@@ -237,7 +237,7 @@ library(tidyverse)
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```
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> ** Note:** You may have noticed that we got some extra
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- > output from R saying ` Attaching packages` and ` Conflicts ` below our code
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+ > output from R regarding attached packages and conflicts below our code
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> line. These are examples of * messages* in R, which give the user more
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> information that might be handy to know. The ` Attaching packages ` message is
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> natural when loading ` tidyverse ` , since ` tidyverse ` actually automatically
@@ -452,7 +452,7 @@ selected_lang <- select(aboriginal_lang, language, mother_tongue)
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selected_lang
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```
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- ## Using ` arrange ` to order and ` slice ` to select rows by index number
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+ ## Using ` arrange ` to order and ` slice ` to select rows by index number {#arrangesliceintro}
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We have used ` filter ` and ` select ` to obtain a table with only the Aboriginal
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languages in the data set and their associated counts. However, we want to know
@@ -500,7 +500,7 @@ counts... But perhaps, seeing these numbers, we became curious about the
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* percentage* of the population of Canada associated with each count. It is
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common to come up with new data analysis questions in the process of answering
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a first one&mdash ; so fear not and explore! To answer this small
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- question- along- the- way, we need to divide each count in the ` mother_tongue `
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+ question along the way, we need to divide each count in the ` mother_tongue `
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column by the total Canadian population according to the 2016
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census&mdash ; i.e., 35,151,728&mdash ; and multiply it by 100. We can perform
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this computation using the ` mutate ` function. We pass the ` ten_lang `
@@ -523,7 +523,7 @@ as a mother tongue by between 0.008% and 0.18% of the Canadian population.
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## Exploring data with visualizations
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- We have now answered our initial question by generating the ` ten_lang ` table!
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+ The ` ten_lang ` table we generated in Section \@ ref(arrangesliceintro) answers our initial data analysis question.
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Are we done? Well, not quite; tables are almost never the best way to present
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the result of your analysis to your audience. Even the ` ten_lang ` table with
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only two columns presents some difficulty: for example, you have to scrutinize
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