Breastmilk_analysis
Molly Lazarus
2024-08-02
Calculate DHM to EMH ratio
#calculate total volume for each individual day in the hospital
data <- data %>%
mutate(total_volume_day = EHM_volume + DHM_volume)
#calculate percentage volume DHM for each individual day in the hospital
data <- data %>%
mutate(Percent_DHM_day = DHM_volume/total_volume_day)
#calculate total volume (DHM + EHM) for all days of life in the hospital
data <- data %>%
mutate(total_volume = total_volume_EHM + total_volume_DHM)
#calculate percentage volume DHM for all days of life in the hospital
data <- data %>%
mutate(Percent_DHM = total_volume_DHM/total_volume)
#check calcs on one participant
data_check <- data %>%
filter(CSN==329930120) %>%
group_by (instance_dol, EHM_volume, DHM_volume, total_volume_day, Percent_DHM_day, total_volume_EHM, total_volume_DHM, total_volume, Percent_DHM) %>%
count() %>%
ungroup() %>%
mutate(percent = n/sum(n))
datatable(data_check)Plot of mean volumes of DHM EHM over the hosptial stay (100 dol)
mean_volume_data <- data %>%
filter(instance_dol >= 0 & instance_dol <= 100) %>%
group_by(instance_dol) %>%
summarize(
mean_EHM_volume = mean(EHM_volume, na.rm = TRUE),
mean_DHM_volume = mean(DHM_volume, na.rm = TRUE),
mean_pump_volume = mean(Pump_volume, na.rm = TRUE),
mean_DHM_percent = mean(Percent_DHM_day, na.rm = TRUE),
)
# Create a plot of the mean volume for each day of life
ggplot(mean_volume_data, aes(x = instance_dol)) +
geom_line(aes(y = mean_EHM_volume, color = "EHM Volume")) +
geom_line(aes(y = mean_DHM_volume, color = "DHM Volume")) +
labs(title = "Mean Volume for Each Day of Life",
x = "Day of Life",
y = "Mean Volume",
color = "Volume Type") +
theme_minimal()
Plot of mean volumes of pumping over the hosptial stay (100 dol)
ggplot(mean_volume_data, aes(x = instance_dol)) +
geom_line(aes(y = mean_pump_volume, color = "Pump Volume")) +
labs(title = "Mean Pump Volume for Each Day of Life",
x = "Day of Life",
y = "Mean Volume",
color = "Volume Type") +
theme_minimal()
Plot of percentage of DHM over the hosptial stay (100 dol)
ggplot(mean_volume_data, aes(x = instance_dol)) +
geom_line(aes(y = mean_DHM_percent, color = "DHM percent")) +
labs(title = "Percent of volume from DHM for each day of life",
x = "Day of Life",
y = "Percent",
color = "DHM percent") +
theme_minimal()
Calculate DHM to EMH ratio for the first 2 weeks of life
#Calculate total EHM for each infant during the first 14 days of life
data <- data %>%
filter(instance_dol <= 14 & instance_dol >= 0) %>%
group_by(CSN) %>%
mutate(volume_EHM_14 = sum(EHM_volume, na.rm = TRUE))
#Calculate total DHM for each infant during the first 14 days of life
data <- data %>%
filter(instance_dol <= 14 & instance_dol >= 0) %>%
group_by(CSN) %>%
mutate(volume_DHM_14 = sum(DHM_volume, na.rm = TRUE))
#Calculate total pump volume for each mother during the first 14 days of life
data <- data %>%
filter(instance_dol <= 14 & instance_dol >= 0) %>%
group_by(CSN) %>%
mutate(volume_pump_14 = sum(Pump_volume, na.rm = TRUE))
#Calculate total milk volume for each infant during the first 14 days of life
data <- data %>%
mutate(total_volume_14 = volume_DHM_14 + volume_EHM_14)
#Calculate percentage of milk that is DHM in the first 14 days of life
data <- data %>%
mutate(Percent_DHM_14 = volume_DHM_14/total_volume_14)
#check calcs on one participant
data_check <- data %>%
filter(CSN==329930120) %>%
group_by (instance_dol, EHM_volume, DHM_volume, volume_EHM_14, volume_DHM_14, total_volume_14, Percent_DHM_14) %>%
count() %>%
ungroup() %>%
mutate(percent = n/sum(n))
datatable(data_check)Plot of mean volumes of DHM, EHM and pumping over the first 14 days
mean_volume_data_14 <- data %>%
filter(instance_dol <= 14 & instance_dol >= 0) %>%
group_by(instance_dol) %>%
summarize(
mean_EHM_volume = mean(EHM_volume, na.rm = TRUE),
mean_DHM_volume = mean(DHM_volume, na.rm = TRUE),
mean_pump_volume = mean(Pump_volume, na.rm = TRUE),
)
# Create a plot of the mean volume for each day of life over the first 14 days
ggplot(mean_volume_data_14, aes(x = instance_dol)) +
geom_line(aes(y = mean_EHM_volume, color = "EHM Volume")) +
geom_line(aes(y = mean_DHM_volume, color = "DHM Volume")) +
labs(title = "Mean Volume for Each Day of Life Over the First 14 Days",
x = "Day of Life",
y = "Mean Volume",
color = "Volume Type") +
theme_minimal()
ggplot(mean_volume_data_14, aes(x = instance_dol)) +
geom_line(aes(y = mean_pump_volume, color = "Pump Volume")) +
labs(title = "Mean Volume for Each Day of Life Over the First 14 Days",
x = "Day of Life",
y = "Mean Volume",
color = "Volume Type") +
theme_minimal()
We can see from these plots that on average there is an increase in
pumping around day of life 5 which seems to correspond to an uptick in
the amounts of mom milk that infants are getting
Inspect Percentage DHM
#limit to one slice
data_slice <- data %>%
group_by(CSN)%>%
slice(1)
descriptives(data_slice, vars = c('volume_pump_14', 'total_volume_pump', 'Percent_DHM_14', 'Percent_DHM'),hist = TRUE, desc = "rows")##
## DESCRIPTIVES
##
## Descriptives
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## N Missing Mean Median SD Minimum Maximum
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## volume_pump_14 134 0 2864.9777612 1867.6500000 2944.1868483 0.000000 11889.680000
## total_volume_pump 122 12 11688.1739344 9512.1750000 11511.0789517 0.000000 77975.200000
## Percent_DHM_14 128 6 0.3488837 0.1910525 0.3510296 0.000000 1.000000
## Percent_DHM 130 4 0.3321199 0.1043970 0.3891418 0.000000 1.000000
## ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
boxplot(data_slice$Percent_DHM)
hist(data_slice$Percent_DHM)
Test SES differences in DHM Percent
Infants in the low SES group get significantly higher proportions of DHM compared to those in the high SES group