Wrangling, String Processing & Date/Time
packages = c(
"dplyr","ggplot2","stringr", "dslabs", "readr", "tidyr", "purrr",
"lubridate", "rvest"
)
existing = as.character(installed.packages()[,1])
for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)rm(list=ls(all=T))
Sys.setlocale("LC_ALL","C")[1] "C"options(digits=4, scipen=12)
library(rvest)
library(readr)
library(dplyr)
library(ggplot2)
library(stringr)
library(lubridate)
library(tidyr)
library(dslabs)Tidy Data
Web Scraping
library(rvest)
url = "https://en.wikipedia.org/wiki/Murder_in_the_United_States_by_state"
h = read_html(url)
tab = html_nodes(h, "table")[[2]] %>%
html_table %>%
setNames(c(
"state","population","total","murders","gun_murders",
"gun_ownersjip","total_rate","murder_rate","gun_murder_rate"))A. String Processing Overview
B. String Processing Part 1
B1. String Parsing
Q1: Which of the following is NOT an application of string parsing?
- Formatting numbers and characters so they can easily be displayed in deliverables like papers and presentations.
B2. Defining Strings: Single and Double Quotes and How to Escape
Q1: Which of the following commands would not give you an error in R?
cat(" LeBron James is 6'8\" ") LeBron James is 6'8" B3. stringr Package
Q1: Which of the following are advantages of the stringr package over string processing functions in base R? Select all that apply.
- Functions in stringr all start with “str_”, which makes them easy to look up using autocomplete.
- Stringr functions work better with pipes.
- he order of arguments is more consistent in stringr functions than in base R.
B4. Case Study 1: US Murders Data
sapply(tab, str_detect, ",") %>% colSums state population total murders
0 51 3 2
gun_murders gun_ownersjip total_rate murder_rate
1 0 0 0
gun_murder_rate
0 # 向量是同一種資料的集合(欄),可以是字串向量,數值向量,邏輯向量等等...
# sapply對一個collcetion(即tab,tab是一個資料框)做detect,回傳一個布林矩陣,再算成colSumstab2 = tab %>% mutate_at(2:3, parse_number)
# mutatae是對某東西做轉換 # parse可以消除逗號並將字串轉回數字
sapply(tab2, str_detect, ",") %>% colSums state population total murders
0 0 0 2
gun_murders gun_ownersjip total_rate murder_rate
1 0 0 0
gun_murder_rate
0 Q1: You have a dataframe of monthly sales and profits in R
dat = read.table("data/sales.txt", header=T, sep="", stringsAsFactors=F)
dat Month Sales Profit
1 January $128,568 $16,234
2 February $109,523 $12,876
3 March $115,468 $17,920
4 April $122,274 $15,825
5 May $117,921 $15,437Which of the following commands could convert the sales and profits columns to numeric? Select all that apply.
dat %>% mutate_at(2:3, parse_number) Month Sales Profit
1 January 128568 16234
2 February 109523 12876
3 March 115468 17920
4 April 122274 15825
5 May 117921 15437dat %>% mutate_at(2:3, funs(str_replace_all(., c("\\$|,"), ""))) Month Sales Profit
1 January 128568 16234
2 February 109523 12876
3 March 115468 17920
4 April 122274 15825
5 May 117921 15437dat %>% mutate_at(2:3, str_replace_all, "\\$|,", "") Month Sales Profit
1 January 128568 16234
2 February 109523 12876
3 March 115468 17920
4 April 122274 15825
5 May 117921 15437# 對$或,做replace,變成沒有東西("":空集合)dat %>% mutate_all(2:3, parse_number)
# mutate_all是將整個資料框都做過一次,但因為第一欄是月份沒有逗號,會失敗dat$Profit <- str_replace_all(dat$Profit, c("\\$|,"), "")
dat$Sales <- parse_number(dat$Sales)
dat Month Sales Profit
1 January 128568 16234
2 February 109523 12876
3 March 115468 17920
4 April 122274 15825
5 May 117921 15437C. String Processing Part 2
C1. Case Study2: Reported Heights
library(dslabs)
data(reported_heights)
reported_heights %>% head time_stamp sex height
1 2014-09-02 13:40:36 Male 75
2 2014-09-02 13:46:59 Male 70
3 2014-09-02 13:59:20 Male 68
4 2014-09-02 14:51:53 Male 74
5 2014-09-02 15:16:15 Male 61
6 2014-09-02 15:16:16 Female 65reported_heights %>%
mutate(new_height = as.numeric(height)) %>%
filter(is.na(new_height)) %>%
getElement("height")NAs introduced by coercion [1] "5' 4\"" "165cm"
[3] "5'7" ">9000"
[5] "5'7\"" "5'3\""
[7] "5 feet and 8.11 inches" "5'11"
[9] "5'9''" "5'10''"
[11] "5,3" "6'"
[13] "6,8" "5' 10"
[15] "Five foot eight inches" "5'5\""
[17] "5'2\"" "5,4"
[19] "5'3" "5'10''"
[21] "5'3''" "5'7''"
[23] "5'12" "2'33"
[25] "5'11" "5'3\""
[27] "5,8" "5'6''"
[29] "5'4" "1,70"
[31] "5'7.5''" "5'7.5''"
[33] "5'2\"" "5' 7.78\""
[35] "yyy" "5'5"
[37] "5'8" "5'6"
[39] "5 feet 7inches" "6*12"
[41] "5 .11" "5 11"
[43] "5'4" "5'8\""
[45] "5'5" "5'7"
[47] "5'6" "5'11\""
[49] "5'7\"" "5'7"
[51] "5'8" "5' 11\""
[53] "6'1\"" "69\""
[55] "5' 7\"" "5'10''"
[57] "5'10" "5'10"
[59] "5ft 9 inches" "5 ft 9 inches"
[61] "5'2" "5'11"
[63] "5'11''" "5'8\""
[65] "708,661" "5 feet 6 inches"
[67] "5'10''" "5'8"
[69] "6'3\"" "649,606"
[71] "728,346" "6 04"
[73] "5'9" "5'5''"
[75] "5'7\"" "6'4\""
[77] "5'4" "170 cm"
[79] "7,283,465" "5'6"
[81] "5'6" # mutate是加一個新的變數 # 轉不成功的會變成NAreported_heights %>%
filter(is.na(as.numeric(height))) %>%
.$heightNAs introduced by coercion [1] "5' 4\"" "165cm"
[3] "5'7" ">9000"
[5] "5'7\"" "5'3\""
[7] "5 feet and 8.11 inches" "5'11"
[9] "5'9''" "5'10''"
[11] "5,3" "6'"
[13] "6,8" "5' 10"
[15] "Five foot eight inches" "5'5\""
[17] "5'2\"" "5,4"
[19] "5'3" "5'10''"
[21] "5'3''" "5'7''"
[23] "5'12" "2'33"
[25] "5'11" "5'3\""
[27] "5,8" "5'6''"
[29] "5'4" "1,70"
[31] "5'7.5''" "5'7.5''"
[33] "5'2\"" "5' 7.78\""
[35] "yyy" "5'5"
[37] "5'8" "5'6"
[39] "5 feet 7inches" "6*12"
[41] "5 .11" "5 11"
[43] "5'4" "5'8\""
[45] "5'5" "5'7"
[47] "5'6" "5'11\""
[49] "5'7\"" "5'7"
[51] "5'8" "5' 11\""
[53] "6'1\"" "69\""
[55] "5' 7\"" "5'10''"
[57] "5'10" "5'10"
[59] "5ft 9 inches" "5 ft 9 inches"
[61] "5'2" "5'11"
[63] "5'11''" "5'8\""
[65] "708,661" "5 feet 6 inches"
[67] "5'10''" "5'8"
[69] "6'3\"" "649,606"
[71] "728,346" "6 04"
[73] "5'9" "5'5''"
[75] "5'7\"" "6'4\""
[77] "5'4" "170 cm"
[79] "7,283,465" "5'6"
[81] "5'6" reported_heights$height %>% .[is.na(as.numeric(.))]NAs introduced by coercion [1] "5' 4\"" "165cm"
[3] "5'7" ">9000"
[5] "5'7\"" "5'3\""
[7] "5 feet and 8.11 inches" "5'11"
[9] "5'9''" "5'10''"
[11] "5,3" "6'"
[13] "6,8" "5' 10"
[15] "Five foot eight inches" "5'5\""
[17] "5'2\"" "5,4"
[19] "5'3" "5'10''"
[21] "5'3''" "5'7''"
[23] "5'12" "2'33"
[25] "5'11" "5'3\""
[27] "5,8" "5'6''"
[29] "5'4" "1,70"
[31] "5'7.5''" "5'7.5''"
[33] "5'2\"" "5' 7.78\""
[35] "yyy" "5'5"
[37] "5'8" "5'6"
[39] "5 feet 7inches" "6*12"
[41] "5 .11" "5 11"
[43] "5'4" "5'8\""
[45] "5'5" "5'7"
[47] "5'6" "5'11\""
[49] "5'7\"" "5'7"
[51] "5'8" "5' 11\""
[53] "6'1\"" "69\""
[55] "5' 7\"" "5'10''"
[57] "5'10" "5'10"
[59] "5ft 9 inches" "5 ft 9 inches"
[61] "5'2" "5'11"
[63] "5'11''" "5'8\""
[65] "708,661" "5 feet 6 inches"
[67] "5'10''" "5'8"
[69] "6'3\"" "649,606"
[71] "728,346" "6 04"
[73] "5'9" "5'5''"
[75] "5'7\"" "6'4\""
[77] "5'4" "170 cm"
[79] "7,283,465" "5'6"
[81] "5'6" not_inches <- function(x, smallest = 50, tallest = 84) {
inches <- suppressWarnings(as.numeric(x))
ind <- is.na(inches) | inches < smallest | inches > tallest
ind}
# 把英吋的大小控制在50-84的合理範圍problems = reported_heights$height %>% .[not_inches(.)]
problems [1] "6" "5' 4\""
[3] "5.3" "165cm"
[5] "511" "6"
[7] "2" "5'7"
[9] ">9000" "5'7\""
[11] "5'3\"" "5 feet and 8.11 inches"
[13] "5.25" "5'11"
[15] "5.5" "11111"
[17] "5'9''" "6"
[19] "6.5" "150"
[21] "5'10''" "103.2"
[23] "5.8" "19"
[25] "5" "5.6"
[27] "175" "177"
[29] "300" "5,3"
[31] "6'" "6"
[33] "5.9" "6,8"
[35] "5' 10" "5.5"
[37] "178" "163"
[39] "6.2" "175"
[41] "Five foot eight inches" "6.2"
[43] "5.8" "5.1"
[45] "178" "165"
[47] "5.11" "5'5\""
[49] "165" "180"
[51] "5'2\"" "5.75"
[53] "169" "5,4"
[55] "7" "5.4"
[57] "157" "6.1"
[59] "169" "5'3"
[61] "5.6" "214"
[63] "183" "5.6"
[65] "6" "162"
[67] "178" "180"
[69] "5'10''" "170"
[71] "5'3''" "178"
[73] "0.7" "190"
[75] "5.4" "184"
[77] "5'7''" "5.9"
[79] "5'12" "5.6"
[81] "5.6" "184"
[83] "6" "167"
[85] "2'33" "5'11"
[87] "5'3\"" "5.5"
[89] "5.2" "180"
[91] "5.5" "5.5"
[93] "6.5" "5,8"
[95] "180" "183"
[97] "170" "5'6''"
[99] "172" "612"
[101] "5.11" "168"
[103] "5'4" "1,70"
[105] "172" "87"
[107] "5.5" "176"
[109] "5'7.5''" "5'7.5''"
[111] "111" "5'2\""
[113] "173" "174"
[115] "176" "175"
[117] "5' 7.78\"" "6.7"
[119] "12" "6"
[121] "5.1" "5.6"
[123] "5.5" "yyy"
[125] "5.2" "5'5"
[127] "5'8" "5'6"
[129] "5 feet 7inches" "89"
[131] "5.6" "5.7"
[133] "183" "172"
[135] "34" "25"
[137] "6" "5.9"
[139] "168" "6.5"
[141] "170" "175"
[143] "6" "22"
[145] "5.11" "684"
[147] "6" "1"
[149] "1" "6*12"
[151] "5 .11" "87"
[153] "162" "165"
[155] "184" "6"
[157] "173" "1.6"
[159] "172" "170"
[161] "5.7" "5.5"
[163] "174" "170"
[165] "160" "120"
[167] "120" "23"
[169] "192" "5 11"
[171] "167" "150"
[173] "1.7" "174"
[175] "5.8" "6"
[177] "5'4" "5'8\""
[179] "5'5" "5.8"
[181] "5.1" "5.11"
[183] "5.7" "5'7"
[185] "5'6" "5'11\""
[187] "5'7\"" "5'7"
[189] "172" "5'8"
[191] "180" "5' 11\""
[193] "5" "180"
[195] "180" "6'1\""
[197] "5.9" "5.2"
[199] "5.5" "69\""
[201] "5' 7\"" "5'10''"
[203] "5.51" "5'10"
[205] "5'10" "5ft 9 inches"
[207] "5 ft 9 inches" "5'2"
[209] "5'11" "5.8"
[211] "5.7" "167"
[213] "168" "6"
[215] "6.1" "5'11''"
[217] "5.69" "178"
[219] "182" "164"
[221] "5'8\"" "185"
[223] "6" "86"
[225] "5.7" "708,661"
[227] "5.25" "5.5"
[229] "5 feet 6 inches" "5'10''"
[231] "172" "6"
[233] "5'8" "160"
[235] "6'3\"" "649,606"
[237] "10000" "5.1"
[239] "152" "1"
[241] "180" "728,346"
[243] "175" "158"
[245] "173" "164"
[247] "6 04" "169"
[249] "0" "185"
[251] "168" "5'9"
[253] "169" "5'5''"
[255] "174" "6.3"
[257] "179" "5'7\""
[259] "5.5" "6"
[261] "6" "170"
[263] "6" "172"
[265] "158" "100"
[267] "159" "190"
[269] "5.7" "170"
[271] "158" "6'4\""
[273] "180" "5.57"
[275] "5'4" "210"
[277] "88" "6"
[279] "162" "170 cm"
[281] "5.7" "170"
[283] "157" "186"
[285] "170" "7,283,465"
[287] "5" "5"
[289] "34" "161"
[291] "5'6" "5'6" # 對一個vector做篩選可以用.[],"."指向前面的東西(在[]外面再包一個{}比較保險)
# 等於reported_heights$height[ not_inches(reported_heights$height) ]的縮寫
# 這是一個string factor str_subset(problems, "cm|inches")[1] "165cm" "5 feet and 8.11 inches"
[3] "Five foot eight inches" "5 feet 7inches"
[5] "5ft 9 inches" "5 ft 9 inches"
[7] "5 feet 6 inches" "170 cm" str_subset(problems, "cm|inches") %>% str_extract("cm|inches")[1] "cm" "inches" "inches" "inches" "inches" "inches" "inches"
[8] "cm" str_extract(problems, "cm|inches") [1] NA NA NA "cm" NA NA NA
[8] NA NA NA NA "inches" NA NA
[15] NA NA NA NA NA NA NA
[22] NA NA NA NA NA NA NA
[29] NA NA NA NA NA NA NA
[36] NA NA NA NA NA "inches" NA
[43] NA NA NA NA NA NA NA
[50] NA NA NA NA NA NA NA
[57] NA NA NA NA NA NA NA
[64] NA NA NA NA NA NA NA
[71] NA NA NA NA NA NA NA
[78] NA NA NA NA NA NA NA
[85] NA NA NA NA NA NA NA
[92] NA NA NA NA NA NA NA
[99] NA NA NA NA NA NA NA
[106] NA NA NA NA NA NA NA
[113] NA NA NA NA NA NA NA
[120] NA NA NA NA NA NA NA
[127] NA NA "inches" NA NA NA NA
[134] NA NA NA NA NA NA NA
[141] NA NA NA NA NA NA NA
[148] NA NA NA NA NA NA NA
[155] NA NA NA NA NA NA NA
[162] NA NA NA NA NA NA NA
[169] NA NA NA NA NA NA NA
[176] NA NA NA NA NA NA NA
[183] NA NA NA NA NA NA NA
[190] NA NA NA NA NA NA NA
[197] NA NA NA NA NA NA NA
[204] NA NA "inches" "inches" NA NA NA
[211] NA NA NA NA NA NA NA
[218] NA NA NA NA NA NA NA
[225] NA NA NA NA "inches" NA NA
[232] NA NA NA NA NA NA NA
[239] NA NA NA NA NA NA NA
[246] NA NA NA NA NA NA NA
[253] NA NA NA NA NA NA NA
[260] NA NA NA NA NA NA NA
[267] NA NA NA NA NA NA NA
[274] NA NA NA NA NA NA "cm"
[281] NA NA NA NA NA NA NA
[288] NA NA NA NA NA Q1: In the video, we use the function not_inches to identify heights that were incorrectly entered
not_inches <- function(x, smallest = 50, tallest = 84) {
inches <- suppressWarnings(as.numeric(x))
ind <- is.na(inches) | inches < smallest | inches > tallest
ind
}In this function, what TWO types of values are identified as not being correctly formatted in inches?
- Values that result in NA’s when converted to numeric
- Values less than 50 inches or greater than 84 inches
Q2: Which of the following arguments, when passed to the function not_inches, would return the vector c(FALSE)?
c(70) %>% not_inches[1] FALSEQ3: Our function not_inches returns the object ind. Which answer correctly describes ind?
indis a logical vector ofTRUEandFALSE, equal in length to the vectorx(in the arguments list).TRUEindicates that a height entry is incorrectly formatted.
C2. Regex(定義pattern的語言)
Q1: Given the following code
s = c("70" ,"5 ft", "4'11", "", ".", "Six feet")What pattern vector yields the following result?
pattern = "\\d|ft"
# 對應阿拉伯數字:\d,但由於在R裡面要在""裡打\,就要打兩次 ex.cat("\\") = "\"
# \' 是 ' \" 是 " \. 是 .
str_subset(s, pattern)[1] "70" "5 ft" "4'11"C3. Character Classes, Anchors, and Qualifiers
Character Classes - []
yes = as.character(4:7)
no = as.character(1:3)
str_detect(c(yes,no), "[4-7]")[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE# [4-7]是4到7 # [A-Z]是全部大寫字母 # [a-zA-Z]是所有英文字母
# []裡面都是character,所以[1-20]是0,1,2而非1~20
# [0-9] == \\dAnchors - ^ and $ (^是開始/$是結束的條件)
yes = c("1","5","9")
no = c("12","123"," 1","a4","b")
str_detect(c(yes,no), "^\\d$")[1] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE# 選擇開始第一個字是阿拉伯數字就結束
# str_detect(c(yes,no), "\\d$") 會變成只要阿拉伯數字結尾就好Qualifiers - {} ({}前面的字元有幾個)
yes = c("1","5","9","12")
no = c("123","a4","b")
str_detect(c(yes,no), "^\\d{1,2}$")[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE# 開始和結尾是阿拉伯數字,可以有1~2個數字Pattern of Feets & Inches
pattern = "^[4-7]'\\d{1,2}\"$"
yes = c("5'7\"", "6'2\"", "5'12\"")
no = c("6,2\"", "6.2\"", "I am 5'11\"", "3'2\"", "64")
str_detect(c(yes,no), pattern)[1] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE# 開頭要是4-7,加個',再來1~2個阿拉伯數字,加個",結束 ex. 5'7"Q1: You enter the following set of commands into your R console. What is your printed result?
animals <- c("cat", "puppy", "Moose", "MONKEY")
pattern <- "[a-z]"
str_detect(animals, pattern)[1] TRUE TRUE TRUE FALSE# 要有小寫字母Q2: You enter the following set of commands into your R console. What is your printed result?
animals <- c("cat", "puppy", "Moose", "MONKEY")
pattern <- "[A-Z]$"
str_detect(animals, pattern)[1] FALSE FALSE FALSE TRUE# 結尾要是大寫字母Q3: You enter the following set of commands into your R console. What is your printed result?
animals <- c("cat", "puppy", "Moose", "MONKEY")
pattern <- "[a-z]{4,5}"
str_detect(animals, pattern)[1] FALSE TRUE TRUE FALSE# 要包含4到5個小寫字母C4. Search and Replace with Regex
Inital Pattern
pattern = "^[4-7]'\\d{1,2}$"
str_subset(problems, pattern) # 24 [1] "5'7" "5'11" "5'3" "5'12" "5'11" "5'4" "5'5" "5'8" "5'6"
[10] "5'4" "5'5" "5'7" "5'6" "5'7" "5'8" "5'10" "5'10" "5'2"
[19] "5'11" "5'8" "5'9" "5'4" "5'6" "5'6" Replace Feet and Inches
pattern = "^[4-7]'\\d{1,2}$"
problems %>%
str_replace("feet|ft|foot","'") %>%
str_replace("inches|in|''|\"","") %>%
str_subset(pattern) [1] "5'7" "5'7" "5'3" "5'11" "5'9" "5'10" "5'5" "5'2" "5'3"
[10] "5'10" "5'3" "5'7" "5'12" "5'11" "5'3" "5'6" "5'4" "5'2"
[19] "5'5" "5'8" "5'6" "5'4" "5'8" "5'5" "5'7" "5'6" "5'11"
[28] "5'7" "5'7" "5'8" "6'1" "5'10" "5'10" "5'10" "5'2" "5'11"
[37] "5'11" "5'8" "5'10" "5'8" "6'3" "5'9" "5'5" "5'7" "6'4"
[46] "5'4" "5'6" "5'6" # str_detect(pattern) %>% sum # 48 More Qualifiers
*: 0 or more+: 1 or more?: 0 or 1
yes = c("AB","A1B","A11B","A111B","A1111B")
no = c("A2B","A21B")
str_detect(c(yes,no), "A1*B")[1] TRUE TRUE TRUE TRUE TRUE FALSE FALSE# 在A後面可以有0或更多的1Space - \\s
pattern = "^[4-7]\\s*'\\s*\\d{1,2}$"
problems %>%
str_replace("feet|ft|foot","'") %>%
str_replace("inches|in|''|\"","") %>%
str_detect(pattern) %>%
sum # 53 [1] 53# 空白鍵: \s , 在R就要: \\s
# 用星號代表不限空白鍵的多寡Q1: Given the following code, which TWO pattern vectors would yield the following result?
animals <- c("moose", "monkey", "meerkat", "mountain lion")
pattern = c("mo*","mo?","mo+","moo*")
sapply(pattern, function(p) str_detect(animals, p)) %>% t [,1] [,2] [,3] [,4]
mo* TRUE TRUE TRUE TRUE
mo? TRUE TRUE TRUE TRUE
mo+ TRUE TRUE FALSE TRUE
moo* TRUE TRUE FALSE TRUE# moo*是在mo後面有0或更多的oQ2: You are working on some data from different universities. You have the following vector
schools = c(
"U. Kentucky","Univ New Hampshire","Univ. of Massachusetts",
"University Georgia","U California","California State University"
)You want to clean this data to match the full names of each university. What of the following commands could accomplish this?
schools %>%
str_replace("^Univ\\.?\\s|^U\\.?\\s", "University ") %>%
str_replace("^University of |^University ", "University of ")[1] "University of Kentucky" "University of New Hampshire"
[3] "University of Massachusetts" "University of Georgia"
[5] "University of California" "California State University"C5. Groups with Regex
Define Groups ()
pattern_no_group = "^[4-7],\\d*$"
pattern_group = "^([4-7]),(\\d*)$"
yes = c("5,9","5,11","6,","6,1")
no = c("5'9",",","2,8","6.1.1")
s = c(yes, no)
# 括號把他們分groupGroups do not affect pattern detection
str_detect(s, pattern_no_group)[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSEstr_detect(s, pattern_group)[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSEThe difference between
str_match()str_extarct()str_subset()str_detect()
str_match(s, pattern_group) [,1] [,2] [,3]
[1,] "5,9" "5" "9"
[2,] "5,11" "5" "11"
[3,] "6," "6" ""
[4,] "6,1" "6" "1"
[5,] NA NA NA
[6,] NA NA NA
[7,] NA NA NA
[8,] NA NA NA str_extract(s, pattern_group)[1] "5,9" "5,11" "6," "6,1" NA NA NA NA str_subset(s, pattern_group)[1] "5,9" "5,11" "6," "6,1" str_detect(s, pattern_group)[1] TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSEReplace with Group
pattern = "^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$"
str_subset(problems, pattern) %>% str_match(pattern) [,1] [,2] [,3]
[1,] "5.3" "5" "3"
[2,] "5.25" "5" "25"
[3,] "5.5" "5" "5"
[4,] "6.5" "6" "5"
[5,] "5.8" "5" "8"
[6,] "5.6" "5" "6"
[7,] "5,3" "5" "3"
[8,] "5.9" "5" "9"
[9,] "6,8" "6" "8"
[10,] "5.5" "5" "5"
[11,] "6.2" "6" "2"
[12,] "6.2" "6" "2"
[13,] "5.8" "5" "8"
[14,] "5.1" "5" "1"
[15,] "5.11" "5" "11"
[16,] "5.75" "5" "75"
[17,] "5,4" "5" "4"
[18,] "5.4" "5" "4"
[19,] "6.1" "6" "1"
[20,] "5.6" "5" "6"
[21,] "5.6" "5" "6"
[22,] "5.4" "5" "4"
[23,] "5.9" "5" "9"
[24,] "5.6" "5" "6"
[25,] "5.6" "5" "6"
[26,] "5.5" "5" "5"
[27,] "5.2" "5" "2"
[28,] "5.5" "5" "5"
[29,] "5.5" "5" "5"
[30,] "6.5" "6" "5"
[31,] "5,8" "5" "8"
[32,] "5.11" "5" "11"
[33,] "5.5" "5" "5"
[34,] "6.7" "6" "7"
[35,] "5.1" "5" "1"
[36,] "5.6" "5" "6"
[37,] "5.5" "5" "5"
[38,] "5.2" "5" "2"
[39,] "5.6" "5" "6"
[40,] "5.7" "5" "7"
[41,] "5.9" "5" "9"
[42,] "6.5" "6" "5"
[43,] "5.11" "5" "11"
[44,] "5 .11" "5" "11"
[45,] "5.7" "5" "7"
[46,] "5.5" "5" "5"
[47,] "5 11" "5" "11"
[48,] "5.8" "5" "8"
[49,] "5.8" "5" "8"
[50,] "5.1" "5" "1"
[51,] "5.11" "5" "11"
[52,] "5.7" "5" "7"
[53,] "5.9" "5" "9"
[54,] "5.2" "5" "2"
[55,] "5.5" "5" "5"
[56,] "5.51" "5" "51"
[57,] "5.8" "5" "8"
[58,] "5.7" "5" "7"
[59,] "6.1" "6" "1"
[60,] "5.69" "5" "69"
[61,] "5.7" "5" "7"
[62,] "5.25" "5" "25"
[63,] "5.5" "5" "5"
[64,] "5.1" "5" "1"
[65,] "6 04" "6" "04"
[66,] "6.3" "6" "3"
[67,] "5.5" "5" "5"
[68,] "5.7" "5" "7"
[69,] "5.57" "5" "57"
[70,] "5.7" "5" "7" str_subset(problems, pattern) %>%
str_replace(pattern, "\\1'\\2") [1] "5'3" "5'25" "5'5" "6'5" "5'8" "5'6" "5'3" "5'9" "6'8"
[10] "5'5" "6'2" "6'2" "5'8" "5'1" "5'11" "5'75" "5'4" "5'4"
[19] "6'1" "5'6" "5'6" "5'4" "5'9" "5'6" "5'6" "5'5" "5'2"
[28] "5'5" "5'5" "6'5" "5'8" "5'11" "5'5" "6'7" "5'1" "5'6"
[37] "5'5" "5'2" "5'6" "5'7" "5'9" "6'5" "5'11" "5'11" "5'7"
[46] "5'5" "5'11" "5'8" "5'8" "5'1" "5'11" "5'7" "5'9" "5'2"
[55] "5'5" "5'51" "5'8" "5'7" "6'1" "5'69" "5'7" "5'25" "5'5"
[64] "5'1" "6'04" "6'3" "5'5" "5'7" "5'57" "5'7" # \\1代表group1;\\2代表group2
# str_replace(pattern, "\\1 feets and \\2 inches")
# 挖出來再重新定義Q1: Rather than using the pattern_with_groups vector from the video, you accidentally write in the following code. What is your result?
pattern_w_groups = "^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$"
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
pattern_with_groups <- "^([4-7])[,\\.](\\d*)$"
str_replace(problems1, pattern_with_groups, "\\1'\\2")[1] "5'3" "5'5" "6 1" "5 .11" "5, 12"Q2: You notice your mistake and correct your pattern regex to the following What is your result?
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
pattern_with_groups <- "^([4-7])[,\\.\\s](\\d*)$"
str_replace(problems1, pattern_with_groups, "\\1'\\2")[1] "5'3" "5'5" "6'1" "5 .11" "5, 12"I think what it intends to do is …
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
pattern_with_groups <- "^([4-7])\\s*[,\\.\\s]\\s*(\\d*)$"
str_replace(problems1, pattern_with_groups, "\\1'\\2")[1] "5'3" "5'5" "6'1" "5'11" "5'12"C6. Testing and Improving
converted <- problems %>%
str_replace("feet|foot|ft", "'") %>%
str_replace("inches|in|''|\"", "") %>%
str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
index <- str_detect(converted, pattern)
mean(index) # 0.42123[1] 0.4212converted[!index] [1] "6" "165cm" "511" "6"
[5] "2" ">9000" "5 ' and 8.11 " "11111"
[9] "6" "150" "103.2" "19"
[13] "5" "175" "177" "300"
[17] "6'" "6" "178" "163"
[21] "175" "Five ' eight " "178" "165"
[25] "165" "180" "169" "7"
[29] "157" "169" "214" "183"
[33] "6" "162" "178" "180"
[37] "170" "178" "0.7" "190"
[41] "184" "184" "6" "167"
[45] "2'33" "180" "180" "183"
[49] "170" "172" "612" "168"
[53] "1,70" "172" "87" "176"
[57] "5'7.5" "5'7.5" "111" "173"
[61] "174" "176" "175" "5' 7.78"
[65] "12" "6" "yyy" "89"
[69] "183" "172" "34" "25"
[73] "6" "168" "170" "175"
[77] "6" "22" "684" "6"
[81] "1" "1" "6*12" "87"
[85] "162" "165" "184" "6"
[89] "173" "1.6" "172" "170"
[93] "174" "170" "160" "120"
[97] "120" "23" "192" "167"
[101] "150" "1.7" "174" "6"
[105] "172" "180" "5" "180"
[109] "180" "69" "5' 9 " "5 ' 9 "
[113] "167" "168" "6" "178"
[117] "182" "164" "185" "6"
[121] "86" "708,661" "5 ' 6 " "172"
[125] "6" "160" "649,606" "10000"
[129] "152" "1" "180" "728,346"
[133] "175" "158" "173" "164"
[137] "169" "0" "185" "168"
[141] "169" "174" "179" "6"
[145] "6" "170" "6" "172"
[149] "158" "100" "159" "190"
[153] "170" "158" "180" "210"
[157] "88" "6" "162" "170 cm"
[161] "170" "157" "186" "170"
[165] "7,283,465" "5" "5" "34"
[169] "161" Q1: In our example, we use the following code to detect height entries that do not match our pattern of x’y”.
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
converted1 <- problems1 %>%
str_replace("feet|foot|ft", "'") %>%
str_replace("inches|in|''|\"", "") %>%
str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")
pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
index <- str_detect(converted1, pattern)
converted1[!index]Which answer best describes the differences between the regex string we use as an argument in
str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")
And the regex string in
pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"?
- The regex used in str_replace looks for either a comma, period or space between the feet and inches digits, while the pattern regex just looks for an apostrophe; the regex in str_replace allows for none or more digits to be entered as inches, while the pattern regex only allows for one or two digits.
Q2: You notice a few entries that are not being properly converted using your str_replace and str_detect code
yes <- c("5 feet 7inches")
no <- c("5ft 9 inches", "5 ft 9 inches")
s <- c(yes, no)
converted <- s %>%
str_replace("feet|foot|ft", "'") %>%
str_replace("inches|in|''|\"", "") %>%
str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")
converted[1] "5 ' 7" "5' 9 " "5 ' 9 "pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
str_detect(converted, pattern)[1] TRUE FALSE FALSEIt seems like the problem may be due to spaces around the words feet|foot|ft and inches|in. What is another way you could fix this problem?
converted <- s %>%
str_replace("\\s*feet|foot|ft\\s*", "'") %>%
str_replace("\\s*inches|in|''|\"\\s*", "") %>%
str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")
converted[1] "5' 7" "5'9" "5 '9"pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
str_detect(converted, pattern)[1] TRUE TRUE TRUED. String Processing Part 3
D2. Separate with Regex
s = c("5'10", "6'1")
tab = data.frame(x = s)
separate(tab, x, c("feet", "inches"), sep="'") feet inches
1 5 10
2 6 1extract(tab, x, c("feet", "inches"), regex="(\\d)'(\\d{1,2})") feet inches
1 5 10
2 6 1s = c("5'10", "6'1\"","5'8inches")
tab = data.frame(x = s)
separate(tab, x, c("feet", "inches"), sep="'") feet inches
1 5 10
2 6 1"
3 5 8inchesextract(tab, x, c("feet", "inches"), regex="(\\d)'(\\d{1,2})") feet inches
1 5 10
2 6 1
3 5 8D1. Using Groups and Quantifiers
** Q1:** If you use the extract code from our video, the decimal point is dropped. What modification of the code would allow you to put the decimals in a third column called “decimal”?
library(tidyr)
s <- c("5'10", "6'1\"", "5'8inches", "5'7.5")
tab <- data.frame(x = s)
rx = c("(\\d)'(\\d{1,2})(\\.)?",
"(\\d)'(\\d{1,2})(\\.\\d+)",
"(\\d)'(\\d{1,2})\\.\\d+?",
"(\\d)'(\\d{1,2})(\\.\\d+)?")
extract(tab, x, into=c("feet", "inches", "decimal"), regex=rx[4]) feet inches decimal
1 5 10 <NA>
2 6 1 <NA>
3 5 8 <NA>
4 5 7 .5D4. String Splitting
filename = system.file("extdata/murders.csv", package="dslabs")
lines = readLines(filename)
head(lines)[1] "state,abb,region,population,total"
[2] "Alabama,AL,South,4779736,135"
[3] "Alaska,AK,West,710231,19"
[4] "Arizona,AZ,West,6392017,232"
[5] "Arkansas,AR,South,2915918,93"
[6] "California,CA,West,37253956,1257" x = str_split(lines, ",", simplify=T)
head(x) [,1] [,2] [,3] [,4] [,5]
[1,] "state" "abb" "region" "population" "total"
[2,] "Alabama" "AL" "South" "4779736" "135"
[3,] "Alaska" "AK" "West" "710231" "19"
[4,] "Arizona" "AZ" "West" "6392017" "232"
[5,] "Arkansas" "AR" "South" "2915918" "93"
[6,] "California" "CA" "West" "37253956" "1257" as.data.frame(x[-1,]) %>%
setNames(x[1,]) %>%
mutate_all(parse_guess) %>%
head(10) state abb region population total
1 Alabama AL South 4779736 135
2 Alaska AK West 710231 19
3 Arizona AZ West 6392017 232
4 Arkansas AR South 2915918 93
5 California CA West 37253956 1257
6 Colorado CO West 5029196 65
7 Connecticut CT Northeast 3574097 97
8 Delaware DE South 897934 38
9 District of Columbia DC South 601723 99
10 Florida FL South 19687653 669Q1: You have the following table
schedule = data.frame(
day = c("Monday", "Tuesday"),
staff = c("Mandy, Chris and Laura", "Steve, Ruth and Frank"))
schedule day staff
1 Monday Mandy, Chris and Laura
2 Tuesday Steve, Ruth and FrankWhich two commands would properly split the text in the “Staff” column into each individual name? Check all that apply.
lapply(c(",|and", ", | and ", ",\\s|\\sand\\s", "\\s?(,|and)\\s?"),
function(r) str_split(schedule$staff, r, simplify=T))[[1]]
[,1] [,2] [,3] [,4]
[1,] "M" "y" " Chris " " Laura"
[2,] "Steve" " Ruth " " Frank" ""
[[2]]
[,1] [,2] [,3]
[1,] "Mandy" "Chris" "Laura"
[2,] "Steve" "Ruth" "Frank"
[[3]]
[,1] [,2] [,3]
[1,] "Mandy" "Chris" "Laura"
[2,] "Steve" "Ruth" "Frank"
[[4]]
[,1] [,2] [,3] [,4]
[1,] "M" "y" "Chris" "Laura"
[2,] "Steve" "Ruth" "Frank" "" Q2: What code would successfully turn your “Schedule” table into the following tidy table
schedule %>%
mutate(staff = str_split(staff, ", | and ")) %>%
unnest() day staff
1 Monday Mandy
2 Monday Chris
3 Monday Laura
4 Tuesday Steve
5 Tuesday Ruth
6 Tuesday Frank# split出來變成一個list,再用unnestD.6 Recoding
library(ggplot2)
data("gapminder")
gapminder %>% filter(region == "Caribbean") %>%
ggplot(aes(year, life_expectancy, color=country)) +
geom_line()
gapminder %>% filter(region == "Caribbean") %>%
filter(str_length(country) >= 12) %>%
distinct(country) country
1 Antigua and Barbuda
2 Dominican Republic
3 St. Vincent and the Grenadines
4 Trinidad and Tobagogapminder %>% filter(region == "Caribbean") %>%
mutate(country = recode(
country,
`Antigua and Barbuda` = "Barbuda",
`Dominican Republic` = "DR",
`St. Vincent and the Grenadines` = "St. Vincent",
`Trinidad and Tobago` = "Trinidad"
)) %>%
ggplot(aes(year, life_expectancy, color=country)) +
geom_line()
Q1: Using the gapminder data, you want to recode countries longer than 12 letters in the region Middle Africa to their abbreviations in a new column, country_short. Which code would accomplish this?
library(dslabs)
data(gapminder)
gapminder %>% filter(region == "Middle Africa") %>%
filter(nchar(as.character(country)) >= 12) %>%
select(region, country) %>% distinct() %>%
mutate(country_short = recode(country,
"Central African Republic" = "CAR",
"Congo, Dem. Rep." = "DRC",
"Equatorial Guinea" = "Eq. Guinea"
) ) region country country_short
1 Middle Africa Central African Republic CAR
2 Middle Africa Congo, Dem. Rep. DRC
3 Middle Africa Equatorial Guinea Eq. GuineaE. Date, Times and Text Mining
E1. Dates and Times
Q1: Which of the following is the standard ISO 8601 format for dates?
- YYYY-MM-DD
Q2: Which of the following commands could convert this string into the correct date format?
library(lubridate)
dates <- c("09-01-02", "01-12-07", "02-03-04")
ymd(dates)[1] "2009-01-02" "2001-12-07" "2002-03-04"mdy(dates)[1] "2002-09-01" "2007-01-12" "2004-02-03"dmy(dates)[1] "2002-01-09" "2007-12-01" "2004-03-02"- It is impossible to know which format is correct without additional information.
---
title: "Wrangling, String Processing & Date/Time"
output: html_notebook
---

<br>

```{r}
packages = c(
  "dplyr","ggplot2","stringr", "dslabs", "readr", "tidyr", "purrr",
  "lubridate", "rvest"
  )
existing = as.character(installed.packages()[,1])
for(pkg in packages[!(packages %in% existing)]) install.packages(pkg)
```

```{r echo=T, message=F, cache=F, warning=F}
rm(list=ls(all=T))
Sys.setlocale("LC_ALL","C")
options(digits=4, scipen=12)
library(rvest)
library(readr)
library(dplyr)
library(ggplot2)
library(stringr)
library(lubridate)
library(tidyr)
library(dslabs)
```

- - -

### Tidy Data

##### Web Scraping
```{r}
library(rvest)
url = "https://en.wikipedia.org/wiki/Murder_in_the_United_States_by_state"
h = read_html(url)

tab = html_nodes(h, "table")[[2]] %>%
  html_table %>% 
  setNames(c(
    "state","population","total","murders","gun_murders",
    "gun_ownersjip","total_rate","murder_rate","gun_murder_rate"))
```

- - -

### A. String Processing Overview

- - -

### B. String Processing Part 1 

##### B1. String Parsing

**Q1:** _Which of the following is NOT an application of string parsing?_

+ Formatting numbers and characters so they can easily be displayed in deliverables like papers and presentations.

##### B2. Defining Strings: Single and Double Quotes and How to Escape

**Q1:** _Which of the following commands would not give you an error in R?_
```{r}
cat(" LeBron James is 6'8\" ")
```

##### B3. `stringr` Package

**Q1:** _Which of the following are advantages of the stringr package over string processing functions in base R? Select all that apply._

+ Functions in stringr all start with “str_”, which makes them easy to look up using autocomplete.
+ Stringr functions work better with pipes.
+ he order of arguments is more consistent in stringr functions than in base R.

##### B4. Case Study 1: US Murders Data

```{r}
sapply(tab, str_detect, ",") %>% colSums
# 向量是同一種資料的集合(欄),可以是字串向量,數值向量,邏輯向量等等...
# sapply對一個collcetion(即tab,tab是一個資料框)做detect,回傳一個布林矩陣,再算成colSums
```

```{r}
tab2 = tab %>% mutate_at(2:3, parse_number) 
# mutatae是對某東西做轉換 # parse可以消除逗號並將字串轉回數字
sapply(tab2, str_detect, ",") %>% colSums
```

**Q1:** You have a dataframe of monthly sales and profits in R
```{r}
dat = read.table("data/sales.txt", header=T, sep="", stringsAsFactors=F)
dat
```

_Which of the following commands could convert the sales and profits columns to numeric? Select all that apply._
```{r}
dat %>% mutate_at(2:3, parse_number)
```

```{r}
dat %>% mutate_at(2:3, funs(str_replace_all(., c("\\$|,"), "")))
```

```{r}
dat %>% mutate_at(2:3, str_replace_all, "\\$|,", "")
# 對$或,做replace,變成沒有東西("":空集合)
```

```{r eval=F}
dat %>% mutate_all(2:3, parse_number)
# mutate_all是將整個資料框都做過一次,但因為第一欄是月份沒有逗號,會失敗
```

```{r}
dat$Profit <- str_replace_all(dat$Profit, c("\\$|,"), "") 
dat$Sales <- parse_number(dat$Sales) 
dat
```

- - -

### C. String Processing Part 2

##### C1. Case Study2: Reported Heights

```{r}
library(dslabs)
data(reported_heights)
reported_heights %>% head
```

```{r}
reported_heights %>%
  mutate(new_height = as.numeric(height)) %>% 
  filter(is.na(new_height)) %>% 
  getElement("height")
# mutate是加一個新的變數 # 轉不成功的會變成NA
```

```{r}
reported_heights %>%
  filter(is.na(as.numeric(height))) %>% 
  .$height
```

```{r}
reported_heights$height %>% .[is.na(as.numeric(.))]
```

```{r}
not_inches <- function(x, smallest = 50, tallest = 84) {
  inches <- suppressWarnings(as.numeric(x))
  ind <- is.na(inches) | inches < smallest | inches > tallest 
  ind}
# 把英吋的大小控制在50-84的合理範圍
```

```{r}
problems = reported_heights$height %>% .[not_inches(.)]
problems
# 對一個vector做篩選可以用.[],"."指向前面的東西(在[]外面再包一個{}比較保險)
# 等於reported_heights$height[ not_inches(reported_heights$height) ]的縮寫
# 這是一個string factor 
```

```{r}
str_subset(problems, "cm|inches")
```

```{r}
str_subset(problems, "cm|inches") %>% str_extract("cm|inches")
```

```{r}
str_extract(problems, "cm|inches")
```

**Q1:** In the video, we use the function `not_inches` to identify heights that were incorrectly entered
```{r}
not_inches <- function(x, smallest = 50, tallest = 84) {
  inches <- suppressWarnings(as.numeric(x))
  ind <- is.na(inches) | inches < smallest | inches > tallest 
  ind
}
```
In this function, _what TWO types of values are identified as not being correctly formatted in inches?_

+ Values that result in NA’s when converted to numeric
+ Values less than 50 inches or greater than 84 inches

**Q2:** Which of the following arguments, when passed to the function not_inches, would return the vector `c(FALSE)`?
```{r}
c(70) %>% not_inches
```

**Q3:** Our function `not_inches` returns the object `ind`. Which answer correctly describes `ind`?

+ `ind` is a logical vector of `TRUE` and `FALSE`, equal in length to the vector `x` (in the arguments list). `TRUE` indicates that a height entry is incorrectly formatted.


##### C2. Regex(定義pattern的語言)

**Q1:** Given the following code
```{r}
s = c("70" ,"5 ft", "4'11", "", ".", "Six feet")
```

_What pattern vector yields the following result?_
```{r}
pattern = "\\d|ft"
# 對應阿拉伯數字:\d,但由於在R裡面要在""裡打\,就要打兩次 ex.cat("\\") = "\"
# \' 是 '  \" 是 " \. 是 .
str_subset(s, pattern)
```

##### C3. Character Classes, Anchors, and Qualifiers

Character Classes - `[]`
```{r}
yes = as.character(4:7)
no = as.character(1:3)
str_detect(c(yes,no), "[4-7]")
# [4-7]是4到7 # [A-Z]是全部大寫字母 # [a-zA-Z]是所有英文字母
# []裡面都是character,所以[1-20]是0,1,2而非1~20
# [0-9] == \\d
```

Anchors - `^` and `$` (^是開始/$是結束的條件)
```{r}
yes = c("1","5","9")
no = c("12","123"," 1","a4","b")
str_detect(c(yes,no), "^\\d$")
# 選擇開始第一個字是阿拉伯數字就結束
# str_detect(c(yes,no), "\\d$") 會變成只要阿拉伯數字結尾就好
```

Qualifiers - `{}` ({}前面的字元有幾個)
```{r}
yes = c("1","5","9","12")
no = c("123","a4","b")
str_detect(c(yes,no), "^\\d{1,2}$")
# 開始和結尾是阿拉伯數字,可以有1~2個數字
```

Pattern of Feets & Inches
```{r}
pattern = "^[4-7]'\\d{1,2}\"$"
yes = c("5'7\"", "6'2\"", "5'12\"")
no = c("6,2\"", "6.2\"", "I am 5'11\"", "3'2\"", "64")
str_detect(c(yes,no), pattern)
# 開頭要是4-7,加個',再來1~2個阿拉伯數字,加個",結束 ex. 5'7"
```

**Q1:** You enter the following set of commands into your R console. _What is your printed result?_
```{r}
animals <- c("cat", "puppy", "Moose", "MONKEY")
pattern <- "[a-z]"
str_detect(animals, pattern)
# 要有小寫字母
```

**Q2:** You enter the following set of commands into your R console. _What is your printed result?_ 
```{r}
animals <- c("cat", "puppy", "Moose", "MONKEY")
pattern <- "[A-Z]$"
str_detect(animals, pattern)
# 結尾要是大寫字母
```

**Q3:** You enter the following set of commands into your R console. _What is your printed result?_
```{r}
animals <- c("cat", "puppy", "Moose", "MONKEY")
pattern <- "[a-z]{4,5}"
str_detect(animals, pattern)
# 要包含4到5個小寫字母
```

##### C4. Search and Replace with Regex

Inital Pattern
```{r}
pattern = "^[4-7]'\\d{1,2}$"
str_subset(problems, pattern)   # 24
```

Replace Feet and Inches
```{r}
pattern = "^[4-7]'\\d{1,2}$"
problems %>% 
  str_replace("feet|ft|foot","'") %>% 
  str_replace("inches|in|''|\"","") %>%
  str_subset(pattern)
#  str_detect(pattern) %>%  sum   # 48 
```

More Qualifiers

+ `*` : 0 or more
+ `+` : 1 or more
+ `?` : 0 or 1

```{r}
yes = c("AB","A1B","A11B","A111B","A1111B")
no = c("A2B","A21B")
str_detect(c(yes,no), "A1*B")
# 在A後面可以有0或更多的1
```

Space - `\\s`
```{r}
pattern = "^[4-7]\\s*'\\s*\\d{1,2}$"
problems %>% 
  str_replace("feet|ft|foot","'") %>% 
  str_replace("inches|in|''|\"","") %>%
  str_detect(pattern) %>% 
  sum                           # 53 
# 空白鍵: \s , 在R就要: \\s
# 用星號代表不限空白鍵的多寡
```

**Q1:** Given the following code, _which TWO `pattern` vectors would yield the following result?_
```{r}
animals <- c("moose", "monkey", "meerkat", "mountain lion")
pattern = c("mo*","mo?","mo+","moo*")
sapply(pattern, function(p) str_detect(animals, p)) %>% t
# moo*是在mo後面有0或更多的o
```


**Q2:** You are working on some data from different universities. You have the following vector
```{r}
schools = c(
  "U. Kentucky","Univ New Hampshire","Univ. of Massachusetts",
  "University Georgia","U California","California State University"
  )
```

You want to clean this data to match the full names of each university. _What of the following commands could accomplish this?_
```{r}
schools %>% 
  str_replace("^Univ\\.?\\s|^U\\.?\\s", "University ") %>% 
  str_replace("^University of |^University ", "University of ")
```

##### C5. Groups with Regex

Define Groups `()` 
```{r}
pattern_no_group = "^[4-7],\\d*$"
pattern_group = "^([4-7]),(\\d*)$"
yes = c("5,9","5,11","6,","6,1")
no = c("5'9",",","2,8","6.1.1")
s = c(yes, no)
# 括號把他們分group
```

Groups do not affect pattern detection
```{r}
str_detect(s, pattern_no_group)
str_detect(s, pattern_group)
```

The difference between 

+ `str_match()`
+ `str_extarct()` 
+ `str_subset()` 
+ `str_detect()` 

```{r}
str_match(s, pattern_group)
```

```{r}
str_extract(s, pattern_group)
```

```{r}
str_subset(s, pattern_group)
```

```{r}
str_detect(s, pattern_group)
```

Replace with Group
```{r}
pattern = "^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$"
str_subset(problems, pattern) %>% str_match(pattern)
```

```{r}
str_subset(problems, pattern) %>% 
  str_replace(pattern, "\\1'\\2")
# \\1代表group1;\\2代表group2
# str_replace(pattern, "\\1 feets and \\2 inches")
# 挖出來再重新定義
```

**Q1:** Rather than using the pattern_with_groups vector from the video, you accidentally write in the following code. _What is your result?_
```{r}
pattern_w_groups = "^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$"
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
pattern_with_groups <- "^([4-7])[,\\.](\\d*)$"
str_replace(problems1, pattern_with_groups, "\\1'\\2")
```

**Q2:** You notice your mistake and correct your pattern regex to the following
_What is your result?_
```{r}
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
pattern_with_groups <- "^([4-7])[,\\.\\s](\\d*)$"
str_replace(problems1, pattern_with_groups, "\\1'\\2")
```

<p style="color:red">I think what it intends to do is ...</p>
```{r}
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
pattern_with_groups <- "^([4-7])\\s*[,\\.\\s]\\s*(\\d*)$"
str_replace(problems1, pattern_with_groups, "\\1'\\2")
```

##### C6. Testing and Improving

```{r}
converted <- problems %>% 
  str_replace("feet|foot|ft", "'") %>% 
  str_replace("inches|in|''|\"", "") %>% 
  str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")
```

```{r}
pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
index <- str_detect(converted, pattern)
mean(index)          # 0.42123
```

```{r}
converted[!index]    
```

**Q1:** In our example, we use the following code to detect height entries that do not match our pattern of x’y”.
```{r eval=F}
problems1 <- c("5.3", "5,5", "6 1", "5 .11", "5, 12")
converted1 <- problems1 %>% 
  str_replace("feet|foot|ft", "'") %>% 
  str_replace("inches|in|''|\"", "") %>% 
  str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")

pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
index <- str_detect(converted1, pattern)
converted1[!index]
```

_Which answer best describes the differences_ between the regex string we use as an argument in <br>
`str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")` <br>
And the regex string in <br>
`pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"?`

+ The regex used in str_replace looks for either a comma, period or space between the feet and inches digits, while the pattern regex just looks for an apostrophe; the regex in str_replace allows for none or more digits to be entered as inches, while the pattern regex only allows for one or two digits.

**Q2:** You notice a few entries that are not being properly converted using your str_replace and str_detect code
```{r}
yes <- c("5 feet 7inches")
no <- c("5ft 9 inches", "5 ft 9 inches")
s <- c(yes, no)

converted <- s %>% 
  str_replace("feet|foot|ft", "'") %>% 
  str_replace("inches|in|''|\"", "") %>% 
  str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")
converted

pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
str_detect(converted, pattern)
```

It seems like the problem may be due to spaces around the words feet|foot|ft and inches|in. _What is another way you could fix this problem?_
```{r}
converted <- s %>% 
  str_replace("\\s*feet|foot|ft\\s*", "'") %>% 
  str_replace("\\s*inches|in|''|\"\\s*", "") %>% 
  str_replace("^([4-7])\\s*[,\\.\\s+]\\s*(\\d*)$", "\\1'\\2")

converted
pattern <- "^[4-7]\\s*'\\s*\\d{1,2}$"
str_detect(converted, pattern)
```

<br>

- - -

### D. String Processing Part 3

##### D2. Separate with Regex
```{r}
s = c("5'10", "6'1")
tab = data.frame(x = s)
separate(tab, x, c("feet", "inches"), sep="'")
extract(tab, x, c("feet", "inches"), regex="(\\d)'(\\d{1,2})")
```

```{r}
s = c("5'10", "6'1\"","5'8inches")
tab = data.frame(x = s)
separate(tab, x, c("feet", "inches"), sep="'")
extract(tab, x, c("feet", "inches"), regex="(\\d)'(\\d{1,2})")
```

##### D1. Using Groups and Quantifiers

** Q1:** If you use the extract code from our video, the decimal point is dropped. What modification of the code would allow you to put the decimals in a third column called “decimal”?
```{r}
library(tidyr)
s <- c("5'10", "6'1\"", "5'8inches", "5'7.5")
tab <- data.frame(x = s)
rx = c("(\\d)'(\\d{1,2})(\\.)?", 
       "(\\d)'(\\d{1,2})(\\.\\d+)",
       "(\\d)'(\\d{1,2})\\.\\d+?",
       "(\\d)'(\\d{1,2})(\\.\\d+)?")
extract(tab, x, into=c("feet", "inches", "decimal"), regex=rx[4])
```

##### D4. String Splitting

```{r}
filename =  system.file("extdata/murders.csv", package="dslabs")
lines = readLines(filename)
head(lines)
```

```{r}
x = str_split(lines, ",", simplify=T)
head(x)
```

```{r}
as.data.frame(x[-1,]) %>% 
  setNames(x[1,]) %>% 
  mutate_all(parse_guess) %>% 
  head(10)
```

**Q1:** You have the following table
```{r}
schedule = data.frame(
  day = c("Monday", "Tuesday"),
  staff = c("Mandy, Chris and Laura", "Steve, Ruth and Frank"))

schedule
```

_Which two commands would properly split the text in the “Staff” column into each individual name? Check all that apply._
```{r}
lapply(c(",|and", ", | and ", ",\\s|\\sand\\s", "\\s?(,|and)\\s?"),
       function(r) str_split(schedule$staff, r, simplify=T))
```

**Q2:** _What code would successfully turn your “Schedule” table into the following tidy table_
```{r}
schedule %>% 
  mutate(staff = str_split(staff, ", | and ")) %>% 
  unnest()
# split出來變成一個list,再用unnest
```

##### D.6 Recoding

```{r}
library(ggplot2)
data("gapminder")
gapminder %>% filter(region == "Caribbean") %>% 
  ggplot(aes(year, life_expectancy, color=country)) +
  geom_line()
```

```{r}
gapminder %>% filter(region == "Caribbean") %>% 
  filter(str_length(country) >= 12) %>% 
  distinct(country)
```

```{r}
gapminder %>% filter(region == "Caribbean") %>% 
  mutate(country = recode(
    country, 
    `Antigua and Barbuda` = "Barbuda",
    `Dominican Republic` = "DR",
    `St. Vincent and the Grenadines` = "St. Vincent",
    `Trinidad and Tobago` = "Trinidad"
  )) %>% 
  ggplot(aes(year, life_expectancy, color=country)) +
  geom_line()
```


**Q1:** 
Using the gapminder data, you want to recode countries longer than 12 letters in the region `Middle Africa` to their abbreviations in a new column, `country_short`. _Which code would accomplish this?_
```{r}
library(dslabs)
data(gapminder)

gapminder %>% filter(region == "Middle Africa") %>% 
  filter(nchar(as.character(country)) >= 12) %>% 
  select(region, country) %>% distinct() %>% 
  mutate(country_short = recode(country, 
    "Central African Republic" = "CAR", 
    "Congo, Dem. Rep." = "DRC",
    "Equatorial Guinea" = "Eq. Guinea"
    ) )
```

<br>

- - -

### E. Date, Times and Text Mining

##### E1. Dates and Times

**Q1:** _Which of the following is the standard ISO 8601 format for dates?_

+ YYYY-MM-DD

**Q2:** _Which of the following commands could convert this string into the correct date format?_

```{r}
library(lubridate)
dates <- c("09-01-02", "01-12-07", "02-03-04")
ymd(dates)
mdy(dates)
dmy(dates)
```

+ It is impossible to know which format is correct without additional information.


- - -

<br><br><br><br><br>

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