Find the candy that I like...

April 16, 2020 • 15:00 Eastern • PD Schloss • 10 min read

For our next Code Club, we will continue our exploration of data collected by Five Thirty Eight that looked at how people feel about different types of candy. In the first code club, we initially played with the data. Last week, we began to use the filter function to generate logical (or boolean) queries to identify rows from the data that met our criteria. This time we’ll focus on using filter with quantitative data and we’ll see how to use it with the group_by and summarize functions.

You will be able to join the conversation using this link to use Zoom. The session should last an hour. Please be sure to see the setup instructions and code of conduct before we get going.

If you would like to revisit Pat’s introduction and the approach taken by several participants, you can watch this video. If you were on the call, you’ll notice a different Introduction. Pat forgot to press record the first time around (again):

Prompt

In our inaugural code club we loaded data that described people’s preferences for each of 85 different types of candy. The folks at Five Thirty Eight characterized each of the candies by their attributes including things like whether the candy has chocolate, nougat, caramel, and so forth. They also created indices to rate the relative prices and amount of sugar in each candy. In that code club, I had participants generate a figure that showed whether chocolate candies are more expensive as a bar or as bite-sized pieces. I’d like to use that question to build upon the discussion we had last week using filter and to expand the discussion to calculate statistics for different groups of data using group_by and summarize. Let’s load the data and get going!

library(tidyverse)

candy_data <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/candy-power-ranking/candy-data.csv",
	col_types="clllllllllddd")

filter revisited

You’ll recall that filter identifies rows in a data frame that satisfy some logical question. If the answer to the question is TRUE, then that row is returned. These candy data have a number of variables that are already TRUE or FALSE values.

candy_data

If you look under the “chocolate” heading, you’ll see which is short for logical. You'll also see that column (and others) have `TRUE` and `FALSE` as their values. Since these already evaluate to a logical value, I can do the following to get all of the candies that have chocolate in them

candy_data %>%
	filter(chocolate)

While you can do

candy_data %>%
	filter(chocolate == TRUE)

It’s a bit redundant and not necessary. Regardless of the approach, we see that 37 of the candies have chocolate in them. Of course, we could have figured that out with the count function that we discussed earlier

candy_data %>%
	count(chocolate)

What the filter command gets us is a new data frame that only contains candies with chocolate. Before moving on to discussing group_by and summarize, I want to show you a few other things we can do with filter.

So far we’ve seen that we can search for specific values using the == and != operators and that we can combine queries with the & and | operators. The == and != operators work well when we’re trying to select for specific values. Sometimes, we want a range of values. Let’s say we want a new data frame that contains all of the candies that have a price index greater than 0.5, we could do the following

candy_data %>%
	filter(pricepercent > 0.5)

Similarly, if we wanted all of the low sugar candies we could do

candy_data %>%
	filter(sugarpercent < 0.33)

And building upon what we did last week, we can combine the queries with a & to get the expensive low sugar candies or with a | to get the candies that are expensive or low in sugar.

candy_data %>%
	filter(pricepercent > 0.5 & sugarpercent < 0.33) #12 rows

candy_data %>%
	filter(pricepercent > 0.5 | sugarpercent < 0.33) #64 rows

Beyond > and <, you can also use >= and <= to ask if things are “greater than or equal to” or “less than or equal to” a specific value.

group_by and summarize

The group_by and summarize functions generally go together. They allow us to group our data by some variable and then to perform some summary operation for each group. Returning to our question of whether candy with chocolate is more expensive as a bar or in bite-sized form we can do the following

candy_data %>%
	filter(chocolate == TRUE) %>%
	group_by(bar) %>%
	summarize(mean_price = mean(pricepercent))

We could also report back the standard deviation and number of candies in each group by adding them to the list of arguments in summarize separated by a comma

candy_data %>%
	filter(chocolate == TRUE) %>%
	group_by(bar) %>%
	summarize(mean_price = mean(pricepercent), sd_price = sd(pricepercent), n=n())

Although we should apply a statistical test, it does appear that candy with chocolate is more expensive in bar form. Other functions that work well with summarize include median, min, max, IQR, range, and any function that returns a single value.

Perhaps we’d like to partition our data frame by two variables. Say we want to look at all candy to determine whether non-chocolate candy is also more expensive as a bar than as bite-sized. We could modify our earlier code chunk

candy_data %>%
	filter(!chocolate) %>% # the ! turns TRUE to FALSE and FALSE to TRUE
	group_by(bar) %>%
	summarize(mean_price = mean(pricepercent))

If we want both chocolaty and non-chocolate candy data in the same data frame, we will need to drop the filter function and modify the group_by function. We can add other groups to partition the data by in the group_by function separated by commas

candy_data %>%
	group_by(chocolate, bar) %>%
	summarize(mean_price = mean(pricepercent))

Sure enough, candy as a bar is more expensive regardless of whether it has chocolate in it!

One final trick to show you is that we can add a logical question to our group_by argument

candy_data %>%
	group_by(sugarpercent > 0.50) %>%
	summarize(mean_price = mean(pricepercent))

Who knew? Candy with more sugar is more expensive.

Exercises

1. How many of the candies that won more than 75% of their matchups had chocolate?

2. Do fruity candies have a different average price than non-fruity candies?

3. How do the prices of the more favored candies compare to those that are less favored?

4. Come up with your own question to answer with the functions we’ve discussed today

Solutions

1. How many of the candies that won more than 75% of their matchups had chocolate?

candy_data %>%
	filter(winpercent > 0.75) %>%
	count(chocolate)

There’s more non-chocolate than chocolate containing candy in the dataset

2. Do fruity candies have a different average price than non-fruity candies?

candy_data %>%
	group_by(fruity) %>%
	summarize(mean_price = mean(pricepercent), sd_price=sd(pricepercent), n=n())

Fruity candy tends to be cheaper than candy without fruity flavor.

3. How do the prices of the more favored candies compare to those that are less favored?

candy_data %>%
	group_by(winpercent > 50) %>%
	summarize(mean_price = mean(pricepercent), sd_price=sd(pricepercent), n=n())

The more favored candies are more expensive.

4. Come up with your own question to answer with the functions we’ve discussed today

Does price differ by sugar content?

candy_data %>%
 	group_by(sugarpercent > 0.5) %>%
	summarize(mean_price = mean(pricepercent), sd_price=sd(pricepercent), n=n())

High sugar candy tends to be more expensive than low sugar candy. Perhaps confounded with people’s preference?

candy_data %>%
 	group_by(sugarpercent > 0.5, winpercent > 50) %>%
	summarize(mean_price = mean(pricepercent), sd_price=sd(pricepercent), n=n())

Maybe people just like good candy.

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