class: title-slide, 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 11: .fancy[Data Frames] ### EMSE 4574: Intro to Programming for Analytics ### John Paul Helveston ### November 10, 2020 ] --- class: inverse # Quiz 5 <div class="countdown" id="timer_5fc810eb" style="bottom:0;left:0;font-size:4em;" 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> .leftcol[ - ### Go to `#classroom` channel in Slack for link - ### Open up RStudio before you start - you'll probably want to use it. ] .rightcol[ <center> <img src="images/quiz_doge.png" width="400"> </center> ] --- # Before we start Make sure you have these packages installed and loaded: ```r install.packages("stringr") install.packages("dplyr") install.packages("ggplot2") install.packages("readr") ``` (they're at the top of the notes.R file) Remember: you only need to install them once! --- .leftcol[ ## "The purpose of computing is insight, not numbers" ### - [Richard Hamming](https://en.wikipedia.org/wiki/Richard_Hamming) ] .rightcol[ <img src="images/Richard_Hamming.jpg" width="400"> ] --- class: inverse, middle # Week 11: .fancy[Data Frames] ## 1. Basics ## 2. Slicing ## 3. External data --- class: inverse, middle # Week 11: .fancy[Data Frames] ## 1. .orange[Basics] ## 2. Slicing ## 3. External data --- # The data frame...in Excel <img src="images/spreadsheet.png" width="600"> --- # The data frame...in R ```r beatles <- tibble( firstName = c("John", "Paul", "Ringo", "George"), lastName = c("Lennon", "McCartney", "Starr", "Harrison"), instrument = c("guitar", "bass", "drums", "guitar"), yearOfBirth = c(1940, 1942, 1940, 1943), deceased = c(TRUE, FALSE, FALSE, TRUE) ) beatles ``` ``` ## # A tibble: 4 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ``` --- # The data frame...in RStudio .leftcol[ ```r View(beatles) ``` ] <img src="images/dataframe.png" width="700"> --- ## **Columns**: _Vectors_ of values (must be same data type) ```r beatles ``` ``` ## # A tibble: 4 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ``` --- ## **Rows**: Information about individual observations ```r beatles ``` ``` ## # A tibble: 4 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ``` Information about John Lennon is in the first row: ``` ## # A tibble: 1 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ``` --- ## Make a data frame with `data.frame()` ```r beatles <- data.frame( firstName = c("John", "Paul", "Ringo", "George"), lastName = c("Lennon", "McCartney", "Starr", "Harrison"), instrument = c("guitar", "bass", "drums", "guitar"), yearOfBirth = c(1940, 1942, 1940, 1943), deceased = c(TRUE, FALSE, FALSE, TRUE) ) ``` -- ```r beatles ``` ``` ## firstName lastName instrument yearOfBirth deceased ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ``` --- ## Make a data frame with `tibble()` ```r library(dplyr) ``` ```r beatles <- tibble( firstName = c("John", "Paul", "Ringo", "George"), lastName = c("Lennon", "McCartney", "Starr", "Harrison"), instrument = c("guitar", "bass", "drums", "guitar"), yearOfBirth = c(1940, 1942, 1940, 1943), deceased = c(TRUE, FALSE, FALSE, TRUE) ) ``` -- ```r beatles ``` ``` ## # A tibble: 4 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ``` --- ## Why I use `tibble()` instead of `data.frame()` -- 1. The `tibble()` shows the **dimensions** and **data type**. -- 2. A tibble will only print the first few rows of data when you enter the object name Example: `faithful` vs. `as_tibble(faithful)` -- 3. Columns of class `character` are _never_ converted into factors (don't worry about this for now...just know that tibbles make life easier when dealing with character type columns). **Note**: I use the word **"data frame"** to refer to both `tibble()` and `data.frame()` objects --- ## Data frame vectors must have the same length ```r beatles <- tibble( * firstName = c("John", "Paul", "Ringo", "George", "Bob"), # Added "Bob" lastName = c("Lennon", "McCartney", "Starr", "Harrison"), instrument = c("guitar", "bass", "drums", "guitar"), yearOfBirth = c(1940, 1942, 1940, 1943), deceased = c(TRUE, FALSE, FALSE, TRUE) ) ``` ``` ## Error: Tibble columns must have compatible sizes. ## * Size 5: Existing data. ## * Size 4: Column `lastName`. ## ℹ Only values of size one are recycled. ``` --- ## Use `NA` for missing values ```r beatles <- tibble( firstName = c("John", "Paul", "Ringo", "George", "Bob"), # Added "Bob" * lastName = c("Lennon", "McCartney", "Starr", "Harrison", NA), * instrument = c("guitar", "bass", "drums", "guitar", NA), * yearOfBirth = c(1940, 1942, 1940, 1943, NA), * deceased = c(TRUE, FALSE, FALSE, TRUE, NA) ) ``` -- ```r beatles ``` ``` ## # A tibble: 5 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ## 5 Bob <NA> <NA> NA NA ``` --- # Dimensions: `nrow()`, `ncol()`, & `dim()` ```r nrow(beatles) # Number of rows ``` ``` ## [1] 5 ``` ```r ncol(beatles) # Number of columns ``` ``` ## [1] 5 ``` ```r dim(beatles) # Number of rows and columns ``` ``` ## [1] 5 5 ``` --- ## Use `names()` to see which variables a data frame has Get the names of columns: ```r names(beatles) ``` ``` ## [1] "firstName" "lastName" "instrument" "yearOfBirth" "deceased" ``` ```r colnames(beatles) ``` ``` ## [1] "firstName" "lastName" "instrument" "yearOfBirth" "deceased" ``` -- Get the names of rows (rarely needed): ```r rownames(beatles) ``` ``` ## [1] "1" "2" "3" "4" "5" ``` --- # Changing the column names Change the column names with `names()` or `colnames()`: ```r names(beatles) <- c('one', 'two', 'three', 'four', 'five') beatles ``` ``` ## # A tibble: 5 x 5 ## one two three four five ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ## 5 Bob <NA> <NA> NA NA ``` --- # Changing the column names Make all the column names upper-case: ```r colnames(beatles) <- stringr::str_to_upper(colnames(beatles)) beatles ``` ``` ## # A tibble: 5 x 5 ## FIRSTNAME LASTNAME INSTRUMENT YEAROFBIRTH DECEASED ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ## 3 Ringo Starr drums 1940 FALSE ## 4 George Harrison guitar 1943 TRUE ## 5 Bob <NA> <NA> NA NA ``` --- ## Combine data frames by columns using `bind_cols()` Note: `bind_cols()` is from the **dplyr** library ```r names <- tibble( firstName = c("John", "Paul", "Ringo", "George"), lastName = c("Lennon", "McCartney", "Starr", "Harrison")) instruments <- tibble( instrument = c("guitar", "bass", "drums", "guitar")) ``` -- ```r bind_cols(names, instruments) ``` ``` ## # A tibble: 4 x 3 ## firstName lastName instrument ## <chr> <chr> <chr> ## 1 John Lennon guitar ## 2 Paul McCartney bass ## 3 Ringo Starr drums ## 4 George Harrison guitar ``` --- ## Combine data frames by rows using `bind_rows()` Note: `bind_rows()` is from the **dplyr** library ```r members1 <- tibble( firstName = c("John", "Paul"), lastName = c("Lennon", "McCartney")) members2 <- tibble( firstName = c("Ringo", "George"), lastName = c("Starr", "Harrison")) ``` -- ```r bind_rows(members1, members2) ``` ``` ## # A tibble: 4 x 2 ## firstName lastName ## <chr> <chr> ## 1 John Lennon ## 2 Paul McCartney ## 3 Ringo Starr ## 4 George Harrison ``` --- ## Note: `bind_rows()` requires the **same** columns names: ```r *colnames(members2) <- c("firstName", "LastName") bind_rows(members1, members2) ``` ``` ## # A tibble: 4 x 3 ## firstName lastName LastName ## <chr> <chr> <chr> ## 1 John Lennon <NA> ## 2 Paul McCartney <NA> ## 3 Ringo <NA> Starr ## 4 George <NA> Harrison ``` Note how `<NA>`s were created --- <div class="countdown" id="timer_5fc811aa" style="top:0;right:0;font-size:2em;" data-warnwhen="30"> <code class="countdown-time"><span class="countdown-digits minutes">03</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code> </div> ## Quick practice Answer these questions using the `animals_farm` and `animals_pet` data frames: 1. Write code to find how many _rows_ are in the `animals_farm` data frame? 2. Write code to find how many _columns_ are in the `animals_pet` data frame? 3. Create a new data frame, `animals`, by combining `animals_farm` and `animals_pet`. 4. Change the column names of `animals` to title case. --- class: inverse, middle # Week 11: .fancy[Data Frames] ## 1. Basics ## 2. .orange[Slicing] ## 3. External data --- ## &zwj; Access data frame columns using the `$` symbol ```r beatles$firstName ``` ``` ## [1] "John" "Paul" "Ringo" "George" ``` -- ```r beatles$lastName ``` ``` ## [1] "Lennon" "McCartney" "Starr" "Harrison" ``` --- # Creating new variables with the `$` symbol -- Add the hometown of the bandmembers: ```r beatles$hometown <- 'Liverpool' beatles ``` ``` ## # A tibble: 4 x 6 ## firstName lastName instrument yearOfBirth deceased hometown ## <chr> <chr> <chr> <dbl> <lgl> <chr> ## 1 John Lennon guitar 1940 TRUE Liverpool ## 2 Paul McCartney bass 1942 FALSE Liverpool ## 3 Ringo Starr drums 1940 FALSE Liverpool ## 4 George Harrison guitar 1943 TRUE Liverpool ``` --- # Creating new variables with the `$` symbol -- Add a new `alive` variable: ```r beatles$alive <- c(FALSE, TRUE, TRUE, FALSE) beatles ``` ``` ## # A tibble: 4 x 7 ## firstName lastName instrument yearOfBirth deceased hometown alive ## <chr> <chr> <chr> <dbl> <lgl> <chr> <lgl> ## 1 John Lennon guitar 1940 TRUE Liverpool FALSE ## 2 Paul McCartney bass 1942 FALSE Liverpool TRUE ## 3 Ringo Starr drums 1940 FALSE Liverpool TRUE ## 4 George Harrison guitar 1943 TRUE Liverpool FALSE ``` --- ## You can compute new variables from current ones -- Compute and add the age of the bandmembers: ```r beatles$age <- 2020 - beatles$yearOfBirth beatles ``` ``` ## # A tibble: 4 x 8 ## firstName lastName instrument yearOfBirth deceased hometown alive ## <chr> <chr> <chr> <dbl> <lgl> <chr> <lgl> ## 1 John Lennon guitar 1940 TRUE Liverpool FALSE ## 2 Paul McCartney bass 1942 FALSE Liverpool TRUE ## 3 Ringo Starr drums 1940 FALSE Liverpool TRUE ## 4 George Harrison guitar 1943 TRUE Liverpool FALSE ## age ## <dbl> ## 1 80 ## 2 78 ## 3 80 ## 4 77 ``` --- ## Access elements by index: `DF[row, column]` General form for indexing elements: ```r DF[row, column] ``` -- .leftcol[ Select the element in row 1, column 2: ```r beatles[1, 2] ``` ``` ## # A tibble: 1 x 1 ## lastName ## <chr> ## 1 Lennon ``` ] -- .rightcol[ Select the elements in rows 1 & 2 and columns 2 & 3: ```r beatles[c(1, 2), c(2, 3)] ``` ``` ## # A tibble: 2 x 2 ## lastName instrument ## <chr> <chr> ## 1 Lennon guitar ## 2 McCartney bass ``` ] --- ## Leave row or column "blank" to select all -- ```r beatles[c(1, 2),] # Selects all COLUMNS for rows 1 & 2 ``` ``` ## # A tibble: 2 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 John Lennon guitar 1940 TRUE ## 2 Paul McCartney bass 1942 FALSE ``` -- ```r beatles[,c(1, 2)] # Selects all ROWS for columns 1 & 2 ``` ``` ## # A tibble: 4 x 2 ## firstName lastName ## <chr> <chr> ## 1 John Lennon ## 2 Paul McCartney ## 3 Ringo Starr ## 4 George Harrison ``` --- ## Negative indices exclude row / column -- ```r beatles[-1, ] # Select all ROWS except the first ``` ``` ## # A tibble: 3 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 Paul McCartney bass 1942 FALSE ## 2 Ringo Starr drums 1940 FALSE ## 3 George Harrison guitar 1943 TRUE ``` -- ```r beatles[,-1] # Select all COLUMNS except the first ``` ``` ## # A tibble: 4 x 4 ## lastName instrument yearOfBirth deceased ## <chr> <chr> <dbl> <lgl> ## 1 Lennon guitar 1940 TRUE ## 2 McCartney bass 1942 FALSE ## 3 Starr drums 1940 FALSE ## 4 Harrison guitar 1943 TRUE ``` --- # You can select columns by their names Note: you don't need the comma to select an entire column -- .leftcol[ One column ```r beatles['firstName'] ``` ``` ## # A tibble: 4 x 1 ## firstName ## <chr> ## 1 John ## 2 Paul ## 3 Ringo ## 4 George ``` ] -- <br>Multiple columns .rightcol[ ```r beatles[c('firstName', 'lastName')] ``` ``` ## # A tibble: 4 x 2 ## firstName lastName ## <chr> <chr> ## 1 John Lennon ## 2 Paul McCartney ## 3 Ringo Starr ## 4 George Harrison ``` ] --- ## Use logical indices to _filter_ rows -- **Which Beatles members are still alive?**<br>Create a logical vector using the `deceased` column: ```r beatles$deceased == FALSE ``` ``` ## [1] FALSE TRUE TRUE FALSE ``` -- Insert this logical vector in the ROW position of `beatles[,]`: ```r beatles[beatles$deceased == FALSE,] ``` ``` ## # A tibble: 2 x 5 ## firstName lastName instrument yearOfBirth deceased ## <chr> <chr> <chr> <dbl> <lgl> ## 1 Paul McCartney bass 1942 FALSE ## 2 Ringo Starr drums 1940 FALSE ``` --- class: inverse <div class="countdown" id="timer_5fc811c8" 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> ## Think-Pair-Share Answer these questions using the `beatles` data frame: 1. Create a new column, `playsGuitar`, which is `TRUE` if the band member plays the guitar and `FALSE` otherwise. 2. Filter the data frame to select only the rows for the band members who have four-letter first names. 3. Create a new column, `fullName`, which contains the band member's first and last name separated by a space (e.g. `"John Lennon"`) --- class: inverse, center # .fancy[Break] <div class="countdown" id="timer_5fc80f2d" 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 11: .fancy[Data Frames] ## 1. Basics ## 2. Slicing ## 3. .orange[External data] --- # Getting data into R <br> ## 1. Load external packages ## 2. Read in external files (usually .csv files) --- ## Getting the data from an R package ```r library(ggplot2) ``` -- ```r data(package = "ggplot2") ``` ``` Data sets in package ‘ggplot2’: diamonds Prices of over 50,000 round cut diamonds economics US economic time series economics_long US economic time series faithfuld 2d density estimate of Old Faithful data luv_colours 'colors()' in Luv space midwest Midwest demographics mpg Fuel economy data from 1999 to 2008 for 38 popular models of cars msleep An updated and expanded version of the mammals sleep dataset presidential Terms of 11 presidents from Eisenhower to Obama seals Vector field of seal movements txhousing Housing sales in TX ``` --- # Find out about package data sets with `?` ```r ?msleep ``` ``` msleep {ggplot2} An updated and expanded version of the mammals sleep dataset Description This is an updated and expanded version of the mammals sleep dataset. Updated sleep times and weights were taken from V. M. Savage and G. B. West. A quantitative, theoretical framework for understanding mammalian sleep. Proceedings of the National Academy of Sciences, 104 (3):1051-1056, 2007. ``` --- # Previewing data frames: `msleep` -- Look at the data in a "spreadsheet"-like way: ```r View(msleep) ``` This is "read-only" so you can't corrupt the data 😄 --- # My favorite quick summary: `glimpse()` Preview each variable with `str()` or `glimpse()` ```r glimpse(msleep) ``` .code80[ ``` ## Rows: 83 ## Columns: 11 ## $ name <chr> "Cheetah", "Owl monkey", "Mountain beaver", "Grea… ## $ genus <chr> "Acinonyx", "Aotus", "Aplodontia", "Blarina", "Bo… ## $ vore <chr> "carni", "omni", "herbi", "omni", "herbi", "herbi… ## $ order <chr> "Carnivora", "Primates", "Rodentia", "Soricomorph… ## $ conservation <chr> "lc", NA, "nt", "lc", "domesticated", NA, "vu", N… ## $ sleep_total <dbl> 12.1, 17.0, 14.4, 14.9, 4.0, 14.4, 8.7, 7.0, 10.1… ## $ sleep_rem <dbl> NA, 1.8, 2.4, 2.3, 0.7, 2.2, 1.4, NA, 2.9, NA, 0.… ## $ sleep_cycle <dbl> NA, NA, NA, 0.1333333, 0.6666667, 0.7666667, 0.38… ## $ awake <dbl> 11.9, 7.0, 9.6, 9.1, 20.0, 9.6, 15.3, 17.0, 13.9,… ## $ brainwt <dbl> NA, 0.01550, NA, 0.00029, 0.42300, NA, NA, NA, 0.… ## $ bodywt <dbl> 50.000, 0.480, 1.350, 0.019, 600.000, 3.850, 20.4… ``` ] --- ## Also very useful for quick checks: `head()` and `tail()` .leftcol[ View the **first** 6 rows with `head()` ```r head(msleep) ``` .code40[ ``` ## # A tibble: 6 x 11 ## name genus vore order conservation ## <chr> <chr> <chr> <chr> <chr> ## 1 Cheetah Acinonyx carni Carnivora lc ## 2 Owl monkey Aotus omni Primates <NA> ## 3 Mountain beaver Aplodontia herbi Rodentia nt ## 4 Greater short-tailed shrew Blarina omni Soricomorpha lc ## 5 Cow Bos herbi Artiodactyla domesticated ## 6 Three-toed sloth Bradypus herbi Pilosa <NA> ## sleep_total sleep_rem sleep_cycle awake brainwt bodywt ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 12.1 NA NA 11.9 NA 50 ## 2 17 1.8 NA 7 0.0155 0.48 ## 3 14.4 2.4 NA 9.6 NA 1.35 ## 4 14.9 2.3 0.133 9.1 0.00029 0.019 ## 5 4 0.7 0.667 20 0.423 600 ## 6 14.4 2.2 0.767 9.6 NA 3.85 ``` ]] .rightcol[ View the **last** 6 rows with `tail()` ```r tail(msleep) ``` .code40[ ``` ## # A tibble: 6 x 11 ## name genus vore order conservation ## <chr> <chr> <chr> <chr> <chr> ## 1 Tenrec Tenrec omni Afrosoricida <NA> ## 2 Tree shrew Tupaia omni Scandentia <NA> ## 3 Bottle-nosed dolphin Tursiops carni Cetacea <NA> ## 4 Genet Genetta carni Carnivora <NA> ## 5 Arctic fox Vulpes carni Carnivora <NA> ## 6 Red fox Vulpes carni Carnivora <NA> ## sleep_total sleep_rem sleep_cycle awake brainwt bodywt ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 15.6 2.3 NA 8.4 0.0026 0.9 ## 2 8.9 2.6 0.233 15.1 0.0025 0.104 ## 3 5.2 NA NA 18.8 NA 173. ## 4 6.3 1.3 NA 17.7 0.0175 2 ## 5 12.5 NA NA 11.5 0.0445 3.38 ## 6 9.8 2.4 0.35 14.2 0.0504 4.23 ``` ]] --- # Importing an external data file <br> Note the `data.csv` file in your `data` folder. - **DO NOT** double-click it! - **DO NOT** open it in Excel! PSA: Excel can **corrupt** your data --- # Steps to importing external data files -- ## 1. Create a path to the data ```r library(here) *pathToData <- here('data', 'data.csv') pathToData ``` ``` ## [1] "/Users/jhelvy/gh/0gw/P4A/2020-Fall/class/11-data-frames/data/data.csv" ``` -- ## 2. Import the data ```r library(readr) *df <- read_csv(pathToData) ``` --- ## PSA: Use the **here** package to make file paths The `here()` function builds the path to your **root** to your _working directory_ <br>(this is where your `.Rproj` file lives!) ```r here() ``` ``` ## [1] "/Users/jhelvy/gh/0gw/P4A/2020-Fall/class/11-data-frames" ``` -- The `here()` function builds the path to files _inside_ your working directory ```r pathToData <- here('data', 'data.csv') pathToData ``` ``` ## [1] "/Users/jhelvy/gh/0gw/P4A/2020-Fall/class/11-data-frames/data/data.csv" ``` --- # Avoid hard-coding file paths! ### (they can break on different computers) ```r pathToData <- 'data/data.csv' pathToData ``` ``` ## [1] "data/data.csv" ``` # 💩💩💩 --- class: center .leftcol40[.left[ ## PSA:<br>Use the **here** package to make file paths ]] .rightcol60[ <center><br> <img src="images/horst_monsters_here.png"> </center>Art by [Allison Horst](https://www.allisonhorst.com/) ] --- # Back to reading in data ```r pathToData <- here('data', 'data.csv') *df <- read_csv(pathToData) ``` **Important**: Note the use of `read_csv()` instead of `read.csv()` I recommend `read_csv()`...it is usually more robust --- class: inverse <div class="countdown" id="timer_5fc811c3" 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> ## Think-Pair-Share .font90[ 1) Use the `here()` and `read_csv()` functions to load the `data.csv` file that is in the `data` folder. Name the data frame object `df`. 2) Use the `df` object to answer the following questions: - How many rows and columns are in the data frame? - What type of data is each column? - Preview the different columns - what do you think this data is about? What might one row represent? - How many unique airports are in the data frame? - What is the earliest and latest observation in the data frame? - What is the lowest and highest cost of any one repair in the data frame? ] --- class: center ## Next week: 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/) --- # Select rows with `filter()` Example: Filter rows to find which Beatles members are still alive? -- Base R: ```r beatles[beatles$deceased == FALSE,] ``` -- &zwj;**dplyr**: ```r filter(beatles, deceased == FALSE) ``` --- # In 2 weeks: plotting with **ggplot2** .leftcol[ ## Translate _data_... ``` ## # A tibble: 11 x 2 ## brainwt bodywt ## <dbl> <dbl> ## 1 0.001 0.06 ## 2 0.0066 1 ## 3 0.000140 0.005 ## 4 0.0108 3.5 ## 5 0.0123 2.95 ## 6 0.0063 1.7 ## 7 4.60 2547 ## 8 0.000300 0.023 ## 9 0.655 521 ## 10 0.419 187 ## 11 0.0035 0.77 ``` ] .rightcol[ ## ...into _information_ <img src="slides-11-data-frames_files/figure-html/unnamed-chunk-76-1.png" width="468" /> ] --- # A note about HW9 - You have what you need to start now. - It will be _much_ easier if you use the **dplyr** functions (i.e. read ahead).