Manipulating Tabular Data
Lesson 3 with Mary Shelley
Contents
- Objectives for this lesson
- Tidy data concept
- Gather
- Sample data
- Key dplyr functions
- Chaining functions
- Mutate
- Split-apply-combine
- Review
- Exercise solutions
Objectives for this lesson
- Review what makes a tidy datasets
- Learn to transform datasets with split-apply-combine procedures
- Pay attention to code clarity
Specific achievements
- Reshape data frames with tidyr
- Summarize data by groups with dplyr
- Combine multiple data frame operations with pipes
Data frames generally occupy a central place in R analysis workflows. While the base R functions provide most necessary tools to subset, reformat and transform data frames, the specialized packages in this lesson offer a more succinct and often computationally faster way to perform the common data frame processing steps. Their straightforward syntax also makes scripts more readable and easier to debug. The key functions from the tidyr and dplyr packages all have close counterparts in SQL (Structured Query Language), which provides the added bonus of facilitating translation between R and relational databases.
Tidy data concept
R developer Hadley Wickham (author of the tidyr, dplyr and ggplot packages, among others) defines tidy datasets (Wickham 2014) as those where:
- each variable forms a column
- each observation forms a row
- each type of observational unit forms a table
These guidelines may be familiar to some of you—they closely map to best practices in database design.
Conser a data frame where the outcome of an experiment has been recorded in a perfectly appropriate way:
| trial | drug_A | drug_B | placebo |
| 1 | 0.22 | 0.58 | 0.31 |
| 2 | 0.12 | 0.98 | 0.47 |
| 3 | 0.42 | 0.19 | 0.4 |
response
trial drug_A drug_B placebo
1 1 0.22 0.58 0.31
2 2 0.12 0.98 0.47
3 3 0.42 0.19 0.40
The response data are present in a compact matrix, as you might record it on a spreadsheet. The form does not match how we think about a statistical model, such as:
response ~ treatment + trial
In a tidy format, each row is a complete observation: it includes one response value and all the predictor values. In this data, some of those values are column headers, so we’ve got to tidy up!
Gather
The tidyr package’s gather function reshapes “wide” data frames into “long” ones.
library(tidyr)
tidy_response <- gather(response,
key = "treatment",
value = "response",
-trial)
All columns, accept for “trial”, are stacked in two columns: a “key” and a “value”. the key column gets the name treatment and the value column reveives the name response. For each row in the result, the key is taken from the name of the column and the value from the data in the column.
tidy_response
trial treatment response
1 1 drug_A 0.22
2 2 drug_A 0.12
3 3 drug_A 0.42
4 1 drug_B 0.58
5 2 drug_B 0.98
6 3 drug_B 0.19
7 1 placebo 0.31
8 2 placebo 0.47
9 3 placebo 0.40
Some notes on the syntax: a big advantage of tidyr and dplyr is that each function takes a data frame as its first argument and returns a new data frame. As we will see later, it makes it very easy to apply these functions in a chain. All functions also use column names as variables without subsetting them from a data frame (i.e. trial instead of response$trial).
Some analyses require a “wide” data format rather than the long format produced by gather. The community ecology package vegan uses a matrix of species counts, where rows correspond to species and columns to sites.
Suppose data are provided in a “entity-attribute-value” format.
| site | species | n |
| 1 | lynx | 2 |
| 1 | hare | 341 |
| 2 | lynx | 7 |
| 2 | hare | 42 |
| 3 | hare | 289 |
Transform the data with the spread function, which “reverses” a gather.
wide_counts <- spread(counts,
key = species,
value = n)
wide_counts
site hare lynx
1 1 341 2
2 2 42 7
3 3 289 NA
- Question
- Why were
speciesandnnot quoted in the call tospread? - Answer
- They refer to existing column names. In
gather, quotes are used to create new column names.
Think about what “missing data” means in this table. Perhaps you can safely do:
wide_counts <- spread(counts,
key = species,
value = n,
fill = 0)
wide_counts
site hare lynx
1 1 341 2
2 2 42 7
3 3 289 0
Exercise 1
Now that we have a wide form of counts, convert it to a tidy_counts data frame
using gather. The only difference between counts and tidy_counts should be
the additional row for zero lynx at site 3. Remember, a tidy dataset has a row
for every observation, even if the value is “implied”.
Sample data
To learn about data transformation with dplyr, we need more data. The County Business Patterns (CBP) dataset provides subnational economic data by industry. The annual series includes the number of establishments, employment, and payroll.
Credit: US Census Bureau
The CBP dataset documentation explains the variables. The key variables we use in this lesson are ## FIXME
library(data.table)
cbp <- fread('data/cbp15co.csv')
str(cbp)
Classes 'data.table' and 'data.frame': 2126601 obs. of 26 variables:
$ FIPSTATE: chr "01" "01" "01" "01" ...
$ FIPSCTY : chr "001" "001" "001" "001" ...
$ NAICS : chr "------" "11----" "113///" "1133//" ...
$ EMPFLAG : chr "" "" "" "" ...
$ EMP_NF : chr "G" "H" "H" "H" ...
$ EMP : int 10454 70 70 70 70 70 0 0 0 0 ...
$ QP1_NF : chr "G" "H" "H" "H" ...
$ QP1 : int 76437 790 790 790 790 790 0 0 0 0 ...
$ AP_NF : chr "G" "H" "H" "H" ...
$ AP : int 321433 3566 3551 3551 3551 3551 0 0 0 0 ...
$ EST : int 844 7 6 6 6 6 1 1 1 1 ...
$ N1_4 : int 430 5 4 4 4 4 1 1 1 1 ...
$ N5_9 : int 171 1 1 1 1 1 0 0 0 0 ...
$ N10_19 : int 118 0 0 0 0 0 0 0 0 0 ...
$ N20_49 : int 81 0 0 0 0 0 0 0 0 0 ...
$ N50_99 : int 35 1 1 1 1 1 0 0 0 0 ...
$ N100_249: int 6 0 0 0 0 0 0 0 0 0 ...
$ N250_499: int 2 0 0 0 0 0 0 0 0 0 ...
$ N500_999: int 1 0 0 0 0 0 0 0 0 0 ...
$ N1000 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_1 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_2 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_3 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_4 : int 0 0 0 0 0 0 0 0 0 0 ...
$ CENSTATE: chr "63" "63" "63" "63" ...
$ CENCTY : chr "001" "001" "001" "001" ...
- attr(*, ".internal.selfref")=<externalptr>
Modify the function to specify what string in the CSV file represents NAs, a.k.a. data that is not-available or missing.
cbp <- fread(
'data/cbp15co.csv',
na.strings = '')
- Question
- What changed?
- Answer
- Using
str()shows that the character string “” in the CSV file is read into R as missing data (anNA) rather than the value''.
Key dplyr functions
| Function | Returns |
|---|---|
filter |
keep rows that staisfy conditions |
select |
keep columns with matching names |
group_by |
split data into groups by an existing factor |
mutate |
apply a transformation to existing [split] columns |
summarize |
summarize across rows [and combine split groups] |
The table above presents the most commonly used functions in dplyr, which we will demonstrate in turn, starting from the cbp data frame.
Subsetting
The cbp table includes character NAICS column. Of the 2 million observations, lets see how many observations are left when we keep only observations from the Health Care and Social Assistance sector.
library(dplyr)
cbp_health_care <- filter(
cbp,
NAICS == '62----')
That may not be what we wanted, because it’s the aggregated data.
str(cbp_health_care)
'data.frame': 3171 obs. of 26 variables:
$ FIPSTATE: chr "01" "01" "01" "01" ...
$ FIPSCTY : chr "001" "003" "005" "007" ...
$ NAICS : chr "62----" "62----" "62----" "62----" ...
$ EMPFLAG : chr NA NA NA NA ...
$ EMP_NF : chr "G" "G" "G" "H" ...
$ EMP : int 1618 7507 679 620 958 404 894 6088 673 449 ...
$ QP1_NF : chr "G" "G" "G" "H" ...
$ QP1 : int 12038 72416 5262 4698 7861 3016 8089 54848 5240 4176 ...
$ AP_NF : chr "G" "G" "G" "H" ...
$ AP : int 50965 308177 22234 20161 33012 12961 32906 238227 22789 17087 ...
$ EST : int 93 493 45 26 53 14 41 270 57 37 ...
$ N1_4 : int 34 191 21 8 20 5 11 104 23 14 ...
$ N5_9 : int 21 123 12 6 14 3 12 65 15 12 ...
$ N10_19 : int 22 112 7 3 9 3 12 47 11 5 ...
$ N20_49 : int 10 43 3 8 6 1 2 31 5 5 ...
$ N50_99 : int 3 15 0 0 1 0 1 15 3 0 ...
$ N100_249: int 2 7 2 0 3 2 3 6 0 1 ...
$ N250_499: int 1 0 0 1 0 0 0 1 0 0 ...
$ N500_999: int 0 2 0 0 0 0 0 0 0 0 ...
$ N1000 : int 0 0 0 0 0 0 0 1 0 0 ...
$ N1000_1 : int 0 0 0 0 0 0 0 1 0 0 ...
$ N1000_2 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_3 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_4 : int 0 0 0 0 0 0 0 0 0 0 ...
$ CENSTATE: chr "63" "63" "63" "63" ...
$ CENCTY : chr "001" "003" "005" "007" ...
- attr(*, ".internal.selfref")=<externalptr>
library(stringr)
cbp_health_care <- filter(
cbp,
str_detect(NAICS, '^62'),
!is.na(as.integer(NAICS))
)
Note that a logical “and” is implied when conditions are separated by commas.
(This is perhaps the main way in which filter differs from the base R subset
function.) Therefore, the example above is equivalent to filter(cbp,
str_detect(NAICS, '^62') & !is.na(as.integer(NAICS))). A logical “or” must be
specified explicitly with the | operator.
To keep particular columns of a data frame (rather than choosing rows) , use the select with arguments that match the column names.
names(cbp)
[1] "FIPSTATE" "FIPSCTY" "NAICS" "EMPFLAG" "EMP_NF" "EMP"
[7] "QP1_NF" "QP1" "AP_NF" "AP" "EST" "N1_4"
[13] "N5_9" "N10_19" "N20_49" "N50_99" "N100_249" "N250_499"
[19] "N500_999" "N1000" "N1000_1" "N1000_2" "N1000_3" "N1000_4"
[25] "CENSTATE" "CENCTY"
One way to “match” is by including complete names, each one you want to keep:
select(cbp_health_care,
FIPSTATE, FIPSCTY,
NAICS,
EMPFLAG, EMP_NF, EMP
)
Alternatively, we can use a “select helper”” to match patterns.
cbp_health_care <- select(cbp_health_care,
starts_with('FIPS'),
NAICS,
starts_with('EMP')
)
str(cbp_health_care)
'data.frame': 63231 obs. of 26 variables:
$ FIPSTATE: chr "01" "01" "01" "01" ...
$ FIPSCTY : chr "001" "001" "001" "001" ...
$ NAICS : chr "621111" "621210" "621310" "621320" ...
$ EMPFLAG : chr NA NA NA NA ...
$ EMP_NF : chr "G" "H" "G" "J" ...
$ EMP : int 156 95 27 36 31 0 0 0 0 0 ...
$ QP1_NF : chr "G" "G" "G" "H" ...
$ QP1 : int 1603 933 232 297 339 0 0 0 0 0 ...
$ AP_NF : chr "G" "G" "G" "H" ...
$ AP : int 7090 4557 970 1328 1411 0 0 0 0 0 ...
$ EST : int 22 16 5 4 5 2 1 3 3 1 ...
$ N1_4 : int 10 6 3 0 2 1 1 1 0 1 ...
$ N5_9 : int 7 5 1 3 1 1 0 0 0 0 ...
$ N10_19 : int 4 5 1 1 2 0 0 2 2 0 ...
$ N20_49 : int 1 0 0 0 0 0 0 0 1 0 ...
$ N50_99 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N100_249: int 0 0 0 0 0 0 0 0 0 0 ...
$ N250_499: int 0 0 0 0 0 0 0 0 0 0 ...
$ N500_999: int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_1 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_2 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_3 : int 0 0 0 0 0 0 0 0 0 0 ...
$ N1000_4 : int 0 0 0 0 0 0 0 0 0 0 ...
$ CENSTATE: chr "63" "63" "63" "63" ...
$ CENCTY : chr "001" "001" "001" "001" ...
- attr(*, ".internal.selfref")=<externalptr>
Exercise 2
Write code that returns the annual payroll data for the top level Construction sector (“23—-“).
Chaining functions
All those functions from the dplyr package take a data frame as their first argument, and they return a data frame. This consistent syntax is on purpose. It is designed for easily chaining data transformations together: processing data frames in easy-to-read steps.
The “pipe” operator (%>%) from the magrittr package is loaded by
dplyr The pipe takes the expression on its left-hand side and hands
it over as the first argument to the function on its right-hand side.
library(magrittr)
Equivalent to sum(c(1,3,5)), for example, we have:
c(1, 3, 5) %>% sum()
[1] 9
Additional arguments are accepted, a pipe only handles the first.
c(1, 3, 5, NA) %>% sum(na.rm = TRUE)
[1] 9
The pipe operator’s main utility is to condense a chain of operations applied to the same piece of data, when you don’t need to save the intermediate results. We can do both the filter and select operations from above with one assignment.
cbp_health_care <- cbp %>%
filter(
str_detect(NAICS, '^62'),
!is.na(as.integer(NAICS))) %>%
select(
starts_with('FIPS'),
NAICS,
starts_with('EMP'))
str(cbp_health_care)
'data.frame': 63231 obs. of 6 variables:
$ FIPSTATE: chr "01" "01" "01" "01" ...
$ FIPSCTY : chr "001" "001" "001" "001" ...
$ NAICS : chr "621111" "621210" "621310" "621320" ...
$ EMPFLAG : chr NA NA NA NA ...
$ EMP_NF : chr "G" "H" "G" "J" ...
$ EMP : int 156 95 27 36 31 0 0 0 0 0 ...
- attr(*, ".internal.selfref")=<externalptr>
Mutate
The mutate function is the dplyr answer to cleaning up your
columns. It performs arbitrary row-wise operations on existing columns and
appends the result as a new column.
Here’s one you’ve probably needed before:
cbp_health_care %<>% mutate(
FIPS = str_c(FIPSTATE, FIPSCTY))
- Question
- Oh c’mon!
%<>%?! - Answer
- I know, but stay calm and
?`%<>%`.
Split-apply-combine
A very common data manipulation procedure is doing some “group-wise” operations on a dataset and combing the results for each group into a single table. For example, say you need to sum the number of employees in each state observed in the health care sector?
Grouping
The dplyr function group_by begins the process by indicating how the data frame should be split into subsets.
state_cbp_health_care <- cbp_health_care %>%
group_by(FIPSTATE)
At this point, nothing has really changed:
str(state_cbp_health_care)
Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame': 63231 obs. of 7 variables:
$ FIPSTATE: chr "01" "01" "01" "01" ...
$ FIPSCTY : chr "001" "001" "001" "001" ...
$ NAICS : chr "621111" "621210" "621310" "621320" ...
$ EMPFLAG : chr NA NA NA NA ...
$ EMP_NF : chr "G" "H" "G" "J" ...
$ EMP : int 156 95 27 36 31 0 0 0 0 0 ...
$ FIPS : chr "01001" "01001" "01001" "01001" ...
- attr(*, "vars")= chr "FIPSTATE"
- attr(*, "drop")= logi TRUE
- attr(*, "indices")=List of 51
..$ : int 0 1 2 3 4 5 6 7 8 9 ...
..$ : int 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 ...
..$ : int 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 ...
..$ : int 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 ...
..$ : int 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 ...
..$ : int 5322 5323 5324 5325 5326 5327 5328 5329 5330 5331 ...
..$ : int 6465 6466 6467 6468 6469 6470 6471 6472 6473 6474 ...
..$ : int 6749 6750 6751 6752 6753 6754 6755 6756 6757 6758 ...
..$ : int 6860 6861 6862 6863 6864 6865 6866 6867 6868 6869 ...
..$ : int 6899 6900 6901 6902 6903 6904 6905 6906 6907 6908 ...
..$ : int 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769 ...
..$ : int 11516 11517 11518 11519 11520 11521 11522 11523 11524 11525 ...
..$ : int 11653 11654 11655 11656 11657 11658 11659 11660 11661 11662 ...
..$ : int 12396 12397 12398 12399 12400 12401 12402 12403 12404 12405 ...
..$ : int 14431 14432 14433 14434 14435 14436 14437 14438 14439 14440 ...
..$ : int 16536 16537 16538 16539 16540 16541 16542 16543 16544 16545 ...
..$ : int 18331 18332 18333 18334 18335 18336 18337 18338 18339 18340 ...
..$ : int 19824 19825 19826 19827 19828 19829 19830 19831 19832 19833 ...
..$ : int 21935 21936 21937 21938 21939 21940 21941 21942 21943 21944 ...
..$ : int 23265 23266 23267 23268 23269 23270 23271 23272 23273 23274 ...
..$ : int 23720 23721 23722 23723 23724 23725 23726 23727 23728 23729 ...
..$ : int 24460 24461 24462 24463 24464 24465 24466 24467 24468 24469 ...
..$ : int 24943 24944 24945 24946 24947 24948 24949 24950 24951 24952 ...
..$ : int 26883 26884 26885 26886 26887 26888 26889 26890 26891 26892 ...
..$ : int 28674 28675 28676 28677 28678 28679 28680 28681 28682 28683 ...
..$ : int 30011 30012 30013 30014 30015 30016 30017 30018 30019 30020 ...
..$ : int 32214 32215 32216 32217 32218 32219 32220 32221 32222 32223 ...
..$ : int 32974 32975 32976 32977 32978 32979 32980 32981 32982 32983 ...
..$ : int 34037 34038 34039 34040 34041 34042 34043 34044 34045 34046 ...
..$ : int 34314 34315 34316 34317 34318 34319 34320 34321 34322 34323 ...
..$ : int 34623 34624 34625 34626 34627 34628 34629 34630 34631 34632 ...
..$ : int 35375 35376 35377 35378 35379 35380 35381 35382 35383 35384 ...
..$ : int 36045 36046 36047 36048 36049 36050 36051 36052 36053 36054 ...
..$ : int 37909 37910 37911 37912 37913 37914 37915 37916 37917 37918 ...
..$ : int 40404 40405 40406 40407 40408 40409 40410 40411 40412 40413 ...
..$ : int 40982 40983 40984 40985 40986 40987 40988 40989 40990 40991 ...
..$ : int 43357 43358 43359 43360 43361 43362 43363 43364 43365 43366 ...
..$ : int 44780 44781 44782 44783 44784 44785 44786 44787 44788 44789 ...
..$ : int 45668 45669 45670 45671 45672 45673 45674 45675 45676 45677 ...
..$ : int 47683 47684 47685 47686 47687 47688 47689 47690 47691 47692 ...
..$ : int 47848 47849 47850 47851 47852 47853 47854 47855 47856 47857 ...
..$ : int 48936 48937 48938 48939 48940 48941 48942 48943 48944 48945 ...
..$ : int 49662 49663 49664 49665 49666 49667 49668 49669 49670 49671 ...
..$ : int 51573 51574 51575 51576 51577 51578 51579 51580 51581 51582 ...
..$ : int 55568 55569 55570 55571 55572 55573 55574 55575 55576 55577 ...
..$ : int 56101 56102 56103 56104 56105 56106 56107 56108 56109 56110 ...
..$ : int 56449 56450 56451 56452 56453 56454 56455 56456 56457 56458 ...
..$ : int 59014 59015 59016 59017 59018 59019 59020 59021 59022 59023 ...
..$ : int 60005 60006 60007 60008 60009 60010 60011 60012 60013 60014 ...
..$ : int 61077 61078 61079 61080 61081 61082 61083 61084 61085 61086 ...
..$ : int 62760 62761 62762 62763 62764 62765 62766 62767 62768 62769 ...
- attr(*, "group_sizes")= int 1394 347 440 1400 1741 1143 284 111 39 1861 ...
- attr(*, "biggest_group_size")= int 3995
- attr(*, "labels")='data.frame': 51 obs. of 1 variable:
..$ FIPSTATE: chr "01" "02" "04" "05" ...
..- attr(*, "vars")= chr "FIPSTATE"
..- attr(*, "drop")= logi TRUE
The group_by statement does not change any values in the data frame; it only
adds attributes to the the original data frame. You can add multiple variables
(separated by commas) in group_by; each distinct combination of values across
these columns defines a different group.
Summarize
The operation to perform on each species is summing: we need to sum employment are in each group.
state_cbp_health_care <- cbp_health_care %>%
group_by(FIPSTATE) %>%
summarize(EMP = sum(EMP))
str(state_cbp_health_care)
Classes 'tbl_df', 'tbl' and 'data.frame': 51 obs. of 2 variables:
$ FIPSTATE: chr "01" "02" "04" "05" ...
$ EMP : int 162202 35063 325251 114101 1789324 240330 262599 60290 65472 950356 ...
The “combine” part of “split-apply-combine” occurs automatically, when the attributes introduced by group_by are dropped. You can see attributes either by running the str() function on the data frame or by inspecting it in the RStudio Environment pane.
The function n() takes no arguments and returns the number of records in a group. Any function that collapses a vector input to a single output is a suitable function to use within summarize.
state_cbp_health_care <- cbp_health_care %>%
group_by(FIPSTATE) %>%
summarize(EMP = sum(EMP),
counties = n())
But group_by can’t read your mind, so be careful to type what you mean!
head(state_cbp_health_care)
# A tibble: 6 x 3
FIPSTATE EMP counties
<chr> <int> <int>
1 01 162202 1394
2 02 35063 347
3 04 325251 440
4 05 114101 1400
5 06 1789324 1741
6 08 240330 1143
Exercise 3
Write code that fixes state_cbp_health_care, which should show the number
of counties and the total employment. Group the data using both FIPSTATE
and FIPSCTY and use the fact that a call to summarize only combines across
the lowest level of grouping.
Review
- Convert between wide and long tables
- Transform columns
- Split-apply-combine routine
RStudio Cheatsheets
Data Wrangling with dplyr and tidyr
One of several cheat sheets available on the RStudio website, it provides a brief, visual summary of all the key functions discussed in this lesson. It also lists some of the auxiliary functions that can be used within each type of expression, e.g. aggregation functions for summarize, “moving window” functions for mutate, etc.
Exercise solutions
Solution 1
gather(wide_counts, key = "species", value = "n", -site)
site species n
1 1 hare 341
2 2 hare 42
3 3 hare 289
4 1 lynx 2
5 2 lynx 7
6 3 lynx 0
Solution 2
cbp_construction <- filter(cbp, NAICS == '23----')
select(cbp_construction, starts_with('FIPS'), starts_with('AP'))
FIPSTATE FIPSCTY AP_NF AP
1 01 001 G 13801
2 01 003 G 140518
3 01 005 G 2963
4 01 007 H 43711
5 01 009 G 13741
6 01 011 D 0
7 01 013 H 13115
8 01 015 G 28162
9 01 017 G 5292
10 01 019 H 2554
11 01 021 G 21331
12 01 023 H 6490
13 01 025 H 3613
14 01 027 G 1483
15 01 029 D 0
16 01 031 G 12278
17 01 033 G 57026
18 01 035 G 3612
19 01 037 H 818
20 01 039 G 20051
21 01 041 H 4044
22 01 043 G 50915
23 01 045 G 25815
24 01 047 G 18281
25 01 049 G 22667
26 01 051 G 36625
27 01 053 H 13628
28 01 055 G 44780
29 01 057 G 1383
30 01 059 H 7220
31 01 061 H 3590
32 01 063 H 4004
33 01 065 H 5853
34 01 067 H 9614
35 01 069 G 71620
36 01 071 G 12013
37 01 073 G 1075284
38 01 075 G 2571
39 01 077 G 57850
40 01 079 G 7249
41 01 081 G 88423
42 01 083 G 39508
43 01 085 J 8695
44 01 087 H 2124
45 01 089 G 216345
46 01 091 G 12516
47 01 093 G 3799
48 01 095 G 34563
49 01 097 G 551875
50 01 099 H 6106
51 01 101 G 189496
52 01 103 G 141995
53 01 105 G 1883
54 01 107 J 11991
55 01 109 J 27089
56 01 111 G 2506
57 01 113 G 16144
58 01 115 G 45301
59 01 117 G 217323
60 01 119 D 0
61 01 121 G 25845
62 01 123 G 13274
63 01 125 G 185758
64 01 127 G 16771
65 01 129 H 2257
66 01 131 G 3806
67 01 133 H 4508
68 01 999 H 64316
69 02 016 J 2930
70 02 020 G 912138
71 02 050 G 2658
72 02 060 D 0
73 02 068 G 1657
74 02 070 J 2493
75 02 090 G 203385
76 02 100 H 6172
77 02 105 G 550
78 02 110 G 48408
79 02 122 G 69893
80 02 130 H 18935
81 02 150 G 12192
82 02 164 J 1009
83 02 170 G 187763
84 02 180 H 2734
85 02 185 D 0
86 02 188 H 3595
87 02 195 G 3645
88 02 198 H 1055
89 02 220 G 12366
90 02 230 H 4451
91 02 240 H 6998
92 02 261 H 10341
93 02 270 D 0
94 02 275 H 1078
95 02 282 D 0
96 02 290 G 530
97 02 999 D 0
98 04 001 G 4147
99 04 003 G 40650
100 04 005 G 94647
101 04 007 G 22715
102 04 009 G 13195
103 04 011 H 2996
104 04 012 H 2755
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1956 37 085 G 54223
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1991 37 155 G 44310
1992 37 157 G 28565
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1995 37 163 G 21186
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1998 37 169 G 11190
1999 37 171 H 288265
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2003 37 179 G 283415
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2007 37 187 H 1290
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2009 37 191 G 58811
2010 37 193 H 20497
2011 37 195 H 96291
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2013 37 199 H 11749
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2734 48 367 G 105269
2735 48 369 H 3078
2736 48 371 H 12166
2737 48 373 G 17701
2738 48 375 G 148213
2739 48 377 G 1629
2740 48 379 G 3670
2741 48 381 G 115426
2742 48 383 G 5831
2743 48 385 G 1228
2744 48 387 H 6119
2745 48 389 H 18258
2746 48 391 H 12785
2747 48 393 D 0
2748 48 395 H 6842
2749 48 397 G 81022
2750 48 399 G 3502
2751 48 401 G 35710
2752 48 403 H 2177
2753 48 405 G 5834
2754 48 407 H 3809
2755 48 409 G 89052
2756 48 411 G 756
2757 48 413 D 0
2758 48 415 H 34703
2759 48 417 G 2695
2760 48 419 H 18999
2761 48 421 G 664
2762 48 423 G 168930
2763 48 425 H 9734
2764 48 427 H 2779
2765 48 429 H 10121
2766 48 431 G 1807
2767 48 433 G 2886
2768 48 435 H 8204
2769 48 437 H 859
2770 48 439 G 2262792
2771 48 441 G 243667
2772 48 443 D 0
2773 48 445 H 4137
2774 48 447 H 525
2775 48 449 G 11927
2776 48 451 G 89637
2777 48 453 G 1976409
2778 48 455 J 2464
2779 48 457 H 1846
2780 48 459 G 34701
2781 48 461 H 5194
2782 48 463 H 7862
2783 48 465 G 7277
2784 48 467 G 49934
2785 48 469 G 85859
2786 48 471 G 18482
2787 48 473 G 49353
2788 48 475 G 25018
2789 48 477 G 29474
2790 48 479 G 61495
2791 48 481 G 34061
2792 48 483 G 3635
2793 48 485 G 67668
2794 48 487 H 2576
2795 48 489 J 1293
2796 48 491 G 607633
2797 48 493 G 18981
2798 48 495 G 10634
2799 48 497 G 51324
2800 48 499 G 30779
2801 48 501 H 10019
2802 48 503 H 23323
2803 48 505 J 13171
2804 48 507 H 3833
2805 48 999 H 1147613
2806 49 001 H 3484
2807 49 003 H 45957
2808 49 005 H 113530
2809 49 007 H 17636
2810 49 009 D 0
2811 49 011 G 301201
2812 49 013 H 27142
2813 49 015 J 14614
2814 49 017 H 1828
2815 49 019 G 9130
2816 49 021 G 26923
2817 49 023 G 6681
2818 49 025 G 2795
2819 49 027 H 1638
2820 49 029 H 19469
2821 49 031 D 0
2822 49 033 H 1292
2823 49 035 G 1694569
2824 49 037 G 5967
2825 49 039 G 11323
2826 49 041 G 9223
2827 49 043 G 65001
2828 49 045 G 23479
2829 49 047 G 67205
2830 49 049 G 642160
2831 49 051 G 48885
2832 49 053 G 174096
2833 49 055 G 5047
2834 49 057 G 231390
2835 49 999 G 17339
2836 50 001 G 35922
2837 50 003 G 22781
2838 50 005 G 25001
2839 50 007 H 313462
2840 50 009 G 1528
2841 50 011 G 21414
2842 50 013 G 5302
2843 50 015 G 31768
2844 50 017 G 27925
2845 50 019 G 29235
2846 50 021 G 64210
2847 50 023 G 63463
2848 50 025 G 43198
2849 50 027 G 54094
2850 50 999 G 3009
2851 51 001 G 9225
2852 51 003 G 99186
2853 51 005 J 4883
2854 51 007 G 11681
2855 51 009 J 21431
2856 51 011 G 6706
2857 51 013 G 145992
2858 51 015 G 70713
2859 51 017 H 3574
2860 51 019 G 47380
2861 51 021 J 2018
2862 51 023 H 102720
2863 51 025 G 6145
2864 51 027 G 8091
2865 51 029 G 7168
2866 51 031 H 79382
2867 51 033 G 13023
2868 51 035 G 11496
2869 51 036 G 17572
2870 51 037 H 2297
2871 51 041 G 470171
2872 51 043 G 20469
2873 51 045 G 179
2874 51 047 G 32216
2875 51 049 G 3551
2876 51 051 G 5489
2877 51 053 H 18752
2878 51 057 G 2567
2879 51 059 G 1569946
2880 51 061 G 101253
2881 51 063 G 6578
2882 51 065 G 19477
2883 51 067 G 40415
2884 51 069 H 76705
2885 51 071 H 5364
2886 51 073 G 22731
2887 51 075 G 49324
2888 51 077 H 1069
2889 51 079 G 8944
2890 51 081 H 810
2891 51 083 H 21096
2892 51 085 G 215809
2893 51 087 G 408674
2894 51 089 G 11841
2895 51 091 H 1021
2896 51 093 G 19237
2897 51 095 G 65169
2898 51 097 H 2911
2899 51 099 G 7647
2900 51 101 H 20488
2901 51 103 G 9869
2902 51 105 H 7999
2903 51 107 G 786825
2904 51 109 G 21641
2905 51 111 H 5213
2906 51 113 G 10098
2907 51 115 G 2288
2908 51 117 G 11665
2909 51 119 G 5301
2910 51 121 H 45815
2911 51 125 G 7576
2912 51 127 G 19117
2913 51 131 G 2317
2914 51 133 G 7299
2915 51 135 H 3778
2916 51 137 G 21128
2917 51 139 H 16218
2918 51 141 H 3251
2919 51 143 G 35228
2920 51 145 G 49950
2921 51 147 H 6320
2922 51 149 G 12867
2923 51 153 G 653867
2924 51 155 H 10514
2925 51 157 G 4309
2926 51 159 H 2890
2927 51 161 G 33817
2928 51 163 G 8594
2929 51 165 G 86397
2930 51 167 G 23057
2931 51 169 J 2263
2932 51 171 H 20656
2933 51 173 H 17881
2934 51 175 G 3114
2935 51 177 G 89731
2936 51 179 G 99472
2937 51 181 G 3454
2938 51 183 H 3747
2939 51 185 G 26391
2940 51 187 G 24158
2941 51 191 G 18364
2942 51 193 G 5240
2943 51 195 H 10354
2944 51 197 H 13527
2945 51 199 G 67825
2946 51 510 H 126887
2947 51 515 H 1726
2948 51 520 H 9981
2949 51 530 J 496
2950 51 540 G 70385
2951 51 550 G 415364
2952 51 570 G 6105
2953 51 580 G 11919
2954 51 590 G 13085
2955 51 595 H 726
2956 51 600 H 117197
2957 51 610 J 55122
2958 51 620 G 1047
2959 51 630 G 23122
2960 51 640 H 2573
2961 51 650 G 76429
2962 51 660 G 42099
2963 51 670 G 21773
2964 51 678 G 2453
2965 51 680 G 76984
2966 51 683 G 96025
2967 51 685 G 57754
2968 51 690 H 6109
2969 51 700 G 123096
2970 51 710 G 185901
2971 51 720 G 2174
2972 51 730 G 20208
2973 51 735 G 4882
2974 51 740 H 94478
2975 51 750 G 3431
2976 51 760 G 314748
2977 51 770 G 149851
2978 51 775 G 31880
2979 51 790 G 10364
2980 51 800 G 46953
2981 51 810 G 456628
2982 51 820 H 12857
2983 51 830 H 4091
2984 51 840 G 13907
2985 51 999 G 142439
2986 53 001 H 3887
2987 53 003 G 21875
2988 53 005 G 267600
2989 53 007 H 126835
2990 53 009 G 51501
2991 53 011 G 652840
2992 53 013 G 462
2993 53 015 H 173688
2994 53 017 G 25407
2995 53 019 H 989
2996 53 021 G 81299
2997 53 023 J 46
2998 53 025 G 50732
2999 53 027 G 72595
3000 53 029 G 46170
3001 53 031 H 21047
3002 53 033 G 4211662
3003 53 035 G 206546
3004 53 037 G 34292
3005 53 039 G 6391
3006 53 041 G 36064
3007 53 043 H 9739
3008 53 045 G 22337
3009 53 047 G 16880
3010 53 049 G 5965
3011 53 051 H 4888
3012 53 053 G 1059916
3013 53 055 G 28105
3014 53 057 H 195510
3015 53 059 H 3837
3016 53 061 G 1204848
3017 53 063 G 580782
3018 53 065 G 20846
3019 53 067 G 212150
3020 53 069 J 2142
3021 53 071 G 40620
3022 53 073 G 402503
3023 53 075 G 15815
3024 53 077 G 150049
3025 53 999 J 12648
3026 54 001 G 2701
3027 54 003 G 32306
3028 54 005 H 2511
3029 54 007 G 7470
3030 54 009 H 12452
3031 54 011 H 116738
3032 54 013 G 20410
3033 54 015 J 2703
3034 54 017 J 6538
3035 54 019 G 9250
3036 54 021 H 1185
3037 54 023 H 10649
3038 54 025 G 10668
3039 54 027 G 4772
3040 54 029 G 2880
3041 54 031 H 1811
3042 54 033 G 78446
3043 54 035 G 20688
3044 54 037 G 18575
3045 54 039 G 246393
3046 54 041 H 26220
3047 54 043 H 6466
3048 54 045 G 3394
3049 54 047 J 389
3050 54 049 G 32435
3051 54 051 H 10459
3052 54 053 G 8530
3053 54 055 G 22118
3054 54 057 G 11105
3055 54 059 G 5069
3056 54 061 G 91708
3057 54 063 H 2978
3058 54 065 G 1869
3059 54 067 G 5783
3060 54 069 G 45913
3061 54 071 G 706
3062 54 073 D 0
3063 54 075 G 2005
3064 54 077 G 31006
3065 54 079 G 122236
3066 54 081 G 40280
3067 54 083 G 8219
3068 54 085 H 4331
3069 54 087 H 4529
3070 54 089 H 1244
3071 54 091 G 6974
3072 54 093 H 3239
3073 54 095 J 834
3074 54 097 G 30159
3075 54 099 G 13009
3076 54 101 J 701
3077 54 103 G 11181
3078 54 105 J 796
3079 54 107 H 62402
3080 54 109 H 3202
3081 54 999 J 10401
3082 55 001 G 6361
3083 55 003 J 37315
3084 55 005 H 30333
3085 55 007 G 8428
3086 55 009 G 364181
3087 55 011 G 4658
3088 55 013 H 7504
3089 55 015 G 26637
3090 55 017 H 61951
3091 55 019 H 32922
3092 55 021 G 43951
3093 55 023 G 3182
3094 55 025 G 865926
3095 55 027 H 391719
3096 55 029 G 28106
3097 55 031 G 79367
3098 55 033 G 42355
3099 55 035 G 79898
3100 55 037 H 1184
3101 55 039 H 164511
3102 55 041 G 1560
3103 55 043 G 21460
3104 55 045 G 21766
3105 55 047 G 20696
3106 55 049 G 13295
3107 55 051 H 6147
3108 55 053 H 100006
3109 55 055 G 63629
3110 55 057 G 9107
3111 55 059 G 103164
3112 55 061 G 17853
3113 55 063 G 164234
3114 55 065 G 12062
3115 55 067 G 7730
3116 55 069 G 21869
3117 55 071 G 56433
3118 55 073 G 135752
3119 55 075 G 20674
3120 55 077 J 21269
3121 55 078 D 0
3122 55 079 G 679894
3123 55 081 H 30103
3124 55 083 G 16525
3125 55 085 G 54117
3126 55 087 G 392681
3127 55 089 G 70609
3128 55 091 J 11022
3129 55 093 G 21666
3130 55 095 G 24059
3131 55 097 G 56107
3132 55 099 G 1997
3133 55 101 G 153535
3134 55 103 G 7197
3135 55 105 G 183633
3136 55 107 G 2764
3137 55 109 G 93127
3138 55 111 G 75950
3139 55 113 G 8322
3140 55 115 G 16215
3141 55 117 G 100629
3142 55 119 J 15907
3143 55 121 G 12660
3144 55 123 G 9252
3145 55 125 H 18418
3146 55 127 G 53703
3147 55 129 G 3727
3148 55 131 G 127903
3149 55 133 G 914862
3150 55 135 G 21631
3151 55 137 G 4966
3152 55 139 H 236679
3153 55 141 G 83438
3154 55 999 D 0
3155 56 001 G 29982
3156 56 003 G 20679
3157 56 005 G 130519
3158 56 007 H 11850
3159 56 009 G 35184
3160 56 011 G 11400
3161 56 013 G 39652
3162 56 015 G 9696
3163 56 017 G 3311
3164 56 019 G 9620
3165 56 021 G 117538
3166 56 023 G 21784
3167 56 025 G 179073
3168 56 027 H 841
3169 56 029 H 48423
3170 56 031 G 3844
3171 56 033 G 48751
3172 56 035 G 15162
3173 56 037 G 45378
3174 56 039 G 105519
3175 56 041 H 148380
3176 56 043 H 12336
3177 56 045 H 7311
3178 56 999 H 975
Solution 3
state_cbp_health_care <- cbp_health_care %>%
group_by(FIPSTATE, FIPSCTY) %>%
summarize(EMP = sum(EMP)) %>%
summarize(EMP = sum(EMP), counties = n())