class: middle, inverse .leftcol30[ <center> <img src="https://github.com/emse-p4a-gwu/emse-p4a-gwu.github.io/raw/master/images/p4a_hex_sticker.png" width=250> </center> ] .rightcol70[ # Week 10: .fancy[Data Wrangling] ### <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M496 128v16a8 8 0 0 1-8 8h-24v12c0 6.627-5.373 12-12 12H60c-6.627 0-12-5.373-12-12v-12H24a8 8 0 0 1-8-8v-16a8 8 0 0 1 4.941-7.392l232-88a7.996 7.996 0 0 1 6.118 0l232 88A8 8 0 0 1 496 128zm-24 304H40c-13.255 0-24 10.745-24 24v16a8 8 0 0 0 8 8h464a8 8 0 0 0 8-8v-16c0-13.255-10.745-24-24-24zM96 192v192H60c-6.627 0-12 5.373-12 12v20h416v-20c0-6.627-5.373-12-12-12h-36V192h-64v192h-64V192h-64v192h-64V192H96z"/></svg> EMSE 4571: Intro to Programming for Analytics ### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M224 256c70.7 0 128-57.3 128-128S294.7 0 224 0 96 57.3 96 128s57.3 128 128 128zm89.6 32h-16.7c-22.2 10.2-46.9 16-72.9 16s-50.6-5.8-72.9-16h-16.7C60.2 288 0 348.2 0 422.4V464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48v-41.6c0-74.2-60.2-134.4-134.4-134.4z"/></svg> John Paul Helveston ### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M0 464c0 26.5 21.5 48 48 48h352c26.5 0 48-21.5 48-48V192H0v272zm320-196c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM192 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12h-40c-6.6 0-12-5.4-12-12v-40zM64 268c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zm0 128c0-6.6 5.4-12 12-12h40c6.6 0 12 5.4 12 12v40c0 6.6-5.4 12-12 12H76c-6.6 0-12-5.4-12-12v-40zM400 64h-48V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H160V16c0-8.8-7.2-16-16-16h-32c-8.8 0-16 7.2-16 16v48H48C21.5 64 0 85.5 0 112v48h448v-48c0-26.5-21.5-48-48-48z"/></svg> March 31, 2022 ] --- class: inverse, middle # Week 10: .fancy[Data Wrangling] ### 1. Selecting & filtering ### 2. Sequences with pipes ### BREAK ### 3. Creating new variables ### 4. Grouped operations --- class: inverse, middle # Week 10: .fancy[Data Wrangling] ### 1. .orange[Selecting & filtering] ### 2. Sequences with pipes ### BREAK ### 3. Creating new variables ### 4. Grouped operations --- # Before we start Make sure you have the "tidyverse" installed ```r install.packages('tidyverse') ``` (this is at the top of the notes.R file) Remember: you only need to install packages once! --- class: center ### The tidyverse: `stringr` + `dplyr` + `readr` + `ggplot2` + ... <center> <img src="images/horst_monsters_tidyverse.jpeg" width="950"> </center>Art by [Allison Horst](https://www.allisonhorst.com/) --- class: center ## Today: better data wrangling with **dplyr** <center> <img src="images/horst_monsters_data_wrangling.png" width="600"> </center>Art by [Allison Horst](https://www.allisonhorst.com/) --- class: center # 80% of the job is data wrangling <center> <img src="images/data-wrangle.png" width="800"> </center> --- # The main `dplyr` "verbs" - `select()`: subset columns - `filter()`: subset rows on conditions - `arrange()`: sort data frame - `mutate()`: create new columns by using information from other columns - `group_by()`: group data to perform grouped operations - `summarize()`: create summary statistics (usually on grouped data) - `count()`: count discrete rows --- # This week's British Band: **The Spice Girls** ```r spicegirls <- tibble( firstName = c("Melanie", "Melanie", "Emma", "Geri", "Victoria"), lastName = c("Brown", "Chisholm", "Bunton", "Halliwell", "Beckham"), spice = c("Scary", "Sporty", "Baby", "Ginger", "Posh"), yearOfBirth = c(1975, 1974, 1976, 1972, 1974), deceased = c(FALSE, FALSE, FALSE, FALSE, FALSE) ) spicegirls ``` ``` #> # A tibble: 5 × 5 #> firstName lastName spice yearOfBirth deceased #> <chr> <chr> <chr> <dbl> <lgl> #> 1 Melanie Brown Scary 1975 FALSE #> 2 Melanie Chisholm Sporty 1974 FALSE #> 3 Emma Bunton Baby 1976 FALSE #> 4 Geri Halliwell Ginger 1972 FALSE #> 5 Victoria Beckham Posh 1974 FALSE ``` --- # Select columns with `select()` <br> <center> <img src="images/rstudio-cheatsheet-select.png" width="900"> </center> --- # Select columns with `select()` Example: Select the columns `firstName` & `lastName` -- &zwj;**Base R**: ```r spicegirls[c('firstName', 'lastName')] ``` ``` #> # A tibble: 5 × 2 #> firstName lastName #> <chr> <chr> #> 1 Melanie Brown #> 2 Melanie Chisholm #> 3 Emma Bunton #> 4 Geri Halliwell #> 5 Victoria Beckham ``` --- # Select columns with `select()` Example: Select the columns `firstName` & `lastName` **dplyr**: (note that you don't need `""` around names) ```r select(spicegirls, firstName, lastName) ``` ``` #> # A tibble: 5 × 2 #> firstName lastName #> <chr> <chr> #> 1 Melanie Brown #> 2 Melanie Chisholm #> 3 Emma Bunton #> 4 Geri Halliwell #> 5 Victoria Beckham ``` --- # Select columns with `select()` Use the `-` sign to drop columns: ```r select(spicegirls, -firstName, -lastName) ``` ``` #> # A tibble: 5 × 3 #> spice yearOfBirth deceased #> <chr> <dbl> <lgl> #> 1 Scary 1975 FALSE #> 2 Sporty 1974 FALSE #> 3 Baby 1976 FALSE #> 4 Ginger 1972 FALSE #> 5 Posh 1974 FALSE ``` --- # Select columns with `select()` Select columns based on name criteria: - `ends_with()` = Select columns that end with a character string - `contains()` = Select columns that contain a character string - `matches()` = Select columns that match a regular expression - `one_of()` = Select column names that are from a group of names --- # Select columns with `select()` Select only the "name" columns ```r select(spicegirls, ends_with('name')) ``` ``` #> # A tibble: 5 × 2 #> firstName lastName #> <chr> <chr> #> 1 Melanie Brown #> 2 Melanie Chisholm #> 3 Emma Bunton #> 4 Geri Halliwell #> 5 Victoria Beckham ``` --- # Select rows with `filter()` <br> <center> <img src="images/rstudio-cheatsheet-filter.png" width="900"> </center> --- # Select rows with `filter()` Example: Filter the band members born after 1974 ```r ## # A tibble: 5 x 5 ## firstName lastName spice yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> *## 1 Melanie Brown Scary 1975 FALSE ## 2 Melanie Chisholm Sporty 1974 FALSE *## 3 Emma Bunton Baby 1976 FALSE ## 4 Geri Halliwell Ginger 1972 FALSE ## 5 Victoria Beckham Posh 1974 FALSE ``` --- # Select rows with `filter()` Example: Filter the band members born after 1974 **Base R**: ```r spicegirls[spicegirls$yearOfBirth > 1974,] ``` ``` #> # A tibble: 2 × 5 #> firstName lastName spice yearOfBirth deceased #> <chr> <chr> <chr> <dbl> <lgl> #> 1 Melanie Brown Scary 1975 FALSE #> 2 Emma Bunton Baby 1976 FALSE ``` --- # Select rows with `filter()` Example: Filter the band members born after 1974 **dplyr**: ```r filter(spicegirls, yearOfBirth > 1974) ``` ``` #> # A tibble: 2 × 5 #> firstName lastName spice yearOfBirth deceased #> <chr> <chr> <chr> <dbl> <lgl> #> 1 Melanie Brown Scary 1975 FALSE #> 2 Emma Bunton Baby 1976 FALSE ``` --- # Select rows with `filter()` Example: Filter the band members born after 1974 **& are named "Melanie"** **dplyr**: ```r filter(spicegirls, yearOfBirth > 1974 & firstName == "Melanie") ``` ``` #> # A tibble: 1 × 5 #> firstName lastName spice yearOfBirth deceased #> <chr> <chr> <chr> <dbl> <lgl> #> 1 Melanie Brown Scary 1975 FALSE ``` --- # .center[Logic operators for `filter()`] <br> Description | Example ------------|------------ Values greater than 1 | `value > 1` Values greater than or equal to 1 | `value >= 1` Values less than 1 | `value < 1` Values less than or equal to 1 | `value <= 1` Values equal to 1 | `value == 1` Values not equal to 1 | `value != 1` Values in the set c(1, 4) | `value %in% c(1, 4)` --- # Removing missing values Drop all rows where `variable` is `NA` ```r filter(data, !is.na(variable)) ``` --- class: inverse <div class="countdown" id="timer_6245cb6e" style="top:0;right:0;font-size:2em;" data-warnwhen="30"> <code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ## Your turn: wildlife impacts data .font90[ 1) Create the data frame object `df` by using `here()` and `read_csv()` to load the `wildlife_impacts.csv` file in the `data` folder. 2) Use the `df` object and the `select()` and `filter()` functions to answer the following questions: - Create a new data frame, `df_birds`, that contains only the variables (columns) about the species of bird. - Create a new data frame, `dc`, that contains only the observations (rows) from DC airports. - Create a new data frame, `dc_birds_known`, that contains only the observations (rows) from DC airports and those where the species of bird is known. - How many _known_ unique species of birds have been involved in accidents at DC airports? ] --- class: inverse, middle # Week 10: .fancy[Data Wrangling] ### 1. Selecting & filtering ### 2. .orange[Sequences with pipes] ### BREAK ### 3. Creating new variables ### 4. Grouped operations --- # Create sequences of operations with "pipes" -- .leftcol[ <br> <center> <img src="images/magrittePipe.jpg" width="400"> </center> [The Treachery of Images](https://en.wikipedia.org/wiki/The_Treachery_of_Images), René Magritte ] -- .rightcol[ <center> <img src="images/magrittr.png" width="350"> <a href = "https://magrittr.tidyverse.org/">magrittr package</a> </center> ] --- # Think of `%>%` as the words "...and then..." -- **Without Pipes** (read from inside-out): ```r leave_house(get_dressed(get_out_of_bed(wake_up(me)))) ``` -- **With Pipes**: ```r me %>% wake_up() %>% get_out_of_bed() %>% get_dressed() %>% leave_house() ``` --- # Sequence operations with pipes: `%>%` -- 1. Filter the band members born after 1974 2. Select only the columns `firstName` & `lastName` -- **Without Pipes**: ```r select(filter(spicegirls, yearOfBirth > 1974), firstName, lastName) ``` ``` #> # A tibble: 2 × 2 #> firstName lastName #> <chr> <chr> #> 1 Melanie Brown #> 2 Emma Bunton ``` --- # Sequence operations with pipes: `%>%` 1. Filter the band members born after 1974 2. Select only the columns `firstName` & `lastName` **With Pipes**: ```r spicegirls %>% filter(yearOfBirth > 1974) %>% select(firstName, lastName) ``` ``` #> # A tibble: 2 × 2 #> firstName lastName #> <chr> <chr> #> 1 Melanie Brown #> 2 Emma Bunton ``` --- # Think of the words "...and then..." **Without Pipes**: ```r select(filter(spicegirls, yearOfBirth > 1974), firstName, lastName) ``` **With Pipes**: Note that you don't need to repeat the dataframe name ```r spicegirls %>% filter(yearOfBirth > 1974) %>% select(firstName, lastName) ``` --- # Sort rows with `arrange()` -- Sort the data frame by year of birth: ```r spicegirls %>% arrange(yearOfBirth) ``` ``` #> # A tibble: 5 × 5 #> firstName lastName spice yearOfBirth deceased #> <chr> <chr> <chr> <dbl> <lgl> #> 1 Geri Halliwell Ginger 1972 FALSE #> 2 Melanie Chisholm Sporty 1974 FALSE #> 3 Victoria Beckham Posh 1974 FALSE #> 4 Melanie Brown Scary 1975 FALSE #> 5 Emma Bunton Baby 1976 FALSE ``` --- # Sort rows with `arrange()` Use the `desc()` function to sort in descending order: ```r spicegirls %>% arrange(desc(yearOfBirth)) ``` ``` #> # A tibble: 5 × 5 #> firstName lastName spice yearOfBirth deceased #> <chr> <chr> <chr> <dbl> <lgl> #> 1 Emma Bunton Baby 1976 FALSE #> 2 Melanie Brown Scary 1975 FALSE #> 3 Melanie Chisholm Sporty 1974 FALSE #> 4 Victoria Beckham Posh 1974 FALSE #> 5 Geri Halliwell Ginger 1972 FALSE ``` --- # Sort rows with `arrange()` Example of filtering, arranging, and selecting: ```r spicegirls %>% filter(yearOfBirth < 1975) %>% arrange(desc(yearOfBirth)) %>% select(ends_with('name')) ``` ``` #> # A tibble: 3 × 2 #> firstName lastName #> <chr> <chr> #> 1 Melanie Chisholm #> 2 Victoria Beckham #> 3 Geri Halliwell ``` --- class: inverse <div class="countdown" id="timer_6245ca63" style="top:0;right:0;font-size:2em;" data-warnwhen="30"> <code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ## Your turn .font90[ 1) Create the data frame object `df` by using `here()` and `read_csv()` to load the `wildlife_impacts.csv` file in the `data` folder. 2) Use the `df` object and `select()`, `filter()`, and `%>%` to answer the following questions: - Create a new data frame, `dc_dawn`, that contains only the observations (rows) from DC airports that occurred at dawn. - Create a new data frame, `dc_dawn_birds`, that contains only the observations (rows) from DC airports that occurred at dawn and only the variables (columns) about the species of bird. - Create a new data frame, `dc_dawn_birds_known`, that contains only the observations (rows) from DC airports that occurred at dawn and only the variables (columns) about the KNOWN species of bird. - How many _known_ unique species of birds have been involved in accidents at DC airports at dawn? ] --- class: inverse, center # .fancy[Break] <div class="countdown" id="timer_6245c9fd" style="top:1;right:0;bottom:0;left:0;margin:5%;font-size:8em;" data-warnwhen="30"> <code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> --- class: inverse, middle # Week 10: .fancy[Data Wrangling] ### 1. Selecting & filtering ### 2. Sequences with pipes ### BREAK ### 3. .orange[Creating new variables] ### 4. Grouped operations --- class: center, middle ## Create new variables with `mutate()` <br> <center> <img src="images/rstudio-cheatsheet-mutate.png" width="900"> </center> --- class: center background-color: #fff <center> <img src="images/horst_monsters_mutate.jpg" width="700"> </center>Art by [Allison Horst](https://www.allisonhorst.com/) --- # Create new variables with `mutate()` Example: Use the `yearOfBirth` variable to compute the age of each band member -- **Base R**: ```r spicegirls$age <- 2022 - spicegirls$yearOfBirth ``` -- **dplyr**: ```r spicegirls %>% mutate(age = 2022 - yearOfBirth) ``` ``` #> # A tibble: 5 × 6 #> firstName lastName spice yearOfBirth deceased age #> <chr> <chr> <chr> <dbl> <lgl> <dbl> #> 1 Melanie Brown Scary 1975 FALSE 47 #> 2 Melanie Chisholm Sporty 1974 FALSE 48 #> 3 Emma Bunton Baby 1976 FALSE 46 #> 4 Geri Halliwell Ginger 1972 FALSE 50 #> 5 Victoria Beckham Posh 1974 FALSE 48 ``` --- # You can _immediately_ use new variables ```r spicegirls %>% mutate( age = 2022 - yearOfBirth, * meanAge = mean(age)) # Immediately using the "age" variable ``` ``` #> # A tibble: 5 × 7 #> firstName lastName spice yearOfBirth deceased age meanAge #> <chr> <chr> <chr> <dbl> <lgl> <dbl> <dbl> #> 1 Melanie Brown Scary 1975 FALSE 47 47.8 #> 2 Melanie Chisholm Sporty 1974 FALSE 48 47.8 #> 3 Emma Bunton Baby 1976 FALSE 46 47.8 #> 4 Geri Halliwell Ginger 1972 FALSE 50 47.8 #> 5 Victoria Beckham Posh 1974 FALSE 48 47.8 ``` --- # .center[Handling if/else conditions] ### .center[`ifelse(<condition>, <if TRUE>, <else>)`] -- ```r spicegirls %>% mutate( yobAfter74 = ifelse(yearOfBirth > 1974, "yes", "no")) ``` ``` #> # A tibble: 5 × 6 #> firstName lastName spice yearOfBirth deceased yobAfter74 #> <chr> <chr> <chr> <dbl> <lgl> <chr> #> 1 Melanie Brown Scary 1975 FALSE yes #> 2 Melanie Chisholm Sporty 1974 FALSE no #> 3 Emma Bunton Baby 1976 FALSE yes #> 4 Geri Halliwell Ginger 1972 FALSE no #> 5 Victoria Beckham Posh 1974 FALSE no ``` --- class: inverse <div class="countdown" id="timer_6245cce2" style="top:0;right:0;font-size:2em;" data-warnwhen="30"> <code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ## Your turn .font90[ 1) Create the data frame object `df` by using `here()` and `read_csv()` to load the `wildlife_impacts.csv` file in the `data` folder. 2) Use the `df` object with `%>%` and `mutate()` to create the following new variables: - `height_miles`: The `height` variable converted to miles (Hint: there are 5,280 feet in a mile). - `cost_mil`: Is `TRUE` if the repair costs was greater or equal to $1 million, `FALSE` otherwise. - `season`: One of four seasons based on the `incident_month` variable: - `spring`: March, April, May - `summer`: June, July, August - `fall`: September, October, November - `winter`: December, January, February ] --- class: inverse, middle # Week 10: .fancy[Data Wrangling] ### 1. Selecting & filtering ### 2. Sequences with pipes ### BREAK ### 3. Creating new variables ### 4. .orange[Grouped operations] --- # Split-apply-combine with `group_by` <br> ### 1. **Split** the data into groups ### 2. **Apply** some analysis to each group ### 3. **Combine** the results --- class: center # Split-apply-combine with `group_by` <br> <center> <img src="images/rstudio-cheatsheet-group_by.png" width="900"> </center> --- ## Split-apply-combine with `group_by` ```r bands ``` ``` #> # A tibble: 9 × 5 #> firstName lastName yearOfBirth deceased band #> <chr> <chr> <dbl> <lgl> <chr> #> 1 Melanie Brown 1975 FALSE spicegirls #> 2 Melanie Chisholm 1974 FALSE spicegirls #> 3 Emma Bunton 1976 FALSE spicegirls #> 4 Geri Halliwell 1972 FALSE spicegirls #> 5 Victoria Beckham 1974 FALSE spicegirls #> 6 John Lennon 1940 TRUE beatles #> 7 Paul McCartney 1942 FALSE beatles #> 8 Ringo Starr 1940 FALSE beatles #> 9 George Harrison 1943 TRUE beatles ``` --- ## Split-apply-combine with `group_by` Compute the mean band member age for **each band** ```r bands %>% mutate( age = 2020 - yearOfBirth, * mean_age = mean(age)) # This is the mean across both bands ``` ``` #> # A tibble: 9 × 7 #> firstName lastName yearOfBirth deceased band age mean_age #> <chr> <chr> <dbl> <lgl> <chr> <dbl> <dbl> #> 1 Melanie Brown 1975 FALSE spicegirls 45 60.4 #> 2 Melanie Chisholm 1974 FALSE spicegirls 46 60.4 #> 3 Emma Bunton 1976 FALSE spicegirls 44 60.4 #> 4 Geri Halliwell 1972 FALSE spicegirls 48 60.4 #> 5 Victoria Beckham 1974 FALSE spicegirls 46 60.4 #> 6 John Lennon 1940 TRUE beatles 80 60.4 #> 7 Paul McCartney 1942 FALSE beatles 78 60.4 #> 8 Ringo Starr 1940 FALSE beatles 80 60.4 #> 9 George Harrison 1943 TRUE beatles 77 60.4 ``` --- ## Split-apply-combine with `group_by` Compute the mean band member age for each band ```r bands %>% mutate(age = 2020 - yearOfBirth) %>% * group_by(band) %>% # Everything after this will be done each band mutate(mean_age = mean(age)) ``` ``` #> # A tibble: 9 × 7 #> # Groups: band [2] #> firstName lastName yearOfBirth deceased band age mean_age #> <chr> <chr> <dbl> <lgl> <chr> <dbl> <dbl> #> 1 Melanie Brown 1975 FALSE spicegirls 45 45.8 #> 2 Melanie Chisholm 1974 FALSE spicegirls 46 45.8 #> 3 Emma Bunton 1976 FALSE spicegirls 44 45.8 #> 4 Geri Halliwell 1972 FALSE spicegirls 48 45.8 #> 5 Victoria Beckham 1974 FALSE spicegirls 46 45.8 #> 6 John Lennon 1940 TRUE beatles 80 78.8 #> 7 Paul McCartney 1942 FALSE beatles 78 78.8 #> 8 Ringo Starr 1940 FALSE beatles 80 78.8 #> 9 George Harrison 1943 TRUE beatles 77 78.8 ``` --- class: center # Summarize data frames with `summarise()` <br> <center> <img src="images/rstudio-cheatsheet-summarise.png" width="900"> </center> --- ## Summarize data frames with `summarise()` Compute the mean band member age for **each band** ```r bands %>% mutate(age = 2020 - yearOfBirth) %>% group_by(band) %>% * summarise(mean_age = mean(age)) # Drops all variables except for group ``` ``` #> # A tibble: 2 × 2 #> band mean_age #> <chr> <dbl> #> 1 beatles 78.8 #> 2 spicegirls 45.8 ``` --- ## Summarize data frames with `summarise()` Compute the mean, min, and max band member age for **each band** ```r bands %>% mutate(age = 2020 - yearOfBirth) %>% group_by(band) %>% summarise( mean_age = mean(age), min_age = min(age), max_age = max(age)) ``` ``` #> # A tibble: 2 × 4 #> band mean_age min_age max_age #> <chr> <dbl> <dbl> <dbl> #> 1 beatles 78.8 77 80 #> 2 spicegirls 45.8 44 48 ``` --- ## Computing counts of observations with `n()` How many members are in each band? ```r bands %>% mutate(age = 2020 - yearOfBirth) %>% group_by(band) %>% summarise( mean_age = mean(age), min_age = min(age), max_age = max(age), * numMembers = n()) ``` ``` #> # A tibble: 2 × 5 #> band mean_age min_age max_age numMembers #> <chr> <dbl> <dbl> <dbl> <int> #> 1 beatles 78.8 77 80 4 #> 2 spicegirls 45.8 44 48 5 ``` --- ## If you only want a quick count, use `count()` These do the same thing: .leftcol[ ```r bands %>% group_by(band) %>% summarise(n = n()) ``` ``` #> # A tibble: 2 × 2 #> band n #> <chr> <int> #> 1 beatles 4 #> 2 spicegirls 5 ``` ] .rightcol[ ```r bands %>% count(band) ``` ``` #> # A tibble: 2 × 2 #> band n #> <chr> <int> #> 1 beatles 4 #> 2 spicegirls 5 ``` ] --- ## If you only want a quick count, use `count()` You can count multiple combinations ```r bands %>% mutate(nameStartsWithG = str_detect(firstName, '^G')) %>% count(band, nameStartsWithG) ``` ``` #> # A tibble: 4 × 3 #> band nameStartsWithG n #> <chr> <lgl> <int> #> 1 beatles FALSE 3 #> 2 beatles TRUE 1 #> 3 spicegirls FALSE 4 #> 4 spicegirls TRUE 1 ``` --- class: inverse <div class="countdown" id="timer_6245cc6e" style="top:0;right:0;font-size:2em;" data-warnwhen="30"> <code class="countdown-time"><span class="countdown-digits minutes">10</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ## Your turn .font90[ 1) Create the data frame object `df` by using `here()` and `read_csv()` to load the `wildlife_impacts.csv` file in the `data` folder. 2) Use the `df` object and `group_by()`, `summarise()`, `count()`, and `%>%` to answer the following questions: - Create a summary data frame that contains the mean `height` for each different time of day. - Create a summary data frame that contains the maximum `cost_repairs_infl_adj` for each year. - Which _month_ has had the greatest number of reported incidents? - Which _year_ has had the greatest number of reported incidents? ] --- # Exporting data ```r ageSummary <- bands %>% mutate(age = 2020 - yearOfBirth) %>% group_by(band) %>% summarise( mean_age = mean(age), min_age = min(age), max_age = max(age), numMembers = n()) ageSummary ``` ``` #> # A tibble: 2 × 5 #> band mean_age min_age max_age numMembers #> <chr> <dbl> <dbl> <dbl> <int> #> 1 beatles 78.8 77 80 4 #> 2 spicegirls 45.8 44 48 5 ``` --- # Exporting data: `here()` + `write_csv()` Save the `ageSummary` data frame in your "data" folder: -- 1) Create a path to where you want to save the data ```r library(here) savePath <- here('data', 'ageSummary.csv') ``` -- 2) Export the data ```r library(readr) write_csv(ageSummary, savePath) ``` --- # HW 10 -- Make sure you install the package `nycflights13` ```r install.packages('nycflights13') ``` -- This package includes **5 data frames**: ```r airlines airports flights planes weather ```