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analysis.R
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# Load libraries
if (!require("dplyr")) { install.packages("dplyr") }; library(dplyr)
if (!require("devtools")) { install.packages("devtools") }
if (!require("readr")) { install.packages("readr") }
if (!require("surveytools2")) { devtools::install_github("peterhurford/surveytools2") }; library(surveytools2)
# Load data
data <- read_csv("../cleaned-data-shareable/all_waves_cleaned.csv")
# Make variables
data$howOftenEatMeat_chg <- data$howOftenEatMeat.1 - data$howOftenEatMeat.3
data$is_veg.1 <- data$FFQfreqTurkey.1 == 0 & data$FFQfreqPork.1 == 0 & data$FFQfreqChicken.1 == 0 & data$FFQfreqFish.1 == 0 & data$FFQfreqBeef.1 == 0
data$is_veg.3 <- data$FFQfreqTurkey.3 == 0 & data$FFQfreqPork.3 == 0 & data$FFQfreqChicken.3 == 0 & data$FFQfreqFish.3 == 0 & data$FFQfreqBeef.3 == 0
data$veg_chg <- data$is_veg.3 - data$is_veg.1
data$mt.1 <- data$FFQfreqTurkey.1 + data$FFQfreqPork.1 + data$FFQfreqChicken.1 + data$FFQfreqFish.1 + data$FFQfreqBeef.1
data$mt.3 <- data$FFQfreqTurkey.3 + data$FFQfreqPork.3 + data$FFQfreqChicken.3 + data$FFQfreqFish.3 + data$FFQfreqBeef.3
data$mt_chg <- data$mt.3 - data$mt.1
data$bogus.1 <- data$FFQfreqFruit.1 + data$FFQfreqNuts.1 + data$FFQfreqVegetables.1 + data$FFQfreqBeans.1 + data$FFQfreqGrains.1
data$bogus.3 <- data$FFQfreqFruit.3 + data$FFQfreqNuts.3 + data$FFQfreqVegetables.3 + data$FFQfreqBeans.3 + data$FFQfreqGrains.3
data$bogus_chg <- data$bogus.3 - data$bogus.1
data$vegetable_chg <- data$FFQfreqVegetables.3 - data$FFQfreqVegetables.1
# Analysis
data %>% ctable(FFQtotalSumMeat_chg, treatment)
# Adding missing grouping variables: `group`
# FFQtotalSumMeat_chg ### treatment
# # A tibble: 3 × 4
# group mean median sd
# <chr> <dbl> <dbl> <dbl>
# 1 control 0.3034188 0 8.054944
# 2 reduce -0.6977077 0 8.595043
# 3 veg -0.7948895 0 9.412867
# Call:
# lm(formula = .)
# Residuals:
# Min 1Q Median 3Q Max
# -32.803 -2.803 0.697 3.198 26.795
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) 0.3034 0.3289 0.923 0.3563
# yreduce -1.0011 0.4658 -2.149 0.0317 *
# yveg -1.0983 0.4615 -2.380 0.0174 *
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Residual standard error: 8.713 on 2121 degrees of freedom
# (113 observations deleted due to missingness)
# Multiple R-squared: 0.003232, Adjusted R-squared: 0.002292
# F-statistic: 3.438 on 2 and 2121 DF, p-value: 0.03229
data %>% ctable(howOftenEatMeat_chg, treatment)
# Adding missing grouping variables: `group`
# howOftenEatMeat_chg ### treatment
# # A tibble: 3 × 4
# group mean median sd
# <chr> <dbl> <int> <dbl>
# 1 control -0.03518268 0 0.9084706
# 2 reduce 0.08119080 0 0.9774757
# 3 veg 0.01192053 0 0.9926071
# Call:
# lm(formula = .)
# Residuals:
# Min 1Q Median 3Q Max
# -5.0812 -0.0812 -0.0119 0.0352 4.9881
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -0.03518 0.03533 -0.996 0.3195
# yreduce 0.11637 0.04997 2.329 0.0199 *
# yveg 0.04710 0.04970 0.948 0.3434
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Residual standard error: 0.9605 on 2230 degrees of freedom
# (4 observations deleted due to missingness)
# Multiple R-squared: 0.002456, Adjusted R-squared: 0.001562
# F-statistic: 2.746 on 2 and 2230 DF, p-value: 0.06443
data %>% filter(treatment == "control") %>% .$veg_chg %>% table
# -1 0 1
# 3 727 6
data %>% filter(treatment != "control") %>% .$veg_chg %>% table
# -1 0 1
# 6 1478 8
data %>% ctable(veg_chg > 0, treatment)
# > veg_chg 0 ### treatment
# control reduce veg
# FALSE 0.9918 0.9973 0.9921
# TRUE 0.0082 0.0027 0.0079
# Pearson's Chi-squared test
# data: x and y
# X-squared = 2.2503, df = 2, p-value = 0.3246
data %>% ctable(mt_chg < 0, treatment == "control")
# < mt_chg 0 ### == treatment control
# FALSE TRUE
# FALSE 0.5755 0.6467
# TRUE 0.4245 0.3533
# Pearson's Chi-squared test with Yates' continuity correction
# data: x and y
# X-squared = 9.9013, df = 1, p-value = 0.001652
data %>% ctable(bogus_chg < 0, treatment == "control")
# p-value: 0.7933
data %>% ctable(bogus_chg < 0, treatment == "control")
# < bogus_chg 0 ### == treatment control
# FALSE TRUE
# FALSE 0.5755 0.5860
# TRUE 0.4245 0.4140
data %>% ctable(vegetable_chg, treatment == "control")
# p-value: 0.1778
# Pearson's Chi-squared test with Yates' continuity correction
# data: x and y
# X-squared = 0.17827, df = 1, p-value = 0.6729
# Beef vs. chicken
data %>% filter(treatment == "control") %>% filter(FFQfreqBeef.3 < FFQfreqBeef.1) %>% { .$FFQfreqChicken.3 - .$FFQfreqChicken.1 } %>% mean(., na.rm = T)
# [1] -0.6649485
data %>% filter(treatment != "control") %>% filter(FFQfreqBeef.3 < FFQfreqBeef.1) %>% { .$FFQfreqChicken.3 - .$FFQfreqChicken.1 } %>% mean(., na.rm = T)
# [1] -2.200758
data %>% filter(treatment == "control") %>% filter(FFQfreqChicken.3 < FFQfreqChicken.1) %>% { .$FFQfreqBeef.3 - .$FFQfreqBeef.1 } %>% mean(., na.rm = T)
# [1] -1
data %>% filter(treatment != "control") %>% filter(FFQfreqChicken.3 < FFQfreqChicken.1) %>% { .$FFQfreqBeef.3 - .$FFQfreqBeef.1 } %>% mean(., na.rm = T)
# [1] -2.283673
# Product elimination analysis
data %>% filter(treatment == "control") %>% { (.$FFQfreqBeef.3 == 0 & .$FFQfreqBeef.1 > 0) } %>% table
# FALSE TRUE
# 721 15
# 2.0380435%
data %>% filter(treatment != "control") %>% { (.$FFQfreqBeef.3 == 0 & .$FFQfreqBeef.1 > 0) } %>% table
# FALSE TRUE
# 1450 40
# 2.684563758%
data %>% filter(treatment == "control") %>% { (.$FFQfreqChicken.3 == 0 & .$FFQfreqChicken.1 > 0) } %>% table
# FALSE TRUE
# 726 10
# 1.358695652%
data %>% filter(treatment != "control") %>% { (.$FFQfreqChicken.3 == 0 & .$FFQfreqChicken.1 > 0) } %>% table
# FALSE TRUE
# 1477 15
# 1.00536193%
data %>% filter(treatment == "control") %>% { (.$FFQfreqFish.3 == 0 & .$FFQfreqFish.1 > 0) } %>% table
# FALSE TRUE
# 694 40
# 5.449591281%
data %>% filter(treatment != "control") %>% { (.$FFQfreqFish.3 == 0 & .$FFQfreqFish.1 > 0) } %>% table
# FALSE TRUE
# 1412 80
# 5.361930295%
data %>% filter(treatment == "control") %>% { (.$FFQfreqEggs.3 == 0 & .$FFQfreqEggs.1 > 0) } %>% table
# FALSE TRUE
# 724 11
# 1.496598639%
data %>% filter(treatment != "control") %>% { (.$FFQfreqEggs.3 == 0 & .$FFQfreqEggs.1 > 0) } %>% table
# FALSE TRUE
# 1440 52
# 3.485254692%
data %>% filter(treatment == "control") %>% { (.$FFQfreqDairy.3 == 0 & .$FFQfreqDairy.1 > 0) } %>% table
# FALSE TRUE
# 726 9
# 1.2244898%
data %>% filter(treatment != "control") %>% { (.$FFQfreqDairy.3 == 0 & .$FFQfreqDairy.1 > 0) } %>% table
# FALSE TRUE
# 1471 15
# 0.605652759%
# Consumption analysis
data %>% filter(treatment == "control") %>% { .$FFQfreqTurkey.3 - .$FFQfreqTurkey.1 } %>% mean(., na.rm = T)
# [1] 0.07901907
data %>% filter(treatment != "control") %>% { .$FFQfreqTurkey.3 - .$FFQfreqTurkey.1 } %>% mean(., na.rm = T)
# [1] -0.1851107
data %>% filter(treatment == "control") %>% { .$FFQfreqPork.3 - .$FFQfreqPork.1 } %>% mean(., na.rm = T)
# [1] 0.1707483
data %>% filter(treatment != "control") %>% { .$FFQfreqPork.3 - .$FFQfreqPork.1 } %>% mean(., na.rm = T)
# [1] -0.3003356
data %>% filter(treatment == "control") %>% { .$FFQfreqChicken.3 - .$FFQfreqChicken.1 } %>% mean(., na.rm = T)
# [1] 0.09332425
data %>% filter(treatment != "control") %>% { .$FFQfreqChicken.3 - .$FFQfreqChicken.1 } %>% mean(., na.rm = T)
# [1] -0.262273
data %>% filter(treatment == "control") %>% { .$FFQfreqFish.3 - .$FFQfreqFish.1 } %>% mean(., na.rm = T)
# [1] 0.1532148
data %>% filter(treatment != "control") %>% { .$FFQfreqFish.3 - .$FFQfreqFish.1 } %>% mean(., na.rm = T)
# [1] 0.07550336
data %>% filter(treatment == "control") %>% { .$FFQfreqBeef.3 - .$FFQfreqBeef.1 } %>% mean(., na.rm = T)
# [1] 0.04774898
data %>% filter(treatment != "control") %>% { .$FFQfreqBeef.3 - .$FFQfreqBeef.1 } %>% mean(., na.rm = T)
# [1] -0.2234543