Exercise 1: Univariate Data Reshaping

Univariate Option 1:Life Expectancy vs Year

1.1 Data in Wider Format

Let’s choose 2 other countries to compare to Canada

gapwide <- gapminder %>% 
  filter((country=="Algeria")|(country=="Bahrain")|(country=="Canada")) %>%
  pivot_wider(id_cols= year, 
              names_from= country,
              values_from = lifeExp) 
  gapwide %>%
    DT::datatable()

1.2 Scatter Plot for 2 countries against each other:

Let’s choose Algeria and Bahrain to plot against each other.

gapwide %>%
  ggplot(aes(x=Algeria, y=Bahrain))+
  geom_point()+
  labs(title="Scatter Plot of Bahrain's Life Expectancy vs Algeria's Life Expectancy",x="Algeria(Years)",y="Bahrain(Years)")+
  theme_bw()

Over time, life Expectancy of Bahrain is higher compared to Algeria

1.3 Relengthen Data:

gapwide %>%
  pivot_longer(cols = c(-year),
               names_to = "Country",
               values_to="Life Expectancy") %>%
  DT::datatable()

Excercise 2: Multivariate Data Reshaping:

Multivariate Option 1: Life Expectancy-Gdp

2.1 Wide Tibble

gap_mulit_wide <- gapminder %>%
filter(country== "Australia"| country == "Bahrain") %>%
  pivot_wider(id_cols = year,
              names_from = country,
              values_from = c(lifeExp,gdpPercap))
gap_mulit_wide %>% knitr::kable()
year lifeExp_Australia lifeExp_Bahrain gdpPercap_Australia gdpPercap_Bahrain
1952 69.120 50.939 10039.60 9867.085
1957 70.330 53.832 10949.65 11635.799
1962 70.930 56.923 12217.23 12753.275
1967 71.100 59.923 14526.12 14804.673
1972 71.930 63.300 16788.63 18268.658
1977 73.490 65.593 18334.20 19340.102
1982 74.740 69.052 19477.01 19211.147
1987 76.320 70.750 21888.89 18524.024
1992 77.560 72.601 23424.77 19035.579
1997 78.830 73.925 26997.94 20292.017
2002 80.370 74.795 30687.75 23403.559
2007 81.235 75.635 34435.37 29796.048

2.2 Relengthen Data:

gap_mulit_wide %>%
  pivot_longer(cols=c(-year),
               names_to = c(".value", "country"),
               names_sep = "_") %>%
  knitr::kable()
year country lifeExp gdpPercap
1952 Australia 69.120 10039.596
1952 Bahrain 50.939 9867.085
1957 Australia 70.330 10949.650
1957 Bahrain 53.832 11635.799
1962 Australia 70.930 12217.227
1962 Bahrain 56.923 12753.275
1967 Australia 71.100 14526.125
1967 Bahrain 59.923 14804.673
1972 Australia 71.930 16788.629
1972 Bahrain 63.300 18268.658
1977 Australia 73.490 18334.198
1977 Bahrain 65.593 19340.102
1982 Australia 74.740 19477.009
1982 Bahrain 69.052 19211.147
1987 Australia 76.320 21888.889
1987 Bahrain 70.750 18524.024
1992 Australia 77.560 23424.767
1992 Bahrain 72.601 19035.579
1997 Australia 78.830 26997.937
1997 Bahrain 73.925 20292.017
2002 Australia 80.370 30687.755
2002 Bahrain 74.795 23403.559
2007 Australia 81.235 34435.367
2007 Bahrain 75.635 29796.048 
## .value separates values for the measures of lifeExp and gdpPerCap

Excercise 3.0 Table Joins:

Loading the guest and email data

guest <- read_csv("https://raw.githubusercontent.com/STAT545-UBC/Classroom/master/data/wedding/attend.csv")
## Parsed with column specification:
## cols(
##   party = col_double(),
##   name = col_character(),
##   meal_wedding = col_character(),
##   meal_brunch = col_character(),
##   attendance_wedding = col_character(),
##   attendance_brunch = col_character(),
##   attendance_golf = col_character()
## )
email <- read_csv("https://raw.githubusercontent.com/STAT545-UBC/Classroom/master/data/wedding/emails.csv")
## Parsed with column specification:
## cols(
##   guest = col_character(),
##   email = col_character()
## )

3.01 First Edit email csv:

Each guest should on one line for each email and guest field changed to name to make it easier to join

Email <- email %>%
  separate_rows(email,guest, sep = ",") %>%
  select(name=guest,email) %>%
 mutate(name=trimws(name, "both"))

## There is a white space in Email dataset, removed this

3.1 Add Emails for Each Guest on List:

Assumption: Only including guests from guest list who have a corresponding email from the email tibble: (no additonal emails, no additional guests):

guest_emails <- Email %>%
  inner_join(guest,Email, by="name")

guest_emails %>% DT::datatable()

3.2 Emails but not on Guest List

The names of peoples with emails but are not on the guest list:

guest_emails %>%
  select(name,email) %>%
  setdiff(Email,.) %>%
  knitr::kable()
name email
Turner Jones tjjones12@hotmail.ca
Albert Marshall themarshallfamily1234@gmail.com
Vivian Marshall themarshallfamily1234@gmail.com

These 3 are not found in the guest dataset.

3.3 Guest List for all those that we have emails for:

A new guest list with names of people on the original guest list with emails and those not on the original guest list who also have emails (found on email dataset but not guest dataset)

Email %>%
  left_join(guest,Email, by="name") %>%
DT::datatable()

3–Extra Analysis:

There are 5 guests who don’t have emails in the email dataset. These are the ones that don’t have a corresponding email in the email tibble but are found on the guest list (guest dataset)

Let’s find out who the five guests on the guest list that don’t have emails are :

guest_emails%>%
  select(-email) %>%
  setdiff(guest,.) %>%
  knitr::kable()
name party meal_wedding meal_brunch attendance_wedding attendance_brunch attendance_golf
Atlanta Connolly 5 PENDING PENDING PENDING PENDING PENDING
Denzel Connolly 5 fish Menu B CONFIRMED CONFIRMED CONFIRMED
Chanelle Shah 5 chicken Menu C CONFIRMED CONFIRMED CONFIRMED
Cosmo Dunkley 10 PENDING PENDING PENDING PENDING PENDING
Cai Mcdaniel 11 fish Menu C CONFIRMED CONFIRMED CONFIRMED