The goal of nemsr is to enable researchers to quickly calculate NEMS-S scores for individual grocery stores using a Qualtrics-based NEMS-S survey developed at BYU.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("byu-transpolab/nemsr")The NEMS-S data should come from a Qualtrics survey collected by a
trained surveyor. Download the data from Qualtrics in the .sav format
(for SPSS). Then you can read the data into R with the read_nemss
function.
library(nemsr)
## basic example code
file <- system.file("extdata", "example_nems.sav", package = "nemsr")
(nemss <- read_nemss(file))
#> # A tibble: 10 × 83
#> STORE_ID store…¹ pharm…² ethnic merch regis…³ self_…⁴ lowfa…⁵ dairy…⁶ lowfa…⁷
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 SL-014 Dollar… FALSE FALSE FALSE 4 0 <NA> FALSE ""
#> 2 SL-013 Grocer… FALSE TRUE FALSE 3 0 FALSE TRUE ""
#> 3 SL-015 Grocer… TRUE FALSE FALSE 9 5 TRUE TRUE "0"
#> 4 SL-010 Dollar… FALSE FALSE FALSE 2 0 FALSE TRUE "0"
#> 5 SL-016 Grocer… FALSE TRUE FALSE 3 0 <NA> FALSE ""
#> 6 SL-017 Dollar… FALSE FALSE FALSE 2 0 FALSE TRUE ""
#> 7 SL-019 Grocer… FALSE TRUE FALSE 5 4 TRUE TRUE ""
#> 8 SL-018 Grocer… FALSE FALSE FALSE 12 7 TRUE TRUE "0"
#> 9 SL-012 Dollar… FALSE FALSE FALSE 2 0 FALSE TRUE "2"
#> 10 SL-011 Grocer… FALSE TRUE FALSE 2 0 FALSE TRUE "0"
#> # … with 73 more variables: lowfat_quart <chr>, lowfat_half_gal <chr>,
#> # lowfat_gal <chr>, whole_pint <chr>, whole_quart <chr>,
#> # whole_half_gal <chr>, whole_gal <chr>, lowfat_quart_price <chr>,
#> # lowfat_half_gal_price <chr>, lowfat_gal_price <chr>,
#> # whole_quart_price <chr>, whole_half_gal_price <chr>, whole_gal_price <chr>,
#> # lean_beef_varieties <chr>, lean_beef_price <chr>, regular_beef_price <chr>,
#> # varieties_of_fruit <chr>, apple_price <chr>, banana_price <chr>, …Once the data are in your R session, there are two basic things you might like to do with them: a NEMS-S score and a market basket score.
The calculate_nems_score() function computes the NEMS scores related
to the cost and availability of healthier options at each grocery store.
Additionally, a few variables relevant to the size and type of the store
are returned in a tibble.
calculate_nems_score(nemss)
#> Warning in baked_goods_cost(as.numeric(nems_data$lowfat_baked_goods_cost)): No
#> regular baked goods price provided
#> # A tibble: 10 × 12
#> ID type pharm…¹ ethnic merch regis…² selfc…³ total…⁴ Total…⁵ Total…⁶
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 SL-014 Dollar S… FALSE FALSE FALSE 4 0 4 5 1
#> 2 SL-013 Grocery … FALSE TRUE FALSE 3 0 3 9 0
#> 3 SL-015 Grocery … TRUE FALSE FALSE 9 5 14 25 1
#> 4 SL-010 Dollar S… FALSE FALSE FALSE 2 0 2 10 1
#> 5 SL-016 Grocery … FALSE TRUE FALSE 3 0 3 8 1
#> 6 SL-017 Dollar S… FALSE FALSE FALSE 2 0 2 13 6
#> 7 SL-019 Grocery … FALSE TRUE FALSE 5 4 9 15 4
#> 8 SL-018 Grocery … FALSE FALSE FALSE 12 7 19 24 0
#> 9 SL-012 Dollar S… FALSE FALSE FALSE 2 0 2 11 4
#> 10 SL-011 Grocery … FALSE TRUE FALSE 2 0 2 11 1
#> # … with 2 more variables: Latitude <chr>, Longitude <chr>, and abbreviated
#> # variable names ¹pharmacy, ²registers, ³selfchecko, ⁴total_registers,
#> # ⁵Total_Availability_Score, ⁶Total_Cost_ScoreThere is also a detail argument to this function that returns data
columns showing the detail of the price and availability calculations.
calculate_nems_score(nemss, detail = TRUE)
#> Warning in baked_goods_cost(as.numeric(nems_data$lowfat_baked_goods_cost)): No
#> regular baked goods price provided
#> # A tibble: 10 × 34
#> ID type pharm…¹ ethnic merch regis…² selfc…³ total…⁴ milk_…⁵ fruit…⁶
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 SL-014 Dollar S… FALSE FALSE FALSE 4 0 4 0 0
#> 2 SL-013 Grocery … FALSE TRUE FALSE 3 0 3 3 0
#> 3 SL-015 Grocery … TRUE FALSE FALSE 9 5 14 3 2
#> 4 SL-010 Dollar S… FALSE FALSE FALSE 2 0 2 3 0
#> 5 SL-016 Grocery … FALSE TRUE FALSE 3 0 3 0 2
#> 6 SL-017 Dollar S… FALSE FALSE FALSE 2 0 2 3 0
#> 7 SL-019 Grocery … FALSE TRUE FALSE 5 4 9 3 2
#> 8 SL-018 Grocery … FALSE FALSE FALSE 12 7 19 2 2
#> 9 SL-012 Dollar S… FALSE FALSE FALSE 2 0 2 3 0
#> 10 SL-011 Grocery … FALSE TRUE FALSE 2 0 2 3 2
#> # … with 24 more variables: vegetable_avail_score <dbl>,
#> # ground_beef_avail_score <dbl>, hot_dog_avail_score <dbl>,
#> # frozen_dinners_avail_score <dbl>, baked_goods_avail_score <dbl>,
#> # soda_avail_score <dbl>, juice_drinks_avail_score <dbl>,
#> # bread_avail_score <dbl>, chip_avail_score <dbl>, cereal_avail_score <dbl>,
#> # Total_Availability_Score <dbl>, milk_cost_score <dbl>,
#> # ground_beef_cost_score <dbl>, wieners_cost_score <dbl>, …The calculate_market_basket() function computes the cost of a common
market basket at that store using the pricing data available weighted
according to the Thrifty Food Plan,
2021
(basket_score <- calculate_market_basket(nemss))
#> # A tibble: 10 × 17
#> ID grain_repl…¹ grain dairy…² dairy veget…³ veget…⁴ fruit…⁵ fruit soda_…⁶
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 SL-014 2 35.7 2 8.75 7 40.8 4 29.6 0
#> 2 SL-013 1 40.8 0 20.1 7 40.8 4 29.6 0
#> 3 SL-015 0 30.0 0 16.5 1 44.2 0 32.0 0
#> 4 SL-010 1 40.8 0 21.5 7 40.8 4 29.6 0
#> 5 SL-016 2 35.7 2 8.75 4 34.3 1 24.6 0
#> 6 SL-017 1 35.8 0 21.5 7 40.8 4 29.6 0
#> 7 SL-019 1 45.1 0 16.5 1 28.2 1 23.9 0
#> 8 SL-018 0 23.1 0 16.4 1 51.6 0 20.7 0
#> 9 SL-012 2 35.7 0 21.5 7 40.8 4 29.6 0
#> 10 SL-011 1 38.2 0 17.4 2 27.1 1 20.6 0
#> # … with 7 more variables: soda <dbl>, beef_replacements <dbl>, beef <dbl>,
#> # frozen_dinner_replacements <dbl>, frozen_dinner <dbl>,
#> # total_replacements <dbl>, total <dbl>, and abbreviated variable names
#> # ¹grain_replacements, ²dairy_replacements, ³vegetable_replacements,
#> # ⁴vegetable, ⁵fruit_replacements, ⁶soda_replacementsFor details on this function, its methods, and return information, see
?calculate_market_basket()