Martin Frigaard 2019-08-03
The fivethirtyeight R package – released by Albert Y. Kim, Chester Ismay, and Jennifer Chunn last March – contains dozens of datasets used in FiveThirtyEight news articles like “A Handful Of Cities Are Driving 2016’s Rise In Murders,” “The Best MLB All-Star Teams Ever,” and “The Dallas Shooting Was Among The Deadliest For Police In U.S. History.”
This tutorial will explore the murder_2015_final dataset using tidyr functions like tibble, gather, arrange and separate from our tidyverse tutorial.
Using RStudio, we’ll install and then load the fivethirtyeight package, as well as tidyr, tibble and dplyr and then call murder_2015_final
## # A tibble: 83 x 5
## city state murders_2014 murders_2015 change
## <chr> <chr> <int> <int> <int>
## 1 Baltimore Maryland 211 344 133
## 2 Chicago Illinois 411 478 67
## 3 Houston Texas 242 303 61
## 4 Cleveland Ohio 63 120 57
## 5 Washington D.C. 105 162 57
## 6 Milwaukee Wisconsin 90 145 55
## 7 Philadelphia Pennsylvania 248 280 32
## 8 Kansas City Missouri 78 109 31
## 9 Nashville Tennessee 41 72 31
## 10 St. Louis Missouri 159 188 29
## # … with 73 more rows
Look at the dataset’s column names
Use names(murder_2015_final) to list out the dataset’s column names.
## [1] "city" "state" "murders_2014" "murders_2015"
## [5] "change"
Lets gather up the two year variables, murders_2014 and murders_2015 into a single year column we’ll name murder_year. We’ll store the number of murders in a column titled murders and call this new object murders_gathered.
murders_gathered <- murder_2015_final %>%
gather(
murder_year,
murders,
murders_2014:murders_2015,
na.rm = TRUE)
murders_gathered
Now let’s arrange these data alphabetically by state and city. We can do this with dplyr::arrange() from the dplyr package. (We’ll learn more about dplyr in the next tutorial!)
So now we have the two years in a single column (murder_year), but the repeating murder_ text is redundant. I want to get the year in a column by itself.
Now I want to put the murder years (2014 & 2015) back into two different columns. I’ll do this with tidyr::separate().
The separate() command uses a name of the existting column we want to separate (murder_year), and the names of the columns that will contain the separated values (c("text", "year")).
murders_separate <- murders_arranged %>%
tidyr::separate(
murder_year,
into = c("text",
"year"))
murders_separate## # A tibble: 166 x 6
## city state change text year murders
## <chr> <chr> <int> <chr> <chr> <int>
## 1 Mobile Alabama -7 murders 2014 31
## 2 Mobile Alabama -7 murders 2015 24
## 3 Anchorage Alaska 14 murders 2014 12
## 4 Anchorage Alaska 14 murders 2015 26
## 5 Chandler Arizona 0 murders 2014 1
## 6 Chandler Arizona 0 murders 2015 1
## 7 Mesa Arizona 3 murders 2014 13
## 8 Mesa Arizona 3 murders 2015 16
## 9 Phoenix Arizona -2 murders 2014 114
## 10 Phoenix Arizona -2 murders 2015 112
## # … with 156 more rows
Great. Now I can use tidyr::spread() to put the year back into two different columns, 2014 & 2015. But I will combine this with dplyr::arrange() so the output is easier to read.
murders_spread <- murders_separate %>%
spread(
year,
murders
) %>%
arrange(
state,
city)
murders_spread
What if I want to combine city and state into a single column city_state?
The final command, tidyr::unite(), lets me paste the contents of columns onto one another. It requires the name of the new column city_state and the columns I want to combine city and state. But I want to sort this new tibble in descending order of change, and I want to remove the text variable.
I can combine all of these together with the pipe (%>%).
murders_final <- murders_spread %>%
unite(
city_state,
city,
state) %>%
arrange(
city_state
) %>%
select(
-(text)
)
murders_final## # A tibble: 83 x 4
## city_state change `2014` `2015`
## <chr> <int> <int> <int>
## 1 Albuquerque_New Mexico 13 30 43
## 2 Anaheim_California 4 14 18
## 3 Anchorage_Alaska 14 12 26
## 4 Arlington_Texas -5 13 8
## 5 Atlanta_Georgia 1 93 94
## 6 Aurora_Colorado 13 11 24
## 7 Austin_Texas -9 32 23
## 8 Bakersfield_California 5 17 22
## 9 Baltimore_Maryland 133 211 344
## 10 Boston_Massachusetts -15 53 38
## # … with 73 more rows
Use readr::write_csv(as_data_frame(murders_final), "murders_final.csv") and voilá, you have a csv.
We used the pipe operator to string together various tidyr and dplyr functions for structuring our data (in tibbles). Remember that:
gather() collects data across columns and puts it into into rows arrange() sorts the data in each column separate() divides the contents of a column apart into new columns spread() distributes data from rows into columns unite() attaches the contents from one column onto the contents of another column
By typing barplot(murders_final$change), you can create a quick barplot of the change in murders by city.
Obviously, the barplot is not visualizing the “change” column sorted low to high. To do that, try your hand with arrange().
Then, plotting barplot(murders_final_sort$change) will produce this:
To extend the y-axis to -20, add:
Finally, we’ll add some labels. There are many different ways to add labels in R. Here’s one way:
midpts <- barplot(murders_final_sort$change,
cex.axis = 1.0, cex.names = 0.6,
ylim = c(-20, 120), main = "Change in murders from 2014 to 2015"
)
text(murder_2015_final$city,
x = midpts,
offset = -0.1,
y = -20,
cex = 0.5,
srt = 60,
xpd = TRUE,
pos = 2
) 