Bivariate Graphing Rana Yousaf, Manpreet Mann, and Dan Hiney 24 Sept, 2017

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1 Bivariate Graphing Rana Yousaf, Manpreet Mann, and Dan Hiney 24 Sept, 2017 load("c:/users/owner/desktop/math315/projects/data/addhealth_clean.rdata") library(ggplot2) library(mass) library(knitr) The following scatter plot compares the respondent s weight against their bmi. This is a quantitative-quantitative variable comparison. ggplot(addhealth, aes(x=wght, y=bmi)) + geom_point() + geom_smooth(se=false) + ylab("respondents BMI") + xlab("respondents weight") + ggtitle("scatterplot of Weight against BMI") ## `geom_smooth()` using method = 'gam' ## Warning: Removed 1543 rows containing non-finite values (stat_smooth). ## Warning: Removed 1543 rows containing missing values (geom_point). Scatterplot of Weight against BMI Respondents BMI Respondents weight 1

2 round(summary(addhealth$bmi), 1) ## summary(addhealth$wght) ## round(cor(addhealth$wght, addhealth$bmi, "pairwise.complete.obs"), 3) ## [1] 0.85 The scatter plot of the respondents weight against their BMI above visually indicates a very strong positive correlation. There are some outliers, but based on the correlation factor of 0.85, which also indicates a strong positive relationship, they have very little effect on the overall data set. This correlation is not unexpected because a person s weight does affect their BMI. The mean BMI of the respondents is 29.1 and the median BMI is The mean weight of the respondents is pounds and the median weight 178. The following box plot and bar graph plot the respondent s general health against their perceived weight. General health is how they rated their overall health and their perceived weight is how they see their weight in terms of what they see and not necessarily what the scale says. This is a categorical-categorical variable comparison. ggplot(addhealth, aes(x=pwght, fill=factor(gnhlth))) + geom_bar(position=position_dodge()) + xlab("perceived Weight") + ylab("count") + ggtitle("relationship of Perceived Weight and General Healt 2

3 Relationship of Perceived Weight and General Health Count factor(gnhlth) excellent very good good fair poor NA 0 very under under normal over very over NA Perceived Weight boxplot(wght~pwght, data=addhealth) 3

4 very under under normal over very over round(prop.table(table(addhealth$gnhlth)),3)*100 ## ## excellent very good good fair poor ## round(prop.table(table(addhealth$pwght)), 3)*100 ## ## very under under normal over very over ## Respondents were asked to rate their overall health as either excellent, very good, normal, fair, or poor while they were also asked to about their perceived weight as very underweight, underweight, normal, overweight, or very overweight. In terms of general health, the most popular answer was very good at 38.4%, however, the most popular response to their perceived weight was overweight at 43.1%. In fact, 71.3% of the respondents state that their health is good or very good, yet 57.0% consider themselves to be either overweight or very overweight. Those responses seem to contradict one another, but one explanation could be that despite how people view themselves by what they see, it does not affect how they say they feel. Examining the box plot, we see that each of the responses has a slight skew to the right, but there is little significant difference in these responses because the middle 50% of each data set overlaps with the others. 4

5 The following violin plot compares a respondent s perceived weight against their actual weight. This ia categorical-quantitative variable comparison. ggplot(addhealth, aes(x=pwght, y=wght, fill=factor(pwght), na.rm=true)) + geom_boxplot() + geom_violin(alpha=.2) + geom_boxplot(alpha=.2, width=.5) + xlab("perceived Weight") + ylab("weight") + ggtitle("violin Plot of Perceived Weight against Actual We ## Warning: Removed 1474 rows containing non-finite values (stat_boxplot). ## Warning: Removed 1474 rows containing non-finite values (stat_ydensity). ## Warning: Removed 1474 rows containing non-finite values (stat_boxplot). Violin Plot of Perceived Weight against Actual Weight 500 Weight factor(pwght) very under under normal over very over 100 very under under normal over very over NA Perceived Weight The above violin box plot compares the actual weight of the respondents against their perceived weight. Each response category of the perceived weight contains outliers that skew the data to the right meaning that, for instance, respondents who said they were very underweight actually had a true weight that indicated they were not very underweight. This contradiction occurs across all of the responses to these two variables. The median weights across all of the perceived weight categories rises slightly from the very underweight category to the very overweight category. There is little significant difference between the data because the middle 50% of the data overlap across all of the responses. This bivariate comparison will be helpful to us as we try to answer our research questions because a person s actual weight may not have a negative impact on how they perceive their weight to be. 5

6 The following histogram plots a respondent s amount of fast food in the last 7 days against whether they had caffeinated drink in the last 24 hours. This is quantitative-binary categorical variable comparison. ggplot(addhealth, aes(x=sevendayff, fill=caff)) + geom_histogram(binwidth=10.5) + facet_wrap("caff") + xlab("fast Food in the last 7 days") + ylab("count") + ggtitle("histogram of Seven Day Fast Food Against Caffiene in the last 24 hours") ## Warning: Removed 1427 rows containing non-finite values (stat_bin). Histogram of Seven Day Fast Food Against Caffiene in the last 24 hours 0 1 NA 3000 count 2000 caff Fast Food in the last 7 days summary(addhealth$sevendayff) ## summary(addhealth$caff) ## The bar graph represents the relationship between the amount of fast foods consumed in seven days and whether they had a caffeinated beverage in the last 24 hours with columns labeled as zero and one. Each bar is a separate figure for caffeinated beverages consumed in 24 hours and amount of fast food consumed in seven days for zero representing caffeinated beverages not being consumed in 24 hours and one representing caffeinated beverages being consumed in 24 hours. Both graphs show a relationship that in a seven day 6

7 period that majority of people went to fast foods zero to seven times in the week with having a caffeinated beverage the first two to three times but soon slow down with the intake of the drinks. The mean from intake of caffeinated beverages in which represents that majority people did consume a caffeinated beverage in 24 hours. The mean for the count of how many times people went to fast foods is 2.3 which shows that most people don t go as much to fast food places and when they do, they ll have a caffeinated beverage. 7