Stephen Onteri 2025-09-03
## Warning: package 'tidyverse' was built under R version 4.4.3
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## Warning: package 'purrr' was built under R version 4.4.3
## Warning: package 'dplyr' was built under R version 4.4.3
## Warning: package 'stringr' was built under R version 4.4.3
## Warning: package 'forcats' was built under R version 4.4.3
## Warning: package 'lubridate' was built under R version 4.4.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsinstall.packages(“rmarkdown”)
# Importing the data and naming it C1_Survey
C1_Survey <- read.csv("https://raw.githubusercontent.com/kijohnson/Advanced-Data-Analysis/main/Class%201%20Survey%20Fall%202025%202.csv")## [1] 58## [1] 27# Rename columns in C1_Survey
C1_Survey <- C1_Survey %>%
rename_with(~ c(
"id", "like_cats", "like_dogs", "have_desert", "slogan", "fav_day",
"larkORowl", "fav_food", "fav_drink", "fav_season", "fav_month",
"hobby", "program", "specialization", "stat_software", "R_exp",
"coding_comfort", "coding_length", "top_three", "public_health_interest",
"fav_num", "bday", "bmonth", "country", "state", "city",
"highest_educ_level"
), .cols = 1:27)# Load dplyr
library(dplyr)
# Rename columns in C1_Survey
C1_Survey <- rename_with(
C1_Survey,
~ c(
"id", "like_cats", "like_dogs", "have_desert", "slogan", "fav_day",
"larkORowl", "fav_food", "fav_drink", "fav_season", "fav_month",
"hobby", "program", "specialization", "stat_software", "R_exp",
"coding_comfort", "coding_length", "top_three", "public_health_interest",
"fav_num", "bday", "bmonth", "country", "state", "city",
"highest_educ_level"
),
.cols = 1:27
)## id like_cats like_dogs have_desert
## 1 1 Yes Yes My phone ofc
## 2 2 No Yes Bible
## 3 3 No Yes Bible
## 4 4 Ambivalent Yes atmospheric water generator
## 5 5 No Ambivalent Water
## 6 6 No Yes Phone Network
## slogan fav_day
## 1 "Living with purpose, leaving with meaning." Friday
## 2 Every moment is a new beginning! Saturday
## 3 Work hard, Play Hard Thursday
## 4 Friday
## 5 Thanks to God for his indescribable gifts Saturday
## 6 Faith over Fear Saturday
## larkORowl
## 1 Lark ( I am a morning person)
## 2 Hummingbird (I am neither a lark nor an owl)
## 3 Lark ( I am a morning person)
## 4 Hummingbird (I am neither a lark nor an owl)
## 5 Owl (I am a night person)
## 6 Owl (I am a night person)
## fav_food
## 1 Injera with anything :-) ( it's an Ethiopian soft Traditional bread)
## 2 Stake
## 3 Ethiopian Cusine-Doro Wot is my favourite
## 4 Steak Sandwich with chimichurri
## 5 Chopped meat
## 6 Ethiopian food (Enjera+Doro Wot). M mom makes it the best.
## fav_drink fav_season fav_month
## 1 Fanta Spring June
## 2 Coffee Fall September
## 3 Coffee Fall September
## 4 Tequila lol Im jk I would say Ginger Ale Spring May
## 5 Water Fall May
## 6 Vanilla Milkshake Fall August
## hobby program specialization stat_software R_exp
## 1 chess MPH only Epidemiology/Biostatistics SPSS 2
## 2 Watching football MPH only Epidemiology/Biostatistics SPSS 2
## 3 Soccer MPH only Epidemiology/Biostatistics SPSS 2
## 4 Language Learning MPH only Epidemiology/Biostatistics SPSS 2
## 5 Soccer MPH only Epidemiology/Biostatistics SPSS 1
## 6 Reading MPH only Epidemiology/Biostatistics SPSS 2
## coding_comfort coding_length
## 1 2 0-3 months
## 2 2 0-3 months
## 3 2 0-3 months
## 4 2 0-3 months
## 5 2 0-3 months
## 6 2 4-6 months
## top_three
## 1 1. Code better\n2. Application in a real-world scenario\n3. How to interpret my analysis better for different audience
## 2 Use R comfortably, start coding confidently, and apply advanced methods to real data
## 3 Coding,data analysis in a very great detail
## 4 All the necessary skill set to be able to handle and interact with biostatisticians as part of a research team, such as improving my R skills and and learn a bit about how to handel complex regression models.
## 5 Coding, R Studio, and Analysis
## 6 Being familiar with different databases, choosing the right statistical tool for data analysis, getting more familiar with coding in R and be proficient in data analysis
## public_health_interest fav_num bday
## 1 Health Insurance and Nutrition. 28 10
## 2 Oncology 1 13
## 3 cancer 7 18
## 4 Cancer 3 5
## 5 Non communicable disease and quality of healthcare delivery 17 13
## 6 Cancer 7 June 2nd
## bmonth country state city highest_educ_level
## 1 6 Ethiopia Addis Ketema Addis Ababa Bachelor's degree
## 2 3 USA MO St. Louis Bachelor's degree
## 3 6 Ethiopia Addis Ababa Addis Ababa Bachelor's degree
## 4 Septemeber Ethiopia Addis Ababa Addis Ababa Doctoral degree
## 5 5 Ethiopia Addis Ababa Addis Ababa Master's degree
## 6 June Ethiopia Addis Ababa Addis Ababa Bachelor's degree##
## character integer
## 23 4##
## 1 10 12 13
## 2 2 2 4 3
## 14 15-Nov 16 17 18
## 1 1 2 2 3
## 19 2 20 21 22
## 1 1 1 4 2
## 23 24 24-Jun 25 26
## 1 2 1 2 1
## 27 28 29 3 30
## 4 2 2 1 1
## 31 5 6 7 8
## 2 1 2 1 1
## 9 August 2nd 1997 June 2nd
## 1 1 1##
## 1 10 11 2 3 4
## 1 3 5 4 5 2 5
## 5 6 7 8 9 August December
## 8 5 4 4 3 1 1
## January june June May November October Septemeber
## 1 1 1 1 1 1 1# Convert bday and bmonth to numeric if they are not already
C1_Survey$bday <- as.numeric(C1_Survey$bday)## Warning: NAs introduced by coercion## Warning: NAs introduced by coercion## 'data.frame': 58 obs. of 2 variables:
## $ bday : num 10 13 18 5 13 NA 1 6 17 20 ...
## $ bmonth: num 6 3 6 NA 5 NA 5 4 6 11 ...# Ensure bday and bmonth are numeric (handles factors imported from CSV)
C1_Survey$bday <- as.numeric(as.character(C1_Survey$bday))
C1_Survey$bmonth <- as.numeric(as.character(C1_Survey$bmonth))# Clean unusual values
C1_Survey$bday[C1_Survey$bday < 1 | C1_Survey$bday > 31] <- NA
C1_Survey$bmonth[C1_Survey$bmonth < 1 | C1_Survey$bmonth > 12] <- NA##
## 1 2 3 5 6 7 8 9 10 12 13 14 16 17 18 19
## 2 1 1 1 2 1 1 1 2 4 3 1 2 2 3 1
## 20 21 22 23 24 25 26 27 28 29 30 31 <NA>
## 1 4 2 1 2 2 1 4 2 2 1 2 6##
## 1 2 3 4 5 6 7 8 9 10 11 <NA>
## 3 5 2 5 8 5 4 4 3 5 4 10# Calculate median bday and bmonth, ignoring NAs
median_bday <- median(C1_Survey$bday, na.rm = TRUE)
median_bmonth <- median(C1_Survey$bmonth, na.rm = TRUE)C1_Survey <- C1_Survey %>%
mutate(bseason = case_when(
(bmonth == 12 & bday >= 1) | bmonth == 1 | bmonth == 2 ~ "Winter",
bmonth >= 3 & bmonth <= 5 ~ "Spring",
bmonth >= 6 & bmonth <= 8 ~ "Summer",
bmonth >= 9 & bmonth <= 11 ~ "Fall",
TRUE ~ NA_character_
))# Cross-tabulation of bmonth (rows) and bseason (columns)
table(C1_Survey$bmonth, C1_Survey$bseason, useNA = "ifany")##
## Fall Spring Summer Winter <NA>
## 1 0 0 0 3 0
## 2 0 0 0 5 0
## 3 0 2 0 0 0
## 4 0 5 0 0 0
## 5 0 8 0 0 0
## 6 0 0 5 0 0
## 7 0 0 4 0 0
## 8 0 0 4 0 0
## 9 3 0 0 0 0
## 10 5 0 0 0 0
## 11 4 0 0 0 0
## <NA> 0 0 0 0 10# Create a cross-tabulation of bmonth (rows) vs bseason (columns)
season_table <- table(C1_Survey$bmonth, C1_Survey$bseason, useNA = "ifany")
# Sum the columns to get number of classmates born in each season
season_summary <- addmargins(season_table, margin = 2) # margin = 2 sums columns
# Print the table with column sums
season_summary##
## Fall Spring Summer Winter <NA> Sum
## 1 0 0 0 3 0 3
## 2 0 0 0 5 0 5
## 3 0 2 0 0 0 2
## 4 0 5 0 0 0 5
## 5 0 8 0 0 0 8
## 6 0 0 5 0 0 5
## 7 0 0 4 0 0 4
## 8 0 0 4 0 0 4
## 9 3 0 0 0 0 3
## 10 5 0 0 0 0 5
## 11 4 0 0 0 0 4
## <NA> 0 0 0 0 10 10# Answer: Number of classmates born in each season
# Winter: season_summary["Sum", "Winter"]
# Spring: season_summary["Sum", "Spring"]
# Summer: season_summary["Sum", "Summer"]
# Fall: season_summary["Sum", "Fall"]# Check the distribution of the hobby variable, including missing values
table(C1_Survey$hobby, useNA = "ifany") # counts per hobby##
## Any activity associated with football "The real football not the NFL") (Playing in real life, playing in video games or watching the game)
## 1
## anyting that involves making/fixing stuff
## 1
## Canoeing
## 1
## chess
## 1
## Cooking
## 2
## Cooking
## 1
## Crochet
## 1
## Crocheting
## 1
## Dancing
## 1
## Gaming! Both Outdoor and Indoor(Board Games,PS5)
## 1
## Gardening
## 1
## Gym workout
## 1
## Hanging out with friends
## 1
## Hiking
## 1
## hiking!
## 1
## Language Learning
## 1
## Photography
## 1
## Pickleball
## 1
## Playing and Listening to Music
## 1
## Playing and watching sports
## 1
## Playing Bass Guitar
## 1
## Playing guitar
## 2
## Playing Lawn Tennis
## 1
## playing table tennis
## 1
## playing tennis
## 1
## Playing Video Games
## 1
## reading
## 1
## Reading
## 6
## Reading
## 1
## Running
## 1
## Running
## 1
## skiing
## 1
## Soccer
## 4
## spend time with loved ones
## 1
## sports
## 1
## Star gazing
## 1
## Table tennis
## 1
## Travelling
## 1
## Video games
## 1
## Watching football
## 1
## watching movie
## 1
## Watching movies
## 1
## Watching Movies
## 1
## watching soccer
## 1
## Watching soccer
## 2
## Watching Sports
## 1
## working-out
## 1##
## Any activity associated with football "The real football not the NFL") (Playing in real life, playing in video games or watching the game)
## 1.724138
## anyting that involves making/fixing stuff
## 1.724138
## Canoeing
## 1.724138
## chess
## 1.724138
## Cooking
## 3.448276
## Cooking
## 1.724138
## Crochet
## 1.724138
## Crocheting
## 1.724138
## Dancing
## 1.724138
## Gaming! Both Outdoor and Indoor(Board Games,PS5)
## 1.724138
## Gardening
## 1.724138
## Gym workout
## 1.724138
## Hanging out with friends
## 1.724138
## Hiking
## 1.724138
## hiking!
## 1.724138
## Language Learning
## 1.724138
## Photography
## 1.724138
## Pickleball
## 1.724138
## Playing and Listening to Music
## 1.724138
## Playing and watching sports
## 1.724138
## Playing Bass Guitar
## 1.724138
## Playing guitar
## 3.448276
## Playing Lawn Tennis
## 1.724138
## playing table tennis
## 1.724138
## playing tennis
## 1.724138
## Playing Video Games
## 1.724138
## reading
## 1.724138
## Reading
## 10.344828
## Reading
## 1.724138
## Running
## 1.724138
## Running
## 1.724138
## skiing
## 1.724138
## Soccer
## 6.896552
## spend time with loved ones
## 1.724138
## sports
## 1.724138
## Star gazing
## 1.724138
## Table tennis
## 1.724138
## Travelling
## 1.724138
## Video games
## 1.724138
## Watching football
## 1.724138
## watching movie
## 1.724138
## Watching movies
## 1.724138
## Watching Movies
## 1.724138
## watching soccer
## 1.724138
## Watching soccer
## 3.448276
## Watching Sports
## 1.724138
## working-out
## 1.724138##
## Any activity associated with football "The real football not the NFL") (Playing in real life, playing in video games or watching the game)
## 1.724138
## anyting that involves making/fixing stuff
## 1.724138
## Canoeing
## 1.724138
## chess
## 1.724138
## Cooking
## 3.448276
## Cooking
## 1.724138
## Crochet
## 1.724138
## Crocheting
## 1.724138
## Dancing
## 1.724138
## Gaming! Both Outdoor and Indoor(Board Games,PS5)
## 1.724138
## Gardening
## 1.724138
## Gym workout
## 1.724138
## Hanging out with friends
## 1.724138
## Hiking
## 1.724138
## hiking!
## 1.724138
## Language Learning
## 1.724138
## Photography
## 1.724138
## Pickleball
## 1.724138
## Playing and Listening to Music
## 1.724138
## Playing and watching sports
## 1.724138
## Playing Bass Guitar
## 1.724138
## Playing guitar
## 3.448276
## Playing Lawn Tennis
## 1.724138
## playing table tennis
## 1.724138
## playing tennis
## 1.724138
## Playing Video Games
## 1.724138
## reading
## 1.724138
## Reading
## 10.344828
## Reading
## 1.724138
## Running
## 1.724138
## Running
## 1.724138
## skiing
## 1.724138
## Soccer
## 6.896552
## spend time with loved ones
## 1.724138
## sports
## 1.724138
## Star gazing
## 1.724138
## Table tennis
## 1.724138
## Travelling
## 1.724138
## Video games
## 1.724138
## Watching football
## 1.724138
## watching movie
## 1.724138
## Watching movies
## 1.724138
## Watching Movies
## 1.724138
## watching soccer
## 1.724138
## Watching soccer
## 3.448276
## Watching Sports
## 1.724138
## working-out
## 1.724138