Assignment-2-2020-s1-team-ZJZR
Jia Zeng and rui zhao
18/05/2020
Instructions to Students
This assignment is designed to simulate a scenario where you are given a dataset, and taking over someone’s existing work, and continuing with it to draw some further insights. This aspect of it is similar to Assignment 1, but it will provide less scaffolding, and ask you to draw more insights, as well as do more communication.
Your previous submission of crime data was well received!
You’ve now been given a different next task to work on. Your colleague at your consulting firm, Amelia (in the text treatment below) has written some helpful hints throughout the assignment to help guide you.
Questions that are worth marks are indicated with “Q” at the start and end of the question, as well as the number of marks in parenthesis. For example
## Q1A some text (0.5 marks)Is question one, part A, worth 0.5 marks
Marking + Grades
This assignment will be worth 6% of your total grade, and is marked out of 58 marks total.
- 9 Marks for grammar and clarity. You must write in complete sentences and do a spell check.
9 Marks for presentation of the data visualisations
- 40 marks for the questions
- Part 1: 7 Marks
- Park 2: 17 Marks
- Park 3: 16 Marks
Your marks will be weighted according to peer evaluation.
A Note on skills
As of week 6, you have seen most of the code for parts 1 - 2 that needs to be used here, and Week 7 will give you the skills to complete part 3. I do not expect you to know immediately what the code below does - this is a challenge for you! We will be covering skills on modelling in the next weeks, but this assignment is designed to simulate a real life work situation - this means that there are some things where you need to “learn on the job”. But the vast majority of the assignment will cover things that you will have seen in class, or the readings.
Remember, you can look up the help file for functions by typing ?function_name. For example, ?mean. Feel free to google questions you have about how to do other kinds of plots, and post on the ED if you have any questions about the assignment.
How to complete this assignment.
To complete the assignment you will need to fill in the blanks for function names, arguments, or other names. These sections are marked with *** or ___. At a minimum, your assignment should be able to be “knitted” using the knit button for your Rmarkdown document.
If you want to look at what the assignment looks like in progress, but you do not have valid R code in all the R code chunks, remember that you can set the chunk options to eval = FALSE like so:
```{r this-chunk-will-not-run, eval = FALSE}`r''`
ggplot()
```If you do this, please remember to ensure that you remove this chunk option or set it to eval = TRUE when you submit the assignment, to ensure all your R code runs.
You will be completing this assignment in your assigned groups. A reminder regarding our recommendations for completing group assignments:
- Each member of the group completes the entire assignment, as best they can.
- Group members compare answers and combine it into one document for the final submission.
Your assignments will be peer reviewed, and results checked for reproducibility. This means:
- 25% of the assignment grade will come from peer evaluation.
- Peer evaluation is an important learning tool.
Each student will be randomly assigned another team’s submission to provide feedback on three things:
- Could you reproduce the analysis?
- Did you learn something new from the other team’s approach?
- What would you suggest to improve their work?
Due Date
This assignment is due in by 4pm on Monday 18th May. You will submit the assignment via ED. Please change the file name to include your teams name. For example, if you are team dplyr, your assignment file name could read: “assignment-2-2020-s1-team-dplyr.Rmd”
Treatment
You work as a data scientist in the well named consulting company, “Consulting for You”.
On your second day at the company, you impressed the team with your work on crime data. Your boss says to you:
Amelia has managed to find yet another treasure trove of data - get this: pedestrian count data in inner city Melbourne! Amelia is still in New Zealand, and now won’t be back now for a while. They discovered this dataset the afternoon before they left on holiday, and got started on doing some data analysis.
We’ve got a meeting coming up soon where we need to discuss some new directions for the company, and we want you to tell us about this dataset and what we can do with it.
Most Importantly, can you get this to me by Monday 18th May, COB (COB = Close of Business at 5pm).
I’ve given this dataset to some of the other new hire data scientists as well, you’ll all be working as a team on this dataset. I’d like you to all try and work on the questions separately, and then combine your answers together to provide the best results.
From here, you are handed a USB stick. You load this into your computer, and you see a folder called “melbourne-walk”. In it is a folder called “data-raw”, and an Rmarkdown file. It contains the start of a data analysis. Your job is to explore the data and answer the questions in the document.
Note that the text that is written was originally written by Amelia, and you need to make sure that their name is kept up top, and to pay attention to what they have to say in the document!
Overview
The City of Melbourne has sensors set up in strategic locations across the inner city to keep hourly tallies of pedestrians. The data is updated on a monthly basis and available for download from Melbourne Open Data Portal. The rwalkr package provides an API in R to easily access sensor counts and geographic locations.
There are three parts to this work:
- Data wrangling and data visualisation of the pedestrian data
- Joining data together weather data
- Performing preliminary modelling
Part 1: Data wrangling and data visualisation of the pedestrian data
Amelia: I’ve downloaded a map chunk of Melbourne. Can you take the map I made, and plot the location of the sensors on top of it? We want to be able to see all of the sensors, but we also want to create different shapes for the following sensors:
- “Southern Cross Station”
- “Melbourne Central”
- “Flinders Street Station Underpass”
- “Birrarung Marr”
First we download the data on the pedestrian sensor locations around Melbourne.
And now we draw a plot on a map tile of the pedestrian sensor locations
Q1A Tell me what this map shows us? (0.5 Mark)
!> Answer:melbourne central has the highest walking people，flinders has the second, southern cross has third and birrarung marr has the lowest.
Q1B Create a markdown table of the number of sensors installed each year (0.5 Mark)
ped_loc %>%
# calculate the year from the date information
mutate(year =year(installation_date)) %>%
# count up the number of sensors
count(year) %>%
# then use `kable()` to create a markdown table
kable()| year | n |
|---|---|
| 2009 | 13 |
| 2013 | 12 |
| 2014 | 2 |
| 2015 | 7 |
| 2016 | 1 |
| 2017 | 9 |
| 2018 | 5 |
| 2019 | 6 |
| 2020 | 4 |
Additionally, how many sensors were added in 2016, and in 2017?
!> Answer:there are one in 2016 and 9 in 2017. > …
Q1C Filter the data down to just look at the selected four sensors (1 mark)
We would like you to focus on the foot traffic at 4 sensors:
- “Southern Cross Station”
- “Melbourne Central”
- “Flinders Street Station Underpass”
- “Birrarung Marr”
We’ve already downloaded the data for you, so your task is to:
- Filter the data down to include only the four sensors above
- add variables that contain the day of the month, month, year, and day in the year.
walk_2018 <- read_rds(
here::here("data-raw/walk_2018.rds")
) %>%
# Filter the data down to include only the four sensors above
filter(Sensor==c("Southern Cross Station",
"Melbourne Central",
"Flinders Street Station Underpass",
"Birrarung Marr")) %>%
# now add four columns, containing month day, month, year, and day of the year
# using functions from lubridate.
mutate(mday = mday(Date),
day=day(Date),
month=month(Date),
year=year(Date))Now we can plot the pedestrian count information for January - April in 2018
ggplot(walk_2018,
aes(x = Date_Time,
y = Count)) +
geom_line(size = 0.3) +
facet_grid(Sensor ~ .,
# this code presents the facets ina nice way
labeller = labeller(Sensor = label_wrap_gen(20))) +
# this code mades the x axis a bit nicer to read
scale_x_datetime(date_labels = "%d %b %Y",
date_minor_breaks = "1 month") +
labs(x = "Date Time")
We can see that there are quite different patterns in each of the sensors. Let’s explore this further.
Q1D How many types of activities might this capture at the four selected places? (0.5 marks)
!> Answer: too many types of activities might be caotured like walking,shopping and working….
Q1E Create a plot of the counts of each sensor over the day (2 Marks)
We’re primarily interested in exploiting pedestrian patterns at various time resolutions and across different locations. In light of people’s daily schedules, let’s plot the counts against time of day for each sensor.
ggplot(walk_2018,
aes(x = Time,
y = Count,
group = Date,
colour = Sensor)) +
geom_line() +
facet_wrap(~ Sensor,
labeller = labeller(Sensor = label_wrap_gen(20))) +
scale_colour_brewer(palette = "Dark2",
name = "Sensor") +
theme(legend.position = "none")
Write a short paragraph that describe what the plot shows:
- What is plotted on the graph? What does each line represent?
- Is the data in these sensors similar, or different?
- Does each panel show the same trend within it, or is there variation?
- What do you learn?
!> Answer: 1.each line of each sensor represent at that time how many people is walking on that street. 2.they are different. 3 they all have difference like southern cross is strange line and others are upward. 4.I learn midnight always have low people at street.
Q1F Exploring non work days (2 marks)
Use the data inside the hols_2018 data to identify weekdays and weekends, and holidays.
hols_2018 <- tsibble::holiday_aus(year = 2018, state = "VIC")
walk_2018_hols <- walk_2018 %>%
mutate(weekday = wday(Date, label = TRUE, week_start = 1),
workday = if_else(
condition = Date %in% (hols_2018$date) | isWeekend(Date),
true = "Non work day",
false = "work day"
))Now create a plot to compare the workdays to the non workdays.
ggplot(walk_2018_hols,
aes(x = Time,
y = Count,
group = Date,
colour = Sensor)) +
geom_line(size = 0.3,
alpha = 0.3) +
facet_grid(Sensor ~ workday,
labeller = labeller(Sensor = label_wrap_gen(20))) +
scale_colour_brewer(palette = "Dark2", name = "Sensor") +
theme(legend.position = "none")
Write a short paragraph that describe what the plot shows, and helps us answer these questions:
- What is plotted on the graph? What does each line represent?
- How are the data in these sensors similar or different?
- Does each panel show the same trend within it, or is there variation?
- What do you learn?
!> Answer: 1.each line of each sensor represent at that time how many people is walking on that street on non working or working time. 2.they are different. 3 they all have difference like working day southern cross station is very popular but if that is holiday or weekend, southern cross station will not have many people. 4.I learn midnight always have low people at street.
Q1G Calendar plot (0.5 mark)
To locate those unusual moments, Flinders Street Station data is calendarised on the canvas, using the sugrrants package. We can spot the unusual weekday patterns on public holidays using their color. Using the calendar plot, try to spot another unusual pattern, do a google search to try to explain the change in foot traffic. (Hint: Look at the plot carefully, does a particular day show a different daily pattern? Is there a specific event or festival happening on that day?)
# filter to just look at flinders st station
flinders <- walk_2018_hols %>% filter(Sensor == "Flinders Street Station Underpass")
flinders_cal <- flinders %>%
frame_calendar(x = Time, y = Count, date = Date)
gg_cal <- flinders_cal %>%
ggplot(aes(x = .Time, y = .Count, colour = workday, group = Date)) +
geom_line()
prettify(gg_cal) +
theme(legend.position = "bottom")
!> Answer:The workday is always a little wavy and weekend alway have the highest people during the afternoon.
Part Two: Combining data sources
Q2A We’ve got pedestrian count data for 2020, can you add that to the data? (2 marks)
You’ll need to ensure that you follow the steps we did earlier to filter the data and add the holiday information.
walk_2020 <- read_rds(
here::here("data-raw/walk_2020.rds")
) %>%
filter(Sensor==c("Southern Cross Station","Melbourne Central", "Flinders Street Station Underpass","Birrarung Marr") ) %>%
# now add four using `mutate` columns which contain the day of the month, month, and year, and day of the year using functions from lubridate.
mutate(mday = mday(Date),month=month(Date),year=year(Date),yday=yday(Date))Now add the holiday data
# also the steps for adding in the holiday info
hols_2020 <- tsibble::holiday_aus(year = 2020, state = "VIC")
walk_2020_hols <-
walk_2020 %>%
mutate(weekday= wday(Date,label = TRUE, week_start = 1),
workday= if_else(condition= wday(Date,label = TRUE, week_start = 1)%in% c("Sat","Sun")|yday(Date)%in% yday(hols_2020$date),
true= "non.working",
false= "work"))
melb_walk_hols <- bind_rows(walk_2018_hols, walk_2020_hols)Q2B There is some repetition in the code above from previous, describe two (or more) ways to limit that repetition, and demonstrate the use of this as a function (1 Mark)
filter_sensor <- function(data, sensors){
data %>% filter(Sensor %in% sensors)
}
add_day_info <- function(data){
# now add four using `mutate` columns which contain the day of the month, month, and year, and day of the year usin functions from lubridate.
data %>%
mutate(mday=mday(Date), month=month(Date),year=year(Date),yday=yday(Date))
}
add_working_day <- function(data){
walk_years <- unique(year(ymd(data$Date)))
hols <- tsibble::holiday_aus(year = walk_years, state = "VIC")
walk_years %>%
mutate(weekday= wday(data$Date,label = TRUE, week_start = 1),
workday= if_else(condition= wday(data$Date,label = TRUE, week_start = 1)%in% c("Sat","Sun")|yday(Date)%in% yday(hols$date),
true= "non.working",
false= "work"))
}Q2C For Flinders st, in the month of April, can you compare 2018 to 2020? (2 Marks)
Write a paragraph that describe what you learn from these plots. Can you describe any similarities, and differences amongst the plots, and why they might be similar or different? (You might need to play with the plot output size to clearly see the pattern)
melb_walk_hols_flinders_april <- melb_walk_hols %>%
filter(month==4, Sensor=="Flinders Street Station Underpass")
ggplot(melb_walk_hols_flinders_april,
aes(x=Time, y=Count,group=Date,
colour = as.factor(year))) +
geom_line() +
facet_wrap(~ Sensor, ncol = 5) +
theme(legend.position = "bottom") 
labs(colour = "Year")## $colour
## [1] "Year"
##
## attr(,"class")
## [1] "labels"!> Answer: In 2018, the flow of people here was very high, but by 2020 there was a significant decline, probably due to the opening of many new subway stations, which resulted in more convenient riding options for people. In 2018, most people should only choose flinders to ride.
Q2D Produce a similar plot (to 2C) that will also allow us to contrast the patterns across the four sensors. (2 marks)
What do you learn? Which Sensors seem the most similar? Or the most different?
melb_walk_hols_april <- melb_walk_hols %>% filter(month == 4)
ggplot(melb_walk_hols_april,
aes(x=Time, y=Count,
group= Date))+
geom_line()+
facet_grid(~year)+
theme(legend.position = "bottom") +
labs(colour = "Year")
!> Answer: in 2020 is most similar and 2018 is most different.
Combining weather data with pedestrian counts
One question we want to answer is: “Does the weather make a difference to the number of people walking out?”
Time of day and day of week are the predominant driving force of the number of pedestrian, depicted in the previous data plots. Apart from these temporal factors, the weather condition could possibly affect how many people are walking in the city. In particular, people are likely to stay indoors, when the day is too hot or too cold, or raining hard.
Daily meteorological data as a separate source, available on National Climatic Data Center, is used and joined to the main pedestrian data table using common dates.
Binary variables are created to serve as the tipping points
We have pulled information on weather stations for the Melbourne area - can you combine it together into one dataset?
Q2E Read in and add flagging variables to the weather data (2 marks)
- high_prcp if
prcp> 5 (if yes, “rain”, if no, “none”) - high_temp if
tmax> 33 (if yes, “hot”, if no, “not”) - low_temp if
tmin< 6 (if yes, “cold”, if no, “not”)
# Now create some flag variables
melb_weather_2018 <- read_csv(
here::here("data-raw/melb_ncdc_2018.csv")
) %>%
mutate(
high_prcp = if_else(condition = prcp > 5,
true ="rain",
false ="none"),
high_temp = if_else(condition = "tmax">33,
true = "hot",
false ="not"),
low_temp = if_else(condition = "tmin"<6,
true = "cold",
false = "not"))Q2F summarise the pedestrian count data and join it to the weather data (2 marks)
The weather data is per day, and the pedestrian count data is every hour. One way to explore this data is to collapse the pedestrian count data down to the total daily counts, so we can compare the total number of people each day to the weather for each day. This means each row is the total number of counts at each sensor, for each day.
Depending on how you do this, you will likely need to merge the pedestrian count data back with the weather data. Remember that we want to look at the data for 2018 only
melb_daily_walk_2018 <- melb_walk_hols %>%
filter(year==2018) %>%
group_by(Sensor, Date) %>%
summarise(Count=sum(Count)) %>%
ungroup()
melb_daily_walk_weather_2018 <- melb_daily_walk_2018 %>%
left_join(melb_weather_2018, by = c("Date"="date"))
melb_daily_walk_weather_2018## # A tibble: 480 x 10
## Sensor Date Count station tmax tmin prcp high_prcp high_temp
## <chr> <date> <int> <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 Birra… 2018-01-01 1362 ASN000… 26.2 14 0 none hot
## 2 Birra… 2018-01-02 1611 ASN000… 23.6 15.5 0 none hot
## 3 Birra… 2018-01-03 870 ASN000… 22.3 11.2 0 none hot
## 4 Birra… 2018-01-04 NA ASN000… 25.5 11.5 0 none hot
## 5 Birra… 2018-01-05 276 ASN000… 30.5 12.2 0 none hot
## 6 Birra… 2018-01-06 2189 ASN000… 41.5 16.6 0 none hot
## 7 Birra… 2018-01-07 323 ASN000… 22 15.7 0 none hot
## 8 Birra… 2018-01-08 1232 ASN000… 23.6 15.9 0 none hot
## 9 Birra… 2018-01-09 1925 ASN000… 22.8 13.9 0 none hot
## 10 Birra… 2018-01-10 2364 ASN000… 25.5 12.1 0 none hot
## # … with 470 more rows, and 1 more variable: low_temp <chr>Q2H Exploring sensor count against weather flegging variables (4 marks)
Create a few plots that look at the spread of the daily totals for each of the sensors, according to the weather flagging variables (high_prcp, high_temp, and low_temp). Write a paragraph that tells us what you learn from these plots, how you think weather might be impacting how people go outside. Make sure to discuss the strengths and limitations of the plots summarised like this, what assumption do they make?
# Plot of count for each sensor against high rain
ggplot(melb_daily_walk_weather_2018,
aes(y = Count,
x = Sensor,
colour = high_prcp)) +
geom_boxplot() +
theme(legend.position = "bottom")
# Plot against high temperature
ggplot(melb_daily_walk_weather_2018,
aes(y = Count,
x = Sensor,
colour = high_temp)) +
geom_boxplot() +
theme(legend.position = "bottom")
# Plot of low temperature
ggplot(melb_daily_walk_weather_2018,
aes(y = Count,
x = Sensor,
colour = low_temp)) +
geom_boxplot() +
theme(legend.position = "bottom")
!> Answer:
Q2F Combine the weather data with the pedestrian count data (2 marks)
The visualisations tell us something interesting about the data, but to really understand the data, we need to perform some modelling. To do this, you need to combine the weather data with the pedestrian data. We have provided the weather data for 2018 and 2020, combine with the pedestrian data for 2018 and 2020.
melb_weather_2018 <- read_csv(here::here("data-raw/melb_ncdc_2018.csv"))
melb_weather_2020 <- read_csv(here::here("data-raw/melb_ncdc_2020.csv"))
# task: combine the weather data together into an object, `melb_weather`
melb_weather <- bind_rows(melb_weather_2018,
melb_weather_2020)
# remember to add info about high precipitation, high temperature, + low temps
melb_weather<-melb_weather %>%
mutate(
high_prcp = if_else(condition = prcp > 5,
true ="rain",
false ="none"),
high_temp = if_else(condition = "tmax">33,
true = "hot",
false ="not"),
low_temp = if_else(condition = "tmin"<6,
true = "cold",
false = "not")
)%>%
rename(Date=date)
# now combine this weather data with the walking data
melb_walk_weather <- melb_walk_hols %>%
left_join(melb_weather, by ="Date")Part 3: Modelling Count data
We have been able to start answering the question, “Does the weather make a difference to the number of people walking out?” by looking at a few exploratory plots. However, we want to get a bit more definitive answer by performing some statistical modelling.
We are going to process the data somewhat so we can fit a linear model to the data. First, let’s take a set relevant variables to be factors. This ensures that the linear model interprets them appropriately.
We also add one to count and then take the natural log of it. The reasons for this are a bit complex, but essentially a linear model is not the most ideal model to fit for this data, and we can help it be more ideal by taking the log of the counts, which helps stabilise the residuals (predictions - observed) when we fit the model.
melb_walk_weather_prep_lm <- melb_walk_weather %>%
mutate_at(.vars = vars(Sensor,
Time,
month,
year,
workday,
high_prcp,
high_temp,
low_temp),
as_factor) %>%
mutate(log_count = log1p(Count))Q3A: Fit a linear model (1 mark)
Now we fit a linear model, predicting logCount using Time, Month, weekday and the weather flag variables (high_prcp, high_temp, and low_temp)
Q3B: Evaluate the model fit statistics (1 mark)
Provide some summary statistics on how well this model fits the data? What do you see? What statistics tell us about our model fit?
Make sure to reference all of the R packages that you used here, along with any links or blog posts that you used to help you answer these questions
Extra code
This code below here is what was used to retrieve the data in the data-raw folder.
#
# walk_2018 <- melb_walk(from = ymd("2018-01-01"), to = ymd("2018-04-30"))
# walk_2020 <- melb_walk(from = ymd("2020-01-01"), to = ymd("2020-04-30")) ##
# #
# write_rds(walk_2018,
# here::here("2020/assignment-2/data-raw/walk_2018.rds"),
# compress = "xz")
#
# write_rds(walk_2020,
# here::here("2020/assignment-2/data-raw/walk_2020.rds"),
# compress = "xz")# melb_bbox <- c(min(ped_loc$longitude) - .001,
# min(ped_loc$latitude) - 0.001,
# max(ped_loc$longitude) + .001,
# max(ped_loc$latitude) + 0.001)
#
# melb_map <- get_map(location = melb_bbox, source = "osm")
# write_rds(melb_map,
# path = here::here("2020/assignment-2/data-raw/melb-map.rds"),
# compress = "xz")# code to download the stations around the airport and the weather times
# this is purely here so you can see how we downloaded this data
# it is not needed for you to complete the assignment, so it is commented out
# melb_stns <- read_table(
# file = "https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt",
# col_names = c("ID",
# "lat",
# "lon",
# "elev",
# "state",
# "name",
# "v1",
# "v2",
# "v3"),
# skip = 353,
# n_max = 17081
# ) %>%
# filter(state == "MELBOURNE AIRPORT")
# #
# get_ncdc <- function(year){
# vroom::vroom(
# glue::glue(
# "https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_year/{year}.csv.gz"
# ),
# col_names = FALSE,
# col_select = 1:4
# )
# }
#
# clean_ncdc <- function(x){
# x %>%
# filter(X1 == melb_stns$ID, X3 %in% c("PRCP", "TMAX", "TMIN")) %>%
# rename_all(~ c("station", "date", "variable", "value")) %>%
# mutate(date = ymd(date), value = value / 10) %>%
# pivot_wider(names_from = variable, values_from = value) %>%
# rename_all(tolower)
# }
# ncdc_2018 <- get_ncdc(2018)
# melb_ncdc_2018 <- clean_ncdc(ncdc_2018)
# write_csv(melb_ncdc_2018,
# path = here::here("2020/assignment-2/data-raw/melb_ncdc_2018.csv"))
#
# ncdc_2020 <- get_ncdc(2020)
# beepr::beep(sound = 4)
# melb_ncdc_2020 <- clean_ncdc(ncdc_2020)
# beepr::beep(sound = 4)
# write_csv(melb_ncdc_2020,
# path = here::here("2020/assignment-2/data-raw/melb_ncdc_2020.csv"))