lab 6 update

lab6/lab6_1.Rmd

@@ -22,12 +22,18 @@ library("janitor")
 ```
 
 ## Load the data
-For this lab, we will use the following dataset:  
+For this lab, we will use the following two datasets:  
 
-1. S. K. Morgan Ernest. 2003. Life history characteristics of placental non-volant mammals. Ecology 84:3402.   [link](http://esapubs.org/archive/ecol/E084/093/)  
+1. 1. Gaeta J., G. Sass, S. Carpenter. 2012. Biocomplexity at North Temperate Lakes LTER: Coordinated Field Studies: Large Mouth Bass Growth 2006. Environmental Data Initiative.   [link](https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-ntl&identifier=267)  
+
+2. S. K. Morgan Ernest. 2003. Life history characteristics of placental non-volant mammals. Ecology 84:3402.   [link](http://esapubs.org/archive/ecol/E084/093/)  
 
 ## Pipes `%>%` 
 Recall that we use pipes to connect the output of code to a subsequent function. This makes our code cleaner and more efficient. One way we can use pipes is to attach the `clean_names()` function from janitor to the `read_csv()` output.  
+```{r}
+fish <- readr::read_csv("data/Gaeta_etal_CLC_data.csv")
+```
+
 ```{r}
 mammals <- read_csv("data/mammal_lifehistories_v2.csv") %>% clean_names()
 ```

lab6/lab6_2.Rmd

@@ -27,8 +27,8 @@ These are data taken from comic books and assembled by fans. The include a good
 
 Check out the way I am loading these data. If I know there are NAs, I can take care of them at the beginning. But, we should do this very cautiously. At times it is better to keep the original columns and data intact.  
 ```{r}
-superhero_info <- read_csv("data/heroes_information.csv", na = c("", "-99", "-"))
-superhero_powers <- read_csv("data/super_hero_powers.csv", na = c("", "-99", "-"))
+#superhero_info <- read_csv("data/heroes_information.csv", na = c("", "-99", "-"))
+#superhero_powers <- read_csv("data/super_hero_powers.csv", na = c("", "-99", "-"))
 ```
 
 ## Data tidy
@@ -37,7 +37,7 @@ superhero_powers <- read_csv("data/super_hero_powers.csv", na = c("", "-99", "-"
 ## `tabyl`
 The `janitor` package has many awesome functions that we will explore. Here is its version of `table` which not only produces counts but also percentages. Very handy! Let's use it to explore the proportion of good guys and bad guys in the `superhero_info` data.  
 ```{r}
-tabyl(superhero_info, alignment)
+#tabyl(superhero_info, alignment)
 ```
 
 1. Who are the publishers of the superheros? Show the proportion of superheros from each publisher. Which publisher has the highest number of superheros?