Cleaning Data & Basic Statistics HW
Luke McKissock
2023-06-01
1 Load Libraries
library(tidyverse) # for the map() command## ── 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() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors2 Import Data
# use the read.csv() command to import your downloaded CSV file
df <- read.csv(file="~/Desktop/P421.R.PROJ/EAMMi2-Data1.2.csv", header=T)3 Viewing Data
# use the names() command to view the list of columns or variables in your dataframe
names(df)## [1] "StartDate" "EndDate" "Status"
## [4] "Progress" "Duration..in.seconds." "Finished"
## [7] "RecordedDate" "ResponseId" "RecipientLastName"
## [10] "RecipientFirstName" "RecipientEmail" "ExternalReference"
## [13] "DistributionChannel" "informedconsent" "moa1.1_1"
## [16] "moa1.1_2" "moa1.1_3" "moa1.1_4"
## [19] "moa1.1_5" "moa1.1_6" "moa1.1_7"
## [22] "moa1.1_8" "moa1.1_9" "moa1.1_10"
## [25] "moa1.2_1" "moa1.2_2" "moa1.2_3"
## [28] "moa1.2_4" "moa1.2_5" "moa1.2_6"
## [31] "moa1.2_7" "moa1.2_8" "moa1.2_9"
## [34] "moa1.2_10" "moa2.1_1" "moa2.1_2"
## [37] "moa2.1_3" "moa2.1_4" "moa2.1_5"
## [40] "moa2.1_6" "moa2.1_7" "moa2.1_8"
## [43] "moa2.1_9" "moa2.1_10" "moa2.2_1"
## [46] "moa2.2_2" "moa2.2_3" "moa2.2_4"
## [49] "moa2.2_5" "moa2.2_6" "moa2.2_7"
## [52] "moa2.2_8" "moa2.2_9" "moa2.2_10"
## [55] "adult_Q" "MOA_IMP_biascheck" "MOA_ach_biascheck"
## [58] "MOA_IMP_dummy" "MOA.ACH_dummy" "Q65_First.Click"
## [61] "Q65_Last.Click" "Q65_Page.Submit" "Q65_Click.Count"
## [64] "IDEA_1" "IDEA_2" "IDEA_3"
## [67] "IDEA_4" "IDEA_5" "IDEA_6"
## [70] "IDEA_7" "IDEA_8" "IDEA.biascheck"
## [73] "IDEA.bias.dummy" "Q66_First.Click" "Q66_Last.Click"
## [76] "Q66_Page.Submit" "Q66_Click.Count" "politics"
## [79] "party" "president" "Q74_First.Click"
## [82] "Q74_Last.Click" "Q74_Page.Submit" "Q74_Click.Count"
## [85] "swb_1" "swb_2" "swb_3"
## [88] "swb_4" "swb_5" "swb_6"
## [91] "Q67_First.Click" "Q67_Last.Click" "Q67_Page.Submit"
## [94] "Q67_Click.Count" "mindful_1" "mindful_2"
## [97] "mindful_3" "mindful_4" "mindful_5"
## [100] "mindful_6" "mindful_7" "mindful_8"
## [103] "mindful_9" "mindful_10" "mindful_11"
## [106] "mindful_12" "mindful_13" "mindful_14"
## [109] "mindful_15" "mindful_biascheck" "mindful_bias_dummy"
## [112] "Q68_First.Click" "Q68_Last.Click" "Q68_Page.Submit"
## [115] "Q68_Click.Count" "belong_1" "belong_2"
## [118] "belong_3" "belong_4" "belong_5"
## [121] "belong_6" "belong_7" "belong_8"
## [124] "belong_9" "belong_10" "belnow"
## [127] "belong_biascheck" "belong_bias_dummy" "Q72_First.Click"
## [130] "Q72_Last.Click" "Q72_Page.Submit" "Q72_Click.Count"
## [133] "efficacy_1" "efficacy_2" "efficacy_3"
## [136] "efficacy_4" "efficacy_5" "efficacy_6"
## [139] "efficacy_7" "efficacy_8" "efficacy_9"
## [142] "efficacy_10" "efficacy_biascheck" "efficacy_bias_dummy"
## [145] "Q77_First.Click" "Q77_Last.Click" "Q77_Page.Submit"
## [148] "Q77_Click.Count" "support_1" "support_2"
## [151] "support_3" "support_4" "support_5"
## [154] "support_6" "support_7" "support_8"
## [157] "support_9" "support_10" "support_11"
## [160] "support_12" "support_biascheck" "support_bias_dummy"
## [163] "Q96_First.Click" "Q96_Last.Click" "Q96_Page.Submit"
## [166] "Q96_Click.Count" "SocMedia_1" "SocMedia_2"
## [169] "SocMedia_3" "SocMedia_4" "SocMedia_5"
## [172] "SocMedia_6" "SocMedia_7" "SocMedia_8"
## [175] "SocMedia_9" "SocMedia_10" "SocMedia_11"
## [178] "SocMedia_biascheck" "SocMedia_bias_dummy" "Q80_First.Click"
## [181] "Q80_Last.Click" "Q80_Page.Submit" "Q80_Click.Count"
## [184] "usdream_1" "usdream_2" "usdream_3"
## [187] "Q73_First.Click" "Q73_Last.Click" "Q73_Page.Submit"
## [190] "Q73_Click.Count" "freq" "transgres"
## [193] "relation" "relation_10_TEXT" "fault"
## [196] "feel" "common" "attenion2"
## [199] "Q78_First.Click" "Q78_Last.Click" "Q78_Page.Submit"
## [202] "Q78_Click.Count" "transgres_1" "transgres_2"
## [205] "transgres_3" "transgres_4" "Q79_First.Click"
## [208] "Q79_Last.Click" "Q79_Page.Submit" "Q79_Click.Count"
## [211] "NPI1" "NPI2" "NPI3"
## [214] "NPI4" "NPI5" "NPI6"
## [217] "NPI7" "NPI8" "NPI9"
## [220] "NPI10" "NPI11" "NPI12"
## [223] "NPI13" "exploit_1" "exploit_2"
## [226] "exploit_3" "NPI_biascheck" "NPI_bias_dummy"
## [229] "Q76_First.Click" "Q76_Last.Click" "Q76_Page.Submit"
## [232] "Q76_Click.Count" "Q11" "Q14_1"
## [235] "Q14_2" "Q14_3" "Q14_4"
## [238] "Q14_5" "Q14_6" "Q14_6_TEXT"
## [241] "Q10_1" "Q10_2" "Q10_3"
## [244] "Q10_4" "Q10_5" "Q10_6"
## [247] "Q10_7" "Q10_8" "Q10_9"
## [250] "Q10_10" "Q10_11" "Q10_12"
## [253] "Q10_13" "Q10_14" "Q10_15"
## [256] "Q71_First.Click" "Q71_Last.Click" "Q71_Page.Submit"
## [259] "Q71_Click.Count" "physSx_1" "physSx_2"
## [262] "physSx_3" "physSx_4" "physSx_5"
## [265] "physSx_6" "physSx_7" "physSx_8"
## [268] "physSx_9" "physSx_10" "physSx_11"
## [271] "physSx_12" "physSx_13" "phys_sx_biaschec"
## [274] "phys_sym_bias_dummy." "Q70_First.Click" "Q70_Last.Click"
## [277] "Q70_Page.Submit" "Q70_Click.Count" "stress_1"
## [280] "stress_2" "stress_3" "stress_4"
## [283] "stress_5" "stress_6" "stress_7"
## [286] "stress_8" "stress_9" "stress_10"
## [289] "stress_biascheck" "stress_bias_dummy" "Q69_First.Click"
## [292] "Q69_Last.Click" "Q69_Page.Submit" "Q69_Click.Count"
## [295] "marriage1_1" "marriage1_2" "marriage1_3"
## [298] "marriage1_4" "marriage2" "marriage3"
## [301] "marriage4" "marriage5" "Q75_First.Click"
## [304] "Q75_Last.Click" "Q75_Page.Submit" "Q75_Click.Count"
## [307] "school" "sex" "age"
## [310] "edu" "sibling" "race"
## [313] "race_6_TEXT" "Q82" "Q83"
## [316] "income" "place2" "Q80"
## [319] "place" "Q81" "Q81_First.Click"
## [322] "Q81_Last.Click" "Q81_Page.Submit" "Q81_Click.Count"
## [325] "comments" "affiliation" "response_bias_SUM"
## [328] "school_coded"# use the head() command to view the first few lines of your dataframe
head(df)## StartDate EndDate Status Progress Duration..in.seconds. Finished
## 1 12/02/16 12/02/16 0 100 1839 1
## 2 11/16/16 11/16/16 0 100 1467 1
## 3 11/09/16 11/09/16 0 99 2185 0
## 4 11/07/16 11/07/16 0 100 2904 1
## 5 11/18/16 11/18/16 0 100 1229 1
## 6 11/07/16 11/07/16 0 100 2068 1
## RecordedDate ResponseId RecipientLastName RecipientFirstName
## 1 12/2/2016 5:38 R_BJN3bQqi1zUMid3 NA NA
## 2 11/16/2016 11:53 R_2TGbiBXmAtxywsD NA NA
## 3 11/16/2016 1:22 R_12G7bIqN2wB2N65 NA NA
## 4 11/7/2016 4:54 R_39pldNoon8CePfP NA NA
## 5 11/18/2016 0:30 R_1QiKb2LdJo1Bhvv NA NA
## 6 11/7/2016 14:42 R_pmwDTZyCyCycXwB NA NA
## RecipientEmail ExternalReference DistributionChannel informedconsent moa1.1_1
## 1 NA NA anonymous 1 4
## 2 NA NA anonymous 1 4
## 3 NA NA anonymous 1 4
## 4 NA NA anonymous 1 4
## 5 NA NA anonymous 1 4
## 6 NA NA anonymous 1 4
## moa1.1_2 moa1.1_3 moa1.1_4 moa1.1_5 moa1.1_6 moa1.1_7 moa1.1_8 moa1.1_9
## 1 4 3 2 2 3 2 1 4
## 2 4 4 2 3 3 4 3 3
## 3 4 4 1 1 4 2 3 4
## 4 3 3 1 1 2 1 1 1
## 5 4 4 1 1 3 1 1 4
## 6 3 4 2 3 4 2 1 3
## moa1.1_10 moa1.2_1 moa1.2_2 moa1.2_3 moa1.2_4 moa1.2_5 moa1.2_6 moa1.2_7
## 1 3 2 1 2 1 1 1 2
## 2 3 1 1 2 2 1 1 1
## 3 3 2 1 1 1 1 1 2
## 4 1 1 1 1 1 1 1 2
## 5 3 2 1 2 1 3 3 1
## 6 4 1 1 1 1 1 1 1
## moa1.2_8 moa1.2_9 moa1.2_10 moa2.1_1 moa2.1_2 moa2.1_3 moa2.1_4 moa2.1_5
## 1 3 3 2 4 4 4 4 3
## 2 1 2 3 3 4 2 4 4
## 3 3 3 3 4 2 2 4 3
## 4 3 2 3 4 2 2 4 3
## 5 1 3 2 4 4 3 4 4
## 6 1 1 3 4 4 4 4 2
## moa2.1_6 moa2.1_7 moa2.1_8 moa2.1_9 moa2.1_10 moa2.2_1 moa2.2_2 moa2.2_3
## 1 4 4 4 3 2 2 1 1
## 2 3 2 4 2 1 3 1 2
## 3 3 4 4 3 2 2 1 1
## 4 2 4 2 3 2 2 1 1
## 5 3 4 4 3 3 3 3 3
## 6 4 3 4 4 4 2 1 1
## moa2.2_4 moa2.2_5 moa2.2_6 moa2.2_7 moa2.2_8 moa2.2_9 moa2.2_10 adult_Q
## 1 3 2 3 3 2 2 1 1
## 2 2 1 2 2 2 2 1 1
## 3 2 1 2 3 1 1 1 1
## 4 2 1 2 2 2 2 1 1
## 5 3 3 2 3 3 2 3 1
## 6 2 1 2 2 1 2 1 1
## MOA_IMP_biascheck MOA_ach_biascheck MOA_IMP_dummy MOA.ACH_dummy
## 1 64 38 0 0
## 2 62 33 0 0
## 3 61 33 0 0
## 4 46 32 0 0
## 5 62 47 0 0
## 6 67 27 0 0
## Q65_First.Click Q65_Last.Click Q65_Page.Submit Q65_Click.Count IDEA_1 IDEA_2
## 1 37.139 307.731 308.890 45 3 4
## 2 120.026 336.428 338.177 58 4 4
## 3 27.705 154.447 155.544 47 4 4
## 4 19.656 297.285 298.509 43 4 4
## 5 12.867 121.932 122.254 46 4 4
## 6 15.652 223.372 225.431 50 3 4
## IDEA_3 IDEA_4 IDEA_5 IDEA_6 IDEA_7 IDEA_8 IDEA.biascheck IDEA.bias.dummy
## 1 4 3 4 4 4 4 30 0
## 2 4 4 3 4 4 4 31 0
## 3 4 4 4 4 3 3 30 0
## 4 3 3 4 4 4 4 30 0
## 5 3 4 3 3 3 4 28 0
## 6 3 3 4 4 3 2 26 0
## Q66_First.Click Q66_Last.Click Q66_Page.Submit Q66_Click.Count politics party
## 1 44.705 86.585 87.514 11 2 3
## 2 19.927 65.200 67.162 13 1 4
## 3 23.170 51.401 52.408 11 2 8
## 4 27.467 172.797 174.119 9 8 8
## 5 23.952 52.176 53.355 9 1 8
## 6 9.475 72.935 73.937 13 8 8
## president
## 1
## 2 None, but I was a US Citizen and had a gun next to my head, I would vote for Trump
## 3 Hillary Clinton
## 4 Hillary Clinton
## 5 Sanders
## 6 No one.
## Q74_First.Click Q74_Last.Click Q74_Page.Submit Q74_Click.Count swb_1 swb_2
## 1 13.052 40.445 46.399 2 4 6
## 2 4.899 28.125 55.107 6 3 4
## 3 34.868 48.402 56.371 4 1 2
## 4 66.886 119.219 135.295 4 5 6
## 5 23.614 32.221 35.338 4 2 5
## 6 12.314 41.232 54.436 6 4 4
## swb_3 swb_4 swb_5 swb_6 Q67_First.Click Q67_Last.Click Q67_Page.Submit
## 1 5 5 3 3 9.627 40.388 41.198
## 2 5 5 4 4 8.607 29.115 29.955
## 3 2 2 2 2 37.656 53.240 54.603
## 4 6 5 6 3 13.587 55.197 56.150
## 5 5 3 2 5 6.798 22.246 23.138
## 6 6 5 1 4 7.927 44.108 48.227
## Q67_Click.Count mindful_1 mindful_2 mindful_3 mindful_4 mindful_5 mindful_6
## 1 7 4 2 2 2 4 1
## 2 7 2 2 2 1 3 1
## 3 9 2 3 1 2 3 1
## 4 7 2 2 1 2 2 1
## 5 7 4 5 3 2 4 1
## 6 7 1 4 3 3 5 1
## mindful_7 mindful_8 mindful_9 mindful_10 mindful_11 mindful_12 mindful_13
## 1 2 2 2 2 2 4 1
## 2 1 1 2 2 1 2 1
## 3 2 2 5 3 2 1 1
## 4 2 2 3 2 2 3 3
## 5 4 2 1 2 2 6 5
## 6 5 6 3 5 1 3 2
## mindful_14 mindful_15 mindful_biascheck mindful_bias_dummy Q68_First.Click
## 1 2 4 36 0 32.692
## 2 1 5 27 0 13.184
## 3 1 4 33 0 48.022
## 4 2 4 33 0 110.432
## 5 2 5 48 0 81.124
## 6 4 5 51 0 33.458
## Q68_Last.Click Q68_Page.Submit Q68_Click.Count belong_1 belong_2 belong_3
## 1 154.123 157.391 15 4 2 4
## 2 76.856 77.629 22 2 3 1
## 3 142.665 143.398 20 4 4 2
## 4 255.734 257.134 17 3 4 1
## 5 134.499 135.848 16 4 3 3
## 6 212.770 213.511 25 2 3 2
## belong_4 belong_5 belong_6 belong_7 belong_8 belong_9 belong_10 belnow
## 1 4 4 2 5 2 4 3 4
## 2 5 4 4 2 4 5 4 4
## 3 5 4 4 2 3 4 4 2
## 4 5 4 5 2 4 4 4 4
## 5 4 4 5 1 3 2 3 4
## 6 5 4 5 1 4 4 4 3
## belong_biascheck belong_bias_dummy Q72_First.Click Q72_Last.Click
## 1 38 0 8.221 80.460
## 2 38 0 6.774 65.987
## 3 38 0 5.697 71.192
## 4 40 0 6.096 86.373
## 5 36 0 58.613 121.436
## 6 37 0 6.124 70.638
## Q72_Page.Submit Q72_Click.Count efficacy_1 efficacy_2 efficacy_3 efficacy_4
## 1 82.781 13 4 3 4 3
## 2 67.158 18 3 3 3 4
## 3 72.176 13 3 3 1 2
## 4 87.231 14 4 1 2 3
## 5 122.620 11 3 3 2 3
## 6 72.503 15 3 2 3 2
## efficacy_5 efficacy_6 efficacy_7 efficacy_8 efficacy_9 efficacy_10
## 1 3 4 3 3 4 3
## 2 4 4 3 3 3 4
## 3 2 3 1 3 2 2
## 4 2 4 2 3 4 3
## 5 3 3 3 3 4 3
## 6 1 3 2 3 3 2
## efficacy_biascheck efficacy_bias_dummy Q77_First.Click Q77_Last.Click
## 1 34 0 15.372 104.315
## 2 34 0 11.711 74.768
## 3 22 0 38.964 179.667
## 4 28 0 16.050 168.886
## 5 30 0 32.150 59.030
## 6 24 0 6.259 69.817
## Q77_Page.Submit Q77_Click.Count support_1 support_2 support_3 support_4
## 1 105.195 11 7 4 6 5
## 2 75.558 21 7 7 7 6
## 3 182.727 13 6 6 5 2
## 4 170.530 10 6 6 7 3
## 5 60.273 11 6 6 5 5
## 6 70.970 13 7 7 6 6
## support_5 support_6 support_7 support_8 support_9 support_10 support_11
## 1 6 6 7 7 7 4 6
## 2 7 6 6 7 7 7 7
## 3 7 5 5 3 6 6 5
## 4 7 6 5 4 6 6 6
## 5 6 6 7 6 6 6 7
## 6 7 2 2 1 1 7 6
## support_12 support_biascheck support_bias_dummy Q96_First.Click
## 1 7 72 0 12.241
## 2 7 81 0 7.482
## 3 6 62 0 18.845
## 4 5 67 0 30.307
## 5 6 72 0 13.096
## 6 2 54 0 24.841
## Q96_Last.Click Q96_Page.Submit Q96_Click.Count SocMedia_1 SocMedia_2
## 1 91.497 92.381 17 4 2
## 2 34.247 35.467 19 3 2
## 3 74.388 76.037 14 3 3
## 4 150.285 151.869 18 4 2
## 5 43.727 45.041 12 3 3
## 6 110.942 111.636 18 1 1
## SocMedia_3 SocMedia_4 SocMedia_5 SocMedia_6 SocMedia_7 SocMedia_8 SocMedia_9
## 1 5 3 5 5 5 4 5
## 2 4 2 1 1 1 1 2
## 3 4 2 3 4 4 2 3
## 4 5 2 2 4 4 1 3
## 5 5 2 2 4 4 2 4
## 6 2 1 1 1 2 1 1
## SocMedia_10 SocMedia_11 SocMedia_biascheck SocMedia_bias_dummy
## 1 5 4 47 0
## 2 4 2 23 0
## 3 3 3 34 0
## 4 4 4 35 0
## 5 4 4 37 0
## 6 1 1 13 0
## Q80_First.Click Q80_Last.Click Q80_Page.Submit Q80_Click.Count usdream_1
## 1 17.470 59.573 60.431 11 4
## 2 10.768 45.340 47.007 15 4
## 3 30.059 66.498 68.018 12 2
## 4 112.432 161.455 163.036 11 1
## 5 22.246 53.406 55.275 12 3
## 6 5.361 89.568 90.785 15 1
## usdream_2 usdream_3 Q73_First.Click Q73_Last.Click Q73_Page.Submit
## 1 4 1 14.144 24.073 27.558
## 2 4 1 5.240 27.206 36.973
## 3 2 1 5.671 16.751 19.891
## 4 3 1 71.153 82.926 98.687
## 5 4 1 21.293 32.533 35.448
## 6 1 2 6.771 37.203 45.268
## Q73_Click.Count freq
## 1 3 3
## 2 5 4
## 3 3 6
## 4 3 3
## 5 3 4
## 6 4 2
## transgres
## 1 told my friend that something he did was wrong and hurtful to somebody else.
## 2 got very passive
## 3 you don't want to see me
## 4 I tried to excuse myself and said I was sorry
## 5 Got angry.
## 6 why didn't you tell him?
## relation relation_10_TEXT fault feel common attenion2 Q78_First.Click
## 1 4 6 3 3 7 22.366
## 2 7 2 1,4,5,6,7,8 4 7 6.174
## 3 5 3 6 5 7 9.705
## 4 4 3 10,4,6 2 7 58.517
## 5 7 2 5,6 3 NA 5.647
## 6 6 3 1 4 7 15.862
## Q78_Last.Click Q78_Page.Submit Q78_Click.Count transgres_1 transgres_2
## 1 148.934 153.134 12 3 1
## 2 72.626 74.609 22 4 3
## 3 109.167 111.847 10 3 1
## 4 195.290 199.227 14 4 2
## 5 61.894 65.304 13 4 2
## 6 174.894 177.914 21 1 3
## transgres_3 transgres_4 Q79_First.Click Q79_Last.Click Q79_Page.Submit
## 1 1 1 16.239 27.208 28.488
## 2 2 1 20.400 31.697 32.941
## 3 4 1 57.079 77.311 82.171
## 4 1 1 23.005 49.246 50.303
## 5 2 1 11.727 23.987 25.130
## 6 1 1 14.701 35.077 40.711
## Q79_Click.Count NPI1 NPI2 NPI3 NPI4 NPI5 NPI6 NPI7 NPI8 NPI9 NPI10 NPI11
## 1 4 1 1 1 2 1 2 2 1 2 2 1
## 2 4 2 2 1 2 1 2 1 1 1 1 2
## 3 5 2 1 1 2 1 2 1 1 1 2 2
## 4 5 2 2 1 2 1 2 2 1 1 2 1
## 5 8 1 2 1 2 1 2 2 2 1 2 1
## 6 6 2 2 2 1 2 2 2 2 1 2 1
## NPI12 NPI13 exploit_1 exploit_2 exploit_3 NPI_biascheck NPI_bias_dummy
## 1 2 1 2 2 2 19 0
## 2 1 1 4 4 3 18 0
## 3 2 1 5 5 3 19 0
## 4 2 1 2 1 2 20 0
## 5 2 2 5 4 3 21 0
## 6 2 2 1 1 2 23 0
## Q76_First.Click Q76_Last.Click Q76_Page.Submit Q76_Click.Count Q11 Q14_1
## 1 25.575 141.688 142.668 16 2 2
## 2 10.473 103.989 105.050 35 2 2
## 3 29.957 129.786 132.515 22 2 2
## 4 49.912 222.748 223.703 18 2 2
## 5 5.315 108.076 109.093 25 2 2
## 6 5.353 94.716 98.083 25 2 2
## Q14_2 Q14_3 Q14_4 Q14_5 Q14_6 Q14_6_TEXT Q10_1 Q10_2 Q10_3 Q10_4 Q10_5 Q10_6
## 1 2 2 2 2 2 NA NA NA NA NA NA
## 2 2 2 2 2 NA NA NA NA NA NA NA
## 3 2 2 1 2 2 4 3 4 2 4 5
## 4 2 2 2 2 2 NA NA NA NA NA NA
## 5 2 2 2 2 NA NA NA NA NA NA NA
## 6 2 2 2 2 2 NA NA NA NA NA NA
## Q10_7 Q10_8 Q10_9 Q10_10 Q10_11 Q10_12 Q10_13 Q10_14 Q10_15 Q71_First.Click
## 1 NA NA NA NA NA NA NA NA NA 5.199
## 2 NA NA NA NA NA NA NA NA NA 6.497
## 3 3 1 4 1 3 1 4 2 2 4.533
## 4 NA NA NA NA NA NA NA NA NA 7.430
## 5 NA NA NA NA NA NA NA NA NA 5.020
## 6 NA NA NA NA NA NA NA NA NA 15.872
## Q71_Last.Click Q71_Page.Submit Q71_Click.Count physSx_1 physSx_2 physSx_3
## 1 28.239 28.669 7 3 1 1
## 2 23.457 24.600 8 2 2 1
## 3 115.299 116.410 21 3 1 1
## 4 122.984 128.490 12 2 3 2
## 5 7.996 17.581 6 1 1 1
## 6 32.719 33.562 7 1 2 2
## physSx_4 physSx_5 physSx_6 physSx_7 physSx_8 physSx_9 physSx_10 physSx_11
## 1 2 1 2 1 2 2 2 2
## 2 1 3 1 1 3 2 1 1
## 3 3 2 2 1 3 2 1 2
## 4 3 1 2 1 2 1 1 2
## 5 2 1 1 1 1 2 1 1
## 6 1 2 2 1 3 1 2 2
## physSx_12 physSx_13 phys_sx_biaschec phys_sym_bias_dummy. Q70_First.Click
## 1 3 2 24 0 13.703
## 2 3 3 24 0 5.209
## 3 3 3 27 0 36.694
## 4 3 2 25 0 47.764
## 5 2 1 16 0 4.561
## 6 3 2 24 0 10.062
## Q70_Last.Click Q70_Page.Submit Q70_Click.Count stress_1 stress_2 stress_3
## 1 54.318 60.887 16 2 4 5
## 2 27.841 28.982 17 4 5 5
## 3 79.053 80.519 18 4 4 5
## 4 77.308 79.068 13 2 4 4
## 5 18.707 20.343 14 4 3 3
## 6 80.372 82.827 18 3 3 5
## stress_4 stress_5 stress_6 stress_7 stress_8 stress_9 stress_10
## 1 3 3 3 4 3 3 3
## 2 4 3 3 2 2 4 4
## 3 2 1 5 2 2 4 4
## 4 3 4 5 4 2 2 2
## 5 4 3 4 3 3 4 4
## 6 2 3 5 2 1 3 2
## stress_biascheck stress_bias_dummy Q69_First.Click Q69_Last.Click
## 1 33 0 10.383 100.428
## 2 36 0 9.729 43.281
## 3 33 0 42.213 89.329
## 4 32 0 43.206 180.767
## 5 35 0 63.797 76.765
## 6 29 0 16.412 94.543
## Q69_Page.Submit Q69_Click.Count marriage1_1 marriage1_2 marriage1_3
## 1 101.287 14 10 25 30
## 2 44.423 13 10 25 35
## 3 91.194 12 1 1 59
## 4 182.932 12 0 0 60
## 5 77.998 13 25 25 25
## 6 95.709 15 13 33 21
## marriage1_4 marriage2 marriage3 marriage4 marriage5 Q75_First.Click
## 1 35 2 20 1 2 20.229
## 2 30 3 19 1 1 11.100
## 3 39 2 19 1 1 40.807
## 4 40 1 16 1 1 74.778
## 5 25 2 14 3 1 21.347
## 6 33 3 17 1 1 19.366
## Q75_Last.Click Q75_Page.Submit Q75_Click.Count school sex
## 1 164.560 165.870 32 ACG 2
## 2 60.223 61.354 17 ACG,Deree 1
## 3 160.366 165.459 23 American College of Greece 1
## 4 155.971 156.974 13 American College of Greece 2
## 5 48.802 49.742 11 American College of Greece 1
## 6 181.747 183.288 25 American College of Greece 2
## age edu sibling race race_6_TEXT Q82 Q83 income place2 Q80 place Q81
## 1 20 2 2 1 3 NA 3 2 NA NA Greece
## 2 23 5 2 1 3 NA 3 2 NA NA Greece
## 3 23 2 5 1 3 NA 1 2 NA NA Greece
## 4 22 2 3,5,7 6 Greek 3 NA 1 2 NA NA Greece
## 5 18 2 3,5,7 1 3 NA 6 2 NA NA Greece
## 6 23 2 2,4 1 3 NA 1 2 NA NA Greece
## Q81_First.Click Q81_Last.Click Q81_Page.Submit Q81_Click.Count
## 1 0.000 0.000 5.781 0
## 2 2.424 2.424 6.621 1
## 3 0.000 0.000 9.294 0
## 4 2.679 2.679 5.803 1
## 5 1.632 1.632 5.500 1
## 6 2.766 2.766 6.995 1
## comments
## 1 I have completed this survey
## 2 i have completed this survey
## 3 Didn't know my household income.\nI have completed the last question about disability wrong. I read "not" the moment I pressed ">>"
## 4 I have completed this survey
## 5 i have completed this survey
## 6 The question saying "Select" was funny.
## affiliation response_bias_SUM school_coded
## 1 acgreece 0 acgreece
## 2 acgreece 0 acgreece
## 3 acgreece 0 acgreece
## 4 acgreece 0 acgreece
## 5 acgreece 0 acgreece
## 6 acgreece 0 acgreece# use the str() command to see what kinds of variables are in your dataframe
str(df)## 'data.frame': 3182 obs. of 328 variables:
## $ StartDate : chr "12/02/16" "11/16/16" "11/09/16" "11/07/16" ...
## $ EndDate : chr "12/02/16" "11/16/16" "11/09/16" "11/07/16" ...
## $ Status : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Progress : int 100 100 99 100 100 100 99 99 100 100 ...
## $ Duration..in.seconds.: int 1839 1467 2185 2904 1229 2068 1656 1839 1160 2134 ...
## $ Finished : int 1 1 0 1 1 1 0 0 1 1 ...
## $ RecordedDate : chr "12/2/2016 5:38" "11/16/2016 11:53" "11/16/2016 1:22" "11/7/2016 4:54" ...
## $ ResponseId : chr "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
## $ RecipientLastName : logi NA NA NA NA NA NA ...
## $ RecipientFirstName : logi NA NA NA NA NA NA ...
## $ RecipientEmail : logi NA NA NA NA NA NA ...
## $ ExternalReference : logi NA NA NA NA NA NA ...
## $ DistributionChannel : chr "anonymous" "anonymous" "anonymous" "anonymous" ...
## $ informedconsent : int 1 1 1 1 1 1 1 1 1 1 ...
## $ moa1.1_1 : int 4 4 4 4 4 4 4 4 4 3 ...
## $ moa1.1_2 : int 4 4 4 3 4 3 4 4 4 2 ...
## $ moa1.1_3 : int 3 4 4 3 4 4 4 4 4 3 ...
## $ moa1.1_4 : int 2 2 1 1 1 2 3 3 4 1 ...
## $ moa1.1_5 : int 2 3 1 1 1 3 3 3 4 1 ...
## $ moa1.1_6 : int 3 3 4 2 3 4 4 4 4 2 ...
## $ moa1.1_7 : int 2 4 2 1 1 2 4 3 1 3 ...
## $ moa1.1_8 : int 1 3 3 1 1 1 4 3 2 3 ...
## $ moa1.1_9 : int 4 3 4 1 4 3 4 4 4 4 ...
## $ moa1.1_10 : int 3 3 3 1 3 4 3 3 4 3 ...
## $ moa1.2_1 : int 2 1 2 1 2 1 1 2 2 2 ...
## $ moa1.2_2 : int 1 1 1 1 1 1 1 2 2 2 ...
## $ moa1.2_3 : int 2 2 1 1 2 1 2 2 2 2 ...
## $ moa1.2_4 : int 1 2 1 1 1 1 1 1 1 1 ...
## $ moa1.2_5 : int 1 1 1 1 3 1 1 1 1 1 ...
## $ moa1.2_6 : int 1 1 1 1 3 1 1 1 2 2 ...
## $ moa1.2_7 : int 2 1 2 2 1 1 3 3 1 3 ...
## $ moa1.2_8 : int 3 1 3 3 1 1 3 3 3 3 ...
## $ moa1.2_9 : int 3 2 3 2 3 1 3 3 3 3 ...
## $ moa1.2_10 : int 2 3 3 3 2 3 3 3 1 3 ...
## $ moa2.1_1 : int 4 3 4 4 4 4 4 4 4 3 ...
## $ moa2.1_2 : int 4 4 2 2 4 4 4 4 4 3 ...
## $ moa2.1_3 : int 4 2 2 2 3 4 3 4 4 3 ...
## $ moa2.1_4 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ moa2.1_5 : int 3 4 3 3 4 2 4 4 4 3 ...
## $ moa2.1_6 : int 4 3 3 2 3 4 4 3 4 4 ...
## $ moa2.1_7 : int 4 2 4 4 4 3 4 4 2 4 ...
## $ moa2.1_8 : int 4 4 4 2 4 4 3 4 4 4 ...
## $ moa2.1_9 : int 3 2 3 3 3 4 4 4 4 4 ...
## $ moa2.1_10 : int 2 1 2 2 3 4 2 4 2 2 ...
## $ moa2.2_1 : int 2 3 2 2 3 2 2 2 3 3 ...
## $ moa2.2_2 : int 1 1 1 1 3 1 1 1 1 2 ...
## $ moa2.2_3 : int 1 2 1 1 3 1 1 1 3 3 ...
## $ moa2.2_4 : int 3 2 2 2 3 2 2 3 3 3 ...
## $ moa2.2_5 : int 2 1 1 1 3 1 1 1 2 2 ...
## $ moa2.2_6 : int 3 2 2 2 2 2 2 2 3 3 ...
## $ moa2.2_7 : int 3 2 3 2 3 2 1 2 1 3 ...
## $ moa2.2_8 : int 2 2 1 2 3 1 2 3 2 2 ...
## $ moa2.2_9 : int 2 2 1 2 2 2 2 2 3 3 ...
## $ moa2.2_10 : int 1 1 1 1 3 1 1 1 1 2 ...
## $ adult_Q : int 1 1 1 1 1 1 1 1 1 2 ...
## $ MOA_IMP_biascheck : int 64 62 61 46 62 67 73 74 71 59 ...
## $ MOA_ach_biascheck : int 38 33 33 32 47 27 34 39 40 48 ...
## $ MOA_IMP_dummy : int 0 0 0 0 0 0 0 0 0 0 ...
## $ MOA.ACH_dummy : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Q65_First.Click : num 37.1 120 27.7 19.7 12.9 ...
## $ Q65_Last.Click : num 308 336 154 297 122 ...
## $ Q65_Page.Submit : num 309 338 156 299 122 ...
## $ Q65_Click.Count : int 45 58 47 43 46 50 45 42 47 47 ...
## $ IDEA_1 : int 3 4 4 4 4 3 4 4 4 4 ...
## $ IDEA_2 : int 4 4 4 4 4 4 3 3 4 4 ...
## $ IDEA_3 : int 4 4 4 3 3 3 4 3 3 2 ...
## $ IDEA_4 : int 3 4 4 3 4 3 4 4 2 2 ...
## $ IDEA_5 : int 4 3 4 4 3 4 3 3 4 4 ...
## $ IDEA_6 : int 4 4 4 4 3 4 4 2 4 4 ...
## $ IDEA_7 : int 4 4 3 4 3 3 3 2 3 3 ...
## $ IDEA_8 : int 4 4 3 4 4 2 3 3 3 4 ...
## $ IDEA.biascheck : int 30 31 30 30 28 26 28 24 27 27 ...
## $ IDEA.bias.dummy : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Q66_First.Click : num 44.7 19.9 23.2 27.5 24 ...
## $ Q66_Last.Click : num 86.6 65.2 51.4 172.8 52.2 ...
## $ Q66_Page.Submit : num 87.5 67.2 52.4 174.1 53.4 ...
## $ Q66_Click.Count : int 11 13 11 9 9 13 9 8 8 9 ...
## $ politics : int 2 1 2 8 1 8 4 2 8 4 ...
## $ party : int 3 4 8 8 8 8 4 1 3 8 ...
## $ president : chr "" "None, but I was a US Citizen and had a gun next to my head, I would vote for Trump" "Hillary Clinton" "Hillary Clinton" ...
## $ Q74_First.Click : num 13.1 4.9 34.9 66.9 23.6 ...
## $ Q74_Last.Click : num 40.4 28.1 48.4 119.2 32.2 ...
## $ Q74_Page.Submit : num 46.4 55.1 56.4 135.3 35.3 ...
## $ Q74_Click.Count : int 2 6 4 4 4 6 3 3 3 3 ...
## $ swb_1 : int 4 3 1 5 2 4 4 5 5 5 ...
## $ swb_2 : int 6 4 2 6 5 4 4 5 5 4 ...
## $ swb_3 : int 5 5 2 6 5 6 5 6 5 6 ...
## $ swb_4 : int 5 5 2 5 3 5 4 5 5 4 ...
## $ swb_5 : int 3 4 2 6 2 1 1 6 5 6 ...
## $ swb_6 : int 3 4 2 3 5 4 4 6 5 4 ...
## $ Q67_First.Click : num 9.63 8.61 37.66 13.59 6.8 ...
## $ Q67_Last.Click : num 40.4 29.1 53.2 55.2 22.2 ...
## $ Q67_Page.Submit : num 41.2 30 54.6 56.1 23.1 ...
## $ Q67_Click.Count : int 7 7 9 7 7 7 7 7 6 13 ...
## $ mindful_1 : int 4 2 2 2 4 1 5 3 3 5 ...
## $ mindful_2 : int 2 2 3 2 5 4 6 2 3 6 ...
## $ mindful_3 : int 2 2 1 1 3 3 5 4 4 4 ...
## $ mindful_4 : int 2 1 2 2 2 3 4 2 3 5 ...
## $ mindful_5 : int 4 3 3 2 4 5 6 2 3 6 ...
## [list output truncated]4 Subsetting Data
# use the subset() command to select which columns to keep in your dataframe
d <- subset(df, select=c(ResponseId, moa1.1_1, moa1.1_2, moa1.1_3, moa1.1_4, moa1.1_5, moa1.1_6, moa1.1_7, moa1.1_8, moa1.1_9, moa1.1_10,
moa2.1_1, moa2.1_2, moa2.1_3, moa2.1_4, moa2.1_5, moa2.1_6, moa2.1_7, moa2.1_8, moa2.1_9, moa2.1_10,
mindful_1, mindful_2, mindful_3, mindful_4, mindful_5, mindful_6, mindful_7, mindful_8, mindful_9, mindful_10, mindful_11, mindful_12, mindful_13, mindful_14, mindful_15,
swb_1, swb_2, swb_3, swb_4, swb_5, swb_6,
stress_1, stress_2, stress_3, stress_4, stress_5, stress_6, stress_7, stress_8, stress_9, stress_10,
politics, sex))5 Basic Data Checking
5.1 Checking Values
# use the map() command to view tables for all of your columns/variables at once
d %>%
map(table, useNA = "always")## $ResponseId
##
## R_00aEuEBExladk8V R_00cbdktvaY86PbL R_02K0VTmup5OMJP3 R_06aTGiZAvuTngdP
## 1 1 1 1
## R_07h1OLlzcoZj7Q9 R_08MSceiScQuWWMV R_0AnPM5x7wfwX1bf R_0B4gtR1WbBTM34Z
## 1 1 1 1
## R_0BCnIHKONiN2fS1 R_0BRR4WuOgQsX7Zr R_0Cj1m8a7TIGCjwl R_0CJcqtXWnOo8uBz
## 1 1 1 1
## R_0cVogNKh17252Lf R_0DHawufGaevNITD R_0dHeG2u1sh8jBD3 R_0DJxVk198bJKW3L
## 1 1 1 1
## R_0dNdRm6ew3q0NTL R_0DO7VdrACYNGD4x R_0eSMVCDA6q7DlTP R_0f9Kp3Yh5d8dR17
## 1 1 1 1
## R_0fhBPYN8EIDum8J R_0fuW0LxSw8UBdNb R_0HfQhzNjRXj43oF R_0IhahecRrYVPWQZ
## 1 1 1 1
## R_0jkTKwF8erY09ed R_0jNsdXaoFwisOcx R_0k4hK2HpuEffOtb R_0NyXCCeAwrZy9MJ
## 1 1 1 1
## R_0OIVG4OddeIkBA9 R_0p0o20iOMuxc813 R_0pRBGCJnrZVUpM1 R_0PWnpufIzF3YIzT
## 1 1 1 1
## R_0q8r4AJ0KRqJD6F R_0qgwLO0x0MViDOp R_0SMFGU1spnVkY4Z R_0SSfg5WWjL2Pv2l
## 1 1 1 1
## R_0SWvon67Pp6xoad R_0U2icEWOOz0WnUl R_0U2W1vrCUsbD3Tv R_0Ujo9ij5Prw5p5z
## 1 1 1 1
## R_0uouoAArB6DgFZr R_0vtxu9tY1OZlZKh R_0wS0wVx2djmhoyx R_0xPHZrS6QJxGCaJ
## 1 1 1 1
## R_10AR1BhlKdqLzd9 R_10DzLeOYwIt65ES R_10GHdAruGK8PKsD R_10HtfDDQWAF0TUu
## 1 1 1 1
## R_10ID9IwsGTH8jKh R_10Iu1euwQG7u8Sd R_10o5g7NBIliz7y7 R_10OPyaWe3FGIg5T
## 1 1 1 1
## R_10osRVYQTozmPQz R_10OtPcHR8FRqYWc R_10OTSb3qKpsUpgY R_10PhoFvpHsSRUzq
## 1 1 1 1
## R_10qEDsOS2lof6ZX R_10qO2qYgU3X0g49 R_10qSpHLSgguQ3P4 R_10SGNtXcRPxkKPL
## 1 1 1 1
## R_10Ta8CCHcxAKrVv R_10TXsy91Zo8PPzr R_10u6G17JITY60xl R_10uS04Ubp4IwfYE
## 1 1 1 1
## R_10Uyk0IFlCgwSSS R_10wSj5XPe0tgJGz R_10Z9bVcmtG3o3nc R_10ZTeK5tAqSKyCl
## 1 1 1 1
## R_114KGbDvLZKVCnL R_114oIOatJSReBAv R_117TeYgqOVRfmSr R_11cg45iNm9C0EkE
## 1 1 1 1
## R_11gSRxVodM1SzNZ R_11gyCdnzpxAimFV R_11h0k4wkLXg6f3l R_11iS5uJleF16Vrt
## 1 1 1 1
## R_11XSc9UH9vYQpQd R_11Z2ipgG06JlwQh R_11ZRxisCLB5shIK R_120eAMA1JEaVzDg
## 1 1 1 1
## R_123cHWaIY9a7lTj R_125dBqVsrnXFXde R_125z5zdnETjS0jd R_126hlQCkLu4yrOw
## 1 1 1 1
## R_12As5dRrLv3frVH R_12DRYQVQXW9FAJy R_12F64OvWX2h8al3 R_12FkPYc6D6AmS8Z
## 1 1 1 1
## R_12fZ4Uf4AjC0nPp R_12G7bIqN2wB2N65 R_12G7i3IHX4oPbyX R_12hunWXMEYOKaOW
## 1 1 1 1
## R_12L1PtHgU43Acrq R_12L3kNuNIqjHsMr R_12LIvhyqfgiboWS R_12LQUiollwEFzsN
## 1 1 1 1
## R_12mbwWnjAW2oAZr R_12moVn7IJsErSy9 R_12QpuSszEB0ZXho R_12qZ4wj8OYFZoSe
## 1 1 1 1
## R_12r6sxr2ZFm7r0I R_12rUfK89ysLhGrV R_12rUozan444C5xF R_12RzALeawDdgvlj
## 1 1 1 1
## R_12sDTIswZPCWT2V R_12sxPUaUqY6MGUu R_12xKKUW6y5uX9p0 R_1AAIjIxYsrkDKF3
## 1 1 1 1
## R_1AOlcS2DxomEQbr R_1B2ErY9UgFeBC9n R_1Bae6Wt1u8czLot R_1BPIWXMM5sqx71l
## 1 1 1 1
## R_1BqeN645IHWLYBz R_1BQSJCdyKLabank R_1BW7rnVAPccRFkB R_1BWarcKJ0Gg3aO8
## 1 1 1 1
## R_1BWV7fsdyMARRcJ R_1C1lWtqTrdnwsl8 R_1C1vwGGStueiMsJ R_1C2g76yekBbh3Rr
## 1 1 1 1
## R_1C2gbqaQIESYVf3 R_1C42QZHqEWxKZXt R_1C8xsbJ9NbVPoyh R_1C8zOh7WDlKOW7N
## 1 1 1 1
## R_1CattXKeuxQvaJh R_1CBaRWVVRJHJfZU R_1CBnDZkcZPCwDdV R_1CE0KCrObpZDxGh
## 1 1 1 1
## R_1CEjf5qVtrqxbwJ R_1CEpgpuL7KJHYEi R_1CI43oP3J1ez8wR R_1CKJEq7siyO6EE5
## 1 1 1 1
## R_1Cl7ONhtvJCpssB R_1CpEVdduwKceHj2 R_1Cpudzi4IuG1xxq R_1cSoMVBYZtTafkJ
## 1 1 1 1
## R_1Csx16E3WHQdMlL R_1cUtktLa0HgUhDN R_1CvdMHOGrSUP42x R_1CvJ7B19kLK377K
## 1 1 1 1
## R_1CwInCbEYMvQx3V R_1CwkvKOZlYs2hfH R_1Cxx41ayuk0rrNb R_1CygJEsM94OqSPG
## 1 1 1 1
## R_1cZdNgSrtSiVSBl R_1cZGRsR5RpMDcIA R_1d44Xg1osep8cZM R_1d4vQ8U7vBSKLJh
## 1 1 1 1
## R_1d5MBBSjWY4FAnk R_1dAdQ4SKO3cCbl2 R_1DBFaq1IJwIxgKV R_1DBgHDRwoNqBoDW
## 1 1 1 1
## R_1DD8qsCXLvPOsuI R_1dEQbcs91OVCSCh R_1dF66xA0Q0neCpg R_1DFNnlVLJ9jjt8Y
## 1 1 1 1
## R_1dGlM4QYRqPAHUw R_1DHZxCSx0PEdq0i R_1DI2ba7koWlmmF0 R_1djp0B5WnReh6Dw
## 1 1 1 1
## R_1dLbD6qt7Whc9kD R_1DLINetRysy8CbS R_1DLJY2Olm6Adpbl R_1DLOJSE9D2PTGQr
## 1 1 1 1
## R_1dM5UPxM3Aba6E0 R_1dMFWARCMxaSuHd R_1dmMdKX9pcNamkb R_1dnKgkMNpCMkUZk
## 1 1 1 1
## R_1DNZvoLmuuExHjU R_1doDW54uiguPOi4 R_1dofwoAs8fbkdUl R_1DOy3jkqsqkJCgP
## 1 1 1 1
## R_1DpN3vHy4zBcf6I R_1DpOgFOzljFJPxH R_1DpSVgNK2WGepmU R_1DRRPakJVtTAalx
## 1 1 1 1
## R_1DRSsWhvzF4nsSa R_1ds2OpjJH90Kgk3 R_1dsDI3zBsKuHUD4 R_1DSFe3GjGeiuCkp
## 1 1 1 1
## R_1dshhiOR4EH7u1t R_1dtBAIcx9qOPEfy R_1DTINR2wS7YiYdr R_1DTKl5osv1GEvKv
## 1 1 1 1
## R_1DtWGmJQ734yfKx R_1Dtz1hwwfIyl22f R_1DU10SWcgZFbjXG R_1DuQkjLlXMzZJUg
## 1 1 1 1
## R_1DVgk1fLU9kPtmg R_1DvstcO4oEM43bF R_1DXNRpaCLK4adEc R_1DXYVEVPhR5tF2j
## 1 1 1 1
## R_1dy8DzF9od00vCL R_1DYF6fdTtLfPGT4 R_1dyoLztJ9flDhsK R_1DYxkI738N4dxAd
## 1 1 1 1
## R_1DzL2YSrjjlJgkk R_1E1goSmnUS3k0nU R_1E1hRiomdr9YcW3 R_1E4He93IpQWmcMq
## 1 1 1 1
## R_1E6GL9aJNI9DzUb R_1E6V08QyHtpTAFU R_1E6VtbRvOaIBQtd R_1E6XMtxrEO4wau4
## 1 1 1 1
## R_1EbQKNeegcIRuww R_1EcJDddZHddkUSR R_1eFadoRZYX8fZiS R_1eG19ADqnxyYVvm
## 1 1 1 1
## R_1EhgfyZuqzIJOPR R_1Ehqy1OHOZae7gG R_1EhuS7PEozygh5K R_1EhxwuV8dcx8nCY
## 1 1 1 1
## R_1EictPZzYdBaHgp R_1EiEtm2ZNl7dQHY R_1EihKePhUenEL9L R_1EimW1PHou5A3cS
## 1 1 1 1
## R_1eODPtY1LDc4aEN R_1eQOswa3suuc7pY R_1eqrAonjbqx7vxX R_1eRdanNNXfHV5TK
## 1 1 1 1
## R_1eRDkVMrVWovIEh R_1es3002vvYiH33q R_1eS6CWOUDS9wCsx R_1esjPnwrno36UUY
## 1 1 1 1
## R_1eUFa1WXhNDBVJK R_1eVH4Bc6yJCdcGn R_1ewVD7tcTWUmjzZ R_1EXs59TJEUsiMYW
## 1 1 1 1
## R_1exz3bknyDCeGzS R_1exZrxCtJfE2tGl R_1ey46oxQ0LGBtZe R_1f26SK42rIxx4tL
## 1 1 1 1
## R_1f2daygD74wsLAo R_1F3eLUtnUqenxLi R_1F3QzG0ZZzqHanH R_1F4keDKnGuApP5u
## 1 1 1 1
## R_1F4VD768SxNs4Rf R_1F5nrKQ29M6CCuo R_1F5uWdCFOIdF5WY R_1f8HIw0e9PPylrr
## 1 1 1 1
## R_1F8MvFpHIqkOysA R_1f9Fkx8HFLhhUaI R_1f9rkj303512Rx7 R_1FA4AioOjlWqsB6
## 1 1 1 1
## R_1FaoAqCUiqfAhwq R_1Fc3679aEemc305 R_1fdTIgxr4jeMPwG R_1FEBtEAKnHsa9GP
## 1 1 1 1
## R_1FEQ8xLWZeRoyqB R_1ff6IAASXqOv2IJ R_1FfcHLRPe8DXuQv R_1FFEr6YMK1zJPHe
## 1 1 1 1
## R_1FfVIhmrj3PdnzQ R_1Fglma4L378sP23 R_1Fh9c0RHPNan3VY R_1fjQVvavBUHCpPQ
## 1 1 1 1
## R_1fjY5WGwT4Ffaow R_1fkkdehLmm3RFdN R_1FQgPZ58XGYbtKq R_1FqP2f60qjz4S1C
## 1 1 1 1
## R_1FquPKcAqTJQzRl R_1FqUxVOZwUYcqvT R_1FRIvDGJw0IvJ7t R_1FUwtEwVNOh04Ux
## 1 1 1 1
## R_1FvEpRoEBuevUt3 R_1FzTcbiHVUsyUhM R_1g5DfwzFmfs5G8J R_1g89MwkzvujpyJm
## 1 1 1 1
## R_1g8B6UZlVVbib5g R_1g8uz0SSU0RgPdC R_1gAxha4oCC3u2bC R_1gbedjw2jC3aTOg
## 1 1 1 1
## R_1gBLu8cuzD7NKct R_1gcLcEFwIzfpWzp R_1GD1DpDmeqH8Kho R_1GDDvFFXIzLCy8s
## 1 1 1 1
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## R_r8rDOSPDzNPpBOp R_R9McNRNrQL6F1Yd R_rcdoWsnuIYY8Xux R_RCgn91Sf5SNf1rH
## 1 1 1 1
## R_rdKG3tP6MB34L4J R_RdZKw6Xwii2Q5Db R_rfDZRSMrgat8F7H R_rfHNUWZC6cIxKbT
## 1 1 1 1
## R_RfTmHpdulLopW9z R_RgxUfZFdXSerZV7 R_riiJjZ879Q1em8F R_rjcsRdDkFU5LcoV
## 1 1 1 1
## R_rje0xML6mcPbXax R_RjWpEAvlQ68XYFb R_rjYJvi9OIHWKiOt R_rk9JA9DSnFO80s9
## 1 1 1 1
## R_rkf1u33aHqkHGy5 R_rkzxn4KkNzgMWRP R_rl0eE7teuulFZU5 R_rls8Q5Sr57PiHh7
## 1 1 1 1
## R_RmJ3Jd3fBgsmoxz R_roKHBENfxrpHTZn R_roTXyW9xm0vaDBv R_RRF50cns0kqXuY9
## 1 1 1 1
## R_rrRQTWbKMr2G9fr R_RrSNupUJB6FNKmZ R_rrttZuiNvJz1D3P R_RrWZyJp3fMZulEJ
## 1 1 1 1
## R_RRyKQPYEIuH5rxv R_RspY7I8PDWHNiRb R_RWXSfCwGSTB5v0d R_RXmKE1fhB5sVYu5
## 1 1 1 1
## R_RzsmorXlKO4eoGR R_s5cpwgYPZxz2HwB R_s5wX18VCu98nlMR R_s6Gk0mY6dgsgPFn
## 1 1 1 1
## R_s6GlOpP1WJSh6oN R_s83qscBROEGLD6p R_sbvGtBjpbY4enCN R_sBZO4p4HjbmmASt
## 1 1 1 1
## R_scJR5aMnulg8yQh R_sCoXRY85aUpiGf7 R_sdJ4kmTgc08ajsd R_SDY9XhwDsN4NOed
## 1 1 1 1
## R_sGbMiZYCnTBXD9L R_shtqlVWdPrOuh8d R_shyncpd3VGLGyM9 R_SISk36cETkIezKh
## 1 1 1 1
## R_SIuZz0Ke1xupnvX R_SJn9b4riEW6cbqp R_sKXTaQN8ofK5Vzr R_sNhyckf10MG1sIx
## 1 1 1 1
## R_SNPTGLYE4O6SJtD R_sohbz6FSyA0w0SZ R_soIZEDHfgWRsX73 R_sOtJxiVoAJUDHZT
## 1 1 1 1
## R_sOwZCK2UG7jHHG1 R_SPh0iKrxFCo3p6h R_spsCbf0lO39qxmp R_sQXfDeC9CBGBfhv
## 1 1 1 1
## R_sQZvt8xr3XGCwXT R_sR0OOAlxyRIVjjj R_sRI8zDWH7X9Ivbr R_SSFWDWshbeTueS5
## 1 1 1 1
## R_sTHLVosHj4jYPmx R_sTrLUHjZowmvUDn R_sTYSKqCkoDk5OOR R_svwlmBGZE2vKTzX
## 1 1 1 1
## R_sySFV365cdiXbgt R_sz1qXSQQ6rVDDGh R_sZHkOxSQb6Msxb3 R_sZi2VtV3vcxIpPz
## 1 1 1 1
## R_sZjpTjX8CpRwXzX R_t0Ay3NtIEyan9TP R_T0CL0aIuAN3iCtP R_T0MI9WY3E2c6tkR
## 1 1 1 1
## R_T11ONpJ8DWTANA5 R_T1V4sUqO8mIWYOl R_T5Wdqoi0tdi9fLX R_T7B0VxgNivNji1P
## 1 1 1 1
## R_TckKoMAaKdnIHnz R_TdpUwVBj2gvWKuR R_tDqwAyc13bB8qRz R_TdUDIBb18UIgCgp
## 1 1 1 1
## R_tFEbTPyWjAKVkzL R_tGafKJlE6jc7TnX R_tGMUaYUREKRXd3H R_tGQz5XVbEXCUYaR
## 1 1 1 1
## R_tKRN7CtFIQqY8qB R_tLonsOXII9JFU09 R_tMvZqrlMZtpbWPT R_TnDBpK0zZ3qgtCF
## 1 1 1 1
## R_tRPFKdAV5oACxoJ R_TtnFEa68rSbSQiB R_TuAwlqvdn6YL6CZ R_tX8uoBHEsDUws9j
## 1 1 1 1
## R_u4SyE6Gm022bGpj R_u8604SCcGZen0yd R_u8nzto5sZMs7yoN R_u9A7Lpuc1R1VwNb
## 1 1 1 1
## R_Ua8rHkWD01EQhnX R_UaPcDZ7hCLzrKFP R_UapRCybZgVkQqNb R_UDCp582qhQSgOwF
## 1 1 1 1
## R_UEZykdV1BiavZG9 R_UFJofn4cAQbXnzz R_UgBHWFNN6KMvHCF R_UgRHuRkOg1iKdax
## 1 1 1 1
## R_UGU7BNbhfTxRH7H R_UgXGC2pm03oBQHv R_Ui5EEg1cOrONd1D R_UihzHe19xeVHFa9
## 1 1 1 1
## R_UJGIWndbnecA4cV R_UK3SOXcuKZBJ3ix R_UKiODuTDvxOlV7z R_UKrL3P70iccKwqR
## 1 1 1 1
## R_UL6BfBeLvCqDPDH R_UL9YToqfo4cUkRH R_ULJQBnBj4oDbtKx R_ulVLZnjX7Fzoout
## 1 1 1 1
## R_Umh7gdM7rk8SPJf R_umI8x8W3uqg4i1H R_UMUo41MhP9kL4GJ R_UMwpzUZEb35d9st
## 1 1 1 1
## R_UmXggVPg6xAGtvb R_UnC9VcijknhlpZv R_UniXPRjV2prZ0vD R_upPIwjCybhkGURb
## 1 1 1 1
## R_UQrDfjqqLSvZ5pD R_urkLUi8fdV4VzMd R_urnzs4hgmaq3fu9 R_USAjSSjwI1s6z5v
## 1 1 1 1
## R_Ut1qCmykaASH4WJ R_uvP3C9DUBZWP46B R_uwB9gPWPPijYH5v R_UXBq31PgA3jyh1f
## 1 1 1 1
## R_V22CAnrHihh9w8V R_V3GO8EX9v1CSu4h R_V3wsZf9dJhdu3fz R_V42Lx45G3Pf2kYV
## 1 1 1 1
## R_V4D8LV3EXOEHTbz R_vAYgUIIHNxtVkGJ R_vC4FtURnQmdSJbj R_vce5QHcFKQLqDtf
## 1 1 1 1
## R_vdeLWZW0nLoYJl7 R_vdgvyY8llNSDDEZ R_vdVCROJS4vLRUJz R_vdxBxWQmRSAhLXj
## 1 1 1 1
## R_vHX4vHKybfiI1jj R_vicsZ8Ejx3P4T05 R_vJlMJBV6y4VajjH R_VJxDonUVSEuQqvD
## 1 1 1 1
## R_VKaYd13vKutFeSZ R_vl4X3T2HwBPd7ax R_vMhsCGa1AGmgQ1z R_vMqbe00ginSC8md
## 1 1 1 1
## R_vP6LiGCEIKmK1cR R_VP7qtsSLSGgUnRv R_VPAHwtPP4h79ri1 R_VPbz3XSFJJVzfDb
## 1 1 1 1
## R_vpk0ZLeId9a33Ud R_vq421Bry1so6GU9 R_vqusUfIe7EnqWoV R_vqxmyWc8xtBdhex
## 1 1 1 1
## R_vrbvpCIPl98Ovu1 R_vrClbXaknWwcA6J R_vrGzouC5t8WSrC1 R_VRpqmK4Qyxsx6xz
## 1 1 1 1
## R_vubHWN2HmfxF85j R_vUpEiSySJBgzsT7 R_VUxUZP1EhUDzNRv R_vV2wMq8yEKDZDNf
## 1 1 1 1
## R_vvJoqvaCodMk8yR R_vw1GVn3jYIKrn7X R_vwdVwZEGDCydWI9 R_VWKgSLoAMBwfbC9
## 1 1 1 1
## R_vxac4k76uyIUt2x R_vxAiSahnhiOuH85 R_VXt7vrIJyBtiolX R_vZCnds9l4KSq1cl
## 1 1 1 1
## R_w0NzIzu6KXyqOBz R_w0qcNYgS9WUdI6l R_W1EGf2QJG0hn77H R_W25VncXfjvum6Up
## 1 1 1 1
## R_w4v6jEcK9Bnui3L R_w5BTS7ojQmTrRWp R_w5ebJR3z0EfKyAh R_W65Gs3Kc27pzxnz
## 1 1 1 1
## R_w6lxAMmtuX3Ek5X R_W6S35WYLHLscFVv R_W6TkEFYJpTlsl6V R_W6WIq7D3KkGS3MR
## 1 1 1 1
## R_W7O7KF7yEHPFdHH R_w7RcOnepWH2wOnT R_W8xMpW2yYzwS45r R_WBUWHjFN8VmL1sZ
## 1 1 1 1
## R_WcA58ouQdChPVTz R_WcEmFSCFvsfZ6XD R_WcyIKDc69mbiwWB R_WcZa6G916pFjJbr
## 1 1 1 1
## R_WDoTF7qZXCfkIbD R_WfEsejIkee3Colr R_WjGB1sKDJBfUDi9 R_WkbXsjnNFOBmO5P
## 1 1 1 1
## R_wL5G1eHIHJt4OE9 R_wLV8uVbwd9Hq0Ct R_wOks2PSuqruQOlj R_WpaxxZp37UO9I2d
## 1 1 1 1
## R_Wr1ZT9xEnPY0V9v R_wRi8WjZEQps2e2d R_wTrsTDnZuVudvep R_WuE77Tmkxe27VJv
## 1 1 1 1
## R_WuGSDCD5Bh7RBuh R_Wv9R6HdzTYHYS7T R_WvbXAzReYzy6IgN R_WvoppNlNMaJwOqJ
## 1 1 1 1
## R_WvwgSXdOAxjf9PX R_wYiU7404tbzVDe9 R_wZcI1elyiIBxsQ1 R_x0fEkXigXSx3YPf
## 1 1 1 1
## R_x2T1pZJRK5q3soF R_x4z9PgGtORz2bDP R_x5x2xw2KhvOjybT R_x9JFVQSWH2rwzYd
## 1 1 1 1
## R_x9tSJfHHlRPYeI1 R_xACwcdS3mhHmRYl R_xbfyhlLMvpvrs0F R_XESJAjmCh7d0obn
## 1 1 1 1
## R_xFbKsCdVWIBSl3z R_XgL6LNATJyhxjTH R_xgrhw19BNeutEvT R_XGrxUS1XcfXxA65
## 1 1 1 1
## R_xgXu2pj6z8Z47El R_XHdyMaik28PToVH R_xinAHaGX73o2ak1 R_XKYWl0uR6LXbwKR
## 1 1 1 1
## R_xlQVDXU3WHKlDfX R_XLZduf1UmcI3dTz R_XM6PgM5UBmZGFzP R_XNx5Ihc9npOoyKB
## 1 1 1 1
## R_XpvMtq8GyXne6tj R_Xqed5qjTP5W3NPb R_xr45aVFqM3Bj6EN R_xryJBM8NY8bAlZD
## 1 1 1 1
## R_XSu8wjE68V9OJEd R_xsUHjhOPeQdEybf R_XTCu6Anhjie1wJj R_XTESNoCsrsfb35T
## 1 1 1 1
## R_XTIAIAIwSt3pSBX R_XtPD6eArGbEnECl R_XtUdI9TpyP1tKAp R_xuDczhEtyooS45X
## 1 1 1 1
## R_XUrkCVzaWN7qSuR R_xuupGEX4vq6A91L R_Xvopl0UvSmAhHrz R_XvTh7edKJftNE4h
## 1 1 1 1
## R_Xwe7JaGKKFYDyRb R_XwhXfHx4EmfiBWh R_Xwi9eReJDcSEZ21 R_xyBn828HK6VyBZT
## 1 1 1 1
## R_xyCNZJ4IykKu8Gl R_xykCZZdnteyZF1T R_XYyvbarNtY0wueR R_xzISJ4ujcslhKEx
## 1 1 1 1
## R_xzp39EAu9iscVW1 R_xzRLqbGz6pe4oX7 R_XzSvdxyrgLSdpXb R_xzTFQ1FmDDQ4wfL
## 1 1 1 1
## R_Y3SKThqEBz6Jjd7 R_Y69XOURBCDXZd0R R_Y91QtI6V8NcgUEx R_YbFSLhmurtOWZJ7
## 1 1 1 1
## R_yCtUbug4qskv83L R_yEjcQBFx9pdSutj R_yjs5RFwSVqGDFhD R_ylrlUzwFzk6D0L7
## 1 1 1 1
## R_ymyvzdaHBIdanpD R_yOb3NwdQ0B8ZBfz R_yOr8H3Xq0bJ3BGV R_yP9ZidBGyFTJmY9
## 1 1 1 1
## R_ypFhvhYUSnjMvex R_yPg2lwzLLvdyRKV R_yqm9uvxabaX1AiZ R_yR612aOKU7sQFih
## 1 1 1 1
## R_yrOn3isgHdPPRAd R_yrq35WjNwdZD4Ix R_yrwZmYEtRe4NycF R_yUqMuFeWrmCoGgF
## 1 1 1 1
## R_YVRsM1SqLTXKZdn R_YVtQ1gDJoao90Ax R_YW8RDLOru3UB5Gp R_ywki5fArUkja91f
## 1 1 1 1
## R_ywqkLvP8SOCmVPz R_yWxpDHDRHE8j7cB R_yx7CYfp2lJbkfmh R_yXdevlGkmVvTG0h
## 1 1 1 1
## R_YXiLIxboMOeLF6h R_YXlkszpOYeSMqQN R_yym10RKiHtU4OJz R_z07k9TQvgpqxnJn
## 1 1 1 1
## R_Z2m4cF3KzhZ2VFv R_Z3FsWrOdrY35khr R_Z8ofSdLse5DeUHD R_z8SXE7RpMWrIOFb
## 1 1 1 1
## R_Za7CjBkxnCNkOiZ R_ZCDOpJHsJiTFiRb R_zdsbKsrHU1kq99L R_zdwzrMmBEVqs5pf
## 1 1 1 1
## R_ZDzzdUEtdg609Fv R_zeapSz3oRBGLfGx R_zeQs23FAQQ9Cy3L R_zeVzInz0dHCKY6t
## 1 1 1 1
## R_zeWrXx8kySc3FCh R_ZFe6soTVT0aGw9j R_zfiAXwOzjsI5ZkJ R_ZgTI5GweP98aBot
## 1 1 1 1
## R_ZkGG5UUVfc1I3MR R_ZLcXEENfgpCWEIp R_Zm13o5i8d4Z4mv7 R_ZpEp1PVdm66wrWV
## 1 1 1 1
## R_ZqrZHpaqfrgPhlL R_Zr6Xylj6BTWq5xf R_ZrApYB5flld5sBz R_ZrD49jng3sxz2W5
## 1 1 1 1
## R_ZsHquDajFMklhId R_zSUlFa0qHFGio9j R_zTIWeOowIY0VPQR R_Zx9FTPo9AtmNN3r
## 1 1 1 1
## R_zYCEZx56Qlj1ytX R_ZymeVsrbugX4jPr <NA>
## 1 1 0
##
## $moa1.1_1
##
## 1 2 3 4 <NA>
## 33 122 581 2437 9
##
## $moa1.1_2
##
## 1 2 3 4 <NA>
## 114 280 910 1852 26
##
## $moa1.1_3
##
## 1 2 3 4 <NA>
## 185 291 577 2110 19
##
## $moa1.1_4
##
## 1 2 3 4 <NA>
## 835 743 837 749 18
##
## $moa1.1_5
##
## 1 2 3 4 <NA>
## 1152 674 614 717 25
##
## $moa1.1_6
##
## 1 2 3 4 <NA>
## 202 378 890 1680 32
##
## $moa1.1_7
##
## 1 2 3 4 <NA>
## 780 910 633 833 26
##
## $moa1.1_8
##
## 1 2 3 4 <NA>
## 414 508 615 1616 29
##
## $moa1.1_9
##
## 1 2 3 4 <NA>
## 218 271 682 1987 24
##
## $moa1.1_10
##
## 1 2 3 4 <NA>
## 450 552 823 1338 19
##
## $moa2.1_1
##
## 1 2 3 4 <NA>
## 3 39 481 2636 23
##
## $moa2.1_2
##
## 1 2 3 4 <NA>
## 151 303 643 2075 10
##
## $moa2.1_3
##
## 1 2 3 4 <NA>
## 246 472 788 1657 19
##
## $moa2.1_4
##
## 1 2 3 4 <NA>
## 9 27 368 2754 24
##
## $moa2.1_5
##
## 1 2 3 4 <NA>
## 97 252 816 1997 20
##
## $moa2.1_6
##
## 1 2 3 4 <NA>
## 38 93 219 2814 18
##
## $moa2.1_7
##
## 1 2 3 4 <NA>
## 81 350 1034 1697 20
##
## $moa2.1_8
##
## 1 2 3 4 <NA>
## 37 219 1188 1719 19
##
## $moa2.1_9
##
## 1 2 3 4 <NA>
## 49 262 1028 1826 17
##
## $moa2.1_10
##
## 1 2 3 4 <NA>
## 303 776 1081 1002 20
##
## $mindful_1
##
## 1 2 3 4 5 6 <NA>
## 84 390 1034 754 578 336 6
##
## $mindful_2
##
## 1 2 3 4 5 6 <NA>
## 60 230 577 592 917 801 5
##
## $mindful_3
##
## 1 2 3 4 5 6 <NA>
## 143 408 833 762 706 324 6
##
## $mindful_4
##
## 1 2 3 4 5 6 <NA>
## 310 667 893 593 486 228 5
##
## $mindful_5
##
## 1 2 3 4 5 6 <NA>
## 179 375 620 714 837 452 5
##
## $mindful_6
##
## 1 2 3 4 5 6 <NA>
## 493 629 627 555 536 337 5
##
## $mindful_7
##
## 1 2 3 4 5 6 <NA>
## 187 442 826 778 632 312 5
##
## $mindful_8
##
## 1 2 3 4 5 6 <NA>
## 109 359 758 881 745 325 5
##
## $mindful_9
##
## 1 2 3 4 5 6 <NA>
## 201 432 775 834 669 266 5
##
## $mindful_10
##
## 1 2 3 4 5 6 <NA>
## 140 400 823 842 683 287 7
##
## $mindful_11
##
## 1 2 3 4 5 6 <NA>
## 262 741 937 569 472 195 6
##
## $mindful_12
##
## 1 2 3 4 5 6 <NA>
## 168 310 482 504 699 1013 6
##
## $mindful_13
##
## 1 2 3 4 5 6 <NA>
## 571 906 818 461 270 151 5
##
## $mindful_14
##
## 1 2 3 4 5 6 <NA>
## 199 521 912 715 549 281 5
##
## $mindful_15
##
## 1 2 3 4 5 6 <NA>
## 198 348 513 558 693 867 5
##
## $swb_1
##
## 1 2 3 4 5 6 7 <NA>
## 234 408 449 467 788 617 215 4
##
## $swb_2
##
## 1 2 3 4 5 6 7 <NA>
## 108 243 382 458 782 812 394 3
##
## $swb_3
##
## 1 2 3 4 5 6 7 <NA>
## 125 200 332 375 767 993 386 4
##
## $swb_4
##
## 1 2 3 4 5 6 7 <NA>
## 137 285 351 416 847 805 337 4
##
## $swb_5
##
## 1 2 3 4 5 6 7 <NA>
## 374 489 597 318 506 567 327 4
##
## $swb_6
##
## 1 2 3 4 5 6 7 <NA>
## 255 394 407 416 730 678 298 4
##
## $stress_1
##
## 1 2 3 4 5 <NA>
## 147 656 1331 775 268 5
##
## $stress_2
##
## 1 2 3 4 5 <NA>
## 209 665 1074 786 443 5
##
## $stress_3
##
## 1 2 3 4 5 <NA>
## 31 128 639 1037 1342 5
##
## $stress_4
##
## 1 2 3 4 5 <NA>
## 68 359 1217 1098 434 6
##
## $stress_5
##
## 1 2 3 4 5 <NA>
## 119 523 1482 857 196 5
##
## $stress_6
##
## 1 2 3 4 5 <NA>
## 221 826 1164 662 304 5
##
## $stress_7
##
## 1 2 3 4 5 <NA>
## 99 461 1395 1007 215 5
##
## $stress_8
##
## 1 2 3 4 5 <NA>
## 135 572 1340 948 181 6
##
## $stress_9
##
## 1 2 3 4 5 <NA>
## 175 646 1123 846 387 5
##
## $stress_10
##
## 1 2 3 4 5 <NA>
## 295 763 1037 660 422 5
##
## $politics
##
## 1 2 3 4 5 6 7 8 <NA>
## 235 772 373 568 308 332 57 532 5
##
## $sex
##
## 1 2 3 <NA>
## 792 2332 54 46 Factor Scores/Composite Variables
# use the str() command to check that your recoded variable is numeric so you can use mathematical operators on it
str(d)## 'data.frame': 3182 obs. of 54 variables:
## $ ResponseId: chr "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
## $ moa1.1_1 : int 4 4 4 4 4 4 4 4 4 3 ...
## $ moa1.1_2 : int 4 4 4 3 4 3 4 4 4 2 ...
## $ moa1.1_3 : int 3 4 4 3 4 4 4 4 4 3 ...
## $ moa1.1_4 : int 2 2 1 1 1 2 3 3 4 1 ...
## $ moa1.1_5 : int 2 3 1 1 1 3 3 3 4 1 ...
## $ moa1.1_6 : int 3 3 4 2 3 4 4 4 4 2 ...
## $ moa1.1_7 : int 2 4 2 1 1 2 4 3 1 3 ...
## $ moa1.1_8 : int 1 3 3 1 1 1 4 3 2 3 ...
## $ moa1.1_9 : int 4 3 4 1 4 3 4 4 4 4 ...
## $ moa1.1_10 : int 3 3 3 1 3 4 3 3 4 3 ...
## $ moa2.1_1 : int 4 3 4 4 4 4 4 4 4 3 ...
## $ moa2.1_2 : int 4 4 2 2 4 4 4 4 4 3 ...
## $ moa2.1_3 : int 4 2 2 2 3 4 3 4 4 3 ...
## $ moa2.1_4 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ moa2.1_5 : int 3 4 3 3 4 2 4 4 4 3 ...
## $ moa2.1_6 : int 4 3 3 2 3 4 4 3 4 4 ...
## $ moa2.1_7 : int 4 2 4 4 4 3 4 4 2 4 ...
## $ moa2.1_8 : int 4 4 4 2 4 4 3 4 4 4 ...
## $ moa2.1_9 : int 3 2 3 3 3 4 4 4 4 4 ...
## $ moa2.1_10 : int 2 1 2 2 3 4 2 4 2 2 ...
## $ mindful_1 : int 4 2 2 2 4 1 5 3 3 5 ...
## $ mindful_2 : int 2 2 3 2 5 4 6 2 3 6 ...
## $ mindful_3 : int 2 2 1 1 3 3 5 4 4 4 ...
## $ mindful_4 : int 2 1 2 2 2 3 4 2 3 5 ...
## $ mindful_5 : int 4 3 3 2 4 5 6 2 3 6 ...
## $ mindful_6 : int 1 1 1 1 1 1 1 5 3 5 ...
## $ mindful_7 : int 2 1 2 2 4 5 4 4 4 3 ...
## $ mindful_8 : int 2 1 2 2 2 6 4 4 4 5 ...
## $ mindful_9 : int 2 2 5 3 1 3 4 3 4 4 ...
## $ mindful_10: int 2 2 3 2 2 5 4 5 4 5 ...
## $ mindful_11: int 2 1 2 2 2 1 3 2 4 4 ...
## $ mindful_12: int 4 2 1 3 6 3 4 1 3 6 ...
## $ mindful_13: int 1 1 1 3 5 2 2 1 3 3 ...
## $ mindful_14: int 2 1 1 2 2 4 4 2 3 6 ...
## $ mindful_15: int 4 5 4 4 5 5 6 5 3 6 ...
## $ swb_1 : int 4 3 1 5 2 4 4 5 5 5 ...
## $ swb_2 : int 6 4 2 6 5 4 4 5 5 4 ...
## $ swb_3 : int 5 5 2 6 5 6 5 6 5 6 ...
## $ swb_4 : int 5 5 2 5 3 5 4 5 5 4 ...
## $ swb_5 : int 3 4 2 6 2 1 1 6 5 6 ...
## $ swb_6 : int 3 4 2 3 5 4 4 6 5 4 ...
## $ stress_1 : int 2 4 4 2 4 3 3 3 3 2 ...
## $ stress_2 : int 4 5 4 4 3 3 4 2 3 3 ...
## $ stress_3 : int 5 5 5 4 3 5 5 3 3 3 ...
## $ stress_4 : int 3 4 2 3 4 2 3 4 3 4 ...
## $ stress_5 : int 3 3 1 4 3 3 3 4 3 3 ...
## $ stress_6 : int 3 3 5 5 4 5 4 3 3 4 ...
## $ stress_7 : int 4 2 2 4 3 2 3 4 3 4 ...
## $ stress_8 : int 3 2 2 2 3 1 2 4 3 4 ...
## $ stress_9 : int 3 4 4 2 4 3 3 2 3 2 ...
## $ stress_10 : int 3 4 4 2 4 2 2 1 2 3 ...
## $ politics : int 2 1 2 8 1 8 4 2 8 4 ...
## $ sex : int 2 1 1 2 1 2 2 2 2 2 ...# create your composite variable by adding the individual items and dividing by the total number of items
d$moa <- (d$moa1.1_1 + d$moa1.1_2 + d$moa1.1_3 + d$moa1.1_4 + d$moa1.1_5 + d$moa1.1_6 + d$moa1.1_7 + d$moa1.1_8 + d$moa1.1_9 + d$moa1.1_10 + d$moa2.1_1 + d$moa2.1_2 + d$moa2.1_3 + d$moa2.1_4 + d$moa2.1_5 + d$moa2.1_6 + d$moa2.1_7 + d$moa2.1_8 + d$moa2.1_9 + d$moa2.1_10)/20
d$swb <- (d$swb_1 + d$swb_2 + d$swb_3 + d$swb_4 + d$swb_5 + d$swb_6)/6
d$stress <- (d$stress_1 + d$stress_2 + d$stress_3 + d$stress_4 + d$stress_5 + d$stress_6 + d$stress_7 + d$stress_8 + d$stress_9 + d$stress_10)/10
d$mindful <- (d$mindful_1 + d$mindful_2 + d$mindful_3 + d$mindful_4 + d$mindful_5 + d$mindful_6 + d$mindful_7 + d$mindful_8 + d$mindful_9 + d$mindful_10 + d$mindful_11 + d$mindful_12 + d$mindful_13 + d$mindful_14 + d$mindful_15)/157 Exporting Data
# use the subset() command to finalize your current dataframe
# in the version of the subset() command below, a dash is added to the 'c' argument so that instead of keeping the columns listed in the parentheses, R will drop them instead
d2 <- subset(d, select=c(ResponseId, politics, sex, moa, swb, stress, mindful))
# use the write.csv() command to export your finalized dataframe
write.csv(d2, file="EAMMi2clean.csv", row.names = F)8 Load Libraries
library(psych) # for the describe() command##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(naniar) # for the gg_miss-upset() command
library(expss) # for the cross_cases() command## Loading required package: maditr##
## To drop variable use NULL: let(mtcars, am = NULL) %>% head()##
## Attaching package: 'maditr'## The following objects are masked from 'package:dplyr':
##
## between, coalesce, first, last## The following object is masked from 'package:purrr':
##
## transpose## The following object is masked from 'package:readr':
##
## cols##
## Attaching package: 'expss'## The following object is masked from 'package:naniar':
##
## is_na## The following objects are masked from 'package:stringr':
##
## fixed, regex## The following objects are masked from 'package:dplyr':
##
## compute, contains, na_if, recode, vars, where## The following objects are masked from 'package:purrr':
##
## keep, modify, modify_if, when## The following objects are masked from 'package:tidyr':
##
## contains, nest## The following object is masked from 'package:ggplot2':
##
## vars9 Import Data
d <- read.csv(file="EAMMi2clean.csv", header=T) # import the file you created in last lab
d <- subset(d, select=c(ResponseId, politics, sex, moa, swb, stress, mindful)) # subset to only your selected variables if necessary. If not necessary, you can skip this step
d$ResponseId <- seq_along(d$ResponseId)10 Check Data
10.1 Formatting
head(d)## ResponseId politics sex moa swb stress mindful
## 1 1 2 2 3.20 4.333333 3.3 2.4
## 2 2 1 1 3.10 4.166667 3.6 1.8
## 3 3 2 1 3.05 1.833333 3.3 2.2
## 4 4 8 2 2.30 5.166667 3.2 2.2
## 5 5 1 1 3.10 3.666667 3.5 3.2
## 6 6 8 2 3.35 4.000000 2.9 3.4str(d)## 'data.frame': 3182 obs. of 7 variables:
## $ ResponseId: int 1 2 3 4 5 6 7 8 9 10 ...
## $ politics : int 2 1 2 8 1 8 4 2 8 4 ...
## $ sex : int 2 1 1 2 1 2 2 2 2 2 ...
## $ moa : num 3.2 3.1 3.05 2.3 3.1 3.35 3.65 3.7 3.55 2.95 ...
## $ swb : num 4.33 4.17 1.83 5.17 3.67 ...
## $ stress : num 3.3 3.6 3.3 3.2 3.5 2.9 3.2 3 2.9 3.2 ...
## $ mindful : num 2.4 1.8 2.2 2.2 3.2 ...# use as.factor() command to make sure your categorical variables are recognized as such
d$ResponseId <- as.factor(d$ResponseId)
d$politics <- as.factor(d$politics)
d$sex <- as.factor(d$sex)10.2 Univariate Normality
describe(d)## vars n mean sd median trimmed mad min max range
## ResponseId* 1 3182 1591.50 918.71 1591.50 1591.50 1179.41 1.00 3182 3181.00
## politics* 2 3177 4.20 2.25 4.00 4.10 2.97 1.00 8 7.00
## sex* 3 3178 1.77 0.46 2.00 1.81 0.00 1.00 3 2.00
## moa 4 3006 3.28 0.45 3.35 3.31 0.44 1.30 4 2.70
## swb 5 3178 4.47 1.32 4.67 4.53 1.48 1.00 7 6.00
## stress 6 3175 3.27 0.41 3.30 3.26 0.44 1.00 5 4.00
## mindful 7 3173 3.71 0.84 3.73 3.71 0.79 1.13 6 4.87
## skew kurtosis se
## ResponseId* 0.00 -1.20 16.29
## politics* 0.43 -1.03 0.04
## sex* -0.73 -0.16 0.01
## moa -0.66 0.07 0.01
## swb -0.36 -0.46 0.02
## stress -0.16 2.67 0.01
## mindful -0.06 -0.13 0.0110.3 Histograms
# use the hist() command to create a histogram for your continuous variables
hist(d$moa)
hist(d$swb)
hist(d$stress)
hist(d$mindful)
# use the table() command to create a table for your categorical variables (other than your ID variable)
table(d$politics, useNA = "always")##
## 1 2 3 4 5 6 7 8 <NA>
## 235 772 373 568 308 332 57 532 5table(d$sex, useNA = "always")##
## 1 2 3 <NA>
## 792 2332 54 411 Missing Data
# use the gg_miss_upset() command to visualize your missing data
gg_miss_upset(d, nsets = "7")
# create a new dataframe with only your complete cases/observations
d2 <- na.omit(d)12 Crosstabs & Scatterplots
12.1 Crosstabs
# use the cross_cases() command to create a crosstab of your categorical variables
cross_cases(d2, politics, sex)| sex | |||
|---|---|---|---|
| 1 | 2 | 3 | |
| politics | |||
| 1 | 41 | 163 | 17 |
| 2 | 141 | 567 | 23 |
| 3 | 93 | 258 | 2 |
| 4 | 144 | 376 | 5 |
| 5 | 104 | 196 | 1 |
| 6 | 92 | 224 | |
| 7 | 23 | 28 | |
| 8 | 111 | 377 | 4 |
| #Total cases | 749 | 2189 | 52 |
12.2 Scatterplots
# use the plot() command to create scatterplots of your continuous variables
plot(d2$moa, d2$swb,
main="Scatterplot of Markers of Adulthood and Social Well-Being",
xlab = "MOA",
ylab = "SWB")
plot(d2$moa, d2$stress,
main="Scatterplot of Markers of Adulthood and Perceived Stress",
xlab = "MOA",
ylab = "Stress")
plot(d2$moa, d2$mindful,
main="Scatterplot of Markers of Adulthood and Mindfulness",
xlab = "MOA",
ylab = "Mindfulness")
plot(d2$swb, d2$mindful,
main="Scatterplot of Social Well-Being and Mindfulness",
xlab = "SWB",
ylab = "Mindfulness")
plot(d2$swb, d2$stress,
main="Scatterplot of Social Well-Being and Perceived Stress",
xlab = "SWB",
ylab = "Stress")
plot(d2$stress, d2$mindful,
main="Scatterplot of Stress and Mindfulness",
xlab = "Stress",
ylab = "Mindfulness")
12.3 Boxplots
# use the boxplot() command to create boxplots of your continuous and categorical variables
boxplot(d2$moa~d2$politics,
main="Boxplot of Politics and Markers of Adulthood",
xlab = "Politics",
ylab = "MOA")
boxplot(d2$swb~d2$politics,
main="Boxplot of Politics and Social Well-Being",
xlab = "Politics",
ylab = "SWB")
boxplot(d2$mindful~d2$politics,
main="Boxplot of Politics and Mindfulness",
xlab = "Politics",
ylab = "Mindfulness")
boxplot(d2$stress~d2$politics,
main="Boxplot of Politics and Perceived Stress",
xlab = "Politics",
ylab = "Stress")
boxplot(d2$moa~d2$sex,
main="Boxplot of Sex and Markers of Adulthood",
xlab = "Sex",
ylab = "MOA")
boxplot(d2$swb~d2$sex,
main="Boxplot of Sex and Social Well-Being",
xlab = "Sex",
ylab = "SWB")
boxplot(d2$mindful~d2$sex,
main="Boxplot of Sex and Mindfulness",
xlab = "Sex",
ylab = "Mindfulness")
boxplot(d2$stress~d2$sex,
main="Boxplot of Sex and Perceived Stress",
xlab = "Sex",
ylab = "Stress")
13 Reflection
Do your continuous variables meet the criteria for univariate normality? (Skew and kurtosis should be between -2 and +2).If they are skewed, identify whether most participants’ responses are below the mean or above the mean. If they are kurtotic, identify whether outliers are more or less likely.
Most participants’ responses fall within the criteria of univariate normality. The exception occurs on the stress variable kurtosis. At 2.67, it falls outside the preferred range. It is expected that this kurtosis is the likely result of an outlier(s).
Do you have any missing data? Once you have removed the cases/participants with missing data, what is your total sample size? Please discuss how much data is missing and whether it’s due to survey design or individual non-response.
Yes, there is missing data. After omitting 192 missing data, the sample size decreased by 6%. Almost all of these missing data points derived from the Markers of Adulthood section, which is at the beginning of the survey. This indicates item non-response.