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Let’s pull up the Facebook dataset for this.
Facebook<-read.delim(file="full_facebook_set.txt", header=TRUE)
This is the full dataset, which includes all the variables I collected. I don’t want to run analyses on all variables, so I’ll pull out the ones most important for this blog post demonstration.
smallFB<-Facebook[,c(1:2,77:80,105:116,122,133:137,170,187)]
First, I’ll run descriptives on this smaller data frame by gender.
library(psych) ## Warning: package 'psych' was built under R version 3.4.4 describeBy(smallFB,smallFB$gender) ## ## Descriptive statistics by group ## group: 0 ## vars n mean sd median trimmed mad min ## RespondentId 1 73 164647.77 1711.78 164943.0 164587.37 2644.96 162373.0 ## gender 2 73 0.00 0.00 0.0 0.00 0.00 0.0 ## Rumination 3 73 37.66 14.27 37.0 37.41 13.34 8.0 ## DepRelat 4 73 21.00 7.86 21.0 20.95 5.93 4.0 ## Brood 5 73 8.49 3.76 9.0 8.42 2.97 1.0 ## Reflect 6 73 8.16 4.44 8.0 8.24 4.45 0.0 ## SavorPos 7 73 64.30 10.93 65.0 64.92 8.90 27.0 ## SavorNeg 8 73 33.30 11.48 33.0 33.08 13.34 12.0 ## SavorTot 9 73 31.00 20.15 34.0 31.15 19.27 -10.0 ## AntPos 10 73 20.85 3.95 21.0 20.93 4.45 10.0 ## AntNeg 11 73 11.30 4.23 11.0 11.22 4.45 4.0 ## AntTot 12 73 9.55 6.90 10.0 9.31 7.41 -3.0 ## MomPos 13 73 21.68 3.95 22.0 21.90 2.97 9.0 ## MomNeg 14 73 11.45 4.63 11.0 11.41 5.93 4.0 ## MomTot 15 73 10.23 7.63 11.0 10.36 8.90 -11.0 ## RemPos 16 73 21.77 4.53 23.0 22.20 4.45 8.0 ## RemNeg 17 73 10.55 4.39 9.0 10.27 4.45 4.0 ## RemTot 18 73 11.22 8.05 14.0 11.68 7.41 -8.0 ## LifeSat 19 73 24.63 6.80 25.0 24.93 7.41 10.0 ## Extravert 20 73 4.32 1.58 4.5 4.33 1.48 1.5 ## Agreeable 21 73 4.79 1.08 5.0 4.85 1.48 1.0 ## Conscient 22 73 5.14 1.34 5.0 5.19 1.48 2.0 ## EmotStab 23 73 5.10 1.22 5.0 5.15 1.48 1.0 ## OpenExp 24 73 5.11 1.29 5.5 5.20 1.48 2.0 ## Health 25 73 28.77 19.56 25.0 26.42 17.79 0.0 ## Depression 26 73 10.26 7.27 9.0 9.56 5.93 0.0 ## max range skew kurtosis se ## RespondentId 168279 5906.0 0.21 -1.36 200.35 ## gender 0 0.0 NaN NaN 0.00 ## Rumination 71 63.0 0.12 -0.53 1.67 ## DepRelat 42 38.0 0.10 -0.04 0.92 ## Brood 17 16.0 0.15 -0.38 0.44 ## Reflect 19 19.0 -0.12 -0.69 0.52 ## SavorPos 84 57.0 -0.69 0.76 1.28 ## SavorNeg 57 45.0 0.14 -0.95 1.34 ## SavorTot 72 82.0 -0.17 -0.75 2.36 ## AntPos 28 18.0 -0.24 -0.46 0.46 ## AntNeg 22 18.0 0.27 -0.55 0.49 ## AntTot 24 27.0 0.11 -0.76 0.81 ## MomPos 28 19.0 -0.69 0.55 0.46 ## MomNeg 22 18.0 0.08 -0.98 0.54 ## MomTot 24 35.0 -0.25 -0.55 0.89 ## RemPos 28 20.0 -0.88 0.35 0.53 ## RemNeg 22 18.0 0.56 -0.66 0.51 ## RemTot 24 32.0 -0.53 -0.77 0.94 ## LifeSat 35 25.0 -0.37 -0.84 0.80 ## Extravert 7 5.5 -0.09 -0.93 0.19 ## Agreeable 7 6.0 -0.60 1.04 0.13 ## Conscient 7 5.0 -0.24 -0.98 0.16 ## EmotStab 7 6.0 -0.60 0.28 0.14 ## OpenExp 7 5.0 -0.49 -0.55 0.15 ## Health 91 91.0 1.13 1.14 2.29 ## Depression 36 36.0 1.02 0.95 0.85 ## -------------------------------------------------------- ## group: 1 ## vars n mean sd median trimmed mad ## RespondentId 1 184 164373.49 1515.34 164388.00 164253.72 1891.80 ## gender 2 184 1.00 0.00 1.00 1.00 0.00 ## Rumination 3 184 38.09 15.28 40.00 38.16 17.05 ## DepRelat 4 184 21.67 8.78 21.00 21.66 8.90 ## Brood 5 184 8.57 4.14 8.50 8.47 3.71 ## Reflect 6 184 7.84 4.06 8.00 7.73 4.45 ## SavorPos 7 184 67.22 9.63 68.00 67.71 8.90 ## SavorNeg 8 184 29.75 11.62 27.50 28.72 9.64 ## SavorTot 9 184 37.47 19.30 40.00 38.66 20.02 ## AntPos 10 184 22.18 3.37 23.00 22.28 2.97 ## AntNeg 11 184 10.10 4.44 9.00 9.78 4.45 ## AntTot 12 184 12.08 6.85 14.00 12.36 5.93 ## MomPos 13 184 22.28 3.88 23.00 22.59 2.97 ## MomNeg 14 184 10.60 4.88 9.50 10.13 5.19 ## MomTot 15 184 11.68 7.75 13.00 12.29 7.41 ## RemPos 16 184 22.76 3.85 23.00 23.10 2.97 ## RemNeg 17 184 9.05 3.79 8.00 8.68 2.97 ## RemTot 18 184 13.71 6.97 15.00 14.34 5.93 ## LifeSat 19 184 23.76 6.25 24.00 24.18 7.41 ## Extravert 20 184 4.66 1.57 5.00 4.74 1.48 ## Agreeable 21 184 5.22 1.06 5.50 5.26 1.48 ## Conscient 22 184 5.32 1.24 5.50 5.42 1.48 ## EmotStab 23 184 4.70 1.31 4.75 4.75 1.11 ## OpenExp 24 184 5.47 1.08 5.50 5.56 0.74 ## Health 25 184 32.54 16.17 30.00 31.43 16.31 ## Depression 26 184 12.19 8.48 9.00 11.09 5.93 ## min max range skew kurtosis se ## RespondentId 162350.0 167714 5364.0 0.46 -0.90 111.71 ## gender 1.0 1 0.0 NaN NaN 0.00 ## Rumination 3.0 74 71.0 -0.05 -0.60 1.13 ## DepRelat 0.0 42 42.0 0.00 -0.46 0.65 ## Brood 0.0 19 19.0 0.19 -0.62 0.31 ## Reflect 0.0 19 19.0 0.25 -0.48 0.30 ## SavorPos 33.0 84 51.0 -0.59 0.36 0.71 ## SavorNeg 12.0 64 52.0 0.79 0.25 0.86 ## SavorTot -18.0 72 90.0 -0.57 -0.10 1.42 ## AntPos 9.0 28 19.0 -0.49 0.41 0.25 ## AntNeg 4.0 22 18.0 0.63 -0.39 0.33 ## AntTot -8.0 24 32.0 -0.43 -0.48 0.50 ## MomPos 10.0 28 18.0 -0.81 0.54 0.29 ## MomNeg 4.0 24 20.0 0.81 -0.03 0.36 ## MomTot -13.0 24 37.0 -0.69 -0.03 0.57 ## RemPos 9.0 28 19.0 -0.87 0.81 0.28 ## RemNeg 4.0 21 17.0 0.83 0.33 0.28 ## RemTot -9.0 24 33.0 -0.82 0.50 0.51 ## LifeSat 8.0 35 27.0 -0.53 -0.32 0.46 ## Extravert 1.0 7 6.0 -0.36 -0.72 0.12 ## Agreeable 2.5 7 4.5 -0.27 -0.63 0.08 ## Conscient 1.0 7 6.0 -0.70 0.13 0.09 ## EmotStab 1.5 7 5.5 -0.35 -0.73 0.10 ## OpenExp 1.5 7 5.5 -0.91 0.62 0.08 ## Health 2.0 85 83.0 0.60 -0.05 1.19 ## Depression 0.0 39 39.0 1.14 0.66 0.62
In this dataset, I coded men as 0 and women as 1. The descriptive statistics table generated includes all scale and subscale scores, and gives me mean, standard deviation, median, a trimmed mean (dropping very low and very high values), median absolute deviation, minimum and maximum values, range, skewness, and kurtosis. I’d need to run t-tests to find out if differences were significant, but this still gives me some idea of how men and women might differ on these measures.
There are certain measures I included that we might hypothesize would show gender differences. For instance, some research suggests gender differences for rumination and depression. In addition to running descriptives by group, I might also want to display these differences in a violin plot. The psych package can quickly generate such a plot by group.
violinBy(smallFB,"Rumination","gender",grp.name=c("M","F"))
violinBy(smallFB,"Depression","gender",grp.name=c("M","F"))