P421 Lab - Data Prep
Heather Perkins
2024-05-01
Data Prep
Load Libraries
# if you haven't run this code before, you'll need to download the below packages first
# instructions on how to do this are included in the video
# but as a reminder, you use the packages tab to the right
library(naniar) # for the gg_miss-upset() command## Warning: package 'naniar' was built under R version 4.3.3Import Data
# for the lab, you'll import the CSV file you downloaded
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" "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_a"
## [40] "isolation_c" "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 pet covid_pos covid_neg big5_open
## 1 3 7-8 hours 0.0 cat 0 0 5.333333
## 2 2 5-6 hours 2.0 cat 0 0 5.333333
## 3 3 7-8 hours 3.0 dog 0 0 5.000000
## 4 2 5-6 hours 1.5 no pets 0 0 6.000000
## 5 <NA> NA <NA> 0 0 NA
## 6 3 7-8 hours 1.0 multiple types of pet 0 0 5.000000
## big5_con big5_agr big5_neu big5_ext pswq iou mfq_26 mfq_state rse
## 1 6.000000 4.333333 6.000000 2.000000 4.937500 3.185185 4.20 3.625 2.3
## 2 3.333333 4.333333 6.666667 1.666667 3.357143 4.000000 3.35 3.000 1.6
## 3 5.333333 6.666667 4.000000 6.000000 1.857143 1.592593 4.65 5.875 3.9
## 4 5.666667 4.666667 4.000000 5.000000 3.937500 3.370370 4.65 4.000 1.7
## 5 NA NA NA NA NA NA NA NA NA
## 6 6.000000 6.333333 2.666667 NA 2.625000 1.703704 4.50 4.625 3.9
## school_covid_support school_att pas_covid pss phq gad edeq12 brs
## 1 NA NA 3.222222 3.25 1.333333 1.857143 1.583333 NA
## 2 NA NA 4.555556 3.75 3.333333 3.857143 1.833333 NA
## 3 NA NA 3.333333 1.00 1.000000 1.142857 1.000000 NA
## 4 NA NA 4.222222 3.25 2.333333 2.000000 1.666667 NA
## 5 NA NA NA NA NA NA NA NA
## 6 NA NA 3.222222 2.00 1.111111 1.428571 1.416667 NA
## swemws isolation_a isolation_c support
## 1 2.857143 2.25 NA 2.500000
## 2 2.285714 NA 3.5 2.166667
## 3 4.285714 NA 1.0 5.000000
## 4 3.285714 2.50 NA 2.500000
## 5 NA NA NA NA
## 6 4.000000 1.75 NA 3.666667str(df)## 'data.frame': 2073 obs. of 41 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 : num 0 2 3 1.5 NA 1 NA 2 2 1.7 ...
## $ 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 4.94 3.36 1.86 3.94 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_a : num 2.25 NA NA 2.5 NA 1.75 NA 2 1.25 NA ...
## $ isolation_c : num NA 3.5 1 NA NA NA NA NA NA 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(age, pet, pswq, rse, phq))Missing Data
We looked at the missing data in our dataset, and found that about 54% of the participants in our sample skipped at least one item. We dropped these participants from our analysis, which is not advisable and runs the risk of dropping vulnerable groups or skewing results. However, we will proceed for the sake of this class using the reduced dataset.
# 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)
2073-957## [1] 11161116/2073## [1] 0.5383502Exporting Data
# last step is to export the data after you've dropped NAs
write.csv(d2, file="Data/mydata.csv", row.names = F)