Gender lang fix2 #40
Gender lang fix2 #40
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chapter4.Rmd
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| @@ -418,7 +412,7 @@ test_object("factor_survey_vector", eq_condition = "equal", undefined_msg = msg, | |||
| test_object("male", undefined_msg = msg, incorrect_msg = msg) | |||
| test_object("female", undefined_msg = msg, incorrect_msg = msg) | |||
| test_output_contains("male > female", incorrect_msg = msg) | |||
| success_msg("Phew, it seems that R is gender-neutral. Or maybe it just wants to stay out of these discussions ;-).") | |||
| success_msg("Phew, it seems that R is not a sexist language.") | |||
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I think I'd make this just a standard "Good job." success_msg.
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Updated via be438ff
chapter4.Rmd
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| @@ -19,7 +19,7 @@ The term factor refers to a statistical data type used to store categorical vari | |||
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| It is important that R knows whether it is dealing with a continuous or a categorical variable, as the statistical models you will develop in the future treat both types differently. (You will see later why this is the case.) | |||
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| A good example of a categorical variable is the variable 'Gender'. A human individual can either be "Male" or "Female", making abstraction of inter-sexes. So here "Male" and "Female" are, in a simplified sense, the two values of the categorical variable "Gender", and every observation can be assigned to either the value "Male" of "Female". | |||
| A good example of a categorical variable is sex. In many circumstances you can limit the sex categories to "Male" or "Female". (Sometimes you may need different categories. For example, you may need to consider chromosomal variation or hermaphroditic animals, but you will always have a finite number of categories.) | |||
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"…you may need to consider chromosomal variation, hermaphroditic animals, or different cultural norms, but you will always…"
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Fixed via 7ae518f
chapter4.Rmd
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| @@ -214,7 +208,7 @@ When you first get a data set, you will often notice that it contains factors wi | |||
| levels(factor_vector) <- c("name1", "name2",...) | |||
| ``` | |||
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| A good illustration is the raw data that is provided to you by a survey. A standard question for every questionnaire is the gender of the respondent. You remember from the previous question that this is a factor and when performing the questionnaire on the streets its levels are often coded as `"M"` and `"F"`. | |||
| A good illustration is the raw data that is provided to you by a survey. A standard question for every questionnaire is the sex of the respondent. You remember from the previous question that this is a factor and when performing the questionnaire on the streets its levels are often coded as `"M"` and `"F"`. | |||
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"A common question is the sex of the respondent, which is still often over-simplified to "M" and "F". However many categories there are, this is a factor."
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Updated via a36c792
chapter4.Rmd
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| @@ -363,10 +357,10 @@ skills: 1 | |||
| key: 90ecc160d1ebf2f75bf53f9c3843fc1632bdd0a5 | |||
| ``` | |||
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| In `factor_survey_vector` we have a factor with two levels: Male and Female. But how does R value these relatively to each other? In other words, who does R think is better, males or females? | |||
| You might wonder what happens when you try to compare elements of a factor. In `factor_survey_vector` you have a factor with two levels: `"Male"` and `"Female"`. But how does R value these relatively to each other? In other words, who does R think is greater, males or females? | |||
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"In other words, which does R think should come first in sorted data, males or females?"
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"Greater" is used because the question is about using then greater than operator on factor values. So I think I'm happy with the sentence as it is.
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Agree with @gvwilson that this phrasing is fraught. Maybe rephrase as:
In other words, which level does R consider to be greater, male or female?
Therfore, we avoid using the word "value", and also by using inline code for male and female it is clear that we aren't referring to men and women in general.
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@ncarchedi I tested on Teach and Campus and this is good to merge.
chapter4.Rmd
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| @@ -363,10 +357,10 @@ skills: 1 | |||
| key: 90ecc160d1ebf2f75bf53f9c3843fc1632bdd0a5 | |||
| ``` | |||
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| In `factor_survey_vector` we have a factor with two levels: Male and Female. But how does R value these relatively to each other? In other words, who does R think is better, males or females? | |||
| You might wonder what happens when you try to compare elements of a factor. In `factor_survey_vector` you have a factor with two levels: `"Male"` and `"Female"`. But how does R value these relatively to each other? In other words, who does R think is greater, males or females? | |||
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"relatively" --> "relative". Also, why not just cut the last sentence? It's annoying and unnecessary.
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@ncarchedi Assignment text has been edited. |
I think this is the minimal fix for the gender-related language.