Upload a PDF file, named with your UC Davis email ID and homework
number (e.g., xjw18_hw2.pdf), to Gradescope (accessible
through Canvas). You will give the commands to answer each question in
its own code block, which will also produce output that will be
automatically embedded in the output file. When asked, answer must be
supported by written statements as well as any code used.
All code used to produce your results must be shown in your PDF
file (e.g., do not use echo = FALSE or
include = FALSE as options anywhere). Rmd
files do not need to be submitted, but may be requested by the TA and
must be available when the assignment is submitted.
Students may choose to collaborate with each other on the homework, but must clearly indicate with whom they collaborated. Every student must upload their own submission.
Start to work on it as early as possible
When you want to show your result as a vector that is too long,
slice the first 10 objects. When you want to show your result as a data
frame, use head() on it. Failure to do so may lead to point
deduction.
Directly knit the Rmd file will give you an html file. Open that file in your browser and then you can print it into a PDF file.
If you have not installed these packages, please install them by uncommenting the code. You only need to install the package once.
#install.packages("ggplot2")
#install.packages("tidyverse")
#install.packages("ggthemes")
#install.packages("ggrepel")
#install.packages("NHANES")
#install.packages("nycflights13")1. Load the dplyr package and the murders dataset. (In future courses before your project you need to write these code yourself to include package and dataset)
library(dplyr)
library(dslabs)
data(murders)You can add columns using the dplyr function
mutate. This function is aware of the column names and
inside the function you can call them unquoted:
murders <- mutate(murders, population_in_millions = population / 10^6)We can write population rather than
murders$population. The function mutate knows
we are grabbing columns from murders.
Use the function mutate to add a murders column named
rate with the per 100,000 murder rate as in the example
code above. Make sure you redefine murders as done in the
example code above ( murders <- [your code]) so we can keep using
this variable.
2. If rank(x) gives you the ranks of x from
lowest to highest, rank(-x) gives you the ranks from
highest to lowest. Use the function mutate to add a column
rank containing the rank, from highest to lowest murder
rate. Make sure you redefine murders so we can keep using
this variable.
3. With dplyr, we can use select to
show only certain columns. For example, with this code we would only
show the states and population sizes:
select(murders, state, population) |> head()Use select to show the state names and abbreviations in
murders. Do not redefine murders, just show
the results.
4. The dplyr function filter is used to
choose specific rows of the data frame to keep. Unlike
select which is for columns, filter is for
rows. For example, you can show just the New York row like this:
filter(murders, state == "New York") state abb region population total population_in_millions
1 New York NY Northeast 19378102 517 19.3781You can use other logical vectors to filter rows.
Use filter to show the top 5 states with the highest
murder rates. After we add murder rate and rank, do not change the
murders dataset, just show the result. Remember that you can filter
based on the rank column.
5. We can remove rows using the != operator. For
example, to remove Florida, we would do this:
no_florida <- filter(murders, state != "Florida")Create a new data frame called no_south that removes
states from the South region. How many states are in this category? You
can use the function nrow for this.
6. We can also use %in% to filter with
dplyr. You can therefore see the data from New York and
Texas like this:
filter(murders, state %in% c("New York", "Texas")) state abb region population total population_in_millions
1 New York NY Northeast 19378102 517 19.37810
2 Texas TX South 25145561 805 25.14556Create a new data frame called murders_nw with only the
states from the Northeast and the West. How many states are in this
category?
7. Suppose you want to live in the Northeast or West
and want the murder rate to be less than 10. We want to
see the data for the states satisfying these options. Note that you can
use logical operators with filter. Here is an example in
which we filter to keep only small states in the Northeast region.
filter(murders, population < 5000000 & region == "Northeast") state abb region population total population_in_millions
1 Connecticut CT Northeast 3574097 97 3.574097
2 Maine ME Northeast 1328361 11 1.328361
3 New Hampshire NH Northeast 1316470 5 1.316470
4 Rhode Island RI Northeast 1052567 16 1.052567
5 Vermont VT Northeast 625741 2 0.625741Make sure murders has been defined with
rate and rank and still has all states. Create
a table called my_states that contains rows for states
satisfying both the conditions: it is in the Northeast or West and the
murder rate is less than 10. Use select to show only the
state name, the rate, and the rank.
Reset murders to the original table by using
data(murders). Use a pipe to create a new data frame called
my_states that considers only states in the Northeast or
West which have a murder rate lower than 1, and contains only the state,
rate and rank columns. The pipe should also have four components
separated by three |>. The code should look something
like this:
my_states <- murders |>
mutate SOMETHING |>
filter SOMETHING |>
select SOMETHINGWe will using data from the nycflights13 package. So please install that package:
install.packages("nycflights13")Now we can library the package and access the data
flights
library(nycflights13)
library(tidyverse)
head(flights)# A tibble: 6 × 19
year month day dep_time sched_dep…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
<int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
1 2013 1 1 517 515 2 830 819 11 UA
2 2013 1 1 533 529 4 850 830 20 UA
3 2013 1 1 542 540 2 923 850 33 AA
4 2013 1 1 544 545 -1 1004 1022 -18 B6
5 2013 1 1 554 600 -6 812 837 -25 DL
6 2013 1 1 554 558 -4 740 728 12 UA
# … with 9 more variables: flight <int>, tailnum <chr>, origin <chr>,
# dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
# time_hour <dttm>, and abbreviated variable names ¹sched_dep_time,
# ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delayYou might notice that this data frame prints a little differently
from other data frames you might have used in the past: it only shows
the first few rows and all the columns that fit on one screen. (To see
the whole dataset, you can run View(flights) which will
open the dataset in the RStudio viewer). It prints differently because
it’s a tibble. Tibbles are data frames, but slightly
tweaked to work better in the tidyverse. For now, you don’t need to
worry about the differences.
You might also have noticed the row of three (or four) letter abbreviations under the column names. These describe the type of each variable:
int stands for integers.
dbl stands for doubles, or real numbers.
chr stands for character vectors, or strings.
dttm stands for date-times (a date + a time).
lgl stands for logical, vectors that contain only TRUE or FALSE.
fctr stands for factors, which R uses to represent categorical variables with fixed possible values.
date stands for dates.
Find all flights that
Had an arrival delay of two or more hours
Flew to Houston (IAH or HOU)
Were operated by United, American, or Delta
Departed in summer (July, August, and September)
Arrived more than two hours late, but didn’t leave late
Were delayed by at least 30 minutes, but made up over 30 minutes in flight
In each of the problem, only show the dim(result) where
result is your result for each sub problem.
Start by loading the dplyr and
ggplot2 library as well as the murders and
heights data.
library(dplyr)
library(ggplot2)
library(dslabs)
data(heights)
data(murders)1. With ggplot2 plots can be saved as objects. For example we can associate a dataset with a plot object like this
p <- ggplot(data = murders)Because data is the first argument we don’t need to
spell it out
p <- ggplot(murders)and we can also use the pipe:
p <- murders |> ggplot()What is class of the object p?
2. Remember that to print an object you can use the command
print or simply type the object. Print the object
p defined in exercise one and describe what you see.
3. Using the pipe |>, create an object p
but this time associated with the heights dataset instead
of the murders dataset.
4. Now we are going to add a layer and the corresponding aesthetic
mappings. For the murders data we plotted total murders versus
population sizes. Explore the murders data frame to remind
yourself what are the names for these two variables and select the
correct answer. Hint: Look at
?murders.
state and abb.total_murders and population_size.total and population.murders and size.5. To create the scatterplot we add a layer with
geom_point. The aesthetic mappings require us to define the
x-axis and y-axis variables, respectively. So the code looks like
this:
murders |> ggplot(aes(x = , y = )) +
geom_point()except we have to define the two variables x and
y. Fill this out with the correct variable names.
6. Note that if we don’t use argument names, we can obtain the same plot by making sure we enter the variable names in the right order like this:
murders |> ggplot(aes(population, total)) +
geom_point()Remake the plot but now with total in the x-axis and population in the y-axis.
7. If instead of points we want to add text, we can use the
geom_text() or geom_label() geometries. The
following code
murders |> ggplot(aes(population, total)) + geom_label()will give us the error message:
Error: geom_label requires the following missing aesthetics: label
Why is this?
geom_label know what character to use in
the plot.geom_label geometry does not require x-axis and
y-axis values.geom_label is not a ggplot2 command.8. Rewrite the code above to use abbreviation as the label through
aes
9. Change the color of the labels to blue. How will we do this?
blue to
murders.aes.color argument in ggplot.geom_label.10. Rewrite the code above to make the labels blue.
11. Now suppose we want to use color to represent the different regions. In this case which of the following is most appropriate:
color to murders
with the color we want to use.aes .color argument in ggplot.geom_label.12. Rewrite the code above to make the labels’ color be determined by the state’s region.
13. Now we are going to change the x-axis to a log scale to account
for the fact the distribution of population is skewed. Let’s start by
defining an object p holding the plot we have made up to
now
p <- murders |>
ggplot(aes(population, total, label = abb, color = region)) +
geom_label() To change the y-axis to a log scale we learned about the
scale_x_log10() function. Add this layer to the object
p to change the scale and render the plot.
14. Repeat the previous exercise but now change both axes to be in the log scale.
15. Now edit the code above to add the title “Gun murder data” to the
plot. Hint: use the ggtitle function.
For these exercises, we will be using the data from the survey collected by the United States National Center for Health Statistics (NCHS). This center has conducted a series of health and nutrition surveys since the 1960’s. Starting in 1999, about 5,000 individuals of all ages have been interviewed every year and they complete the health examination component of the survey. Part of the data is made available via the NHANES package. Once you install the NHANES package, you can load the data like this:
library(NHANES)
data(NHANES)The NHANES data has many missing values. The
mean and sd functions in R will return
NA if any of the entries of the input vector is an
NA. Here is an example:
library(dslabs)
data(na_example)
mean(na_example)[1] NAsd(na_example)[1] NATo ignore the NAs we can use the na.rm
argument:
mean(na_example, na.rm = TRUE)[1] 2.301754sd(na_example, na.rm = TRUE)[1] 1.22338Let’s now explore the NHANES data.
1. We will provide some basic facts about blood pressure. First let’s
select a group to set the standard. We will use 20-to-29-year-old
females. AgeDecade is a categorical variable with these
ages. Note that the category is coded like ” 20-29”, with a
space in front! What is the average and standard deviation of
systolic blood pressure as saved in the BPSysAve variable?
Save it to a variable called ref.
Hint: Use filter and summarize and use the
na.rm = TRUE argument when computing the average and
standard deviation. You can also filter the NA values using
filter.
2. Using a pipe, assign the average to a numeric variable
ref_avg. Hint: Use the code similar to above and then
pull.
3. Now report the min and max values for the same group.
4. Compute the average and standard deviation for females, but for
each age group separately rather than a selected decade as in question
1. Note that the age groups are defined by AgeDecade. Hint:
rather than filtering by age and gender, filter by Gender
and then use group_by.
5. We can actually combine both gender summaries into one line of
code. This is because group_by permits us to group by more
than one variable. Obtain one big summary table using
group_by(AgeDecade, Gender).
6. For males between the ages of 40-49, summarise the average
systolic blood pressure across race as reported in the
Race1 variable. Order the resulting table from lowest to
highest average systolic blood pressure. hint:
arrange()
We’ll be illustrating visualization process using a dataset comprising observations of life expectancies at birth for men, women, and the general population, along with GDP per capita and total population for 158 countries at approximately five-year intervals from 2000 to 2019.
Observational units: countries.
Variables: country, year, life expectancy at birth (men, women, overall), GDP per capita, total population, region (continent), and subregion.
Here we will load the data for you. In this problem you just need to
work on the dataset named data.
myfile <- "https://raw.githubusercontent.com/xjw1001001/xjw1001001.github.io/main/data/homework%20data/HW2data.csv"
data = read.csv(myfile) %>%
select(-X) %>%
mutate(Year = as.factor(Year))
head(data) Country.Name Year Life.Expectancy Male.Life.Expectancy Female.Life.Expectancy
1 Afghanistan 2019 63.2 63.3 63.2
2 Afghanistan 2015 61.7 61.0 62.3
3 Afghanistan 2010 59.9 59.6 60.3
4 Albania 2019 78.0 76.3 79.9
5 Albania 2015 77.8 76.1 79.7
6 Albania 2010 76.2 74.2 78.3
GDP.per.capita region sub.region Population
1 507.1034 Asia Southern Asia 38041754
2 578.4664 Asia Southern Asia 34413603
3 543.3030 Asia Southern Asia 29185507
4 5353.2449 Europe Southern Europe 2854191
5 3952.8012 Europe Southern Europe 2880703
6 4094.3503 Europe Southern Europe 29130211. Construct a scatterplot of Life.Expectancy against
GDP.per.capita; each point corresponds to one country in
one year. Change the X axis into ‘GDP per capita’ and Y axis into ‘Life
Expectancy at Birth’. Change the title into ‘Scatterplot of life
expectancy at birth against GDP per capita’. Change the scale of X axis
into log2 scale.
Name the plot object as p. Show the plot by call
p like this:
p = ggplot... +
geom_point... +
... +
...
# Show the plot
ptrans='log2' inside some function’s (). What’s
that function?2. Map Year (as nominal) to color in part 1 plot. Change the opacity
of all points into 0.8. (alpha=0.8 in
geom_point()) Does this color give you any information?
I have already transform the Year into factor by
mutate(Year = as.factor(Year)). Without that Year will be a
numeric variable and the color will be assigned continuous.
Start from the part 1 code. Just make a small modification in
aes part will be able to do that.
3. Instead, map region to color, population to size in the
aes(). Change the opacity of all points into 0.8. Does this
plot give you more information?
4. Now based on the part 3 plot, facet by the year. Use
facet_wrap.
5. Apply a new theme from the ggthemes package.
library(ggthemes)6. Use ggrepel to label the countries in the country list:
library(ggrepel)
country_list = c("Afghanistan","Belgium","Canada","Djibouti","France","Indonesia","Mexico","Sudan")Here is the code that you can generate the label for these selected
countries. Generate the plot. If you find the result plot texts are
colored, it may because you put color and size as global aes mapping.
You need to change it into local ones in geom_point().
box.padding, max.overlaps, force
to make it nicer.geom_text_repel(data =
subset(data, Country.Name %in% country_list),
aes(label = Country.Name),
box.padding = 0.5, max.overlaps = Inf,
force = 1.5) +
facet_wrap(~Year, ncol = 2)Names:
sessionInfo()R version 4.2.3 (2023-03-15 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22621)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggrepel_0.9.3 ggthemes_4.2.4 NHANES_2.1.0 lubridate_1.9.2
[5] forcats_1.0.0 stringr_1.5.0 purrr_1.0.1 readr_2.1.4
[9] tidyr_1.3.0 tibble_3.2.0 ggplot2_3.4.1 tidyverse_2.0.0
[13] nycflights13_1.0.2 dslabs_0.7.4 dplyr_1.1.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 pillar_1.8.1 bslib_0.4.2 compiler_4.2.3
[5] jquerylib_0.1.4 tools_4.2.3 digest_0.6.31 timechange_0.2.0
[9] jsonlite_1.8.4 evaluate_0.20 lifecycle_1.0.3 gtable_0.3.2
[13] pkgconfig_2.0.3 rlang_1.1.0 cli_3.6.0 rstudioapi_0.14
[17] yaml_2.3.7 xfun_0.37 fastmap_1.1.1 withr_2.5.0
[21] knitr_1.42 hms_1.1.3 generics_0.1.3 vctrs_0.6.0
[25] sass_0.4.5 grid_4.2.3 tidyselect_1.2.0 glue_1.6.2
[29] R6_2.5.1 fansi_1.0.4 rmarkdown_2.20 tzdb_0.3.0
[33] magrittr_2.0.3 scales_1.2.1 htmltools_0.5.4 colorspace_2.1-0
[37] utf8_1.2.3 stringi_1.7.12 munsell_0.5.0 cachem_1.0.7