Code Handout  Data Wrangling with dplyr & tidyr
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This document contains all of the functions that we have covered thus far in the course. It will be updated every week, after we’ve added new skills. Each function is presented alongside an example of how it is used.
All of the examples below are in the context of the Palmer Penguins, found here (link).
Packages

library()
– loads packages into yourR
session
R
library(tidyverse)
library(palmerpenguins)
Inspecting Data

glimpse()
– shows a summary of the dataset, the number of rows and columns, variable names, and the first 10 entries of each variable
R
glimpse(penguins)
Working with Data

<
– “assignment arrow”, assigns a value (vector, dataframe, single value) to the name of a variable
R
penguins_2007 < penguins %>%
filter(year == 2007)

c()
– the “concatenate” function combines inputs to form a vector, the values have to be the same data type.
R
cat_variables < c("Species", "Island", "Sex")
\newpage
Verbs of Data Wrangling

select()
– selects variables (columns) from a dataframe
R
penguins %>%
select(species)

filter()
– filters observations (rows) out of / into a dataframe, where the inputs (arguments) are the conditions to be satisfied in the data that are kept
R
## It's nice to have a new line for each condition, so your code is easier to read!
penguins %>%
filter(species == "Adelie",
body_mass_g > 3000,
year == 2008)
Logical operators: Filtering for certain
observations (e.g. flights from a particular airport) is often of
interest in data frames where we might want to examine observations with
certain characteristics separately from the rest of the data. To do so,
you can use the filter
function and a series of
logical operators. The most commonly used logical
operators for data analysis are as follows:
==
means “equal to”!=
means “not equal to”>
or<
means “greater than” or “less than”>=
or<=
means “greater than or equal to” or “less than or equal to”mutate()
– creates new variables or modifies existing variables
R
penguins %>%
filter(is.na(bill_length_mm) != TRUE,
is.na(bill_depth_mm) != TRUE) %>%
mutate(body_mass_kg = body_mass_g / 1000)

group_by()
– groups the dataframe based on levels of a categorical variable, usually used alongsidesummarize()
R
penguins %>%
group_by(island)
 summarize()
 creates data summaries of variables in a dataframe, for grouped summaries use alongside
group_by()`
R
penguins %>%
filter(is.na(body_mass_g) != TRUE) %>%
group_by(island) %>%
summarize(mean_mass = mean(body_mass_g))

ungroup()
– removes the grouping of a dataframe, typically used after group summaries when additional ungrouped operations are required
R
penguins %>%
filter(is.na(body_mass_g) != TRUE) %>%
group_by(island) %>%
summarize(mean_mass = mean(body_mass_g)) %>%
ungroup()

arrange()
– orders a dataframe based on the values of a numerical variable, paired withdesc()
to order in descending order
R
penguins %>%
filter(is.na(body_mass_g) != TRUE) %>%
group_by(island) %>%
summarize(mean_mass = mean(body_mass_g)) %>%
arrange(desc(mean_mass))

%>%
– the “pipe” operator, joins sequences of data wrangling steps together, works with any function that hasdata =
as the first argument
R
penguins %>%
select(species, island, body_mass_g, sex, year) %>%
filter(island == "Torgersen",
is.na(body_mass_g) != TRUE) %>%
group_by(species, year) %>%
summarize(mean_mass = mean(body_mass_g),
median_mass = median(body_mass_g),
observations = n()) %>%
arrange(desc(mean_mass))
Other Data Wrangling Tools

count()
– counts the number of observations (rows) of the different levels of a categorical variable can add
sort = TRUE
to sort the table in descending order (similar to usingarrange(desc())
)
 can add
R
penguins %>%
count(species)

mean()
– finds the mean of a numerical variable, not resistant toNA
values, so either filter out prior or usena.omit = TRUE
argument Other summary functions include:

var()
– find the variance of a numerical variable 
sd()
– finds the standard deviation of a numerical variable 
IQR()
– find the innerquartile range (Q3  Q1) of a numerical variable 
median()
– finds the median of a numerical variable

 Other summary functions include:
is.na()
– returns a vector ofTRUE
andFALSE
values corresponding to whether a particular row of a variable wasNA
(missing)
R
penguins %>%
mutate(missing_weight = is.na(body_mass_g))

sample_n()
– selects \(n\) rows from the dataframe, based on the value ofsize
specified
R
penguins %>%
sample_n(size = 10)

replace_na()
– replaces NA values with the value specified The values to be replaced must be passed to the function (input) as
a
list()
object.
 The values to be replaced must be passed to the function (input) as
a
R
penguins %>%
replace_na(list(bill_length_mm = "no_measurement",
bill_depth_mm = "no_measurement")) %>%
glimpse()

separate_rows()
– separates a variable with multiple values based on the delimiter specified. Variables whose entries are stored as a list with commas or semicolons are great candidates for this function!

rowSums()
– forms row sums for numeric variables Note: In the lesson
rowSums()
was used on alogical
variable, because logical values can be numerically represented as 0 (FALSE) and 1 (TRUE)
 Note: In the lesson
R
x < tibble(x1 = 3, x2 = c(4:1, 2:5))
rowSums(x)
Pivoting Dataframes

pivot_wider()
– transforms a dataframe from long to wide format takes three principal arguments:
 the data
 the names_from column variable whose values will become new column names
 the values_from column variable whose values will fill the new column variables.
 Further arguments include
values_fill
which, if set, fills in missing values with the value provided.
 takes three principal arguments:
R
wide < penguins %>%
mutate(island_logical = TRUE) %>%
pivot_wider(names_from = species,
values_from = island_logical,
values_fill = list(island_logical = FALSE))
glimpse(wide)

pivot_longer()
– transforms a dataframe from wide to long format takes four principal arguments:
 the data
 cols are the names of the columns we use to fill the a new values variable (or to drop).
 the names_to column variable we wish to create from the cols provided.
 the values_to column variable we wish to create and fill with values associated with the cols provided.
R
wide %>%
pivot_longer(cols = Adelie:Gentoo,
names_to = "species",
values_to = "island_logical")
Extracting Data

write_csv()
– writes a dataframe to a csv file, output into the file path specified
R
write_csv(wide, path = "data/penguins_wide.csv")