Update the naming of challenges and add missing code chunk labels

episodes/02-data-structures.Rmd

@@ -97,7 +97,7 @@ abcd_vector
 
 ::::::::::::::::::::::::::::::::::::: challenge 
 
-### Exercise 
+### Challenge: combining vectors 
 
 Combine the `abcd_vector` with the `numeric_vector` in R. What is the data type of this new vector and why?
 

episodes/03-explore-data.Rmd

@@ -91,7 +91,7 @@ In each line after a `$` sign, we see the name of each column, its type and firs
 There are multiple ways to explore a data set. Here are just a few examples:
 
 
-```{r}
+```{r examine-data}
 
 head(gapminder) # shows first 6  rows of the data set
 
@@ -159,7 +159,7 @@ First we define data set, then - with the use of pipe we pass it on to the `sele
 We already know how to select only the needed columns. But now, we also want to filter the rows of our data set via certain conditions with `filter()` function. Instead of doing it in separate steps, we can do it all together. 
 
 In the `gapminder` data set, we want to see the results from outside of Europe for the 21st century. 
-```{r}
+```{r filter-data}
 year_country_gdp_euro <- gapminder %>%
   filter(continent != "Europe" & year >= 2000) %>%
   select(year, country, gdpPercap)
@@ -170,7 +170,7 @@ head(year_country_gdp_euro)
 
 ::: challenge
 
-##  Exercise 1
+##  Challenge: filtered data frame
 
 Write a single command (which can span multiple lines and includes pipes) that will produce a data frame that has the values for life expectancy, country and year, only for Eurasia. How many rows does your data frame have and why? 
 
@@ -201,7 +201,7 @@ gapminder %>% # select the dataset
 
 ::: challenge
 
-## Exercise 2
+## Challenge: longest and shortest life expectancy
 
 Calculate the average life expectancy per country. Which country has the longest average life expectancy and which has the shortest average life expectancy?