geom_jitter() function You can use the _ function to put a text label on your plot to call out specific data points. The _ creates a scatterplot and then adds a small amount of random noise to each point in the plot to make the points easier to find. You use the aes() function to define the mapping between your data and your plot. Mapping means matching up a specific variable in your data set with a specific aesthetic.
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They write the following code chunk: bias(actual_temp, predicted_temp) What will this code chunk calculate? The average difference between the actual and predicted values In ggplot2, the term mapping refers to the connection between variables and _. In R, which statistical measure demonstrates how strong the relationship is between two variables? Correlation A data analyst is studying weather data. Finally, the argument sep="," places a comma between the city and state data in the location column. In the parentheses of the function, the analyst writes the name of the data frame, then the name of the new column in quotation marks, followed by the names of the two columns they want to combine. The unite() function lets the analyst combine the city and state data into a single column.
Lets the analyst create the location column. What code chunk lets the analyst create the location column? unite(stores, "location", city, state, sep=",") The analyst wants to combine the two columns into a single column named location, with the city and state separated by a comma. It has separate columns for city (city) and state (state). A data analyst is working with a data frame named stores. The mutate() function can create a new column without affecting any existing columns. The analyst can use the mutate() function to create a new column for wages divided by 40 called hourly_salary. What code chunk lets the analyst create the hourly_salary column? mutate(salary_data, hourly_salary = wages / 40) They want to create a new column named hourly_salary that includes data from the wages column divided by 40.
A data analyst is working with a data frame called salary_data. Two equal signs in an argument mean "exactly equal to." Using this operator instead of the assignment operator <- calls only the data about Gentoo penguins to the dataset. The filter function allows the data analyst to specify which part of the data they want to view. What code chunk does the analyst add to create a data frame that only includes the Gentoo species? filter(species = "Gentoo") The variable species includes three penguin species: Adelie, Chinstrap, and Gentoo.
A data analyst is working with the penguins data.
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