my.seq <- seq(from = 1, to = 11, by = 2)
my.seq[1] 1 3 5 7 9 11Tutorial 4 Answers
Problem Set 4: Questions and Answers
- When we change from a histogram that reports the number of years by number of hurricanes to share of years by number of hurricanes, the numbering on the y axis changes, but the height of the bars is constant. Why?
Answer:
Let the height of the bars be \(x_i\), where \(i\) is the index of the \(N\) groups, and \(\sum_{i=1}^N x_i = Z\). If the bar were the share of the total, rather than the group’s total, it would be \(\frac{x_i}{N}\). In other words, either in levels or shares, the bars are simply scaled by a different denominator, keeping their relative position the same.
- Why does the income histogram look so odd when bins are very narrow? Explain in your own words.
Answer:
The income histogram looks wacky when the bins are narrow for multiple reasons
- the series is choppy, rather than smooth, as it is with larger bins
- the end of the series has a very very high bar (literally), making it dominate the distribution
- the individual categories on the x-axis are very hard to read
- We created the histogram with income distribution for all four states. In one of those we have the number of block groups on the vertical axis; in the other the share. Explain (a) what the share is of (number of block groups from what total number) and (b) why the share makes a more reasonable comparison than the levels.
Answer:
- Number of block groups divided by the total number of block groups in that state
- When you compare levels, you are mostly comparing the number of block groups, not the relative frequency of block groups. In the type of comparison we were making, the object of greater interest was the relative frequency.
- Create a sequence of odd numbers from 1 to 11, inclusive. (Not a graph, just a list of numbers.) Report the code and output.
Answer:
- Choose a new variable in the block group dataframe with which to make a histogram from the block group data (not income!). Here I define histogram broadly, as any of the types of charts we’ve explored in this tutorial. Your chart should show the four states in the DC area, grouped or faceted as you please. (However, West Virginia has very few observations and can sometimes complicate things; you can drop it if you prefer.)
# load library
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.7 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()# load the data
# data are created in
# /groups/brooksgrp/pppa_dataviz/r_programs/2021/acsinv03.R
block.groups <- readRDS("h:/pppa_data_viz/2021/tutorial_data/tutorial_04/acs5_bg_dmv_2019_20210202.rds")
names(block.groups) [1] "NAME" "B01001_001E" "B17017_001E" "B17017_002E" "B19013_001E"
[6] "B19057_001E" "B19057_002E" "B25003_001E" "B25003_002E" "B11007_001E"
[11] "B11007_003E" "B25057_001E" "B25058_001E" "B25059_001E" "B25076_001E"
[16] "B25077_001E" "B25079_001E" "B25107_001E" "B25107_002E" "B25107_003E"
[21] "B25107_004E" "B25107_005E" "B25107_006E" "B25107_007E" "B25107_008E"
[26] "B25107_009E" "B25107_010E" "B25107_011E" "state" "county"
[31] "tract" "block.group" "acs" head(block.groups$B25077_001E)[1] 558000 614100 206400 282400 354900 240600summary(block.groups$B25077_001E) Min. 1st Qu. Median Mean 3rd Qu. Max.
-666666666 280350 396650 -42124085 587300 2000001 # get rid of super-low value in 25th percentile distribution
block.groups$p25 <- ifelse(test = block.groups$B25077_001E > 0,
yes = block.groups$B25077_001E,
no = NA)
# make a distribution by state of 25th p of housing value
h1 <- ggplot() +
geom_density(data = block.groups,
mapping = aes(x = p25, y = ..density.., color = as.factor(state))) +
labs(x="25th percentile of housing value",
y="share of block groups")
h1Warning: Removed 228 rows containing non-finite values (stat_density).
- Make two histograms from a dataset (one dataset is sufficient) that we have not used in this class. A good place to start if you have no favorite data is Open Data DC here.
- use whatever variables you please
- write 2 to 3 sentences describing what you find
Answer:
Here is one example, using 311 data from the city of Los Angeles.
# location of la's 311 data for 2022
# https://data.lacity.org/City-Infrastructure-Service-Requests/MyLA311-Service-Request-Data-2022/i5ke-k6by
three11 <- "https://data.lacity.org/resource/i5ke-k6by.csv"
# load the data
la3 <- read_csv(three11)Rows: 1000 Columns: 34
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (26): SRNumber, CreatedDate, UpdatedDate, ActionTaken, Owner, RequestTyp...
dbl (8): HouseNumber, ZipCode, Latitude, Longitude, TBMPage, TBMRow, CD, NC
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(la3)spec_tbl_df [1,000 × 34] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ SRNumber : chr [1:1000] "1-2154996101" "1-2154995181" "1-2154996311" "1-2154996331" ...
$ CreatedDate : chr [1:1000] "01/01/2022 12:08:14 AM" "01/01/2022 12:15:59 AM" "01/01/2022 12:24:31 AM" "01/01/2022 12:24:38 AM" ...
$ UpdatedDate : chr [1:1000] "01/03/2022 10:39:18 PM" "01/01/2022 01:06:13 PM" "01/03/2022 12:21:42 AM" "01/03/2022 09:37:09 AM" ...
$ ActionTaken : chr [1:1000] "SR Created" "SR Created" "SR Created" "SR Created" ...
$ Owner : chr [1:1000] "LASAN" "LASAN" "LASAN" "LASAN" ...
$ RequestType : chr [1:1000] "Bulky Items" "Dead Animal Removal" "Bulky Items" "Metal/Household Appliances" ...
$ Status : chr [1:1000] "Closed" "Closed" "Cancelled" "Cancelled" ...
$ RequestSource : chr [1:1000] "Self Service" "Call" "Self Service" "Self Service" ...
$ CreatedByUserOrganization: chr [1:1000] "Self Service_SAN" "LASAN" "Self Service" "Self Service_SAN" ...
$ MobileOS : chr [1:1000] NA NA NA NA ...
$ Anonymous : chr [1:1000] "N" "N" "N" "N" ...
$ AssignTo : chr [1:1000] "WLA" "HB" "EV" "WV" ...
$ ServiceDate : chr [1:1000] "01/03/2022 12:00:00 AM" NA "01/05/2022 12:00:00 AM" "01/04/2022 12:00:00 AM" ...
$ ClosedDate : chr [1:1000] "01/03/2022 02:58:20 PM" "01/01/2022 01:06:13 PM" "01/03/2022 12:21:40 AM" "01/03/2022 09:37:08 AM" ...
$ AddressVerified : chr [1:1000] "Y" "Y" "Y" "Y" ...
$ ApproximateAddress : chr [1:1000] "N" "N" "N" "N" ...
$ Address : chr [1:1000] "4776 S LA VILLA MARINA, 90292" "HOOVER ST AT IMPERIAL HWY, 90044" "4144 N TUJUNGA AVE, 91604" "10118 N LURLINE AVE, 91311" ...
$ HouseNumber : num [1:1000] 4776 NA 4144 10118 17101 ...
$ Direction : chr [1:1000] "S" NA "N" "N" ...
$ StreetName : chr [1:1000] "LA VILLA MARINA" NA "TUJUNGA" "LURLINE" ...
$ Suffix : chr [1:1000] NA NA "AVE" "AVE" ...
$ ZipCode : num [1:1000] 90292 90044 91604 91311 91344 ...
$ Latitude : num [1:1000] 34 33.9 34.1 34.3 34.3 ...
$ Longitude : num [1:1000] -118 -118 -118 -119 -119 ...
$ Location : chr [1:1000] "(33.9812287953, -118.433950454)" "(33.930968583, -118.286997573)" "(34.1436314704, -118.378844267)" "(34.2540709722, -118.584098582)" ...
$ TBMPage : num [1:1000] 672 704 562 500 481 594 501 501 501 501 ...
$ TBMColumn : chr [1:1000] "C" "B" "J" "C" ...
$ TBMRow : num [1:1000] 7 6 5 4 6 6 2 2 2 2 ...
$ APC : chr [1:1000] "West Los Angeles APC" "South Los Angeles APC" "South Valley APC" "North Valley APC" ...
$ CD : num [1:1000] 11 8 2 12 12 13 12 12 12 12 ...
$ CDMember : chr [1:1000] "Mike Bonin" "Marqueece Harris-Dawson" "Paul Krekorian" "John Lee" ...
$ NC : num [1:1000] 70 90 27 99 4 38 118 118 118 118 ...
$ NCName : chr [1:1000] "Del Rey" "Harbor Gateway North" "Studio City" "Chatsworth" ...
$ PolicePrecinct : chr [1:1000] "PACIFIC" "SOUTHEAST" "NORTH HOLLYWOOD" "DEVONSHIRE" ...
- attr(*, "spec")=
.. cols(
.. SRNumber = col_character(),
.. CreatedDate = col_character(),
.. UpdatedDate = col_character(),
.. ActionTaken = col_character(),
.. Owner = col_character(),
.. RequestType = col_character(),
.. Status = col_character(),
.. RequestSource = col_character(),
.. CreatedByUserOrganization = col_character(),
.. MobileOS = col_character(),
.. Anonymous = col_character(),
.. AssignTo = col_character(),
.. ServiceDate = col_character(),
.. ClosedDate = col_character(),
.. AddressVerified = col_character(),
.. ApproximateAddress = col_character(),
.. Address = col_character(),
.. HouseNumber = col_double(),
.. Direction = col_character(),
.. StreetName = col_character(),
.. Suffix = col_character(),
.. ZipCode = col_double(),
.. Latitude = col_double(),
.. Longitude = col_double(),
.. Location = col_character(),
.. TBMPage = col_double(),
.. TBMColumn = col_character(),
.. TBMRow = col_double(),
.. APC = col_character(),
.. CD = col_double(),
.. CDMember = col_character(),
.. NC = col_double(),
.. NCName = col_character(),
.. PolicePrecinct = col_character()
.. )
- attr(*, "problems")=<externalptr> # --- calculate the length of time from start to close
# start date
la3$start.date <- as.Date(x = substr(la3$CreatedDate, start = 1, stop = 10), format = "%m/%d/%Y")
# stop date
la3$stop.date <- as.Date(x = substr(la3$ClosedDate, start = 1, stop = 10), format = "%m/%d/%Y")
# number of days between these two
la3$days <- la3$stop.date - la3$start.date
# double-check it
table(la3$days)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 17 18 19 24 25 32
61 45 234 191 142 154 74 29 4 10 5 4 3 2 2 1 1 4 6 1
36 37 40 43 44 45 54 59 78 96 97 101 120 163 174 325
5 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 # make a histogram
la.hist <- ggplot() +
geom_histogram(data = la3,
mapping = aes(x = days))
la.histDon't know how to automatically pick scale for object of type difftime. Defaulting to continuous.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 5 rows containing non-finite values (stat_bin).