Group Analysis Assignment

Load Packages

if (!require(haven)){
  install.packages("haven", dependencies = TRUE)
  require(haven)
}
Loading required package: haven
if (!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
  require(tidyverse)
}
Loading required package: tidyverse
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
✔ tibble  3.1.8      ✔ dplyr   1.0.10
✔ tidyr   1.2.1      ✔ stringr 1.5.0 
✔ readr   2.1.3      ✔ forcats 0.5.2 
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
if (!require(afex)){
  install.packages("afex", dependencies = TRUE)
  require(afex)
}
Loading required package: afex
Loading required package: lme4
Loading required package: Matrix

Attaching package: 'Matrix'
The following objects are masked from 'package:tidyr':

    expand, pack, unpack
************
Welcome to afex. For support visit: http://afex.singmann.science/
- Functions for ANOVAs: aov_car(), aov_ez(), and aov_4()
- Methods for calculating p-values with mixed(): 'S', 'KR', 'LRT', and 'PB'
- 'afex_aov' and 'mixed' objects can be passed to emmeans() for follow-up tests
- NEWS: emmeans() for ANOVA models now uses model = 'multivariate' as default.
- Get and set global package options with: afex_options()
- Set orthogonal sum-to-zero contrasts globally: set_sum_contrasts()
- For example analyses see: browseVignettes("afex")
************

Attaching package: 'afex'
The following object is masked from 'package:lme4':

    lmer
if (!require(summarytools)){
  install.packages("summarytools", dependencies = TRUE)
  require(summarytools)
}
Loading required package: summarytools
Warning in fun(libname, pkgname): couldn't connect to display ":0"
system might not have X11 capabilities; in case of errors when using dfSummary(), set st_options(use.x11 = FALSE)

Attaching package: 'summarytools'
The following object is masked from 'package:tibble':

    view

Importing Data

dataset <- read_sav("Project9.sav")

Cleaning Data

(dataset %>%
  filter(Duration__in_seconds_ > 45) -> dataset.clean)
# A tibble: 66 × 27
   StartDate           EndDate             Status        IPAdd…¹ Progr…² Durat…³
   <dttm>              <dttm>              <dbl+lbl>     <chr>     <dbl>   <dbl>
 1 2023-04-03 11:51:11 2023-04-06 14:23:36 0 [IP Addres… 104.13…     100  268344
 2 2023-04-06 14:33:42 2023-04-06 14:34:35 0 [IP Addres… 173.8.…     100      53
 3 2023-04-06 14:31:45 2023-04-06 14:35:14 0 [IP Addres… 72.210…     100     208
 4 2023-04-06 14:44:52 2023-04-06 14:45:54 0 [IP Addres… 174.20…     100      62
 5 2023-04-06 14:52:47 2023-04-06 14:54:18 0 [IP Addres… 107.11…     100      90
 6 2023-04-06 14:56:39 2023-04-06 14:57:40 0 [IP Addres… 174.20…     100      61
 7 2023-04-06 15:07:01 2023-04-06 15:08:52 0 [IP Addres… 24.113…     100     110
 8 2023-04-06 15:09:34 2023-04-06 15:10:54 0 [IP Addres… 172.56…     100      79
 9 2023-04-06 15:16:58 2023-04-06 15:18:13 0 [IP Addres… 107.11…     100      74
10 2023-04-06 15:17:44 2023-04-06 15:18:42 0 [IP Addres… 73.242…     100      57
# … with 56 more rows, 21 more variables: Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Welcome_and_Consent <dbl+lbl>,
#   Dependent_Measure <dbl+lbl>, Gender <chr>, Race <chr>, Age <dbl>,
#   Hypothesis <chr>, FL_18_DO_Condition1_WithFood_Healthy <dbl>, …

Codebook

print(dfSummary(dataset.clean, graph.magnif = .75), method = 'render')
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''
Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
graph.magnif, : unable to open connection to X11 display ''

Data Frame Summary

dataset.clean

Dimensions: 66 x 27
Duplicates: 0
No Variable Label Stats / Values Freqs (% of Valid) Graph Valid Missing
1 StartDate [POSIXct, POSIXt] Start Date
min : 2023-04-03 11:51:11
med : 2023-04-06 19:15:04
max : 2023-04-18 22:26:04
range : 15d 10H 34M 53S
 65 distinct values 66 (100.0%) 0 (0.0%)
2 EndDate [POSIXct, POSIXt] End Date
min : 2023-04-06 14:23:36
med : 2023-04-06 19:21:30
max : 2023-04-18 22:34:35
range : 12d 8H 10M 59S
 66 distinct values 66 (100.0%) 0 (0.0%)
3 Status [haven_labelled, vctrs_vctr, double] Response Type 1 distinct value
0:66(100.0%)
 66 (100.0%) 0 (0.0%)
4 IPAddress [character] IP Address
1. 73.104.161.128
2. 104.13.3.250
3. 98.231.81.18
4. 104.60.110.59
5. 107.115.165.50
6. 24.10.76.39
7. 73.194.91.100
8. 73.224.229.155
9. 73.242.235.153
10. 96.8.26.116
[ 42 others ]
4(6.1%)
3(4.5%)
3(4.5%)
2(3.0%)
2(3.0%)
2(3.0%)
2(3.0%)
2(3.0%)
2(3.0%)
2(3.0%)
42(63.6%)
 66 (100.0%) 0 (0.0%)
5 Progress [numeric] Progress
Mean (sd) : 92.6 (17.3)
min ≤ med ≤ max:
36 ≤ 100 ≤ 100
IQR (CV) : 0 (0.2)
36:2(3.0%)
45:2(3.0%)
55:4(6.1%)
91:8(12.1%)
100:50(75.8%)
 66 (100.0%) 0 (0.0%)
6 Duration__in_seconds_ [numeric] Duration (in seconds)
Mean (sd) : 5288.6 (33758.7)
min ≤ med ≤ max:
47 ≤ 83.5 ≤ 268344
IQR (CV) : 96 (6.4)
 53 distinct values 66 (100.0%) 0 (0.0%)
7 Finished [haven_labelled, vctrs_vctr, double] Finished
Min : 0
Mean : 0.8
Max : 1
0:16(24.2%)
1:50(75.8%)
 66 (100.0%) 0 (0.0%)
8 RecordedDate [POSIXct, POSIXt] Recorded Date
min : 2023-04-06 14:23:36
med : 2023-04-06 21:21:33
max : 2023-04-18 22:34:35
range : 12d 8H 10M 59S
 66 distinct values 66 (100.0%) 0 (0.0%)
9 ResponseId [character] Response ID
1. R_1eLhVLIXjJc3OTE
2. R_1g6ZLZTZZas5nMY
3. R_1ghGR6vJC3WeBro
4. R_1gklgrZacVLdE8m
5. R_1GOg5ZtOCL79UVA
6. R_1GOHTQpIkdHAxQi
7. R_1Hc4NR1YVgKjIC5
8. R_1JJYP93cCXCojDc
9. R_1jlFwAeGHRTdQcl
10. R_1laR9U24TFu2apu
[ 56 others ]
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
56(84.8%)
 66 (100.0%) 0 (0.0%)
10 RecipientLastName [character] Recipient Last Name
All empty strings
 66 (100.0%) 0 (0.0%)
11 RecipientFirstName [character] Recipient First Name
All empty strings
 66 (100.0%) 0 (0.0%)
12 RecipientEmail [character] Recipient Email
All empty strings
 66 (100.0%) 0 (0.0%)
13 ExternalReference [character] External Data Reference
All empty strings
 66 (100.0%) 0 (0.0%)
14 LocationLatitude [character] Location Latitude
1. (Empty string)
2. 30.0955
3. 30.161
4. 30.3341
5. 30.4566
6. 30.1172
7. 30.3562
8. 35.8663
9. 26.0276
10. 28.5344
[ 28 others ]
16(24.2%)
4(6.1%)
4(6.1%)
3(4.5%)
3(4.5%)
2(3.0%)
2(3.0%)
2(3.0%)
1(1.5%)
1(1.5%)
28(42.4%)
 66 (100.0%) 0 (0.0%)
15 LocationLongitude [character] Location Longitude
1. (Empty string)
2. -81.7198
3. -81.7311
4. -81.6544
5. -84.3223
6. -106.2676
7. -81.4119
8. -81.688
9. -111.9956
10. -121.1644
[ 28 others ]
16(24.2%)
4(6.1%)
4(6.1%)
3(4.5%)
3(4.5%)
2(3.0%)
2(3.0%)
2(3.0%)
1(1.5%)
1(1.5%)
28(42.4%)
 66 (100.0%) 0 (0.0%)
16 DistributionChannel [character] Distribution Channel
1. anonymous
2. qr
15(22.7%)
51(77.3%)
 66 (100.0%) 0 (0.0%)
17 UserLanguage [character] User Language 1. EN
66(100.0%)
 66 (100.0%) 0 (0.0%)
18 Welcome_and_Consent [haven_labelled, vctrs_vctr, double] Purpose of Research and Specific procedures to be used:In this study, you will be asked to participa 1 distinct value
1:66(100.0%)
 66 (100.0%) 0 (0.0%)
19 Dependent_Measure [haven_labelled, vctrs_vctr, double] How hungry are you?
Mean (sd) : 4.2 (2.4)
min ≤ med ≤ max:
1 ≤ 5 ≤ 10
IQR (CV) : 4 (0.6)
1:14(22.6%)
2:6(9.7%)
3:3(4.8%)
4:2(3.2%)
5:20(32.3%)
6:7(11.3%)
7:5(8.1%)
8:3(4.8%)
9:1(1.6%)
10:1(1.6%)
 62 (93.9%) 4 (6.1%)
20 Gender [character] What is your gender?
1. (Empty string)
2. Array
3. Cis Female
4. F
5. Femake
6. female
7. Female
8. male
9. Male
10. non-binary
11. Woman
8(12.1%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
5(7.6%)
18(27.3%)
1(1.5%)
28(42.4%)
1(1.5%)
1(1.5%)
 66 (100.0%) 0 (0.0%)
21 Race [character] What is your race?
1. White
2. (Empty string)
3. white
4. Caucasian
5. African American
6. Hispanic
7. Asian
8. Black
9. Array
10. Asian/Latina
[ 8 others ]
27(40.9%)
8(12.1%)
6(9.1%)
5(7.6%)
3(4.5%)
3(4.5%)
2(3.0%)
2(3.0%)
1(1.5%)
1(1.5%)
8(12.1%)
 66 (100.0%) 0 (0.0%)
22 Age [numeric] What is your age?
Mean (sd) : 28.5 (13.2)
min ≤ med ≤ max:
18 ≤ 22 ≤ 76
IQR (CV) : 10 (0.5)
 22 distinct values 57 (86.4%) 9 (13.6%)
23 Hypothesis [character] What did you think our hypotheses were from this survey?
1. (Empty string)
2. Idk
3. N/A
4. Not sure
5. Depictions of food influe
6. do certain images affect
7. do pictures of food make
8. Do social media posts inf
9. Does photo stimuli have i
10. Does seeing food make you
[ 44 others ]
10(15.2%)
2(3.0%)
2(3.0%)
2(3.0%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
1(1.5%)
44(66.7%)
 66 (100.0%) 0 (0.0%)
24 FL_18_DO_Condition1_WithFood_Healthy [numeric] FL_18 - Block Randomizer - Display Order Condition1:WithFood,Healthy 1 distinct value
1:18(100.0%)
 18 (27.3%) 48 (72.7%)
25 FL_18_DO_Condition2_WithFood_Unhealthy [numeric] FL_18 - Block Randomizer - Display Order Condition2:WithFood,Unhealthy 1 distinct value
1:15(100.0%)
 15 (22.7%) 51 (77.3%)
26 FL_18_DO_Condition3_Withoutfood_unhealthy [numeric] FL_18 - Block Randomizer - Display Order Condition3:Withoutfood,unhealthy 1 distinct value
1:11(100.0%)
 11 (16.7%) 55 (83.3%)
27 FL_18_DO_Condition4_Withoutfood_healthy [numeric] FL_18 - Block Randomizer - Display Order Condition4:Withoutfood,healthy 1 distinct value
1:22(100.0%)
 22 (33.3%) 44 (66.7%)

Generated by summarytools 1.0.1 (R version 4.2.2)
2023-04-24

Create Food IV

(dataset.clean %>%
  mutate(FoodIV = case_when(FL_18_DO_Condition1_WithFood_Healthy == 1 ~ "food", FL_18_DO_Condition2_WithFood_Unhealthy == 1 ~ "food", FL_18_DO_Condition3_Withoutfood_unhealthy == 1 ~ "no food", FL_18_DO_Condition4_Withoutfood_healthy == 1 ~ "no food")) -> dataset.clean)
# A tibble: 66 × 28
   StartDate           EndDate             Status        IPAdd…¹ Progr…² Durat…³
   <dttm>              <dttm>              <dbl+lbl>     <chr>     <dbl>   <dbl>
 1 2023-04-03 11:51:11 2023-04-06 14:23:36 0 [IP Addres… 104.13…     100  268344
 2 2023-04-06 14:33:42 2023-04-06 14:34:35 0 [IP Addres… 173.8.…     100      53
 3 2023-04-06 14:31:45 2023-04-06 14:35:14 0 [IP Addres… 72.210…     100     208
 4 2023-04-06 14:44:52 2023-04-06 14:45:54 0 [IP Addres… 174.20…     100      62
 5 2023-04-06 14:52:47 2023-04-06 14:54:18 0 [IP Addres… 107.11…     100      90
 6 2023-04-06 14:56:39 2023-04-06 14:57:40 0 [IP Addres… 174.20…     100      61
 7 2023-04-06 15:07:01 2023-04-06 15:08:52 0 [IP Addres… 24.113…     100     110
 8 2023-04-06 15:09:34 2023-04-06 15:10:54 0 [IP Addres… 172.56…     100      79
 9 2023-04-06 15:16:58 2023-04-06 15:18:13 0 [IP Addres… 107.11…     100      74
10 2023-04-06 15:17:44 2023-04-06 15:18:42 0 [IP Addres… 73.242…     100      57
# … with 56 more rows, 22 more variables: Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Welcome_and_Consent <dbl+lbl>,
#   Dependent_Measure <dbl+lbl>, Gender <chr>, Race <chr>, Age <dbl>,
#   Hypothesis <chr>, FL_18_DO_Condition1_WithFood_Healthy <dbl>, …

Create HealthyIV

(dataset.clean %>%
  mutate(HealthyIV = case_when(FL_18_DO_Condition1_WithFood_Healthy == 1 ~ "healthy", FL_18_DO_Condition2_WithFood_Unhealthy == 1 ~ "unhealthy", FL_18_DO_Condition3_Withoutfood_unhealthy == 1 ~ "unhealthy", FL_18_DO_Condition4_Withoutfood_healthy == 1 ~ "healthy")) -> dataset.clean)
# A tibble: 66 × 29
   StartDate           EndDate             Status        IPAdd…¹ Progr…² Durat…³
   <dttm>              <dttm>              <dbl+lbl>     <chr>     <dbl>   <dbl>
 1 2023-04-03 11:51:11 2023-04-06 14:23:36 0 [IP Addres… 104.13…     100  268344
 2 2023-04-06 14:33:42 2023-04-06 14:34:35 0 [IP Addres… 173.8.…     100      53
 3 2023-04-06 14:31:45 2023-04-06 14:35:14 0 [IP Addres… 72.210…     100     208
 4 2023-04-06 14:44:52 2023-04-06 14:45:54 0 [IP Addres… 174.20…     100      62
 5 2023-04-06 14:52:47 2023-04-06 14:54:18 0 [IP Addres… 107.11…     100      90
 6 2023-04-06 14:56:39 2023-04-06 14:57:40 0 [IP Addres… 174.20…     100      61
 7 2023-04-06 15:07:01 2023-04-06 15:08:52 0 [IP Addres… 24.113…     100     110
 8 2023-04-06 15:09:34 2023-04-06 15:10:54 0 [IP Addres… 172.56…     100      79
 9 2023-04-06 15:16:58 2023-04-06 15:18:13 0 [IP Addres… 107.11…     100      74
10 2023-04-06 15:17:44 2023-04-06 15:18:42 0 [IP Addres… 73.242…     100      57
# … with 56 more rows, 23 more variables: Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Welcome_and_Consent <dbl+lbl>,
#   Dependent_Measure <dbl+lbl>, Gender <chr>, Race <chr>, Age <dbl>,
#   Hypothesis <chr>, FL_18_DO_Condition1_WithFood_Healthy <dbl>, …

ANOVA

(dataset.clean %>%
  mutate(hungerDV = as.numeric(Dependent_Measure)) -> dataset.clean)
# A tibble: 66 × 30
   StartDate           EndDate             Status        IPAdd…¹ Progr…² Durat…³
   <dttm>              <dttm>              <dbl+lbl>     <chr>     <dbl>   <dbl>
 1 2023-04-03 11:51:11 2023-04-06 14:23:36 0 [IP Addres… 104.13…     100  268344
 2 2023-04-06 14:33:42 2023-04-06 14:34:35 0 [IP Addres… 173.8.…     100      53
 3 2023-04-06 14:31:45 2023-04-06 14:35:14 0 [IP Addres… 72.210…     100     208
 4 2023-04-06 14:44:52 2023-04-06 14:45:54 0 [IP Addres… 174.20…     100      62
 5 2023-04-06 14:52:47 2023-04-06 14:54:18 0 [IP Addres… 107.11…     100      90
 6 2023-04-06 14:56:39 2023-04-06 14:57:40 0 [IP Addres… 174.20…     100      61
 7 2023-04-06 15:07:01 2023-04-06 15:08:52 0 [IP Addres… 24.113…     100     110
 8 2023-04-06 15:09:34 2023-04-06 15:10:54 0 [IP Addres… 172.56…     100      79
 9 2023-04-06 15:16:58 2023-04-06 15:18:13 0 [IP Addres… 107.11…     100      74
10 2023-04-06 15:17:44 2023-04-06 15:18:42 0 [IP Addres… 73.242…     100      57
# … with 56 more rows, 24 more variables: Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Welcome_and_Consent <dbl+lbl>,
#   Dependent_Measure <dbl+lbl>, Gender <chr>, Race <chr>, Age <dbl>,
#   Hypothesis <chr>, FL_18_DO_Condition1_WithFood_Healthy <dbl>, …
aov_ez(id = "ResponseId", 
       dv = "hungerDV", 
       data = dataset.clean, 
       between=c("HealthyIV", "FoodIV"))
Converting to factor: HealthyIV, FoodIV
Warning: Missing values for following ID(s):
R_1eLhVLIXjJc3OTE, R_2OTOBFmnx7tGf26, R_2ScvaTLRYt1NDJj, R_3kcvEuOt7yYp5JR
Removing those cases from the analysis.
Contrasts set to contr.sum for the following variables: HealthyIV, FoodIV
Anova Table (Type 3 tests)

Response: hungerDV
            Effect    df  MSE      F  ges p.value
1        HealthyIV 1, 58 5.24 4.16 * .067    .046
2           FoodIV 1, 58 5.24   1.43 .024    .237
3 HealthyIV:FoodIV 1, 58 5.24   2.16 .036    .147
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Study Design Table Numbers

dataset.clean %>%
  group_by(HealthyIV, FoodIV) %>%
  summarise(mean = mean(hungerDV, na.rm = TRUE), sd = sd(hungerDV, na.rm = TRUE))
`summarise()` has grouped output by 'HealthyIV'. You can override using the
`.groups` argument.
# A tibble: 4 × 4
# Groups:   HealthyIV [2]
  HealthyIV FoodIV   mean    sd
  <chr>     <chr>   <dbl> <dbl>
1 healthy   food     4.59 2.94 
2 healthy   no food  3    2.25 
3 unhealthy food     4.93 2.27 
4 unhealthy no food  5.09 0.539
dataset.clean %>%
  group_by(FoodIV) %>%
  summarise(mean = mean(hungerDV, na.rm = TRUE), sd = sd(hungerDV, na.rm = TRUE))
# A tibble: 2 × 3
  FoodIV   mean    sd
  <chr>   <dbl> <dbl>
1 food     4.74  2.62
2 no food  3.74  2.08
dataset.clean %>%
  group_by(HealthyIV) %>%
  summarise(mean = mean(hungerDV, na.rm = TRUE), sd = sd(hungerDV, na.rm = TRUE))
# A tibble: 2 × 3
  HealthyIV  mean    sd
  <chr>     <dbl> <dbl>
1 healthy    3.73  2.67
2 unhealthy  5     1.71