# Empirical Project 3 Working in R

Don’t forget to also download the data into your working directory by following the steps in this project.

## R-specific learning objectives

In addition to the learning objectives for this project, in this section you will learn how to use the piping technique to run a sequence of functions.

## Getting started in R

For this project you will need the following packages:

• tidyverse, to help with data manipulation
• readxl, to import an Excel spreadsheet
• mosaic, to help create frequency tables
• readstata13, to read in a Stata datafile.

You will also use the ggplot2 package to produce accurate graphs, but that comes as part of the tidyverse package.

If you need to install any of these packages, run the following code:

install.packages(c("readxl", "tidyverse",


You can import these libraries now, or when they are used in the R walk-throughs below.

library(readxl)
library(tidyverse)
library(mosaic)


## Part 3.1 Before-and-after comparisons of retail prices

We will first look at price data from the treatment group (stores in Berkeley) to see what happened to the price of sugary and non-sugary beverages after the tax.

• Download the data from the Global Food Research Program’s website, and select the ‘Berkeley Store Price Survey’ Excel dataset. Then upload the dataset into R.
• The first tab of the Excel file contains the data dictionary. Make sure you read the data description column carefully, and check that each variable is in the Data tab.

### R walk-through 3.1 Importing the datafile into R

The data is in .xlsx format, so we use the readxl package to import it. We also load the tidyverse library as this includes packages that we will use later for drawing charts and making code easier to read. If you open the data in Excel, you will see that there are two tabs: ‘Data Dictionary’, which contains some information about the variables, and ‘Data’, which contains the actual data. Let’s import both into R, so that do not need to refer back to Excel for the additional information about variables.

library(readxl)
library(tidyverse)

# Set your working directory to the correct folder.
# Insert your file path for 'YOURFILEPATH'.
setwd("YOURFILEPATH")

sheet = "Data Dictionary")
dat <- read_excel("sps_public.xlsx", sheet = "Data")


Let’s use the str function to check that the variables were classified correctly.

str(dat)

## Classes 'tbl_df', 'tbl' and 'data.frame': 2175 obs. of 12 variables:
## $store_id : num 16 16 16 16 16 16 16 16 16 16 ... ##$ type          : chr "WATER" "TEA" "TEA" "WATER" ...
## $store_type : num 2 2 2 2 2 2 2 2 2 2 ... ##$ type2         : chr NA NA NA NA ...
## $size : num 33.8 23 23 33.8 128 64 128 64 63.9 144 ... ##$ price         : num 1.69 0.99 0.99 1.69 3.79 2.79 3.79 2.79 4.59 5.99 ...
## $price_per_oz : num 0.05 0.043 0.043 0.05 0.0296 ... ##$ price_per_oz_c: num 5 4.3 4.3 5 2.96 ...
## $taxed : num 0 1 1 0 0 0 0 0 0 1 ... ##$ supp          : num 0 0 0 0 0 0 0 0 0 1 ...
## $time : chr "DEC2014" "DEC2014" "DEC2014" "DEC2014" ... ##$ product_id    : num 29 32 33 38 40 41 42 43 44 50 ...


R classified all the variables containing numbers as numerical (num). However, for some of these variables (specifically, type, taxed, supp, store_id, store_type, type2 and product_id), the numbers actually represent categories (known as ‘factors’ in R). So let’s use the factor function to convert the variables to factor variables.

dat$type <- factor(dat$type)
dat$taxed <- factor(dat$taxed,
labels = c("not taxed", "taxed"))
dat$supp <- factor(dat$supp,
labels = c("Standard", "Supplemental"))
dat$store_id <- factor(dat$store_id)
dat$store_type <- factor(dat$store_type,
labels = c("Large Supermarket",
"Small Supermarket",
"Pharmacy",
"Gas Station"))
dat$type2 <- factor(dat$type2)
dat$product_id <- factor(dat$product_id)


You can see that we used the labels option to specify the names of different categories (where they are clearly defined).

There is another variable, time, which is classified as a chr variable (chr stands for ‘characters’, meaning letters and numbers), but should be a factor variable. Before we do this, we use the unique command to check the categories of this variable.

unique(dat$time)  ## [1] "DEC2014" "JUN2015" "MAR2015"  If you look at the timeline in the Silver et al. (2017) paper, you will notice that the third price survey was in March 2016, not in March 2015, so the data has been labelled incorrectly. We shall therefore change all the values MAR2015 to MAR2016. # Selects all observations with “time” equal to "MAR2015". dat$time[dat$time == "MAR2015"] <- "MAR2016"  We can now change time into a factor variable. dat$time <- factor(dat$time)  1. Read ‘S1 Text’, from the journal paper’s supporting information, which explains how the Store Price Survey data was collected. • In your own words, explain how the product information was recorded, and the measures that researchers took to ensure that the data was accurate and representative of the treatment group. What were some of the data collection issues that they encountered? • Instead of using the name of the store, each store was given a unique ID number (recorded as store_id on the spreadsheet). Verify that the number of stores in the dataset is the same as that stated in the ‘S1 Text’ (26). Similarly, each product was given a unique ID number (product_id). How many different products are in the dataset? ### R walk-through 3.2 Counting the number of unique elements in a variable We use two functions here: unique to obtain a list of the unique elements of the variables of interest (dat$store_id and dat$product_id), then length to count how long the list is. We will create two variables, no_stores and no_products, that contain the number of stores and products respectively. no_stores <- length(unique(dat$store_id))
no_products <- length(unique(dat$product_id)) paste("Stores:", no_stores)  ## [1] "Stores: 26"  paste("Products:", no_products)  ## [1] "Products: 247"  Following the approach described in ‘S1 Text’, we will compare the variable price per ounce in US$ cents (price_per_oz_c). We will look at what happened to prices in the two treatment groups before the tax (time = DEC2014) and after the tax (time = JUN2015):

• treatment group one: large supermarkets (store_type = 1)
• treatment group two: pharmacies (store_type = 3).

Before doing this analysis, we will use summary measures to see how many observations are in the treatment and control group, and how the two groups differ across some variables of interest. For example, if there are very few observations in a group, we might be concerned about the precision of our estimates and will need to interpret our results in light of this fact.

We will now create frequency tables containing the summary measures that we are interested in.

1. Create the following tables:
• A frequency table showing the number (count) of store observations (store type) in December 2014 and June 2015, with ‘store type’ as the row variable and ‘time period’ as the column variable. For each store type, is the number of observations similar in each time period?
• A frequency table showing the number of taxed and non-taxed beverages in December 2014 and June 2015, with ‘store type’ as the row variable and ‘taxed’ as the column variable. (‘Taxed’ equals 1 if the sugar tax applied to that product, and 0 if the tax did not apply). For each store type, is the number of taxed and non-taxed beverages similar?
• A frequency table showing the number of each product type (type), with ‘product type’ as the row variable and ‘time period’ as the column variable. Which product types have the highest number of observations and which have the lowest number of observations? Why might some products have more observations than others?

### R walk-through 3.3 Creating frequency tables

#### Frequency table for store type and time period

We use the tally function, which allows us to produce frequency tables using the format = "count" option. We start with the frequency table that shows the number of stores of different types in each time period.

library(mosaic)
tally(~store_type + time, data = dat,
margins = TRUE, format = "count")

##                    time
## store_type          DEC2014 JUN2015 MAR2016 Total
##   Large Supermarket     177     209     158   544
##   Small Supermarket     407     391     327  1125
##   Pharmacy               87     102      73   262
##   Gas Station            73      96      75   244
##   Total                 744     798     633  2175


There are fewer observations taken from gas stations and pharmacies and more from small supermarkets.

#### Frequency table for store type and taxed

Now we repeat the steps above to make a frequency table with store_type as the row variable and taxed as the column variable. We add +time to the code because we also want separate values for each time period.

tally(~store_type + taxed + time, data = dat,
margins = TRUE, format = "count")

## , , time = DEC2014
##
##                    taxed
## store_type          not taxed taxed Total
##   Large Supermarket        92    85   177
##   Small Supermarket       196   211   407
##   Pharmacy                 44    43    87
##   Gas Station              34    39    73
##   Total                   366   378   744
##
## , , time = JUN2015
##
##                    taxed
## store_type          not taxed taxed Total
##   Large Supermarket       111    98   209
##   Small Supermarket       192   199   391
##   Pharmacy                 52    50   102
##   Gas Station              44    52    96
##   Total                   399   399   798
##
## , , time = MAR2016
##
##                    taxed
## store_type          not taxed taxed Total
##   Large Supermarket        88    70   158
##   Small Supermarket       154   173   327
##   Pharmacy                 36    37    73
##   Gas Station              31    44    75
##   Total                   309   324   633
##
## , , time = Total
##
##                    taxed
## store_type          not taxed taxed Total
##   Large Supermarket       291   253   544
##   Small Supermarket       542   583  1125
##   Pharmacy                132   130   262
##   Gas Station             109   135   244
##   Total                  1074  1101  2175


#### Frequency table for product type and time period

Now we make a frequency table with product type (type) as the row variable and time period (time) as the column variable.

tally(~type + time, data = dat,
margins = TRUE, format = "count")

##              time
## type          DEC2014 JUN2015 MAR2016 Total
##   ENERGY           56      58      49   163
##   ENERGY-DIET      49      54      35   138
##   JUICE            70      64      52   186
##   JUICE DRINK      19      17       6    42
##   MILK             63      61      53   177
##   SODA            239     262     215   716
##   SODA-DIET       128     174     127   429
##   SPORT            11      16      12    39
##   SPORT-DIET        2       2       0     4
##   TEA              52      45      41   138
##   TEA-DIET          6       6       8    20
##   WATER            48      38      34   120
##   WATER-SWEET       1       1       1     3
##   Total           744     798     633  2175


This table shows that there were no observations for the category Sport-diet in March 2016. As this is a drink which even in the other months has very few observations, it may be a product that is offered only in one shop, and it is possible that this shop was not visited in March 2016. The product may also be a seasonal product that is not available in March. It is also likely that Water-sweet is offered in only one shop.

conditional mean
An average of a variable, taken over a subgroup of observations that satisfy certain conditions, rather than all observations.

Besides counting the number of observations in a particular group, we can also calculate the mean by only using observations that satisfy certain conditions (known as the conditional mean). In this case, we are interested in comparing the mean price of taxed and untaxed beverages, before and after the tax.

1. Calculate and compare conditional means:
• Create a table similar to Figure 3.1, showing the average price per ounce (in cents) for taxed and untaxed beverages separately, with ‘store type’ as the row variable, and ‘taxed’ and ‘time’ as the column variables. To follow the methodology used in the journal paper, make sure to only include products that are present in all time periods, and non-supplementary products (supp = 0).
• Without doing any calculations, summarize any differences or general patterns between December 2014 and June 2015 that you find in the table.
• Would we be able to assess the effect of sugar taxes on product prices by comparing the average price of untaxed goods with that of taxed goods in any given period? Why or why not?
Non-taxed Taxed
Store type Dec 2014 Jun 2015 Dec 2014 Jun 2015
1
3

Figure 3.1 The average price of taxed and non-taxed beverages, according to time period and store type.

### R walk-through 3.4 Calculating conditional means

Calculating conditional means is not a straightforward task (in R or in any other statistical program), but is, however, a common data cleaning operation you will encounter. Here is one way to do this.

In order to identify products (product_id) that have observations for all three periods (DEC2014 , JUN2015 and MAR2016), we will first create a new variable called period_test, which takes the value 1 (or TRUE) if we have observations in all periods for a product in a particular store, and 0 (FALSE) otherwise. These true/false variables are called ‘boolean variables’.

The easiest way to create this variable is through a loop. For each store and product ID, we will check whether there are observations in all periods (i.e. the variable time has the time periods DEC2014 , JUN2015 and MAR2016), and temporarily store this information in the variable test. We then transfer this information to the rows of the new period_test variable that correspond to that store and product ID.

dat$period_test <- NA # List of all store IDs sid_list = unique(dat$store_id)

# List of all product IDs
pid_list = unique(dat$product_id) for (s in sid_list) { for (p in pid_list) { temp <- subset(dat, product_id == p & store_id == s) temp_time <- temp$time
test <- (
any(temp_time == "DEC2014") &
any(temp_time == "JUN2015") &
any(temp_time == "MAR2016"))
dat$period_test[dat$product_id == p &
table_res$D2 <- table_res$MAR2016 - table_res$DEC2014 print("Group Means")  ## [1] "Group Means"  table_res  ## # A tibble: 8 x 8 ## # Groups: taxed, store_type [8] ## taxed store_type n DEC2014 JUN2015 MAR2016 D1 D2 ## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 not taxed Large Supermar~ 36 0.112 0.115 0.117 2.88e-3 0.00510 ## 2 not taxed Small Supermar~ 70 0.137 0.138 0.134 1.46e-3 -0.00304 ## 3 not taxed Pharmacy 18 0.152 0.161 0.154 8.80e-3 0.00240 ## 4 not taxed Gas Station 12 0.169 0.170 0.170 2.90e-4 0.00106 ## 5 taxed Large Supermar~ 36 0.156 0.169 0.167 1.31e-2 0.0107 ## 6 taxed Small Supermar~ 101 0.159 0.160 0.155 1.44e-3 -0.00359 ## 7 taxed Pharmacy 18 0.182 0.191 0.186 8.97e-3 0.00448 ## 8 taxed Gas Station 22 0.194 0.203 0.192 9.25e-3 -0.00174  #### Plot a column chart for average price changes To display D1 and D2 in a column chart, we will use the ggplot2 package (which is part of the tidyverse package we loaded for R walk-through 3.1). Let’s start with displaying the average price change from December 2014 to June 2015 (which is stored in D1): ggplot(table_res, aes(fill = taxed, y = D1, x = store_type)) + geom_bar(position = "dodge", stat = "identity") + # Add the axes labels labs(y = "Price change (US$/oz)", x = "Store type") +
# Add the title and legend labels
scale_fill_discrete(name = "Beverages",
labels = c("Non-taxed", "Taxed")) +
ggtitle("Average price change from Dec 2014 to Jun 2015")


Figure 3.2 Average price change from December 2014 to June 2015.

Now we do the same for the price change from Dec 2014 to Mar 2016:

ggplot(table_res,
aes(fill = taxed, y = D2, x = store_type)) +
geom_bar(position = "dodge", stat = "identity") +
labs(y = "Price change (US$/oz)", x = "Store type") + # Add the title and legend labels scale_fill_discrete(name = "Beverages", labels = c("Non-taxed", "Taxed")) + ggtitle("Average price change from Dec 2014 to Mar 2016")  Figure 3.3 Average price change from December 2014 to March 2016. statistically significant When a relationship between two or more variables is unlikely to be due to chance, given the assumptions made about the variables (for example, having the same mean). Statistical significance does not tell us whether there is a causal link between the variables. To assess whether the difference in mean prices before and after the tax could have happened by chance due to the samples chosen (and there are no differences in the population means), we could calculate the p-value. (Here, ‘population means’ refer to the mean prices before/after the tax that we would calculate if we had all prices for all stores in Berkeley.) The authors of the journal article calculate p-values, and use the idea of statistical significance to interpret them. Whenever they get a p-value of less than 5%, they conclude that the assumption of no differences in the population is unlikely to be true: they say that the price difference is statistically significant. If they get a p-value higher than 5%, they say that the difference is not statistically significant, meaning that they think it could be due to chance variation in prices. Using a particular cutoff level for the p-value, and concluding that a result is only statistically significant if the p-value is below the cutoff, is common in statistical studies, and 5% is often used as the cutoff level. But this approach has been criticized recently by statisticians and social scientists. The main criticisms raised are that any cutoffs are arbitrary. Instead of using a cutoff, we prefer to calculate p-values and use them to assess the strength of the evidence against our assumption that there are no differences in the population means. Whether the statistical evidence is strong enough for us to draw a conclusion about a policy, such as a sugar tax, will always be a matter of judgement. According to the journal paper, the p-value is 0.02 for large supermarkets, and 0.99 for pharmacies. 1. Based on these p-values and your chart from Question 4, what can you conclude about the difference in means? (Hint: You may find the discussion in Part 2.3 helpful.) ### Extension R walk-through 3.6 Calculating the p-value for price changes In this walk-through, we show the calculations required to obtain the p- values in Table S3 of the Silver et al. (2017) paper. Since the p-values are already provided, this walk-through is only for those who want to see how these p-values were calculated. For the categories of Large Supermarkets and Pharmacies, we conduct a hypothesis test, which tests the null hypothesis that the price difference between June 2015 and December 2014 (and March 2016 and December 2014) for the taxed and untaxed beverages in the two store types could be due to chance. We are interested in whether the difference in average price between JUN2015 and DEC2014 (or MAR2016 and DEC2014) for one group (say, Large Supermarket and taxed) is zero (i.e. there is no difference in the means of the two populations). Note that we are dealing with paired observations (the same product in both time periods). Let’s use the price difference between June 2015 and December 2014 in Large Supermarkets for taxed beverages as an example. First, we extract the prices for both periods (the vectors p1 and p2) and then calculate the difference, element by element (stored as d_t). p1 <- dat_c$price_per_oz[
dat_c$store_type == "Large Supermarket" & dat_c$taxed == "taxed" &
dat_c$time == "DEC2014"] p2 <- dat_c$price_per_oz[
dat_c$store_type == "Large Supermarket" & dat_c$taxed == "taxed" &
dat_c$time == "JUN2015"] # Price difference for taxed products d_t <- p2 - p1  All three new variables are vectors with 36 elements. For d_t to correctly represent the price difference for a particular product in a particular store, we need to be certain that each element in both vectors corresponds to the same product in the same store. To check that the elements match, we will extract the store and product IDs along with the prices, and compare the ordering in p1_alt and p2_alt. p1_alt <- dat_c[ dat_c$store_type == "Large Supermarket" &
dat_c$taxed == "taxed" & dat_c$time == "DEC2014",
c("product_id", "store_id", "price_per_oz")]

p2_alt <- dat_c[
dat_c$store_type == "Large Supermarket" & dat_c$taxed == "taxed" &
dat_c$time == "JUN2015", c("product_id", "store_id", "price_per_oz")]  You can see that the ordering matches, since the original datafile was ordered in a way (first according to time, then store_id and then product_id ) that in this instance guarantees identical ordering. The average value of the price difference is 0.0131222, and our task is to evaluate whether this is likely to be due to sampling variation (given the assumption that there is no difference between the populations) or not. To do this, we can use the t.test function, which provides the associated p-value. standard error A measure of the degree to which the sample mean deviates from the population mean. It is calculated by dividing the standard deviation of the sample by the square root of the number of observations. Alternatively, we can calculate the respective test statistic manually, which also requires us to calculate the standard error of this value: t <- mean(d_t) / sqrt(var(d_t) / (length(d_t)))  Alternatively, we can use the available t.test function in R, which gives exactly the same results but has the advantage of directly obtaining p-values and confidence intervals. # Recognize that the differences come from paired samples. t.test(p2, p1, paired = TRUE)  ## ## Paired t-test ## ## data: p2 and p1 ## t = 4.7681, df = 35, p-value = 3.226e-05 ## alternative hypothesis: true difference in means is not equal to 0 ## 95 percent confidence interval: ## 0.007535221 0.018709202 ## sample estimates: ## mean of the differences ## 0.01312221  # Or get the same result using the function directly on d # t.test(d)  To compare this result to the journal paper, look at the extract from Table S3 (the section on Large Supermarkets) shown in Figure 3.4 below. The cell with ‘**’ shows the mean price difference of 1.31 cents ($0.0131).

Figure 3.4 Table S3 in Silver et al. (2017), showing means and confidence intervals.

n = number of stores of each type.

In our test output we get a very small p-value (0.0000323) which in the table is indicated by the double asterisk.

Tests for other store types are calculated similarly, by changing the data extracted to p1 and p2. Let’s do that for one more example: the price difference between June 2015 and December 2014 in Large Supermarkets for untaxed beverages.

p1 <- dat_c$price_per_oz[ dat_c$store_type == "Large Supermarket" &
dat_c$taxed == "not taxed" & dat_c$time == "DEC2014"]

p2 <- dat_c$price_per_oz[ dat_c$store_type == "Large Supermarket" &
dat_c$taxed == "not taxed" & dat_c$time == "JUN2015"]

d_nt <- p2 - p1

t.test(p2, p1, paired = TRUE)

##
## Paired t-test
##
## data: p2 and p1
## t = 1.8179, df = 35, p-value = 0.07765
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0003367942 0.0061057804
## sample estimates:
## mean of the differences
## 0.002884493


You should be able to recognize the mean difference, the p-value, and the confidence interval in the excerpt of Table S3 provided in Figure 3.4.

Let’s also replicate the last section of Table S3, which shows the difference between the price changes in taxed and untaxed products, that is, we want to know whether d_tAND d_nt have different means. We will apply the two sample hypothesis tests, but this time for unpaired data, as the products differ across samples.

t.test(d_t,d_nt)

##
## Welch two sample t-test
##
## data: d_t and d_nt
## t = 3.2227, df = 55.955, p-value = 0.002119
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.003873834 0.016601603
## sample estimates:
## mean of x mean of y
## 0.013122212 0.002884493


Again you should be able to identify the corresponding entries in Table S3 shown in Figure 3.4. The main entry in the table is 1.02, indicating that the means of the two groups differ by 1.02 cents. This is confirmed in our calculations, as $0.01312 −$0.00288 is about $0.0102 or 1.02 cents. The p-value of 0.002 is also the same as the one in Table S3. ## Part 3.2 Before-and-after comparisons with prices in other areas When looking for any price patterns, it is possible that the observed changes in Berkeley were not solely due to the tax, but instead were also influenced by other events that happened in Berkeley and in neighbouring areas. To investigate whether this is the case, the researchers conducted another differences-in-differences analysis, using a different treatment and control group: • The treatment group: Beverages in Berkeley • The control group: Beverages in surrounding areas. The researchers collected price data from stores in the surrounding areas and compared them with prices in Berkeley. If prices changed in a similar way in nearby areas (which were not subject to the tax), then what we observed in Berkeley may not be primarily a result of the tax. We will be using the data the researchers collected to make our own comparisons. Download the following files: • The Berkeley Point-of-Sale Stata file on the Global Food Research Program’s website, containing the price data they collected, including information on the date (year and month), location (Berkeley or Non-Berkeley), beverage group (soda, fruit drinks, milk substitutes, milk and water), and the average price for that month. Stata is another popular statistical software package, and the data is provided as a .dta file. • ‘S5 Table’ comparing the neighbourhood characteristics of the Berkeley and non-Berkeley stores. 1. Based on ‘S5 Table’, do you think the researchers chose suitable comparison stores? Why or why not? We will now plot a line chart similar to Figure 3 in the journal paper, to compare prices of similar goods in different locations and see how they have changed over time. To do this, we will need to summarize the data so that there is one value (the mean price) for each location and type of good in each month. 1. Assess the effects of a tax on prices: • Create a table similar to the one provided in Figure 3.5 to show the average price in each month for taxed and non-taxed beverages, according to location. Use ‘year and month’ as the row variables, and ‘tax’ and ‘location’ as the column variables. (Hint: You may find R walk-through 3.4 helpful.) • Plot the four columns of your table on the same line chart, with average price on the vertical axis and time (months) on the horizontal axis. Describe any differences you see between the prices of non-taxed goods in Berkeley and those outside Berkeley, both before the tax (January 2013 to December 2014) and after the tax (March 2015 onwards). Do the same for prices of taxed goods. • Based on your chart, is it reasonable to conclude that the sugar tax had an effect on prices? Non-taxed Taxed Year/Month Berkeley Non-Berkeley Berkeley Non-Berkeley January 2013 February 2013 March 2013 December 2013 January 2014 February 2016 Figure 3.5 The average price of taxed and non-taxed beverages, according to location and month. ### R walk-through 3.7 Importing data from a Stata file and plotting a line chart #### Import data and create a table of average monthly prices To import data from a Stata file (.dta format) we need the readstata13 package. library(readstata13) PoSd <- read.dta13("public_use_weighted_prices2.dta")  Before proceeding, use the command str(PoSd) to look at the structure of this dataset (output not shown here). You will see that for each month and location (Berkeley or Non-Berkeley), there are prices for a variety of beverage categories, and we know whether the category is taxed or not. For any particular time-location-tax status combination we want the average price of all products. To make the summary table, we use the piping technique again: table_test <- PoSd %>% group_by(year, month, location, tax) %>% summarize(avg.price = mean(price)) %>% spread(location, avg.price) %>% print()  ## # A tibble: 78 x 5 ## # Groups: year, month [39] ## year month tax Berkeley Non-Berkeley ## <dbl> <dbl> <chr> <dbl> <dbl> ## 1 2013 1 Non-taxed 5.72 5.35 ## 2 2013 1 Taxed 8.69 7.99 ## 3 2013 2 Non-taxed 5.81 5.36 ## 4 2013 2 Taxed 8.65 8.18 ## 5 2013 3 Non-taxed 5.86 5.42 ## 6 2013 3 Taxed 8.82 8.19 ## 7 2013 4 Non-taxed 5.86 5.64 ## 8 2013 4 Taxed 9.02 8.25 ## 9 2013 5 Non-taxed 5.79 5.18 ## 10 2013 5 Taxed 8.68 7.76 ## # ... with 68 more rows  We have the necessary information, but the data is not quite in the right shape yet, because we need to separate the Taxed from the Non-taxed data (which we do using the subset function). tax_table <- subset(table_test, tax == "Taxed") ntax_table <- subset(table_test, tax == "Non-taxed")  #### Plot a line chart Now we can create a line chart. Before we do this, we will convert the Berkeley and Non-Berkeley columns of both datasets to time series data using the ts function, which will make plotting a little easier. We can then plot tax_table$Berkeley and add lines for the three other variables.

# Use inverted commas ('') to refer to the 'Non-Berkeley'
# variable, since R interprets the hyphen as a minus sign.

tax_table$Berkeley <- ts(tax_table$Berkeley,
start = c(2013, 1), end = c(2016, 3), frequency = 12)
tax_table$'Non-Berkeley' <- ts(tax_table$'Non-Berkeley',
start = c(2013, 1), end = c(2016, 3), frequency = 12)
ntax_table$Berkeley <- ts(ntax_table$Berkeley,
start = c(2013, 1), end = c(2016, 3), frequency = 12)
ntax_table$'Non-Berkeley' <- ts(ntax_table$'Non-Berkeley',
start = c(2013, 1), end = c(2016, 3), frequency = 12)

plot(tax_table$Berkeley, col = "deepskyblue4", lwd = 2, ylab = "Average price", xlab = "Time", ylim = c(4, 12)) # \n creates a line break. title("Average price of taxed and non-taxed beverages \n in Berkeley and non-Berkeley areas") lines(tax_table$'Non-Berkeley',
col = "deeppink", lwd = 2)
lines(ntax_table$Berkeley, col = "darkgreen", lwd = 2) lines(ntax_table$'Non-Berkeley',
col = "darkorange", lwd = 2)

abline(v = 2015.1, col = "grey")
abline(v = 2015.3, col = "grey")

text(2014.6, 4, "Pre-tax")
text(2015.8, 4, "Post-tax")
legend(2013.1, 12, lwd = 2, lty = 1, cex = 0.8,
legend = c("Taxed (Berkeley)", "Taxed (non-Berkeley)",
"Non-taxed (Berkeley)", "Non-taxed (non-Berkeley)"),
col = c("deepskyblue4", "deeppink",
"darkgreen", "darkorange"))


Figure 3.6 Average price of taxed and non-taxed beverages in Berkeley and non-Berkeley areas.

How strong is the evidence that the sugar tax affected prices? According to the journal paper, when comparing the mean Berkeley and non-Berkeley price of sugary beverages after the tax, the p-value is smaller than 0.00001, and it is 0.63 for non-sugary beverages after the tax.

1. What do the p-values tell us about the difference in means and the effect of the sugar tax on the price of sugary beverages? (Hint: You may find the discussion in Part 2.3 helpful.)

The aim of the sugar tax was to decrease consumption of sugary beverages. Figure 3.7 shows the mean number of calories consumed and the mean volume consumed before and after the tax. The researchers reported the p-values for the difference in means before and after the tax in the last column.

Usual intake Pre-tax (Nov–Dec 2014),
n = 623
Post-tax (Nov–Dec 2015),
n = 613
Pre-tax–post-tax difference
Caloric intake (kilocalories/capita/day)
Taxed beverages 45.1 38.7 −6.4, p = 0.56
Non-taxed beverages 115.7 147.6 31.9, p = 0.04
Volume of intake (grams/capita/day)
Taxed beverages 121.0 97.0 −24.0, p = 0.24
Non-taxed beverages 1,839.4 1,896.5 57.1, p = 0.22
Models account for age, gender, race/ethnicity, income level, and educational attainment. n is the sample size at each round of the survey after excluding participants with missing values on self-reported race/ethnicity, age, education, income, or monthly intake of sugar-sweetened beverages.

Figure 3.7 Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: A before-and-after study.

Lynn D. Silver, Shu Wen Ng, Suzanne Ryan-Ibarra, Lindsey Smith Taillie, Marta Induni, Donna R. Miles, Jennifer M. Poti, and Barry M. Popkin. 2017. Table 1 in ‘Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: A before-and-after study’. PLoS Med 14 (4): e1002283.

1. Based on Figure 3.7, what can you say about consumption behaviour in Berkeley after the tax? Suggest some explanations for the evidence.
1. Read the ‘Limitations’ in the ‘Discussions’ section of the paper and discuss the strengths and limitations of this study. How could future studies on the sugar tax in Berkeley address these problems? (Some issues you may want to discuss are: the number of stores observed, number of people surveyed, and the reliability of the price data collected.)
1. Suppose that you have the authority to conduct your own sugar tax natural experiment in two neighbouring towns, Town A and Town B. Outline how you would conduct the experiment to ensure that any changes in outcomes (prices, consumption of sugary beverages) are due to the tax and not due to other factors. (Hint: think about what factors you need to hold constant.)
1. Lynn D. Silver, Shu Wen Ng, Suzanne Ryan-Ibarra, Lindsey Smith Taillie, Marta Induni, Donna R. Miles, Jennifer M. Poti, and Barry M. Popkin. 2017. S3 Table in ‘Changes in prices, sales, consumer spending, and beverage consumption one year after a tax on sugar-sweetened beverages in Berkeley, California, US: A before-and-after study’. PLoS Med 14 (4): e1002283.