introduction

string basics

chac_data <- "I'm 'very' hungry."

stringr::str_length("I am hungry.")
## [1] 12
stringr::str_c(c("I", " am"), collapse = "")
## [1] "I am"
stringr::str_c("I", " am", sep = " ;")
## [1] "I ; am"
str_sort(c("John", "Mary", "Aaron"))
## [1] "Aaron" "John"  "Mary"

matching pattern with regular expressions

flights %>% glimpse()
## Rows: 336,776
## Columns: 19
## $ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
## $ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
## $ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
## $ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
## $ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
## $ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
## $ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
## $ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
## $ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
## $ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
## $ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
## $ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
## $ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
## $ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
## $ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…
flights_small <- flights %>% select(where(is.character)) %>% head(n =10)
flights_small %>% filter(str_detect(origin, "M"))
## # A tibble: 0 × 4
## # … with 4 variables: carrier <chr>, tailnum <chr>, origin <chr>, dest <chr>
flights_small %>% filter(str_detect(origin, ".M"))
## # A tibble: 0 × 4
## # … with 4 variables: carrier <chr>, tailnum <chr>, origin <chr>, dest <chr>
flights_small %>% filter(str_detect(origin, "M."))
## # A tibble: 0 × 4
## # … with 4 variables: carrier <chr>, tailnum <chr>, origin <chr>, dest <chr>
flights_small %>% filter(str_detect(origin, "M\\."))
## # A tibble: 0 × 4
## # … with 4 variables: carrier <chr>, tailnum <chr>, origin <chr>, dest <chr>

anchors

flights_small %>% filter(str_detect(origin, "E$"))
## # A tibble: 0 × 4
## # … with 4 variables: carrier <chr>, tailnum <chr>, origin <chr>, dest <chr>

character classes and alternatives

flights_small %>% filter(str_detect(carrier, "\\d"))
## # A tibble: 3 × 4
##   carrier tailnum origin dest 
##   <chr>   <chr>   <chr>  <chr>
## 1 B6      N804JB  JFK    BQN  
## 2 B6      N516JB  EWR    FLL  
## 3 B6      N593JB  JFK    MCO
flights_small %>% filter(str_detect(carrier, "\\s"))
## # A tibble: 0 × 4
## # … with 4 variables: carrier <chr>, tailnum <chr>, origin <chr>, dest <chr>
flights_small %>% filter(str_detect(carrier, "[^ABD]"))
## # A tibble: 8 × 4
##   carrier tailnum origin dest 
##   <chr>   <chr>   <chr>  <chr>
## 1 UA      N14228  EWR    IAH  
## 2 UA      N24211  LGA    IAH  
## 3 B6      N804JB  JFK    BQN  
## 4 DL      N668DN  LGA    ATL  
## 5 UA      N39463  EWR    ORD  
## 6 B6      N516JB  EWR    FLL  
## 7 EV      N829AS  LGA    IAD  
## 8 B6      N593JB  JFK    MCO

repetition

# ? 0 or 1
flights %>% filter(str_detect(carrier, "A?"))
## # A tibble: 336,776 × 19
##     year month   day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
##    <int> <int> <int>    <int>      <int>   <dbl>   <int>   <int>   <dbl> <chr>  
##  1  2013     1     1      517        515       2     830     819      11 UA     
##  2  2013     1     1      533        529       4     850     830      20 UA     
##  3  2013     1     1      542        540       2     923     850      33 AA     
##  4  2013     1     1      544        545      -1    1004    1022     -18 B6     
##  5  2013     1     1      554        600      -6     812     837     -25 DL     
##  6  2013     1     1      554        558      -4     740     728      12 UA     
##  7  2013     1     1      555        600      -5     913     854      19 B6     
##  8  2013     1     1      557        600      -3     709     723     -14 EV     
##  9  2013     1     1      557        600      -3     838     846      -8 B6     
## 10  2013     1     1      558        600      -2     753     745       8 AA     
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
## #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay
# + 1 or more
flights %>% filter(str_detect(carrier, "A+"))
## # A tibble: 92,450 × 19
##     year month   day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
##    <int> <int> <int>    <int>      <int>   <dbl>   <int>   <int>   <dbl> <chr>  
##  1  2013     1     1      517        515       2     830     819      11 UA     
##  2  2013     1     1      533        529       4     850     830      20 UA     
##  3  2013     1     1      542        540       2     923     850      33 AA     
##  4  2013     1     1      554        558      -4     740     728      12 UA     
##  5  2013     1     1      558        600      -2     753     745       8 AA     
##  6  2013     1     1      558        600      -2     924     917       7 UA     
##  7  2013     1     1      558        600      -2     923     937     -14 UA     
##  8  2013     1     1      559        600      -1     941     910      31 AA     
##  9  2013     1     1      559        600      -1     854     902      -8 UA     
## 10  2013     1     1      606        610      -4     858     910     -12 AA     
## # … with 92,440 more rows, 9 more variables: flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
## #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay
# * 0 or more
flights %>% filter(str_detect(carrier, "A*"))
## # A tibble: 336,776 × 19
##     year month   day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
##    <int> <int> <int>    <int>      <int>   <dbl>   <int>   <int>   <dbl> <chr>  
##  1  2013     1     1      517        515       2     830     819      11 UA     
##  2  2013     1     1      533        529       4     850     830      20 UA     
##  3  2013     1     1      542        540       2     923     850      33 AA     
##  4  2013     1     1      544        545      -1    1004    1022     -18 B6     
##  5  2013     1     1      554        600      -6     812     837     -25 DL     
##  6  2013     1     1      554        558      -4     740     728      12 UA     
##  7  2013     1     1      555        600      -5     913     854      19 B6     
##  8  2013     1     1      557        600      -3     709     723     -14 EV     
##  9  2013     1     1      557        600      -3     838     846      -8 B6     
## 10  2013     1     1      558        600      -2     753     745       8 AA     
## # … with 336,766 more rows, 9 more variables: flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>, and abbreviated variable names
## #   ¹​sched_dep_time, ²​dep_delay, ³​arr_time, ⁴​sched_arr_time, ⁵​arr_delay

grouping and backrefrences

# (..)\\1

flights %>% select(where(is.character)) %>% filter(str_detect(tailnum, "(\\d{2})\\1"))
## # A tibble: 1,990 × 4
##    carrier tailnum origin dest 
##    <chr>   <chr>   <chr>  <chr>
##  1 EV      N15555  EWR    MKE  
##  2 EV      N11119  LGA    CLE  
##  3 UA      N14242  EWR    TPA  
##  4 EV      N14143  EWR    PIT  
##  5 EV      N15555  EWR    SAV  
##  6 UA      N12125  EWR    LAX  
##  7 EV      N15555  EWR    PWM  
##  8 EV      N15555  EWR    BUF  
##  9 EV      N15555  EWR    RIC  
## 10 EV      N13133  EWR    DTW  
## # … with 1,980 more rows

other types of pattern

flights_small %>% filter(str_detect(tailnum, regex("^e", ignore_case = TRUE)))
## # A tibble: 0 × 4
## # … with 4 variables: carrier <chr>, tailnum <chr>, origin <chr>, dest <chr>