| title | output | ||||||
|---|---|---|---|---|---|---|---|
dplyr functions for a single dataset |
|
In the introduction to dplyr, we used two very important verbs and an operator:
filter()for subsetting data with row logicselect()for subsetting data variable- or column-wise- the pipe operator
%>%, which feeds the LHS as the first argument to the expression on the RHS
We also discussed dplyr's role inside the tidyverse and tibbles:
- dplyr is a core package in the tidyverse meta-package. Since we often make incidental usage of the others, we will load dplyr and the others via
library(tidyverse). - The tidyverse embraces a special flavor of data frame, called a tibble. The
gapminderdataset is stored as a tibble.
I choose to load the tidyverse, which will load dplyr, among other packages we use incidentally below. Also load gapminder.
library(gapminder)
library(tidyverse)## ── Attaching packages ────────────────────────────────────────── tidyverse 1.2.1 ──
## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.99.9004 ✔ dplyr 0.7.99.9000
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.2.0 ✔ forcats 0.3.0
## ── Conflicts ───────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
We're going to make changes to the gapminder tibble. To eliminate any fear that you're damaging the data that comes with the package, we create an explicit copy of gapminder for our experiments.
(my_gap <- gapminder)## # A tibble: 1,704 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,694 more rows
Pay close attention to when we evaluate statements but let the output just print to screen:
## let output print to screen, but do not store
my_gap %>% filter(country == "Canada")... versus when we assign the output to an object, possibly overwriting an existing object.
## store the output as an R object
my_precious <- my_gap %>% filter(country == "Canada")Imagine we wanted to recover each country's GDP. After all, the Gapminder data has a variable for population and GDP per capita. Let's multiply them together.
mutate() is a function that defines and inserts new variables into a tibble. You can refer to existing variables by name.
my_gap %>%
mutate(gdp = pop * gdpPercap)## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap gdp
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 6567086330.
## 2 Afghanistan Asia 1957 30.3 9240934 821. 7585448670.
## 3 Afghanistan Asia 1962 32.0 10267083 853. 8758855797.
## 4 Afghanistan Asia 1967 34.0 11537966 836. 9648014150.
## 5 Afghanistan Asia 1972 36.1 13079460 740. 9678553274.
## 6 Afghanistan Asia 1977 38.4 14880372 786. 11697659231.
## 7 Afghanistan Asia 1982 39.9 12881816 978. 12598563401.
## 8 Afghanistan Asia 1987 40.8 13867957 852. 11820990309.
## 9 Afghanistan Asia 1992 41.7 16317921 649. 10595901589.
## 10 Afghanistan Asia 1997 41.8 22227415 635. 14121995875.
## # … with 1,694 more rows
Hmmmm ... those GDP numbers are almost uselessly large and abstract. Consider the advice of Randall Munroe of xkcd:
One thing that bothers me is large numbers presented without context... 'If I added a zero to this number, would the sentence containing it mean something different to me?' If the answer is 'no,' maybe the number has no business being in the sentence in the first place."
Maybe it would be more meaningful to consumers of my tables and figures to stick with GDP per capita. But what if I reported GDP per capita, relative to some benchmark country. Since Canada is my adopted home, I'll go with that.
I need to create a new variable that is gdpPercap divided by Canadian gdpPercap, taking care that I always divide two numbers that pertain to the same year.
How I achieve:
- Filter down to the rows for Canada.
- Create a new temporary variable in
my_gap:- Extract the
gdpPercapvariable from the Canadian data. - Replicate it once per country in the dataset, so it has the right length.
- Extract the
- Divide raw
gdpPercapby this Canadian figure. - Discard the temporary variable of replicated Canadian
gdpPercap.
ctib <- my_gap %>%
filter(country == "Canada")
## this is a semi-dangerous way to add this variable
## I'd prefer to join on year, but we haven't covered joins yet
my_gap <- my_gap %>%
mutate(tmp = rep(ctib$gdpPercap, nlevels(country)),
gdpPercapRel = gdpPercap / tmp,
tmp = NULL)Note that, mutate() builds new variables sequentially so you can reference earlier ones (like tmp) when defining later ones (like gdpPercapRel). Also, you can get rid of a variable by setting it to NULL.
How could we sanity check that this worked? The Canadian values for gdpPercapRel better all be 1!
my_gap %>%
filter(country == "Canada") %>%
select(country, year, gdpPercapRel)## # A tibble: 12 x 3
## country year gdpPercapRel
## <fct> <int> <dbl>
## 1 Canada 1952 1
## 2 Canada 1957 1
## 3 Canada 1962 1
## 4 Canada 1967 1
## 5 Canada 1972 1
## 6 Canada 1977 1
## 7 Canada 1982 1
## 8 Canada 1987 1
## 9 Canada 1992 1
## 10 Canada 1997 1
## 11 Canada 2002 1
## 12 Canada 2007 1
I perceive Canada to be a "high GDP" country, so I predict that the distribution of gdpPercapRel is located below 1, possibly even well below. Check your intuition!
summary(my_gap$gdpPercapRel)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.007236 0.061648 0.171521 0.326659 0.446564 9.534690
The relative GDP per capita numbers are, in general, well below 1. We see that most of the countries covered by this dataset have substantially lower GDP per capita, relative to Canada, across the entire time period.
Remember: Trust No One. Including (especially?) yourself. Always try to find a way to check that you've done what meant to. Prepare to be horrified.
arrange() reorders the rows in a data frame. Imagine you wanted this data ordered by year then country, as opposed to by country then year.
my_gap %>%
arrange(year, country)## # A tibble: 1,704 x 7
## country continent year lifeExp pop gdpPercap gdpPercapRel
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 0.0686
## 2 Albania Europe 1952 55.2 1282697 1601. 0.141
## 3 Algeria Africa 1952 43.1 9279525 2449. 0.215
## 4 Angola Africa 1952 30.0 4232095 3521. 0.310
## 5 Argentina Americas 1952 62.5 17876956 5911. 0.520
## 6 Australia Oceania 1952 69.1 8691212 10040. 0.883
## 7 Austria Europe 1952 66.8 6927772 6137. 0.540
## 8 Bahrain Asia 1952 50.9 120447 9867. 0.868
## 9 Bangladesh Asia 1952 37.5 46886859 684. 0.0602
## 10 Belgium Europe 1952 68 8730405 8343. 0.734
## # … with 1,694 more rows
Or maybe you want just the data from 2007, sorted on life expectancy?
my_gap %>%
filter(year == 2007) %>%
arrange(lifeExp)## # A tibble: 142 x 7
## country continent year lifeExp pop gdpPercap gdpPercapRel
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Swaziland Africa 2007 39.6 1.13e6 4513. 0.124
## 2 Mozambique Africa 2007 42.1 2.00e7 824. 0.0227
## 3 Zambia Africa 2007 42.4 1.17e7 1271. 0.0350
## 4 Sierra Leone Africa 2007 42.6 6.14e6 863. 0.0237
## 5 Lesotho Africa 2007 42.6 2.01e6 1569. 0.0432
## 6 Angola Africa 2007 42.7 1.24e7 4797. 0.132
## 7 Zimbabwe Africa 2007 43.5 1.23e7 470. 0.0129
## 8 Afghanistan Asia 2007 43.8 3.19e7 975. 0.0268
## 9 Central African… Africa 2007 44.7 4.37e6 706. 0.0194
## 10 Liberia Africa 2007 45.7 3.19e6 415. 0.0114
## # … with 132 more rows
Oh, you'd like to sort on life expectancy in descending order? Then use desc().
my_gap %>%
filter(year == 2007) %>%
arrange(desc(lifeExp))## # A tibble: 142 x 7
## country continent year lifeExp pop gdpPercap gdpPercapRel
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Japan Asia 2007 82.6 1.27e8 31656. 0.872
## 2 Hong Kong, Chi… Asia 2007 82.2 6.98e6 39725. 1.09
## 3 Iceland Europe 2007 81.8 3.02e5 36181. 0.996
## 4 Switzerland Europe 2007 81.7 7.55e6 37506. 1.03
## 5 Australia Oceania 2007 81.2 2.04e7 34435. 0.948
## 6 Spain Europe 2007 80.9 4.04e7 28821. 0.794
## 7 Sweden Europe 2007 80.9 9.03e6 33860. 0.932
## 8 Israel Asia 2007 80.7 6.43e6 25523. 0.703
## 9 France Europe 2007 80.7 6.11e7 30470. 0.839
## 10 Canada Americas 2007 80.7 3.34e7 36319. 1
## # … with 132 more rows
I advise that your analyses NEVER rely on rows or variables being in a specific order. But it's still true that human beings write the code and the interactive development process can be much nicer if you reorder the rows of your data as you go along. Also, once you are preparing tables for human eyeballs, it is imperative that you step up and take control of row order.
When I first cleaned this Gapminder excerpt, I was a camelCase person, but now I'm all about snake_case. So I am vexed by the variable names I chose when I cleaned this data years ago. Let's rename some variables!
my_gap %>%
rename(life_exp = lifeExp,
gdp_percap = gdpPercap,
gdp_percap_rel = gdpPercapRel)## # A tibble: 1,704 x 7
## country continent year life_exp pop gdp_percap gdp_percap_rel
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 0.0686
## 2 Afghanistan Asia 1957 30.3 9240934 821. 0.0657
## 3 Afghanistan Asia 1962 32.0 10267083 853. 0.0634
## 4 Afghanistan Asia 1967 34.0 11537966 836. 0.0520
## 5 Afghanistan Asia 1972 36.1 13079460 740. 0.0390
## 6 Afghanistan Asia 1977 38.4 14880372 786. 0.0356
## 7 Afghanistan Asia 1982 39.9 12881816 978. 0.0427
## 8 Afghanistan Asia 1987 40.8 13867957 852. 0.0320
## 9 Afghanistan Asia 1992 41.7 16317921 649. 0.0246
## 10 Afghanistan Asia 1997 41.8 22227415 635. 0.0219
## # … with 1,694 more rows
I did NOT assign the post-rename object back to my_gap because that would make the chunks in this tutorial harder to copy/paste and run out of order. In real life, I would probably assign this back to my_gap, in a data preparation script, and proceed with the new variable names.
You've seen simple use of select(). There are two tricks you might enjoy:
select()can rename the variables you request to keep.select()can be used witheverything()to hoist a variable up to the front of the tibble.
my_gap %>%
filter(country == "Burundi", year > 1996) %>%
select(yr = year, lifeExp, gdpPercap) %>%
select(gdpPercap, everything())## # A tibble: 3 x 3
## gdpPercap yr lifeExp
## <dbl> <int> <dbl>
## 1 463. 1997 45.3
## 2 446. 2002 47.4
## 3 430. 2007 49.6
everything() is one of several helpers for variable selection. Read its help to see the rest.
I have found friends and family collaborators love to ask seemingly innocuous questions like, "which country experienced the sharpest 5-year drop in life expectancy?". In fact, that is a totally natural question to ask. But if you are using a language that doesn't know about data, it's an incredibly annoying question to answer.
dplyr offers powerful tools to solve this class of problem.
-
group_by()adds extra structure to your dataset -- grouping information -- which lays the groundwork for computations within the groups. -
summarize()takes a dataset with$n$ observations, computes requested summaries, and returns a dataset with 1 observation. - Window functions take a dataset with
$n$ observations and return a dataset with$n$ observations. -
mutate()andsummarize()will honor groups. - You can also do very general computations on your groups with
do(), though elsewhere in this course, I advocate for other approaches that I find more intuitive, using thepurrrpackage.
Combined with the verbs you already know, these new tools allow you to solve an extremely diverse set of problems with relative ease.
Let's start with simple counting. How many observations do we have per continent?
my_gap %>%
group_by(continent) %>%
summarize(n = n())## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 624
## 2 Americas 300
## 3 Asia 396
## 4 Europe 360
## 5 Oceania 24
Let us pause here to think about the tidyverse. You could get these same frequencies using table() from base R.
table(gapminder$continent)##
## Africa Americas Asia Europe Oceania
## 624 300 396 360 24
str(table(gapminder$continent))## 'table' int [1:5(1d)] 624 300 396 360 24
## - attr(*, "dimnames")=List of 1
## ..$ : chr [1:5] "Africa" "Americas" "Asia" "Europe" ...
But the object of class table that is returned makes downstream computation a bit fiddlier than you'd like. For example, it's too bad the continent levels come back only as names and not as a proper factor, with the original set of levels. This is an example of how the tidyverse smooths transitions where you want the output of step i to become the input of step i + 1.
The tally() function is a convenience function that knows to count rows. It honors groups.
my_gap %>%
group_by(continent) %>%
tally()## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 624
## 2 Americas 300
## 3 Asia 396
## 4 Europe 360
## 5 Oceania 24
The count() function is an even more convenient function that does both grouping and counting.
my_gap %>%
count(continent)## # A tibble: 5 x 2
## continent n
## <fct> <int>
## 1 Africa 624
## 2 Americas 300
## 3 Asia 396
## 4 Europe 360
## 5 Oceania 24
What if we wanted to add the number of unique countries for each continent? You can compute multiple summaries inside summarize(). Use the n_distinct() function to count the number of distinct countries within each continent.
my_gap %>%
group_by(continent) %>%
summarize(n = n(),
n_countries = n_distinct(country))## # A tibble: 5 x 3
## continent n n_countries
## <fct> <int> <int>
## 1 Africa 624 52
## 2 Americas 300 25
## 3 Asia 396 33
## 4 Europe 360 30
## 5 Oceania 24 2
The functions you'll apply within summarize() include classical statistical summaries, like mean(), median(), var(), sd(), mad(), IQR(), min(), and max(). Remember they are functions that take
Although this may be statistically ill-advised, let's compute the average life expectancy by continent.
my_gap %>%
group_by(continent) %>%
summarize(avg_lifeExp = mean(lifeExp))## # A tibble: 5 x 2
## continent avg_lifeExp
## <fct> <dbl>
## 1 Africa 48.9
## 2 Americas 64.7
## 3 Asia 60.1
## 4 Europe 71.9
## 5 Oceania 74.3
summarize_at() applies the same summary function(s) to multiple variables. Let's compute average and median life expectancy and GDP per capita by continent by year ... but only for 1952 and 2007.
my_gap %>%
filter(year %in% c(1952, 2007)) %>%
group_by(continent, year) %>%
summarize_at(vars(lifeExp, gdpPercap), funs(mean, median))## # A tibble: 10 x 6
## # Groups: continent [5]
## continent year lifeExp_mean gdpPercap_mean lifeExp_median
## <fct> <int> <dbl> <dbl> <dbl>
## 1 Africa 1952 39.1 1253. 38.8
## 2 Africa 2007 54.8 3089. 52.9
## 3 Americas 1952 53.3 4079. 54.7
## 4 Americas 2007 73.6 11003. 72.9
## 5 Asia 1952 46.3 5195. 44.9
## 6 Asia 2007 70.7 12473. 72.4
## 7 Europe 1952 64.4 5661. 65.9
## 8 Europe 2007 77.6 25054. 78.6
## 9 Oceania 1952 69.3 10298. 69.3
## 10 Oceania 2007 80.7 29810. 80.7
## # … with 1 more variable: gdpPercap_median <dbl>
Let's focus just on Asia. What are the minimum and maximum life expectancies seen by year?
my_gap %>%
filter(continent == "Asia") %>%
group_by(year) %>%
summarize(min_lifeExp = min(lifeExp), max_lifeExp = max(lifeExp))## # A tibble: 12 x 3
## year min_lifeExp max_lifeExp
## <int> <dbl> <dbl>
## 1 1952 28.8 65.4
## 2 1957 30.3 67.8
## 3 1962 32.0 69.4
## 4 1967 34.0 71.4
## 5 1972 36.1 73.4
## 6 1977 31.2 75.4
## 7 1982 39.9 77.1
## 8 1987 40.8 78.7
## 9 1992 41.7 79.4
## 10 1997 41.8 80.7
## 11 2002 42.1 82
## 12 2007 43.8 82.6
Of course it would be much more interesting to see which country contributed these extreme observations. Is the minimum (maximum) always coming from the same country? We tackle that with window functions shortly.
Sometimes you don't want to collapse the
Let's make a new variable that is the years of life expectancy gained (lost) relative to 1952, for each individual country. We group by country and use mutate() to make a new variable. The first() function extracts the first value from a vector. Notice that first() is operating on the vector of life expectancies within each country group.
my_gap %>%
group_by(country) %>%
select(country, year, lifeExp) %>%
mutate(lifeExp_gain = lifeExp - first(lifeExp)) %>%
filter(year < 1963)## # A tibble: 426 x 4
## # Groups: country [142]
## country year lifeExp lifeExp_gain
## <fct> <int> <dbl> <dbl>
## 1 Afghanistan 1952 28.8 0
## 2 Afghanistan 1957 30.3 1.53
## 3 Afghanistan 1962 32.0 3.20
## 4 Albania 1952 55.2 0
## 5 Albania 1957 59.3 4.05
## 6 Albania 1962 64.8 9.59
## 7 Algeria 1952 43.1 0
## 8 Algeria 1957 45.7 2.61
## 9 Algeria 1962 48.3 5.23
## 10 Angola 1952 30.0 0
## # … with 416 more rows
Within country, we take the difference between life expectancy in year
Window functions take rank() is a window function but log() is not. Here we use window functions based on ranks and offsets.
Let's revisit the worst and best life expectancies in Asia over time, but retaining info about which country contributes these extreme values.
my_gap %>%
filter(continent == "Asia") %>%
select(year, country, lifeExp) %>%
group_by(year) %>%
filter(min_rank(desc(lifeExp)) < 2 | min_rank(lifeExp) < 2) %>%
arrange(year) %>%
print(n = Inf)## # A tibble: 24 x 3
## # Groups: year [12]
## year country lifeExp
## <int> <fct> <dbl>
## 1 1952 Afghanistan 28.8
## 2 1952 Israel 65.4
## 3 1957 Afghanistan 30.3
## 4 1957 Israel 67.8
## 5 1962 Afghanistan 32.0
## 6 1962 Israel 69.4
## 7 1967 Afghanistan 34.0
## 8 1967 Japan 71.4
## 9 1972 Afghanistan 36.1
## 10 1972 Japan 73.4
## 11 1977 Cambodia 31.2
## 12 1977 Japan 75.4
## 13 1982 Afghanistan 39.9
## 14 1982 Japan 77.1
## 15 1987 Afghanistan 40.8
## 16 1987 Japan 78.7
## 17 1992 Afghanistan 41.7
## 18 1992 Japan 79.4
## 19 1997 Afghanistan 41.8
## 20 1997 Japan 80.7
## 21 2002 Afghanistan 42.1
## 22 2002 Japan 82
## 23 2007 Afghanistan 43.8
## 24 2007 Japan 82.6
We see that (min = Afghanistan, max = Japan) is the most frequent result, but Cambodia and Israel pop up at least once each as the min or max, respectively. That table should make you impatient for our upcoming work on tidying and reshaping data! Wouldn't it be nice to have one row per year?
How did that actually work? First, I store and view a partial that leaves off the filter() statement. All of these operations should be familiar.
asia <- my_gap %>%
filter(continent == "Asia") %>%
select(year, country, lifeExp) %>%
group_by(year)
asia## # A tibble: 396 x 3
## # Groups: year [12]
## year country lifeExp
## <int> <fct> <dbl>
## 1 1952 Afghanistan 28.8
## 2 1957 Afghanistan 30.3
## 3 1962 Afghanistan 32.0
## 4 1967 Afghanistan 34.0
## 5 1972 Afghanistan 36.1
## 6 1977 Afghanistan 38.4
## 7 1982 Afghanistan 39.9
## 8 1987 Afghanistan 40.8
## 9 1992 Afghanistan 41.7
## 10 1997 Afghanistan 41.8
## # … with 386 more rows
Now we apply a window function -- min_rank(). Since asia is grouped by year, min_rank() operates within mini-datasets, each for a specific year. Applied to the variable lifeExp, min_rank() returns the rank of each country's observed life expectancy. FYI, the min part just specifies how ties are broken. Here is an explicit peek at these within-year life expectancy ranks, in both the (default) ascending and descending order.
For concreteness, I use mutate() to actually create these variables, even though I dropped this in the solution above. Let's look at a bit of that.
asia %>%
mutate(le_rank = min_rank(lifeExp),
le_desc_rank = min_rank(desc(lifeExp))) %>%
filter(country %in% c("Afghanistan", "Japan", "Thailand"), year > 1995)## # A tibble: 9 x 5
## # Groups: year [12]
## year country lifeExp le_rank le_desc_rank
## <int> <fct> <dbl> <int> <int>
## 1 1997 Afghanistan 41.8 1 33
## 2 1997 Japan 80.7 33 1
## 3 1997 Thailand 67.5 12 22
## 4 2002 Afghanistan 42.1 1 33
## 5 2002 Japan 82 33 1
## 6 2002 Thailand 68.6 12 22
## 7 2007 Afghanistan 43.8 1 33
## 8 2007 Japan 82.6 33 1
## 9 2007 Thailand 70.6 12 22
Afghanistan tends to present 1's in the le_rank variable, Japan tends to present 1's in the le_desc_rank variable and other countries, like Thailand, present less extreme ranks.
You can understand the original filter() statement now:
filter(min_rank(desc(lifeExp)) < 2 | min_rank(lifeExp) < 2)These two sets of ranks are formed on-the-fly, within year group, and filter() retains rows with rank less than 2, which means ... the row with rank = 1. Since we do for ascending and descending ranks, we get both the min and the max.
If we had wanted just the min OR the max, an alternative approach using top_n() would have worked.
my_gap %>%
filter(continent == "Asia") %>%
select(year, country, lifeExp) %>%
arrange(year) %>%
group_by(year) %>%
#top_n(1, wt = lifeExp) ## gets the min
top_n(1, wt = desc(lifeExp)) ## gets the max## # A tibble: 12 x 3
## # Groups: year [12]
## year country lifeExp
## <int> <fct> <dbl>
## 1 1952 Afghanistan 28.8
## 2 1957 Afghanistan 30.3
## 3 1962 Afghanistan 32.0
## 4 1967 Afghanistan 34.0
## 5 1972 Afghanistan 36.1
## 6 1977 Cambodia 31.2
## 7 1982 Afghanistan 39.9
## 8 1987 Afghanistan 40.8
## 9 1992 Afghanistan 41.7
## 10 1997 Afghanistan 41.8
## 11 2002 Afghanistan 42.1
## 12 2007 Afghanistan 43.8
So let's answer that "simple" question: which country experienced the sharpest 5-year drop in life expectancy? Recall that this excerpt of the Gapminder data only has data every five years, e.g. for 1952, 1957, etc. So this really means looking at life expectancy changes between adjacent timepoints.
At this point, that's just too easy, so let's do it by continent while we're at it.
my_gap %>%
select(country, year, continent, lifeExp) %>%
group_by(continent, country) %>%
## within country, take (lifeExp in year i) - (lifeExp in year i - 1)
## positive means lifeExp went up, negative means it went down
mutate(le_delta = lifeExp - lag(lifeExp)) %>%
## within country, retain the worst lifeExp change = smallest or most negative
summarize(worst_le_delta = min(le_delta, na.rm = TRUE)) %>%
## within continent, retain the row with the lowest worst_le_delta
top_n(-1, wt = worst_le_delta) %>%
arrange(worst_le_delta)## # A tibble: 5 x 3
## # Groups: continent [5]
## continent country worst_le_delta
## <fct> <fct> <dbl>
## 1 Africa Rwanda -20.4
## 2 Asia Cambodia -9.10
## 3 Americas El Salvador -1.51
## 4 Europe Montenegro -1.46
## 5 Oceania Australia 0.170
Ponder that for a while. The subject matter and the code. Mostly you're seeing what genocide looks like in dry statistics on average life expectancy.
Break the code into pieces, starting at the top, and inspect the intermediate results. That's certainly how I was able to write such a thing. These commands do not leap fully formed out of anyone's forehead -- they are built up gradually, with lots of errors and refinements along the way. I'm not even sure it's a great idea to do so much manipulation in one fell swoop. Is the statement above really hard for you to read? If yes, then by all means break it into pieces and make some intermediate objects. Your code should be easy to write and read when you're done.
In later tutorials, we'll explore more of dplyr, such as operations based on two datasets.
dplyr official stuff
- package home on CRAN
- note there are several vignettes, with the introduction being the most relevant right now
- the one on window functions will also be interesting to you now
- development home on GitHub
- tutorial HW delivered (note this links to a DropBox folder) at useR! 2014 conference
RStudio Data Wrangling cheatsheet, covering dplyr and tidyr. Remember you can get to these via Help > Cheatsheets.
Data transformation chapter of R for Data Science
Excellent slides on pipelines and dplyr by TJ Mahr, talk given to the Madison R Users Group.
Blog post Hands-on dplyr tutorial for faster data manipulation in R by Data School, that includes a link to an R Markdown document and links to videos
Cheatsheet I made for dplyr join functions (not relevant yet but soon)