Tutorial 1 Answers

Questions and Answers

  1. Why do we do table(was.counties$statefips) and summary(was.counties$cv1) and not vice-versa?

We use table() for categorical or integer variables, and summary() for continuous variables. While statefips is a “number,” it has no intrinsic meaning – the number is a code that represents a state. As a matter of practice, you could take an average of this numeric categorical variable – it just won’t mean anything!

  1. Why does the first summary in part G.3. yield 11 observations, but the second 44?

Load the data:

library(tidyverse)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ 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 csv data
was.counties <- read.csv("h:/pppa_data_viz/2019/tutorial_data/was_msas_1910_2010_20190107.csv")
str(was.counties)
'data.frame':   246 obs. of  5 variables:
 $ statefips : int  11 24 24 24 24 24 51 51 51 51 ...
 $ countyfips: int  1 9 17 21 31 33 13 43 47 59 ...
 $ cv1       : int  331069 10325 16386 52673 32089 36147 10231 7468 13472 20536 ...
 $ year      : int  1910 1910 1910 1910 1910 1910 1910 1910 1910 1910 ...
 $ cv28      : int  NA NA NA NA NA NA NA NA NA NA ...

Here is the first summary:

# summarize by year w/o missings
print("find average by year w/o missing values")
[1] "find average by year w/o missing values"
was.counties.grp.yr <- group_by(.data = was.counties, year)
was.by.year <- summarize(.data = was.counties.grp.yr, cv1.yr=mean(cv1, na.rm = TRUE))
was.by.year
# A tibble: 11 × 2
    year  cv1.yr
   <int>   <dbl>
 1  1910  32892.
 2  1920  39282.
 3  1930  44222.
 4  1940  59891.
 5  1950  81966.
 6  1960 110812.
 7  1970 143834.
 8  1980 142777 
 9  1990 173222.
10  2000 201560.
11  2010 234843

And here is the second:

# summarize by state and year 
print("find info by state and year")
[1] "find info by state and year"
was.counties.grp.st <- group_by(.data = was.counties,year,statefips)
was.by.state.yr <- summarize(.data = was.counties.grp.st,
                             cv.st.total = sum(cv1, na.rm = TRUE))
`summarise()` has grouped output by 'year'. You can override using the
`.groups` argument.
was.by.state.yr
# A tibble: 44 × 3
# Groups:   year [11]
    year statefips cv.st.total
   <int>     <int>       <int>
 1  1910        11      331069
 2  1910        24      147620
 3  1910        51      163267
 4  1910        54       15889
 5  1920        11      437571
 6  1920        24      158258
 7  1920        51      174085
 8  1920        54       15729
 9  1930        11      486869
10  1930        24      189435
# … with 34 more rows

The first reports one observation by year, and there are 11 years in the data (1910-2010). The second summarize reports data by year and state, so that there are 11 years x 4 states (DC, MD, VA, WV) observations, or 44.

  1. Find and report the average population in DC for the entire period 1910-2010
# keep dc only
was.counties.dc <- was.counties[which(was.counties$statefips == 11),]
# mean population for all years 
mean(was.counties.dc$cv1, na.rm = TRUE)
[1] 605478.1
  1. Find state-level (or the part of the state we observe) average population over the entire period. Put a table with this information in your final output. Describe the results in a sentence or two.

The important logic here is the following

  1. first make a dataset at the state-year level
  2. then take the average by state

If you take the average without creating a state-year level dataframe, you are taking the average across counties, not across state observations.

Here is my code to do this.

# find state-level population by year
str(was.counties)
'data.frame':   246 obs. of  5 variables:
 $ statefips : int  11 24 24 24 24 24 51 51 51 51 ...
 $ countyfips: int  1 9 17 21 31 33 13 43 47 59 ...
 $ cv1       : int  331069 10325 16386 52673 32089 36147 10231 7468 13472 20536 ...
 $ year      : int  1910 1910 1910 1910 1910 1910 1910 1910 1910 1910 ...
 $ cv28      : int  NA NA NA NA NA NA NA NA NA NA ...
was.counties.st <- group_by(.data = was.counties, statefips, year)
# add up to state-year level
state.year <- summarize(.data = was.counties.st, state_pop = sum(cv1, na.rm = TRUE))
`summarise()` has grouped output by 'statefips'. You can override using the
`.groups` argument.
# now take a state-level average
state.year <- group_by(.data = state.year, statefips)
state.overall <- summarize(.data = state.year, state_pop_all = mean(state_pop, na.rm = TRUE))
state.overall
# A tibble: 4 × 2
  statefips state_pop_all
      <int>         <dbl>
1        11       605478.
2        24       999115.
3        51       987486.
4        54        25746.
  1. For each of the four states, are there more or fewer jurisdictions in this dataset now than in 1910? (Hint: sum(!is.na(variable.name)) tells you the total number of non-missing observations.)

Here I count jurisdictions by year for all years

# group at state-year level
was.counties.st <- group_by(.data = was.counties, statefips, year)
# count jurisdictions by state-year
state.year <- summarize(.data = was.counties.st, no_jurisdictions = sum(!is.na(cv1), na.rm = TRUE))
`summarise()` has grouped output by 'statefips'. You can override using the
`.groups` argument.
# just print 1910 and 2010
state.year[which(state.year$year %in% c(1910,2010)),]
# A tibble: 8 × 3
# Groups:   statefips [4]
  statefips  year no_jurisdictions
      <int> <int>            <int>
1        11  1910                1
2        11  2010                1
3        24  1910                5
4        24  2010                5
5        51  1910               13
6        51  2010               17
7        54  1910                1
8        54  2010                1

This table shows each state in each year, where no_jurisdictions reports the number of jurisdictions.

  1. What is the most populous jurisdiction in the DC area in 2010?
# just limit to 2010 counties
was.counties.2010 <- was.counties[which(was.counties$year == 2010),]
# print the maximum population
max.pop <- max(was.counties.2010$cv1, na.rm = TRUE)
print("maximum population for all 2010 counties")
[1] "maximum population for all 2010 counties"
max.pop
[1] 1081726
# list all counties
was.counties.2010[,c("statefips","countyfips","cv1")]
    statefips countyfips     cv1
223        11          1  601723
224        24          9   88737
225        24         17  146551
226        24         21  233385
227        24         31  971777
228        24         33  863420
229        51         13  207627
230        51         43   14034
231        51         47   46689
232        51         59 1081726
233        51         61   65203
234        51        107  312311
235        51        153  402002
236        51        157    7373
237        51        177  122397
238        51        179  128961
239        51        187   37575
240        51        510  139966
241        51        600   22565
242        51        610   12332
243        51        630   24286
244        51        683   37821
245        51        685   14273
246        54         37   53498
# or be even fancier
was.counties.2010[which(was.counties.2010$cv1 == max.pop),
                  c("statefips","countyfips","cv1")]
    statefips countyfips     cv1
232        51         59 1081726

The maximum matches to Fairfax County, VA: state 51, county 59.