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sakhinov case study patch #4

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sakhinov case study patch #4

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c722739
Update 02-case-study-exercises.Rmd
sakhinov May 20, 2019
1d8717d
Update 02-case-study-solutions.Rmd
sakhinov May 20, 2019
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2 changes: 1 addition & 1 deletion docs/case-study/02-case-study-exercises.Rmd
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Expand Up @@ -165,7 +165,7 @@ glimpse(df)
```


12. The "Factor_D" variable contains 15 unique values (i.e. 10, 15, 20, 21, ..., 85, 90). There is at least one single observation where `Factor_D = 26` (possibly more). Assume these observations were improperly recorded and, in fact, the value should be 25.
12. The "Factor_D" variable contains 13 unique values (i.e. 10, 15, 20, 21, ..., 70, 90). There is at least one single observation where `Factor_D = 26` (possibly more). Assume these observations were improperly recorded and, in fact, the value should be 25.

Using `ifelse()` (or __dplyr__'s `if_else()`) inside `mutate()`, recode any values where `Factor_D == 26` to be 25.

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2 changes: 1 addition & 1 deletion docs/case-study/02-case-study-solutions.Rmd
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Expand Up @@ -114,7 +114,7 @@ glimpse(df)
```


12. The "Factor_D" variable contains 15 unique values (i.e. 10, 15, 20, 21, ..., 85, 90). There is at least one single observation where `Factor_D = 26` (possibly more). Assume these observations were improperly recorded and, in fact, the value should be 25.
12. The "Factor_D" variable contains 13 unique values (i.e. 10, 15, 20, 21, ..., 70, 90). There is at least one single observation where `Factor_D = 26` (possibly more). Assume these observations were improperly recorded and, in fact, the value should be 25.

Using `ifelse()` (or __dplyr__'s `if_else()`) inside `mutate()`, recode any values where `Factor_D == 26` to be 25.

Expand Down