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(psych) # for the describe() command
library(naniar) # for the gg_miss-upset() command
library(expss) # for the cross_cases() command
## Loading required package: maditr
## 
## To select rows from data: rows(mtcars, am==0)
## 
## Attaching package: 'expss'
## The following object is masked from 'package:naniar':
## 
##     is_na

Import Data

# for the lab, you'll import the CSV file you downloaded along with the current file we're working in (an RMD file)
# for the homework, you'll download the CSV file from your chosen README page (should be titled arc_data_final.csv or eammi2_data_final.csv)
df <- read.csv(file="HWData/eammi2_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] "ResponseId"       "gender"           "race_rc"          "age"             
##  [5] "income"           "edu"              "sibling"          "party_rc"        
##  [9] "disability"       "marriage5"        "phys_sym"         "pipwd"           
## [13] "moa_independence" "moa_role"         "moa_safety"       "moa_maturity"    
## [17] "idea"             "swb"              "mindful"          "belong"          
## [21] "efficacy"         "support"          "socmeduse"        "usdream"         
## [25] "npi"              "exploit"          "stress"
head(df)
##          ResponseId gender race_rc                 age         income
## 1 R_BJN3bQqi1zUMid3      f   white 1 between 18 and 25          1 low
## 2 R_2TGbiBXmAtxywsD      m   white 1 between 18 and 25          1 low
## 3 R_12G7bIqN2wB2N65      m   white 1 between 18 and 25 rather not say
## 4 R_39pldNoon8CePfP      f   other 1 between 18 and 25 rather not say
## 5 R_1QiKb2LdJo1Bhvv      m   white 1 between 18 and 25       2 middle
## 6 R_pmwDTZyCyCycXwB      f   white 1 between 18 and 25 rather not say
##                            edu              sibling    party_rc  disability
## 1       2 Currently in college at least one sibling    democrat        <NA>
## 2 5 Completed Bachelors Degree at least one sibling independent        <NA>
## 3       2 Currently in college at least one sibling  apolitical psychiatric
## 4       2 Currently in college at least one sibling  apolitical        <NA>
## 5       2 Currently in college at least one sibling  apolitical        <NA>
## 6       2 Currently in college at least one sibling  apolitical        <NA>
##                                 marriage5                phys_sym    pipwd
## 1 are currently divorced from one another high number of symptoms       NA
## 2    are currently married to one another high number of symptoms       NA
## 3    are currently married to one another high number of symptoms 2.333333
## 4    are currently married to one another high number of symptoms       NA
## 5    are currently married to one another  low number of symptoms       NA
## 6    are currently married to one another high number of symptoms       NA
##   moa_independence moa_role moa_safety moa_maturity  idea      swb mindful
## 1         3.666667 3.000000       2.75     3.666667 3.750 4.333333     2.4
## 2         3.666667 2.666667       3.25     3.333333 3.875 4.166667     1.8
## 3         3.500000 2.500000       3.00     3.666667 3.750 1.833333     2.2
## 4         3.000000 2.000000       1.25     3.000000 3.750 5.166667     2.2
## 5         3.833333 2.666667       2.25     3.666667 3.500 3.666667     3.2
## 6         3.500000 3.333333       2.50     4.000000 3.250 4.000000     3.4
##   belong efficacy  support socmeduse
## 1    2.8      3.4 6.000000        47
## 2    4.2      3.4 6.750000        23
## 3    3.6      2.2 5.166667        34
## 4    4.0      2.8 5.583333        35
## 5    3.4      3.0 6.000000        37
## 6    4.2      2.4 4.500000        13
##                                                           usdream        npi
## 1               american dream is important and achievable for me 0.69230769
## 2               american dream is important and achievable for me 0.15384615
## 3 american dream is not important and maybe not achievable for me 0.07692308
## 4 american dream is not important and maybe not achievable for me 0.07692308
## 5                            not sure if american dream important 0.76923077
## 6 american dream is not important and maybe not achievable for me 0.23076923
##    exploit stress
## 1 2.000000    3.3
## 2 3.666667    3.3
## 3 4.333333    4.0
## 4 1.666667    3.2
## 5 4.000000    3.1
## 6 1.333333    3.5
str(df)
## 'data.frame':    3182 obs. of  27 variables:
##  $ ResponseId      : chr  "R_BJN3bQqi1zUMid3" "R_2TGbiBXmAtxywsD" "R_12G7bIqN2wB2N65" "R_39pldNoon8CePfP" ...
##  $ gender          : chr  "f" "m" "m" "f" ...
##  $ race_rc         : chr  "white" "white" "white" "other" ...
##  $ age             : chr  "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" "1 between 18 and 25" ...
##  $ income          : chr  "1 low" "1 low" "rather not say" "rather not say" ...
##  $ edu             : chr  "2 Currently in college" "5 Completed Bachelors Degree" "2 Currently in college" "2 Currently in college" ...
##  $ sibling         : chr  "at least one sibling" "at least one sibling" "at least one sibling" "at least one sibling" ...
##  $ party_rc        : chr  "democrat" "independent" "apolitical" "apolitical" ...
##  $ disability      : chr  NA NA "psychiatric" NA ...
##  $ marriage5       : chr  "are currently divorced from one another" "are currently married to one another" "are currently married to one another" "are currently married to one another" ...
##  $ phys_sym        : chr  "high number of symptoms" "high number of symptoms" "high number of symptoms" "high number of symptoms" ...
##  $ pipwd           : num  NA NA 2.33 NA NA ...
##  $ moa_independence: num  3.67 3.67 3.5 3 3.83 ...
##  $ moa_role        : num  3 2.67 2.5 2 2.67 ...
##  $ moa_safety      : num  2.75 3.25 3 1.25 2.25 2.5 4 3.25 2.75 3.5 ...
##  $ moa_maturity    : num  3.67 3.33 3.67 3 3.67 ...
##  $ idea            : num  3.75 3.88 3.75 3.75 3.5 ...
##  $ swb             : num  4.33 4.17 1.83 5.17 3.67 ...
##  $ mindful         : num  2.4 1.8 2.2 2.2 3.2 ...
##  $ belong          : num  2.8 4.2 3.6 4 3.4 4.2 3.9 3.6 2.9 2.5 ...
##  $ efficacy        : num  3.4 3.4 2.2 2.8 3 2.4 2.3 3 3 3.7 ...
##  $ support         : num  6 6.75 5.17 5.58 6 ...
##  $ socmeduse       : int  47 23 34 35 37 13 37 43 37 29 ...
##  $ usdream         : chr  "american dream is important and achievable for me" "american dream is important and achievable for me" "american dream is not important and maybe not achievable for me" "american dream is not important and maybe not achievable for me" ...
##  $ npi             : num  0.6923 0.1538 0.0769 0.0769 0.7692 ...
##  $ exploit         : num  2 3.67 4.33 1.67 4 ...
##  $ stress          : num  3.3 3.3 4 3.2 3.1 3.5 3.3 2.4 2.9 2.7 ...

Subsetting Data

# for the HW: 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(income, usdream, idea, swb, efficacy, support))

Recoding Variables

# categorical variables need to be recoded as factors
# the content of the variable will stay the same, but R will treat the variable differently at times
d$income <- as.factor(d$income)
d$usdream <- as.factor(d$usdream)
str(d)
## 'data.frame':    3182 obs. of  6 variables:
##  $ income  : Factor w/ 4 levels "1 low","2 middle",..: 1 1 4 4 2 4 1 1 3 4 ...
##  $ usdream : Factor w/ 5 levels "american dream is important and achievable for me",..: 1 1 3 3 5 3 2 4 1 3 ...
##  $ idea    : num  3.75 3.88 3.75 3.75 3.5 ...
##  $ swb     : num  4.33 4.17 1.83 5.17 3.67 ...
##  $ efficacy: num  3.4 3.4 2.2 2.8 3 2.4 2.3 3 3 3.7 ...
##  $ support : num  6 6.75 5.17 5.58 6 ...

Missing Data

I looked at the missing data in the Eammi2 dataset and found that about 1% of participants in our sample skipped at least one item. I am dropping these participants from the analysis per instructions, which is not advisable and runs the risk of dropping vulnerable groups or skewing results. However, I 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[-1], 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)

Exporting Data

# last step is to export the data after you've dropped NAs
# for the HW, the file you're exporting here is what you'll use for all future HW assignments (labs will use the files I provide you)
# make sure you give it a name that is memorable!
# and make sure you save it to your Data folder!
write.csv(d2, file="HWData/updated_eammi2_data.csv", row.names = F)

# since we've created a cleaned dataframe in d2, we'll use that for the rest of the lab/HW

Basic Statistics

Univariate Plots: Histograms & Tables

table(d2$income)
## 
##          1 low       2 middle         3 high rather not say 
##            876            879            534            852
table(d2$usdream)
## 
##               american dream is important and achievable for me 
##                                                            1454 
##     american dream is important but maybe not achievable for me 
##                                                             341 
## american dream is not important and maybe not achievable for me 
##                                                             585 
##        american dream is not important but is achievable for me 
##                                                             182 
##                            not sure if american dream important 
##                                                             579
hist(d2$idea)

hist(d2$swb)

hist(d2$efficacy)

hist(d2$support)

Univariate Normality

I analyzed the skew and kurtosis of our continuous variables and all were within the accepted range (-2/+2)

describe(d2)
##          vars    n mean   sd median trimmed  mad min max range  skew kurtosis
## income*     1 3141 2.43 1.16   2.00    2.42 1.48 1.0   4   3.0  0.15    -1.43
## usdream*    2 3141 2.39 1.54   2.00    2.24 1.48 1.0   5   4.0  0.62    -1.13
## idea        3 3141 3.57 0.38   3.62    3.62 0.37 1.0   4   3.0 -1.52     4.33
## swb         4 3141 4.48 1.32   4.67    4.53 1.48 1.0   7   6.0 -0.36    -0.45
## efficacy    5 3141 3.13 0.45   3.10    3.13 0.44 1.1   4   2.9 -0.24     0.44
## support     6 3141 5.54 1.13   5.75    5.66 0.99 0.0   7   7.0 -1.09     1.42
##            se
## income*  0.02
## usdream* 0.03
## idea     0.01
## swb      0.02
## efficacy 0.01
## support  0.02

Bivariate Plots

Crosstabs

cross_cases(d2, usdream, income)
 income 
 1 low   2 middle   3 high   rather not say 
 usdream 
   american dream is important and achievable for me  379 456 289 330
   american dream is important but maybe not achievable for me  111 81 59 90
   american dream is not important and maybe not achievable for me  186 153 63 183
   american dream is not important but is achievable for me  56 46 43 37
   not sure if american dream important  144 143 80 212
   #Total cases  876 879 534 852

Scatterplots

plot(d2$idea, d2$efficacy,
     main="Scatterplot of Scale of Emerging Adulthood and Self-efficacy",
     xlab = "Emerging Adulthood",
     ylab = "Self-efficacy")

plot(d2$swb, d2$support,
     main="Scatterplot of Satisfaction with Life and Perceived Social Support",
     xlab = "Satisfaction with Life",
     ylab = "Perceived Social Support")

Boxplots

boxplot(data=d2, swb~income,
##ALWAYS CONTINUOUS~CATEGORICAL OR R WILL DIE
        main="Boxplot of Income and Satisfaction with Life",
        xlab = "x (Income)",
        ylab = "Y (Satisfaction with Life)")

boxplot(data=d2, efficacy~usdream,
##ALWAYS CONTINUOUS~CATEGORICAL OR R WILL DIE
        main="Boxplot of Self-efficacy and Belief in American Dream",
        xlab = "x (Belief in American Dream)",
        ylab = "Y (Self-efficacy)")