Tidyverse: Difference between revisions

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* [http://genomicsclass.github.io/book/pages/dplyr_tutorial.html dplyr tutorial] from PH525x series (Biomedical Data Science by Rafael Irizarry and Michael Love). For select() function, some additional options to select columns based on a specific criteria include
* [http://genomicsclass.github.io/book/pages/dplyr_tutorial.html dplyr tutorial] from PH525x series (Biomedical Data Science by Rafael Irizarry and Michael Love). For select() function, some additional options to select columns based on a specific criteria include
** start_with()/ ends_with() = Select columns that start/end with a character string
** [https://tidyselect.r-lib.org/reference/starts_with.html starts_with()]/ ends_with() = Select columns that start/end with a character string
** contains() = Select columns that contain a character string
** contains() = Select columns that contain a character string
** matches() = Select columns that match a regular expression
** matches() = Select columns that match a regular expression

Revision as of 14:11, 28 September 2023

Tidyverse

   Import
     |
     | readr, readxl
     | haven, DBI, httr   +----- Visualize ------+
     |                    |    ggplot2, ggvis    |
     |                    |                      |
   Tidy ------------- Transform 
   tibble               dplyr                   Model 
   tidyr                  |                    broom
                          +------ Model ---------+

Cheat sheet

The cheat sheets are downloaded from RStudio

Books

Going from Beginner to Advanced in the Tidyverse

Online

Animation to explain

Base-R and Tidyverse

Examples

A Gentle Introduction to Tidy Statistics in R

A Gentle Introduction to Tidy Statistics in R by Thomas Mock on RStudio webinar. Good coverage with step-by-step explanation. See part 1 & part 2 about the data and markdown document. All documents are available in github repository.

Task R code Graph
Load the libraries 
library(tidyverse)
library(readxl)
library(broom)
library(knitr)
Read Excel file 
raw_df <- readxl::read_xlsx("ad_treatment.xlsx")

dplyr::glimpse(raw_df)
Check distribution 
g2 <- ggplot(raw_df, aes(x = age)) +
  geom_density(fill = "blue")
g2
raw_df %>% summarize(min = min(age),
                     max = max(age))
File:Check dist.svg
Data cleaning 
raw_df %>% 
  summarize(na_count = sum(is.na(mmse)))
Experimental variables

levels 

# check Ns and levels for our variables
table(raw_df$drug_treatment, raw_df$health_status)
table(raw_df$drug_treatment, raw_df$health_status, raw_df$sex)

# tidy way of looking at variables
raw_df %>% 
  group_by(drug_treatment, health_status, sex) %>% 
  count()
Visual Exploratory

Data Analysis 

ggplot(data = raw_df, # add the data
       aes(x = drug_treatment, y = mmse, # set x, y coordinates
           color = drug_treatment)) +    # color by treatment
  geom_boxplot() +
  facet_grid(~health_status)
File:Onefacet.svg
Summary Statistics 
raw_df %>% 
  glimpse()
sum_df <- raw_df %>% 
            mutate(
              sex = factor(sex, 
                  labels = c("Male", "Female")),
              drug_treatment =  factor(drug_treatment, 
                  levels = c("Placebo", "Low dose", "High Dose")),
              health_status = factor(health_status, 
                  levels = c("Healthy", "Alzheimer's"))
              ) %>% 
            group_by(sex, health_status, drug_treatment # group by categorical variables
              ) %>%  
            summarize(
              mmse_mean = mean(mmse),      # calc mean
              mmse_se = sd(mmse)/sqrt(n()) # calc standard error
              ) %>%  
            ungroup() # ungrouping variable is a good habit to prevent errors

kable(sum_df)

write.csv(sum_df, "adx37_sum_stats.csv")
Plotting summary

statistics 

g <- ggplot(data = sum_df, # add the data
       aes(x = drug_treatment,  #set x, y coordinates
           y = mmse_mean,
           group = drug_treatment,  # group by treatment
           color = drug_treatment)) +    # color by treatment
  geom_point(size = 3) + # set size of the dots
  facet_grid(sex~health_status) # create facets by sex and status
g
File:Twofacets.svg
ANOVA 
# set up the statistics df
stats_df <- raw_df %>% # start with data
   mutate(drug_treatment = factor(drug_treatment, levels = c("Placebo", "Low dose", "High Dose")),
         sex = factor(sex, labels = c("Male", "Female")),
         health_status = factor(health_status, levels = c("Healthy", "Alzheimer's")))

glimpse(stats_df)
# this gives main effects AND interactions
ad_aov <- aov(mmse ~ sex * drug_treatment * health_status, 
        data = stats_df)

summary(ad_aov)


# this extracts ANOVA output into a nice tidy dataframe
tidy_ad_aov <- tidy(ad_aov)
# which we can save to Excel
write.csv(tidy_ad_aov, "ad_aov.csv")
Post-hocs 
# pairwise t.tests
ad_pairwise <- pairwise.t.test(stats_df$mmse,
                               stats_df$sex:stats_df$drug_treatment:stats_df$health_status, 
                               p.adj = "none")
# look at the posthoc p.values in a tidy dataframe
kable(head(tidy(ad_pairwise)))


# call and tidy the tukey posthoc
tidy_ad_tukey <- tidy(
                      TukeyHSD(ad_aov, 
                              which = 'sex:drug_treatment:health_status'))
Publication plot 
sig_df <- tribble(
  ~drug_treatment, ~ health_status, ~sex, ~mmse_mean,
  "Low dose", "Alzheimer's", "Male", 17,
  "High Dose", "Alzheimer's", "Male", 25,
  "Low dose", "Alzheimer's", "Female", 18, 
  "High Dose", "Alzheimer's", "Female", 24
  )

sig_df <- sig_df %>% 
  mutate(drug_treatment = factor(drug_treatment, levels = c("Placebo", "Low dose", "High Dose")),
         sex = factor(sex, levels = c("Male", "Female")),
         health_status = factor(health_status, levels = c("Healthy", "Alzheimer's")))
sig_df
# plot of cognitive function health and drug treatment
g1 <- ggplot(data = sum_df, 
       aes(x = drug_treatment, y = mmse_mean, fill = drug_treatment,  
           group = drug_treatment)) +
  geom_errorbar(aes(ymin = mmse_mean - mmse_se, 
                    ymax = mmse_mean + mmse_se), width = 0.5) +
  geom_bar(color = "black", stat = "identity", width = 0.7) +
  
  facet_grid(sex~health_status) +
  theme_bw() +
  scale_fill_manual(values = c("white", "grey", "black")) +
  theme(legend.position = "NULL",
        legend.title = element_blank(),
        axis.title = element_text(size = 20),
        legend.background = element_blank(),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        axis.text = element_text(size = 12)) +
  geom_text(data = sig_df, label = "*", size = 8) +
  labs(x = "\nDrug Treatment", 
       y = "Cognitive Function (MMSE)\n",
       caption = "\nFigure 1. Effect of novel drug treatment AD-x37 on cognitive function in 
                    healthy and demented elderly adults. 
                  \nn = 100/treatment group (total n = 600), * indicates significance 
                    at p < 0.001")
g1

# save the graph!
ggsave("ad_publication_graph.png", g1, height = 7, width = 8, units = "in")
File:Ad public.svg

glm() and ggplot2()

data(mtcars)

# Fit a Poisson regression model to predict "mpg" based on "wt"
model <- mtcars %>% 
  select(mpg, wt) %>% 
  mutate(wt = as.numeric(wt)) %>% 
  glm(mpg ~ wt, family = poisson(link = "log"), data = .)

# Print the summary of the model
summary(model)

# Make predictions on new data
new_data <- data.frame(wt = c(2.5, 3.0, 3.5))
predictions <- predict(model, new_data, type = "response")
print(predictions)

# Visualize the results with ggplot2
ggplot(data = mtcars, aes(x = wt, y = mpg)) + 
  geom_point() +
  stat_smooth(method = "glm", formula = "y ~ x", 
              method.args = list(family = poisson(link = "log")), 
              se = FALSE, color = "red") +
  labs(x = "Weight", y = "Miles per gallon")

Opioid prescribing habits in texas

https://juliasilge.com/blog/texas-opioids/.

  • It can read multiple sheets (27 sheets) at a time and merge them by rows.
  • case_when(): A general vectorised if. This function allows you to vectorise multiple if_else() statements. How to use the R case_when function. 
    x %>% mutate(group = case_when(
  PredScore > quantile(PredScore, .5) ~ 'High',
  PredScore < quantile(PredScore, .5) ~ 'Low',
  TRUE ~ NA_character_
))
  • top_n(). weight parameter. top_n(n=5, wt=x) won't order rows by weight in the output actually. slice_max(order_by = x, n = 5) does it. 
    set.seed(1)
d <- data.frame(
  x   = runif(90),
  grp = gl(3, 30)
) 

> d %>% group_by(grp) %>% top_n(5, wt=x)
# A tibble: 15 x 2
# Groups:   grp [3]
       x grp  
   <dbl> <fct>
 1 0.908 1    
 2 0.898 1    
...
15 0.961 3 

> d %>% group_by(grp) %>% slice_max(order_by = x, n = 5)
# A tibble: 15 x 2
# Groups:   grp [3]
       x grp  
   <dbl> <fct>
 1 0.992 1    
 2 0.945 1    
...
15 0.864 3

Tidying the Freedom Index

https://pacha.dev/blog/2023/06/05/freedom-index/index.html

tidyverse

  • gsub()
  • read_excel()
  • filter()
  • pivot_longer()
  • case_when()
  • fill()
  • group_by(), mutate(), row_number(), ungroup()
  • pivot_wider()
  • drop_na()
  • ungroup(), distinct()
  • left_join()

ggplot2

  • geom_line()
  • facet_wrap()
  • theme_minimal()
  • theme()
  • labs()

Useful dplyr functions (with examples)

Supervised machine learning case studies in R

Supervised machine learning case studies in R - A Free, Interactive Course Using Tidy Tools.

Time series data

Calculating change from baseline

group_by() + mutate() + ungroup(). We can accomplish the task by using split() + lapply() + do.call(). 

trial_data_chg <- trial_data %>%
  arrange(USUBJID, AVISITN) %>%
  group_by(USUBJID) %>%
  mutate(CHG = AVAL - AVAL[1L]) %>%
  ungroup()

# If the baseline is missing
trial_data_chg2 <- trial_data %>%
  group_by(USUBJID) %>%
  mutate(
    CHG = if (any(AVISIT == "Baseline")) AVAL - AVAL[AVISIT == "Baseline"] else NA
  ) %>%
  ungroup()

Split data and fitting models to subsets

https://twitter.com/romain_francois/status/1226967548144635907?s=20 

library(dplyr)
iris %>% 
  group_by(Species) %>%
  summarise(broom::tidy(lm(Petal.Length ~ Sepal.Length))

Show all possible group combinations

Ten Tremendous Tricks in the Tidyverse

https://youtu.be/NDHSBUN_rVU (video).

  • count(),
  • add_count(),
  • summarize() w/ a list column,
  • fct_reorder() + geom_col() + coord_flip(),
  • fct_lump(),
  • scale_x/y_log10(),
  • crossing(),
  • separate(),
  • extract().

Gapminder dataset

Hands-on R and dplyr – Analyzing the Gapminder Dataset

Install on Ubuntu

sudo apt install r-cran-tidyverse

# Ubuntu >= 18.04. However, I get unmet dependencies errors on R 3.5.3.
# r-cran-curl : Depends: r-api-3.4
sudo apt-get install r-cran-curl r-cran-openssl r-cran-xml2

# Works fine on Ubuntu 16.04, 18.04, 20.04
sudo apt install libcurl4-openssl-dev libssl-dev libxml2-dev

80 R packages will be installed after tidyverse has been installed.

For RStudio server docker version (Debian 10), I also need to install zlib1g-dev

Install on Raspberry Pi/(ARM based) Chromebook

In additional to the requirements of installing on Ubuntu, I got an error when it is installing a dependent package fs: undefined symbol: pthread_atfork. The fs package version is 1.2.6. The solution is to add one line in fs/src/Makevars file and then install the "fs" package using the source on the local machine.

5 most useful data manipulation functions

  • subset() for making subsets of data (natch)
  • merge() for combining data sets in a smart and easy way
  • melt()-reshape2 package for converting from wide to long data formats. See an example here where we want to combine multiple columns of values into 1 column. melt() is replaced by gather().
  • dcast()-reshape2 package for converting from long to wide data formats (or just use tapply()), and for making summary tables
  • ddply()-plyr package for doing split-apply-combine operations, which covers a huge swath of the most tricky data operations

Miscellaneous examples using tibble or dplyr packages

Print all columns or rows

?print.tbl_df

  • print(x, width = Inf) # all columns
  • print(x, n = Inf) # all rows

Move a column to rownames

?tibble::column_to_rownames 

# It assumes the input data frame has no row names; otherwise we will get
# Error: `df` must be a data frame without row names in `column_to_rownames()`
# 
tibble::column_to_rownames(data.frame(x=letters[1:5], y = rnorm(5)), "x")

Move rownames to a variable

https://tibble.tidyverse.org/reference/rownames.html 

tibble::rownames_to_column(trees, "newVar")
# Still a data frame

Old way add_rownames() 

data.frame(x=1:5, y=2:6) %>% magrittr::set_rownames(letters[1:5]) %>% add_rownames("newvar")
# tibble object

Remove rows or columns only containing NAs

Surgically removing specific rows or columns that only contains `NA`s 

library(dplyr)
df <- tibble(x = c(NA, NA, NA),
             y = c(2, 3, NA),
             z = c(NA, 5, NA) )

# removing columns where all elements are NA
df %>% select(where(~ !all(is.na(.x))))

# removing rows where all elements are NA
df %>% filter(if_any(.fns = ~ !is.na(.x)))

Rename variables

dplyr::rename(os, newName = oldName)

Drop/remove a variable/column

select(df, -x) # 'x' is the name of the variable

Drop a level

group_by() has a .drop argument so you can also group by factor levels that don't appear in the data. See this example.

Remove rownames

tibble has_rownames(), rownames_to_column(), column_to_rownames() 

has_rownames(mtcars)
#> [1] TRUE

# Remove row names
remove_rownames(mtcars) %>% has_rownames()
#> [1] FALSE

> tibble::has_rownames(trees)
[1] FALSE
> rownames(trees)
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31"
> rownames(trees) <- NULL
> rownames(trees)
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31"

relocate: change column order

relocate() 

# Move Petal.Width column to appear next to Sepal.Width
iris %>% relocate(Petal.Width, .after = Sepal.Width) %>% head() 

# Move Petal.Width to the last column
iris %>% relocate(Petal.Width, .after = last_col()) %>% head()

pull: extract a single column

pull() 

x <- iris %>% filter(Species == 'setosa') %>% select(Sepal.Length) %>% pull()
# x <- iris %>% filter(Species == 'setosa') %>% pull(Sepal.Length)
# x <- iris %>% filter(Species == 'setosa') %>% .$Sepal.Length
y <- iris %>% filter(Species == 'virginica') %>% select(Sepal.Length) %>% pull()
t.test(x, y)

Convert Multiple Columns to Numeric

Convert Multiple Columns to Numeric in R. mutate_at(), mutate_if()

select(): extract multiple columns

slice(): select rows by index

?slice 

mtcars %>% slice_max(mpg, n = 1)
#                 mpg cyl disp hp drat    wt qsec vs am gear carb
# Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.9  1  1    4    1

mtcars %>% slice(which.max(mpg))
#                 mpg cyl disp hp drat    wt qsec vs am gear carb
# Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.9  1  1    4    1

Reorder columns

reorder()

iris %>% ggplot(aes(x=Species, y = Sepal.Width)) + 
         geom_boxplot() +
         xlab=("Species")

# reorder the boxplot based on the Species' median
iris %>% ggplot(aes(x=reorder(Species, Sepal.Width, FUN = median),
                    y=Sepal.Width)) + 
         geom_boxplot() +
         xlab=("Species")

fct_reorder()

10 Tidyverse functions that might save your day

Standardize variables

How to Standardize Data in R?

Anonymous functions

Transformation on multiple columns

  • How to apply a transformation to multiple columns in R?
    • df %>% mutate(across(c(col1, col2), function(x) x*2))
    • df %>% summarise(across(c(col1, col2), mean, na.rm=TRUE))
  • select() vs across()
    • the across() and select() functions are both used to manipulate columns in a data frame
    • The select() function is used to select columns from a data frame.
    • The across() function is used to apply a function to multiple columns in a data frame. It’s often used inside other functions like mutate() or summarize().
data.frame(
  x = c(1, 2, 3),
  y = c(4, 5, 6)
) %>% 
mutate(across(everything(), ~ .x * 2)) # purrr-style lambda
#  x  y
#1 2  8
#2 4 10
#3 6 12

Reading and writing data

Speeding up Reading and Writing in R

data.table

Fast aggregation of large data (e.g. 100GB in RAM or just several GB size file), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread).

Note: data.table has its own ways (cf base R and dplyr) to subset columns.

Some resources:

OpenMP enabled compiler for Mac. This instruction works on my Mac El Capitan (10.11.6) when I need to upgrade the data.table version from 1.11.4 to 1.11.6.

Question: how to make use multicore with data.table package?

dtplyr

https://www.tidyverse.org/blog/2019/11/dtplyr-1-0-0/

reshape & reshape2 (superceded by tidyr package)

tidyr

Missing values

Handling Missing Values in R using tidyr

Pivot

  • tidyr package. pivot vignette, pivot_wider() 
    R> d2 <- tibble(o=rep(LETTERS[1:2], each=3), n=rep(letters[1:3], 2), v=1:6); d2
# A tibble: 6 × 3
  o     n         v
  <chr> <chr> <int>
1 A     a         1
2 A     b         2
3 A     c         3
4 B     a         4
5 B     b         5
6 B     c         6
R> d1 <- d2%>% pivot_wider(names_from=n, values_from=v); d1
# A tibble: 2 × 4
  o         a     b     c
  <chr> <int> <int> <int>
1 A         1     2     3
2 B         4     5     6

    pivot_longer() 

    R> d1 %>% pivot_longer(!o, names_to = 'n', values_to = 'v')
# Pivot all columns except 'o' column
# A tibble: 6 × 3
  o     n         v
  <chr> <chr> <int>
1 A     a         1
2 A     b         2
3 A     c         3
4 B     a         4
5 B     b         5
6 B     c         6
    • In addition to the names_from and values_from columns, the data must have other columns
    • For each (combination) of unique value from other columns, the values from names_from variable must be unique
  • Conversion from gather() to pivot_longer() 
    gather(df, key=KeyName, value = valueName, col1, col2, ...) # No quotes around KeyName and valueName

pivot_longer(df, cols, names_to = "keyName", values_to = "valueName") 
  # cols can be everything()
  # cols can be numerical numbers or column names
  • A Tidy Transcriptomics introduction to RNA-Seq analyses 
    data %>% pivot_longer(cols = c("counts", "counts_scaled"), names_to = "source", values_to = "abundance")
  • Using R: setting a colour scheme in ggplot2. Note the new (default) column names value and name after the function pivot_longer(data, cols). 
    set(1)
dat1 <- data.frame(y=rnorm(10), x1=rnorm(10), x2=rnorm(10))
dat2 <- pivot_longer(dat1, -y)
head(dat2, 2)
# A tibble: 2 x 3
      y name   value
  <dbl> <chr>  <dbl>
1 -1.28 x1     0.717
2 -1.28 x2    -0.320

dat3 <- pivot_wider(dat2)
range(dat1 - dat3)

Benchmark

An evolution of reshape2. It's designed specifically for data tidying (not general reshaping or aggregating) and works well with dplyr data pipelines.

Make wide tables long with gather() (see 6.3.1 of Efficient R Programming) 

library(tidyr)
library(efficient)
data(pew) # wide table
dim(pew) # 18 x 10,  (religion, '<$10k', '$10--20k', '$20--30k', ..., '>150k') 
pewt <- gather(data = pew, key = Income, value = Count, -religion)
dim(pew) # 162 x 3,  (religion, Income, Count)

args(gather)
# function(data, key, value, ..., na.rm = FALSE, convert = FALSE, factor_key = FALSE)

where the three arguments of gather() requires:

  • data: a data frame in which column names will become row values. If the data is a matrix, use %>% as.data.frame() beforehand.
  • key: the name of the categorical variable into which the column names in the original datasets are converted.
  • value: the name of cell value columns

In this example, the 'religion' column will not be included (-religion).

dplyr, plyr packages

  • plyr package suffered from being slow in some cases. dplyr addresses this by porting much of the computation to C++. Another additional feature is the ability to work with data stored directly in an external database. The benefits of doing this are the data can be managed natively in a relational database, queries can be conducted on that database, and only the results of query returned.
  • It's amazing the things one can do in base R (without installing or loading any other #rstats packages)
  • Essential functions: 3 rows functions, 3 column functions and 1 mixed function.
           select, mutate, rename, recode
            +------------------+
filter      +                  +
arrange     +                  +
group_by    +                  +
drop_na     +                  +
ungroup     + summarise        +
            +------------------+
  • These functions works on data frames and tibble objects. Note stats package also has a filter() function for time series data. If we have not loaded the dplyr package, the filter() function below will give an error (count() also is from dplyr).
iris %>% filter(Species == "setosa") %>% count()
head(iris %>% filter(Species == "setosa") %>% arrange(Sepal.Length))
  • dplyr tutorial from PH525x series (Biomedical Data Science by Rafael Irizarry and Michael Love). For select() function, some additional options to select columns based on a specific criteria include
    • starts_with()/ ends_with() = Select columns that start/end with a character string
    • contains() = Select columns that contain a character string
    • matches() = Select columns that match a regular expression
    • one_of() = Select columns names that are from a group of names
  • Data Transformation in the book R for Data Science. Five key functions in the dplyr package: 
# filter
jan1 <- filter(flights, month == 1, day == 1)
filter(flights, month == 11 | month == 12)
filter(flights, arr_delay <= 120, dep_delay <= 120)
df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)
filter(df, is.na(x) | x > 1)

# arrange
arrange(flights, year, month, day)
arrange(flights, desc(arr_delay))

# select
select(flights, year, month, day)
select(flights, year:day)
select(flights, -(year:day))

# mutate
flights_sml <- select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time
)
mutate(flights_sml,
  gain = arr_delay - dep_delay,
  speed = distance / air_time * 60
)
# if you only want to keep the new variables
transmute(flights,
  gain = arr_delay - dep_delay,
  hours = air_time / 60,
  gain_per_hour = gain / hours
)

# summarise()
by_day <- group_by(flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))

# pipe. Note summarise() can return more than 1 variable.
delays <- flights %>% 
  group_by(dest) %>% 
  summarise(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>% 
  filter(count > 20, dest != "HNL")
flights %>% 
  group_by(year, month, day) %>% 
  summarise(mean = mean(dep_delay, na.rm = TRUE))
  • the dot. 
    matrix(rnorm(12),4, 3) %>% .[1:2, 1:2]

.x symbol

dplyr piping data - difference between `.` and `.x`

select()

Select columns from a data frame 

select(my_data_frame, column_one, column_two, ...)
select(my_data_frame, new_column_name = current_column, ...)
select(my_data_frame, column_start:column_end)
select(my_data_frame, index_one, index_two, ...)
select(my_data_frame, index_start:index_end)

select() + everything()

If we want one particular column (say the dependent variable y) to appear first or last in the dataset. We can use the everything(). 

iris %>% select(Species, everything()) %>% head()
iris %>% select(-Species, everything()) %>% head() # put Species to the last col

group_by()

  • ?group_by and ungroup(),
  • Grouped data
  • Is ungroup() recommended after every group_by()? Always ungroup() when you’ve finished with your calculations. See here or this.
  • You might want to use ungroup() if you want to perform further calculations or manipulations on the data that don’t depend on the grouping. For example, after ungrouping the data, you could add new columns or filter rows without being restricted by the grouping.
                  +-- mutate() + ungroup() 
x -- group_by() --|
                  +-- summarise() # reduce the dimension, no way to get back

Subset rows by group

Subset rows based on their integer locations-slice in R

Rank by Group

How to Rank by Group in R? 

df %>% arrange(team, points) %>%
    group_by(team) %>%
    mutate(rank = rank(points))

arrange and reorder

How to do the following in pipe A <- A[myorder]

How to sort rows of a data frame based on a vector using dplyr pipe, Order data frame rows according to vector with specific order 

# reorder rows so 'var2' will be the same as 'target'
dat %>% arrange(match(var2, target))

group_by() + summarise(), arrange(desc())

Data transformation from R for Data Science

Function in summarise()

  • group_by(var1) %>% summarise(varY = mean(var2)) %>% ggplot(aes(x = varX, y = varY, fill = varF)) + geom_bar(stat = "identity") + theme_classic()
  • summarise(newvar = sum(var1) / sum(var2))
  • arrange(desc(var1, var2))
  • Distinct number of observation: n_distinct()
  • Count the number of rows: n()
  • nth observation of the group: nth()
  • First observation of the group: first()
  • Last observation of the group: last()

group_by() + summarise() + across()

group_by() + nest(), mutate(, map()), unnest(), list-columns

nest(data=) is a function in the tidyr package in R that allows you to create nested data frames, where one column contains another data frame or list. This is useful when you want to perform analysis or visualization on each group separately. PS: it seems group_by() is not needed. 

histogram <- gss_cat |> 
  nest(data = -marital) |>  # OR nest(.by = marital). 6x2 tibble. Col1=marital, col2=data.
  mutate(
    histogram = pmap(
      .l = list(marital, data),
      .f = \(marital, data) {
        ggplot(data, aes(x = tvhours)) +
          geom_histogram(binwidth = 1) +
          labs(
            title = marital
          )
      }
    )
  )
histogram$histogram[[1]]

Many models from R for Data Science

  • ?unnest, vignette('rectangle'), vignette('nest') & vignette('pivot') 
    tibble(x = 1:2, y = list(1:4, 2:3)) %>% unnest(y) %>% group_by(x) %>% nest()
# returns to tibble(x = 1:2, y = list(1:4, 2:3)) with 'groups' information
  • annotate boxplot in ggplot2
  • Coding in R: Nest and map your way to efficient code 
          group_by() + nest()    mutate(, map())   unnest()
data  -------------------->  --------------->  ------->

    install.packages('gapminder'); library(gapminder)

gapminder_nest <- gapminder %>% 
  group_by(country) %>% 
  nest()  # country, data
          # each row of 'data' is a tibble

gapminder_nest$data[[1]]  # tibble 57 x 8

gapminder_nest <- gapminder_nest %>%
          mutate(pop_mean = map(.x = data, ~mean(.x$pop, na.rm = T)))
                                    # country, data, pop_mean

gapminder_nest %>% unnest(pop_mean) # country, data, pop_mean

gapminder_plot <- gapminder_nest %>% 
  unnest(pop_mean) %>% 
  select(country, pop_mean) %>% 
  ungroup() %>% 
  top_n(pop_mean, n = -10) %>% 
  mutate(pop_mean = pop_mean/10^3)
gapminder_plot %>% 
  ggplot(aes(x = reorder(country, pop_mean), y = pop_mean)) +
  geom_point(colour = "#FF6699", size = 5) +
  geom_segment(aes(xend = country, yend = 0), colour = "#FF6699") +
  geom_text(aes(label = round(pop_mean, 0)), hjust = -1) +
  theme_minimal() +
  labs(title = "Countries with smallest mean population from 1960 to 2016",
       subtitle = "(thousands)",
       x = "",
       y = "") +
  theme(legend.position = "none",
        axis.text.x = element_blank(),
        plot.title = element_text(size = 14, face = "bold"),
        panel.grid.major.y = element_blank()) +
  coord_flip() +
  scale_y_continuous()
  • Tidy analysis from tidymodels
  • Is nest() + mutate() + map() + unnest() really the best alternative to dplyr::do()

across()

filter by date

What Is the Best Way to Filter by Date in R?

ave() - Adding a column of means by group to original data

mutate vs tapply

Base-R is alive and well

mutate + replace() or ifelse()

Hash table

  • Create new column based on 4 values in another column. The trick is to create a named vector; like a Dictionary in Python. Here is my example: 
    hashtable <- data.frame(value=1:4, key=c("B", "C", "A", "D"))
input <- c("A", "B", "C", "D", "B", "B", "A", "A") # input to be matched with keys, 
                                                   # this could be very long
# Trick: convert the hash table into a named vector
htb <- hashtable$value; names(htb) <- hashtable$key

# return the values according to the names
out <- htb[input]; out
A B C D B B A A
3 1 2 4 1 1 3 3

    We can implement using Python by creating a variable of dictionary type/structure. 

    hashtable = {'B': 1, 'C': 2, 'A': 3, 'D': 4}
input = ['A', 'B', 'C', 'D', 'B', 'B', 'A', 'A']
out = [hashtable[key] for key in input]

    Or using C 

    #include <stdio.h>

int main() {
    int hashtable[4] = {3, 1, 2, 4};
    char input[] = {'A', 'B', 'C', 'D', 'B', 'B', 'A', 'A'};
    int out[sizeof(input)/sizeof(input[0])];

    for (int i = 0; i < sizeof(input)/sizeof(input[0]); i++) {
        out[i] = hashtable[input[i] - 'A'];
    }

    for (int i = 0; i < sizeof(out)/sizeof(out[0]); i++) {
        printf("%d ", out[i]);
    }
    printf("\n");

    return 0;
}
  • hash package
  • digest package

inner_join, left_join, full_join

plyr::rbind.fill()

Videos

dbplyr

stringr

  • stringr is part of the tidyverse but is not a core package. You need to load it separately.
  • Handling Strings with R(ebook) by Gaston Sanchez.
  • https://www.rstudio.com/wp-content/uploads/2016/09/RegExCheatsheet.pdf
  • stringr Cheat sheet (2 pages, this will immediately download the pdf file)
    • Detect Matches: str_detect(), str_which(), str_count(), str_locate()
    • Subset: str_sub(), str_subset(), str_extract(), str_match()
    • Manage Lengths: str_length(), str_pad(), str_trunc(), str_trim()
    • Mutate Strings: str_sub(), str_replace(), str_replace_all(), str_remove()
      • Case Conversion: str_to_lower(), str_to_upper(), str_to_title()
    • Joint and Split: str_c(), str_dup(), str_split_fixed(), str_glue(), str_glue_date()
  • Efficient data carpentry → Regular expressions from Efficient R programming book by Gillespie & Lovelace.

str_replace()

Replace a string in a column: dplyr::across() & str_replace() 

df <- data.frame(country=c('India', 'USA', 'CHINA', 'Algeria'),
                 position=c('1', '1', '2', '3'),
                 points=c(22, 25, 29, 13))

df %>%
  mutate(across('country', str_replace, 'India', 'Albania'))

df %>%
  mutate(across('country', str_replace, 'A|I', ''))

split

Split Data Frame Variable into Multiple Columns in R (3 Examples)

Three ways: 

x <- c("a-1", "b-2", "c-3")

stringr::str_split_fixed(x, "-", 2)
#      [,1] [,2]
# [1,] "a"  "1" 
# [2,] "b"  "2" 
# [3,] "c"  "3" 

tidyr::separate(data.frame(x), x, c('x1', 'x2'), "-")
   # The first argument must be a data frame
   # The 2nd argument is the column names
#   x1 x2
# 1  a  1
# 2  b  2
# 3  c  3

magrittr: pipe

x %>% f     # f(x)
x %>% f(y)  # f(x, y)
x %>% f(arg=y)  # f(x, arg=y)
x %>% f(z, .) # f(z, x)
x %>% f(y) %>% g(z)  #  g(f(x, y), z)

x %>% select(which(colSums(!is.na(.))>0))  # remove columns with all missing data
x %>% select(which(colSums(!is.na(.))>0)) %>% filter((rowSums(!is.na(.))>0)) # remove all-NA columns _and_ rows
suppressPackageStartupMessages(library("dplyr"))
starwars %>%
  filter(., height > 200) %>%
  select(., height, mass) %>%
  head(.)
# instead of 
starwars %>%
  filter(height > 200) %>%
  select(height, mass) %>%
  head
iris$Species
iris[["Species"]]

iris %>%
`[[`("Species")

iris %>%
`[[`(5)

iris %>%
  subset(select = "Species")
  • Split-apply-combine: group + summarize + sort/arrange + top n. The following example is from Efficient R programming.
data(wb_ineq, package = "efficient")
wb_ineq %>% 
  filter(grepl("g", Country)) %>%
  group_by(Year) %>%
  summarise(gini = mean(gini, na.rm  = TRUE)) %>%
  arrange(desc(gini)) %>%
  top_n(n = 5)
f <- function(x) {
  (y - x) %>% 
    '^'(2) %>% 
    sum %>%
    '/'(length(x)) %>% 
    sqrt %>% 
    round(2)
}
# Examples from R for Data Science-Import, Tidy, Transform, Visualize, and Model
diamonds <- ggplot2::diamonds
diamonds2 <- diamonds %>% dplyr::mutate(price_per_carat = price / carat)

pryr::object_size(diamonds)
pryr::object_size(diamonds2)
pryr::object_size(diamonds, diamonds2)

rnorm(100) %>% matrix(ncol = 2) %>% plot() %>% str()
rnorm(100) %>% matrix(ncol = 2) %T>% plot() %>% str() # 'tee' pipe
    # %T>% works like %>% except that it returns the lefthand side (rnorm(100) %>% matrix(ncol = 2))  
    # instead of the righthand side.

# If a function does not have a data frame based api, you can use %$%.
# It explodes out the variables in a data frame.
mtcars %$% cor(disp, mpg) 

# For assignment, magrittr provides the %<>% operator
mtcars <- mtcars %>% transform(cyl = cyl * 2) # can be simplified by
mtcars %<>% transform(cyl = cyl * 2)

Upsides of using magrittr: no need to create intermediate objects, code is easy to read.

When not to use the pipe

  • your pipes are longer than (say) 10 steps
  • you have multiple inputs or outputs
  • Functions that use the current environment: assign(), get(), load()
  • Functions that use lazy evaluation: tryCatch(), try()

Dollar sign .$

%$%

Expose the names in lhs to the rhs expression. This is useful when functions do not have a built-in data argument. 

lhs %$% rhs
# lhs:	A list, environment, or a data.frame.
# rhs: An expression where the names in lhs is available.

iris %>%
  subset(Sepal.Length > mean(Sepal.Length)) %$%
  cor(Sepal.Length, Sepal.Width)

set_rownames() and set_colnames()

https://stackoverflow.com/a/56613460, https://www.rdocumentation.org/packages/magrittr/versions/1.5/topics/extract 

data.frame(x=1:5, y=2:6) %>% magrittr::set_rownames(letters[1:5])

cbind(1:5, 2:6) %>% magrittr::set_colnames(letters[1:2])

dtrackr

dtrackr: Track your Data Pipelines

purrr: : Functional Programming Tools

While there is nothing fundamentally wrong with the base R apply functions, the syntax is somewhat inconsistent across the different apply functions, and the expected type of the object they return is often ambiguous (at least it is for sapply…). See Learn to purrr.

  • What does the tilde mean in this context of R code, What is meaning of first tilde in purrr::map
  • Getting started with the purrr package in R, especially the map() and map_df() functions. 
    library(purrr)
# map() is a replacement of lapply()
# lapply(dat, function(x) mean(x$Open))
map(dat, function(x)mean(x$Open))  

# map allows us to bypass the function function. 
# Using a tilda (~) in place of function and a dot (.) in place of x
map(dat, ~mean(.$Open))

# map allows you to specify the structure of your output.
map_dbl(dat, ~mean(.$Open))

# map2() is a replacement of mapply()
# mapply(function(x,y)plot(x$Close, type = "l", main = y), x = dat, y = stocks)
map2(dat, stocks, ~plot(.x$Close, type="l", main = .y))
data <- map(paths, read.csv)
data <- map_dfr(paths, read.csv, id = "path")

out1 <- mtcars %>% map_dbl(mean, na.rm = TRUE)
out2 <- mtcars %>% map_dbl(median, na.rm = TRUE)

map() and map_dbl()

  • An example from https://purrr.tidyverse.org/ 
    mtcars |> 
     split(mtcars$cyl) |>  # from base R
     map(\(df) lm(mpg ~ wt, data = df)) |> 
     map(summary) |> map_dbl("r.squared")
#         4         6         8 
# 0.5086326 0.4645102 0.4229655
  • Solution by base R lapply() and sapply(). See the article purrr <-> base R 
    mtcars |>
     split(mtcars$cyl) |>
     lapply(function(df) lm(mpg ~ wt, data = df)) |>
     lapply(summary) |>
     sapply(function(x) x$r.squared)
#         4         6         8 
# 0.5086326 0.4645102 0.4229655

tilde

  • The lambda syntax and tilde notation provided by purrr allow you to write concise and readable anonymous functions in R.
x <- 1:3
map_dbl(x, ~ .x^2)  # [1] 1 4 9
The notation ~ .x^2 is equivalent to writing function(.x) .x^2 or function(z) z^2 or \(y) y^2
x <- list(a = 1:3, b = 4:6)
y <- list(a = 10, b = 100)
map2_dbl(x, y, ~ sum(.x * .y))
#   a    b 
#  60 1500

.x symbol

Function argument naming conventions (`.x` vs `x`). Se purrr::map

negate()

How to select non-numeric columns using dplyr::select_if 

library(tidyverse)
iris %>% select_if(negate(is.numeric))

pmap()

?pmap - Map over multiple input simultaneously (in "parallel") 

# Create two lists with multiple elements
list1 <- list(1, 2, 3)
list2 <- list(10, 20, 30)

# Define a function to add the elements of each list
my_func <- function(x, y) {
  x + y
}

# Use pmap to apply the function to each element of the lists in parallel
result <- pmap(list(list1, list2), my_func); result
[[1]]
[1] 11

[[2]]
[1] 22

[[3]]
[1] 33

A more practical example when we want to run analysis or visualization on each element of some group/class variable. nest() + pmap(). 

# Create a data frame
df <- mpg %>% 
  filter(manufacturer %in% c("audi", "volkswagen")) %>% 
  select(manufacturer, year, cty)

# Nest the data by manufacturer
df_nested <- df %>% 
  nest(data = -manufacturer)

# Create a function that takes a data frame and creates a ggplot object
my_plot_func <- function(data, manuf) {
     ggplot(data, aes(x = year, y = cty)) +
         geom_point() +
         ggtitle(manuf)
 }

# Use pmap to apply the function to each element of the list-column in df_nested
df_nested_plot <- df_nested %>% 
     mutate(plot = pmap(list(data, manufacturer), my_plot_func))

df_nested_plot[[1]]

Another example: fitting regressions for data in each group 

library(tidyverse)

# create example data
data <- tibble(
  x = rnorm(100),
  y = rnorm(100),
  group = sample(c("A", "B", "C"), 100, replace = TRUE)
)

# create a nested dataframe
nested_data <- data %>% 
  nest(data = -group)

# define a function that runs linear regression on each dataset
lm_func <- function(data) {
  lm(y ~ x, data = data)
}

# apply lm_func() to each row of the nested dataframe
results <- nested_data %>% 
  mutate(model = pmap(list(data), lm_func))

reduce

Reducing my for loop usage with purrr::reduce()

filter, subset data

Four Filters for Functional (Programming) Friends

purrr vs base R

https://purrr.tidyverse.org/dev/articles/base.html

forcats

https://forcats.tidyverse.org/

JAMA retraction after miscoding – new Finalfit function to check recoding

outer()

Genomic sequence

  • chartr
> yourSeq <- "AAAACCCGGGTTTNNN"
> chartr("ACGT", "TGCA", yourSeq)
[1] "TTTTGGGCCCAAANNN"

broom

  • broom: Convert Statistical Analysis Objects into Tidy Tibbles
  • Especially the tidy() function. 
    R> str(survfit(Surv(time, status) ~ x, data = aml))
List of 17
 $ n        : int [1:2] 11 12
 $ time     : num [1:20] 9 13 18 23 28 31 34 45 48 161 ...
 $ n.risk   : num [1:20] 11 10 8 7 6 5 4 3 2 1 ...
 $ n.event  : num [1:20] 1 1 1 1 0 1 1 0 1 0 ...
...

R> tidy(survfit(Surv(time, status) ~ x, data = aml))
# A tibble: 20 x 9
    time n.risk n.event n.censor estimate std.error conf.high conf.low strata         
   <dbl>  <dbl>   <dbl>    <dbl>    <dbl>     <dbl>     <dbl>    <dbl> <chr>          
 1     9     11       1        0   0.909     0.0953     1       0.754  x=Maintained   
 2    13     10       1        1   0.818     0.142      1       0.619  x=Maintained   
...
18    33      3       1        0   0.194     0.627      0.664   0.0569 x=Nonmaintained
19    43      2       1        0   0.0972    0.945      0.620   0.0153 x=Nonmaintained
20    45      1       1        0   0       Inf         NA      NA      x=Nonmaintained
  • Tables from journal papers
  • Multiple univariate models 
    library(tidyverse)
library(broom)

mtcars %>%
  select(-mpg) %>%
  names() %>%
  map_dfr(~ tidy(lm(as.formula(paste("mpg ~", .x)), data = mtcars)))
# A tibble: 20 × 5
#   term        estimate std.error statistic  p.value
#   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
# 1 (Intercept)  37.9      2.07       18.3   8.37e-18
# 2 cyl          -2.88     0.322      -8.92  6.11e-10
# 3 (Intercept)  29.6      1.23       24.1   3.58e-21
# 4 disp         -0.0412   0.00471    -8.75  9.38e-10
  • Multivariate model 
    lm(mpg ~ ., data = mtcars) |> tidy()
# A tibble: 11 × 5
#   term        estimate std.error statistic p.value
#   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
# 1 (Intercept)  12.3      18.7        0.657  0.518 
# 2 cyl          -0.111     1.05      -0.107  0.916 
# 3 disp          0.0133    0.0179     0.747  0.463

lobstr package - dig into the internal representation and structure of R objects

lobstr 1.0.0

Other packages

Great R packages for data import, wrangling, and visualization

Great R packages for data import, wrangling, and visualization

tidytext

https://juliasilge.shinyapps.io/learntidytext/

tidytuesdayR

install.packages("tidytuesdayR")
library("tidytuesdayR")
tt_datasets(2020)  # get the exact day of the data we want to load
coffee_ratings <- tt_load("2020-07-07")
print(coffee_ratings)  #  readme(coffee_ratings)

janitor

funneljoin

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