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Getting help in R is easy. **Usage** `?something` * This command pulls up the help file for whatever you write in the place of `something`. **Examples** Click to view. Hover to learn.

? The quick way to access the help function in R. cars The name of a dataset can be typed to open the help file for that dataset. Press Enter to run the code. Click to Show Output Click to View Output.

? The quick way to access the help function in R. data The name of an R function, like `data` can also be used to open the help file for that function. Press Enter to run the code. Click to Show Output Click to View Output.

? The quick way to access the help function in R. mean The `mean` function computes the mean of a column of quantitative data. Typing the name of an R function, like `mean` can also be used to open the help file for that function. Press Enter to run the code. Click to Show Output Click to View Output.

Once you have a dataset, you need to be able to access columns from it. **Usage** `DataSetName$ColumnName` * The `$` operator allows you to access the individual columns of a dataset. Tip: think of the data set as a "store" from which you "purchase" a column using "money": `$`. **Example Code**

airquality The `airqaulity` dataset. This could be the name of any dataset instead of `airquality`. $ Grabs the column, or variable, from the dataset to be used. This is typically used when computing say the mean (or other statistic) of a single column of the data. Wind The name of any column of the dataset can be entered after the dollar sign. In the airquality dataset, this includes: `Ozone`, `Solar.R`, `Wind`, `Temp`, `Month`, or `Day` as shown by `View(airquality)`. Press Enter to run the code. Click to Show Output Click to View Output.

This allows you to compute things about that column, like the mean or standard deviation.

mean( The `mean` function computes the mean of a column of quantitative data. airquality The `airqaulity` dataset. This could be the name of any dataset instead of `airquality`. $ Grabs the column, or variable, from the dataset to be used. This is typically used when computing say the mean (or other statistic) of a single column of the data. Wind The name of any column of the dataset can be entered after the dollar sign. In the airquality dataset, this includes: `Ozone`, `Solar.R`, `Wind`, `Temp`, `Month`, or `Day` as shown by `View(airquality)`. ) Closing parenthesis to the mean() function. Press Enter to run the code. Click to Show Output Click to View Output.

sd( The `sd` function computes the standard deviation of a column of quantitative data. airquality The `airqaulity` dataset. This could be the name of any dataset instead of `airquality`. $ Grabs the column, or variable, from the dataset to be used. This is typically used when computing say the mean (or other statistic) of a single column of the data. Wind The name of any column of the dataset can be entered after the dollar sign. In the airquality dataset, this includes: `Ozone`, `Solar.R`, `Wind`, `Temp`, `Month`, or `Day` as shown by `View(airquality)`. ) Closing parenthesis to the sd() function. Press Enter to run the code. Click to Show Output Click to View Output.

Being able to save your work is important! **Usage** Keyboard Shortcut: `Alt` `-` `NameYouCreate <- some R commands` * `<-` (Less than symbol `<` with a hyphen `-`) is called the assignment operator and lets you store the results of the `some R commands` into an object called `NameYouCreate`. * `NameYouCreate` is any name that *begins with a letter*, but can use numbers, periods, and underscores thereafter. To use spaces in the name, you must use \``your Name`\` encased in back-ticks, but this is not recommended. **Example Code**

cars2 First we name the object we are creating. In this case, we are making a copy of the cars dataset, so it is logical to call it `cars2`, but it could be `bob`, `c2` or any name you wanted to use. Just be careful to not use names that are already in use! <- The `<-` assignment operator will take whatever is on the right hand side and save it into the name written on the left hand side. cars In this case the `cars` dataset is being copied to `cars2` so that we can change `cars2` without changing the original `cars` dataset. Press Enter to run the code. cars2 The new copy of the `cars` dataset that we just created \$ftpersec The `$` selection operator can be used to create a new column in a dataset when used with the `<-` assignment operator. <- The `<-` assignment operator will take the results of the right-hand-side and save them into the name on the left-hand-side. cars2$speed \* 5280 / 3600 This calculation converts the miles per hour of the `cars2` `speed` column into feet per seconds because there are 5280 feet in a mile and 60 minutes in an hour and 60 seconds in a minute. View(cars2) The `cars2` dataset now contains a 3rd column called `feetpersec`. Compare this to the original `cars` dataset to see how it changed. Click to Show Output Click to View Output.

Think of this function as the "back-pack" function, just like putting different books into one back-pack. **Usage** `c(value 1, value 2, value 3, ... )` * The `c( )` function combines `values` into a single object called a "vector". * `values 1, 2, 3, ...` can be numbers or characters, i.e., words, but must be all of one type or the other. **Example Code**

Classlist <- `Classlist` is a new object being created using the assignment operator `<-` that will contain the four names listed above. c( The combine function `c( )` is being used in this case to group character values representing names of students into a single object named "Classlist". "Jackson", "Jared", "Jill", "Jane") These are the values we are grouping into the object named `Classlist`. Press Enter to run the code. Ages <- The assignment operator `<-` is being used to create the object called `Ages` that will contain the ages of each student on the `Classlist`. c( The R function "c()" allows us to group together values in order to save them into an object. 8, 9, 7, 8 The values, separated by comma's, that are being grouped together. In this case, numbers are being grouped together. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Colors <- The assignment operator `<-` is being used to create the object called `Colors` that will have one color for each student on the `Classlist`. c( The R function "c()" allows us to group together values in order to save them into an object. "red", "blue", "green", "yellow" The values, separated by comma's, that are being grouped together. In this case, characters are being grouped together. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

This is a way to quickly count how many times each value occurs in a column or columns. **Usage** `table(NameOfDataset$columnName)` `table(NameOfDataset$columnName1, NameOfDataset$columnName2)` * The `table( )` function counts how many times each `value` in a column of data occurs. * `NameOfDataset` is the ane of a data set, like `cars` or `airquality` or `KidsFeet`. * `columnName` is the name of a column from the data set. * `columnName1` and `columnName2` are two different names of columns from the data set. **Example Code**

speedCounts <- `speedCounts` is a new object being created using the assignment operator `<-` that will contain the counts of how many times each "speed" occurs in the `cars` data set speed column. table( The table function `table( )` is being used in this case to count how many times each speed occurs in the cars data set speed column. cars This is the name of the data set. $ The $ is used to access a given column from the data set. speed This is the name of the column we are interested in from the cars data set. ) Always close off your functions in R with a closing parathesis.
speedCounts Typing the name of an object will print the results to the screen. Press Enter to run the code. Click to Show Output Click to View Output.

library(mosaicData) `library(mosaicData)` is needed to access the KidsFeet data set that is used in this example. If you don't have the mosaic library, you will need to run `install.packages("mosaic")` to install it first. From then on, you can open mosaic to use it with the command library(mosaicData). You need only install packages once. You must library them each time you wish to use them.
birthdays <- `birthdays` is a new object being created using the assignment operator `<-` that will contain the counts of how many birthdays occur in each month for each gender in the KidsFeet dataset. table( The table function `table( )` is being used in this case to count how many birthdays occur in each month for children of each gender. KidsFeet This is the name of the data set. $ The $ is used to access a given column from the data set. sex This is the name of the column we are interested in becoming the rows of our final table. , Comma separating the two columns of the data set you want to table. KidsFeet This is the name of the data set. $ The $ is used to access a given column from the data set. birthmonth This is the name of the column we are interested in becoming the columns of our final table. ) Always close off your functions in R with a closing parathesis.
birthdays Typing the name of an object will print the results to the screen. Press Enter to run the code. Click to Show Output Click to View Output.

Used to reduce a dataset to a smaller set of rows than the original dataset contained. **Usage** `filter(NameOfDataset, columnName filteringRules)` * `filter()` is the function that filters out certain rows of the dataset. * `NameOfDataset` is the name of a dataset, like `cars` or `airquality` or `KidsFeet`. * `columnName` is the name of one of the columns from the dataset. You can use `colnames(NameOfDataset)` or `View(NameOfDataset)` to see the names. * `filteringRules` consists of some **Logical Expression** (see table below) that selects only the rows from the original dataset that meet the criterion.

| **Filtering Rule** | **Logical Expression** | |--------------------------------------------|------------| | Equals one "thing" | `columnName` `==` `something` | | Equals Any Of Several "things" | `columnName` `%in%` `c(something1,something2,...)` | | Not Equal (one thing)| `columnName` `!=` `something` | | Not Equals Any of (several things) | `!columnName` `%in%` `c(something1,something2,...)` | | Less Than | `columnName` `<` `value` | | Less Then or Equal to | `columnName` `<=` `value` | | Greater Than | `columnName` `>` `value` | | Greater Than or Equal to | `columnName` `>=` `value` | | AND | `expression1` `&` `expression2` | | OR | `expression1` `|` `expression2` | | Equals `NA` | `is.na(columnName)` | | Not `NA` | `!is.na(columnName)` |

**Example Code**

library(tidyverse) The tidyverse library is needed to access the filter function used in the following example codes.
library(mosaicData) The mosaic library is needed to access the KidsFeet data set used in the following example codes.

*Equals one "thing"*...

Kids87 <- Kids87 is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. birthyear A quantitative column of the KidsFeet dataset that we want to use to reduce the dataset. == 87 This "filtering rule" filters the data down to just those children who had a birthyear equal to 87. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

KidsBoys <- KidsBoys is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. sex A categorical column of the KidsFeet dataset that we want to use to reduce the dataset. == "B" This "filtering rule" filters the data down to just those children who are boys. Words must be quoted "B" but values are just typed directly. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*Equals Any of Several "things"*...

KidsSummer <- KidsSummer is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. birthmonth The column of the KidsFeet dataset that we want to use to reduce the dataset. %in% c(6,7,8) This is the "filtering rule". It will filter the data down to just those children who were born during the summer, i.e., birthmonth equal to either 6, 7, or 8. Notice how the c( ) function is being used to combine the values of 6, 7, and 8 together into a single list of numbers. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*Does not equal one thing*...

KidsNotJosh <- KidsNotJosh is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. name The column of the KidsFeet dataset that we want to use to reduce the dataset. != "Josh" This is the "filtering rule". It will filter the data down to just those children who are NOT named "Josh". In this case, it removed just two students who were named "Josh". ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*Less than*...

KidsLength24 <- KidsLength24 is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. length The column of the KidsFeet dataset that we want to use to reduce the dataset. < 24 This is the "filtering rule". It will filter the data down to just those children who have a foot length less than 24 cm. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*Less than or equal to*...

KidsLessEq24 <- KidsLessEq24 is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. length The column of the KidsFeet dataset that we want to use to reduce the dataset. <= 24 This is the "filtering rule". It will filter the data down to just those children who have a foot length less than or equal to 24 cm. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*Greater than*...

KidsWider9 <- KidsNotJosh is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. width The column of the KidsFeet dataset that we want to use to reduce the dataset. > 9 This is the "filtering rule". It will filter the data down to just those children who have a foot width greater than 9 cm. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*Greater than or equal to*...

KidsWiderEq9 <- KidsWiderEq9 is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. width The column of the KidsFeet dataset that we want to use to reduce the dataset. >= 9 This is the "filtering rule". It will filter the data down to just those children who have a foot width greater than or equal to 9 cm. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*The "and" statement*...

GirlsWide9 <- GirlsWide9 is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. sex The first column of the KidsFeet dataset that we want to use to reduce the dataset. == "G" This is the first "filtering rule". It will filter the data down to just those children who are girls. & The & is the AND statement. It joins to filtering criteria together into a single criteria where both conditions must be met. In this case, it ensures we get only girls with foot widths greater than 9 cm. width The second column of the KidsFeet dataset that we want to use to reduce the dataset. > 9 This is the second "filtering rule". It will filter the data down to just those children who have a foot width greater than 9 cm. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

*The "or" statement*...

KidsWinter <- KidsWinter is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `filter(...)` function into this name. filter(KidsFeet, "filter" is a function from library(tidyverse) that reduces the number of rows in the KidsFeet dataset by filtering according to certain criteria. Click on this code to see the original and filtered datasets. birthmonth The first column of the KidsFeet dataset that we want to use to reduce the dataset. <= 2 This is the first "filtering rule". It will filter the data down to just those children who are born in January or February. | The | is the OR statement. It joins to filtering criteria together into a single criteria where either condition gives us what we want. In this case, it keeps any child born in January, February, November, or December. birthmonth The second column of the KidsFeet dataset that we want to use to reduce the dataset. In this case, it is the same as the first column. >= 11 This is the second "filtering rule". It will filter the data down to just those children who are born in November or December. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

Used to select out certain columns from a dataset. **Usage** `select(NameOfDataset, listOfColumnNames)` * `select( )` is the function that selects out certain columns of the dataset. * `NameOfDataset` is the name of a dataset, like `cars` or `airquality` or `KidsFeet`. * `listOfColumnNames` is a vector of names of columns from the dataset, usually supplied inside a combine `c(...)` statement. **Example Code**

KidsNameBirth <- KidsNameBirth is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `select(...)` function into this name. select(KidsFeet, "select" is a function from library(tidyverse) that selects out specified columns from the original dataset in the order specified. c(name, birthyear, birthmonth) The columns of the KidsFeet dataset that we want to select out of the original dataset. Notice how the concatenation function `c(...)` is used to list out the columns we want. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

KidsBigLength <- KidsBigLength is a name we made up. The assignment operator `<-` will save the reduced version of the `KidsFeet` dataset created by the `select(...)` function into this name. select(KidsFeet, "select" is a function from library(tidyverse) that selects out specified columns from the original dataset in the order specified. c(name, length) The columns of the KidsFeet dataset that we want to select out of the original dataset. Notice how the concatenation function `c(...)` is used to list out the columns we want. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

Just like the pipes in your kitchen sink, the pipe operator takes "water from the sink" and "sends it down to somewhere else." **Usage** Keyboard Shortcut: `Ctrl` `Shift` `M` `NameOfDataset %>% ` `some R commands that follow on the next line` * `%>%`, the pipe operator, is created by typing percent symbols `%` on both sides of a greater than symbol `>`. It lets you take whatever is on the *left* of the symbol and "pipe it down into" `some R commands` that follow on the next line. * `NameOfDataset` is the name of a dataset, like `cars` or `airquality` or `KidsFeet`. **Note**: you should load `library(tidyverse)` before using the `%>%` operator. **Example Code**

Kids2 <- This provides a name for the new reduced version of the `KidsFeet` dataset that is going to be created by the combined use of `filter(...)` and `select(...)`. KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. Click on this code to View the dataset and the resulting Kids2 dataset. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
filter( "filter" is a function from library(tidyverse) that allows us to reduce the number of rows in the KidsFeet dataset by filtering according to certain criteria. birthyear Represents the column of data that we want to use to reduce the rows of the dataset. == 87 This is the "filtering rule". It will filter the data down to just those children who had a birthyear equal to 87. ) Always close off your functions in R with a closing parathesis. %>% The pipe operator that will send the filtered version of the `KidsFeet` dataset down inside of the code on the following line.
select( "select" is a function from library(tidyverse) that selects out specified columns from the current dataset in the order specified. c(name, birthyear, length) The columns of the filtered KidsFeet dataset that we want to select. Notice how the concatenation function `c(...)` is used to list out the columns we want. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

Compute numerical summaries on data or on groupings within the data. **Usage** `NameofDataset %>%` `summarise(nameYouLike = some_stats_function(columnName))` OR `NameofDataset %>%` `group_by(columnGroupsName) %>%` `summarise(nameYouLike = some_stats_function(columnName))` * `NameOfDataset` is the name of a dataset, like `cars` or `airquality` or `KidsFeet`. * `%>%` is the pipe operator that "pipes data" down into R commands on the next line. * `group_by(...)` is an R function from `library(tidyverse)` that groups data according to a specified column (or columns). * `summarise(...)` is an R function from `library(tidyverse)` that computes numerical summaries on data or groups of data. * `columnGroupsName` is the name of a column that represents qualitative (categorical) data. This column is used to separate the dataset into little datasets, one "little dataset" for each group or category in the `columnGroupsName` column. * `nameYouLike` is just that. Some name you come up with. * `some_stats_function(...)` is a stats function like `mean(...)`, `sd(...)`, `n(...)` or so on. * `columnName` is the name of a column from the dataset that you want to compute numerical summaries on. **Example Code**

KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
summarise( "summarise" is a function from library(tidyverse) that allows us to compute numerical summaries on data. aveLength A name we came up with that will store the results of the numerical summary. = mean(length) This computes the `mean(...)` of the `length` column from the KidsFeet dataset. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
   summarise( "summarise" is a function from library(tidyverse) that allows us to compute numerical summaries on data. aveLength A name we came up with that will store the results of the numerical summary. = mean(length), This computes the `mean(...)` of the `length` column from the KidsFeet dataset.
             sdLength A name we came up with that will store the results of the numerical summary. = sd(length), This computes the `sd(...)` of the `length` column from the KidsFeet dataset.
             sampleSize A name we came up with that will store the results of the numerical summary. = n( ) This computes the `n(...)`, or sample size, of the `length` column from the KidsFeet dataset. ) Always close off your functions in R with a closing parathesis.      Press Enter to run the code. Click to Show Output Click to View Output.

KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
   group_by( "group_by" is a function from library(tidyverse) that allows us to split the dataset up into "little groups" according to the column specified. sex "sex" is a column from the KidsFeet dataset that records the gender of each child. ) Always close off your functions in R with a closing parathesis. %>% The pipe operator that will send the grouped according to gender version of the `KidsFeet` dataset down inside of the code on the following line.
   summarise( "summarise" is a function from library(tidyverse) that allows us to compute numerical summaries on data. aveLength A name we came up with that will store the results of the numerical summary. = mean(length), This computes the `mean(...)` of the `length` column from the KidsFeet dataset.
             sdLength A name we came up with that will store the results of the numerical summary. = sd(length), This computes the `sd(...)` of the `length` column from the KidsFeet dataset.
             sampleSize A name we came up with that will store the results of the numerical summary. = n( ) This computes the `n(...)`, or sample size, of the `length` column from the KidsFeet dataset. ) Always close off your functions in R with a closing parathesis.      Press Enter to run the code. Click to Show Output Click to View Output.

Transform a column of data. **Usage** `NameofDataset %>%` `mutate(nameYouLike = some_transformation)` * `NameOfDataset` is the name of a dataset, like `cars` or `airquality` or `KidsFeet`. * `%>%` is the pipe operator that "pipes data" down into R commands on the next line. * `nameYouLike` is just that. Some name you come up with that will be the name of a new column in the dataset. * `some_transformation` is just that. See the example codes for ideas. **Example Code**

mtcars2 <- `mtcars2` is a new dataset we are creating that will contain all of `mtcars` data set along with a couple new columns we are creating. mtcars `mtcars` is a dataset found in base R. Typing `View(mtcars)` and `?mtcars` in the console will help you learn more about the dataset. %>% The pipe operator that will send the `mtcars` dataset down inside of the code on the following line.
   mutate( "mutate" is a function from library(tidyverse) that allows us to transform columns of data.
         cyl_factor = as.factor(cyl), "cyl_factor" is a name we came up with that will store the results of the transformation of the "cyl" column. Here we are simply converting the "cyl" column from type numeric to a factor. Treating the "cyl" column as a factor could be useful in certain situations.
         weight = wt * 1000 "weight" is a name we came up with that will store the results of the transformation of the "wt" column. Taking a closer look with `?mtcars` shows us that wt is in 1000 lbs. Here we are just multiplying each row in the column by 1000.
   ) Closing parenthesis for the mutate(...) function.      Press Enter to run the code. Click to Show Output Click to View Output.

Kids3 <- `Kids3` is a new dataset we are creating that will contain all of `KidsFeet` data set along with a couple new columns we are creating. KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
   mutate( "mutate" is a function from library(tidyverse) that allows us to transform columns of data.
         season = case_when( "season" is a name we came up with that will store the results of the transformation of the "birthmonth" column. The case_when(...) function from library(tidyverse) allows us to perform more complicated transformations with columns.
           birthmonth %in% c(12,1,2) ~ "Winter", The body of case_when(...) is of the form `logical expression ~ "newValueName"`. This statement says that we want the values in the column "birthmonth" that are equal to 12, 1, and 2 to be assigned to the value "Winter" in the new "season" column.
           birthmonth %in% c(3,4,5) ~ "Spring", The body of case_when(...) is of the form `logical expression ~ "newValueName"`. This statement says that we want the values in the column "birthmonth" that are equal to 3, 4, and 5 to be assigned to the value "Spring" in the new "season" column.
           birthmonth %in% c(6,7,8) ~ "Summer", The body of case_when(...) is of the form `logical expression ~ "newValueName"`. This statement says that we want the values in the column "birthmonth" that are equal to 6, 7, and 8 to be assigned to the value "Summer" in the new "season" column.
           birthmonth %in% c(9,10,11) ~ "Fall" The body of case_when(...) is of the form `logical expression ~ "newValueName"`. This statement says that we want the values in the column "birthmonth" that are equal to 9, 10, and 11 to be assigned to the value "Fall" in the new "season" column.
          ) Closing parenthesis of the case_when(...) function.
        ) Closing parenthesis for the mutate(...) function.      Press Enter to run the code. Click to Show Output Click to View Output.

Kids4 <- `Kids4` is a new dataset we are creating that will contain all of `KidsFeet` data set along with a couple new columns we are creating. KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
   mutate( "mutate" is a function from library(tidyverse) that allows us to transform columns of data.
         lengthIN = length / 2.54, "lengthIN" is a name we came up with that will store the results of the transformation of the "length" column. This is just converting the length data from cm to inches.
         widthIN = width / 2.54, "widthIN" is a name we came up with that will store the results of the transformation of the "width" column. This is just converting the width data from cm to inches.
         lengthSplit = ifelse(length < median(length),
         "Under 50th Percentile",
         "50th Percentile or Greater"), "lengthSplit" is a name we came up with that will store the results of the ifelse(...) function. The ifelse(...) function in this case is being used to split the length column by the median of that column. The ifelse(...) function is of the form ifelse( Logical Condition , valueIfConditionTrue, valueIfConditionFalse).
         gender = case_when( "gender" is a name we came up with that will store the results of the transformation of the "sex" column. The case_when(...) function from library(tidyverse) allows us to perform more complicated transformations with columns.
           sex == "B" ~ "Boy", The body of case_when(...) is of the form `logical expression ~ "newValueName"`. This part of the case_when(...) function is being used to change the value of "B" to "Boy".
           sex == "G" ~ "Girl" The body of case_when(...) is of the form `logical expression ~ "newValueName"`. This part of the case_when(...) function is being used to change the value of "G" to "Girl".
          ) Closing parenthesis for the case_when(...) function.
        ) Closing parenthesis for the mutate(...) function.      Press Enter to run the code. Click to Show Output Click to View Output.

airquality2 <- `airquality` is a new dataset we are creating that will contain all of the `airquality` data set along with a new column we are creating. airquality `airquality` is a dataset found in base R. Typing `View(airquality)` and `?airquality` in the console will help you learn more about the dataset. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
mutate( "mutate" is a function from library(tidyverse) that allows us to transform columns of data. Month_Full = "Month_Full" is a name we came up with that will store the results of the transformation of the "Month" column. month( month(...) is from library(lubridate) and changes the "Month" column from type integer to type datetime. Month, "Month" is the "Month" column from airquality. label = TRUE, "label = TRUE" tells month(...) to change the month numbers to abbreviated month names. abbr = FALSE "abbr = FALSE" changes the abbreviated month names to the full month names. ) Closing parenthesis for the month(...) function. ) Closing parenthesis for the mutate(...) function. Press Enter to run the code. Click to Show Output Click to View Output.

**Other case_when( ) Uses** ```{} case_when(length > 25 & width > 9 ~ "Long and Wide", length < 25 & width > 9 ~ "Short and Wide", length > 25 & width < 9 ~ "Long and Thin", length < 25 & width < 9 ~ "Short and Thin") ``` **replace_na( ) Function** ```{} replace_na(columnName, value) ``` **as.numeric( ) Function** ```{} as.numeric(columnName) ``` **as.character( ) Function** ```{} as.character(columnName) ``` **as.factor( ) Function** ```{} as.factor(columnName) ```

Arrange data by a certain column, or columns, i.e. "sort" the data. **Usage** `NameofDataset %>%` `arrange(columnName1)` Note: `arrange(columnName1, columnName2, ...)` is also possible. * `NameOfDataset` is the name of a dataset, like `cars` or `airquality` or `KidsFeet`. * `%>%` is the pipe operator that "pipes data" down into R commands on the next line. * `arrange(...)` is an R function from `library(tidyverse)` that arranges a data set by order for the column given. * `columnName1` is the name of a column from the dataset that you want to compute numerical summaries on. * `columnName2` is the name of a column from the dataset that you want to compute numerical summaries on. * `...` implies that you can arrange by as many columns as you want. **Example Code**

KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
arrange( "arrange" is an R function from `library(tidyverse)` that arranges a data set by order for the column given. birthmonth birthmonth is the name of one of the columns of the KidsFeet data set. Specifying this name will cause the data to be sorted by birthmonth from 1 to 12. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
arrange( "arrange" is an R function from `library(tidyverse)` that arranges a data set by order for the column given. desc( This causes the arranging to be done in descending order (highest to lowest). birthmonth birthmonth is the name of one of the columns of the KidsFeet data set. Specifying this name will cause the data to be sorted by birthmonth from 1 to 12. ) Always close off your functions in R with a closing parathesis. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

Makes output of most commands "beautiful". **Usage** `library(pander)` then... `pander(someCode)` OR `someCode %>%` `pander( )` Note: `pander(stuff, caption="Some useful caption", ...)` is also possible. * `someCode` is exactly that, some coding you have done that creates output that you want displayed nicely. * `%>%` is the pipe operator that "pipes data" down into R commands on the next line. * `pander(...)` is an R function from `library(pander)` that makes most R output look nice. * `...` other useful commands like `split.table=Inf`. **Example Code**

pander( `pander` is an R function that makes output look nice. table(KidsFeet\$sex, KidsFeet\$birthmonth), Code that makes a table of how many boys and girls were born in each month of the year. caption="Counts of Birthdays by Month" The caption=" " command is very useful for giving your output a small title. ) Always close off your functions in R with a closing parathesis. Press Enter to run the code. Click to Show Output Click to View Output.

KidsFeet `KidsFeet` is a dataset found in `library(mosaicData)`. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
   group_by( "group_by" is a function from library(tidyverse) that allows us to split the dataset up into "little groups" according to the column specified. sex "sex" is a column from the KidsFeet dataset that records the gender of each child. ) Always close off your functions in R with a closing parathesis. %>% The pipe operator that will send the grouped according to gender version of the `KidsFeet` dataset down inside of the code on the following line.
   summarise( "summarise" is a function from library(tidyverse) that allows us to compute numerical summaries on data. aveLength A name we came up with that will store the results of the numerical summary. = mean(length), This computes the `mean(...)` of the `length` column from the KidsFeet dataset.
             sdLength A name we came up with that will store the results of the numerical summary. = sd(length), This computes the `sd(...)` of the `length` column from the KidsFeet dataset.
             sampleSize A name we came up with that will store the results of the numerical summary. = n( ) This computes the `n(...)`, or sample size, of the `length` column from the KidsFeet dataset. ) Always close off your functions in R with a closing parathesis. %>% The pipe operator that will send the `KidsFeet` dataset down inside of the code on the following line.
   pander( The pander function will make the output of the above code look nice. caption="Doesn't that look nice?") Always close off your functions in R with a closing parathesis.      Press Enter to run the code. Click to Show Output Click to View Output.