Import your data

data <- read_excel("../00_data/MyData-Charts.xlsx")
data
## # A tibble: 1,222 × 11
##     year months    state colon…¹ colon…² colon…³ colon…⁴ colon…⁵ colon…⁶ colon…⁷
##    <dbl> <chr>     <chr>   <dbl> <chr>     <dbl>   <dbl> <chr>   <chr>   <chr>  
##  1  2015 January-… Alab…    7000 7000       1800      26 2800    250     4      
##  2  2015 January-… Ariz…   35000 35000      4600      13 3400    2100    6      
##  3  2015 January-… Arka…   13000 14000      1500      11 1200    90      1      
##  4  2015 January-… Cali… 1440000 1690000  255000      15 250000  124000  7      
##  5  2015 January-… Colo…    3500 12500      1500      12 200     140     1      
##  6  2015 January-… Conn…    3900 3900        870      22 290     NA      NA     
##  7  2015 January-… Flor…  305000 315000    42000      13 54000   25000   8      
##  8  2015 January-… Geor…  104000 105000    14500      14 47000   9500    9      
##  9  2015 January-… Hawa…   10500 10500       380       4 3400    760     7      
## 10  2015 January-… Idaho   81000 88000      3700       4 2600    8000    9      
## # … with 1,212 more rows, 1 more variable: `Growth of colonies` <dbl>, and
## #   abbreviated variable names ¹​colony_n, ²​colony_max, ³​colony_lost,
## #   ⁴​colony_lost_pct, ⁵​colony_added, ⁶​colony_reno, ⁷​colony_reno_pct

Chapter 14

Tools

Detect matches

data$year
##    [1] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##   [15] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##   [29] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##   [43] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##   [57] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##   [71] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##   [85] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##   [99] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##  [113] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##  [127] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##  [141] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##  [155] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##  [169] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015
##  [183] 2015 2015 2015 2015 2015 2015 2016 2016 2016 2016 2016 2016 2016 2016
##  [197] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [211] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [225] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [239] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [253] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [267] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [281] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [295] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [309] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [323] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [337] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [351] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016
##  [365] 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2016 2017 2017
##  [379] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [393] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [407] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [421] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [435] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [449] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [463] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [477] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [491] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [505] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [519] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [533] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [547] 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017 2017
##  [561] 2017 2017 2017 2017 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [575] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [589] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [603] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [617] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [631] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [645] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [659] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [673] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [687] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [701] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [715] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [729] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018
##  [743] 2018 2018 2018 2018 2018 2018 2018 2018 2018 2018 2019 2019 2019 2019
##  [757] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [771] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [785] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [799] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [813] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [827] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [841] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [855] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [869] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [883] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [897] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [911] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [925] 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019
##  [939] 2019 2019 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
##  [953] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
##  [967] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
##  [981] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
##  [995] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1009] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1023] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1037] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1051] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1065] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1079] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1093] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1107] 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020
## [1121] 2020 2020 2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021
## [1135] 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021
## [1149] 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021
## [1163] 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021
## [1177] 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021
## [1191] 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021
## [1205] 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021
## [1219] 2021 2021 2021 2021
str_detect(data$year, "2015")
##    [1]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [13]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [25]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [37]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [49]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [61]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [73]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [85]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##   [97]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [109]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [121]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [133]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [145]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [157]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [169]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
##  [181]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE
##  [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [613] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [637] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [649] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [685] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [709] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [745] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [781] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [805] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [817] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [829] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [865] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [877] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [889] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [901] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [913] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [925] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [937] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [985] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [997] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1009] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1021] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1033] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1045] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1057] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1069] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1081] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1093] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1105] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1117] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1129] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1141] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1153] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1165] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1177] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1189] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1201] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [1213] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
sum(str_detect(data$year, "2015"))
## [1] 188
data %>%
    summarise(num_2015 = sum(str_detect(year, "2015")))
## # A tibble: 1 × 1
##   num_2015
##      <int>
## 1      188

Extract matches

data %>%
    mutate(col_2015 = str_replace(year, "2016", "2015")) %>%
    select(year, col_2015)
## # A tibble: 1,222 × 2
##     year col_2015
##    <dbl> <chr>   
##  1  2015 2015    
##  2  2015 2015    
##  3  2015 2015    
##  4  2015 2015    
##  5  2015 2015    
##  6  2015 2015    
##  7  2015 2015    
##  8  2015 2015    
##  9  2015 2015    
## 10  2015 2015    
## # … with 1,212 more rows

Replacing matches

data %>% mutate(year_rev = year %>% str_replace("^[1-9]", "3"))
## # A tibble: 1,222 × 12
##     year months    state colon…¹ colon…² colon…³ colon…⁴ colon…⁵ colon…⁶ colon…⁷
##    <dbl> <chr>     <chr>   <dbl> <chr>     <dbl>   <dbl> <chr>   <chr>   <chr>  
##  1  2015 January-… Alab…    7000 7000       1800      26 2800    250     4      
##  2  2015 January-… Ariz…   35000 35000      4600      13 3400    2100    6      
##  3  2015 January-… Arka…   13000 14000      1500      11 1200    90      1      
##  4  2015 January-… Cali… 1440000 1690000  255000      15 250000  124000  7      
##  5  2015 January-… Colo…    3500 12500      1500      12 200     140     1      
##  6  2015 January-… Conn…    3900 3900        870      22 290     NA      NA     
##  7  2015 January-… Flor…  305000 315000    42000      13 54000   25000   8      
##  8  2015 January-… Geor…  104000 105000    14500      14 47000   9500    9      
##  9  2015 January-… Hawa…   10500 10500       380       4 3400    760     7      
## 10  2015 January-… Idaho   81000 88000      3700       4 2600    8000    9      
## # … with 1,212 more rows, 2 more variables: `Growth of colonies` <dbl>,
## #   year_rev <chr>, and abbreviated variable names ¹​colony_n, ²​colony_max,
## #   ³​colony_lost, ⁴​colony_lost_pct, ⁵​colony_added, ⁶​colony_reno,
## #   ⁷​colony_reno_pct
data %>% mutate(year_rev = year %>% str_replace_all("^[1-9]", "3"))
## # A tibble: 1,222 × 12
##     year months    state colon…¹ colon…² colon…³ colon…⁴ colon…⁵ colon…⁶ colon…⁷
##    <dbl> <chr>     <chr>   <dbl> <chr>     <dbl>   <dbl> <chr>   <chr>   <chr>  
##  1  2015 January-… Alab…    7000 7000       1800      26 2800    250     4      
##  2  2015 January-… Ariz…   35000 35000      4600      13 3400    2100    6      
##  3  2015 January-… Arka…   13000 14000      1500      11 1200    90      1      
##  4  2015 January-… Cali… 1440000 1690000  255000      15 250000  124000  7      
##  5  2015 January-… Colo…    3500 12500      1500      12 200     140     1      
##  6  2015 January-… Conn…    3900 3900        870      22 290     NA      NA     
##  7  2015 January-… Flor…  305000 315000    42000      13 54000   25000   8      
##  8  2015 January-… Geor…  104000 105000    14500      14 47000   9500    9      
##  9  2015 January-… Hawa…   10500 10500       380       4 3400    760     7      
## 10  2015 January-… Idaho   81000 88000      3700       4 2600    8000    9      
## # … with 1,212 more rows, 2 more variables: `Growth of colonies` <dbl>,
## #   year_rev <chr>, and abbreviated variable names ¹​colony_n, ²​colony_max,
## #   ³​colony_lost, ⁴​colony_lost_pct, ⁵​colony_added, ⁶​colony_reno,
## #   ⁷​colony_reno_pct