rename, retype 3 variables
retype a variable that is incorrect, or reclassify one to be incorrect
calculate sum of ‘NAs’ in 10 columns
Identify positions of ‘NAs’ in 1 column
Recode a categorical variable
Produce a frequency table for the original and recoded variables side-by-side
create a derived variable
export data set as .csv or .xlsx
# load the Stata file
dat <- read_dta("//qpa1/Data/Intern/Spokane_PRAMS_phase8_2016_2022.dta")
# checking the column type for filtering
str(dat$cnty_res)## num [1:8344] 88 32 88 88 88 32 88 32 88 88 ...
## - attr(*, "format.stata")= chr "%12.0g"# filter to Spokane County, which is 32
dat <- dat |>
filter(cnty_res == 32)Dat has 463 rows across 682 columns. For the most part,
columns have a small percentage of NA responses. Some
columns have a higher level of NA responses than others,
like the dat$momlbs column which is missing 9% of
responses.
Resource: https://stackoverflow.com/questions/tagged/r?tab=Newest
’str(dat$momlbs) unique() table()
sum(is.na()) which(is.na())’
library(haven) Spokane_PRAMS_phase8_2016_2022 <- read_dta(“//qpa1/Data/Intern/Spokane_PRAMS_phase8_2016_2022.dta”) View(Spokane_PRAMS_phase8_2016_2022)
Calculate Sum of NA values
# Sum NAs of moms who smoke
sum(is.na(dat$momsmoke))## [1] 11#Number of moms who smoke w/o NA
sum(!is.na(dat$momsmoke))## [1] 452#Sum NA Breastfed
sum(is.na(dat$brstfed))## [1] 8#Sum NA ever breastfed
sum(is.na(dat$bf5ever))## [1] 16#Sum NA birth defects
sum(is.na(dat$defect))## [1] 0#Sum NA Infertility Treatment
sum(is.na(dat$infer_tr))## [1] 2#Sum NA BMI by Group
sum(is.na(dat$mom_bmig_qx_rev))## [1] 8# Sum NA Diabetes
sum(is.na(dat$bpg_diab8))## [1] 6#sum NA High BP
sum(is.na(dat$bpg_hbp8))## [1] 6#Sum NA received flu shot before delivery
sum(is.na(dat$flupreg))## [1] 6# Sum NA tdap shot while pregnant
sum(is.na(dat$pg_tdap8))## [1] 32#tdap values that contain 'NA'
which(is.na(dat$pg_tdap8))## [1] 10 28 48 83 95 99 106 118 146 148 153 157 163 173 219 220 232 249 302
## [20] 306 309 321 345 368 369 371 377 379 432 434 446 458# Table of Mom's Weight Gain
table(dat$momlbs)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
## 16 4 2 2 1 3 4 2 5 3 8 1 4 10 7 3 7 4 7 6 14 5 7 10 6 21
## 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
## 11 11 8 3 37 15 7 12 7 18 5 6 13 6 12 4 6 3 11 8 3 6 5 5 3 2
## 52 53 54 55 56 57 59 60 61 63 64 66 68 70 73 75 76 80 85 93
## 1 4 2 2 1 1 3 4 2 1 1 2 1 1 1 1 1 1 1 1Rename Variables
#Flu shot table, rename rows
flu_table = table(dat$flupreg)
row.names(flu_table) <- c("no","before","during")
#Print Flu Table
flu_table##
## no before during
## 229 57 171#Infant Birth Month
table(dat$idob_mth)##
## 1 2 3 4 5 6 7 8 9 10 11 12
## 36 38 44 38 48 38 38 45 30 36 35 31#Rename from number to month
birth_mth = table(dat$idob_mth)
row.names(birth_mth) <-c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
#Print Infant Birth Month Table
birth_mth##
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 36 38 44 38 48 38 38 45 30 36 35 31#Pregnancy Intention
table(dat$pgint3)##
## 0 1 2
## 99 271 82intention_table = table(dat$pgint3)
row.names(intention_table) <- c("Unintended", "Intended", "Unsure")
#Print Intention Table
intention_table##
## Unintended Intended Unsure
## 99 271 82#Table Baby's Sex
dat$sex_num## [1] 1 1 2 2 1 1 1 2 2 1 1 2 2 2 2 1 2 1 1 2 1 1 2 1 2 1 2 2 2 1 2 2 2 2 2 1 1
## [38] 1 2 2 1 1 2 1 1 2 2 1 2 2 1 2 1 1 2 1 1 1 2 2 1 1 1 2 1 1 2 1 2 1 2 2 1 1
## [75] 2 1 2 2 1 1 2 2 2 1 2 2 2 1 1 2 2 1 1 1 1 1 2 2 2 1 2 2 2 2 2 1 2 2 2 1 2
## [112] 1 2 1 2 2 2 1 2 2 1 2 2 2 1 2 1 1 2 1 1 2 1 2 2 1 2 2 2 1 1 1 2 1 1 1 1 1
## [149] 1 2 2 2 2 2 2 2 2 1 2 2 1 1 1 2 1 2 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 1 1 2 2
## [186] 1 2 1 1 2 2 1 1 1 2 1 2 1 2 1 2 1 1 1 2 2 2 2 1 2 2 1 2 1 2 2 2 2 2 1 2 1
## [223] 2 1 1 2 2 2 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 2 2 1 2 2 2 1 2 1 2 2 1 1 2 2 2
## [260] 1 2 2 1 1 1 2 1 2 1 2 1 1 2 2 1 2 2 1 2 2 1 1 1 1 2 1 1 1 2 1 1 1 1 2 1 2
## [297] 2 2 1 1 2 1 1 2 1 1 1 1 2 2 2 2 1 1 1 2 1 2 2 2 1 1 1 1 1 1 2 1 1 1 1 1 2
## [334] 2 1 2 2 2 1 1 2 1 1 2 1 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 2 2 1
## [371] 2 1 1 2 2 2 1 1 1 2 1 2 1 1 2 1 1 2 2 2 2 2 1 2 2 2 1 2 2 1 1 1 1 1 2 1 1
## [408] 1 1 2 1 1 2 2 2 1 1 2 2 1 1 2 1 1 1 2 1 1 1 1 1 2 1 1 2 2 1 2 1 2 1 1 2 1
## [445] 1 2 1 1 2 2 1 1 1 1 1 1 1 1 2 2 1 1 2
## attr(,"label")
## [1] "Infant sex - numeric"
## attr(,"format.stata")
## [1] "%12.0g"table(dat$sex_num)##
## 1 2
## 240 223bby_sex = table(dat$sex_num)
row.names(bby_sex) <- c("Male", "Female")
#Print Baby's Sex as Table, updated labels
bby_sex##
## Male Female
## 240 223#Changing Variable Type
glimpse(dat$sex)## chr [1:463] "M" "M" "F" "F" "M" "M" "M" "F" "F" "M" "M" "F" "F" "F" "F" ...
## - attr(*, "label")= chr "Sex"
## - attr(*, "format.stata")= chr "%2s"class(dat$sex)## [1] "character"table(dat$sex)##
## F M
## 223 240sex_ordered <- (as.ordered(dat$sex))
#Print Orderd Sex Table
sex_ordered## [1] M M F F M M M F F M M F F F F M F M M F M M F M F M F F F M F F F F F M M
## [38] M F F M M F M M F F M F F M F M M F M M M F F M M M F M M F M F M F F M M
## [75] F M F F M M F F F M F F F M M F F M M M M M F F F M F F F F F M F F F M F
## [112] M F M F F F M F F M F F F M F M M F M M F M F F M F F F M M M F M M M M M
## [149] M F F F F F F F F M F F M M M F M F M M M F F M M F F F F F F F F M M F F
## [186] M F M M F F M M M F M F M F M F M M M F F F F M F F M F M F F F F F M F M
## [223] F M M F F F M M F M F M F M M M M F F F M F F M F F F M F M F F M M F F F
## [260] M F F M M M F M F M F M M F F M F F M F F M M M M F M M M F M M M M F M F
## [297] F F M M F M M F M M M M F F F F M M M F M F F F M M M M M M F M M M M M F
## [334] F M F F F M M F M M F M F F M M M F M M M M M M M M M M M F M F F M F F M
## [371] F M M F F F M M M F M F M M F M M F F F F F M F F F M F F M M M M M F M M
## [408] M M F M M F F F M M F F M M F M M M F M M M M M F M M F F M F M F M M F M
## [445] M F M M F F M M M M M M M M F F M M F
## Levels: F < M#Side by side
table(dat$sex, dat$sex_num)##
## 1 2
## F 0 223
## M 240 0Derive New Variable
#Baby Weight Range
range(dat$birth_weight_grams)## [1] 1191 5330#Average Birth Weight (Grams)
avg_weight <- mean(dat$birth_weight_grams)
avg_weight## [1] 3294.382#Average Birth Weight of Male Babies
avg_wt_male <- mean(dat$birth_weight_grams, dat$sex_num [1])
avg_wt_male## [1] 3317Recode States
library(dplyr)
dat$mother_birthplace_region <- case_when(
dat$mother_birthplace_state %in% c(
"CONNECTICUT","MAINE","MASSACHUSETTS","NEW HAMPSHIRE",
"RHODE ISLAND","VERMONT","NEW JERSEY","NEW YORK","PENNSYLVANIA"
) ~ "Northeast",
dat$mother_birthplace_state %in% c(
"ILLINOIS","INDIANA","MICHIGAN","OHIO","WISCONSIN",
"IOWA","KANSAS","MINNESOTA","MISSOURI","NEBRASKA",
"NORTH DAKOTA","SOUTH DAKOTA"
) ~ "Midwest",
dat$mother_birthplace_state %in% c(
"DELAWARE","DISTRICT OF COLUMBIA","FLORIDA","GEORGIA",
"MARYLAND","NORTH CAROLINA","SOUTH CAROLINA","VIRGINIA",
"WEST VIRGINIA","ALABAMA","KENTUCKY","MISSISSIPPI",
"TENNESSEE","ARKANSAS","LOUISIANA","OKLAHOMA","TEXAS"
) ~ "South",
dat$mother_birthplace_state %in% c(
"ARIZONA","COLORADO","IDAHO","MONTANA","NEVADA",
"NEW MEXICO","UTAH","WYOMING","ALASKA","CALIFORNIA",
"HAWAII","OREGON","WASHINGTON"
) ~ "West",
TRUE ~ NA_character_
)
table(dat$mother_birthplace_region, useNA = "ifany")##
## Midwest Northeast South West <NA>
## 16 5 18 296 128Side by Side Frequency Table
table(dat$mother_birthplace_region, dat$mother_birthplace_state)##
## ALASKA ARIZONA ARKANSAS CALIFORNIA COLORADO CONNECTICUT FLORIDA
## Midwest 0 0 0 0 0 0 0 0
## Northeast 0 0 0 0 0 0 1 0
## South 0 0 0 2 0 0 0 2
## West 0 10 4 0 32 3 0 0
##
## GEORGIA HAWAII IDAHO ILLINOIS INDIANA IOWA KANSAS LOUISIANA
## Midwest 0 0 0 3 1 1 1 0
## Northeast 0 0 0 0 0 0 0 0
## South 1 0 0 0 0 0 0 1
## West 0 2 20 0 0 0 0 0
##
## MICHIGAN MINNESOTA MISSOURI MONTANA NEBRASKA NEVADA NEW HAMPSHIRE
## Midwest 2 1 1 0 1 0 0
## Northeast 0 0 0 0 0 0 1
## South 0 0 0 0 0 0 0
## West 0 0 0 6 0 4 0
##
## NEW JERSEY NEW MEXICO NEW YORK NORTH CAROLINA NORTH DAKOTA OHIO
## Midwest 0 0 0 0 3 2
## Northeast 1 0 1 0 0 0
## South 0 0 0 1 0 0
## West 0 1 0 0 0 0
##
## OKLAHOMA OREGON PENNSYLVANIA SOUTH CAROLINA TENNESSEE TEXAS UNKNOWN
## Midwest 0 0 0 0 0 0 0
## Northeast 0 0 1 0 0 0 0
## South 3 0 0 2 1 5 0
## West 0 13 0 0 0 0 0
##
## UTAH WASHINGTON
## Midwest 0 0
## Northeast 0 0
## South 0 0
## West 3 198Export
write.csv(dat, "practice.csv")