Group 6 Analysis

Load Packages

if(!require(haven)){
  install.packages("haven", dependencies = TRUE)
  library(haven)}
Loading required package: haven
if(!require(tidyverse)){
  install.packages("tidyverse", dependencies = TRUE)
  library(tidyverse)}
Loading required package: tidyverse
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.2     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
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✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
if(!require(afex)){
  install.packages("afex", dependencies = TRUE)
  library(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
- Get and set global package options with: afex_options()
- Set 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)
  library(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'
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    view
if(!require(psych)){
  install.packages("psych", dependencies = TRUE)
  library(psych)}
Loading required package: psych

Attaching package: 'psych'
The following objects are masked from 'package:ggplot2':

    %+%, alpha

Import Data

dataset <- read_sav("Group6mood.sav")

Tidy Data

(dataset %>%
  filter(Duration__in_seconds_>= 120) %>%
  mutate(age_as_a_number = as.numeric(Age)) -> dataset.clean)
# A tibble: 24 × 38
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2023-07-24 17:06:19 2023-07-24 17:10:30 0 [IP Address] 73.193.50.147      100
 2 2023-07-24 20:28:17 2023-07-24 20:33:07 0 [IP Address] 97.104.170.1…      100
 3 2023-07-24 21:59:02 2023-07-24 22:05:46 0 [IP Address] 71.199.246.2…      100
 4 2023-07-25 10:20:29 2023-07-25 10:26:27 0 [IP Address] 47.203.128.1…      100
 5 2023-07-25 16:06:45 2023-07-25 16:10:39 0 [IP Address] 73.35.117.177      100
 6 2023-07-25 21:44:40 2023-07-25 21:52:33 0 [IP Address] 99.166.166.1…      100
 7 2023-07-27 22:20:13 2023-07-27 22:22:45 0 [IP Address] 70.171.22.8        100
 8 2023-07-28 18:49:34 2023-07-28 18:51:40 0 [IP Address] 73.152.15.149      100
 9 2023-07-28 23:13:22 2023-07-28 23:21:28 0 [IP Address] 172.56.105.1…      100
10 2023-07-28 23:55:15 2023-07-29 00:04:22 0 [IP Address] 173.185.87.2…      100
# ℹ 14 more rows
# ℹ 33 more variables: Duration__in_seconds_ <dbl>, Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Informed_Consent <dbl+lbl>, Water <dbl+lbl>,
#   Exercise <dbl+lbl>, Exercise.0 <dbl+lbl>, Water.0 <dbl+lbl>, …

Create Codebook

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

Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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Warning in png(png_loc <- tempfile(fileext = ".png"), width = 150 *
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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: 24 x 38
Duplicates: 0
No Variable Label Stats / Values Freqs (% of Valid) Graph Valid Missing
1 StartDate [POSIXct, POSIXt] Start Date
min : 2023-07-24 17:06:19
med : 2023-07-29 07:30:45.5
max : 2023-07-31 16:38:13
range : 6d 23H 31M 54S
24 distinct values 24 (100.0%) 0 (0.0%)
2 EndDate [POSIXct, POSIXt] End Date
min : 2023-07-24 17:10:30
med : 2023-07-29 07:35:07
max : 2023-07-31 16:42:04
range : 6d 23H 31M 34S
24 distinct values 24 (100.0%) 0 (0.0%)
3 Status [haven_labelled, vctrs_vctr, double] Response Type 1 distinct value
0 : 24 ( 100.0% )
24 (100.0%) 0 (0.0%)
4 IPAddress [character] IP Address
1. 73.35.117.177
2. 104.178.163.241
3. 104.230.83.200
4. 142.154.135.218
5. 142.190.118.126
6. 172.56.105.155
7. 172.59.65.203
8. 173.185.87.230
9. 24.129.115.217
10. 47.203.128.161
[ 13 others ]
2 ( 8.3% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
13 ( 54.2% )
24 (100.0%) 0 (0.0%)
5 Progress [numeric] Progress 1 distinct value
100 : 24 ( 100.0% )
24 (100.0%) 0 (0.0%)
6 Duration__in_seconds_ [numeric] Duration (in seconds)
Mean (sd) : 305.9 (166.6)
min ≤ med ≤ max:
125 ≤ 246 ≤ 736
IQR (CV) : 149.5 (0.5)
23 distinct values 24 (100.0%) 0 (0.0%)
7 Finished [haven_labelled, vctrs_vctr, double] Finished 1 distinct value
1 : 24 ( 100.0% )
24 (100.0%) 0 (0.0%)
8 RecordedDate [POSIXct, POSIXt] Recorded Date
min : 2023-07-24 17:10:31
med : 2023-07-29 07:35:08
max : 2023-07-31 16:42:04
range : 6d 23H 31M 33S
24 distinct values 24 (100.0%) 0 (0.0%)
9 ResponseId [character] Response ID
1. R_01BICbsi0Xx4yL7
2. R_0IJYalGdagKmznP
3. R_1dAGgpdOMq05Omw
4. R_1jvWkdxwCKMuFPL
5. R_1Nbkg7OShe2XL9G
6. R_24OvZYOIoPP1G8p
7. R_25AehjbVXfn7HNK
8. R_2aOBGL4IunjAKA7
9. R_2BapuU4kVCqLkZ6
10. R_2CD2KmOEXyitJrw
[ 14 others ]
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
14 ( 58.3% )
24 (100.0%) 0 (0.0%)
10 RecipientLastName [character] Recipient Last Name
All empty strings
24 (100.0%) 0 (0.0%)
11 RecipientFirstName [character] Recipient First Name
All empty strings
24 (100.0%) 0 (0.0%)
12 RecipientEmail [character] Recipient Email
All empty strings
24 (100.0%) 0 (0.0%)
13 ExternalReference [character] External Data Reference
All empty strings
24 (100.0%) 0 (0.0%)
14 LocationLatitude [character] Location Latitude
1. 30.3341
2. 29.5554
3. 25.7689
4. 28.2238
5. 29.1383
6. 29.6138
7. 29.8378
8. 29.9149
9. 30.0955
10. 30.1172
[ 10 others ]
4 ( 16.7% )
2 ( 8.3% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
10 ( 41.7% )
24 (100.0%) 0 (0.0%)
15 LocationLongitude [character] Location Longitude
1. -81.6544
2. -81.2207
3. -122.3414
4. -122.4133
5. -78.4797
6. -80.1946
7. -80.9956
8. -81.3672
9. -81.4119
10. -81.5462
[ 10 others ]
4 ( 16.7% )
2 ( 8.3% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
10 ( 41.7% )
24 (100.0%) 0 (0.0%)
16 DistributionChannel [character] Distribution Channel 1. anonymous
24 ( 100.0% )
24 (100.0%) 0 (0.0%)
17 UserLanguage [character] User Language 1. EN
24 ( 100.0% )
24 (100.0%) 0 (0.0%)
18 Informed_Consent [haven_labelled, vctrs_vctr, double] Informed Consent University of North Florida Department of Psychological Sciences Purpose of Research and Specific procedures to be used: In this study, you will be asked to participate in exercise and you will take a short survey on mood. It should take approximately 5 minutes to complete. All answers will remain anonymous. Please answer the questions to the best of your ability. There are no right or wrong answers. Duration of Participation: Your participation should take 5 minutes. Benefits to the Individual: Your participation in this research will contribute to the body of psychological knowledge about mood. You will have the opportunity to gain a deeper understanding of psychological research. In addition, you will receive information about mood at the end of this study. Risks to the Individual: This study poses no risks greater than those encountered in daily social interactions. Anonymity: Strict anonymity of all data will be upheld. Your responses will remain anonymous and will n 1 distinct value
1 : 24 ( 100.0% )
24 (100.0%) 0 (0.0%)
19 Water [haven_labelled, vctrs_vctr, double] Did you drink 8oz of water? 1 distinct value
2 : 4 ( 100.0% )
4 (16.7%) 20 (83.3%)
20 Exercise [haven_labelled, vctrs_vctr, double] Did you complete 1 minute of exercise? 1 distinct value
2 : 4 ( 100.0% )
4 (16.7%) 20 (83.3%)
21 Exercise.0 [haven_labelled, vctrs_vctr, double] Did you complete 1 minute of exercise? 1 distinct value
2 : 8 ( 100.0% )
8 (33.3%) 16 (66.7%)
22 Water.0 [haven_labelled, vctrs_vctr, double] Did you drink 8oz of water? 1 distinct value
2 : 5 ( 100.0% )
5 (20.8%) 19 (79.2%)
23 Exercise.1 [haven_labelled, vctrs_vctr, double] Did you complete 30 seconds of exercise?
Min : 1
Mean : 1.8
Max : 2
1 : 1 ( 20.0% )
2 : 4 ( 80.0% )
5 (20.8%) 19 (79.2%)
24 Exercise.2 [haven_labelled, vctrs_vctr, double] Did you complete 30 seconds of exercise?
Min : 1
Mean : 1.9
Max : 2
1 : 1 ( 14.3% )
2 : 6 ( 85.7% )
7 (29.2%) 17 (70.8%)
25 Mood_1 [haven_labelled, vctrs_vctr, double] How would you describe your current mood?
Mean (sd) : 3.4 (1)
min ≤ med ≤ max:
1 ≤ 3.5 ≤ 5
IQR (CV) : 1 (0.3)
1 : 2 ( 8.3% )
2 : 1 ( 4.2% )
3 : 9 ( 37.5% )
4 : 10 ( 41.7% )
5 : 2 ( 8.3% )
24 (100.0%) 0 (0.0%)
26 Mood_2 [haven_labelled, vctrs_vctr, double] Do you feel more refreshed and energized?”
Mean (sd) : 3.3 (1)
min ≤ med ≤ max:
1 ≤ 3.5 ≤ 5
IQR (CV) : 1 (0.3)
1 : 2 ( 8.3% )
2 : 2 ( 8.3% )
3 : 8 ( 33.3% )
4 : 10 ( 41.7% )
5 : 2 ( 8.3% )
24 (100.0%) 0 (0.0%)
27 Mood_3 [haven_labelled, vctrs_vctr, double] How prepared are you to handle the rest of your day?
Mean (sd) : 3.5 (1)
min ≤ med ≤ max:
1 ≤ 3.5 ≤ 5
IQR (CV) : 1 (0.3)
1 : 2 ( 8.3% )
3 : 10 ( 41.7% )
4 : 9 ( 37.5% )
5 : 3 ( 12.5% )
24 (100.0%) 0 (0.0%)
28 Mood_4 [haven_labelled, vctrs_vctr, double] How would you describe your current feelings of sadness or low mood?
Mean (sd) : 3.3 (0.9)
min ≤ med ≤ max:
1 ≤ 3 ≤ 5
IQR (CV) : 1 (0.3)
1 : 1 ( 4.2% )
2 : 2 ( 8.3% )
3 : 12 ( 50.0% )
4 : 7 ( 29.2% )
5 : 2 ( 8.3% )
24 (100.0%) 0 (0.0%)
29 Mood_5 [haven_labelled, vctrs_vctr, double] How would you describe your current feelings of fatigue?
Mean (sd) : 3.1 (1.1)
min ≤ med ≤ max:
1 ≤ 3 ≤ 5
IQR (CV) : 1.2 (0.3)
1 : 2 ( 8.3% )
2 : 4 ( 16.7% )
3 : 10 ( 41.7% )
4 : 6 ( 25.0% )
5 : 2 ( 8.3% )
24 (100.0%) 0 (0.0%)
30 Gender [character] How do you identify your gender?
1. Female
2. He/him
3. Intersex
4. Male
5. Non-binary
6. Woman
15 ( 62.5% )
1 ( 4.2% )
1 ( 4.2% )
5 ( 20.8% )
1 ( 4.2% )
1 ( 4.2% )
24 (100.0%) 0 (0.0%)
31 Race [character] What is your race/ethnicity?
1. Black
2. Caucasian
3. Mixed
4. White
5. White/American
1 ( 4.2% )
2 ( 8.3% )
1 ( 4.2% )
19 ( 79.2% )
1 ( 4.2% )
24 (100.0%) 0 (0.0%)
32 Age [character] What is your age (give a number, must be 18+)?
1. 26
2. 27
3. 28
4. 31
5. 59
6. 20
7. 33
8. 35
9. 37
10. 50
[ 7 others ]
3 ( 12.5% )
3 ( 12.5% )
2 ( 8.3% )
2 ( 8.3% )
2 ( 8.3% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
7 ( 29.2% )
24 (100.0%) 0 (0.0%)
33 Exit_question [character] What do you think the hypothesis of the experiment was?
1. A glass of water before e
2. A small amount of hydrati
3. Affects of exercise on mo
4. Changes in mood based off
5. Does exercise have the ab
6. Does hydration anf exerci
7. Does physical activity im
8. Don’t know
9. exercise and how it chang
10. Exercise makes you feel b
[ 14 others ]
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
1 ( 4.2% )
14 ( 58.3% )
24 (100.0%) 0 (0.0%)
34 FL_10_DO_1minuteofexerciseandwater [numeric] FL_10 - Block Randomizer - Display Order 1minuteofexerciseandwater 1 distinct value
1 : 4 ( 100.0% )
4 (16.7%) 20 (83.3%)
35 FL_10_DO_1minuteofexerciseandnowater [numeric] FL_10 - Block Randomizer - Display Order 1minuteofexerciseandnowater 1 distinct value
1 : 8 ( 100.0% )
8 (33.3%) 16 (66.7%)
36 FL_10_DO_30secondsofexerciseandwater [numeric] FL_10 - Block Randomizer - Display Order 30secondsofexerciseandwater 1 distinct value
1 : 5 ( 100.0% )
5 (20.8%) 19 (79.2%)
37 FL_10_DO_30secondsofexerciseandnowater [numeric] FL_10 - Block Randomizer - Display Order 30secondsofexerciseandnowater 1 distinct value
1 : 7 ( 100.0% )
7 (29.2%) 17 (70.8%)
38 age_as_a_number [numeric]
Mean (sd) : 41.2 (15.9)
min ≤ med ≤ max:
20 ≤ 34 ≤ 70
IQR (CV) : 30.2 (0.4)
17 distinct values 24 (100.0%) 0 (0.0%)

Generated by summarytools 1.0.1 (R version 4.3.0)
2023-08-02

Create Exercise Duration IV

(dataset.clean %>%
  mutate(ExerciseDurationIV = case_when(FL_10_DO_1minuteofexerciseandwater == 1 ~ "1 minute",
                                        FL_10_DO_1minuteofexerciseandnowater == 1 ~ "1 minute",
                                        FL_10_DO_30secondsofexerciseandwater == 1 ~ "30 seconds",
                                        FL_10_DO_30secondsofexerciseandnowater == 1 ~ "30 seconds")) -> dataset.clean)
# A tibble: 24 × 39
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2023-07-24 17:06:19 2023-07-24 17:10:30 0 [IP Address] 73.193.50.147      100
 2 2023-07-24 20:28:17 2023-07-24 20:33:07 0 [IP Address] 97.104.170.1…      100
 3 2023-07-24 21:59:02 2023-07-24 22:05:46 0 [IP Address] 71.199.246.2…      100
 4 2023-07-25 10:20:29 2023-07-25 10:26:27 0 [IP Address] 47.203.128.1…      100
 5 2023-07-25 16:06:45 2023-07-25 16:10:39 0 [IP Address] 73.35.117.177      100
 6 2023-07-25 21:44:40 2023-07-25 21:52:33 0 [IP Address] 99.166.166.1…      100
 7 2023-07-27 22:20:13 2023-07-27 22:22:45 0 [IP Address] 70.171.22.8        100
 8 2023-07-28 18:49:34 2023-07-28 18:51:40 0 [IP Address] 73.152.15.149      100
 9 2023-07-28 23:13:22 2023-07-28 23:21:28 0 [IP Address] 172.56.105.1…      100
10 2023-07-28 23:55:15 2023-07-29 00:04:22 0 [IP Address] 173.185.87.2…      100
# ℹ 14 more rows
# ℹ 34 more variables: Duration__in_seconds_ <dbl>, Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Informed_Consent <dbl+lbl>, Water <dbl+lbl>,
#   Exercise <dbl+lbl>, Exercise.0 <dbl+lbl>, Water.0 <dbl+lbl>, …

Create Water IV

(dataset.clean %>%
  mutate(WaterIV = case_when(FL_10_DO_1minuteofexerciseandwater == 1 ~ "water",
                                        FL_10_DO_1minuteofexerciseandnowater == 1 ~ "no water",
                                        FL_10_DO_30secondsofexerciseandwater == 1 ~ "water",
                                        FL_10_DO_30secondsofexerciseandnowater == 1 ~ "no water")) -> dataset.clean)
# A tibble: 24 × 40
   StartDate           EndDate             Status         IPAddress     Progress
   <dttm>              <dttm>              <dbl+lbl>      <chr>            <dbl>
 1 2023-07-24 17:06:19 2023-07-24 17:10:30 0 [IP Address] 73.193.50.147      100
 2 2023-07-24 20:28:17 2023-07-24 20:33:07 0 [IP Address] 97.104.170.1…      100
 3 2023-07-24 21:59:02 2023-07-24 22:05:46 0 [IP Address] 71.199.246.2…      100
 4 2023-07-25 10:20:29 2023-07-25 10:26:27 0 [IP Address] 47.203.128.1…      100
 5 2023-07-25 16:06:45 2023-07-25 16:10:39 0 [IP Address] 73.35.117.177      100
 6 2023-07-25 21:44:40 2023-07-25 21:52:33 0 [IP Address] 99.166.166.1…      100
 7 2023-07-27 22:20:13 2023-07-27 22:22:45 0 [IP Address] 70.171.22.8        100
 8 2023-07-28 18:49:34 2023-07-28 18:51:40 0 [IP Address] 73.152.15.149      100
 9 2023-07-28 23:13:22 2023-07-28 23:21:28 0 [IP Address] 172.56.105.1…      100
10 2023-07-28 23:55:15 2023-07-29 00:04:22 0 [IP Address] 173.185.87.2…      100
# ℹ 14 more rows
# ℹ 35 more variables: Duration__in_seconds_ <dbl>, Finished <dbl+lbl>,
#   RecordedDate <dttm>, ResponseId <chr>, RecipientLastName <chr>,
#   RecipientFirstName <chr>, RecipientEmail <chr>, ExternalReference <chr>,
#   LocationLatitude <chr>, LocationLongitude <chr>, DistributionChannel <chr>,
#   UserLanguage <chr>, Informed_Consent <dbl+lbl>, Water <dbl+lbl>,
#   Exercise <dbl+lbl>, Exercise.0 <dbl+lbl>, Water.0 <dbl+lbl>, …

Create Mood DV

#create dataframe with only relevant variables to work with
Mood <- data.frame (dataset.clean$Mood_1, dataset.clean$Mood_2, dataset.clean$Mood_3, dataset.clean$Mood_4, dataset.clean$Mood_5)

#create list of 'keys'. The  numbers just refer to the order of the question in the data.frame() you just made. The most important thing is to mark the questions that should be reversed scored with a '-'. 
Mood.keys <- make.keys(Mood, list(Mood=c(1,2,3,4,5)))

#score the scale
Mood.scales <- scoreItems (Mood.keys, Mood)

#save the scores
Mood.scores <- Mood.scales$scores

#save the scores back in 'dataset'
dataset.clean$Mood <- Mood.scores[,]

#print the cronbach alpha
Mood.scales$alpha
           Mood
alpha 0.9170831

ANOVA

 aov_ez(id = "ResponseId",
        dv = "Mood", 
        data = dataset.clean,
        between=c("ExerciseDurationIV", "WaterIV"),
        anova_table = list(es = "pes"))
Converting to factor: ExerciseDurationIV, WaterIV
Contrasts set to contr.sum for the following variables: ExerciseDurationIV, WaterIV
Anova Table (Type 3 tests)

Response: Mood
                      Effect    df  MSE      F  pes p.value
1         ExerciseDurationIV 1, 20 0.71 3.33 + .143    .083
2                    WaterIV 1, 20 0.71   0.62 .030    .442
3 ExerciseDurationIV:WaterIV 1, 20 0.71   2.57 .114    .124
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1

Study Design Table Numbers

dataset.clean %>%
  group_by(ExerciseDurationIV) %>%
  summarise(mean = mean(Mood),
            sd = sd(Mood))
# A tibble: 2 × 3
  ExerciseDurationIV  mean    sd
  <chr>              <dbl> <dbl>
1 1 minute            3.07 1.10 
2 30 seconds          3.55 0.527
dataset.clean %>%
  group_by(WaterIV) %>%
  summarise(mean = mean(Mood),
            sd = sd(Mood))
# A tibble: 2 × 3
  WaterIV   mean    sd
  <chr>    <dbl> <dbl>
1 no water  3.39 0.867
2 water     3.18 0.930
dataset.clean %>%
  group_by(ExerciseDurationIV, WaterIV) %>%
  summarise(mean = mean(Mood),
            sd = sd(Mood))
`summarise()` has grouped output by 'ExerciseDurationIV'. You can override
using the `.groups` argument.
# A tibble: 4 × 4
# Groups:   ExerciseDurationIV [2]
  ExerciseDurationIV WaterIV   mean    sd
  <chr>              <chr>    <dbl> <dbl>
1 1 minute           no water  3.35 1.10 
2 1 minute           water     2.5  0.959
3 30 seconds         no water  3.43 0.571
4 30 seconds         water     3.72 0.460