# if you haven't run this code before, you'll need to download the below packages first
# you should see a prompt near the top of the page (in a yellow bar within the RStudio window)
# you can also use the packages tab to the right
library(naniar) # for the gg_miss-upset() commandData Prep
Load Libraries
Import Data
# # for the HW, you'll import the CSV file of your chosen dataset
df <- read.csv(file="Data/arc_data_final.csv", header=T)Viewing Data
# # these are commands useful for viewing a dataframe
# # you can also click the object in the environment tab to view it in a new window
names(df) [1] "X" "gender" "trans"
[4] "sexual_orientation" "ethnicity" "relationship_status"
[7] "age" "urban_rural" "income"
[10] "education" "employment" "treatment"
[13] "health" "mhealth" "sleep_hours"
[16] "exercise_cat" "pet" "covid_pos"
[19] "covid_neg" "big5_open" "big5_con"
[22] "big5_agr" "big5_neu" "big5_ext"
[25] "pswq" "iou" "mfq_26"
[28] "mfq_state" "rse" "school_covid_support"
[31] "school_att" "pas_covid" "pss"
[34] "phq" "gad" "edeq12"
[37] "brs" "swemws" "isolation"
[40] "support" head(df) X gender trans sexual_orientation ethnicity
1 1 female no Heterosexual/Straight White - British, Irish, other
2 20 male no Heterosexual/Straight White - British, Irish, other
3 30 female no Heterosexual/Straight White - British, Irish, other
4 31 female no Heterosexual/Straight White - British, Irish, other
5 32 <NA> <NA> <NA> <NA>
6 33 female no Heterosexual/Straight White - British, Irish, other
relationship_status age urban_rural
1 In a relationship/married and cohabiting <NA> city
2 Prefer not to say 1 under 18 city
3 Prefer not to say 1 under 18 city
4 In a relationship/married and cohabiting 4 between 36 and 45 town
5 <NA> <NA> <NA>
6 In a relationship/married and cohabiting 4 between 36 and 45 city
income education employment
1 3 high 6 graduate degree or higher 3 employed
2 <NA> prefer not to say 1 high school equivalent
3 <NA> 2 equivalent to high school completion 1 high school equivalent
4 2 middle 5 undergraduate degree 3 employed
5 <NA> <NA> <NA>
6 2 middle 6 graduate degree or higher 3 employed
treatment health mhealth
1 no psychological disorders something else or not applicable none or NA
2 in treatment something else or not applicable anxiety disorder
3 not in treatment something else or not applicable none or NA
4 no psychological disorders two conditions none or NA
5 <NA> <NA> none or NA
6 not in treatment something else or not applicable none or NA
sleep_hours exercise_cat pet covid_pos covid_neg
1 3 7-8 hours 1 less than 1 hour cat 0 0
2 2 5-6 hours 2 1-2 hours cat 0 0
3 3 7-8 hours 3 2-5 hours dog 0 0
4 2 5-6 hours 2 1-2 hours no pets 0 0
5 <NA> <NA> <NA> 0 0
6 3 7-8 hours 2 1-2 hours multiple types of pet 0 0
big5_open big5_con big5_agr big5_neu big5_ext pswq iou mfq_26
1 5.333333 6.000000 4.333333 6.000000 2.000000 2.3094514 3.185185 4.20
2 5.333333 3.333333 4.333333 6.666667 1.666667 0.8509744 4.000000 3.35
3 5.000000 5.333333 6.666667 4.000000 6.000000 -1.1235082 1.592593 4.65
4 6.000000 5.666667 4.666667 4.000000 5.000000 1.1626810 3.370370 4.65
5 NA NA NA NA NA NA NA NA
6 5.000000 6.000000 6.333333 2.666667 NA -0.3424552 1.703704 4.50
mfq_state rse school_covid_support school_att pas_covid pss phq
1 3.625 2.3 NA NA 3.222222 3.25 1.333333
2 3.000 1.6 NA NA 4.555556 3.75 3.333333
3 5.875 3.9 NA NA 3.333333 1.00 1.000000
4 4.000 1.7 NA NA 4.222222 3.25 2.333333
5 NA NA NA NA NA NA NA
6 4.625 3.9 NA NA 3.222222 2.00 1.111111
gad edeq12 brs swemws isolation support
1 1.857143 1.583333 NA 2.857143 2.25 2.500000
2 3.857143 1.833333 NA 2.285714 3.50 2.166667
3 1.142857 1.000000 NA 4.285714 1.00 5.000000
4 2.000000 1.666667 NA 3.285714 2.50 2.500000
5 NA NA NA NA NA NA
6 1.428571 1.416667 NA 4.000000 1.75 3.666667 str(df)'data.frame': 2073 obs. of 40 variables:
$ X : int 1 20 30 31 32 33 48 49 57 58 ...
$ gender : chr "female" "male" "female" "female" ...
$ trans : chr "no" "no" "no" "no" ...
$ sexual_orientation : chr "Heterosexual/Straight" "Heterosexual/Straight" "Heterosexual/Straight" "Heterosexual/Straight" ...
$ ethnicity : chr "White - British, Irish, other" "White - British, Irish, other" "White - British, Irish, other" "White - British, Irish, other" ...
$ relationship_status : chr "In a relationship/married and cohabiting" "Prefer not to say" "Prefer not to say" "In a relationship/married and cohabiting" ...
$ age : chr NA "1 under 18" "1 under 18" "4 between 36 and 45" ...
$ urban_rural : chr "city" "city" "city" "town" ...
$ income : chr "3 high" NA NA "2 middle" ...
$ education : chr "6 graduate degree or higher" "prefer not to say" "2 equivalent to high school completion" "5 undergraduate degree" ...
$ employment : chr "3 employed" "1 high school equivalent" "1 high school equivalent" "3 employed" ...
$ treatment : chr "no psychological disorders" "in treatment" "not in treatment" "no psychological disorders" ...
$ health : chr "something else or not applicable" "something else or not applicable" "something else or not applicable" "two conditions" ...
$ mhealth : chr "none or NA" "anxiety disorder" "none or NA" "none or NA" ...
$ sleep_hours : chr "3 7-8 hours" "2 5-6 hours" "3 7-8 hours" "2 5-6 hours" ...
$ exercise_cat : chr "1 less than 1 hour" "2 1-2 hours" "3 2-5 hours" "2 1-2 hours" ...
$ pet : chr "cat" "cat" "dog" "no pets" ...
$ covid_pos : int 0 0 0 0 0 0 0 0 0 0 ...
$ covid_neg : int 0 0 0 0 0 0 0 0 0 0 ...
$ big5_open : num 5.33 5.33 5 6 NA ...
$ big5_con : num 6 3.33 5.33 5.67 NA ...
$ big5_agr : num 4.33 4.33 6.67 4.67 NA ...
$ big5_neu : num 6 6.67 4 4 NA ...
$ big5_ext : num 2 1.67 6 5 NA ...
$ pswq : num 2.309 0.851 -1.124 1.163 NA ...
$ iou : num 3.19 4 1.59 3.37 NA ...
$ mfq_26 : num 4.2 3.35 4.65 4.65 NA 4.5 NA 4.3 5.25 4.45 ...
$ mfq_state : num 3.62 3 5.88 4 NA ...
$ rse : num 2.3 1.6 3.9 1.7 NA 3.9 NA 2.4 1.8 NA ...
$ school_covid_support: num NA NA NA NA NA NA NA NA NA NA ...
$ school_att : num NA NA NA NA NA NA NA NA NA NA ...
$ pas_covid : num 3.22 4.56 3.33 4.22 NA ...
$ pss : num 3.25 3.75 1 3.25 NA 2 NA 2 4 1.25 ...
$ phq : num 1.33 3.33 1 2.33 NA ...
$ gad : num 1.86 3.86 1.14 2 NA ...
$ edeq12 : num 1.58 1.83 1 1.67 NA ...
$ brs : num NA NA NA NA NA NA NA NA NA NA ...
$ swemws : num 2.86 2.29 4.29 3.29 NA ...
$ isolation : num 2.25 3.5 1 2.5 NA 1.75 NA 2 1.25 1 ...
$ support : num 2.5 2.17 5 2.5 NA ...Subsetting Data
# # use the codebook you created in the codebook activity to get the names of your variables (first column)
# # enter this list of names in the select=c() argument to subset those columns from the dataframe
d <- subset(df, select=c(pet, mhealth, iou, rse, phq, pss))Missing Data
# # use the gg_miss_upset() command for a visualization of your missing data
gg_miss_upset(d, nsets = 6)
#
# # use the na.omit() command to create a new dataframe in which any participants with missing data are dropped from the dataframe
d2 <- na.omit(d)
#
# # use a bit of math to see what percentage of participants had missing data
2073-1201[1] 872872/2073[1] 0.4206464Exporting Data
# # last step is to export the data after you've dropped NAs
write.csv(d2, file="Data/labdata.csv", row.names = F)
# NAME MYDATA INSTEAD OF LABDATA ON HWWrite-Up
In this section, you should create a write-up of what you did, using language that would be suitable for a manuscript. Make sure you include: selecting six variables to focus on, dropping participants with missing data, your percentage of how many participants were dropped, and your final number of participants. I have given you a template you can follow below – you should delete the other text in this section and only include your write-up.
Remember – this should be writing appropriate for a manuscript! We use shortened abbreviations when referring to variables in R, but these labels don’t make sense to include in your manuscript. You should include something more descriptive for your writeup.
We selected six variables from the [placeholder] dataset to focus on in our analysis: [placeholder]. Participants with missing data ([placeholder]%) in these six variables were dropped from our analysis, leaving us a final sample of n = [placeholder].