Hi again. Now that we've got a little more experience with the data frames, we can start doing some interesting things like clean, standardize, manipulate, and visualize data. We'll go through some common tasks that you'll perform as a data analyst. But we're just scratching the surface of what you might want to do in R. We'll start with the basics and learn how to clean up our columns. There will be a reading with a handy list you can refer to afterwards too. Let's install the Here, Skimr and Janitor packages now. We'll go ahead and open our console. First, we'll add the Here package. This package makes referencing files easier. To install it, we'll just write install.packages. Then in the parentheses, we'll put Here and RStudio will install it. After we install it, we'll also need to load it using library. Next, we'll install Skimr and Janitor. As a quick reminder, these packages simplify data cleaning tasks. They're both really useful and do slightly different things. The Skimr package makes summarizing data really easy and let's you skim through it more quickly. We'll install it now. The Janitor package has functions for cleaning data. After it's done installing, we'll still need to load it. Finally, we want to make sure the dplyr package is loaded since we are going to be using some of its features. There, now we've got all the packages we need for basic data cleaning. Now, let's load some data in. Later, when you're practicing with your own data, you can use read to grab a file. For example, if you had a CSV you wanted to load, you could write, read underscore CSV, and input the file name in the parentheses. This is where the Here package comes in handy. Be sure to install and load the Here package before trying to save CSV files. For now, we'll load a really fun package to practice with, the palmer penguin package. This is a dataset we've used before, but just as a quick reminder, the palmer penguin data has lots of information about three penguin species in the Palmer Archipelago, including size measurements, clutch sizes, and blood isotope ratios. Who doesn't love penguins? First, we'll install the package. We'll type install.packages and input palmerpenguins. Then remember to load it by using the library function. Now that we've got this data loaded into our library, we can try some cleaning functions on our columns. There's a few different functions that we can use to get summaries of our data frame. Skim without charts, glimpse, head, and select. The skim without charts function gives us a pretty comprehensive summary of a dataset. Let's try it out. When we run this, we get a lot of info back. First, it gives us a summary with the name of the dataset and the number of rows and columns. It also gives us the column types and a summary of the different data types contained in the data frame. Or we could use Glimpse to get a really quick idea of what's in this dataset. When we run this command, it'll show us a summary of the data. There's 344 rows and eight columns. We have species, island, measurements for bills, which are basically beaks and flippers, the penguins' body mass in grams, the sex, and finally, the year the data was recorded. We can also use Head to get a preview of the column names and the first few rows of this data set. Having the column names summarized like this will make it easier to clean them up. We can use select to specify certain columns or to exclude columns we don't need right now. Let's say we only need to check the species column. We can input penguins, then a pipe to indicate we'll add another command, and our select. We'll jump back into an R script because it will be easier to see. Now we have just the species column, or maybe we want everything except the species column. We'll put minus species instead of species, and now we have every column but species. The select statement is useful for pulling just a subset of variables from a large dataset. This lets you focus on specific groups of variables. There's a lot of other select functions that build on this that we'll cover later on. Now that we know our column names, we've got a better idea of what we might want to change. The rename function makes it easy to change column names. Starting with the penguin data, we'll type rename and change the name of our island column to island underscore new. Now, looking at our column names, we can see the column name has changed. Or let's say we want to change our columns so that they're spelled and formatted correctly. In spreadsheet programs, as long as our column names are meaningful, they're fine. But since we have to type the column names over and over in R, we need them to be consistent. Similar to the rename function, the rename_with() function can change column names to be more consistent. For example, maybe we want all of our column names to be in uppercase. We can use the rename_with() function to do that. This will automatically make our column names uppercase. But since variable names are usually lowercase, we'll use the "To lower" option to change it back. The clean names function in the Janitor package will automatically make sure that the column names are unique and consistent. Let's try the clean names function on our penguins data. This ensures that there's only characters, numbers, and underscores in the names. Now you know some functions for cleaning columns in your datasets. Try practicing them on your own with the palmer penguins data. Once you're comfortable with these functions, we'll learn even more about data cleaning in R. See you soon.
