title: “R Notebook - Importing CDC Data” author: Adilene Garcia output: html_document: df_print: paged —
# load necessary packages
library(ggplot2)# look at the BRFSS data
source("http://www.openintro.org/stat/data/cdc.R")
# another way to load data (from the internet)
# read.csv(url name)0 means ‘no’ and 1 means ‘yes’ for the variables
hlthplan and smoke100
# plot weight v height
plot(cdc$weight ~ cdc$height)
Practice: Take 3-5 min to try to make this plot in
ggplot. Hint: you will use + geom_line()
instead of geom_histogram()
# histogram
ggplot(data = cdc, aes(x = height)) +
geom_histogram(binwidth = 20) 
# line graph
ggplot(data = cdc, aes(x = height, y = weight)) + geom_line()
# scatter plot graph
ggplot(data = cdc, aes(x = height, y = weight)) + geom_point() 
# access certain rows or columns with brackets
# this is the 567th observation for the 6th column
cdc[567,6]## [1] 160# To see the weight of the first 10 people
cdc[1:10, 6]## [1] 175 125 105 132 150 114 194 170 150 180# rows, columns
# if you want all the rows of a certain column
# cdc[,6]
# if you want all the columns for the first ten observations
cdc[1:10,]## genhlth exerany hlthplan smoke100 height weight wtdesire age gender
## 1 good 0 1 0 70 175 175 77 m
## 2 good 0 1 1 64 125 115 33 f
## 3 good 1 1 1 60 105 105 49 f
## 4 good 1 1 0 66 132 124 42 f
## 5 very good 0 1 0 61 150 130 55 f
## 6 very good 1 1 0 64 114 114 55 f
## 7 very good 1 1 0 71 194 185 31 m
## 8 very good 0 1 0 67 170 160 45 m
## 9 good 0 1 1 65 150 130 27 f
## 10 good 1 1 0 70 180 170 44 m# access columns by name
#cdc$heightPractice: Use bracket notation to make a scatterplot of height and weight for the first 100 respondents. There are multiple ways to do this—find one that works!
# class example
randomrows <- sample(20000, 100)# using ggplot
# right now, it uses all the data. Make a change so that the only first 100 rows are used
# a) change to last one hundred rows or random 100 rows
# scatter plot graph
ggplot(data = cdc[randomrows,], aes(x = height, y = weight)) + geom_point() 
# base R plot
plot(cdc$height[1:100], cdc$weight[1:100])
# enter comments from picture on phone feb 2# calculating 5-number summary
summary(cdc)## genhlth exerany hlthplan smoke100
## excellent:4657 Min. :0.0000 Min. :0.0000 Min. :0.0000
## very good:6972 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## good :5675 Median :1.0000 Median :1.0000 Median :0.0000
## fair :2019 Mean :0.7457 Mean :0.8738 Mean :0.4721
## poor : 677 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## height weight wtdesire age gender
## Min. :48.00 Min. : 68.0 Min. : 68.0 Min. :18.00 m: 9569
## 1st Qu.:64.00 1st Qu.:140.0 1st Qu.:130.0 1st Qu.:31.00 f:10431
## Median :67.00 Median :165.0 Median :150.0 Median :43.00
## Mean :67.18 Mean :169.7 Mean :155.1 Mean :45.07
## 3rd Qu.:70.00 3rd Qu.:190.0 3rd Qu.:175.0 3rd Qu.:57.00
## Max. :93.00 Max. :500.0 Max. :680.0 Max. :99.00# creating a box plot
ggplot(data = cdc, aes(x = age)) + geom_boxplot() 
# conditioning (which function) for age <= 30 creacting a subset
cdc30_rows <- which(cdc$age <= 30)
# use the rows to make a new data set
cdc30 <- cdc[cdc30_rows, ]
4634/20000## [1] 0.2317# count how many people in cdc30 identify as 'f'
#uncomment the 2 lines below after uou create the cdc30 dataset
length(which(cdc30$gender == 'f'))## [1] 2409ggplot(data = cdc30, aes(x = gender)) + geom_bar()
Practice:
- Using numerical and graphical summaries, describe in words the distribution of ages for the study participants.
The ages of the participant for this study were between 18 to 99 years old. About half of the participants inn this study are between the ages of 31-57 years old. Most of the participants were around 40 years old. There are two out liers in this study which are the people that are 99 years old.
- What proportion of people are over 50 years old? The proportion of people that are over 50 is 37%.
# calculating 5-number summary
summary(cdc)## genhlth exerany hlthplan smoke100
## excellent:4657 Min. :0.0000 Min. :0.0000 Min. :0.0000
## very good:6972 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## good :5675 Median :1.0000 Median :1.0000 Median :0.0000
## fair :2019 Mean :0.7457 Mean :0.8738 Mean :0.4721
## poor : 677 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
## height weight wtdesire age gender
## Min. :48.00 Min. : 68.0 Min. : 68.0 Min. :18.00 m: 9569
## 1st Qu.:64.00 1st Qu.:140.0 1st Qu.:130.0 1st Qu.:31.00 f:10431
## Median :67.00 Median :165.0 Median :150.0 Median :43.00
## Mean :67.18 Mean :169.7 Mean :155.1 Mean :45.07
## 3rd Qu.:70.00 3rd Qu.:190.0 3rd Qu.:175.0 3rd Qu.:57.00
## Max. :93.00 Max. :500.0 Max. :680.0 Max. :99.00# creating a box plot
ggplot(data = cdc, aes(x = age)) + geom_boxplot()
# conditioning (which function) for age >= 50 creating a subset
cdc50_rows <- which(cdc$age >= 50)
# use the rows to make a new data set
cdc50 <- cdc[cdc50_rows, ]
7343/20000## [1] 0.36715