Data Science Assignment 2
2024-03-02
Load R Package
# Make sure the `tidyverse` package is installed.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── 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 errorsLoad a data file
Here is a link to the survey questions I created using Google Forms. Please click here to access the survey.
df <- read_csv("Data Science Assignment 2 CSV File.csv")## Rows: 200 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Gender, Employment, Age, Income, Education
## dbl (11): SQ1, SQ2, SQ3, SQ4, SQ5, SQ6, SQ7, SQ8, SQ9, SQ10, SQ11
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Examine the loaded data set
# The data set has 64 rows and 16 columns
dim(df)## [1] 200 16# I also used this command to display the first six rows of my data set; this is useful for getting a quick glimpse of my data.
head(df)## # A tibble: 6 × 16
## Gender Employment Age Income Education SQ1 SQ2 SQ3 SQ4 SQ5 SQ6
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Male Employed fu… 35-44 <NA> Master's… 5 4 3 5 3 5
## 2 Male Employed fu… 35-44 <NA> Master's… 5 5 5 5 5 5
## 3 Male Employed pa… 25-34 <NA> Master's… 5 5 4 4 5 5
## 4 Male Employed fu… 35-44 <NA> Master's… 5 5 4 3 2 4
## 5 Male Employed pa… 25-34 Less … Master's… 5 5 4 4 5 5
## 6 Female Employed fu… 45-54 $75,0… Master's… 3 4 3 3 3 3
## # ℹ 5 more variables: SQ7 <dbl>, SQ8 <dbl>, SQ9 <dbl>, SQ10 <dbl>, SQ11 <dbl># Using the summary command will help determine missing values in the data set. Even though I did not see NA's using the summary(df) command, I did see incomplete data for three respondents using the head() command in the above R code chuck.
summary(df)## Gender Employment Age Income
## Length:200 Length:200 Length:200 Length:200
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Education SQ1 SQ2 SQ3
## Length:200 Min. :2.00 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:3.000
## Mode :character Median :5.00 Median :4.000 Median :4.000
## Mean :4.51 Mean :4.165 Mean :3.945
## 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.00 Max. :5.000 Max. :5.000
## SQ4 SQ5 SQ6 SQ7 SQ8
## Min. :1.000 Min. :1.0 Min. :1.00 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:3.0 1st Qu.:4.00 1st Qu.:4.000 1st Qu.:3.75
## Median :4.000 Median :4.0 Median :4.00 Median :5.000 Median :4.00
## Mean :4.115 Mean :3.9 Mean :4.09 Mean :4.305 Mean :3.94
## 3rd Qu.:5.000 3rd Qu.:5.0 3rd Qu.:5.00 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.0 Max. :5.00 Max. :5.000 Max. :5.00
## SQ9 SQ10 SQ11
## Min. :1.00 Min. :1 Min. :1.000
## 1st Qu.:3.00 1st Qu.:4 1st Qu.:4.000
## Median :4.00 Median :4 Median :4.000
## Mean :3.92 Mean :4 Mean :4.155
## 3rd Qu.:5.00 3rd Qu.:5 3rd Qu.:5.000
## Max. :5.00 Max. :5 Max. :5.000# The R command will eliminate rows with missing values.
df_no_na <- na.omit(df)
# 3 rows were eliminated due to missing values.
dim(df_no_na)## [1] 196 16summary(df_no_na)## Gender Employment Age Income
## Length:196 Length:196 Length:196 Length:196
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Education SQ1 SQ2 SQ3
## Length:196 Min. :2.0 Min. :1.000 Min. :1.000
## Class :character 1st Qu.:4.0 1st Qu.:4.000 1st Qu.:3.000
## Mode :character Median :5.0 Median :4.000 Median :4.000
## Mean :4.5 Mean :4.153 Mean :3.944
## 3rd Qu.:5.0 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.0 Max. :5.000 Max. :5.000
## SQ4 SQ5 SQ6 SQ7
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.000 Median :5.000
## Mean :4.112 Mean :3.903 Mean :4.077 Mean :4.316
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## SQ8 SQ9 SQ10 SQ11
## Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.00 1st Qu.:4.000
## Median :4.000 Median :4.000 Median :4.00 Median :4.000
## Mean :3.929 Mean :3.903 Mean :3.99 Mean :4.143
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.00 Max. :5.000I will use the unique() command for each categorical
variable (Gender, Employment, Age, Income, and Education) to identify
the values in each column.
unique(df_no_na$Gender)## [1] "Male" "Female"
## [3] "Prefer not to say" "Non-binary/third gender"unique(df_no_na$Employment)## [1] "Employed part-time" "Employed full-time" "Other"unique(df_no_na$Age)## [1] "25-34" "45-54" "55-64" "35-44" "18-24" "65+"unique(df_no_na$Income)## [1] "Less than $25000" "$75,000-$99,999+" "$25,000-$49,999" "$50,000-$74,999"unique(df_no_na$Education)## [1] "Master's Degree" "Doctorate/Professional Degree"
## [3] "Bachelor's Degree" "High School/GED"
## [5] "Some College/Associate Degree"I will use the factor() function to encode the following categorical variables and identify the levels of each.
factor(df_no_na$Gender)## [1] Male Female Male
## [4] Male Female Female
## [7] Male Female Male
## [10] Female Male Male
## [13] Male Female Male
## [16] Prefer not to say Female Male
## [19] Female Male Female
## [22] Female Male Female
## [25] Female Female Female
## [28] Male Male Male
## [31] Female Female Male
## [34] Male Male Male
## [37] Male Female Male
## [40] Female Female Non-binary/third gender
## [43] Male Female Male
## [46] Female Female Female
## [49] Female Female Male
## [52] Male Female Female
## [55] Female Female Female
## [58] Female Male Female
## [61] Male Male Female
## [64] Male Male Female
## [67] Female Male Female
## [70] Male Female Male
## [73] Male Male Female
## [76] Male Prefer not to say Female
## [79] Male Female Male
## [82] Female Female Male
## [85] Male Male Female
## [88] Male Prefer not to say Female
## [91] Male Female Male
## [94] Female Female Male
## [97] Female Female Female
## [100] Female Male Male
## [103] Male Female Female
## [106] Male Male Male
## [109] Male Male Female
## [112] Male Female Female
## [115] Non-binary/third gender Male Female
## [118] Male Female Female
## [121] Female Female Female
## [124] Male Male Female
## [127] Female Female Female
## [130] Female Female Male
## [133] Male Female Female
## [136] Female Female Female
## [139] Female Male Female
## [142] Male Male Female
## [145] Male Male Female
## [148] Female Male Female
## [151] Male Female Male
## [154] Male Male Female
## [157] Male Prefer not to say Female
## [160] Male Female Male
## [163] Female Female Male
## [166] Male Male Female
## [169] Male Prefer not to say Female
## [172] Male Female Male
## [175] Female Female Male
## [178] Female Female Female
## [181] Female Male Male
## [184] Male Female Female
## [187] Male Male Female
## [190] Female Female Female
## [193] Female Male Male
## [196] Female
## Levels: Female Male Non-binary/third gender Prefer not to sayfactor(df_no_na$Employment)## [1] Employed part-time Employed full-time Employed full-time
## [4] Employed full-time Employed full-time Employed full-time
## [7] Employed full-time Employed full-time Employed full-time
## [10] Employed full-time Employed full-time Employed full-time
## [13] Employed full-time Employed full-time Employed full-time
## [16] Employed full-time Employed full-time Employed full-time
## [19] Employed full-time Employed full-time Employed part-time
## [22] Other Employed full-time Employed part-time
## [25] Employed part-time Employed full-time Employed full-time
## [28] Employed full-time Employed full-time Employed full-time
## [31] Employed full-time Employed full-time Employed part-time
## [34] Employed full-time Employed full-time Employed full-time
## [37] Employed full-time Employed full-time Employed full-time
## [40] Employed full-time Employed part-time Employed full-time
## [43] Employed full-time Employed full-time Employed full-time
## [46] Employed full-time Employed full-time Employed full-time
## [49] Employed full-time Employed part-time Employed full-time
## [52] Employed full-time Employed full-time Employed part-time
## [55] Employed full-time Employed full-time Employed full-time
## [58] Employed full-time Employed full-time Employed full-time
## [61] Employed full-time Employed part-time Employed full-time
## [64] Employed full-time Employed full-time Employed full-time
## [67] Employed full-time Employed full-time Employed full-time
## [70] Employed full-time Employed full-time Employed full-time
## [73] Employed full-time Employed full-time Employed full-time
## [76] Employed full-time Employed full-time Employed full-time
## [79] Employed full-time Employed full-time Employed full-time
## [82] Employed part-time Other Employed full-time
## [85] Employed full-time Employed full-time Employed full-time
## [88] Employed full-time Employed full-time Employed full-time
## [91] Employed full-time Employed full-time Employed full-time
## [94] Employed part-time Other Employed full-time
## [97] Employed part-time Employed part-time Employed full-time
## [100] Employed full-time Employed full-time Employed full-time
## [103] Employed full-time Employed full-time Employed full-time
## [106] Employed part-time Employed full-time Employed full-time
## [109] Employed full-time Employed full-time Employed full-time
## [112] Employed full-time Employed full-time Employed part-time
## [115] Employed full-time Employed full-time Employed full-time
## [118] Employed full-time Employed full-time Employed full-time
## [121] Employed full-time Employed full-time Employed part-time
## [124] Employed full-time Employed full-time Employed full-time
## [127] Employed part-time Employed full-time Employed full-time
## [130] Employed full-time Employed full-time Employed full-time
## [133] Employed full-time Employed full-time Employed part-time
## [136] Employed full-time Employed full-time Employed full-time
## [139] Employed full-time Employed full-time Employed full-time
## [142] Employed full-time Employed part-time Employed full-time
## [145] Employed full-time Employed full-time Employed full-time
## [148] Employed full-time Employed full-time Employed full-time
## [151] Employed full-time Employed full-time Employed full-time
## [154] Employed full-time Employed full-time Employed full-time
## [157] Employed full-time Employed full-time Employed full-time
## [160] Employed full-time Employed full-time Employed full-time
## [163] Employed part-time Other Employed full-time
## [166] Employed full-time Employed full-time Employed full-time
## [169] Employed full-time Employed full-time Employed full-time
## [172] Employed full-time Employed full-time Employed full-time
## [175] Employed part-time Other Employed full-time
## [178] Employed part-time Employed part-time Employed full-time
## [181] Employed full-time Employed full-time Employed full-time
## [184] Employed full-time Employed full-time Employed full-time
## [187] Employed part-time Employed full-time Employed full-time
## [190] Employed full-time Employed full-time Employed full-time
## [193] Employed part-time Employed full-time Employed full-time
## [196] Employed full-time
## Levels: Employed full-time Employed part-time Otherfactor(df_no_na$Age)## [1] 25-34 45-54 55-64 35-44 55-64 45-54 35-44 45-54 35-44 35-44 35-44 35-44
## [13] 35-44 35-44 55-64 25-34 35-44 45-54 35-44 55-64 18-24 25-34 35-44 45-54
## [25] 35-44 45-54 35-44 35-44 45-54 35-44 55-64 45-54 25-34 35-44 35-44 35-44
## [37] 45-54 45-54 45-54 35-44 65+ 35-44 35-44 45-54 55-64 45-54 55-64 25-34
## [49] 55-64 65+ 25-34 35-44 35-44 18-24 45-54 25-34 35-44 18-24 35-44 55-64
## [61] 35-44 25-34 45-54 55-64 35-44 55-64 45-54 35-44 45-54 35-44 35-44 35-44
## [73] 35-44 35-44 35-44 55-64 25-34 35-44 45-54 35-44 55-64 18-24 25-34 35-44
## [85] 35-44 35-44 35-44 55-64 25-34 35-44 45-54 35-44 55-64 18-24 25-34 35-44
## [97] 45-54 35-44 45-54 35-44 35-44 45-54 35-44 55-64 45-54 25-34 35-44 35-44
## [109] 35-44 45-54 45-54 45-54 35-44 65+ 35-44 35-44 45-54 55-64 45-54 55-64
## [121] 25-34 55-64 65+ 25-34 35-44 35-44 18-24 45-54 25-34 35-44 18-24 25-34
## [133] 35-44 35-44 18-24 45-54 25-34 35-44 18-24 35-44 55-64 35-44 25-34 45-54
## [145] 55-64 35-44 55-64 45-54 35-44 45-54 35-44 35-44 35-44 35-44 35-44 35-44
## [157] 55-64 25-34 35-44 45-54 35-44 55-64 18-24 25-34 35-44 35-44 35-44 35-44
## [169] 55-64 25-34 35-44 45-54 35-44 55-64 18-24 25-34 35-44 45-54 35-44 45-54
## [181] 35-44 35-44 45-54 35-44 55-64 45-54 25-34 55-64 45-54 55-64 25-34 55-64
## [193] 65+ 25-34 35-44 35-44
## Levels: 18-24 25-34 35-44 45-54 55-64 65+factor(df_no_na$Income)## [1] Less than $25000 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [5] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [9] $25,000-$49,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [13] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $25,000-$49,999
## [17] $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [21] Less than $25000 $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999
## [25] $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [29] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [33] $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [37] $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+
## [41] $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999
## [45] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [49] $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999
## [53] $50,000-$74,999 Less than $25000 $75,000-$99,999+ $75,000-$99,999+
## [57] $50,000-$74,999 Less than $25000 $75,000-$99,999+ $75,000-$99,999+
## [61] $75,000-$99,999+ Less than $25000 $75,000-$99,999+ $75,000-$99,999+
## [65] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [69] $75,000-$99,999+ $25,000-$49,999 $75,000-$99,999+ $75,000-$99,999+
## [73] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [77] $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [81] $75,000-$99,999+ Less than $25000 $50,000-$74,999 $75,000-$99,999+
## [85] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [89] $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [93] $75,000-$99,999+ Less than $25000 $50,000-$74,999 $75,000-$99,999+
## [97] $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [101] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [105] $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+
## [109] $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999
## [113] $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+
## [117] $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [121] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999
## [125] $50,000-$74,999 $50,000-$74,999 Less than $25000 $75,000-$99,999+
## [129] $75,000-$99,999+ $50,000-$74,999 Less than $25000 $50,000-$74,999
## [133] $50,000-$74,999 $50,000-$74,999 Less than $25000 $75,000-$99,999+
## [137] $75,000-$99,999+ $50,000-$74,999 Less than $25000 $75,000-$99,999+
## [141] $75,000-$99,999+ $75,000-$99,999+ Less than $25000 $75,000-$99,999+
## [145] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [149] $75,000-$99,999+ $75,000-$99,999+ $25,000-$49,999 $75,000-$99,999+
## [153] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [157] $75,000-$99,999+ $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+
## [161] $75,000-$99,999+ $75,000-$99,999+ Less than $25000 $50,000-$74,999
## [165] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [169] $75,000-$99,999+ $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+
## [173] $75,000-$99,999+ $75,000-$99,999+ Less than $25000 $50,000-$74,999
## [177] $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+
## [181] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [185] $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+
## [189] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [193] $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999 $50,000-$74,999
## 4 Levels: $25,000-$49,999 $50,000-$74,999 ... Less than $25000factor(df_no_na$Education)## [1] Master's Degree Master's Degree
## [3] Doctorate/Professional Degree Doctorate/Professional Degree
## [5] Doctorate/Professional Degree Master's Degree
## [7] Master's Degree Master's Degree
## [9] Doctorate/Professional Degree Doctorate/Professional Degree
## [11] Master's Degree Doctorate/Professional Degree
## [13] Doctorate/Professional Degree Master's Degree
## [15] Bachelor's Degree High School/GED
## [17] Master's Degree Master's Degree
## [19] Doctorate/Professional Degree Master's Degree
## [21] Some College/Associate Degree Bachelor's Degree
## [23] Master's Degree Bachelor's Degree
## [25] Bachelor's Degree Master's Degree
## [27] Master's Degree Master's Degree
## [29] Master's Degree Master's Degree
## [31] Master's Degree Master's Degree
## [33] Master's Degree Master's Degree
## [35] Master's Degree Doctorate/Professional Degree
## [37] Bachelor's Degree Master's Degree
## [39] Doctorate/Professional Degree Doctorate/Professional Degree
## [41] Doctorate/Professional Degree Master's Degree
## [43] Doctorate/Professional Degree Master's Degree
## [45] Master's Degree Master's Degree
## [47] Doctorate/Professional Degree Master's Degree
## [49] Doctorate/Professional Degree Doctorate/Professional Degree
## [51] Bachelor's Degree Doctorate/Professional Degree
## [53] Master's Degree Some College/Associate Degree
## [55] Master's Degree Master's Degree
## [57] Master's Degree Bachelor's Degree
## [59] Master's Degree Doctorate/Professional Degree
## [61] Master's Degree Master's Degree
## [63] Master's Degree Doctorate/Professional Degree
## [65] Doctorate/Professional Degree Doctorate/Professional Degree
## [67] Master's Degree Master's Degree
## [69] Master's Degree Doctorate/Professional Degree
## [71] Doctorate/Professional Degree Master's Degree
## [73] Doctorate/Professional Degree Doctorate/Professional Degree
## [75] Master's Degree Bachelor's Degree
## [77] High School/GED Master's Degree
## [79] Master's Degree Doctorate/Professional Degree
## [81] Master's Degree Some College/Associate Degree
## [83] Bachelor's Degree Master's Degree
## [85] Doctorate/Professional Degree Doctorate/Professional Degree
## [87] Master's Degree Bachelor's Degree
## [89] High School/GED Master's Degree
## [91] Master's Degree Doctorate/Professional Degree
## [93] Master's Degree Some College/Associate Degree
## [95] Bachelor's Degree Master's Degree
## [97] Bachelor's Degree Bachelor's Degree
## [99] Master's Degree Master's Degree
## [101] Master's Degree Master's Degree
## [103] Master's Degree Master's Degree
## [105] Master's Degree Master's Degree
## [107] Master's Degree Master's Degree
## [109] Doctorate/Professional Degree Bachelor's Degree
## [111] Master's Degree Doctorate/Professional Degree
## [113] Doctorate/Professional Degree Doctorate/Professional Degree
## [115] Master's Degree Doctorate/Professional Degree
## [117] Master's Degree Master's Degree
## [119] Master's Degree Doctorate/Professional Degree
## [121] Master's Degree Doctorate/Professional Degree
## [123] Doctorate/Professional Degree Bachelor's Degree
## [125] Doctorate/Professional Degree Master's Degree
## [127] Some College/Associate Degree Master's Degree
## [129] Master's Degree Master's Degree
## [131] Bachelor's Degree Bachelor's Degree
## [133] Doctorate/Professional Degree Master's Degree
## [135] Some College/Associate Degree Master's Degree
## [137] Master's Degree Master's Degree
## [139] Bachelor's Degree Master's Degree
## [141] Doctorate/Professional Degree Master's Degree
## [143] Master's Degree Master's Degree
## [145] Doctorate/Professional Degree Doctorate/Professional Degree
## [147] Doctorate/Professional Degree Master's Degree
## [149] Master's Degree Master's Degree
## [151] Doctorate/Professional Degree Doctorate/Professional Degree
## [153] Master's Degree Doctorate/Professional Degree
## [155] Doctorate/Professional Degree Master's Degree
## [157] Bachelor's Degree High School/GED
## [159] Master's Degree Master's Degree
## [161] Doctorate/Professional Degree Master's Degree
## [163] Some College/Associate Degree Bachelor's Degree
## [165] Master's Degree Doctorate/Professional Degree
## [167] Doctorate/Professional Degree Master's Degree
## [169] Bachelor's Degree High School/GED
## [171] Master's Degree Master's Degree
## [173] Doctorate/Professional Degree Master's Degree
## [175] Some College/Associate Degree Bachelor's Degree
## [177] Master's Degree Bachelor's Degree
## [179] Bachelor's Degree Master's Degree
## [181] Master's Degree Master's Degree
## [183] Master's Degree Master's Degree
## [185] Master's Degree Master's Degree
## [187] Master's Degree Master's Degree
## [189] Master's Degree Doctorate/Professional Degree
## [191] Master's Degree Doctorate/Professional Degree
## [193] Doctorate/Professional Degree Bachelor's Degree
## [195] Doctorate/Professional Degree Master's Degree
## 5 Levels: Bachelor's Degree Doctorate/Professional Degree ... Some College/Associate Degreefactor(df_no_na$Income, levels = c("Less than $25000", "$25,000-$49,999", "$50,000-$74,999", "$75,000-$99,999+"))## [1] Less than $25000 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [5] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [9] $25,000-$49,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [13] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $25,000-$49,999
## [17] $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [21] Less than $25000 $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999
## [25] $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [29] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [33] $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [37] $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+
## [41] $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999
## [45] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [49] $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999
## [53] $50,000-$74,999 Less than $25000 $75,000-$99,999+ $75,000-$99,999+
## [57] $50,000-$74,999 Less than $25000 $75,000-$99,999+ $75,000-$99,999+
## [61] $75,000-$99,999+ Less than $25000 $75,000-$99,999+ $75,000-$99,999+
## [65] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [69] $75,000-$99,999+ $25,000-$49,999 $75,000-$99,999+ $75,000-$99,999+
## [73] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [77] $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [81] $75,000-$99,999+ Less than $25000 $50,000-$74,999 $75,000-$99,999+
## [85] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [89] $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [93] $75,000-$99,999+ Less than $25000 $50,000-$74,999 $75,000-$99,999+
## [97] $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+
## [101] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [105] $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+
## [109] $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+ $50,000-$74,999
## [113] $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+
## [117] $50,000-$74,999 $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [121] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999
## [125] $50,000-$74,999 $50,000-$74,999 Less than $25000 $75,000-$99,999+
## [129] $75,000-$99,999+ $50,000-$74,999 Less than $25000 $50,000-$74,999
## [133] $50,000-$74,999 $50,000-$74,999 Less than $25000 $75,000-$99,999+
## [137] $75,000-$99,999+ $50,000-$74,999 Less than $25000 $75,000-$99,999+
## [141] $75,000-$99,999+ $75,000-$99,999+ Less than $25000 $75,000-$99,999+
## [145] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [149] $75,000-$99,999+ $75,000-$99,999+ $25,000-$49,999 $75,000-$99,999+
## [153] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [157] $75,000-$99,999+ $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+
## [161] $75,000-$99,999+ $75,000-$99,999+ Less than $25000 $50,000-$74,999
## [165] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [169] $75,000-$99,999+ $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+
## [173] $75,000-$99,999+ $75,000-$99,999+ Less than $25000 $50,000-$74,999
## [177] $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999 $75,000-$99,999+
## [181] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [185] $75,000-$99,999+ $75,000-$99,999+ $50,000-$74,999 $75,000-$99,999+
## [189] $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+ $75,000-$99,999+
## [193] $75,000-$99,999+ $50,000-$74,999 $50,000-$74,999 $50,000-$74,999
## 4 Levels: Less than $25000 $25,000-$49,999 ... $75,000-$99,999+factor(df_no_na$Education, levels = c("High School/GED", "Some College/Associate Degree", "Bachelor's Degree", "Master's Degree", "Doctorate/Professional Degree", "Other"))## [1] Master's Degree Master's Degree
## [3] Doctorate/Professional Degree Doctorate/Professional Degree
## [5] Doctorate/Professional Degree Master's Degree
## [7] Master's Degree Master's Degree
## [9] Doctorate/Professional Degree Doctorate/Professional Degree
## [11] Master's Degree Doctorate/Professional Degree
## [13] Doctorate/Professional Degree Master's Degree
## [15] Bachelor's Degree High School/GED
## [17] Master's Degree Master's Degree
## [19] Doctorate/Professional Degree Master's Degree
## [21] Some College/Associate Degree Bachelor's Degree
## [23] Master's Degree Bachelor's Degree
## [25] Bachelor's Degree Master's Degree
## [27] Master's Degree Master's Degree
## [29] Master's Degree Master's Degree
## [31] Master's Degree Master's Degree
## [33] Master's Degree Master's Degree
## [35] Master's Degree Doctorate/Professional Degree
## [37] Bachelor's Degree Master's Degree
## [39] Doctorate/Professional Degree Doctorate/Professional Degree
## [41] Doctorate/Professional Degree Master's Degree
## [43] Doctorate/Professional Degree Master's Degree
## [45] Master's Degree Master's Degree
## [47] Doctorate/Professional Degree Master's Degree
## [49] Doctorate/Professional Degree Doctorate/Professional Degree
## [51] Bachelor's Degree Doctorate/Professional Degree
## [53] Master's Degree Some College/Associate Degree
## [55] Master's Degree Master's Degree
## [57] Master's Degree Bachelor's Degree
## [59] Master's Degree Doctorate/Professional Degree
## [61] Master's Degree Master's Degree
## [63] Master's Degree Doctorate/Professional Degree
## [65] Doctorate/Professional Degree Doctorate/Professional Degree
## [67] Master's Degree Master's Degree
## [69] Master's Degree Doctorate/Professional Degree
## [71] Doctorate/Professional Degree Master's Degree
## [73] Doctorate/Professional Degree Doctorate/Professional Degree
## [75] Master's Degree Bachelor's Degree
## [77] High School/GED Master's Degree
## [79] Master's Degree Doctorate/Professional Degree
## [81] Master's Degree Some College/Associate Degree
## [83] Bachelor's Degree Master's Degree
## [85] Doctorate/Professional Degree Doctorate/Professional Degree
## [87] Master's Degree Bachelor's Degree
## [89] High School/GED Master's Degree
## [91] Master's Degree Doctorate/Professional Degree
## [93] Master's Degree Some College/Associate Degree
## [95] Bachelor's Degree Master's Degree
## [97] Bachelor's Degree Bachelor's Degree
## [99] Master's Degree Master's Degree
## [101] Master's Degree Master's Degree
## [103] Master's Degree Master's Degree
## [105] Master's Degree Master's Degree
## [107] Master's Degree Master's Degree
## [109] Doctorate/Professional Degree Bachelor's Degree
## [111] Master's Degree Doctorate/Professional Degree
## [113] Doctorate/Professional Degree Doctorate/Professional Degree
## [115] Master's Degree Doctorate/Professional Degree
## [117] Master's Degree Master's Degree
## [119] Master's Degree Doctorate/Professional Degree
## [121] Master's Degree Doctorate/Professional Degree
## [123] Doctorate/Professional Degree Bachelor's Degree
## [125] Doctorate/Professional Degree Master's Degree
## [127] Some College/Associate Degree Master's Degree
## [129] Master's Degree Master's Degree
## [131] Bachelor's Degree Bachelor's Degree
## [133] Doctorate/Professional Degree Master's Degree
## [135] Some College/Associate Degree Master's Degree
## [137] Master's Degree Master's Degree
## [139] Bachelor's Degree Master's Degree
## [141] Doctorate/Professional Degree Master's Degree
## [143] Master's Degree Master's Degree
## [145] Doctorate/Professional Degree Doctorate/Professional Degree
## [147] Doctorate/Professional Degree Master's Degree
## [149] Master's Degree Master's Degree
## [151] Doctorate/Professional Degree Doctorate/Professional Degree
## [153] Master's Degree Doctorate/Professional Degree
## [155] Doctorate/Professional Degree Master's Degree
## [157] Bachelor's Degree High School/GED
## [159] Master's Degree Master's Degree
## [161] Doctorate/Professional Degree Master's Degree
## [163] Some College/Associate Degree Bachelor's Degree
## [165] Master's Degree Doctorate/Professional Degree
## [167] Doctorate/Professional Degree Master's Degree
## [169] Bachelor's Degree High School/GED
## [171] Master's Degree Master's Degree
## [173] Doctorate/Professional Degree Master's Degree
## [175] Some College/Associate Degree Bachelor's Degree
## [177] Master's Degree Bachelor's Degree
## [179] Bachelor's Degree Master's Degree
## [181] Master's Degree Master's Degree
## [183] Master's Degree Master's Degree
## [185] Master's Degree Master's Degree
## [187] Master's Degree Master's Degree
## [189] Master's Degree Doctorate/Professional Degree
## [191] Master's Degree Doctorate/Professional Degree
## [193] Doctorate/Professional Degree Bachelor's Degree
## [195] Doctorate/Professional Degree Master's Degree
## 6 Levels: High School/GED Some College/Associate Degree ... Otherdf2 <- df_no_na %>%
mutate(Gender = factor(df_no_na$Gender)) %>%
mutate(Employment = factor(df_no_na$Employment)) %>%
mutate(Age = factor(df_no_na$Age)) %>%
mutate(Income = factor(df_no_na$Income, levels = c("Less than $25000", "$25,000-$49,999", "$50,000-$74,999", "$75,000-$99,999+"))) %>%
mutate(Education = factor(df_no_na$Education, levels = c("High School/GED", "Some College/Associate Degree", "Bachelor's Degree", "Master's Degree", "Doctorate/Professional Degree", "Other")))
head(df2)## # A tibble: 6 × 16
## Gender Employment Age Income Education SQ1 SQ2 SQ3 SQ4 SQ5 SQ6
## <fct> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Male Employed pa… 25-34 Less … Master's… 5 5 4 4 5 5
## 2 Female Employed fu… 45-54 $75,0… Master's… 3 4 3 3 3 3
## 3 Male Employed fu… 55-64 $75,0… Doctorat… 4 2 2 2 4 4
## 4 Male Employed fu… 35-44 $75,0… Doctorat… 5 5 4 3 4 5
## 5 Female Employed fu… 55-64 $75,0… Doctorat… 5 5 4 4 4 4
## 6 Female Employed fu… 45-54 $75,0… Master's… 5 5 5 4 4 4
## # ℹ 5 more variables: SQ7 <dbl>, SQ8 <dbl>, SQ9 <dbl>, SQ10 <dbl>, SQ11 <dbl>summary(df2)## Gender Employment Age
## Female :102 Employed full-time:166 18-24:11
## Male : 87 Employed part-time: 25 25-34:26
## Non-binary/third gender: 2 Other : 5 35-44:84
## Prefer not to say : 5 45-54:40
## 55-64:30
## 65+ : 5
## Income Education SQ1
## Less than $25000: 14 High School/GED : 5 Min. :2.0
## $25,000-$49,999 : 8 Some College/Associate Degree: 8 1st Qu.:4.0
## $50,000-$74,999 : 44 Bachelor's Degree : 25 Median :5.0
## $75,000-$99,999+:130 Master's Degree :103 Mean :4.5
## Doctorate/Professional Degree: 55 3rd Qu.:5.0
## Other : 0 Max. :5.0
## SQ2 SQ3 SQ4 SQ5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.153 Mean :3.944 Mean :4.112 Mean :3.903
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## SQ6 SQ7 SQ8 SQ9 SQ10
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.00
## Median :4.000 Median :5.000 Median :4.000 Median :4.000 Median :4.00
## Mean :4.077 Mean :4.316 Mean :3.929 Mean :3.903 Mean :3.99
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## SQ11
## Min. :1.000
## 1st Qu.:4.000
## Median :4.000
## Mean :4.143
## 3rd Qu.:5.000
## Max. :5.000Descriptive Statistics
Frequency Table
apply(df2, 2, table)## $Gender
##
## Female Male Non-binary/third gender
## 102 87 2
## Prefer not to say
## 5
##
## $Employment
##
## Employed full-time Employed part-time Other
## 166 25 5
##
## $Age
##
## 18-24 25-34 35-44 45-54 55-64 65+
## 11 26 84 40 30 5
##
## $Income
##
## $25,000-$49,999 $50,000-$74,999 $75,000-$99,999+ Less than $25000
## 8 44 130 14
##
## $Education
##
## Bachelor's Degree Doctorate/Professional Degree
## 25 55
## High School/GED Master's Degree
## 5 103
## Some College/Associate Degree
## 8
##
## $SQ1
##
## 2 3 4 5
## 3 12 65 116
##
## $SQ2
##
## 1 2 3 4 5
## 9 8 16 74 89
##
## $SQ3
##
## 1 2 3 4 5
## 5 21 24 76 70
##
## $SQ4
##
## 1 2 3 4 5
## 5 6 31 74 80
##
## $SQ5
##
## 1 2 3 4 5
## 5 10 39 87 55
##
## $SQ6
##
## 1 2 3 4 5
## 5 8 18 101 64
##
## $SQ7
##
## 1 2 3 4 5
## 5 10 15 54 112
##
## $SQ8
##
## 1 2 3 4 5
## 10 15 25 75 71
##
## $SQ9
##
## 1 2 3 4 5
## 8 14 35 71 68
##
## $SQ10
##
## 1 2 3 4 5
## 5 19 21 79 72
##
## $SQ11
##
## 1 2 3 4 5
## 5 12 16 80 83Mean Values of each column
apply(df2, 2, mean)## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(newX[, i], ...): argument is not numeric or logical:
## returning NA## Gender Employment Age Income Education SQ1 SQ2
## NA NA NA NA NA NA NA
## SQ3 SQ4 SQ5 SQ6 SQ7 SQ8 SQ9
## NA NA NA NA NA NA NA
## SQ10 SQ11
## NA NA# Since Gender, Employment, Age, Income, and Education are non-numeric variables, we exclude them to compute mean correctly.
df3 <- df2 %>% select(-Gender, -Employment, -Age, -Income, -Education)
head(df3)## # A tibble: 6 × 11
## SQ1 SQ2 SQ3 SQ4 SQ5 SQ6 SQ7 SQ8 SQ9 SQ10 SQ11
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 5 5 4 4 5 5 5 5 5 5 5
## 2 3 4 3 3 3 3 4 3 3 3 4
## 3 4 2 2 2 4 4 2 4 2 2 4
## 4 5 5 4 3 4 5 4 4 3 4 5
## 5 5 5 4 4 4 4 4 4 4 4 4
## 6 5 5 5 4 4 4 5 4 4 4 5# We use the sort command to list the means listing the values from largest to smallest.
sort(apply(df3, 2, mean), decreasing = TRUE)## SQ1 SQ7 SQ2 SQ11 SQ4 SQ6 SQ10 SQ3
## 4.500000 4.316327 4.153061 4.142857 4.112245 4.076531 3.989796 3.943878
## SQ8 SQ5 SQ9
## 3.928571 3.903061 3.903061# Composite Score. For example, a 3.8 composite score indicates moderately comfortable with technology per person in the class.
df4 <- df2 %>%
mutate(composite_score = (SQ1+SQ2+SQ3+SQ4+SQ5+SQ6+SQ7+SQ8+SQ9+SQ10+SQ11)/11)
head(df4)## # A tibble: 6 × 17
## Gender Employment Age Income Education SQ1 SQ2 SQ3 SQ4 SQ5 SQ6
## <fct> <fct> <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Male Employed pa… 25-34 Less … Master's… 5 5 4 4 5 5
## 2 Female Employed fu… 45-54 $75,0… Master's… 3 4 3 3 3 3
## 3 Male Employed fu… 55-64 $75,0… Doctorat… 4 2 2 2 4 4
## 4 Male Employed fu… 35-44 $75,0… Doctorat… 5 5 4 3 4 5
## 5 Female Employed fu… 55-64 $75,0… Doctorat… 5 5 4 4 4 4
## 6 Female Employed fu… 45-54 $75,0… Master's… 5 5 5 4 4 4
## # ℹ 6 more variables: SQ7 <dbl>, SQ8 <dbl>, SQ9 <dbl>, SQ10 <dbl>, SQ11 <dbl>,
## # composite_score <dbl># We will look at a summary of the data
summary(df4)## Gender Employment Age
## Female :102 Employed full-time:166 18-24:11
## Male : 87 Employed part-time: 25 25-34:26
## Non-binary/third gender: 2 Other : 5 35-44:84
## Prefer not to say : 5 45-54:40
## 55-64:30
## 65+ : 5
## Income Education SQ1
## Less than $25000: 14 High School/GED : 5 Min. :2.0
## $25,000-$49,999 : 8 Some College/Associate Degree: 8 1st Qu.:4.0
## $50,000-$74,999 : 44 Bachelor's Degree : 25 Median :5.0
## $75,000-$99,999+:130 Master's Degree :103 Mean :4.5
## Doctorate/Professional Degree: 55 3rd Qu.:5.0
## Other : 0 Max. :5.0
## SQ2 SQ3 SQ4 SQ5
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:4.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :4.153 Mean :3.944 Mean :4.112 Mean :3.903
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## SQ6 SQ7 SQ8 SQ9 SQ10
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:4.00
## Median :4.000 Median :5.000 Median :4.000 Median :4.000 Median :4.00
## Mean :4.077 Mean :4.316 Mean :3.929 Mean :3.903 Mean :3.99
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## SQ11 composite_score
## Min. :1.000 Min. :1.727
## 1st Qu.:4.000 1st Qu.:3.795
## Median :4.000 Median :4.182
## Mean :4.143 Mean :4.088
## 3rd Qu.:5.000 3rd Qu.:4.636
## Max. :5.000 Max. :5.000Visualizing Data
# Box and Whisker Plot of Gender to Composite Score
df4 %>%
ggplot(aes(x=Gender, y=composite_score))+
geom_boxplot()+
labs(x="Gender", y="Composite Score")
# Box and Whisker Plot of Employment to Composite Score
df4 %>%
ggplot(aes(x=Employment, y=composite_score))+
geom_boxplot()+
labs(x="Employment", y="Composite Score")
# Box and Whisker Plot of Age to Composite Score
df4 %>%
ggplot(aes(x=Age, y=composite_score))+
geom_boxplot()+
labs(x="Age", y="Composite Score")
# Box and Whisker Plot of Income to Composite Score
df4 %>%
ggplot(aes(x=Income, y=composite_score))+
geom_boxplot()+
labs(x="Income", y="Composite Score")
# Histogram of participants answering survey question Number 5
df4 %>%
ggplot(aes(x=SQ5))+
geom_histogram(fill="white", color="black")+
labs(x="SQ5", y="Count", title="Number of Participants")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Histogram of participants answering survey question Number 6
df4 %>%
ggplot(aes(x=SQ6))+
geom_histogram(fill="white", color="black")+
labs(x="SQ6", y="Count", title="Number of Participants")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Box and Whisker Plot of Gender and how each person answered the Survey Question number 5
df4 %>%
ggplot(aes(x= Gender, y= SQ5))+
geom_boxplot()+
labs(x="Gender", y="Survey Question 5 Responses", title="Survey Question Number 5 across Gender")
Statistical Tests
# We will look at mean, median, and standard deviation of Survey Question 5
df4 %>%
group_by(Gender) %>%
summarize(mean = mean(SQ5),
median = median(SQ5),
sd = sd(SQ5))## # A tibble: 4 × 4
## Gender mean median sd
## <fct> <dbl> <dbl> <dbl>
## 1 Female 3.86 4 1.03
## 2 Male 4.02 4 0.849
## 3 Non-binary/third gender 3 3 0
## 4 Prefer not to say 3 3 0# We use the Shapiro test to see if the data is normally distributed
shapiro.test(df4$SQ5)##
## Shapiro-Wilk normality test
##
## data: df4$SQ5
## W = 0.84542, p-value = 3.68e-13My Response
H0: The Survey Questions #5 are normally distributed
HA: The Survey Questions $5 are NOT normally distributed
The p-value of the Shapiro-Wilk test for the survey questions is 3.68e-13 and the W statistic of 0.84542. The p - value of 3.68e-13 indicates that if the null hypothesis (H0) is true, there is approximately a 0.0006% chance to observe the values in SQ5 (very unlikely). Given our significance level (alpha) of 0.05, since p(3.68e-13) < alpha (0.05), we reject the null hypothesis. Thus, this suggests that the survey question 5 is NOT normally distributed. Therefore, we cannot use a t-test to examine the difference in the mean values.
# We use the Shapiro test to see if the data is normally distributed
shapiro.test(df4$SQ6)##
## Shapiro-Wilk normality test
##
## data: df4$SQ6
## W = 0.77563, p-value = 4.911e-16My Response
H0: The Survey Questions #6 are normally distributed
HA: The Survey Questions $6 are NOT normally distributed
The p-value of the Shapiro-Wilk test for the survey questions is 4.911e-16 and the W statistic of 0.77563. The p - value of 4.911e-16 indicates that if the null hypothesis (H0) is true, there is approximately a 0.00000040560% chance to observe the values in SQ6 (very unlikely). Given our significance level (alpha) of 0.05, since p(4.911e-16) < alpha (0.05), we reject the null hypothesis. Thus, this suggests that the survey question 5 is NOT normally distributed. Therefore, we cannot use a t-test to examine the difference in the mean values.
# Given that the data for both survey questions are not normally distributed, we will compare the medians instead using the Wilcox test.
wilcox.test(df4$SQ5, df4$SQ6)##
## Wilcoxon rank sum test with continuity correction
##
## data: df4$SQ5 and df4$SQ6
## W = 17071, p-value = 0.03981
## alternative hypothesis: true location shift is not equal to 0My Response
H0: No difference between survey questions 5 and 6 (same).
HA: There is a difference between survey questions 5 and 6.
The p-value of the Wilcoxon rank sum test is 0.03981 and the W statistic of 17071. The p - value of 0.03981 indicates that if the null hypothesis (H0) is true, there is approximately a 3.981% chance to observe the values in survey question 5 and 6 (highlt unlikely to occur). Given our significance level (alpha) of 0.05, since p(0.03981) < alpha (0.05), we reject the null hypothesis. Thus, this suggests that there IS a significant difference between survey question 5 and 6.
# Since I have 4 levels and not 2, I will use an ANOVA test to compare survey questions across Gender.
anova_result1 <- aov(SQ5 ~ Gender, data = df4)
summary(anova_result1)## Df Sum Sq Mean Sq F value Pr(>F)
## Gender 3 7.13 2.3752 2.682 0.0481 *
## Residuals 192 170.03 0.8856
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1My Response
H0: There is no difference in the mean responses for Survey Question 5 across the different gender groups. In other words, the mean value of SQ5 is the same across all gender groups.
HA: At least one gender’s group mean response to Survey Question 5 is different from the others.
In the ANOVA test, we compute an F statistic (2.682) to compare the variance between the groups to the variance within the groups. Our p - value, which estimates the probability of observing such ab F statistic under the null hypothesis, is 0.0481. This p- value is slightly less than our predetermined significanc level of 0.05, leading us to rejects the null hypothesis. Therefore, we conclude there iS sufficient evidence to suggest a difference in the mean for survey question 5 across the different gender groups.
Scores of Male versus Scores of Female
male_SQ5 <- df4 %>%
filter(Gender == "Male") %>%
pull(SQ5)
male_SQ5## [1] 5 4 4 4 5 4 5 4 5 4 4 4 2 4 3 5 5 4 5 4 3 2 4 3 4 5 2 5 4 4 4 5 4 5 4 5 4 4
## [39] 4 5 4 5 4 4 4 2 4 3 5 5 4 5 4 3 2 4 3 4 3 4 5 2 5 4 4 4 5 4 5 4 5 4 4 4 5 4
## [77] 5 4 4 4 2 4 3 5 4 3 4# We use the Shapiro test to see if the data is normally distributed
shapiro.test(male_SQ5)##
## Shapiro-Wilk normality test
##
## data: male_SQ5
## W = 0.80292, p-value = 1.977e-09My Response
H0: The male score values are normally distributed.
HA: The male score values are NOT normally distributed
The p-value of the Shapiro-Wilk test for the survey questions is 1.977e-09 and the W statistic of 0.80292. The p - value of 1.977e-09 indicates that if the null hypothesis (H0) is true, there is approximately a 0.0000001977% chance to observe the values in SQ6 (very unlikely). Given our significance level (alpha) of 0.05, since p(1.977e-09) < alpha (0.05), we reject the null hypothesis. Thus, this suggests that the male scores on Survey Question 5 are NOT normally distributed. Therefore, we cannot use a t-test to examine the difference in the mean values.
female_SQ5 <- df4 %>%
filter(Gender == "Female") %>%
pull(SQ5)
female_SQ5## [1] 3 4 4 4 5 4 5 1 4 5 4 3 5 5 4 2 3 4 3 4 5 3 5 4 3 3 4 5 3 4 4 5 3 4 4 4 5
## [38] 4 5 1 4 5 4 5 1 4 5 4 3 5 5 4 2 3 4 3 4 5 3 5 4 3 3 4 5 3 4 4 3 4 5 3 4 4
## [75] 5 3 4 4 4 5 4 5 1 4 5 4 5 1 4 5 4 3 5 5 4 2 5 3 5 4 3 3# We use the Shapiro test to see if the data is normally distributed
shapiro.test(female_SQ5)##
## Shapiro-Wilk normality test
##
## data: female_SQ5
## W = 0.83828, p-value = 3.472e-09My Response
H0: The female score values are normally distributed.
HA: The female score values are NOT normally distributed
The p-value of the Shapiro-Wilk test for the survey questions is 3.472e-09 and the W statistic of 0.83828. The p - value of 3.472e-09 indicates that if the null hypothesis (H0) is true, there is approximately a 0.0000003472% chance to observe the values in SQ6 (very unlikely). Given our significance level (alpha) of 0.05, since p(3.472e-09) < alpha (0.05), we reject the null hypothesis. Thus, this suggests that the male scores on Survey Question 5 are NOT normally distributed. Therefore, we cannot use a t-test to examine the difference in the mean values.
# Given that the groups are not normally distributed, we will compare the medians instead using the Wilcox test.
wilcox.test(male_SQ5, female_SQ5)##
## Wilcoxon rank sum test with continuity correction
##
## data: male_SQ5 and female_SQ5
## W = 4765, p-value = 0.3497
## alternative hypothesis: true location shift is not equal to 0My Response
H0: No difference between male and female scores (same).
HA: There is a difference between male and female scores.
The p-value of the Wilcoxon rank sum test is 0.3497 and the W statistic of 4765. The p - value of 0.3497 indicates that if the null hypothesis (H0) is true, there is approximately a 34.97% chance to observe the values in male_SQ5 (fairly likely to occur). Given our significance level (alpha) of 0.05, since p(0.3497) > alpha (0.05), we fail to reject the null hypothesis. Thus, this suggests that there IS NOT a significant difference between male and female scores. Given the warning about the presence of ties in our data, which complicates the interpretation of the Wilcoxon test results, we may need to consider alternative methods for further analysis.
# Since I have 4 levels and not 2, I will use an ANOVA test to compare the composite scores.
anova_result <- aov(composite_score ~ Gender, data = df4)
summary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Gender 3 15.47 5.156 10.76 1.44e-06 ***
## Residuals 192 92.02 0.479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1My Response
H0: There is no difference in the mean compsite scores across the gender groups. In other words, the mean value of compsotite scores is the same across all gender groups.
HA: At least one gender’s group mean composite score is different from the others.
In the ANOVA test, we compute an F statistic (10.76) to compare the variance between the groups to the variance within the groups. Our p - value, which estimates the probability of observing such ab F statistic under the null hypothesis, is 1.44e-06. This p- value is smaller than our predetermined significanc level of 0.05, leading us to rejects the null hypothesis. Therefore, we conclude there iS sufficient evidence to suggest a difference in the mean composite scores across the different gender groups.
Composite score versus degree
# I will use an ANOVA test again since I have 4 levels to compare the composite scores.
anova_results2 <- aov(composite_score ~ Education, data = df4)
summary(anova_results2)## Df Sum Sq Mean Sq F value Pr(>F)
## Education 4 20.39 5.098 11.18 3.6e-08 ***
## Residuals 191 87.09 0.456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1My Response
H0: There is no difference in the mean compsite scores across the education groups (degrees). In other words, the mean value of compsotite scores is the same across all degrees.
HA: The mean composite score is difference (somehow) acrossd degrees.
In the ANOVA test, we compute an F statistic (11.18) to compare the variance between the groups to the variance within the groups. Our p - value, which estimates the probability of observing such ab F statistic under the null hypothesis, is 3.6e-08. This p- value is much smaller than our predetermined significanc level of 0.05, leading us to rejects the null hypothesis. Therefore, we conclude there iS sufficient evidence to suggest a difference in the mean composite scores across the different educational levels (degrees).
# We will compute the effect size since the p value of 0.0662 gives us a marginal significance result given a the data set provided.
library(effectsize)
effectsize(anova_results2, type="omega")## For one-way between subjects designs, partial omega squared is
## equivalent to omega squared. Returning omega squared.## # Effect Size for ANOVA
##
## Parameter | Omega2 | 95% CI
## ---------------------------------
## Education | 0.17 | [0.09, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].Since omega squared of 0.17 is between 0.06 and 0.14 (0.06 <w2 < 0.14), we have a medium effect size respectively. In other words, omega squared indicates a medium difference in composite scores across the different education levels. It implies that education level has a noticeable impact on composite scores, a factor that might be worth considering for educational interventions or future research given a larger sample size.
# We will compute ANOVA TO compare the composite scores and income.
anova_result3 <- aov(composite_score ~ Income, data = df4)
summary(anova_result3)## Df Sum Sq Mean Sq F value Pr(>F)
## Income 3 8.43 2.8109 5.449 0.00129 **
## Residuals 192 99.05 0.5159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1My Response
H0: There is no difference in the mean compsite scores across different income groups (degrees). In other words, the mean value of compsotite scores is the same across all income.
HA: The mean composite score is difference (somehow) acrossd income.
In the ANOVA test, we compute an F statistic (5.449) to compare the variance between the income groups to the variance within the groups. Our p - value, which estimates the probability of observing such an F statistic under the null hypothesis, is 0.00129. Since this p- value (0.00129) is larger than our predetermined signifigance level of 0.05, we fail to reject the null hypothesis. Therefore, we conclude there iS not sufficient evidence to suggest a difference in the mean composite scores across the different income levels.
# We will compute the effect size.
library(effectsize)
effectsize(anova_result3, type="omega")## For one-way between subjects designs, partial omega squared is
## equivalent to omega squared. Returning omega squared.## # Effect Size for ANOVA
##
## Parameter | Omega2 | 95% CI
## ---------------------------------
## Income | 0.06 | [0.01, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].The omega squared value of 0.06 suggests a small effect size, indicating a modest association between income and the composite scores. The 95% confidence interval for this effect size ranges from 0.01 to 1.00, suggesting some variability in the possible strength of this assocation, Although the effect size is not 0, this indicates that the associatiion is relatively weak.
# We will compute ANOVA TO compare the composite scores and Age
anova_result4 <- aov(composite_score ~ Age, data = df4)
summary(anova_result4)## Df Sum Sq Mean Sq F value Pr(>F)
## Age 5 1.36 0.2723 0.487 0.785
## Residuals 190 106.12 0.5586My Response
H0: There is no difference in the mean compsite scores across different age groups (degrees). In other words, the mean value of compsotite scores is the same across all age groups.
HA: The mean composite score is difference (somehow) across age groups.
In the ANOVA test, we compute an F statistic (0.487) to compare the variance between the age groups to the variance within the groups. Our p - value, which estimates the probability of observing such an F statistic under the null hypothesis, is 0.785. Since this p- value ( 0.785) is larger than our predetermined signifigance level of 0.05, we fail to reject the null hypothesis. Therefore, we conclude there iS not sufficient evidence to suggest a difference in the mean composite scores across the different age levels.
# We will compute the effect size.
library(effectsize)
effectsize(anova_result4, type="omega")## For one-way between subjects designs, partial omega squared is
## equivalent to omega squared. Returning omega squared.## # Effect Size for ANOVA
##
## Parameter | Omega2 | 95% CI
## ---------------------------------
## Age | 0.00 | [0.00, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].