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library(readxl) library(plotly)
Attaching package: 'plotly'
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This graph shows the frequency of race in the survey population.
survey |>mutate(spanking =as_factor(spanking)) |>mutate(spanking =fct_recode(spanking,"Strongly Agree"="STRONGLY AGREE","Agree"="AGREE","Disagree"="DISAGREE","Strongly Disagree"="STRONGLY DISAGREE",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web")) |>drop_na(spanking) |>plot_ly(x =~spanking) |>add_histogram()
This graph shows opinions on spanking across the survey population.
survey |>mutate(spanking =as_factor(spanking)) |>mutate(spanking =fct_recode(spanking,"Strongly Agree"="STRONGLY AGREE","Agree"="AGREE","Disagree"="DISAGREE","Strongly Disagree"="STRONGLY DISAGREE",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web")) |>mutate(spanking =fct_relevel(spanking,c("Strongly Agree","Agree","Disagree","Strongly Disagree"))) |>mutate(spanking =as.numeric(spanking)) |>drop_na(spanking) |>mutate(race =as_factor(race)) |>mutate(race =fct_recode(race, NULL =".i: Inapplicable")) |>drop_na(race) |>mutate(race =fct_infreq(race)) |>plot_ly(x =~race, y =~spanking) |>add_boxplot()
This graph shows the opinions of spanking in the survey population based on race.
survey |>mutate(childs =as_factor(childs)) |>mutate(childs =fct_recode(childs,"8"="8 or more",NULL =".i: Inapplicable",NULL =".d: Do not Know/Cannot Choose")) |>mutate(childs =fct_relevel(childs,c("0","1","2","3","4","5","6","7","8"))) |>drop_na(childs) |>plot_ly(x =~childs) |>add_histogram()
This graph shows the number of children people have within the survey population.
survey |>mutate(spanking =as_factor(spanking)) |>mutate(spanking =fct_recode(spanking,"Strongly Agree"="STRONGLY AGREE","Agree"="AGREE","Disagree"="DISAGREE","Strongly Disagree"="STRONGLY DISAGREE",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web")) |>mutate(spanking =fct_relevel(spanking,c("Strongly Agree","Agree","Disagree","Strongly Disagree"))) |>drop_na(spanking) |>mutate(childs =as_factor(childs)) |>mutate(childs =fct_recode(childs,"8"="8 or more",NULL =".i: Inapplicable",NULL =".d: Do not Know/Cannot Choose")) |>mutate(childs =fct_relevel(childs,c("0","1","2","3","4","5","6","7","8"))) |>drop_na(childs) |>plot_ly(x =~spanking, y =~childs) |>add_boxplot()
This graph shows the opinions on spanking in the survey population based on the number of children a family has.
survey |>mutate(hapmar =as_factor(hapmar)) |>mutate(hapmar =fct_recode(hapmar,"Not Too Happy"="NOT TOO HAPPY","Pretty Happy"="PRETTY HAPPY","Very Happy"="VERY HAPPY",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web")) |>mutate(hapmar =fct_relevel(hapmar,c("Not Too Happy","Pretty Happy","Very Happy"))) |>drop_na(hapmar) |>plot_ly(x =~hapmar) |>add_histogram()
This graph shows how happy people are with their marriages in the survey population.
survey |>mutate(class_ =as_factor(class_)) |>mutate(class_ =fct_recode(class_,NULL =".d: Do not Know/Cannot Choose",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>drop_na(class_) |>mutate(class_ =fct_relevel(class_,"Lower class","Working class","Middle class","Upper class")) |>plot_ly(x =~class_) |>add_histogram()
This graph shows the spread of economic classes within the survey population.
survey |>mutate(class_ =as_factor(class_)) |>mutate(class_ =fct_recode(class_,NULL =".d: Do not Know/Cannot Choose",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>drop_na(class_) |>mutate(class_ =fct_relevel(class_,"Lower class","Working class","Middle class","Upper class")) |>mutate(hapmar =as_factor(hapmar)) |>mutate(hapmar =fct_recode(hapmar,"Not Too Happy"="NOT TOO HAPPY","Pretty Happy"="PRETTY HAPPY","Very Happy"="VERY HAPPY",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web")) |>mutate(hapmar =fct_relevel(hapmar,c("Not Too Happy","Pretty Happy","Very Happy"))) |>drop_na(hapmar) |>plot_ly(x =~class_, color =~hapmar) |>add_histogram()
This graph shows level of happiness in ones marriage according to each economic class with in the survey population.
survey |>mutate(age =as_factor(age)) |>mutate(age =fct_recode(age,NULL =".n: No answer")) |>drop_na(age) |>mutate(age =fct_collapse(age,"Under 30"=c("18","19","20","21","22","23","24","25","26","27","28","29"),"30s"=c("30","31","32","33","34","35","36","37","38","39"),"40s"=c("40","41","42","43","44","45","46","47","48","49"),"50s"=c("50","51","52","53","54","55","56","57","58","59"),"60s"=c("60","61","62","63","64","65","66","67","68","69"),"70s"=c("70","71","72","73","74","75","76","77","78","79"),"80 and up"=c("80","81","82","83","84","85","86","87","88","89 or older"))) |>mutate(age =fct_relevel(age,c("Under 30","30s","40s","50s","60s","70s","80 and up"))) |>plot_ly(x =~age) |>add_histogram()
This graph shows the frequency of age within the survey population.
survey |>mutate(age =as_factor(age)) |>mutate(age =fct_recode(age,NULL =".n: No answer")) |>drop_na(age) |>mutate(age =fct_collapse(age,"Under 30"=c("18","19","20","21","22","23","24","25","26","27","28","29"),"30s"=c("30","31","32","33","34","35","36","37","38","39"),"40s"=c("40","41","42","43","44","45","46","47","48","49"),"50s"=c("50","51","52","53","54","55","56","57","58","59"),"60s"=c("60","61","62","63","64","65","66","67","68","69"),"70s"=c("70","71","72","73","74","75","76","77","78","79"),"80 and up"=c("80","81","82","83","84","85","86","87","88","89 or older"))) |>mutate(age =fct_relevel(age,c("Under 30","30s","40s","50s","60s","70s","80 and up"))) |>mutate(hapmar =as_factor(hapmar)) |>mutate(hapmar =fct_recode(hapmar,"Not Too Happy"="NOT TOO HAPPY","Pretty Happy"="PRETTY HAPPY","Very Happy"="VERY HAPPY",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web")) |>mutate(hapmar =fct_relevel(hapmar,c("Not Too Happy","Pretty Happy","Very Happy"))) |>drop_na(hapmar) |>plot_ly(x =~age, y =~hapmar) |>add_histogram2d()
This graph shows the level of happiness in ones marriage based on age of the survey population.