MECH481A6: Engineering Data Analysis in R
Chapter 8 Homework: Functional Programming
Connor Stephan
08 November, 2023
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Chapter 8 Homework
This homework will give you practice at writing functions, mapping functions, and cleaning/plotting data.
When a question asks you to make a plot, remember to set a theme, title, subtitle, labels, colors, etc. It is up to you how to personalize your plots, but put in some effort and make the plotting approach consistent throughout the document. For example, you could use the same theme for all plots. I also like to use the subtitle as a place for the main summary for the viewer.
Question 1
Write a function named sort_abs()that
takes a vector of numbers as input, calculates the absolute values of
each entry, and then outputs that vector sorted from smallest to largest
value.
## [1] 0 1 2 3 5 7 9Question 2
Modify the function import.w.name() to import the “date”
part of the filename (in addition to the sensor ID). Create a new column
variable called “date_created” with this information. Hint: you will
need to apply a regex pattern like this:
"(?<=_)[:alnum:]+(?=\\.)"
Question 3
This question is designed to give you practice at data cleaning.
First, create a pipeline that (1) uses purrr::map_dfr() and
import.w.name() to read in all the the PurpleAir data files
into a single data frame. Call that new data frame
PA_data_merged. (2) Then, have the pipeline convert the
character vector UTCDateTime into new column of class POSIXct using a
lubridate:: function (note - not all the indices in
UTCDateTime will parse correctly; we will address this in Question 4).
Finally, (3) finish the pipeline by renaming the
current_temp_f and current_humidity column
names to shorter names.
## # A tibble: 5 × 36
## date_time mac_address firmware_ver hardware temp humidity
## <dttm> <chr> <chr> <chr> <dbl> <dbl>
## 1 2018-10-24 00:00:35 68:c6:3a:89:1d:5b 2.50i 2.0+OPENLOG… 68 30
## 2 2018-10-24 00:01:55 68:c6:3a:89:1d:5b 2.50i 2.0+OPENLOG… 68 30
## 3 2018-10-24 00:03:15 68:c6:3a:89:1d:5b 2.50i 2.0+OPENLOG… 69 29
## 4 2018-10-24 00:04:35 68:c6:3a:89:1d:5b 2.50i 2.0+OPENLOG… 69 29
## 5 2018-10-24 00:05:55 68:c6:3a:89:1d:5b 2.50i 2.0+OPENLOG… 69 29
## # ℹ 30 more variables: current_dewpoint_f <dbl>, pressure <dbl>, adc <dbl>,
## # mem <dbl>, rssi <dbl>, uptime <dbl>, pm1_0_atm <dbl>, pm2_5_atm <dbl>,
## # pm10_0_atm <dbl>, pm1_0_cf_1 <dbl>, pm2_5_cf_1 <dbl>, pm10_0_cf_1 <dbl>,
## # p_0_3_um <dbl>, p_0_5_um <dbl>, p_1_0_um <dbl>, p_2_5_um <dbl>,
## # p_5_0_um <dbl>, p_10_0_um <dbl>, pm1_0_atm_b <dbl>, pm2_5_atm_b <dbl>,
## # pm10_0_atm_b <dbl>, pm1_0_cf_1_b <dbl>, pm2_5_cf_1_b <dbl>,
## # pm10_0_cf_1_b <dbl>, p_0_3_um_b <dbl>, p_0_5_um_b <dbl>, …Question 4
Can you find the 3 indices of UTCDateTime in PA_data_merged that
failed to parse with lubridate::? Hint: use the is.na()
function nested wihtin which() to return the row numbers in
question. Both of these are baseR functions. Once you have the row
entries identified you can View() them with a call to
slice().: normal entries in UTCDateTime are all the same
number of characters nchar() or entries that failed to parse in the new
date column will have NA associated with them.
Question 5
Create a series of EDA plots (cdf, boxplot, histogram, time series)
of the pm2_5_atm variable from PA_data_merged.
Use color = or fill = as an aesthetic to
differentiate each sensor by sensor_ID. Do the data have a
central tendency? Do they appear normally distributed? Do events show up
in the time series? Note: the variable pm2_5_atm is the
concentration of fine particulate matter air pollution in micrograms per
cubic meter (\(\mu g/m^3\)).
Extra Credit
Create the EDA figures within a single plot (hint: use the
gridExtra:: package). Show only one legend and place it
within the body of the CDF plot (hint: to move or remove a legend, add a
call that uses a version of
theme(legend.position = ...)).
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