[fix datacarpentry#60] name all plot chunks

_episodes_rmd/02-starting-with-data.Rmd

@@ -325,7 +325,7 @@ Let's extract the `memb_assoc` column from our data frame, convert it into a
 factor, and use it to look at the number of interview respondents who were or
 were not members of an irrigation association:
 
-```{r, purl=TRUE}
+```{r factor-plot-default-order, purl=TRUE}
 ## create a vector from the data frame column "memb_assoc"
 memb_assoc <- interviews$memb_assoc
 ## convert it into a factor
@@ -344,7 +344,7 @@ been recorded, and encoded as missing data. They do not appear on the plot.
 Let's encode them differently so they can counted and visualized in our plot.
 
 
-```{r, purl=TRUE}
+```{r factor-plot-reorder, purl=TRUE}
 ## Let's recreate the vector from the data frame column "memb_assoc"
 memb_assoc <- interviews$memb_assoc
 ## replace the missing data with "undetermined"
@@ -368,7 +368,8 @@ plot(memb_assoc)
 >   recreate the barplot such that "Undetermined" is last (after "Yes")?
 >
 > > ## Solution
-> > ```{r}
+> >
+> > ```{r factor-plot-exercise}
 > > levels(memb_assoc) <- c("No", "Undetermined", "Yes")
 > > memb_assoc <- factor(memb_assoc, levels = c("No", "Yes", "Undetermined"))
 > > plot(memb_assoc)

_episodes_rmd/04-ggplot2.Rmd

@@ -68,13 +68,13 @@ ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +  <GEOM_FUNCTION>()
 - use the `ggplot()` function and bind the plot to a specific data frame using
   the `data` argument
 
-```{r, eval=FALSE, purl=FALSE}
+```{r ggplot-steps-1, eval=FALSE, purl=FALSE}
 ggplot(data = interviews_plotting)
 ```
 
 - define a mapping (using the aesthetic (`aes`) function), by selecting the variables to be plotted and specifying how to present them in the graph, e.g. as x/y positions or characteristics such as size, shape, color, etc.
 
-```{r, eval=FALSE, purl=FALSE}
+```{r ggplot-steps-2, eval=FALSE, purl=FALSE}
 ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items))
 ```
 
@@ -91,15 +91,15 @@ let's use `geom_point()` first:
 
 ```{r first-ggplot, purl=FALSE}
 ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items)) +
-geom_point()
+    geom_point()
 ```
 
 The `+` in the **`ggplot2`** package is particularly useful because it allows
 you to modify existing `ggplot` objects. This means you can easily set up plot
 templates and conveniently explore different types of plots, so the above plot
 can also be generated with code like this:
 
-```{r, first-ggplot-with-plus, eval=FALSE, purl=FALSE}
+```{r first-ggplot-with-plus, eval=FALSE, purl=FALSE}
 # Assign plot to a variable
 interviews_plot <- ggplot(data = interviews_plotting, aes(x = no_membrs, y = number_items))
 
@@ -121,7 +121,7 @@ interviews_plot +
 >   the new layer and will return an error message.
 {: .callout}
 
-```{r, ggplot-with-plus-position, eval=FALSE, purl=FALSE}
+```{r ggplot-with-plus-position, eval=FALSE, purl=FALSE}
 ## This is the correct syntax for adding layers
 interviews_plot +
     geom_point()
@@ -231,7 +231,7 @@ hidden?
 > 
 > > ## Solution
 > >
-> > ```{r}
+> > ```{r violin-plot}
 > > ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = rooms)) +
 > >   geom_violin(alpha = 0) +
 > >   geom_jitter(alpha = 0.5, color = "tomato")
@@ -246,7 +246,7 @@ hidden?
 >   layer on a jitter layer to show actual measurements.
 >
 > > ## Solution
-> > ```{r}
+> > ```{r boxplot-exercise}
 > > ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = liv_count)) +
 > >   geom_boxplot(alpha = 0) +
 > >   geom_jitter(alpha = 0.5)
@@ -257,7 +257,7 @@ hidden?
 >   respondent is a member of an irrigation association (`memb_assoc`).
 >
 > > ## Solution
-> > ```{r}
+> > ```{r boxplot-exercise-factor}
 > > ggplot(data = interviews_plotting, aes(x = respondent_wall_type, y = liv_count)) +
 > >   geom_boxplot(alpha = 0) +
 > >   geom_jitter(aes(alpha = 0.5, color = memb_assoc))
@@ -271,15 +271,15 @@ Barplots are also useful for visualizing categorical data. By default,
 `geom_bar` accepts a variable for x, and plots the number of instances each
 value of x (in this case, wall type) appears in the dataset.
 
-```{r}
+```{r barplot-1}
 ggplot(data = interviews_plotting, aes(x = respondent_wall_type)) +
     geom_bar()
 ```
 
 We can use the `fill` asthetic for the `geom_bar()` geom to color bars by
 the portion of each count that is from each village.
 
-```{r}
+```{r barplot-stack}
 ggplot(data = interviews_plotting, aes(x = respondent_wall_type)) +
     geom_bar(aes(fill = village))
 ```
@@ -290,7 +290,7 @@ correspond to each village and put them side-by-side by using the `position`
 argument for `geom_bar()` and setting it to "dodge".
 
 
-```{r}
+```{r barplot-dodge}
 ggplot(data = interviews_plotting, aes(x = respondent_wall_type)) +
     geom_bar(aes(fill = village), position = "dodge")
 ```
@@ -304,7 +304,7 @@ representing the percent of each house type in each village. We will remove
 houses with cement walls, as there was only one in the dataset.
 
 
-```{r}
+```{r wall-type-data}
 percent_wall_type <- interviews_plotting %>%
     filter(respondent_wall_type != "cement") %>%
     count(village, respondent_wall_type) %>%
@@ -314,7 +314,7 @@ percent_wall_type <- interviews_plotting %>%
 Now we can use this new data frame to create our plot showing the
 percentage of each house type in each village.
 
-```{r}
+```{r barplot-wall-type}
 ggplot(percent_wall_type, aes(x = village, y = percent, fill = respondent_wall_type)) +
     geom_bar(stat = "identity", position = "dodge")
 ```
@@ -329,7 +329,7 @@ ggplot(percent_wall_type, aes(x = village, y = percent, fill = respondent_wall_t
 >
 > > ## Solution
 > >
-> > ```{r}
+> > ```{r barplot-memb-assoc}
 > > percent_memb_assoc <- interviews_plotting %>%
 > >   filter(!is.na(memb_assoc)) %>%
 > >   count(village, memb_assoc) %>%
@@ -353,7 +353,7 @@ relatively few lines of code. We will add more informative x and y axis
 labels to our plot of proportion of house type by village and also add
 a title.
 
-```{r}
+```{r barplot-wall-types-labelled}
 ggplot(percent_wall_type, aes(x = village, y = percent, fill = respondent_wall_type)) +
     geom_bar(stat = "identity", position = "dodge") +
     ylab("Percent") +
@@ -374,7 +374,7 @@ plot into multiple plots based on a factor included in the dataset. We
 will use it to split our barplot of housing type proportion by village
 so that each village has it's own panel in a multi-panel plot:
 
-```{r}
+```{r barplot-faceting}
 ggplot(percent_wall_type, aes(x = respondent_wall_type, y = percent)) +
     geom_bar(stat = "identity", position = "dodge") +
     ylab("Percent") +
@@ -390,7 +390,7 @@ Usually plots with white background look more readable when printed.  We can set
 the background to white using the function `theme_bw()`. Additionally, you can remove
 the grid:
 
-```{r, purl=FALSE}
+```{r barplot-theme-bw, purl=FALSE}
 ggplot(percent_wall_type, aes(x = respondent_wall_type, y = percent)) +
     geom_bar(stat = "identity", position = "dodge") +
     ylab("Percent") +
@@ -407,7 +407,7 @@ in each village who own each item and then create a faceted series of
 bar plots where each plot is a particular item. First we need to
 calculate the percentage of people in each village who own each item:
 
-```{r}
+```{r percent-items-data}
 percent_items <- interviews_plotting %>%
     count(items_owned, village) %>%
     ## add a column with the number of people in each village
@@ -425,7 +425,7 @@ denominator in our percentage calculation. Instead, we need to specify the
 number of respondents in each village. Using this data frame, we can now create
 a multi-paneled bar plot.
 
-```{r}
+```{r percent-items-barplot}
 ggplot(percent_items, aes(x = village, y = percent)) +
     geom_bar(stat = "identity", position = "dodge") +
     facet_wrap(~ items_owned) +
@@ -464,7 +464,7 @@ and think of ways you could improve the plot.
 Now, let's change names of axes to something more informative than 'village' and
 'percent' and add a title to the figure:
 
-```{r, purl=FALSE}
+```{r ggplot-customization, purl=FALSE}
 ggplot(percent_items, aes(x = village, y = percent)) +
     geom_bar(stat = "identity", position = "dodge") +
     facet_wrap(~ items_owned) +
@@ -477,7 +477,7 @@ ggplot(percent_items, aes(x = village, y = percent)) +
 The axes have more informative names, but their readability can be improved by
 increasing the font size:
 
-```{r number-species-year-with-right-labels-xfont-size, purl=FALSE}
+```{r ggplot-customization-font-size, purl=FALSE}
 ggplot(percent_items, aes(x = village, y = percent)) +
     geom_bar(stat = "identity", position = "dodge") +
     facet_wrap(~ items_owned) +
@@ -499,7 +499,7 @@ them vertically and horizontally so they don't overlap. You can use a 90-degree
 angle, or experiment to find the appropriate angle for diagonally oriented
 labels:
 
-```{r number-species-year-with-theme, purl=FALSE}
+```{r ggplot-customization-label-orientation, purl=FALSE}
 ggplot(percent_items, aes(x = village, y = percent)) +
     geom_bar(stat = "identity", position = "dodge") +
     facet_wrap(~ items_owned) +
@@ -516,7 +516,7 @@ If you like the changes you created better than the default theme, you can save
 them as an object to be able to easily apply them to other plots you may create:
 
 
-```{r number-species-year-with-right-labels-xfont-orientation, purl=FALSE}
+```{r ggplot-custom-themes, purl=FALSE}
 grey_theme <- theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 45, hjust = 0.5, vjust = 0.5),
                     axis.text.y = element_text(colour = "grey20", size = 12),
                     text = element_text(size = 16))