PARTICIPANT NAME:NORBERT NAWE

Data

date_of_birth <- c("1997-01-22", "1994-05-21", "2009-11-14", "2010-09-14", "1979-02-17", 
         "1997-06-24", "2002-10-09", "1980-01-19", "2011-05-02", "2003-07-11", 
         "1997-04-03", "1983-10-16", "1986-08-01", "1978-09-16", "2011-11-24", 
         "2000-11-09", "2020-05-19", "2019-02-07", "2018-07-01", "2013-12-20", 
         "1999-09-12", "2006-12-20", "1998-12-11", "1979-05-03", "1991-01-28", 
         "1992-05-18", "1975-01-17", "2017-05-12", "2011-05-24", "2011-12-13", 
         "2007-10-22", "2018-06-23", "2020-02-27", "1983-03-14", "1987-08-17", 
         "2017-08-27", "2006-06-08", "2006-08-23", "1987-10-10", "2021-01-29", 
         "2008-04-18", "1979-12-29", "1994-11-11", "2009-04-18", "1977-12-07", 
         "2018-09-29", "2016-12-14", "2001-12-14", "2018-06-22", "1986-06-22")

district <- c("Rubavu", "Musanze", "Huye", "Huye", "Musanze", "Kigali", "Huye", 
              "Huye", "Rubavu", "Musanze", "Rubavu", "Kigali", "Kigali", "Kigali", 
              "Musanze", "Rusizi", "Huye", "Rubavu", "Kigali", "Rusizi", "Musanze", 
              "Musanze", "Huye", "Musanze", "Rusizi", "Rusizi", "Musanze", 
              "Kigali", "Rusizi", "Musanze", "Kigali", "Huye", "Rusizi", "Kigali", 
              "Musanze", "Kigali", "Rubavu", "Rubavu", "Huye", "Huye", "Rubavu", 
              "Rubavu", "Rusizi", "Musanze", "Huye", "Musanze", "Rubavu", "Rubavu", 
              "Musanze", "Musanze")

education <- c("PhD", "Master", "Bachelor", "PhD", "Bachelor", "Bachelor", 
               "PhD", "High School", "PhD", "Master", "PhD", "Bachelor", "Bachelor", 
               "Bachelor", "Master", "High School", "Bachelor", "High School", 
               "Bachelor", "Bachelor", "Bachelor", "PhD", "PhD", "Master", "PhD", 
               "Bachelor", "High School", "PhD", "Bachelor", "High School", 
               "PhD", "High School", "High School", "High School", "Bachelor", 
               "Bachelor", "Bachelor", "High School", "High School", "Master", 
               "Master", "High School", "Bachelor", "High School", "High School", 
               "Master", "Master", "High School", "Master", "PhD")

DATA PREPARATION

Let us convert date_of_birth to Date format

date_of_birth <- as.Date(date_of_birth) # as.date converts string into date format

Create a data frame combining the vectors

df <- data.frame(
  Birth_date = date_of_birth,
  District = district,
  Education = education,
  stringsAsFactors = FALSE
)
print(df)

##    Birth_date District   Education
## 1  1997-01-22   Rubavu         PhD
## 2  1994-05-21  Musanze      Master
## 3  2009-11-14     Huye    Bachelor
## 4  2010-09-14     Huye         PhD
## 5  1979-02-17  Musanze    Bachelor
## 6  1997-06-24   Kigali    Bachelor
## 7  2002-10-09     Huye         PhD
## 8  1980-01-19     Huye High School
## 9  2011-05-02   Rubavu         PhD
## 10 2003-07-11  Musanze      Master
## 11 1997-04-03   Rubavu         PhD
## 12 1983-10-16   Kigali    Bachelor
## 13 1986-08-01   Kigali    Bachelor
## 14 1978-09-16   Kigali    Bachelor
## 15 2011-11-24  Musanze      Master
## 16 2000-11-09   Rusizi High School
## 17 2020-05-19     Huye    Bachelor
## 18 2019-02-07   Rubavu High School
## 19 2018-07-01   Kigali    Bachelor
## 20 2013-12-20   Rusizi    Bachelor
## 21 1999-09-12  Musanze    Bachelor
## 22 2006-12-20  Musanze         PhD
## 23 1998-12-11     Huye         PhD
## 24 1979-05-03  Musanze      Master
## 25 1991-01-28   Rusizi         PhD
## 26 1992-05-18   Rusizi    Bachelor
## 27 1975-01-17  Musanze High School
## 28 2017-05-12   Kigali         PhD
## 29 2011-05-24   Rusizi    Bachelor
## 30 2011-12-13  Musanze High School
## 31 2007-10-22   Kigali         PhD
## 32 2018-06-23     Huye High School
## 33 2020-02-27   Rusizi High School
## 34 1983-03-14   Kigali High School
## 35 1987-08-17  Musanze    Bachelor
## 36 2017-08-27   Kigali    Bachelor
## 37 2006-06-08   Rubavu    Bachelor
## 38 2006-08-23   Rubavu High School
## 39 1987-10-10     Huye High School
## 40 2021-01-29     Huye      Master
## 41 2008-04-18   Rubavu      Master
## 42 1979-12-29   Rubavu High School
## 43 1994-11-11   Rusizi    Bachelor
## 44 2009-04-18  Musanze High School
## 45 1977-12-07     Huye High School
## 46 2018-09-29  Musanze      Master
## 47 2016-12-14   Rubavu      Master
## 48 2001-12-14   Rubavu High School
## 49 2018-06-22  Musanze      Master
## 50 1986-06-22  Musanze         PhD

Questions

Q1. Calculate the current age of each participant (Hint: use `Sys.Date()` to get today’s date. You also need to convert `date_of_birth` into Date.)

ANSWER

df$Age <- as.numeric(difftime(Sys.Date(), df$Birth_date, units = "days")) %/% 365

Convert Birth_date to Date format

df$Birth_date <- as.Date(df$Birth_date)

# Calculating Age in years and adding it as a new column named “Age_in_years”

#df\(Age_in_years <- as.numeric(difftime(Sys.Date(), df\)Birth_date, units = “days”)) %/% 365

View the updated data frame

head(df)

##   Birth_date District Education Age
## 1 1997-01-22   Rubavu       PhD  27
## 2 1994-05-21  Musanze    Master  30
## 3 2009-11-14     Huye  Bachelor  15
## 4 2010-09-14     Huye       PhD  14
## 5 1979-02-17  Musanze  Bachelor  45
## 6 1997-06-24   Kigali  Bachelor  27

Q2. Determine how many participants are older than 25 years.

#ANSWER

sum(df$Age > 25)

## [1] 21

Q3. Filter participants born after the year 2000.

ANSWER

df[which(df$Birth_date > as.Date("2000-12-31")), ]

##    Birth_date District   Education Age
## 3  2009-11-14     Huye    Bachelor  15
## 4  2010-09-14     Huye         PhD  14
## 7  2002-10-09     Huye         PhD  22
## 9  2011-05-02   Rubavu         PhD  13
## 10 2003-07-11  Musanze      Master  21
## 15 2011-11-24  Musanze      Master  13
## 17 2020-05-19     Huye    Bachelor   4
## 18 2019-02-07   Rubavu High School   5
## 19 2018-07-01   Kigali    Bachelor   6
## 20 2013-12-20   Rusizi    Bachelor  10
## 22 2006-12-20  Musanze         PhD  17
## 28 2017-05-12   Kigali         PhD   7
## 29 2011-05-24   Rusizi    Bachelor  13
## 30 2011-12-13  Musanze High School  12
## 31 2007-10-22   Kigali         PhD  17
## 32 2018-06-23     Huye High School   6
## 33 2020-02-27   Rusizi High School   4
## 36 2017-08-27   Kigali    Bachelor   7
## 37 2006-06-08   Rubavu    Bachelor  18
## 38 2006-08-23   Rubavu High School  18
## 40 2021-01-29     Huye      Master   3
## 41 2008-04-18   Rubavu      Master  16
## 44 2009-04-18  Musanze High School  15
## 46 2018-09-29  Musanze      Master   6
## 47 2016-12-14   Rubavu      Master   7
## 48 2001-12-14   Rubavu High School  22
## 49 2018-06-22  Musanze      Master   6

Q4. Identify the youngest participant and their location.

ANSWER

youngest <- df[which.min(df$Age), ] 

youngest

##    Birth_date District Education Age
## 40 2021-01-29     Huye    Master   3

Q5. Check which participants have “Master” as their education level.

ANSWER

df[which(df$Education=="Master"), ]

##    Birth_date District Education Age
## 2  1994-05-21  Musanze    Master  30
## 10 2003-07-11  Musanze    Master  21
## 15 2011-11-24  Musanze    Master  13
## 24 1979-05-03  Musanze    Master  45
## 40 2021-01-29     Huye    Master   3
## 41 2008-04-18   Rubavu    Master  16
## 46 2018-09-29  Musanze    Master   6
## 47 2016-12-14   Rubavu    Master   7
## 49 2018-06-22  Musanze    Master   6

Q6. Count how many participants are in “Kigali.”

ANSWER

sum(df$District=="Kigali")

## [1] 9

Q7. Find participants who are younger than 30 and live in “Huye.”

ANSWER

df[which(df$Age < 30 & df$District=="Huye"), ] #Or

##    Birth_date District   Education Age
## 3  2009-11-14     Huye    Bachelor  15
## 4  2010-09-14     Huye         PhD  14
## 7  2002-10-09     Huye         PhD  22
## 17 2020-05-19     Huye    Bachelor   4
## 23 1998-12-11     Huye         PhD  26
## 32 2018-06-23     Huye High School   6
## 40 2021-01-29     Huye      Master   3

subset(df, Age < 30 & District == "Huye")

##    Birth_date District   Education Age
## 3  2009-11-14     Huye    Bachelor  15
## 4  2010-09-14     Huye         PhD  14
## 7  2002-10-09     Huye         PhD  22
## 17 2020-05-19     Huye    Bachelor   4
## 23 1998-12-11     Huye         PhD  26
## 32 2018-06-23     Huye High School   6
## 40 2021-01-29     Huye      Master   3

Q8. Calculate the difference in days between the oldest and youngest participant’s date of birth.

ANSWER

Age_Diff_in_years <- diff(range(df$Birth_date))
Age_Diff_in_years

## Time difference of 16814 days

Q9. Sort the participants by their age in descending order.

ANSWER

sort(df$Age, decreasing = TRUE)

##  [1] 49 47 46 45 45 44 44 41 41 38 38 37 37 33 32 30 30 27 27 27 26 25 24 22 22
## [26] 21 18 18 17 17 16 15 15 14 13 13 13 12 10  7  7  7  6  6  6  6  5  4  4  3

Q10. Filter participants with “High School” education level and identify their locations.

ANSWER

df[df$Education == "High School", c("District", "Education")]

##    District   Education
## 8      Huye High School
## 16   Rusizi High School
## 18   Rubavu High School
## 27  Musanze High School
## 30  Musanze High School
## 32     Huye High School
## 33   Rusizi High School
## 34   Kigali High School
## 38   Rubavu High School
## 39     Huye High School
## 42   Rubavu High School
## 44  Musanze High School
## 45     Huye High School
## 48   Rubavu High School

Q11. Combine the three vectors into a data frame and view it.

ANSWER

df

##    Birth_date District   Education Age
## 1  1997-01-22   Rubavu         PhD  27
## 2  1994-05-21  Musanze      Master  30
## 3  2009-11-14     Huye    Bachelor  15
## 4  2010-09-14     Huye         PhD  14
## 5  1979-02-17  Musanze    Bachelor  45
## 6  1997-06-24   Kigali    Bachelor  27
## 7  2002-10-09     Huye         PhD  22
## 8  1980-01-19     Huye High School  44
## 9  2011-05-02   Rubavu         PhD  13
## 10 2003-07-11  Musanze      Master  21
## 11 1997-04-03   Rubavu         PhD  27
## 12 1983-10-16   Kigali    Bachelor  41
## 13 1986-08-01   Kigali    Bachelor  38
## 14 1978-09-16   Kigali    Bachelor  46
## 15 2011-11-24  Musanze      Master  13
## 16 2000-11-09   Rusizi High School  24
## 17 2020-05-19     Huye    Bachelor   4
## 18 2019-02-07   Rubavu High School   5
## 19 2018-07-01   Kigali    Bachelor   6
## 20 2013-12-20   Rusizi    Bachelor  10
## 21 1999-09-12  Musanze    Bachelor  25
## 22 2006-12-20  Musanze         PhD  17
## 23 1998-12-11     Huye         PhD  26
## 24 1979-05-03  Musanze      Master  45
## 25 1991-01-28   Rusizi         PhD  33
## 26 1992-05-18   Rusizi    Bachelor  32
## 27 1975-01-17  Musanze High School  49
## 28 2017-05-12   Kigali         PhD   7
## 29 2011-05-24   Rusizi    Bachelor  13
## 30 2011-12-13  Musanze High School  12
## 31 2007-10-22   Kigali         PhD  17
## 32 2018-06-23     Huye High School   6
## 33 2020-02-27   Rusizi High School   4
## 34 1983-03-14   Kigali High School  41
## 35 1987-08-17  Musanze    Bachelor  37
## 36 2017-08-27   Kigali    Bachelor   7
## 37 2006-06-08   Rubavu    Bachelor  18
## 38 2006-08-23   Rubavu High School  18
## 39 1987-10-10     Huye High School  37
## 40 2021-01-29     Huye      Master   3
## 41 2008-04-18   Rubavu      Master  16
## 42 1979-12-29   Rubavu High School  44
## 43 1994-11-11   Rusizi    Bachelor  30
## 44 2009-04-18  Musanze High School  15
## 45 1977-12-07     Huye High School  47
## 46 2018-09-29  Musanze      Master   6
## 47 2016-12-14   Rubavu      Master   7
## 48 2001-12-14   Rubavu High School  22
## 49 2018-06-22  Musanze      Master   6
## 50 1986-06-22  Musanze         PhD  38

Combining the three columns into a data frame

df1 <- data.frame(
  Birth_date = date_of_birth,
  District = district,
  Education = education,
  stringsAsFactors = FALSE
)

View the data frame

print(df1)

##    Birth_date District   Education
## 1  1997-01-22   Rubavu         PhD
## 2  1994-05-21  Musanze      Master
## 3  2009-11-14     Huye    Bachelor
## 4  2010-09-14     Huye         PhD
## 5  1979-02-17  Musanze    Bachelor
## 6  1997-06-24   Kigali    Bachelor
## 7  2002-10-09     Huye         PhD
## 8  1980-01-19     Huye High School
## 9  2011-05-02   Rubavu         PhD
## 10 2003-07-11  Musanze      Master
## 11 1997-04-03   Rubavu         PhD
## 12 1983-10-16   Kigali    Bachelor
## 13 1986-08-01   Kigali    Bachelor
## 14 1978-09-16   Kigali    Bachelor
## 15 2011-11-24  Musanze      Master
## 16 2000-11-09   Rusizi High School
## 17 2020-05-19     Huye    Bachelor
## 18 2019-02-07   Rubavu High School
## 19 2018-07-01   Kigali    Bachelor
## 20 2013-12-20   Rusizi    Bachelor
## 21 1999-09-12  Musanze    Bachelor
## 22 2006-12-20  Musanze         PhD
## 23 1998-12-11     Huye         PhD
## 24 1979-05-03  Musanze      Master
## 25 1991-01-28   Rusizi         PhD
## 26 1992-05-18   Rusizi    Bachelor
## 27 1975-01-17  Musanze High School
## 28 2017-05-12   Kigali         PhD
## 29 2011-05-24   Rusizi    Bachelor
## 30 2011-12-13  Musanze High School
## 31 2007-10-22   Kigali         PhD
## 32 2018-06-23     Huye High School
## 33 2020-02-27   Rusizi High School
## 34 1983-03-14   Kigali High School
## 35 1987-08-17  Musanze    Bachelor
## 36 2017-08-27   Kigali    Bachelor
## 37 2006-06-08   Rubavu    Bachelor
## 38 2006-08-23   Rubavu High School
## 39 1987-10-10     Huye High School
## 40 2021-01-29     Huye      Master
## 41 2008-04-18   Rubavu      Master
## 42 1979-12-29   Rubavu High School
## 43 1994-11-11   Rusizi    Bachelor
## 44 2009-04-18  Musanze High School
## 45 1977-12-07     Huye High School
## 46 2018-09-29  Musanze      Master
## 47 2016-12-14   Rubavu      Master
## 48 2001-12-14   Rubavu High School
## 49 2018-06-22  Musanze      Master
## 50 1986-06-22  Musanze         PhD

Q12. Create a logical vector to check which participants are either in “Kigali” or “Musanze.”

ANSWER

df$In_Kigali_or_Musanze <- df$District %in% c("Kigali", "Musanze")

Q13. Find participants whose age is a multiple of 5.

ANSWER

subset(df, Age %% 5 == 0)

##    Birth_date District   Education Age In_Kigali_or_Musanze
## 2  1994-05-21  Musanze      Master  30                 TRUE
## 3  2009-11-14     Huye    Bachelor  15                FALSE
## 5  1979-02-17  Musanze    Bachelor  45                 TRUE
## 18 2019-02-07   Rubavu High School   5                FALSE
## 20 2013-12-20   Rusizi    Bachelor  10                FALSE
## 21 1999-09-12  Musanze    Bachelor  25                 TRUE
## 24 1979-05-03  Musanze      Master  45                 TRUE
## 43 1994-11-11   Rusizi    Bachelor  30                FALSE
## 44 2009-04-18  Musanze High School  15                 TRUE

Q14. Determine the percentage of participants with a “Master” level of education.

ANSWER

mean(df$Education == "Master")*100

## [1] 18

Q15. Replace all occurrences of “PhD” in `education` with “Doctorate.”

ANSWER

df$Education <- replace(df$Education, df$Education == "PhD", "Doctorate")
df

##    Birth_date District   Education Age In_Kigali_or_Musanze
## 1  1997-01-22   Rubavu   Doctorate  27                FALSE
## 2  1994-05-21  Musanze      Master  30                 TRUE
## 3  2009-11-14     Huye    Bachelor  15                FALSE
## 4  2010-09-14     Huye   Doctorate  14                FALSE
## 5  1979-02-17  Musanze    Bachelor  45                 TRUE
## 6  1997-06-24   Kigali    Bachelor  27                 TRUE
## 7  2002-10-09     Huye   Doctorate  22                FALSE
## 8  1980-01-19     Huye High School  44                FALSE
## 9  2011-05-02   Rubavu   Doctorate  13                FALSE
## 10 2003-07-11  Musanze      Master  21                 TRUE
## 11 1997-04-03   Rubavu   Doctorate  27                FALSE
## 12 1983-10-16   Kigali    Bachelor  41                 TRUE
## 13 1986-08-01   Kigali    Bachelor  38                 TRUE
## 14 1978-09-16   Kigali    Bachelor  46                 TRUE
## 15 2011-11-24  Musanze      Master  13                 TRUE
## 16 2000-11-09   Rusizi High School  24                FALSE
## 17 2020-05-19     Huye    Bachelor   4                FALSE
## 18 2019-02-07   Rubavu High School   5                FALSE
## 19 2018-07-01   Kigali    Bachelor   6                 TRUE
## 20 2013-12-20   Rusizi    Bachelor  10                FALSE
## 21 1999-09-12  Musanze    Bachelor  25                 TRUE
## 22 2006-12-20  Musanze   Doctorate  17                 TRUE
## 23 1998-12-11     Huye   Doctorate  26                FALSE
## 24 1979-05-03  Musanze      Master  45                 TRUE
## 25 1991-01-28   Rusizi   Doctorate  33                FALSE
## 26 1992-05-18   Rusizi    Bachelor  32                FALSE
## 27 1975-01-17  Musanze High School  49                 TRUE
## 28 2017-05-12   Kigali   Doctorate   7                 TRUE
## 29 2011-05-24   Rusizi    Bachelor  13                FALSE
## 30 2011-12-13  Musanze High School  12                 TRUE
## 31 2007-10-22   Kigali   Doctorate  17                 TRUE
## 32 2018-06-23     Huye High School   6                FALSE
## 33 2020-02-27   Rusizi High School   4                FALSE
## 34 1983-03-14   Kigali High School  41                 TRUE
## 35 1987-08-17  Musanze    Bachelor  37                 TRUE
## 36 2017-08-27   Kigali    Bachelor   7                 TRUE
## 37 2006-06-08   Rubavu    Bachelor  18                FALSE
## 38 2006-08-23   Rubavu High School  18                FALSE
## 39 1987-10-10     Huye High School  37                FALSE
## 40 2021-01-29     Huye      Master   3                FALSE
## 41 2008-04-18   Rubavu      Master  16                FALSE
## 42 1979-12-29   Rubavu High School  44                FALSE
## 43 1994-11-11   Rusizi    Bachelor  30                FALSE
## 44 2009-04-18  Musanze High School  15                 TRUE
## 45 1977-12-07     Huye High School  47                FALSE
## 46 2018-09-29  Musanze      Master   6                 TRUE
## 47 2016-12-14   Rubavu      Master   7                FALSE
## 48 2001-12-14   Rubavu High School  22                FALSE
## 49 2018-06-22  Musanze      Master   6                 TRUE
## 50 1986-06-22  Musanze   Doctorate  38                 TRUE

Q16. Identify which participants have a `location` other than “Kigali.”

ANSWER

subset(df, District != "Kigali")

##    Birth_date District   Education Age In_Kigali_or_Musanze
## 1  1997-01-22   Rubavu   Doctorate  27                FALSE
## 2  1994-05-21  Musanze      Master  30                 TRUE
## 3  2009-11-14     Huye    Bachelor  15                FALSE
## 4  2010-09-14     Huye   Doctorate  14                FALSE
## 5  1979-02-17  Musanze    Bachelor  45                 TRUE
## 7  2002-10-09     Huye   Doctorate  22                FALSE
## 8  1980-01-19     Huye High School  44                FALSE
## 9  2011-05-02   Rubavu   Doctorate  13                FALSE
## 10 2003-07-11  Musanze      Master  21                 TRUE
## 11 1997-04-03   Rubavu   Doctorate  27                FALSE
## 15 2011-11-24  Musanze      Master  13                 TRUE
## 16 2000-11-09   Rusizi High School  24                FALSE
## 17 2020-05-19     Huye    Bachelor   4                FALSE
## 18 2019-02-07   Rubavu High School   5                FALSE
## 20 2013-12-20   Rusizi    Bachelor  10                FALSE
## 21 1999-09-12  Musanze    Bachelor  25                 TRUE
## 22 2006-12-20  Musanze   Doctorate  17                 TRUE
## 23 1998-12-11     Huye   Doctorate  26                FALSE
## 24 1979-05-03  Musanze      Master  45                 TRUE
## 25 1991-01-28   Rusizi   Doctorate  33                FALSE
## 26 1992-05-18   Rusizi    Bachelor  32                FALSE
## 27 1975-01-17  Musanze High School  49                 TRUE
## 29 2011-05-24   Rusizi    Bachelor  13                FALSE
## 30 2011-12-13  Musanze High School  12                 TRUE
## 32 2018-06-23     Huye High School   6                FALSE
## 33 2020-02-27   Rusizi High School   4                FALSE
## 35 1987-08-17  Musanze    Bachelor  37                 TRUE
## 37 2006-06-08   Rubavu    Bachelor  18                FALSE
## 38 2006-08-23   Rubavu High School  18                FALSE
## 39 1987-10-10     Huye High School  37                FALSE
## 40 2021-01-29     Huye      Master   3                FALSE
## 41 2008-04-18   Rubavu      Master  16                FALSE
## 42 1979-12-29   Rubavu High School  44                FALSE
## 43 1994-11-11   Rusizi    Bachelor  30                FALSE
## 44 2009-04-18  Musanze High School  15                 TRUE
## 45 1977-12-07     Huye High School  47                FALSE
## 46 2018-09-29  Musanze      Master   6                 TRUE
## 47 2016-12-14   Rubavu      Master   7                FALSE
## 48 2001-12-14   Rubavu High School  22                FALSE
## 49 2018-06-22  Musanze      Master   6                 TRUE
## 50 1986-06-22  Musanze   Doctorate  38                 TRUE

OR

df[which(df$District != "Kigali"), ]

##    Birth_date District   Education Age In_Kigali_or_Musanze
## 1  1997-01-22   Rubavu   Doctorate  27                FALSE
## 2  1994-05-21  Musanze      Master  30                 TRUE
## 3  2009-11-14     Huye    Bachelor  15                FALSE
## 4  2010-09-14     Huye   Doctorate  14                FALSE
## 5  1979-02-17  Musanze    Bachelor  45                 TRUE
## 7  2002-10-09     Huye   Doctorate  22                FALSE
## 8  1980-01-19     Huye High School  44                FALSE
## 9  2011-05-02   Rubavu   Doctorate  13                FALSE
## 10 2003-07-11  Musanze      Master  21                 TRUE
## 11 1997-04-03   Rubavu   Doctorate  27                FALSE
## 15 2011-11-24  Musanze      Master  13                 TRUE
## 16 2000-11-09   Rusizi High School  24                FALSE
## 17 2020-05-19     Huye    Bachelor   4                FALSE
## 18 2019-02-07   Rubavu High School   5                FALSE
## 20 2013-12-20   Rusizi    Bachelor  10                FALSE
## 21 1999-09-12  Musanze    Bachelor  25                 TRUE
## 22 2006-12-20  Musanze   Doctorate  17                 TRUE
## 23 1998-12-11     Huye   Doctorate  26                FALSE
## 24 1979-05-03  Musanze      Master  45                 TRUE
## 25 1991-01-28   Rusizi   Doctorate  33                FALSE
## 26 1992-05-18   Rusizi    Bachelor  32                FALSE
## 27 1975-01-17  Musanze High School  49                 TRUE
## 29 2011-05-24   Rusizi    Bachelor  13                FALSE
## 30 2011-12-13  Musanze High School  12                 TRUE
## 32 2018-06-23     Huye High School   6                FALSE
## 33 2020-02-27   Rusizi High School   4                FALSE
## 35 1987-08-17  Musanze    Bachelor  37                 TRUE
## 37 2006-06-08   Rubavu    Bachelor  18                FALSE
## 38 2006-08-23   Rubavu High School  18                FALSE
## 39 1987-10-10     Huye High School  37                FALSE
## 40 2021-01-29     Huye      Master   3                FALSE
## 41 2008-04-18   Rubavu      Master  16                FALSE
## 42 1979-12-29   Rubavu High School  44                FALSE
## 43 1994-11-11   Rusizi    Bachelor  30                FALSE
## 44 2009-04-18  Musanze High School  15                 TRUE
## 45 1977-12-07     Huye High School  47                FALSE
## 46 2018-09-29  Musanze      Master   6                 TRUE
## 47 2016-12-14   Rubavu      Master   7                FALSE
## 48 2001-12-14   Rubavu High School  22                FALSE
## 49 2018-06-22  Musanze      Master   6                 TRUE
## 50 1986-06-22  Musanze   Doctorate  38                 TRUE

17. Calculate the mean age of participants and identify participants older than the mean.

ANSWER

mean_age <- mean(df$Age)
mean_age

## [1] 23

The articipants older than the mean.

df[which(df$Age  > mean_age), ]

##    Birth_date District   Education Age In_Kigali_or_Musanze
## 1  1997-01-22   Rubavu   Doctorate  27                FALSE
## 2  1994-05-21  Musanze      Master  30                 TRUE
## 5  1979-02-17  Musanze    Bachelor  45                 TRUE
## 6  1997-06-24   Kigali    Bachelor  27                 TRUE
## 8  1980-01-19     Huye High School  44                FALSE
## 11 1997-04-03   Rubavu   Doctorate  27                FALSE
## 12 1983-10-16   Kigali    Bachelor  41                 TRUE
## 13 1986-08-01   Kigali    Bachelor  38                 TRUE
## 14 1978-09-16   Kigali    Bachelor  46                 TRUE
## 16 2000-11-09   Rusizi High School  24                FALSE
## 21 1999-09-12  Musanze    Bachelor  25                 TRUE
## 23 1998-12-11     Huye   Doctorate  26                FALSE
## 24 1979-05-03  Musanze      Master  45                 TRUE
## 25 1991-01-28   Rusizi   Doctorate  33                FALSE
## 26 1992-05-18   Rusizi    Bachelor  32                FALSE
## 27 1975-01-17  Musanze High School  49                 TRUE
## 34 1983-03-14   Kigali High School  41                 TRUE
## 35 1987-08-17  Musanze    Bachelor  37                 TRUE
## 39 1987-10-10     Huye High School  37                FALSE
## 42 1979-12-29   Rubavu High School  44                FALSE
## 43 1994-11-11   Rusizi    Bachelor  30                FALSE
## 45 1977-12-07     Huye High School  47                FALSE
## 50 1986-06-22  Musanze   Doctorate  38                 TRUE

18. Group participants by `education` and count how many are in each group.

ANSWER

by(df$Education, df$Education, length)

## df$Education: Bachelor
## [1] 16
## ------------------------------------------------------------ 
## df$Education: Doctorate
## [1] 11
## ------------------------------------------------------------ 
## df$Education: High School
## [1] 14
## ------------------------------------------------------------ 
## df$Education: Master
## [1] 9

OR

table(df$Education)

## 
##    Bachelor   Doctorate High School      Master 
##          16          11          14           9

19. Filter participants born in the 1990s.

ANSWER

with(df, df[Birth_date >= as.Date("1990-01-01") & Birth_date < as.Date("2000-01-01"), ])  #OR

##    Birth_date District Education Age In_Kigali_or_Musanze
## 1  1997-01-22   Rubavu Doctorate  27                FALSE
## 2  1994-05-21  Musanze    Master  30                 TRUE
## 6  1997-06-24   Kigali  Bachelor  27                 TRUE
## 11 1997-04-03   Rubavu Doctorate  27                FALSE
## 21 1999-09-12  Musanze  Bachelor  25                 TRUE
## 23 1998-12-11     Huye Doctorate  26                FALSE
## 25 1991-01-28   Rusizi Doctorate  33                FALSE
## 26 1992-05-18   Rusizi  Bachelor  32                FALSE
## 43 1994-11-11   Rusizi  Bachelor  30                FALSE

subset(df, Birth_date >= as.Date("1990-01-01") & Birth_date < as.Date("2000-01-01"))

##    Birth_date District Education Age In_Kigali_or_Musanze
## 1  1997-01-22   Rubavu Doctorate  27                FALSE
## 2  1994-05-21  Musanze    Master  30                 TRUE
## 6  1997-06-24   Kigali  Bachelor  27                 TRUE
## 11 1997-04-03   Rubavu Doctorate  27                FALSE
## 21 1999-09-12  Musanze  Bachelor  25                 TRUE
## 23 1998-12-11     Huye Doctorate  26                FALSE
## 25 1991-01-28   Rusizi Doctorate  33                FALSE
## 26 1992-05-18   Rusizi  Bachelor  32                FALSE
## 43 1994-11-11   Rusizi  Bachelor  30                FALSE

20. Identify participants with “Bachelor” education level who are also younger than 30.

ANSWER

with(df, df[Education == "Bachelor" & Age < 30, ])

##    Birth_date District Education Age In_Kigali_or_Musanze
## 3  2009-11-14     Huye  Bachelor  15                FALSE
## 6  1997-06-24   Kigali  Bachelor  27                 TRUE
## 17 2020-05-19     Huye  Bachelor   4                FALSE
## 19 2018-07-01   Kigali  Bachelor   6                 TRUE
## 20 2013-12-20   Rusizi  Bachelor  10                FALSE
## 21 1999-09-12  Musanze  Bachelor  25                 TRUE
## 29 2011-05-24   Rusizi  Bachelor  13                FALSE
## 36 2017-08-27   Kigali  Bachelor   7                 TRUE
## 37 2006-06-08   Rubavu  Bachelor  18                FALSE

OR

subset(df, Education == "Bachelor" & Age < 30)

##    Birth_date District Education Age In_Kigali_or_Musanze
## 3  2009-11-14     Huye  Bachelor  15                FALSE
## 6  1997-06-24   Kigali  Bachelor  27                 TRUE
## 17 2020-05-19     Huye  Bachelor   4                FALSE
## 19 2018-07-01   Kigali  Bachelor   6                 TRUE
## 20 2013-12-20   Rusizi  Bachelor  10                FALSE
## 21 1999-09-12  Musanze  Bachelor  25                 TRUE
## 29 2011-05-24   Rusizi  Bachelor  13                FALSE
## 36 2017-08-27   Kigali  Bachelor   7                 TRUE
## 37 2006-06-08   Rubavu  Bachelor  18                FALSE

END

```

PARTICIPANT NAME:NORBERT NAWE

Data

DATA PREPARATION

Let us convert date_of_birth to Date format

Create a data frame combining the vectors

Questions

Q1. Calculate the current age of each participant (Hint: use Sys.Date() to get today’s date. You also need to convert date_of_birth into Date.)

ANSWER

Convert Birth_date to Date format

# Calculating Age in years and adding it as a new column named “Age_in_years”

View the updated data frame

Q2. Determine how many participants are older than 25 years.

Q3. Filter participants born after the year 2000.

ANSWER

Q4. Identify the youngest participant and their location.

ANSWER

Q5. Check which participants have “Master” as their education level.

ANSWER

Q6. Count how many participants are in “Kigali.”

ANSWER

Q7. Find participants who are younger than 30 and live in “Huye.”

ANSWER

Q8. Calculate the difference in days between the oldest and youngest participant’s date of birth.

ANSWER

Q9. Sort the participants by their age in descending order.

ANSWER

Q10. Filter participants with “High School” education level and identify their locations.

ANSWER

Q11. Combine the three vectors into a data frame and view it.

ANSWER

Combining the three columns into a data frame

View the data frame

Q12. Create a logical vector to check which participants are either in “Kigali” or “Musanze.”

ANSWER

Q13. Find participants whose age is a multiple of 5.

ANSWER

Q14. Determine the percentage of participants with a “Master” level of education.

ANSWER

Q15. Replace all occurrences of “PhD” in education with “Doctorate.”

ANSWER

Q16. Identify which participants have a location other than “Kigali.”

ANSWER

OR

17. Calculate the mean age of participants and identify participants older than the mean.

ANSWER

The articipants older than the mean.

18. Group participants by education and count how many are in each group.

ANSWER

OR

19. Filter participants born in the 1990s.

ANSWER

20. Identify participants with “Bachelor” education level who are also younger than 30.

ANSWER

OR

END

Q1. Calculate the current age of each participant (Hint: use `Sys.Date()` to get today’s date. You also need to convert `date_of_birth` into Date.)

Q15. Replace all occurrences of “PhD” in `education` with “Doctorate.”

Q16. Identify which participants have a `location` other than “Kigali.”

18. Group participants by `education` and count how many are in each group.