birthweight <- read.csv("birthweight.csv")
Exercise 1
Use the min(), max(), and mean() functions on subsets of the data frame to answer one or more of the following questions. Work together. Often there will be more than one way to arrive at the answer.
- What is the range of paternal ages in the data set?
summary(birthweight$paternal.age, na.rm = TRUE)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 19.00 23.00 28.00 28.76 31.75 46.00 4
What is the mean maternal age?
mean(birthweight$maternal.age)
## [1] 25.54762
What is the age of the mother with the highest maternal.cigarettes value?
birthweight$maternal.age[birthweight$maternal.cigarettes == max(birthweight$maternal.cigarettes)]
## [1] 37
TRUE or FALSE: The mean pre-pregnant weight is lower among women who gave birth to low birth weight children.
mean(birthweight$maternal.prepregnant.weight[birthweight$low.birthweight == 1]) < mean(birthweight$maternal.prepregnant.weight[birthweight$low.birthweight == 0])
## [1] TRUE
Exercise 2
Convert “low.birthweight” from numeric to logical.
birthweight$low.birthweight <- as.logical(birthweight$low.birthweight)
Convert “smoker” from character to logical.
birthweight$smoker <- birthweight$smoker == "yes"
Exercise 3
Are preterm babies more likely to have low birth weight?
tapply(birthweight$birthweight, birthweight$weeks.gestation < 40, mean)
## FALSE TRUE
## 3.643810 2.981905
chisq.test(birthweight$low.birthweight, birthweight$weeks.gestation < 40)
## Warning in chisq.test(birthweight$low.birthweight, birthweight$weeks.gestation
## < : Chi-squared approximation may be incorrect
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: birthweight$low.birthweight and birthweight$weeks.gestation < 40
## X-squared = 4.8611, df = 1, p-value = 0.02747
What is the ratio of head circumference to length for births at each of the hospitals?
tapply(birthweight$head.circumference / birthweight$length, birthweight$location, mean)
## General Memorial Silver Hill
## 0.6584533 0.6697763 0.6865171
Exercise 4
Mean birth weight by smoking status (base R).
tapply(birthweight$birthweight, birthweight$smoker, mean)
## FALSE TRUE
## 3.509500 3.134091
Mean birth weight by combination of smoking status and low birth weight status (base R).
tapply(birthweight$birthweight, paste0("smoker:", birthweight$smoker, ", low:", birthweight$low.birthweight), mean)
## smoker:FALSE, low:FALSE smoker:FALSE, low:TRUE smoker:TRUE, low:FALSE
## 3.554737 2.650000 3.378824
## smoker:TRUE, low:TRUE
## 2.302000
Mean birth weight by pre-term or full-term status (tidyverse).
library(tidyr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(magrittr)
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:tidyr':
##
## extract
birthweight %>%
mutate(full.term = weeks.gestation >= 40) %>%
group_by(full.term) %>%
summarise(mean.birthweight = mean(birthweight),
sd.birthweight = sd(birthweight))
## # A tibble: 2 × 3
## full.term mean.birthweight sd.birthweight
## <lgl> <dbl> <dbl>
## 1 FALSE 2.98 0.530
## 2 TRUE 3.64 0.487
Ratio of head circumference to length at each hospital (tidyverse).
birthweight %>%
mutate(head.length.ratio = head.circumference / length) %>%
group_by(location) %>%
summarise(mean(head.length.ratio))
## # A tibble: 3 × 2
## location `mean(head.length.ratio)`
## <chr> <dbl>
## 1 General 0.658
## 2 Memorial 0.670
## 3 Silver Hill 0.687
Exercise 5
Are preterm babies more likely to have low birth weight?
library(ggplot2)
ggplot(data = birthweight, mapping = aes(x = weeks.gestation < 40, y = birthweight, fill = weeks.gestation < 40)) +
geom_boxplot(notch = TRUE) +
scale_fill_viridis_d() +
guides(fill = "none") +
theme_bw()
What is the ratio of head circumference to length for births at each of the hospitals? Box plot version:
ggplot(data = birthweight, mapping = aes(y = head.circumference / length, fill = location)) +
geom_boxplot() +
scale_fill_viridis_d(option = "magma") +
theme_bw() +
theme(legend.title = element_blank())
Scatter plot version:
ggplot(data = birthweight, mapping = aes(x = head.circumference, y = length, colour = location)) +
geom_point() +
geom_smooth() +
scale_color_viridis_d(option = "magma")
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 29.97
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 4.03
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 0
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : There are other near singularities as well. 4.1209
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 29.97
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius
## 4.03
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 0
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4.1209
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 35
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 2
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 1.3072e-17
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 35
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.3072e-17
What is the relationship between maternal and paternal height?
ggplot(data = birthweight, mapping = aes(x = maternal.height, y = paternal.height)) +
geom_point() +
geom_smooth() +
coord_equal() +
labs(x = "maternal height (cm)", y = "paternal height (cm)") +
theme_classic()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
What is the relationship between maternal height and length at birth?
ggplot(data = birthweight, mapping = aes(x = maternal.height, y = length, color = smoker)) +
geom_point() +
geom_smooth() +
labs(x = "maternal height (cm)", y = "birth length (cm)") +
theme_classic()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
