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library(readxl)library(plotly)
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This graph shows the frequency of each race in the population.
survey |>mutate(spanking =as_factor(spanking)) |>mutate(spanking =fct_recode(spanking,NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>drop_na(spanking)|>plot_ly(x =~spanking) |>add_histogram()
This graph shows the frequency of people’s opinions on spanking.
survey |>mutate(race =as_factor(race)) |>mutate(race =fct_recode(race,NULL =".i: Inapplicable")) |>drop_na(race) |>mutate(spanking =as_factor(spanking)) |>mutate(spanking =fct_recode(spanking,NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>mutate(spanking =fct_relevel(spanking,c("STRONGLY AGREE","AGREE","DISAGREE","STRONGLY DISAGREE"))) |>drop_na(spanking) |>mutate(spanking =as.numeric(spanking)) |>plot_ly(x =~race, y =~spanking) |>add_boxplot()
This graph shows the median and ranges for each race’s opinion on spanking. The lower the number, the more they agree.
survey |>mutate(childs =as_factor(childs)) |>mutate(childs =fct_recode(childs,"8"="8 or more",NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable")) |>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 frequency of each number of children per family in the population.
survey |>mutate(childs =as_factor(childs)) |>mutate(childs =fct_recode(childs,"8"="8 or more",NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable")) |>mutate(childs =fct_relevel(childs,c("0","1","2","3","4","5","6","7","8"))) |>drop_na(childs) |>mutate(spanking =as_factor(spanking)) |>mutate(spanking =fct_recode(spanking,NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>mutate(attend =fct_relevel(spanking,c("STRONGLY AGREE","AGREE","DISAGREE","STRONGLY DISAGREE"))) |>drop_na(spanking) |>plot_ly(x =~spanking, y =~childs) |>add_boxplot()
This graph shows the relation between the number of children you have and your opinions on spanking. It shows that people with more children are more okay with spanking.
survey |>mutate(hapmar =as_factor(hapmar)) |>mutate(hapmar =fct_recode(hapmar,NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>drop_na(hapmar) |>mutate(hapmar =fct_relevel(hapmar,c("NOT TOO HAPPY","PRETTY HAPPY","VERY HAPPY"))) |>plot_ly(x =~hapmar) |>add_histogram()
This graph shows the frequency of how happy people say they are in their marriage, from least to most
survey |>mutate(class_ =as_factor(class_)) |>mutate(class_ =fct_recode(class_,NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web",NULL =".n: No answer")) |>drop_na(class_) |>mutate(class_ =fct_relevel(class_,c("Lower class","Working class","Middle Class","Upper Class"))) |>plot_ly(x =~class_) |>add_histogram()
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `class_ = fct_relevel(...)`.
Caused by warning:
! 2 unknown levels in `f`: Middle Class and Upper Class
This graph shows the frequency of each class in the population.
survey |>mutate(class_ =as_factor(class_)) |>mutate(class_ =fct_recode(class_,NULL =".d: Do not Know/Cannot Choose",NULL =".s: Skipped on Web",NULL =".n: No answer")) |>drop_na(class_) |>mutate(class_ =fct_relevel(class_,c("Lower class","Working class","Middle Class","Upper Class"))) |>mutate(hapmar =as_factor(hapmar)) |>mutate(hapmar =fct_recode(hapmar,NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>drop_na(hapmar) |>mutate(hapmar =fct_relevel(hapmar,c("NOT TOO HAPPY","PRETTY HAPPY","VERY HAPPY"))) |>plot_ly(x =~class_, color =~hapmar) |>add_histogram()
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `class_ = fct_relevel(...)`.
Caused by warning:
! 2 unknown levels in `f`: Middle Class and Upper Class
This graph shows the frequency of how happy each person’s marriage is by class.
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 each age group in the 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,NULL =".d: Do not Know/Cannot Choose",NULL =".i: Inapplicable",NULL =".n: No answer",NULL =".s: Skipped on Web")) |>drop_na(hapmar) |>mutate(hapmar =fct_relevel(hapmar,c("NOT TOO HAPPY","PRETTY HAPPY","VERY HAPPY"))) |>plot_ly(x =~age, y =~hapmar) |>add_histogram2d()
This graph shows the frequency of how happy one’s marriage is by one’s age group.