~ 5.8
## 3 3 Bus 335.8190~ 338.6750~ 340. 352.1619~ 354.3670~ 351.
## 4 4 Commu~ 142.1999~ 138.5 138. 139.5 139 143
## 5 5 Heavy~ 435.2939~ 448.2710~ 459. 472.2010~ 474.5790~ 477.
## 6 6 Light~ 227.2309~ 240.262 241. 255.5569~ 248.262 246.
## # ... with 52 more variables: `2007-07` <chr>, `2007-08` <chr>,
## # `2007-09` <dbl>, `2007-10` <chr>, `2007-11` <chr>, `2007-12` <dbl>,
## # `2008-01` <chr>, `2008-02` <chr>, `2008-03` <dbl>, `2008-04` <chr>,
## # `2008-05` <chr>, `2008-06` <dbl>, `2008-07` <chr>, `2008-08` <chr>,
## # `2008-09` <dbl>, `2008-10` <chr>, `2008-11` <chr>, `2008-12` <dbl>,
## # `2009-01` <chr>, `2009-02` <chr>, `2009-03` <dbl>, `2009-04` <chr>,
## # `2009-05` <chr>, `2009-06` <dbl>, `2009-07` <chr>, `2009-08` <chr>,
## # `2009-09` <dbl>, `2009-10` <chr>, `2009-11` <chr>, `2009-12` <dbl>,
## # `2010-01` <chr>, `2010-02` <chr>, `2010-03` <dbl>, `2010-04` <chr>,
## # `2010-05` <chr>, `2010-06` <dbl>, `2010-07` <chr>, `2010-08` <chr>,
## # `2010-09` <dbl>, `2010-10` <chr>, `2010-11` <chr>, `2010-12` <dbl>,
## # `2011-01` <chr>, `2011-02` <chr>, `2011-03` <dbl>, `2011-04` <chr>,
## # `2011-05` <chr>, `2011-06` <dbl>, `2011-07` <chr>, `2011-08` <chr>,
## # `2011-09` <dbl>, `2011-10` <chr>
summary(mbta)
## X__1 mode 2007-01 2007-02
## Min. : 1.0 Length:11 Length:11 Length:11
## 1st Qu.: 3.5 Class :character Class :character Class :character
## Median : 6.0 Mode :character Mode :character Mode :character
## Mean : 6.0
## 3rd Qu.: 8.5
## Max. :11.0
## 2007-03 2007-04 2007-05
## Min. : 0.114 Length:11 Length:11
## 1st Qu.: 9.278 Class :character Class :character
## Median : 137.700 Mode :character Mode :character
## Mean : 330.293
## 3rd Qu.: 399.225
## Max. :1204.725
## 2007-06 2007-07 2007-08
## Min. : 0.096 Length:11 Length:11
## 1st Qu.: 5.700 Class :character Class :character
## Median : 143.000 Mode :character Mode :character
## Mean : 339.846
## 3rd Qu.: 413.788
## Max. :1246.129
## 2007-09 2007-10 2007-11
## Min. : -0.007 Length:11 Length:11
## 1st Qu.: 5.539 Class :character Class :character
## Median : 143.051 Mode :character Mode :character
## Mean : 352.554
## 3rd Qu.: 436.082
## Max. :1310.764
## 2007-12 2008-01 2008-02
## Min. : -0.060 Length:11 Length:11
## 1st Qu.: 4.385 Class :character Class :character
## Median : 141.585 Mode :character Mode :character
## Mean : 321.588
## 3rd Qu.: 380.594
## Max. :1216.890
## 2008-03 2008-04 2008-05
## Min. : 0.058 Length:11 Length:11
## 1st Qu.: 5.170 Class :character Class :character
## Median : 137.453 Mode :character Mode :character
## Mean : 345.604
## 3rd Qu.: 427.601
## Max. :1274.031
## 2008-06 2008-07 2008-08
## Min. : 0.060 Length:11 Length:11
## 1st Qu.: 5.742 Class :character Class :character
## Median : 142.057 Mode :character Mode :character
## Mean : 359.667
## 3rd Qu.: 440.656
## Max. :1320.728
## 2008-09 2008-10 2008-11
## Min. : 0.021 Length:11 Length:11
## 1st Qu.: 5.691 Class :character Class :character
## Median : 141.907 Mode :character Mode :character
## Mean : 362.099
## 3rd Qu.: 453.430
## Max. :1338.015
## 2008-12 2009-01 2009-02
## Min. : -0.015 Length:11 Length:11
## 1st Qu.: 4.689 Class :character Class :character
## Median : 140.810 Mode :character Mode :character
## Mean : 319.882
## 3rd Qu.: 386.255
## Max. :1232.655
## 2009-03 2009-04 2009-05
## Min. : -0.050 Length:11 Length:11
## 1st Qu.: 5.003 Class :character Class :character
## Median : 142.893 Mode :character Mode :character
## Mean : 330.142
## 3rd Qu.: 410.455
## Max. :1210.912
## 2009-06 2009-07 2009-08
## Min. : -0.079 Length:11 Length:11
## 1st Qu.: 5.845 Class :character Class :character
## Median : 142.006 Mode :character Mode :character
## Mean : 333.194
## 3rd Qu.: 410.482
## Max. :1233.085
## 2009-09 2009-10 2009-11
## Min. : -0.035 Length:11 Length:11
## 1st Qu.: 5.693 Class :character Class :character
## Median : 139.087 Mode :character Mode :character
## Mean : 346.687
## 3rd Qu.: 437.332
## Max. :1291.564
## 2009-12 2010-01 2010-02
## Min. : -0.022 Length:11 Length:11
## 1st Qu.: 4.784 Class :character Class :character
## Median : 126.066 Mode :character Mode :character
## Mean : 312.962
## 3rd Qu.: 386.659
## Max. :1207.845
## 2010-03 2010-04 2010-05
## Min. : 0.012 Length:11 Length:11
## 1st Qu.: 5.274 Class :character Class :character
## Median : 131.252 Mode :character Mode :character
## Mean : 332.726
## 3rd Qu.: 428.420
## Max. :1225.556
## 2010-06 2010-07 2010-08
## Min. : 0.008 Length:11 Length:11
## 1st Qu.: 6.436 Class :character Class :character
## Median : 129.144 Mode :character Mode :character
## Mean : 335.964
## 3rd Qu.: 426.769
## Max. :1244.409
## 2010-09 2010-10 2010-11
## Min. : 0.001 Length:11 Length:11
## 1st Qu.: 5.567 Class :character Class :character
## Median : 132.892 Mode :character Mode :character
## Mean : 346.524
## 3rd Qu.: 451.361
## Max. :1293.117
## 2010-12 2011-01 2011-02
## Min. : -0.004 Length:11 Length:11
## 1st Qu.: 4.466 Class :character Class :character
## Median : 121.422 Mode :character Mode :character
## Mean : 312.917
## 3rd Qu.: 388.385
## Max. :1216.262
## 2011-03 2011-04 2011-05
## Min. : 0.05 Length:11 Length:11
## 1st Qu.: 6.03 Class :character Class :character
## Median : 134.37 Mode :character Mode :character
## Mean : 345.17
## 3rd Qu.: 448.56
## Max. :1286.66
## 2011-06 2011-07 2011-08
## Min. : 0.054 Length:11 Length:11
## 1st Qu.: 6.926 Class :character Class :character
## Median : 135.581 Mode :character Mode :character
## Mean : 353.331
## 3rd Qu.: 452.923
## Max. :1302.414
## 2011-09 2011-10
## Min. : 0.043 Length:11
## 1st Qu.: 6.660 Class :character
## Median : 136.901 Mode :character
## Mean : 362.555
## 3rd Qu.: 469.204
## Max. :1348.754to note
observations stored as columns rather than as rows
Ex3: Removing unnecessary rows/cols
address the missing data
Remove rows: “All Modes by Qtr” row = NA values this row should bein a different data frame it is: quarterly average of weekday MBTA ridership this dataset tracks monthly average ridership
Analysis i.e. NOT observations 7th row (“Pct Chg / Yr”) 11th row (“TOTAL”)
# Remove rows 1, 7, and 11 of mbta: mbta2
mbta2 <- mbta[-c(1, 7, 11), ]
# Use negative indices to quickly remove the rows and column you don't need
# alternatively,
keep <- !(mbta$mode %in% c('All Modes by Qtr', 'Pct Chg / Yr', 'TOTAL'))
mbta2 <- mbta[keep,]
# think about which rows you want to keep and use those indices in your subset1st col = listing the row numbers
# Remove the first column of mbta2: mbta3
mbta3 <- mbta2[,-1]
# Use negative indices to quickly remove the rows and column you don't need. Ex4: Observations are stored in columns
# different modes of transportation (commuter rail, bus, subway, ferry, ...) are variables
# providing information about each month's average ridership
# months = observations
# as you go through time, the month changes, but the modes of transport offered by the T do not.
head(mbta3)
## # A tibble: 6 x 59
## mode `2007-01` `2007-02` `2007-03` `2007-04` `2007-05` `2007-06`
## <chr> <chr> <chr> <dbl> <chr> <chr> <dbl>
## 1 Boat 4 3.6 40 4.3 4.9000000~ 5.8
## 2 Bus 335.819000~ 338.67500~ 340. 352.16199~ 354.36700~ 351.
## 3 Commut~ 142.199999~ 138.5 138. 139.5 139 143
## 4 Heavy ~ 435.293999~ 448.27100~ 459. 472.20100~ 474.57900~ 477.
## 5 Light ~ 227.230999~ 240.262 241. 255.55699~ 248.262 246.
## 6 Privat~ 4.77200000~ 4.4169999~ 4.57 4.5419999~ 4.7679999~ 4.72
## # ... with 52 more variables: `2007-07` <chr>, `2007-08` <chr>,
## # `2007-09` <dbl>, `2007-10` <chr>, `2007-11` <chr>, `2007-12` <dbl>,
## # `2008-01` <chr>, `2008-02` <chr>, `2008-03` <dbl>, `2008-04` <chr>,
## # `2008-05` <chr>, `2008-06` <dbl>, `2008-07` <chr>, `2008-08` <chr>,
## # `2008-09` <dbl>, `2008-10` <chr>, `2008-11` <chr>, `2008-12` <dbl>,
## # `2009-01` <chr>, `2009-02` <chr>, `2009-03` <dbl>, `2009-04` <chr>,
## # `2009-05` <chr>, `2009-06` <dbl>, `2009-07` <chr>, `2009-08` <chr>,
## # `2009-09` <dbl>, `2009-10` <chr>, `2009-11` <chr>, `2009-12` <dbl>,
## # `2010-01` <chr>, `2010-02` <chr>, `2010-03` <dbl>, `2010-04` <chr>,
## # `2010-05` <chr>, `2010-06` <dbl>, `2010-07` <chr>, `2010-08` <chr>,
## # `2010-09` <dbl>, `2010-10` <chr>, `2010-11` <chr>, `2010-12` <dbl>,
## # `2011-01` <chr>, `2011-02` <chr>, `2011-03` <dbl>, `2011-04` <chr>,
## # `2011-05` <chr>, `2011-06` <dbl>, `2011-07` <chr>, `2011-08` <chr>,
## # `2011-09` <dbl>, `2011-10` <chr>
# want variables in columns
# Load tidyr
library(tidyr)
# Gather columns of mbta3: mbta4
# into key-value pairs
mbta4 <- gather(mbta3, month, thou_riders, -mode)
head(mbta4) # dataset is long now
## # A tibble: 6 x 3
## mode month thou_riders
## <chr> <chr> <chr>
## 1 Boat 2007-01 4
## 2 Bus 2007-01 335.81900000000002
## 3 Commuter Rail 2007-01 142.19999999999999
## 4 Heavy Rail 2007-01 435.29399999999998
## 5 Light Rail 2007-01 227.23099999999999
## 6 Private Bus 2007-01 4.7720000000000002Ex5: Type conversions
# thou_riders = average weekday ridership column = character strings
# Coerce into numeric values
mbta4$thou_riders <- as.numeric(mbta4$thou_riders)Ex6: Variables are stored in both rows and columns
variables are stored as “keys” in the “mode” column
# tidyr function spread()
# to make "keys" into columns containing average weekday ridership for the given month and mode of transport
# Spread the contents of mbta4: mbta5
mbta5 <- spread(mbta4, mode, thou_riders)
head(mbta5)
## # A tibble: 6 x 9
## month Boat Bus `Commuter Rail` `Heavy Rail` `Light Rail`
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2007-01 4 336. 142. 435. 227.
## 2 2007-02 3.6 339. 138. 448. 240.
## 3 2007-03 40 340. 138. 459. 241.
## 4 2007-04 4.3 352. 140. 472. 256.
## 5 2007-05 4.9 354. 139 475. 248.
## 6 2007-06 5.8 351. 143 477. 246.
## # ... with 3 more variables: `Private Bus` <dbl>, RIDE <dbl>, `Trackless
## # Trolley` <dbl>Ex7: Separating columns
month and year are together in the same column
separate() should take 3 arguments: * A data frame * A column name (no quotes) to separate * A character vector containing names of new columns (with quotes)
# Split month column into month and year: mbta6
# at the dash
mbta6 <- separate(mbta5, month, c('year', 'month'))
head(mbta6)
## # A tibble: 6 x 10
## year month Boat Bus `Commuter Rail` `Heavy Rail` `Light Rail`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2007 01 4 336. 142. 435. 227.
## 2 2007 02 3.6 339. 138. 448. 240.
## 3 2007 03 40 340. 138. 459. 241.
## 4 2007 04 4.3 352. 140. 472. 256.
## 5 2007 05 4.9 354. 139 475. 248.
## 6 2007 06 5.8 351. 143 477. 246.
## # ... with 3 more variables: `Private Bus` <dbl>, RIDE <dbl>, `Trackless
## # Trolley` <dbl>Ex8: values = reasonable?
screen the data for any obvious mistakes and/or outliers
summary(mbta6) # note Boat column stats.
## year month Boat Bus
## Length:58 Length:58 Min. : 2.985 Min. :312.9
## Class :character Class :character 1st Qu.: 3.494 1st Qu.:345.6
## Mode :character Mode :character Median : 4.293 Median :359.9
## Mean : 5.068 Mean :358.6
## 3rd Qu.: 5.356 3rd Qu.:372.2
## Max. :40.000 Max. :398.5
## Commuter Rail Heavy Rail Light Rail Private Bus
## Min. :121.4 Min. :435.3 Min. :194.4 Min. :2.213
## 1st Qu.:131.4 1st Qu.:471.1 1st Qu.:220.6 1st Qu.:2.641
## Median :138.8 Median :487.3 Median :231.9 Median :2.820
## Mean :137.4 Mean :489.3 Mean :233.0 Mean :3.352
## 3rd Qu.:142.4 3rd Qu.:511.3 3rd Qu.:244.5 3rd Qu.:4.167
## Max. :153.0 Max. :554.9 Max. :271.1 Max. :4.878
## RIDE Trackless Trolley
## Min. :4.900 Min. : 5.777
## 1st Qu.:5.965 1st Qu.:11.679
## Median :6.615 Median :12.598
## Mean :6.604 Mean :12.125
## 3rd Qu.:7.149 3rd Qu.:13.320
## Max. :8.598 Max. :15.109hist(mbta6$Boat) # every value clustered around 4 and one loner out around 40
Ex9: entry error
Every month, average weekday commuter boat ridership was on either side of four thousand jumped to 40 thousand!? error => locate and amend to 4
Find the row number of the incorrect value
# numeric variable i
# store the index of the incorrect Boat value in mbta6
# Combine a call to which()
# with a comparison operator (i.e. >)
# to determine the row number
i <- which(mbta6$Boat == 40)Replace the incorrect value with 4
mbta6$Boat[i] <- 4Verify that the change was made by looking at another histogram of mbta6$Boat
hist(mbta6$Boat)
following plots: use Exercise 4: data: long version
set up the df for use in the ggplot() functions
head(mbta6)
## # A tibble: 6 x 10
## year month Boat Bus `Commuter Rail` `Heavy Rail` `Light Rail`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2007 01 4 336. 142. 435. 227.
## 2 2007 02 3.6 339. 138. 448. 240.
## 3 2007 03 4 340. 138. 459. 241.
## 4 2007 04 4.3 352. 140. 472. 256.
## 5 2007 05 4.9 354. 139 475. 248.
## 6 2007 06 5.8 351. 143 477. 246.
## # ... with 3 more variables: `Private Bus` <dbl>, RIDE <dbl>, `Trackless
## # Trolley` <dbl>table(mbta4$mode)
##
## Boat Bus Commuter Rail Heavy Rail
## 58 58 58 58
## Light Rail Private Bus RIDE Trackless Trolley
## 58 58 58 58mbta_all <- mbta6 %>%
unite(year_mon, year, month, sep = "") %>%
gather(mode, thou_riders, -year_mon)
mbta_boat <- mbta_all %>%
filter(mode %in% c("Boat","Trackless Trolley"))head(mbta_boat)
## # A tibble: 6 x 3
## year_mon mode thou_riders
## <chr> <chr> <dbl>
## 1 200701 Boat 4
## 2 200702 Boat 3.6
## 3 200703 Boat 4
## 4 200704 Boat 4.3
## 5 200705 Boat 4.9
## 6 200706 Boat 5.8table(mbta_boat$mode)
##
## Boat Trackless Trolley
## 58 58Look at Boat and Trackless Trolley ridership over time Note: seasonal variation in Boat ridership
# install.packages("ggplot2")
library(ggplot2)
ggplot(mbta_boat, aes(x = year_mon, y = thou_riders, col = mode)) + geom_point() +
scale_x_discrete(name = "Month", breaks = c(200701, 200801, 200901, 201001, 201101)) +
scale_y_continuous(name = "Avg Weekday Ridership (thousands)")
Look at all T ridership over time
ggplot(mbta_all, aes(x = year_mon, y = thou_riders, col = mode)) + geom_point() +
scale_x_discrete(name = "Month", breaks = c(200701, 200801, 200901, 201001, 201101)) +
scale_y_continuous(name = "Avg Weekday Ridership (thousands)")
World Food
nutrition analysis / sugar content
Ex1: Import data
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:lubridate':
##
## hour, isoweek, mday, minute, month, quarter, second, wday,
## week, yday, year
## The following objects are masked from 'package:dplyr':
##
## between, first, last
url_food <- 'http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/food.csv'
download.file(url_food, "food.csv")
# data table
dt_food <- fread(url_food) # FASTER than read.csv()
# Convert food to a data frame
df_food <- data.frame(dt_food)
food <- df_foodEx2: Examine data
summary(food)
## V1 code url creator
## Min. : 1.0 Min. :100030 Length:1500 Length:1500
## 1st Qu.: 375.8 1st Qu.:124975 Class :character Class :character
## Median : 750.5 Median :149514 Mode :character Mode :character
## Mean : 750.5 Mean :149613
## 3rd Qu.:1125.2 3rd Qu.:174506
## Max. :1500.0 Max. :199880
##
## created_t created_datetime last_modified_t
## Min. :1.332e+09 Length:1500 Min. :1.340e+09
## 1st Qu.:1.394e+09 Class :character 1st Qu.:1.424e+09
## Median :1.425e+09 Mode :character Median :1.437e+09
## Mean :1.414e+09 Mean :1.430e+09
## 3rd Qu.:1.436e+09 3rd Qu.:1.446e+09
## Max. :1.453e+09 Max. :1.453e+09
##
## last_modified_datetime product_name generic_name
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## quantity packaging packaging_tags
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## brands brands_tags categories
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## categories_tags categories_en origins
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## origins_tags manufacturing_places manufacturing_places_tags
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## labels labels_tags labels_en
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## emb_codes emb_codes_tags first_packaging_code_geo
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## cities cities_tags purchase_places stores
## Mode:logical Length:1500 Length:1500 Length:1500
## NA's:1500 Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## countries countries_tags countries_en
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## ingredients_text allergens allergens_en traces
## Length:1500 Length:1500 Mode:logical Length:1500
## Class :character Class :character NA's:1500 Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## traces_tags traces_en serving_size no_nutriments
## Length:1500 Length:1500 Length:1500 Mode:logical
## Class :character Class :character Class :character NA's:1500
## Mode :character Mode :character Mode :character
##
##
##
##
## additives_n additives additives_tags additives_en
## Min. : 0.000 Length:1500 Length:1500 Length:1500
## 1st Qu.: 0.000 Class :character Class :character Class :character
## Median : 1.000 Mode :character Mode :character Mode :character
## Mean : 1.846
## 3rd Qu.: 3.000
## Max. :17.000
## NA's :514
## ingredients_from_palm_oil_n ingredients_from_palm_oil
## Min. :0.0000 Mode:logical
## 1st Qu.:0.0000 NA's:1500
## Median :0.0000
## Mean :0.0487
## 3rd Qu.:0.0000
## Max. :1.0000
## NA's :514
## ingredients_from_palm_oil_tags ingredients_that_may_be_from_palm_oil_n
## Length:1500 Min. :0.0000
## Class :character 1st Qu.:0.0000
## Mode :character Median :0.0000
## Mean :0.1379
## 3rd Qu.:0.0000
## Max. :4.0000
## NA's :514
## ingredients_that_may_be_from_palm_oil
## Mode:logical
## NA's:1500
##
##
##
##
##
## ingredients_that_may_be_from_palm_oil_tags nutrition_grade_uk
## Length:1500 Mode:logical
## Class :character NA's:1500
## Mode :character
##
##
##
##
## nutrition_grade_fr pnns_groups_1 pnns_groups_2
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## states states_tags states_en
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## main_category main_category_en image_url
## Length:1500 Length:1500 Length:1500
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## image_small_url energy_100g energy_from_fat_100g fat_100g
## Length:1500 Min. : 0.0 Min. : 0.00 Min. : 0.00
## Class :character 1st Qu.: 369.8 1st Qu.: 35.98 1st Qu.: 0.90
## Mode :character Median : 966.5 Median : 237.00 Median : 6.00
## Mean :1083.2 Mean : 668.41 Mean : 13.39
## 3rd Qu.:1641.5 3rd Qu.: 974.00 3rd Qu.: 20.00
## Max. :3700.0 Max. :2900.00 Max. :100.00
## NA's :700 NA's :1486 NA's :708
## saturated_fat_100g butyric_acid_100g caproic_acid_100g caprylic_acid_100g
## Min. : 0.000 Mode:logical Mode:logical Mode:logical
## 1st Qu.: 0.200 NA's:1500 NA's:1500 NA's:1500
## Median : 1.700
## Mean : 4.874
## 3rd Qu.: 6.500
## Max. :57.000
## NA's :797
## capric_acid_100g lauric_acid_100g myristic_acid_100g palmitic_acid_100g
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## stearic_acid_100g arachidic_acid_100g behenic_acid_100g
## Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## lignoceric_acid_100g cerotic_acid_100g montanic_acid_100g
## Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## melissic_acid_100g monounsaturated_fat_100g polyunsaturated_fat_100g
## Mode:logical Min. : 0.00 Min. : 0.400
## NA's:1500 1st Qu.: 3.87 1st Qu.: 1.653
## Median : 9.50 Median : 3.900
## Mean :19.77 Mean : 9.986
## 3rd Qu.:29.00 3rd Qu.:12.700
## Max. :75.00 Max. :46.200
## NA's :1465 NA's :1464
## omega_3_fat_100g alpha_linolenic_acid_100g eicosapentaenoic_acid_100g
## Min. : 0.033 Min. :0.0800 Min. :0.721
## 1st Qu.: 1.300 1st Qu.:0.0905 1st Qu.:0.721
## Median : 3.000 Median :0.1010 Median :0.721
## Mean : 3.726 Mean :0.1737 Mean :0.721
## 3rd Qu.: 3.200 3rd Qu.:0.2205 3rd Qu.:0.721
## Max. :12.400 Max. :0.3400 Max. :0.721
## NA's :1491 NA's :1497 NA's :1499
## docosahexaenoic_acid_100g omega_6_fat_100g linoleic_acid_100g
## Min. :1.09 Min. :0.25 Min. :0.5000
## 1st Qu.:1.09 1st Qu.:0.25 1st Qu.:0.5165
## Median :1.09 Median :0.25 Median :0.5330
## Mean :1.09 Mean :0.25 Mean :0.5330
## 3rd Qu.:1.09 3rd Qu.:0.25 3rd Qu.:0.5495
## Max. :1.09 Max. :0.25 Max. :0.5660
## NA's :1499 NA's :1499 NA's :1498
## arachidonic_acid_100g gamma_linolenic_acid_100g
## Mode:logical Mode:logical
## NA's:1500 NA's:1500
##
##
##
##
##
## dihomo_gamma_linolenic_acid_100g omega_9_fat_100g oleic_acid_100g
## Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## elaidic_acid_100g gondoic_acid_100g mead_acid_100g erucic_acid_100g
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## nervonic_acid_100g trans_fat_100g cholesterol_100g carbohydrates_100g
## Mode:logical Min. :0.0000 Min. :0.0000 Min. : 0.000
## NA's:1500 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 3.792
## Median :0.0000 Median :0.0000 Median : 13.500
## Mean :0.0105 Mean :0.0265 Mean : 27.958
## 3rd Qu.:0.0000 3rd Qu.:0.0026 3rd Qu.: 55.000
## Max. :0.1000 Max. :0.4300 Max. :100.000
## NA's :1481 NA's :1477 NA's :708
## sugars_100g sucrose_100g glucose_100g fructose_100g
## Min. : 0.00 Mode:logical Mode:logical Min. :100
## 1st Qu.: 1.00 NA's:1500 NA's:1500 1st Qu.:100
## Median : 4.05 Median :100
## Mean : 12.66 Mean :100
## 3rd Qu.: 14.70 3rd Qu.:100
## Max. :100.00 Max. :100
## NA's :788 NA's :1499
## lactose_100g maltose_100g maltodextrins_100g starch_100g
## Min. :0.000 Mode:logical Mode:logical Min. : 0.00
## 1st Qu.:0.250 NA's:1500 NA's:1500 1st Qu.: 9.45
## Median :0.500 Median :39.50
## Mean :2.933 Mean :30.73
## 3rd Qu.:4.400 3rd Qu.:42.85
## Max. :8.300 Max. :71.00
## NA's :1497 NA's :1493
## polyols_100g fiber_100g proteins_100g casein_100g
## Min. : 8.60 Min. : 0.000 Min. : 0.000 Min. :1.1
## 1st Qu.:59.10 1st Qu.: 0.500 1st Qu.: 1.500 1st Qu.:1.1
## Median :67.00 Median : 1.750 Median : 6.000 Median :1.1
## Mean :56.06 Mean : 2.823 Mean : 7.563 Mean :1.1
## 3rd Qu.:69.80 3rd Qu.: 3.500 3rd Qu.:10.675 3rd Qu.:1.1
## Max. :70.00 Max. :46.700 Max. :61.000 Max. :1.1
## NA's :1491 NA's :994 NA's :710 NA's :1499
## serum_proteins_100g nucleotides_100g salt_100g sodium_100g
## Mode:logical Mode:logical Min. : 0.0000 Min. : 0.0000
## NA's:1500 NA's:1500 1st Qu.: 0.0438 1st Qu.: 0.0172
## Median : 0.4498 Median : 0.1771
## Mean : 1.1205 Mean : 0.4409
## 3rd Qu.: 1.1938 3rd Qu.: 0.4700
## Max. :102.0000 Max. :40.0000
## NA's :780 NA's :780
## alcohol_100g vitamin_a_100g beta_carotene_100g vitamin_d_100g
## Min. : 0.00 Min. :0.0000 Mode:logical Min. :0e+00
## 1st Qu.: 0.00 1st Qu.:0.0000 NA's:1500 1st Qu.:0e+00
## Median : 5.50 Median :0.0001 Median :0e+00
## Mean :10.07 Mean :0.0003 Mean :0e+00
## 3rd Qu.:13.00 3rd Qu.:0.0006 3rd Qu.:0e+00
## Max. :50.00 Max. :0.0013 Max. :1e-04
## NA's :1433 NA's :1477 NA's :1485
## vitamin_e_100g vitamin_k_100g vitamin_c_100g vitamin_b1_100g
## Min. :0.0005 Min. :0 Min. :0.000 Min. :0.0001
## 1st Qu.:0.0021 1st Qu.:0 1st Qu.:0.002 1st Qu.:0.0003
## Median :0.0044 Median :0 Median :0.019 Median :0.0004
## Mean :0.0069 Mean :0 Mean :0.025 Mean :0.0006
## 3rd Qu.:0.0097 3rd Qu.:0 3rd Qu.:0.030 3rd Qu.:0.0010
## Max. :0.0320 Max. :0 Max. :0.217 Max. :0.0013
## NA's :1478 NA's :1498 NA's :1459 NA's :1478
## vitamin_b2_100g vitamin_pp_100g vitamin_b6_100g vitamin_b9_100g
## Min. :0.0002 Min. :0.0006 Min. :0.0001 Min. :0e+00
## 1st Qu.:0.0003 1st Qu.:0.0033 1st Qu.:0.0002 1st Qu.:0e+00
## Median :0.0009 Median :0.0069 Median :0.0008 Median :1e-04
## Mean :0.0011 Mean :0.0086 Mean :0.0112 Mean :1e-04
## 3rd Qu.:0.0013 3rd Qu.:0.0140 3rd Qu.:0.0012 3rd Qu.:2e-04
## Max. :0.0066 Max. :0.0160 Max. :0.2000 Max. :2e-04
## NA's :1483 NA's :1484 NA's :1481 NA's :1483
## vitamin_b12_100g biotin_100g pantothenic_acid_100g silica_100g
## Min. :0 Min. :0 Min. :0.0000 Min. :8e-04
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.0007 1st Qu.:8e-04
## Median :0 Median :0 Median :0.0020 Median :8e-04
## Mean :0 Mean :0 Mean :0.0027 Mean :8e-04
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.0051 3rd Qu.:8e-04
## Max. :0 Max. :0 Max. :0.0060 Max. :8e-04
## NA's :1489 NA's :1498 NA's :1486 NA's :1499
## bicarbonate_100g potassium_100g chloride_100g calcium_100g
## Min. :0.0006 Min. :0.0000 Min. :0.0003 Min. :0.0000
## 1st Qu.:0.0678 1st Qu.:0.0650 1st Qu.:0.0006 1st Qu.:0.0450
## Median :0.1350 Median :0.1940 Median :0.0009 Median :0.1200
## Mean :0.1692 Mean :0.3288 Mean :0.0144 Mean :0.2040
## 3rd Qu.:0.2535 3rd Qu.:0.3670 3rd Qu.:0.0214 3rd Qu.:0.1985
## Max. :0.3720 Max. :1.4300 Max. :0.0420 Max. :1.0000
## NA's :1497 NA's :1487 NA's :1497 NA's :1449
## phosphorus_100g iron_100g magnesium_100g zinc_100g
## Min. :0.0430 Min. :0.0000 Min. :0.0000 Min. :0.0005
## 1st Qu.:0.1938 1st Qu.:0.0012 1st Qu.:0.0670 1st Qu.:0.0009
## Median :0.3185 Median :0.0042 Median :0.1040 Median :0.0017
## Mean :0.3777 Mean :0.0045 Mean :0.1066 Mean :0.0016
## 3rd Qu.:0.4340 3rd Qu.:0.0077 3rd Qu.:0.1300 3rd Qu.:0.0022
## Max. :1.1550 Max. :0.0137 Max. :0.3330 Max. :0.0026
## NA's :1488 NA's :1463 NA's :1479 NA's :1493
## copper_100g manganese_100g fluoride_100g selenium_100g
## Min. :0e+00 Min. :0 Min. :0 Min. :0
## 1st Qu.:1e-04 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :1e-04 Median :0 Median :0 Median :0
## Mean :1e-04 Mean :0 Mean :0 Mean :0
## 3rd Qu.:1e-04 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :1e-04 Max. :0 Max. :0 Max. :0
## NA's :1498 NA's :1499 NA's :1498 NA's :1499
## chromium_100g molybdenum_100g iodine_100g caffeine_100g
## Mode:logical Mode:logical Min. :0 Mode:logical
## NA's:1500 NA's:1500 1st Qu.:0 NA's:1500
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :1499
## taurine_100g ph_100g fruits_vegetables_nuts_100g
## Mode:logical Mode:logical Min. : 2.00
## NA's:1500 NA's:1500 1st Qu.:11.25
## Median :42.00
## Mean :36.88
## 3rd Qu.:52.25
## Max. :80.00
## NA's :1470
## collagen_meat_protein_ratio_100g cocoa_100g chlorophyl_100g
## Min. :12.00 Min. :30 Mode:logical
## 1st Qu.:13.50 1st Qu.:47 NA's:1500
## Median :15.00 Median :60
## Mean :15.67 Mean :57
## 3rd Qu.:17.50 3rd Qu.:70
## Max. :20.00 Max. :81
## NA's :1497 NA's :1491
## carbon_footprint_100g nutrition_score_fr_100g nutrition_score_uk_100g
## Min. : 12.00 Min. :-12.000 Min. :-12.000
## 1st Qu.: 97.42 1st Qu.: 1.000 1st Qu.: 0.000
## Median :182.85 Median : 7.000 Median : 6.000
## Mean :131.18 Mean : 7.941 Mean : 7.631
## 3rd Qu.:190.78 3rd Qu.: 15.000 3rd Qu.: 16.000
## Max. :198.70 Max. : 28.000 Max. : 28.000
## NA's :1497 NA's :825 NA's :825head(food)
## V1 code
## 1 1 100030
## 2 2 100050
## 3 3 100079
## 4 4 100094
## 5 5 100124
## 6 6 100136
## url
## 1 http://world-en.openfoodfacts.org/product/3222475745867/confiture-de-fraise-fraise-des-bois-au-sucre-de-canne-casino-delices
## 2 http://world-en.openfoodfacts.org/product/5410976880110/guylian-sea-shells-selection
## 3 http://world-en.openfoodfacts.org/product/3264750423503/pates-de-fruits-aromatisees-jacquot
## 4 http://world-en.openfoodfacts.org/product/8006040247001/nata-vegetal-a-base-de-soja-valsoia
## 5 http://world-en.openfoodfacts.org/product/8480000340764/semillas-de-girasol-con-cascara-tostadas-aguasal-hacendado
## 6 http://world-en.openfoodfacts.org/product/0087703177727/soft-drink
## creator created_t created_datetime last_modified_t
## 1 sebleouf 1424747544 2015-02-24T03:12:24Z 1438445887
## 2 foodorigins 1450316429 2015-12-17T01:40:29Z 1450817956
## 3 domdom26 1428674916 2015-04-10T14:08:36Z 1428739289
## 4 javichu 1420416591 2015-01-05T00:09:51Z 1420417876
## 5 javichu 1420501121 2015-01-05T23:38:41Z 1445700917
## 6 foodorigins 1437983923 2015-07-27T07:58:43Z 1445577476
## last_modified_datetime
## 1 2015-08-01T16:18:07Z
## 2 2015-12-22T20:59:16Z
## 3 2015-04-11T08:01:29Z
## 4 2015-01-05T00:31:16Z
## 5 2015-10-24T15:35:17Z
## 6 2015-10-23T05:17:56Z
## product_name
## 1 Confiture de fraise fraise des bois au sucre de canne
## 2 Guylian Sea Shells Selection
## 3 Pâtes de fruits aromatisées
## 4 Nata vegetal a base de soja "Valsoia"
## 5 Semillas de girasol con cáscara tostadas aguasal
## 6 Soft Drink
## generic_name quantity
## 1 265 g
## 2 375g
## 3 Pâtes de fruits 1 kg
## 4 Nata vegetal a base de soja 200 ml
## 5 Semillas de girasol con cáscara tostadas aguasal 200 g
## 6
## packaging
## 1 Bocal,Verre
## 2 Plastic,Box
## 3 Carton,plastique
## 4 Tetra Brik
## 5 Bolsa de plástico,Envasado en atmósfera protectora
## 6
## packaging_tags
## 1 bocal,verre
## 2 plastic,box
## 3 carton,plastique
## 4 tetra-brik
## 5 bolsa-de-plastico,envasado-en-atmosfera-protectora
## 6
## brands
## 1 Casino Délices
## 2 Guylian
## 3 Jacquot
## 4 Valsoia,//Propiedad de://,Valsoia S.p.A.
## 5 Hacendado,//Propiedad de://,Mercadona S.A.
## 6
## brands_tags
## 1 casino-delices
## 2 guylian
## 3 jacquot
## 4 valsoia,propiedad-de,valsoia-s-p-a
## 5 hacendado,propiedad-de,mercadona-s-a
## 6
## categories
## 1 Aliments et boissons à base de végétaux,Aliments d'origine végétale,Aliments à base de fruits et de légumes,Petit-déjeuners,Produits à tartiner,Fruits et produits dérivés,Pâtes à tartiner végétaux,Produits à tartiner sucrés,Confitures et marmelades,Confitures,Confitures de fruits,Confitures de fruits rouges,Confitures de fraises
## 2 Chocolate
## 3 pâtes de fruits
## 4 Alimentos y bebidas de origen vegetal,Alimentos de origen vegetal,Natas vegetales,Natas vegetales a base de soja para cocinar,Natas vegetales para cocinar
## 5 Semillas de girasol y derivados, Semillas, Semillas de girasol, Semillas de girasol con cáscara, Semillas de girasol tostadas, Semillas de girasol con cáscara tostadas, Semillas de girasol con cáscara tostadas aguasal
## 6
## categories_tags
## 1 en:plant-based-foods-and-beverages,en:plant-based-foods,en:fruits-and-vegetables-based-foods,en:breakfasts,en:spreads,en:fruits-based-foods,en:plant-based-spreads,en:sweet-spreads,en:fruit-preserves,en:jams,en:fruit-jams,en:berry-jams,en:strawberry-jams
## 2 en:sugary-snacks,en:chocolates
## 3 en:plant-based-foods-and-beverages,en:plant-based-foods,en:fruits-and-vegetables-based-foods,en:sugary-snacks,en:confectioneries,en:fruits-based-foods,en:fruit-pastes
## 4 en:plant-based-foods-and-beverages,en:plant-based-foods,en:plant-based-creams,en:plant-based-creams-for-cooking,en:soy-based-creams-for-cooking
## 5 en:plant-based-foods-and-beverages,en:plant-based-foods,en:seeds,en:sunflower-seeds-and-their-products,en:sunflower-seeds,en:roasted-sunflower-seeds,en:unshelled-sunflower-seeds,en:roasted-unshelled-sunflower-seeds,es:semillas-de-girasol-con-cascara-tostadas-aguasal
## 6
## categories_en
## 1 Plant-based foods and beverages,Plant-based foods,Fruits and vegetables based foods,Breakfasts,Spreads,Fruits based foods,Plant-based spreads,Sweet spreads,Fruit preserves,Jams,Fruit jams,Berry jams,Strawberry jams
## 2 Sugary snacks,Chocolates
## 3 Plant-based foods and beverages,Plant-based foods,Fruits and vegetables based foods,Sugary snacks,Confectioneries,Fruits based foods,Fruit pastes
## 4 Plant-based foods and beverages,Plant-based foods,Plant-based creams,Plant-based creams for cooking,Soy-based creams for cooking
## 5 Plant-based foods and beverages,Plant-based foods,Seeds,Sunflower seeds and their products,Sunflower seeds,Roasted sunflower seeds,Unshelled sunflower seeds,Roasted unshelled sunflower seeds,es:Semillas-de-girasol-con-cascara-tostadas-aguasal
## 6
## origins origins_tags
## 1
## 2
## 3
## 4
## 5 Argentina argentina
## 6 South Korea south-korea
## manufacturing_places
## 1 France
## 2 Belgium
## 3
## 4 Italia
## 5 Beniparrell,Valencia (provincia),Comunidad Valenciana,España
## 6 South Korea
## manufacturing_places_tags
## 1 france
## 2 belgium
## 3
## 4 italia
## 5 beniparrell,valencia-provincia,comunidad-valenciana,espana
## 6 south-korea
## labels
## 1
## 2
## 3
## 4 Vegetariano,Vegano,Sin gluten,Sin OMG,Sin lactosa
## 5 Vegetariano,Vegano,Sin gluten
## 6
## labels_tags
## 1
## 2
## 3
## 4 en:vegetarian,en:vegan,en:gluten-free,en:no-gmos,en:no-lactose
## 5 en:vegetarian,en:vegan,en:gluten-free
## 6
## labels_en
## 1
## 2
## 3
## 4 Vegetarian,Vegan,Gluten-free,No GMOs,No lactose
## 5 Vegetarian,Vegan,Gluten-free
## 6
## emb_codes
## 1 EMB 78015
## 2
## 3
## 4
## 5 ES 21.016540/V EC,ENVASADOR:,IMPORTACO S.A.
## 6
## emb_codes_tags first_packaging_code_geo
## 1 emb-78015 48.983333,2.066667
## 2
## 3
## 4
## 5 es-21-016540-v-ec,envasador,importaco-s-a
## 6
## cities cities_tags purchase_places stores
## 1 NA andresy-yvelines-france Lyon,France Casino
## 2 NA NSW,Australia
## 3 NA France
## 4 NA Madrid,España El Corte Inglés
## 5 NA Madrid,España Mercadona
## 6 NA
## countries countries_tags countries_en
## 1 France en:france France
## 2 Australia en:australia Australia
## 3 France en:france France
## 4 España en:spain Spain
## 5 España en:spain Spain
## 6 Australia en:australia Australia
## ingredients_text
## 1 Sucre de canne, fraises 40 g, fraises des bois 14 g, gélifiant : pectines de fruits, jus de citron concentré. Préparée avec 54 g de fruits pour 100 g de produit fini.
## 2
## 3 Pulpe de pommes 50% , sucre, sirop de glucose, gélifiant : pectine, acidifiant : acide citrique, arômes, colorants naturels : extrait de paprika â\200 complexes cuivreâ\200chlorophyllines â\200 curcumine â\200 antnocyanes
## 4 Extracto de soja (78%) (agua, semillas de soja 8,3%), grasas vegetales, jarabe de glucosa, dextrosa, emulsionante: mono- y diglicéridos de ácidos grasos (E-471), sal marina, estabilizantes: goma xantana (E-415), carragenatos (E-407), goma guar (E-412); aromas, antioxidante: extractos de tocoferoles (de soja) (E-306). (Nota: el envase en italiano del paquete -que puede verse en el enlace-, especifica que el producto es 100% vegetal. Por tanto los mono- y diglicéridos de ácidos grasos (E-471) son de origen no animal).
## 5 Pipas de girasol y sal.
## 6
## allergens allergens_en traces traces_tags
## 1 NA Lait,Fruits à coque en:milk,en:nuts
## 2 NA
## 3 NA
## 4 NA
## 5 NA Frutos de cáscara,Cacahuetes en:nuts,en:peanuts
## 6 NA
## traces_en serving_size no_nutriments additives_n
## 1 Milk,Nuts 15 g NA 1
## 2 NA NA
## 3 NA 2
## 4 NA 5
## 5 Nuts,Peanuts NA 0
## 6 NA NA
## additives
## 1 [ sucre-de-canne -> fr:sucre-de-canne ] [ sucre-de -> fr:sucre-de ] [ sucre -> fr:sucre ] [ fraises-40-g -> fr:fraises-40-g ] [ fraises-40 -> fr:fraises-40 ] [ fraises -> fr:fraises ] [ fraises-des-bois-14-g -> fr:fraises-des-bois-14-g ] [ fraises-des-bois-14 -> fr:fraises-des-bois-14 ] [ fraises-des-bois -> fr:fraises-des-bois ] [ fraises-des -> fr:fraises-des ] [ fraises -> fr:fraises ] [ pectines-de-fruits -> fr:pectines-de-fruits ] [ pectines-de -> fr:pectines-de ] [ pectines -> en:e440 -> exists ] [ jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g-de-produit-fini -> fr:jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g-de-produit-fini ] [ jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g-de-produit -> fr:jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g-de-produit ] [ jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g-de -> fr:jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g-de ] [ jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g -> fr:jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100-g ] [ jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100 -> fr:jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour-100 ] [ jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour -> fr:jus-de-citron-concentre-preparee-avec-54-g-de-fruits-pour ] [ jus-de-citron-concentre-preparee-avec-54-g-de-fruits -> fr:jus-de-citron-concentre-preparee-avec-54-g-de-fruits ] [ jus-de-citron-concentre-preparee-avec-54-g-de -> fr:jus-de-citron-concentre-preparee-avec-54-g-de ] [ jus-de-citron-concentre-preparee-avec-54-g -> fr:jus-de-citron-concentre-preparee-avec-54-g ] [ jus-de-citron-concentre-preparee-avec-54 -> fr:jus-de-citron-concentre-preparee-avec-54 ] [ jus-de-citron-concentre-preparee-avec -> fr:jus-de-citron-concentre-preparee-avec ] [ jus-de-citron-concentre-preparee -> fr:jus-de-citron-concentre-preparee ] [ jus-de-citron-concentre -> fr:jus-de-citron-concentre ] [ jus-de-citron -> fr:jus-de-citron ] [ jus-de -> fr:jus-de ] [ jus -> fr:jus ]
## 2
## 3 [ pulpe-de-pommes-50 -> fr:pulpe-de-pommes-50 ] [ pulpe-de-pommes -> fr:pulpe-de-pommes ] [ pulpe-de -> fr:pulpe-de ] [ pulpe -> fr:pulpe ] [ sucre -> fr:sucre ] [ sirop-de-glucose -> fr:sirop-de-glucose ] [ sirop-de -> fr:sirop-de ] [ sirop -> fr:sirop ] [ pectine -> en:e440 -> exists ] [ acide-citrique -> en:e330 -> exists ] [ aromes -> fr:aromes ] [ naturels -> fr:naturels ] [ extrait-de-paprika-complexes-cuivre-chlorophyllines-curcumine-antnocyanes -> fr:extrait-de-paprika-complexes-cuivre-chlorophyllines-curcumine-antnocyanes ] [ extrait-de-paprika-complexes-cuivre-chlorophyllines-curcumine -> fr:extrait-de-paprika-complexes-cuivre-chlorophyllines-curcumine ] [ extrait-de-paprika-complexes-cuivre-chlorophyllines -> fr:extrait-de-paprika-complexes-cuivre-chlorophyllines ] [ extrait-de-paprika-complexes-cuivre -> fr:extrait-de-paprika-complexes-cuivre ] [ extrait-de-paprika-complexes -> fr:extrait-de-paprika-complexes ] [ extrait-de-paprika -> fr:extrait-de-paprika ] [ extrait-de -> fr:extrait-de ] [ extrait -> fr:extrait ]
## 4 [ extracto-de-soja -> es:extracto-de-soja ] [ 78 -> es:78 ] [ agua -> es:agua ] [ semillas-de-soja-8 -> es:semillas-de-soja-8 ] [ 3 -> en:fd-c ] [ grasas-vegetales -> es:grasas-vegetales ] [ jarabe-de-glucosa -> es:jarabe-de-glucosa ] [ dextrosa -> es:dextrosa ] [ emulsionante -> es:emulsionante ] [ mono-y-digliceridos-de-acidos-grasos -> en:e471 -> exists ] [ e471 -> en:e471 ] [ sal-marina -> es:sal-marina ] [ estabilizantes -> es:estabilizantes ] [ goma-xantana -> en:e415 -> exists ] [ e415 -> en:e415 ] [ carragenatos -> en:e407 -> exists ] [ e407 -> en:e407 ] [ goma-guar -> en:e412 -> exists ] [ e412 -> en:e412 ] [ aromas -> es:aromas ] [ antioxidante -> es:antioxidante ] [ extractos-de-tocoferoles -> es:extractos-de-tocoferoles ] [ de-soja -> es:de-soja ] [ e306 -> en:e306 -> exists ] [ nota -> es:nota ] [ el-envase-en-italiano-del-paquete-que-puede-verse-en-el-enlace -> es:el-envase-en-italiano-del-paquete-que-puede-verse-en-el-enlace ] [ especifica-que-el-producto-es-100-vegetal-por-tanto-los-mono-y-digliceridos-de-acidos-grasos -> es:especifica-que-el-producto-es-100-vegetal-por-tanto-los-mono-y-digliceridos-de-acidos-grasos ] [ e471 -> en:e471 ] [ son-de-origen-no-animal -> es:son-de-origen-no-animal ] [ -> es: ]
## 5 [ pipas-de-girasol-y-sal -> es:pipas-de-girasol-y-sal ]
## 6
## additives_tags
## 1 en:e440
## 2
## 3 en:e440,en:e330
## 4 en:e471,en:e415,en:e407,en:e412,en:e306
## 5
## 6
## additives_en
## 1 E440 - Pectins
## 2
## 3 E440 - Pectins,E330 - Citric acid
## 4 E471 - Mono- and diglycerides of fatty acids,E415 - Xanthan gum,E407 - Carrageenan,E412 - Guar gum,E306 - Tocopherol-rich extract
## 5
## 6
## ingredients_from_palm_oil_n ingredients_from_palm_oil
## 1 0 NA
## 2 NA NA
## 3 0 NA
## 4 0 NA
## 5 0 NA
## 6 NA NA
## ingredients_from_palm_oil_tags ingredients_that_may_be_from_palm_oil_n
## 1 0
## 2 NA
## 3 0
## 4 1
## 5 0
## 6 NA
## ingredients_that_may_be_from_palm_oil
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 NA
## 6 NA
## ingredients_that_may_be_from_palm_oil_tags nutrition_grade_uk
## 1 NA
## 2 NA
## 3 NA
## 4 e471-mono-et-diglycerides-d-acides-gras-alimentaires NA
## 5 NA
## 6 NA
## nutrition_grade_fr pnns_groups_1 pnns_groups_2
## 1 d Sugary snacks Sweets
## 2 Sugary snacks Chocolate products
## 3 Fruits and vegetables Fruits
## 4 d unknown unknown
## 5 d unknown unknown
## 6 unknown unknown
## states
## 1 en:to-be-checked, en:complete, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:characteristics-completed, en:photos-validated, en:photos-uploaded
## 2 en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:characteristics-completed, en:photos-validated, en:photos-uploaded
## 3 en:to-be-checked, en:complete, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be-completed, en:characteristics-completed, en:photos-validated, en:photos-uploaded
## 4 en:to-be-checked, en:complete, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:characteristics-completed, en:photos-validated, en:photos-uploaded
## 5 en:to-be-checked, en:complete, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:characteristics-completed, en:photos-validated, en:photos-uploaded
## 6 en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:characteristics-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:photos-to-be-validated, en:photos-uploaded
## states_tags
## 1 en:to-be-checked,en:complete,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:characteristics-completed,en:photos-validated,en:photos-uploaded
## 2 en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:characteristics-completed,en:photos-validated,en:photos-uploaded
## 3 en:to-be-checked,en:complete,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:characteristics-completed,en:photos-validated,en:photos-uploaded
## 4 en:to-be-checked,en:complete,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:characteristics-completed,en:photos-validated,en:photos-uploaded
## 5 en:to-be-checked,en:complete,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:characteristics-completed,en:photos-validated,en:photos-uploaded
## 6 en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:characteristics-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:photos-to-be-validated,en:photos-uploaded
## states_en
## 1 To be checked,Complete,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Characteristics completed,Photos validated,Photos uploaded
## 2 To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Characteristics completed,Photos validated,Photos uploaded
## 3 To be checked,Complete,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Characteristics completed,Photos validated,Photos uploaded
## 4 To be checked,Complete,Nutrition facts completed,Ingredients completed,Expiration date completed,Characteristics completed,Photos validated,Photos uploaded
## 5 To be checked,Complete,Nutrition facts completed,Ingredients completed,Expiration date completed,Characteristics completed,Photos validated,Photos uploaded
## 6 To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Characteristics to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Photos to be validated,Photos uploaded
## main_category main_category_en
## 1 en:plant-based-foods-and-beverages Plant-based foods and beverages
## 2 en:sugary-snacks Sugary snacks
## 3 en:plant-based-foods-and-beverages Plant-based foods and beverages
## 4 en:plant-based-foods-and-beverages Plant-based foods and beverages
## 5 en:plant-based-foods-and-beverages Plant-based foods and beverages
## 6
## image_url
## 1 http://en.openfoodfacts.org/images/products/322/247/574/5867/front.8.400.jpg
## 2 http://en.openfoodfacts.org/images/products/541/097/688/0110/front.7.400.jpg
## 3 http://en.openfoodfacts.org/images/products/326/475/042/3503/front.6.400.jpg
## 4 http://en.openfoodfacts.org/images/products/800/604/024/7001/front.7.400.jpg
## 5 http://en.openfoodfacts.org/images/products/848/000/034/0764/front.6.400.jpg
## 6 http://en.openfoodfacts.org/images/products/008/770/317/7727/front.8.400.jpg
## image_small_url
## 1 http://en.openfoodfacts.org/images/products/322/247/574/5867/front.8.200.jpg
## 2 http://en.openfoodfacts.org/images/products/541/097/688/0110/front.7.200.jpg
## 3 http://en.openfoodfacts.org/images/products/326/475/042/3503/front.6.200.jpg
## 4 http://en.openfoodfacts.org/images/products/800/604/024/7001/front.7.200.jpg
## 5 http://en.openfoodfacts.org/images/products/848/000/034/0764/front.6.200.jpg
## 6 http://en.openfoodfacts.org/images/products/008/770/317/7727/front.8.200.jpg
## energy_100g energy_from_fat_100g fat_100g saturated_fat_100g
## 1 918 NA 0.0 0.0
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 766 NA 16.7 9.9
## 5 2359 NA 45.5 5.2
## 6 NA NA NA NA
## butyric_acid_100g caproic_acid_100g caprylic_acid_100g capric_acid_100g
## 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
## lauric_acid_100g myristic_acid_100g palmitic_acid_100g stearic_acid_100g
## 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
## arachidic_acid_100g behenic_acid_100g lignoceric_acid_100g
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## cerotic_acid_100g montanic_acid_100g melissic_acid_100g
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## monounsaturated_fat_100g polyunsaturated_fat_100g omega_3_fat_100g
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 2.9 3.9 NA
## 5 9.5 32.8 NA
## 6 NA NA NA
## alpha_linolenic_acid_100g eicosapentaenoic_acid_100g
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## docosahexaenoic_acid_100g omega_6_fat_100g linoleic_acid_100g
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## arachidonic_acid_100g gamma_linolenic_acid_100g
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## dihomo_gamma_linolenic_acid_100g omega_9_fat_100g oleic_acid_100g
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## elaidic_acid_100g gondoic_acid_100g mead_acid_100g erucic_acid_100g
## 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
## nervonic_acid_100g trans_fat_100g cholesterol_100g carbohydrates_100g
## 1 NA NA NA 54.0
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA 2e-04 5.7
## 5 NA NA NA 17.3
## 6 NA NA NA NA
## sugars_100g sucrose_100g glucose_100g fructose_100g lactose_100g
## 1 54.0 NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 4.2 NA NA NA NA
## 5 2.7 NA NA NA NA
## 6 NA NA NA NA NA
## maltose_100g maltodextrins_100g starch_100g polyols_100g fiber_100g
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA 0.2
## 5 NA NA NA NA 9.0
## 6 NA NA NA NA NA
## proteins_100g casein_100g serum_proteins_100g nucleotides_100g salt_100g
## 1 0.0 NA NA NA 0.0000
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 2.9 NA NA NA 0.0508
## 5 18.2 NA NA NA 3.9878
## 6 NA NA NA NA NA
## sodium_100g alcohol_100g vitamin_a_100g beta_carotene_100g
## 1 0.00 NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 0.02 NA NA NA
## 5 1.57 NA NA NA
## 6 NA NA NA NA
## vitamin_d_100g vitamin_e_100g vitamin_k_100g vitamin_c_100g
## 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
## vitamin_b1_100g vitamin_b2_100g vitamin_pp_100g vitamin_b6_100g
## 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
## vitamin_b9_100g vitamin_b12_100g biotin_100g pantothenic_acid_100g
## 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
## silica_100g bicarbonate_100g potassium_100g chloride_100g calcium_100g
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## phosphorus_100g iron_100g magnesium_100g zinc_100g copper_100g
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 1.155 0.0038 0.129 NA NA
## 6 NA NA NA NA NA
## manganese_100g fluoride_100g selenium_100g chromium_100g molybdenum_100g
## 1 NA NA NA NA NA
## 2 NA NA NA NA NA
## 3 NA NA NA NA NA
## 4 NA NA NA NA NA
## 5 NA NA NA NA NA
## 6 NA NA NA NA NA
## iodine_100g caffeine_100g taurine_100g ph_100g
## 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
## fruits_vegetables_nuts_100g collagen_meat_protein_ratio_100g cocoa_100g
## 1 54 NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## chlorophyl_100g carbon_footprint_100g nutrition_score_fr_100g
## 1 NA NA 11
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA 11
## 5 NA NA 17
## 6 NA NA NA
## nutrition_score_uk_100g
## 1 11
## 2 NA
## 3 NA
## 4 11
## 5 17
## 6 NAstr(food)
## 'data.frame': 1500 obs. of 160 variables:
## $ V1 : int 1 2 3 4 5 6 7 8 9 10 ...
## $ code : int 100030 100050 100079 100094 100124 100136 100194 100221 100257 100258 ...
## $ url : chr "http://world-en.openfoodfacts.org/product/3222475745867/confiture-de-fraise-fraise-des-bois-au-sucre-de-canne-casino-delices" "http://world-en.openfoodfacts.org/product/5410976880110/guylian-sea-shells-selection" "http://world-en.openfoodfacts.org/product/3264750423503/pates-de-fruits-aromatisees-jacquot" "http://world-en.openfoodfacts.org/product/8006040247001/nata-vegetal-a-base-de-soja-valsoia" ...
## $ creator : chr "sebleouf" "foodorigins" "domdom26" "javichu" ...
## $ created_t : int 1424747544 1450316429 1428674916 1420416591 1420501121 1437983923 1442420988 1435686217 1436991777 1400516512 ...
## $ created_datetime : chr "2015-02-24T03:12:24Z" "2015-12-17T01:40:29Z" "2015-04-10T14:08:36Z" "2015-01-05T00:09:51Z" ...
## $ last_modified_t : int 1438445887 1450817956 1428739289 1420417876 1445700917 1445577476 1442420988 1451405288 1436991779 1437236856 ...
## $ last_modified_datetime : chr "2015-08-01T16:18:07Z" "2015-12-22T20:59:16Z" "2015-04-11T08:01:29Z" "2015-01-05T00:31:16Z" ...
## $ product_name : chr "Confiture de fraise fraise des bois au sucre de canne" "Guylian Sea Shells Selection" "Pâtes de fruits aromatisées" "Nata vegetal a base de soja "Valsoia"" ...
## $ generic_name : chr "" "" "Pâtes de fruits" "Nata vegetal a base de soja" ...
## $ quantity : chr "265 g" "375g" "1 kg" "200 ml" ...
## $ packaging : chr "Bocal,Verre" "Plastic,Box" "Carton,plastique" "Tetra Brik" ...
## $ packaging_tags : chr "bocal,verre" "plastic,box" "carton,plastique" "tetra-brik" ...
## $ brands : chr "Casino Délices" "Guylian" "Jacquot" "Valsoia,//Propiedad de://,Valsoia S.p.A." ...
## $ brands_tags : chr "casino-delices" "guylian" "jacquot" "valsoia,propiedad-de,valsoia-s-p-a" ...
## $ categories : chr "Aliments et boissons à base de végétaux,Aliments d'origine végétale,Aliments à base de fruits et de légu"| __truncated__ "Chocolate" "pâtes de fruits" "Alimentos y bebidas de origen vegetal,Alimentos de origen vegetal,Natas vegetales,Natas vegetales a base de soj"| __truncated__ ...
## $ categories_tags : chr "en:plant-based-foods-and-beverages,en:plant-based-foods,en:fruits-and-vegetables-based-foods,en:breakfasts,en:s"| __truncated__ "en:sugary-snacks,en:chocolates" "en:plant-based-foods-and-beverages,en:plant-based-foods,en:fruits-and-vegetables-based-foods,en:sugary-snacks,e"| __truncated__ "en:plant-based-foods-and-beverages,en:plant-based-foods,en:plant-based-creams,en:plant-based-creams-for-cooking"| __truncated__ ...
## $ categories_en : chr "Plant-based foods and beverages,Plant-based foods,Fruits and vegetables based foods,Breakfasts,Spreads,Fruits b"| __truncated__ "Sugary snacks,Chocolates" "Plant-based foods and beverages,Plant-based foods,Fruits and vegetables based foods,Sugary snacks,Confectioneri"| __truncated__ "Plant-based foods and beverages,Plant-based foods,Plant-based creams,Plant-based creams for cooking,Soy-based c"| __truncated__ ...
## $ origins : chr "" "" "" "" ...
## $ origins_tags : chr "" "" "" "" ...
## $ manufacturing_places : chr "France" "Belgium" "" "Italia" ...
## $ manufacturing_places_tags : chr "france" "belgium" "" "italia" ...
## $ labels : chr "" "" "" "Vegetariano,Vegano,Sin gluten,Sin OMG,Sin lactosa" ...
## $ labels_tags : chr "" "" "" "en:vegetarian,en:vegan,en:gluten-free,en:no-gmos,en:no-lactose" ...
## $ labels_en : chr "" "" "" "Vegetarian,Vegan,Gluten-free,No GMOs,No lactose" ...
## $ emb_codes : chr "EMB 78015" "" "" "" ...
## $ emb_codes_tags : chr "emb-78015" "" "" "" ...
## $ first_packaging_code_geo : chr "48.983333,2.066667" "" "" "" ...
## $ cities : logi NA NA NA NA NA NA ...
## $ cities_tags : chr "andresy-yvelines-france" "" "" "" ...
## $ purchase_places : chr "Lyon,France" "NSW,Australia" "France" "Madrid,España" ...
## $ stores : chr "Casino" "" "" "El Corte Inglés" ...
## $ countries : chr "France" "Australia" "France" "España" ...
## $ countries_tags : chr "en:france" "en:australia" "en:france" "en:spain" ...
## $ countries_en : chr "France" "Australia" "France" "Spain" ...
## $ ingredients_text : chr "Sucre de canne, fraises 40 g, fraises des bois 14 g, gélifiant : pectines de fruits, jus de citron concentré."| __truncated__ "" "Pulpe de pommes 50% , sucre, sirop de glucose, gélifiant : pectine, acidifiant : acide citrique, arômes, colo"| __truncated__ "Extracto de soja (78%) (agua, semillas de soja 8,3%), grasas vegetales, jarabe de glucosa, dextrosa, emulsionan"| __truncated__ ...
## $ allergens : chr "" "" "" "" ...
## $ allergens_en : logi NA NA NA NA NA NA ...
## $ traces : chr "Lait,Fruits à coque" "" "" "" ...
## $ traces_tags : chr "en:milk,en:nuts" "" "" "" ...
## $ traces_en : chr "Milk,Nuts" "" "" "" ...
## $ serving_size : chr "15 g" "" "" "" ...
## $ no_nutriments : logi NA NA NA NA NA NA ...
## $ additives_n : int 1 NA 2 5 0 NA NA 0 NA 1 ...
## $ additives : chr "[ sucre-de-canne -> fr:sucre-de-canne ] [ sucre-de -> fr:sucre-de ] [ sucre -> fr:sucre ] [ fraises-40-g "| __truncated__ "" "[ pulpe-de-pommes-50 -> fr:pulpe-de-pommes-50 ] [ pulpe-de-pommes -> fr:pulpe-de-pommes ] [ pulpe-de -> fr:"| __truncated__ "[ extracto-de-soja -> es:extracto-de-soja ] [ 78 -> es:78 ] [ agua -> es:agua ] [ semillas-de-soja-8 -> e"| __truncated__ ...
## $ additives_tags : chr "en:e440" "" "en:e440,en:e330" "en:e471,en:e415,en:e407,en:e412,en:e306" ...
## $ additives_en : chr "E440 - Pectins" "" "E440 - Pectins,E330 - Citric acid" "E471 - Mono- and diglycerides of fatty acids,E415 - Xanthan gum,E407 - Carrageenan,E412 - Guar gum,E306 - Tocop"| __truncated__ ...
## $ ingredients_from_palm_oil_n : int 0 NA 0 0 0 NA NA 0 NA 0 ...
## $ ingredients_from_palm_oil : logi NA NA NA NA NA NA ...
## $ ingredients_from_palm_oil_tags : chr "" "" "" "" ...
## $ ingredients_that_may_be_from_palm_oil_n : int 0 NA 0 1 0 NA NA 0 NA 0 ...
## $ ingredients_that_may_be_from_palm_oil : logi NA NA NA NA NA NA ...
## $ ingredients_that_may_be_from_palm_oil_tags: chr "" "" "" "e471-mono-et-diglycerides-d-acides-gras-alimentaires" ...
## $ nutrition_grade_uk : logi NA NA NA NA NA NA ...
## $ nutrition_grade_fr : chr "d" "" "" "d" ...
## $ pnns_groups_1 : chr "Sugary snacks" "Sugary snacks" "Fruits and vegetables" "unknown" ...
## $ pnns_groups_2 : chr "Sweets" "Chocolate products" "Fruits" "unknown" ...
## $ states : chr "en:to-be-checked, en:complete, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be"| __truncated__ "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-b"| __truncated__ "en:to-be-checked, en:complete, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-to-be"| __truncated__ "en:to-be-checked, en:complete, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-compl"| __truncated__ ...
## $ states_tags : chr "en:to-be-checked,en:complete,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-com"| __truncated__ "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-c"| __truncated__ "en:to-be-checked,en:complete,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-com"| __truncated__ "en:to-be-checked,en:complete,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed"| __truncated__ ...
## $ states_en : chr "To be checked,Complete,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Characte"| __truncated__ "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Cha"| __truncated__ "To be checked,Complete,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Characte"| __truncated__ "To be checked,Complete,Nutrition facts completed,Ingredients completed,Expiration date completed,Characteristic"| __truncated__ ...
## $ main_category : chr "en:plant-based-foods-and-beverages" "en:sugary-snacks" "en:plant-based-foods-and-beverages" "en:plant-based-foods-and-beverages" ...
## $ main_category_en : chr "Plant-based foods and beverages" "Sugary snacks" "Plant-based foods and beverages" "Plant-based foods and beverages" ...
## $ image_url : chr "http://en.openfoodfacts.org/images/products/322/247/574/5867/front.8.400.jpg" "http://en.openfoodfacts.org/images/products/541/097/688/0110/front.7.400.jpg" "http://en.openfoodfacts.org/images/products/326/475/042/3503/front.6.400.jpg" "http://en.openfoodfacts.org/images/products/800/604/024/7001/front.7.400.jpg" ...
## $ image_small_url : chr "http://en.openfoodfacts.org/images/products/322/247/574/5867/front.8.200.jpg" "http://en.openfoodfacts.org/images/products/541/097/688/0110/front.7.200.jpg" "http://en.openfoodfacts.org/images/products/326/475/042/3503/front.6.200.jpg" "http://en.openfoodfacts.org/images/products/800/604/024/7001/front.7.200.jpg" ...
## $ energy_100g : num 918 NA NA 766 2359 ...
## $ energy_from_fat_100g : num NA NA NA NA NA NA NA NA NA NA ...
## $ fat_100g : num 0 NA NA 16.7 45.5 NA NA 25 NA 4 ...
## $ saturated_fat_100g : num 0 NA NA 9.9 5.2 NA NA 17 NA 0.54 ...
## $ butyric_acid_100g : logi NA NA NA NA NA NA ...
## $ caproic_acid_100g : logi NA NA NA NA NA NA ...
## $ caprylic_acid_100g : logi NA NA NA NA NA NA ...
## $ capric_acid_100g : logi NA NA NA NA NA NA ...
## $ lauric_acid_100g : logi NA NA NA NA NA NA ...
## $ myristic_acid_100g : logi NA NA NA NA NA NA ...
## $ palmitic_acid_100g : logi NA NA NA NA NA NA ...
## $ stearic_acid_100g : logi NA NA NA NA NA NA ...
## $ arachidic_acid_100g : logi NA NA NA NA NA NA ...
## $ behenic_acid_100g : logi NA NA NA NA NA NA ...
## $ lignoceric_acid_100g : logi NA NA NA NA NA NA ...
## $ cerotic_acid_100g : logi NA NA NA NA NA NA ...
## $ montanic_acid_100g : logi NA NA NA NA NA NA ...
## $ melissic_acid_100g : logi NA NA NA NA NA NA ...
## $ monounsaturated_fat_100g : num NA NA NA 2.9 9.5 NA NA NA NA NA ...
## $ polyunsaturated_fat_100g : num NA NA NA 3.9 32.8 NA NA NA NA NA ...
## $ omega_3_fat_100g : num NA NA NA NA NA NA NA NA NA NA ...
## $ alpha_linolenic_acid_100g : num NA NA NA NA NA NA NA NA NA NA ...
## $ eicosapentaenoic_acid_100g : num NA NA NA NA NA NA NA NA NA NA ...
## $ docosahexaenoic_acid_100g : num NA NA NA NA NA NA NA NA NA NA ...
## $ omega_6_fat_100g : num NA NA NA NA NA NA NA NA NA NA ...
## $ linoleic_acid_100g : num NA NA NA NA NA NA NA NA NA NA ...
## $ arachidonic_acid_100g : logi NA NA NA NA NA NA ...
## $ gamma_linolenic_acid_100g : logi NA NA NA NA NA NA ...
## $ dihomo_gamma_linolenic_acid_100g : logi NA NA NA NA NA NA ...
## $ omega_9_fat_100g : logi NA NA NA NA NA NA ...
## $ oleic_acid_100g : logi NA NA NA NA NA NA ...
## $ elaidic_acid_100g : logi NA NA NA NA NA NA ...
## $ gondoic_acid_100g : logi NA NA NA NA NA NA ...
## $ mead_acid_100g : logi NA NA NA NA NA NA ...
## $ erucic_acid_100g : logi NA NA NA NA NA NA ...
## [list output truncated]Ex3: Inspecting variables
# library(dplyr)
glimpse(food) # formats information in a more approachable way
## Observations: 1,500
## Variables: 160
## $ V1 <int> 1, 2, 3, 4, 5, 6, 7...
## $ code <int> 100030, 100050, 100...
## $ url <chr> "http://world-en.op...
## $ creator <chr> "sebleouf", "foodor...
## $ created_t <int> 1424747544, 1450316...
## $ created_datetime <chr> "2015-02-24T03:12:2...
## $ last_modified_t <int> 1438445887, 1450817...
## $ last_modified_datetime <chr> "2015-08-01T16:18:0...
## $ product_name <chr> "Confiture de frais...
## $ generic_name <chr> "", "", "Pâtes de ...
## $ quantity <chr> "265 g", "375g", "1...
## $ packaging <chr> "Bocal,Verre", "Pla...
## $ packaging_tags <chr> "bocal,verre", "pla...
## $ brands <chr> "Casino Délices", ...
## $ brands_tags <chr> "casino-delices", "...
## $ categories <chr> "Aliments et boisso...
## $ categories_tags <chr> "en:plant-based-foo...
## $ categories_en <chr> "Plant-based foods ...
## $ origins <chr> "", "", "", "", "Ar...
## $ origins_tags <chr> "", "", "", "", "ar...
## $ manufacturing_places <chr> "France", "Belgium"...
## $ manufacturing_places_tags <chr> "france", "belgium"...
## $ labels <chr> "", "", "", "Vegeta...
## $ labels_tags <chr> "", "", "", "en:veg...
## $ labels_en <chr> "", "", "", "Vegeta...
## $ emb_codes <chr> "EMB 78015", "", ""...
## $ emb_codes_tags <chr> "emb-78015", "", ""...
## $ first_packaging_code_geo <chr> "48.983333,2.066667...
## $ cities <lgl> NA, NA, NA, NA, NA,...
## $ cities_tags <chr> "andresy-yvelines-f...
## $ purchase_places <chr> "Lyon,France", "NSW...
## $ stores <chr> "Casino", "", "", "...
## $ countries <chr> "France", "Australi...
## $ countries_tags <chr> "en:france", "en:au...
## $ countries_en <chr> "France", "Australi...
## $ ingredients_text <chr> "Sucre de canne, fr...
## $ allergens <chr> "", "", "", "", "",...
## $ allergens_en <lgl> NA, NA, NA, NA, NA,...
## $ traces <chr> "Lait,Fruits à coq...
## $ traces_tags <chr> "en:milk,en:nuts", ...
## $ traces_en <chr> "Milk,Nuts", "", ""...
## $ serving_size <chr> "15 g", "", "", "",...
## $ no_nutriments <lgl> NA, NA, NA, NA, NA,...
## $ additives_n <int> 1, NA, 2, 5, 0, NA,...
## $ additives <chr> "[ sucre-de-canne -...
## $ additives_tags <chr> "en:e440", "", "en:...
## $ additives_en <chr> "E440 - Pectins", "...
## $ ingredients_from_palm_oil_n <int> 0, NA, 0, 0, 0, NA,...
## $ ingredients_from_palm_oil <lgl> NA, NA, NA, NA, NA,...
## $ ingredients_from_palm_oil_tags <chr> "", "", "", "", "",...
## $ ingredients_that_may_be_from_palm_oil_n <int> 0, NA, 0, 1, 0, NA,...
## $ ingredients_that_may_be_from_palm_oil <lgl> NA, NA, NA, NA, NA,...
## $ ingredients_that_may_be_from_palm_oil_tags <chr> "", "", "", "e471-m...
## $ nutrition_grade_uk <lgl> NA, NA, NA, NA, NA,...
## $ nutrition_grade_fr <chr> "d", "", "", "d", "...
## $ pnns_groups_1 <chr> "Sugary snacks", "S...
## $ pnns_groups_2 <chr> "Sweets", "Chocolat...
## $ states <chr> "en:to-be-checked, ...
## $ states_tags <chr> "en:to-be-checked,e...
## $ states_en <chr> "To be checked,Comp...
## $ main_category <chr> "en:plant-based-foo...
## $ main_category_en <chr> "Plant-based foods ...
## $ image_url <chr> "http://en.openfood...
## $ image_small_url <chr> "http://en.openfood...
## $ energy_100g <dbl> 918, NA, NA, 766, 2...
## $ energy_from_fat_100g <dbl> NA, NA, NA, NA, NA,...
## $ fat_100g <dbl> 0.00, NA, NA, 16.70...
## $ saturated_fat_100g <dbl> 0.000, NA, NA, 9.90...
## $ butyric_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ caproic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ caprylic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ capric_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ lauric_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ myristic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ palmitic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ stearic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ arachidic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ behenic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ lignoceric_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ cerotic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ montanic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ melissic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ monounsaturated_fat_100g <dbl> NA, NA, NA, 2.9, 9....
## $ polyunsaturated_fat_100g <dbl> NA, NA, NA, 3.9, 32...
## $ omega_3_fat_100g <dbl> NA, NA, NA, NA, NA,...
## $ alpha_linolenic_acid_100g <dbl> NA, NA, NA, NA, NA,...
## $ eicosapentaenoic_acid_100g <dbl> NA, NA, NA, NA, NA,...
## $ docosahexaenoic_acid_100g <dbl> NA, NA, NA, NA, NA,...
## $ omega_6_fat_100g <dbl> NA, NA, NA, NA, NA,...
## $ linoleic_acid_100g <dbl> NA, NA, NA, NA, NA,...
## $ arachidonic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ gamma_linolenic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ dihomo_gamma_linolenic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ omega_9_fat_100g <lgl> NA, NA, NA, NA, NA,...
## $ oleic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ elaidic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ gondoic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ mead_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ erucic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ nervonic_acid_100g <lgl> NA, NA, NA, NA, NA,...
## $ trans_fat_100g <dbl> NA, NA, NA, NA, NA,...
## $ cholesterol_100g <dbl> NA, NA, NA, 0.00020...
## $ carbohydrates_100g <dbl> 54.00, NA, NA, 5.70...
## $ sugars_100g <dbl> 54.00, NA, NA, 4.20...
## $ sucrose_100g <lgl> NA, NA, NA, NA, NA,...
## $ glucose_100g <lgl> NA, NA, NA, NA, NA,...
## $ fructose_100g <int> NA, NA, NA, NA, NA,...
## $ lactose_100g <dbl> NA, NA, NA, NA, NA,...
## $ maltose_100g <lgl> NA, NA, NA, NA, NA,...
## $ maltodextrins_100g <lgl> NA, NA, NA, NA, NA,...
## $ starch_100g <dbl> NA, NA, NA, NA, NA,...
## $ polyols_100g <dbl> NA, NA, NA, NA, NA,...
## $ fiber_100g <dbl> NA, NA, NA, 0.2, 9....
## $ proteins_100g <dbl> 0.00, NA, NA, 2.90,...
## $ casein_100g <dbl> NA, NA, NA, NA, NA,...
## $ serum_proteins_100g <lgl> NA, NA, NA, NA, NA,...
## $ nucleotides_100g <lgl> NA, NA, NA, NA, NA,...
## $ salt_100g <dbl> 0.0000000, NA, NA, ...
## $ sodium_100g <dbl> 0.0000000, NA, NA, ...
## $ alcohol_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_a_100g <dbl> NA, NA, NA, NA, NA,...
## $ beta_carotene_100g <lgl> NA, NA, NA, NA, NA,...
## $ vitamin_d_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_e_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_k_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_c_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_b1_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_b2_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_pp_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_b6_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_b9_100g <dbl> NA, NA, NA, NA, NA,...
## $ vitamin_b12_100g <dbl> NA, NA, NA, NA, NA,...
## $ biotin_100g <dbl> NA, NA, NA, NA, NA,...
## $ pantothenic_acid_100g <dbl> NA, NA, NA, NA, NA,...
## $ silica_100g <dbl> NA, NA, NA, NA, NA,...
## $ bicarbonate_100g <dbl> NA, NA, NA, NA, NA,...
## $ potassium_100g <dbl> NA, NA, NA, NA, NA,...
## $ chloride_100g <dbl> NA, NA, NA, NA, NA,...
## $ calcium_100g <dbl> NA, NA, NA, NA, NA,...
## $ phosphorus_100g <dbl> NA, NA, NA, NA, 1.1...
## $ iron_100g <dbl> NA, NA, NA, NA, 0.0...
## $ magnesium_100g <dbl> NA, NA, NA, NA, 0.1...
## $ zinc_100g <dbl> NA, NA, NA, NA, NA,...
## $ copper_100g <dbl> NA, NA, NA, NA, NA,...
## $ manganese_100g <dbl> NA, NA, NA, NA, NA,...
## $ fluoride_100g <dbl> NA, NA, NA, NA, NA,...
## $ selenium_100g <dbl> NA, NA, NA, NA, NA,...
## $ chromium_100g <lgl> NA, NA, NA, NA, NA,...
## $ molybdenum_100g <lgl> NA, NA, NA, NA, NA,...
## $ iodine_100g <dbl> NA, NA, NA, NA, NA,...
## $ caffeine_100g <lgl> NA, NA, NA, NA, NA,...
## $ taurine_100g <lgl> NA, NA, NA, NA, NA,...
## $ ph_100g <lgl> NA, NA, NA, NA, NA,...
## $ fruits_vegetables_nuts_100g <dbl> 54, NA, NA, NA, NA,...
## $ collagen_meat_protein_ratio_100g <int> NA, NA, NA, NA, NA,...
## $ cocoa_100g <int> NA, NA, NA, NA, NA,...
## $ chlorophyl_100g <lgl> NA, NA, NA, NA, NA,...
## $ carbon_footprint_100g <dbl> NA, NA, NA, NA, NA,...
## $ nutrition_score_fr_100g <int> 11, NA, NA, 11, 17,...
## $ nutrition_score_uk_100g <int> 11, NA, NA, 11, 17,...names(food) # column names -- (any pairs that might be duplicates?)
## [1] "V1"
## [2] "code"
## [3] "url"
## [4] "creator"
## [5] "created_t"
## [6] "created_datetime"
## [7] "last_modified_t"
## [8] "last_modified_datetime"
## [9] "product_name"
## [10] "generic_name"
## [11] "quantity"
## [12] "packaging"
## [13] "packaging_tags"
## [14] "brands"
## [15] "brands_tags"
## [16] "categories"
## [17] "categories_tags"
## [18] "categories_en"
## [19] "origins"
## [20] "origins_tags"
## [21] "manufacturing_places"
## [22] "manufacturing_places_tags"
## [23] "labels"
## [24] "labels_tags"
## [25] "labels_en"
## [26] "emb_codes"
## [27] "emb_codes_tags"
## [28] "first_packaging_code_geo"
## [29] "cities"
## [30] "cities_tags"
## [31] "purchase_places"
## [32] "stores"
## [33] "countries"
## [34] "countries_tags"
## [35] "countries_en"
## [36] "ingredients_text"
## [37] "allergens"
## [38] "allergens_en"
## [39] "traces"
## [40] "traces_tags"
## [41] "traces_en"
## [42] "serving_size"
## [43] "no_nutriments"
## [44] "additives_n"
## [45] "additives"
## [46] "additives_tags"
## [47] "additives_en"
## [48] "ingredients_from_palm_oil_n"
## [49] "ingredients_from_palm_oil"
## [50] "ingredients_from_palm_oil_tags"
## [51] "ingredients_that_may_be_from_palm_oil_n"
## [52] "ingredients_that_may_be_from_palm_oil"
## [53] "ingredients_that_may_be_from_palm_oil_tags"
## [54] "nutrition_grade_uk"
## [55] "nutrition_grade_fr"
## [56] "pnns_groups_1"
## [57] "pnns_groups_2"
## [58] "states"
## [59] "states_tags"
## [60] "states_en"
## [61] "main_category"
## [62] "main_category_en"
## [63] "image_url"
## [64] "image_small_url"
## [65] "energy_100g"
## [66] "energy_from_fat_100g"
## [67] "fat_100g"
## [68] "saturated_fat_100g"
## [69] "butyric_acid_100g"
## [70] "caproic_acid_100g"
## [71] "caprylic_acid_100g"
## [72] "capric_acid_100g"
## [73] "lauric_acid_100g"
## [74] "myristic_acid_100g"
## [75] "palmitic_acid_100g"
## [76] "stearic_acid_100g"
## [77] "arachidic_acid_100g"
## [78] "behenic_acid_100g"
## [79] "lignoceric_acid_100g"
## [80] "cerotic_acid_100g"
## [81] "montanic_acid_100g"
## [82] "melissic_acid_100g"
## [83] "monounsaturated_fat_100g"
## [84] "polyunsaturated_fat_100g"
## [85] "omega_3_fat_100g"
## [86] "alpha_linolenic_acid_100g"
## [87] "eicosapentaenoic_acid_100g"
## [88] "docosahexaenoic_acid_100g"
## [89] "omega_6_fat_100g"
## [90] "linoleic_acid_100g"
## [91] "arachidonic_acid_100g"
## [92] "gamma_linolenic_acid_100g"
## [93] "dihomo_gamma_linolenic_acid_100g"
## [94] "omega_9_fat_100g"
## [95] "oleic_acid_100g"
## [96] "elaidic_acid_100g"
## [97] "gondoic_acid_100g"
## [98] "mead_acid_100g"
## [99] "erucic_acid_100g"
## [100] "nervonic_acid_100g"
## [101] "trans_fat_100g"
## [102] "cholesterol_100g"
## [103] "carbohydrates_100g"
## [104] "sugars_100g"
## [105] "sucrose_100g"
## [106] "glucose_100g"
## [107] "fructose_100g"
## [108] "lactose_100g"
## [109] "maltose_100g"
## [110] "maltodextrins_100g"
## [111] "starch_100g"
## [112] "polyols_100g"
## [113] "fiber_100g"
## [114] "proteins_100g"
## [115] "casein_100g"
## [116] "serum_proteins_100g"
## [117] "nucleotides_100g"
## [118] "salt_100g"
## [119] "sodium_100g"
## [120] "alcohol_100g"
## [121] "vitamin_a_100g"
## [122] "beta_carotene_100g"
## [123] "vitamin_d_100g"
## [124] "vitamin_e_100g"
## [125] "vitamin_k_100g"
## [126] "vitamin_c_100g"
## [127] "vitamin_b1_100g"
## [128] "vitamin_b2_100g"
## [129] "vitamin_pp_100g"
## [130] "vitamin_b6_100g"
## [131] "vitamin_b9_100g"
## [132] "vitamin_b12_100g"
## [133] "biotin_100g"
## [134] "pantothenic_acid_100g"
## [135] "silica_100g"
## [136] "bicarbonate_100g"
## [137] "potassium_100g"
## [138] "chloride_100g"
## [139] "calcium_100g"
## [140] "phosphorus_100g"
## [141] "iron_100g"
## [142] "magnesium_100g"
## [143] "zinc_100g"
## [144] "copper_100g"
## [145] "manganese_100g"
## [146] "fluoride_100g"
## [147] "selenium_100g"
## [148] "chromium_100g"
## [149] "molybdenum_100g"
## [150] "iodine_100g"
## [151] "caffeine_100g"
## [152] "taurine_100g"
## [153] "ph_100g"
## [154] "fruits_vegetables_nuts_100g"
## [155] "collagen_meat_protein_ratio_100g"
## [156] "cocoa_100g"
## [157] "chlorophyl_100g"
## [158] "carbon_footprint_100g"
## [159] "nutrition_score_fr_100g"
## [160] "nutrition_score_uk_100g"Ex4: Remove Duplicates
- some information on what and when information was added (1:9)
- meta information about food (10:17, 22:27)
- where it came from (18:21, 28:34)
- what it’s made of (35:52)
- nutrition grades (53:54)
- some unclear (55:63)
- some nutritional information (64:159)
# Define vector of duplicate cols
duplicates <- c(4, 6, 11, 13, 15, 17, 18, 20, 22,
24, 25, 28, 32, 34, 36, 38, 40,
44, 46, 48, 51, 54, 65, 158)
# Remove duplicates from food: food2
food2 <- food[,-duplicates]
Ex5: Removing useless info
- For example
- first few columns contain internal codes that don’t have any meaning to us
- some column names that aren’t clear enough to tell what they contain.
# Define useless vector
useless <- c(1, 2, 3, 32:41)
# Remove useless columns from food2: food3
food3 <- food2[,-useless]
Ex6: Finding columns
All of the columns with nutrition info contain the character string “100g” as part of their name, which makes it easy to identify them.
# library(stringr)
# Create vector of column indices: nutrition
nutrition <- str_detect(names(food3), "100g")
# View a summary of nutrition columns
summary(nutrition) # this is not necessary - duh.
## Mode FALSE TRUE
## logical 29 94
summary(food3[, nutrition])
## energy_from_fat_100g fat_100g saturated_fat_100g
## Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 35.98 1st Qu.: 0.90 1st Qu.: 0.200
## Median : 237.00 Median : 6.00 Median : 1.700
## Mean : 668.41 Mean : 13.39 Mean : 4.874
## 3rd Qu.: 974.00 3rd Qu.: 20.00 3rd Qu.: 6.500
## Max. :2900.00 Max. :100.00 Max. :57.000
## NA's :1486 NA's :708 NA's :797
## butyric_acid_100g caproic_acid_100g caprylic_acid_100g capric_acid_100g
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## lauric_acid_100g myristic_acid_100g palmitic_acid_100g stearic_acid_100g
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## arachidic_acid_100g behenic_acid_100g lignoceric_acid_100g
## Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## cerotic_acid_100g montanic_acid_100g melissic_acid_100g
## Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## monounsaturated_fat_100g polyunsaturated_fat_100g omega_3_fat_100g
## Min. : 0.00 Min. : 0.400 Min. : 0.033
## 1st Qu.: 3.87 1st Qu.: 1.653 1st Qu.: 1.300
## Median : 9.50 Median : 3.900 Median : 3.000
## Mean :19.77 Mean : 9.986 Mean : 3.726
## 3rd Qu.:29.00 3rd Qu.:12.700 3rd Qu.: 3.200
## Max. :75.00 Max. :46.200 Max. :12.400
## NA's :1465 NA's :1464 NA's :1491
## alpha_linolenic_acid_100g eicosapentaenoic_acid_100g
## Min. :0.0800 Min. :0.721
## 1st Qu.:0.0905 1st Qu.:0.721
## Median :0.1010 Median :0.721
## Mean :0.1737 Mean :0.721
## 3rd Qu.:0.2205 3rd Qu.:0.721
## Max. :0.3400 Max. :0.721
## NA's :1497 NA's :1499
## docosahexaenoic_acid_100g omega_6_fat_100g linoleic_acid_100g
## Min. :1.09 Min. :0.25 Min. :0.5000
## 1st Qu.:1.09 1st Qu.:0.25 1st Qu.:0.5165
## Median :1.09 Median :0.25 Median :0.5330
## Mean :1.09 Mean :0.25 Mean :0.5330
## 3rd Qu.:1.09 3rd Qu.:0.25 3rd Qu.:0.5495
## Max. :1.09 Max. :0.25 Max. :0.5660
## NA's :1499 NA's :1499 NA's :1498
## arachidonic_acid_100g gamma_linolenic_acid_100g
## Mode:logical Mode:logical
## NA's:1500 NA's:1500
##
##
##
##
##
## dihomo_gamma_linolenic_acid_100g omega_9_fat_100g oleic_acid_100g
## Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## elaidic_acid_100g gondoic_acid_100g mead_acid_100g erucic_acid_100g
## Mode:logical Mode:logical Mode:logical Mode:logical
## NA's:1500 NA's:1500 NA's:1500 NA's:1500
##
##
##
##
##
## nervonic_acid_100g trans_fat_100g cholesterol_100g carbohydrates_100g
## Mode:logical Min. :0.0000 Min. :0.0000 Min. : 0.000
## NA's:1500 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 3.792
## Median :0.0000 Median :0.0000 Median : 13.500
## Mean :0.0105 Mean :0.0265 Mean : 27.958
## 3rd Qu.:0.0000 3rd Qu.:0.0026 3rd Qu.: 55.000
## Max. :0.1000 Max. :0.4300 Max. :100.000
## NA's :1481 NA's :1477 NA's :708
## sugars_100g sucrose_100g glucose_100g fructose_100g
## Min. : 0.00 Mode:logical Mode:logical Min. :100
## 1st Qu.: 1.00 NA's:1500 NA's:1500 1st Qu.:100
## Median : 4.05 Median :100
## Mean : 12.66 Mean :100
## 3rd Qu.: 14.70 3rd Qu.:100
## Max. :100.00 Max. :100
## NA's :788 NA's :1499
## lactose_100g maltose_100g maltodextrins_100g starch_100g
## Min. :0.000 Mode:logical Mode:logical Min. : 0.00
## 1st Qu.:0.250 NA's:1500 NA's:1500 1st Qu.: 9.45
## Median :0.500 Median :39.50
## Mean :2.933 Mean :30.73
## 3rd Qu.:4.400 3rd Qu.:42.85
## Max. :8.300 Max. :71.00
## NA's :1497 NA's :1493
## polyols_100g fiber_100g proteins_100g casein_100g
## Min. : 8.60 Min. : 0.000 Min. : 0.000 Min. :1.1
## 1st Qu.:59.10 1st Qu.: 0.500 1st Qu.: 1.500 1st Qu.:1.1
## Median :67.00 Median : 1.750 Median : 6.000 Median :1.1
## Mean :56.06 Mean : 2.823 Mean : 7.563 Mean :1.1
## 3rd Qu.:69.80 3rd Qu.: 3.500 3rd Qu.:10.675 3rd Qu.:1.1
## Max. :70.00 Max. :46.700 Max. :61.000 Max. :1.1
## NA's :1491 NA's :994 NA's :710 NA's :1499
## serum_proteins_100g nucleotides_100g salt_100g sodium_100g
## Mode:logical Mode:logical Min. : 0.0000 Min. : 0.0000
## NA's:1500 NA's:1500 1st Qu.: 0.0438 1st Qu.: 0.0172
## Median : 0.4498 Median : 0.1771
## Mean : 1.1205 Mean : 0.4409
## 3rd Qu.: 1.1938 3rd Qu.: 0.4700
## Max. :102.0000 Max. :40.0000
## NA's :780 NA's :780
## alcohol_100g vitamin_a_100g beta_carotene_100g vitamin_d_100g
## Min. : 0.00 Min. :0.0000 Mode:logical Min. :0e+00
## 1st Qu.: 0.00 1st Qu.:0.0000 NA's:1500 1st Qu.:0e+00
## Median : 5.50 Median :0.0001 Median :0e+00
## Mean :10.07 Mean :0.0003 Mean :0e+00
## 3rd Qu.:13.00 3rd Qu.:0.0006 3rd Qu.:0e+00
## Max. :50.00 Max. :0.0013 Max. :1e-04
## NA's :1433 NA's :1477 NA's :1485
## vitamin_e_100g vitamin_k_100g vitamin_c_100g vitamin_b1_100g
## Min. :0.0005 Min. :0 Min. :0.000 Min. :0.0001
## 1st Qu.:0.0021 1st Qu.:0 1st Qu.:0.002 1st Qu.:0.0003
## Median :0.0044 Median :0 Median :0.019 Median :0.0004
## Mean :0.0069 Mean :0 Mean :0.025 Mean :0.0006
## 3rd Qu.:0.0097 3rd Qu.:0 3rd Qu.:0.030 3rd Qu.:0.0010
## Max. :0.0320 Max. :0 Max. :0.217 Max. :0.0013
## NA's :1478 NA's :1498 NA's :1459 NA's :1478
## vitamin_b2_100g vitamin_pp_100g vitamin_b6_100g vitamin_b9_100g
## Min. :0.0002 Min. :0.0006 Min. :0.0001 Min. :0e+00
## 1st Qu.:0.0003 1st Qu.:0.0033 1st Qu.:0.0002 1st Qu.:0e+00
## Median :0.0009 Median :0.0069 Median :0.0008 Median :1e-04
## Mean :0.0011 Mean :0.0086 Mean :0.0112 Mean :1e-04
## 3rd Qu.:0.0013 3rd Qu.:0.0140 3rd Qu.:0.0012 3rd Qu.:2e-04
## Max. :0.0066 Max. :0.0160 Max. :0.2000 Max. :2e-04
## NA's :1483 NA's :1484 NA's :1481 NA's :1483
## vitamin_b12_100g biotin_100g pantothenic_acid_100g silica_100g
## Min. :0 Min. :0 Min. :0.0000 Min. :8e-04
## 1st Qu.:0 1st Qu.:0 1st Qu.:0.0007 1st Qu.:8e-04
## Median :0 Median :0 Median :0.0020 Median :8e-04
## Mean :0 Mean :0 Mean :0.0027 Mean :8e-04
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0.0051 3rd Qu.:8e-04
## Max. :0 Max. :0 Max. :0.0060 Max. :8e-04
## NA's :1489 NA's :1498 NA's :1486 NA's :1499
## bicarbonate_100g potassium_100g chloride_100g calcium_100g
## Min. :0.0006 Min. :0.0000 Min. :0.0003 Min. :0.0000
## 1st Qu.:0.0678 1st Qu.:0.0650 1st Qu.:0.0006 1st Qu.:0.0450
## Median :0.1350 Median :0.1940 Median :0.0009 Median :0.1200
## Mean :0.1692 Mean :0.3288 Mean :0.0144 Mean :0.2040
## 3rd Qu.:0.2535 3rd Qu.:0.3670 3rd Qu.:0.0214 3rd Qu.:0.1985
## Max. :0.3720 Max. :1.4300 Max. :0.0420 Max. :1.0000
## NA's :1497 NA's :1487 NA's :1497 NA's :1449
## phosphorus_100g iron_100g magnesium_100g zinc_100g
## Min. :0.0430 Min. :0.0000 Min. :0.0000 Min. :0.0005
## 1st Qu.:0.1938 1st Qu.:0.0012 1st Qu.:0.0670 1st Qu.:0.0009
## Median :0.3185 Median :0.0042 Median :0.1040 Median :0.0017
## Mean :0.3777 Mean :0.0045 Mean :0.1066 Mean :0.0016
## 3rd Qu.:0.4340 3rd Qu.:0.0077 3rd Qu.:0.1300 3rd Qu.:0.0022
## Max. :1.1550 Max. :0.0137 Max. :0.3330 Max. :0.0026
## NA's :1488 NA's :1463 NA's :1479 NA's :1493
## copper_100g manganese_100g fluoride_100g selenium_100g
## Min. :0e+00 Min. :0 Min. :0 Min. :0
## 1st Qu.:1e-04 1st Qu.:0 1st Qu.:0 1st Qu.:0
## Median :1e-04 Median :0 Median :0 Median :0
## Mean :1e-04 Mean :0 Mean :0 Mean :0
## 3rd Qu.:1e-04 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
## Max. :1e-04 Max. :0 Max. :0 Max. :0
## NA's :1498 NA's :1499 NA's :1498 NA's :1499
## chromium_100g molybdenum_100g iodine_100g caffeine_100g
## Mode:logical Mode:logical Min. :0 Mode:logical
## NA's:1500 NA's:1500 1st Qu.:0 NA's:1500
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0
## NA's :1499
## taurine_100g ph_100g fruits_vegetables_nuts_100g
## Mode:logical Mode:logical Min. : 2.00
## NA's:1500 NA's:1500 1st Qu.:11.25
## Median :42.00
## Mean :36.88
## 3rd Qu.:52.25
## Max. :80.00
## NA's :1470
## collagen_meat_protein_ratio_100g cocoa_100g chlorophyl_100g
## Min. :12.00 Min. :30 Mode:logical
## 1st Qu.:13.50 1st Qu.:47 NA's:1500
## Median :15.00 Median :60
## Mean :15.67 Mean :57
## 3rd Qu.:17.50 3rd Qu.:70
## Max. :20.00 Max. :81
## NA's :1497 NA's :1491
## nutrition_score_fr_100g nutrition_score_uk_100g
## Min. :-12.000 Min. :-12.000
## 1st Qu.: 1.000 1st Qu.: 0.000
## Median : 7.000 Median : 6.000
## Mean : 7.941 Mean : 7.631
## 3rd Qu.: 15.000 3rd Qu.: 16.000
## Max. : 28.000 Max. : 28.000
## NA's :825 NA's :825Ex7: Replacing missing values
summary revealed that the nutrition data are mostly NA values Reason: food just doesn’t have those nutrients
sugar content, zero values are sometimes entered explicitly, but sometimes the values are just left empty to denote a zero
# Find indices of sugar NA values: missing
# in the sugars_100g column
missing <- is.na(food3$sugars_100g)
# Replace NA values with 0
# Subset food3$sugars_100g using missing to replace the missing values with zeros
food3$sugars_100g[missing] <- 0
# Create first histogram
hist(food3$sugars_100g, breaks = 100)
# Create food4
# exclude the observations which have no sugar
# subset of food3 that excludes the foods with zero sugar
food4 <- food3[food3$sugars_100g != 0, ]
# Create second histogram
# see how the distribution changes
hist(food4$sugars_100g, breaks = 100)
Ex8: messy data
how many of these foods come in some sort of plastic packaging?
packaging info in dataset = stored in several different languages (Spanish, French, and English).
root word for plastic is same: * English (plastic) * French (plastique) * Spanish (plastico)
# library(stringr)
# look through the packaging column for the string "plasti".
# Find entries containing "plasti": plastic
plastic <- str_detect(food3$packaging,"plasti")
# count how many of the foods are packaged in plastic.
# Print the sum of plastic
sum(plastic)
## [1] 232School Attendance
attendance data from public schools in the US, organized by school level and state, during the 2007-2008 academic year
average daily attendance (ADA) as a percentage of total enrollment, school day length, and school year length.
Ex1: Import Data
Load the gdata package
# install.packages("gdata")
library(gdata)Import the spreadsheet
url_att = "http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/attendance.xls"
# Error in findPerl(verbose = verbose): perl executable not found.
# Use perl= argument to specify the correct path.
# Error in file.exists(tfn) : invalid 'file' argument
# => requires perl arguement "C:\\Perl\\bin\\perl.exe")
# https://www.activestate.com/activeperl/downloads
# att <- read.xls(url_att, perl = "C:\\Perl64\\bin\\perl.exe")
# this function is part of an archieved CRAN repository
# https://cran.r-project.org/web/packages/xlsReadWrite/
# found here also:
# https://www.rdocumentation.org/packages/gdata/versions/2.18.0/topics/read.xls
# does not work!?
# download.file(url_att, "C:/Temp/attendance.xls")
library(readxl)
library(httr)
url <- "http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/attendance.xls"
GET(url, write_disk("attendance.xls", overwrite=TRUE))
## Response [http://s3.amazonaws.com/assets.datacamp.com/production/course_1294/datasets/attendance.xls]
## Date: 2018-05-04 13:41
## Status: 200
## Content-Type:
## Size: 28.2 kB
## <ON DISK> D:\DATA\python\R - notes\Data Scientist with R\7 - importing-cleaning-data-in-r-case-studies\attendance.xls
att <- read_excel("attendance.xls")
head(att, 3)
## # A tibble: 3 x 17
## `Table 43. Averag~ X__1 X__2 X__3 X__4 X__5 X__6 X__7 X__8 X__9
## <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr>
## 1 <NA> Tota~ NA <NA> NA <NA> NA <NA> NA Elem~
## 2 <NA> ADA ~ NA Aver~ NA Aver~ NA Aver~ NA ADA ~
## 3 1 2 NA 3 NA 4 NA 5 NA 6
## # ... with 7 more variables: X__10 <dbl>, X__11 <chr>, X__12 <dbl>,
## # X__13 <chr>, X__14 <chr>, X__15 <chr>, X__16 <chr>Ex2: Examine Data
first row is a description of the data second row is a variable itself that groups multiple columns together fourth row gives numbers for the columns – nice in a spreadsheet – not very useful
messy data - column names are mostly missing
names(att)
## [1] "Table 43. Average daily attendance (ADA) as a percentage of total enrollment, school day length, and school year length in public schools, by school level and state: 2007-08"
## [2] "X__1"
## [3] "X__2"
## [4] "X__3"
## [5] "X__4"
## [6] "X__5"
## [7] "X__6"
## [8] "X__7"
## [9] "X__8"
## [10] "X__9"
## [11] "X__10"
## [12] "X__11"
## [13] "X__12"
## [14] "X__13"
## [15] "X__14"
## [16] "X__15"
## [17] "X__16"head(att)
## # A tibble: 6 x 17
## `Table 43. Average~ X__1 X__2 X__3 X__4 X__5 X__6 X__7 X__8
## <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl>
## 1 <NA> Total~ NA <NA> NA <NA> NA <NA> NA
## 2 <NA> ADA a~ NA Aver~ NA Aver~ NA Aver~ NA
## 3 1 2 NA 3 NA 4 NA 5 NA
## 4 United States ....~ 93.07~ 0.219 6.64~ 0.0176 180 0.143 1192~ 3.09
## 5 Alabama ..........~ 93.81~ 1.24 7.02~ 0.0656 180 0.755 1266~ 12.3
## 6 Alaska ...........~ 89.91~ 1.22 6.47~ 0.0499 180 3.43 1162~ 22.9
## # ... with 8 more variables: X__9 <chr>, X__10 <dbl>, X__11 <chr>,
## # X__12 <dbl>, X__13 <chr>, X__14 <chr>, X__15 <chr>, X__16 <chr>messy data - irrelevant notes at the end of the data frame
tail(att)
## # A tibble: 6 x 17
## `Table 43. Average ~ X__1 X__2 X__3 X__4 X__5 X__6 X__7 X__8
## <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl>
## 1 Wisconsin .........~ 94.9~ 0.566 6.91~ 0.0427 180 0.736 1246~ 8.63
## 2 Wyoming ...........~ 92.3~ 1.15 6.85~ 0.0458 175 1.28 1200~ 8.33
## 3 Not applicable. <NA> NA <NA> NA <NA> NA <NA> NA
## 4 Reporting standard~ <NA> NA <NA> NA <NA> NA <NA> NA
## 5 NOTE: Averages refl~ <NA> NA <NA> NA <NA> NA <NA> NA
## 6 "SOURCE: U.S. Depar~ <NA> NA <NA> NA <NA> NA <NA> NA
## # ... with 8 more variables: X__9 <chr>, X__10 <dbl>, X__11 <chr>,
## # X__12 <dbl>, X__13 <chr>, X__14 <chr>, X__15 <chr>, X__16 <chr>messy data - numeric data were imported as factors
str(att)
## Classes 'tbl_df', 'tbl' and 'data.frame': 68 obs. of 17 variables:
## $ Table 43. Average daily attendance (ADA) as a percentage of total enrollment, school day length, and school year length in public schools, by school level and state: 2007-08: chr NA NA "1" "United States ........" ...
## $ X__1 : chr "Total elementary, secondary, and combined elementary/secondary schools" "ADA as percent of enrollment" "2" "93.078962000000004" ...
## $ X__2 : num NA NA NA 0.219 1.237 ...
## $ X__3 : chr NA "Average hours in school day" "3" "6.6447000000000003" ...
## $ X__4 : num NA NA NA 0.0176 0.0656 ...
## $ X__5 : chr NA "Average days in school year" "4" "180" ...
## $ X__6 : num NA NA NA 0.143 0.755 ...
## $ X__7 : chr NA "Average hours in school year" "5" "1192.6472000000001" ...
## $ X__8 : num NA NA NA 3.09 12.33 ...
## $ X__9 : chr "Elementary schools" "ADA as percent of enrollment" "6" "94.004982999999996" ...
## $ X__10 : num NA NA NA 0.269 1.839 ...
## $ X__11 : chr NA "Average hours in school day" "7" "6.6560560000000004" ...
## $ X__12 : num NA NA NA 0.016 0.0759 ...
## $ X__13 : chr "Secondary schools" "ADA as percent of enrollment" "8" "91.118081000000004" ...
## $ X__14 : chr NA NA NA "0.43222300000000002" ...
## $ X__15 : chr NA "Average hours in school day" "9" "6.5943940000000003" ...
## $ X__16 : chr NA NA NA "0.040307999999999997" ...Ex3: Removing unnecessary rows
useless rows: 1, 4, 11, & 17
Do NOT do this: att2 <- att[-c(1, 4, 11, 17), ]
Because: * read.xls() function imported the first row of the original data frame as the variable name for the first column * first 6 rows of att aren’t the same as the first six rows you saw in the original spreadsheet * read.xls() function skips empty rows such as the 11th and 17th
get rid of the third row of att, as well as rows 56 through 59.
remove <- c(3, 56:59)
att2 <- att[-remove,]
head(att2)
## # A tibble: 6 x 17
## `Table 43. Average~ X__1 X__2 X__3 X__4 X__5 X__6 X__7 X__8
## <chr> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl>
## 1 <NA> Total~ NA <NA> NA <NA> NA <NA> NA
## 2 <NA> ADA a~ NA Aver~ NA Aver~ NA Aver~ NA
## 3 United States ....~ 93.07~ 0.219 6.64~ 0.0176 180 0.143 1192~ 3.09
## 4 Alabama ..........~ 93.81~ 1.24 7.02~ 0.0656 180 0.755 1266~ 12.3
## 5 Alaska ...........~ 89.91~ 1.22 6.47~ 0.0499 180 3.43 1162~ 22.9
## 6 Arizona ..........~ 89.03~ 2.95 6.43~ 0.0919 181 1.68 1159~ 14.4
## # ... with 8 more variables: X__9 <chr>, X__10 <dbl>, X__11 <chr>,
## # X__12 <dbl>, X__13 <chr>, X__14 <chr>, X__15 <chr>, X__16 <chr>
str(att2)
## Classes 'tbl_df', 'tbl' and 'data.frame': 63 obs. of 17 variables:
## $ Table 43. Average daily attendance (ADA) as a percentage of total enrollment, school day length, and school year length in public schools, by school level and state: 2007-08: chr NA NA "United States ........" "Alabama ................." ...
## $ X__1 : chr "Total elementary, secondary, and combined elementary/secondary schools" "ADA as percent of enrollment" "93.078962000000004" "93.812370999999999" ...
## $ X__2 : num NA NA 0.219 1.237 1.216 ...
## $ X__3 : chr NA "Average hours in school day" "6.6447000000000003" "7.0285200000000003" ...
## $ X__4 : num NA NA 0.0176 0.0656 0.0499 ...
## $ X__5 : chr NA "Average days in school year" "180" "180" ...
## $ X__6 : num NA NA 0.143 0.755 3.428 ...
## $ X__7 : chr NA "Average hours in school year" "1192.6472000000001" "1266.6205" ...
## $ X__8 : num NA NA 3.09 12.33 22.86 ...
## $ X__9 : chr "Elementary schools" "ADA as percent of enrollment" "94.004982999999996" "93.776375000000002" ...
## $ X__10 : num NA NA 0.269 1.839 1.563 ...
## $ X__11 : chr NA "Average hours in school day" "6.6560560000000004" "7.0384729999999998" ...
## $ X__12 : num NA NA 0.016 0.0759 0.0531 ...
## $ X__13 : chr "Secondary schools" "ADA as percent of enrollment" "91.118081000000004" "94.561001000000005" ...
## $ X__14 : chr NA NA "0.43222300000000002" "0.37928000000000001" ...
## $ X__15 : chr NA "Average hours in school day" "6.5943940000000003" "7.1372390000000001" ...
## $ X__16 : chr NA NA "0.040307999999999997" "0.17312" ...Ex4: Removing useless columns
columns 3, 5, 7, 9, 11, 13, 15, and 17 (or columns C, E, G, I, K, M, O, Q in Excel) don’t contain the values of average daily attendance (ADA)
remove <- c(seq(3, 17, 2))
att3 <- att2[,-remove]
head(att3)
## # A tibble: 6 x 9
## `Table 43. Average ~ X__1 X__3 X__5 X__7 X__9 X__11 X__13 X__15
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 <NA> Total ~ <NA> <NA> <NA> Elem~ <NA> Seco~ <NA>
## 2 <NA> ADA as~ Avera~ Avera~ Aver~ ADA ~ Aver~ ADA ~ Aver~
## 3 United States .....~ 93.078~ 6.644~ 180 1192~ 94.0~ 6.65~ 91.1~ 6.59~
## 4 Alabama ...........~ 93.812~ 7.028~ 180 1266~ 93.7~ 7.03~ 94.5~ 7.13~
## 5 Alaska ............~ 89.917~ 6.476~ 180 1162~ 91.2~ 6.48~ 93.2~ 6.24~
## 6 Arizona ...........~ 89.036~ 6.433~ 181 1159~ 88.9~ 6.44~ 88.9~ 6.36~
str(att3)
## Classes 'tbl_df', 'tbl' and 'data.frame': 63 obs. of 9 variables:
## $ Table 43. Average daily attendance (ADA) as a percentage of total enrollment, school day length, and school year length in public schools, by school level and state: 2007-08: chr NA NA "United States ........" "Alabama ................." ...
## $ X__1 : chr "Total elementary, secondary, and combined elementary/secondary schools" "ADA as percent of enrollment" "93.078962000000004" "93.812370999999999" ...
## $ X__3 : chr NA "Average hours in school day" "6.6447000000000003" "7.0285200000000003" ...
## $ X__5 : chr NA "Average days in school year" "180" "180" ...
## $ X__7 : chr NA "Average hours in school year" "1192.6472000000001" "1266.6205" ...
## $ X__9 : chr "Elementary schools" "ADA as percent of enrollment" "94.004982999999996" "93.776375000000002" ...
## $ X__11 : chr NA "Average hours in school day" "6.6560560000000004" "7.0384729999999998" ...
## $ X__13 : chr "Secondary schools" "ADA as percent of enrollment" "91.118081000000004" "94.561001000000005" ...
## $ X__15 : chr NA "Average hours in school day" "6.5943940000000003" "7.1372390000000001" ...Ex5: Splitting data
problem: single data frame can store multiple “tables” of information diagnose: look at the column names and noticing duplicate rows * columns 1, 6, and 7 represent attendance data for US elementary schools * columns 1, 8, and 9 represent data for secondary schools * columns 1 through 5 represent data for all schools in the US
# Subset just elementary schools: att_elem
att_elem <- att3[,c(1,6,7)]
# Subset just secondary schools: att_sec
att_sec <- att3[,c(1,8,9)]
# Subset all schools: att4
att4 <- att3[,c(1:5)]
head(att4)
## # A tibble: 6 x 5
## `Table 43. Average daily attendan~ X__1 X__3 X__5 X__7
## <chr> <chr> <chr> <chr> <chr>
## 1 <NA> Total elemen~ <NA> <NA> <NA>
## 2 <NA> ADA as perce~ Averag~ Averag~ Averag~
## 3 United States ........ 93.078962000~ 6.6447~ 180 1192.6~
## 4 Alabama ................. 93.812370999~ 7.0285~ 180 1266.6~
## 5 Alaska .................. 89.917597000~ 6.4768~ 180 1162.9~
## 6 Arizona ................. 89.036961000~ 6.4336~ 181 1159.1~
str(att4)
## Classes 'tbl_df', 'tbl' and 'data.frame': 63 obs. of 5 variables:
## $ Table 43. Average daily attendance (ADA) as a percentage of total enrollment, school day length, and school year length in public schools, by school level and state: 2007-08: chr NA NA "United States ........" "Alabama ................." ...
## $ X__1 : chr "Total elementary, secondary, and combined elementary/secondary schools" "ADA as percent of enrollment" "93.078962000000004" "93.812370999999999" ...
## $ X__3 : chr NA "Average hours in school day" "6.6447000000000003" "7.0285200000000003" ...
## $ X__5 : chr NA "Average days in school year" "180" "180" ...
## $ X__7 : chr NA "Average hours in school year" "1192.6472000000001" "1266.6205" ...Ex6: Replacing the names
# row storing the variable names
# => actual column names of the data frame
# more stylistically sound:
cnames <- c("state", "avg_attend_pct", "avg_hr_per_day",
"avg_day_per_yr", "avg_hr_per_yr")
# Assign column names of att4
colnames(att4) <- cnames
# Remove first two rows of att4: att5
att5 <- att4[-c(1:2),]
# View the names of att5
colnames(att5)
## [1] "state" "avg_attend_pct" "avg_hr_per_day" "avg_day_per_yr"
## [5] "avg_hr_per_yr"
head(att5)
## # A tibble: 6 x 5
## state avg_attend_pct avg_hr_per_day avg_day_per_yr avg_hr_per_yr
## <chr> <chr> <chr> <chr> <chr>
## 1 United St~ 93.07896200000~ 6.644700000000~ 180 1192.64720000~
## 2 Alabama .~ 93.81237099999~ 7.028520000000~ 180 1266.6205
## 3 Alaska ..~ 89.91759700000~ 6.476880000000~ 180 1162.9084
## 4 Arizona .~ 89.03696100000~ 6.433690000000~ 181 1159.11439999~
## 5 Arkansas ~ 91.82711100000~ 6.885419999999~ 179 1228.88809999~
## 6 Californi~ 93.24101699999~ 6.24064 181 1128.76939999~
str(att5)
## Classes 'tbl_df', 'tbl' and 'data.frame': 61 obs. of 5 variables:
## $ state : chr "United States ........" "Alabama ................." "Alaska .................." "Arizona ................." ...
## $ avg_attend_pct: chr "93.078962000000004" "93.812370999999999" "89.917597000000001" "89.036961000000005" ...
## $ avg_hr_per_day: chr "6.6447000000000003" "7.0285200000000003" "6.4768800000000004" "6.4336900000000004" ...
## $ avg_day_per_yr: chr "180" "180" "180" "181" ...
## $ avg_hr_per_yr : chr "1192.6472000000001" "1266.6205" "1162.9084" "1159.1143999999999" ...Ex7: Cleaning up extra characters
# state names are all stored as the same number of characters,
# with periods padding the ends of the shorter states.
# be careful
# . is a special character in the language of regular expressions (a.k.a. regex)
# To specify that you want to remove periods => "\\." => "escape" sequence.
# str_replace_all()
# 3 arguments
# 1) string vector
# 2) pattern to match
# 3) replacement string.
# Remove all periods in state column
att5$state <- str_replace_all(att5$state, "\\.", "")
# Remove white space around state names
att5$state <- str_trim(att5$state)
# View the head of att5
head(att5)
## # A tibble: 6 x 5
## state avg_attend_pct avg_hr_per_day avg_day_per_yr avg_hr_per_yr
## <chr> <chr> <chr> <chr> <chr>
## 1 United States 93.0789620000~ 6.64470000000~ 180 1192.6472000~
## 2 Alabama 93.8123709999~ 7.02852000000~ 180 1266.6205
## 3 Alaska 89.9175970000~ 6.47688000000~ 180 1162.9084
## 4 Arizona 89.0369610000~ 6.43369000000~ 181 1159.1143999~
## 5 Arkansas 91.8271110000~ 6.88541999999~ 179 1228.8880999~
## 6 California 93.2410169999~ 6.24064 181 1128.7693999~
str(att5)
## Classes 'tbl_df', 'tbl' and 'data.frame': 61 obs. of 5 variables:
## $ state : chr "United States" "Alabama" "Alaska" "Arizona" ...
## $ avg_attend_pct: chr "93.078962000000004" "93.812370999999999" "89.917597000000001" "89.036961000000005" ...
## $ avg_hr_per_day: chr "6.6447000000000003" "7.0285200000000003" "6.4768800000000004" "6.4336900000000004" ...
## $ avg_day_per_yr: chr "180" "180" "180" "181" ...
## $ avg_hr_per_yr : chr "1192.6472000000001" "1266.6205" "1162.9084" "1159.1143999999999" ...Ex8: Some final type conversions
# dplyr package offers an efficient method for applying a function to many columns at once
library(dplyr)
# convert the values in certain variables to numerics (instead of factors)
# Change columns to numeric
example <- mutate_each(att5, funs(as.numeric), -state)
## `mutate_each()` is deprecated.
## Use `mutate_all()`, `mutate_at()` or `mutate_if()` instead.
## To map `funs` over a selection of variables, use `mutate_at()`
# Define vector containing numerical columns: cols
cols <- 2:ncol(att5)
# contains indices of columns containing numerical data
# only column without numerical data is column 1
# Use sapply to coerce cols to numeric
att5[, cols] <- sapply(att5[, cols], as.numeric)
# single bracket subsetting
head(att5)
## # A tibble: 6 x 5
## state avg_attend_pct avg_hr_per_day avg_day_per_yr avg_hr_per_yr
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 United States 93.1 6.64 180 1193.
## 2 Alabama 93.8 7.03 180 1267.
## 3 Alaska 89.9 6.48 180 1163.
## 4 Arizona 89.0 6.43 181 1159.
## 5 Arkansas 91.8 6.89 179 1229.
## 6 California 93.2 6.24 181 1129.
str(att5)
## Classes 'tbl_df', 'tbl' and 'data.frame': 61 obs. of 5 variables:
## $ state : chr "United States" "Alabama" "Alaska" "Arizona" ...
## $ avg_attend_pct: num 93.1 93.8 89.9 89 91.8 ...
## $ avg_hr_per_day: num 6.64 7.03 6.48 6.43 6.89 ...
## $ avg_day_per_yr: num 180 180 180 181 179 181 NA 171 181 181 ...
## $ avg_hr_per_yr : num 1193 1267 1163 1159 1229 ...