CW02132025

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#Create a vector
x<-c(12,7,3,4.2,18,2,54,-21,8,-5,NA)
#find mean.
result.mean <- mean(x)
print(result.mean)

## [1] NA

#find mean dropping NA values.
result.mean <- mean(x,na.rm= TRUE)
print(result.mean)

## [1] 8.22

result.mean <- mean(x,na.rm= FALSE)
print(result.mean)

## [1] NA

#find median.
median(x)

## [1] NA

median(x,na.rm=TRUE)

## [1] 5.6

library(readr)

# Load the renamed file
ChildrenAsthma_Survey <- read_csv("C:/Users/yeu3178/Downloads/ChildrenAsthmaSurvey.csv")

## New names:
## Rows: 20 Columns: 9
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," dbl
## (9): ...1, Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`

# Check if it loaded correctly
head(ChildrenAsthma_Survey)

## # A tibble: 6 × 9
##    ...1    Q1    Q2    Q3    Q4    Q5    Q6    Q7    Q8
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1     1    13     2     1     1     2     1     3     1
## 2     2     5     2     4     1     3     1     3     0
## 3     3     7     1     5     1     1     1     6     1
## 4     4    14     2     6     1     4     9     1     0
## 5     5     6     1     6     1     5     0     1     0
## 6     6     2     1     5     1     1     1     6     1

#find mode
mode(x)

## [1] "numeric"

library(tibble)

CAS <- ChildrenAsthma_Survey  # Use the correct variable name

View(CAS)
table(CAS$Q2)

## 
##  1  2 
## 10 10

prop.table(table(CAS$Q2))

## 
##   1   2 
## 0.5 0.5

prop.table(table(CAS$Q3))

## 
##    1    2    3    4    5    6    7 
## 0.15 0.10 0.10 0.15 0.25 0.20 0.05

communityChildAsthma<-(600/54786)*1000
print(communityChildAsthma)

## [1] 10.9517

# Ensure CAS is a dataframe
AA <- CAS[CAS$Q3 == 5, ]  # Subset rows where Q3 equals 5

# Create a frequency table for Q5 in the filtered data
table(AA$Q5)

## 
## 1 3 
## 3 2

AA

## # A tibble: 5 × 9
##    ...1    Q1    Q2    Q3    Q4    Q5    Q6    Q7    Q8
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1     3     7     1     5     1     1     1     6     1
## 2     6     2     1     5     1     1     1     6     1
## 3    12     3     1     5     1     1     1     5     0
## 4    13     4     1     5     1     3     1     3     0
## 5    19    11     2     5     1     3     0     4     0

prop.table(table(AA$Q5))

## 
##   1   3 
## 0.6 0.4

prop.table(table(AA$Q5!=5))

## 
## TRUE 
##    1

table(table(AA$Q5!=5))

## 
## 5 
## 1

white <- CAS[CAS$Q3 == 6, ]
table(white$Q5)

## 
## 1 4 5 
## 2 1 1

prop.table(table(AA$Q5))

## 
##   1   3 
## 0.6 0.4

prop.table(table(AA$Q5!=5))

## 
## TRUE 
##    1

table(table(AA$Q5!=5))

## 
## 5 
## 1

table(AA$Q2)

## 
## 1 2 
## 4 1

AA$Q2

## [1] 1 1 1 1 2

table(AA$Q4)

## 
## 1 
## 5

prop.table(AA$Q4)*100

## [1] 20 20 20 20 20

prop.table(table(AA$Q4))*100

## 
##   1 
## 100

WK <- CAS[CAS$Q3 == 6, ]  # White kids

table(WK$Q4)

## 
## 1 
## 4

table(WK$Q5)

## 
## 1 4 5 
## 2 1 1

prop.table(table(WK$Q5))

## 
##    1    4    5 
## 0.50 0.25 0.25

LK <- CAS[CAS$Q3 == 3, ]  # Latino Kids (assuming Q3 == 3 means Latino)

table(LK$Q4)

## 
## 1 
## 2

table(LK$Q5)

## 
## 3 
## 2

prop.table(table(LK$Q5))

## 
## 3 
## 1

AK <- CAS[CAS$Q3 == 4, ]  # Asian Kids (assuming Q3 == 4 means Asian)
table(AK$Q4)

## 
## 1 
## 3

table(AK$Q5)

## 
## 1 3 4 
## 1 1 1

prop.table(table(AK$Q5))

## 
##         1         3         4 
## 0.3333333 0.3333333 0.3333333

CW02132025

Chris Yeu

2025-02-13

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