dplyr Tutorial (With 50 Examples)

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dplyr R Tutorial

The dplyr package is one of the most powerful and popular package in R. This package was written by the most popular R programmer Hadley Wickham who has written many useful R packages such as ggplot2, tidyr etc. This post includes several examples and tips of how to use dply package for cleaning and transforming data. It's a complete tutorial on data manipulation and data wrangling with R.

What is dplyr?

The dplyr is a powerful R-package to manipulate, clean and summarize unstructured data. In short, it makes data exploration and data manipulation easy and fast in R.

What's special about dplyr?

The package "dplyr" comprises many functions that perform mostly used data manipulation operations such as applying filter, selecting specific columns, sorting data, adding or deleting columns and aggregating data. Another most important advantage of this package is that it's very easy to learn and use dplyr functions. Also easy to recall these functions. For example, filter() is used to filter rows.

dplyr Tutorial

dplyr vs. Base R Functions

dplyr functions process faster than base R functions. It is because dplyr functions were written in a computationally efficient manner. They are also more stable in the syntax and better supports data frames than vectors.


SQL Queries vs. dplyr

People have been utilizing SQL for analyzing data for decades. Every modern data analysis software such as Python, R, SAS etc supports SQL commands. But SQL was never designed to perform data analysis. It was rather designed for querying and managing data. There are many data analysis operations where SQL fails or makes simple things difficult. For example, calculating median for multiple variables, converting wide format data to long format etc. Whereas, dplyr package was designed to do data analysis.
The names of dplyr functions are similar to SQL commands such as select() for selecting variables, group_by() - group data by grouping variable, join() - joining two data sets. Also includes inner_join() and left_join(). It also supports sub queries for which SQL was popular for.

How to install and load dplyr package

To install the dplyr package, type the following command.
install.packages("dplyr")
To load dplyr package, type the command below
library(dplyr) 

Important dplyr Functions to remember

dplyr Function Description Equivalent SQL
select() Selecting columns (variables) SELECT
filter() Filter (subset) rows. WHERE
group_by() Group the data GROUP BY
summarise() Summarise (or aggregate) data -
arrange() Sort the data ORDER BY
join() Joining data frames (tables) JOIN
mutate() Creating New Variables COLUMN ALIAS

Data : Income Data by States

In this tutorial, we are using the following data which contains income generated by states from year 2002 to 2015. Note : This data do not contain actual income figures of the states.

This dataset contains 51 observations (rows) and 16 variables (columns). The snapshot of few rows and columns of the dataset is shown below.

Input Dataset

Download the Dataset

How to load Data

Submit the following code. Change the file path in the code below.
mydata = read.csv("C:\\Users\\Deepanshu\\Documents\\sampledata.csv")
Example 1 : Selecting Random N Rows

The sample_n function selects random rows from a data frame (or table). The second parameter of the function tells R the number of rows to select.
sample_n(mydata,3)

Example 2 : Selecting Random Fraction of Rows

The sample_frac function returns randomly N% of rows. In the example below, it returns randomly 10% of rows.
sample_frac(mydata,0.1)

Example 3 : Remove Duplicate Rows based on all the variables (Complete Row)

The distinct function is used to eliminate duplicates.
x1 = distinct(mydata)

Example 4 : Remove Duplicate Rows based on a variable

The .keep_all function is used to retain all other variables in the output data frame.
x2 = distinct(mydata, Index, .keep_all= TRUE)

Example 5 : Remove Duplicates Rows based on multiple variables

In the example below, we are using two variables - Index, Y2010 to determine uniqueness.
x2 = distinct(mydata, Index, Y2010, .keep_all= TRUE)

select( ) Function

It is used to select only desired variables.
select() syntax : select(data , ....)
data :
Data Frame
.... : Variables by name or by function

Example 6 : Selecting Variables (or Columns)

Suppose you are asked to select only a few variables. The code below selects variables "Index", columns from "State" to "Y2008".
mydata2 = select(mydata, Index, State:Y2008)

Example 7 : Dropping Variables

The minus sign before a variable tells R to drop the variable.
mydata = select(mydata, -Index, -State)
The above code can also be written like :
mydata = select(mydata, -c(Index,State))

Example 8 : Selecting or Dropping Variables starts with 'Y'

The starts_with() function is used to select variables starts with an alphabet.
mydata3 = select(mydata, starts_with("Y"))
 Adding a negative sign before starts_with() implies dropping the variables starts with 'Y'
mydata33 = select(mydata, -starts_with("Y"))
The following functions helps you to select variables based on their names.

Helpers Description
starts_with() Starts with a prefix
ends_with() Ends with a prefix
contains() Contains a literal string
matches() Matches a regular expression
num_range() Numerical range like x01, x02, x03.
one_of() Variables in character vector.
everything() All variables.

Example 9 : Selecting Variables contain 'I' in their names
mydata4 = select(mydata, contains("I"))

Example 10 : Reorder Variables

The code below keeps variable 'State' in the front and the remaining variables follow that.
mydata5 = select(mydata, State, everything())
rename( ) Function

It is used to change variable name.
rename() syntax : rename(data , new_name = old_name)
data : Data Frame
new_name : New variable name you want to keep
old_name : Existing Variable Name

Example 11 : Rename Variables

The rename function can be used to rename variables.

In the following code, we are renaming 'Index' variable to 'Index1'.
mydata6 = rename(mydata, Index1=Index)
Output

filter( ) Function

It is used to subset data with matching logical conditions.
filter() syntax : filter(data , ....)
data : Data Frame
.... : Logical Condition

Example 12 : Filter Rows

Suppose you need to subset data. You want to filter rows and retain only those values in which Index is equal to A.
mydata7 = filter(mydata, Index == "A")
  
Example 13 : Multiple Selection Criteria

The %in% operator can be used to select multiple items. In the following program, we are telling R to select rows against 'A' and 'C' in column 'Index'.
mydata7 = filter(mydata6, Index %in% c("A", "C"))

Example 14 : 'AND' Condition in Selection Criteria

Suppose you need to apply 'AND' condition. In this case, we are picking data for 'A' and 'C' in the column 'Index' and income greater than 1.3 million in Year 2002.
mydata8 = filter(mydata6, Index %in% c("A", "C") & Y2002 >= 1300000 )

Example 15 : 'OR' Condition in Selection Criteria

The 'I' denotes OR in the logical condition. It means any of the two conditions.
mydata9 = filter(mydata6, Index %in% c("A", "C") | Y2002 >= 1300000)
Example 16 : NOT Condition

The "!" sign is used to reverse the logical condition.
mydata10 = filter(mydata6, !Index %in% c("A", "C"))

Example 17 : CONTAINS Condition

The grepl function is used to search for pattern matching. In the following code, we are looking for records wherein column state contains 'Ar' in their name.
mydata10 = filter(mydata6, grepl("Ar", State))

 summarise( ) Function

It is used to summarize data.
summarise() syntax : summarise(data , ....)
data : Data Frame
..... : Summary Functions such as mean, median etc

Example 18 : Summarize selected variables

In the example below, we are calculating mean and median for the variable Y2015.
summarise(mydata, Y2015_mean = mean(Y2015), Y2015_med=median(Y2015))
Output

Example 19 : Summarize Multiple Variables

In the following example, we are calculating number of records, mean and median for variables Y2005 and Y2006. The summarise_at function allows us to select multiple variables by their names.
summarise_at(mydata, vars(Y2005, Y2006), funs(n(), mean, median))
Output
Example 20 : Summarize with Custom Functions

We can also use custom functions in the summarise function. In this case, we are computing the number of records, number of missing values, mean and median for variables Y2011 and Y2012. The dot (.) denotes each variables specified in the second argument of the function.
summarise_at(mydata, vars(Y2011, Y2012),
funs(n(), missing = sum(is.na(.)), mean(., na.rm = TRUE), median(.,na.rm = TRUE)))
Summarize : Output

How to apply Non-Standard Functions

Suppose you want to subtract mean from its original value and then calculate variance of it.
set.seed(222)
mydata <- data.frame(X1=sample(1:100,100), X2=runif(100))
summarise_at(mydata,vars(X1,X2), function(x) var(x - mean(x)))
        X1         X2
1 841.6667 0.08142161

Example 21 : Summarize all Numeric Variables

The summarise_if function allows you to summarise conditionally.
summarise_if(mydata, is.numeric, funs(n(),mean,median))
Alternative Method :

First, store data for all the numeric variables
numdata = mydata[sapply(mydata,is.numeric)]
Second, the summarise_all function calculates summary statistics for all the columns in a data frame
summarise_all(numdata, funs(n(),mean,median))

Example 22 : Summarize Factor Variable

We are checking the number of levels/categories and count of missing observations in a categorical (factor) variable.
summarise_all(mydata["Index"], funs(nlevels(.), sum(is.na(.))))

arrange() function :

Use : Sort data

Syntax
arrange(data_frame, variable(s)_to_sort)
or
data_frame %>% arrange(variable(s)_to_sort)
To sort a variable in descending order, use desc(x).

Example 23 : Sort Data by Multiple Variables

The default sorting order of arrange() function is ascending. In this example, we are sorting data by multiple variables.
arrange(mydata, Index, Y2011)
Suppose you need to sort one variable by descending order and other variable by ascending oder.
arrange(mydata, desc(Index), Y2011)
Pipe Operator %>%

It is important to understand the pipe (%>%) operator before knowing the other functions of dplyr package. dplyr utilizes pipe operator from another package (magrittr).
It allows you to write sub-queries like we do it in sql.
Note : All the functions in dplyr package can be used without the pipe operator. The question arises "Why to use pipe operator %>%". The answer is it lets to wrap multiple functions together with the use of  %>%.

Syntax :
filter(data_frame, variable == value)
or
data_frame %>% filter(variable == value)
The %>% is NOT restricted to filter function. It can be used with any function. 

Example :

The code below demonstrates the usage of pipe %>% operator. In this example, we are selecting 10 random observations of two variables "Index" "State" from the data frame "mydata".
dt = sample_n(select(mydata, Index, State),10)
or 
dt = mydata %>% select(Index, State) %>% sample_n(10)
Output

group_by() function :

Use : Group data by categorical variable

Syntax :
group_by(data, variables)
or
data %>% group_by(variables)

Example 24 : Summarise Data by Categorical Variable

We are calculating count and mean of variables Y2011 and Y2012 by variable Index.
t = summarise_at(group_by(mydata, Index), vars(Y2011, Y2012), funs(n(), mean(., na.rm = TRUE)))
The above code can also be written like
t = mydata %>% group_by(Index) %>%
  summarise_at(vars(Y2011:Y2015), funs(n(), mean(., na.rm = TRUE)))

do() function :

Use : Compute within groups

Syntax :
do(data_frame, expressions_to_apply_to_each_group)
Note : The dot (.) is required to refer to a data frame.

Example 25 : Filter Data within a Categorical Variable

Suppose you need to pull top 2 rows from 'A', 'C' and 'I' categories of variable Index. 
t = mydata %>% filter(Index %in% c("A", "C","I")) %>% group_by(Index) %>%
  do(head( . , 2))
Output : do() function

Example 26 : Selecting 3rd Maximum Value by Categorical Variable

We are calculating third maximum value of variable Y2015 by variable Index. The following code first selects only two variables Index and Y2015. Then it filters the variable Index with 'A', 'C' and 'I' and then it groups the same variable and sorts the variable Y2015 in descending order. At last, it selects the third row.
t = mydata %>% select(Index, Y2015) %>%
  filter(Index %in% c("A", "C","I")) %>%
  group_by(Index) %>%
  do(arrange(.,desc(Y2015))) %>%  slice(3)
The slice() function is used to select rows by position.

Output
Using Window Functions

Like SQL, dplyr uses window functions that are used to subset data within a group. It returns a vector of values. We could use min_rank() function that calculates rank in the preceding example,
t = mydata %>% select(Index, Y2015) %>%
  filter(Index %in% c("A", "C","I")) %>%
  group_by(Index) %>%
  filter(min_rank(desc(Y2015)) == 3)
Example 27 : Summarize, Group and Sort Together 

In this case, we are computing mean of variables Y2014 and Y2015 by variable Index. Then sort the result by calculated mean variable Y2015.
t = mydata %>%
  group_by(Index)%>%
  summarise(Mean_2014 = mean(Y2014, na.rm=TRUE),
            Mean_2015 = mean(Y2015, na.rm=TRUE)) %>%
  arrange(desc(Mean_2015))

mutate() function :

Use : Creates new variables

Syntax :
mutate(data_frame, expression(s) )
or
data_frame %>% mutate(expression(s))
Example 28 : Create a new variable

The following code calculates division of Y2015 by Y2014 and name it "change".
mydata1 = mutate(mydata, change=Y2015/Y2014)
Example 29 : Multiply all the variables by 1000

It creates new variables and name them with suffix "_new".
mydata11 = mutate_all(mydata, funs("new" = .* 1000))
Output
The output shown in the image above is truncated due to high number of variables.

Note - The above code returns the following error messages -

Warning messages:
1: In Ops.factor(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 4L, 5L, 6L,  :
  ‘*’ not meaningful for factors
2: In Ops.factor(1:51, 1000) : ‘*’ not meaningful for factors

It implies you are multiplying 1000 to string(character) values which are stored as factor variables. These variables are 'Index', 'State'. It does not make sense to apply multiplication operation on character variables. For these two variables, it creates newly created variables which contain only NA.

Solution : See Example 45 - Apply multiplication on only numeric variables

Example 30 : Calculate Rank for Variables

Suppose you need to calculate rank for variables Y2008 to Y2010.
mydata12 = mutate_at(mydata, vars(Y2008:Y2010), funs(Rank=min_rank(.)))
Output
By default, min_rank() assigns 1 to the smallest value and high number to the largest value. In case, you need to assign rank 1 to the largest value of a variable, use min_rank(desc(.))
mydata13 = mutate_at(mydata, vars(Y2008:Y2010), funs(Rank=min_rank(desc(.))))
Example 31 : Select State that generated highest income among the variable 'Index'
out = mydata %>% group_by(Index) %>% filter(min_rank(desc(Y2015)) == 1) %>%
  select(Index, Y2015)

Example 32 : Cumulative Income of 'Index' variable

 The cumsum function calculates cumulative sum of a variable. With mutate function, we insert a new variable called 'Total' which contains values of cumulative income of variable Index.
out2 = mydata %>% group_by(Index) %>% mutate(Total=cumsum(Y2015)) %>%
select(Index, Y2015, Total)

join() function :

Use : Join two datasets

Syntax :
inner_join(x, y, by = )
left_join(x, y, by = )
right_join(x, y, by = )
full_join(x, y, by = )
semi_join(x, y, by = )
anti_join(x, y, by = )
x, y - datasets (or tables) to merge / join
by - common variable (primary key) to join by.

Example 33 : Common rows in both the tables

Let's create two data frames say df1 and df2.
df1 <- data.frame(ID = c(1, 2, 3, 4, 5),
                  w = c('a', 'b', 'c', 'd', 'e'),
                  x = c(1, 1, 0, 0, 1),
                  y=rnorm(5),
                  z=letters[1:5])
df2 <- data.frame(ID = c(1, 7, 3, 6, 8),
                  a = c('z', 'b', 'k', 'd', 'l'),
                  b = c(1, 2, 3, 0, 4),
                  c =rnorm(5),
                  d =letters[2:6])

INNER JOIN returns rows when there is a match in both tables. In this example, we are merging df1 and df2 with ID as common variable (primary key).
df3 = inner_join(df1, df2, by = "ID")
Output : INNER JOIN 
If the primary key does not have same name in both the tables, try the following way:
inner_join(df1, df2, by = c("ID"="ID1"))
Example 34 : Applying LEFT JOIN

LEFT JOIN : It returns all rows from the left table, even if there are no matches in the right table.
left_join(df1, df2, by = "ID")
Output : LEFT JOIN

Combine Data Vertically


intersect(x, y)
Rows that appear in both x and y.

union(x, y)
Rows that appear in either or both x and y.

setdiff(x, y)
Rows that appear in x but not y.

Example 35 : Applying INTERSECT

Prepare Sample Data for Demonstration
mtcars$model <- rownames(mtcars)
first <- mtcars[1:20, ]
second <- mtcars[10:32, ]
INTERSECT selects unique rows that are common to both the data frames.
intersect(first, second)

Example 36 : Applying UNION

UNION displays all rows from both the tables and removes duplicate records from the combined dataset. By using union_all function, it allows duplicate rows in the combined dataset.
x=data.frame(ID = 1:6, ID1= 1:6)
y=data.frame(ID = 1:6,  ID1 = 1:6)
union(x,y)
union_all(x,y)

Example 37 : Rows appear in one table but not in other table
setdiff(first, second)

Example 38 : IF ELSE Statement

Syntax :
if_else(condition, true, false, missing = NULL)
true  : Value if condition meets
false : Value if condition does not meet
missing : Value if missing cases.It will be used to replace missing values (Default : NULL)
df <- c(-10,2, NA)
if_else(df < 0, "negative", "positive", missing = "missing value")
Create a new variable with IF_ELSE

If a value is less than 5, add it to 1 and if it is greater than or equal to 5, add it to 2. Otherwise 0.
df =data.frame(x = c(1,5,6,NA))
df %>% mutate(newvar=if_else(x<5, x+1, x+2,0))
Output

Nested IF ELSE

Multiple IF ELSE statement can be written using if_else() function. See the example below -
mydf =data.frame(x = c(1:5,NA))
mydf %>% mutate(newvar= if_else(is.na(x),"I am missing",
if_else(x==1,"I am one",
if_else(x==2,"I am two",
if_else(x==3,"I am three","Others")))))
Output
x       newvar
1     I am one
2     I am two
3   I am three
4       Others
5       Others
NA I am missing

SQL-Style CASE WHEN Statement

We can use case_when() function to write nested if-else queries. In case_when(), you cannot use variables directly within case_when() wrapper so it should be written like .$x which is equivalent to mydf$x. TRUE refers to ELSE statement.
mydf %>% mutate(flag = case_when(is.na(.$x) ~ "I am missing",
                                .$x == 1 ~ "I am one",
                                .$x == 2 ~ "I am two",
                                .$x == 3 ~ "I am three",
                                TRUE ~ "Others"))

Important Point
Make sure you set is.na() condition at the beginning in nested ifelse. Otherwise, it would not be executed.


Example 39 :  Apply ROW WISE Operation 

Suppose you want to find maximum value in each row of variables 2012, 2013, 2014, 2015. The rowwise() function allows you to apply functions to rows.
df = mydata %>%
  rowwise() %>% mutate(Max= max(Y2012:Y2015)) %>%
  select(Y2012:Y2015,Max)
Output
Example 40 : Combine Data Frames

Suppose you are asked to combine two data frames. Let's first create two sample datasets.
df1=data.frame(ID = 1:6,  x=letters[1:6])
df2=data.frame(ID = 7:12, x=letters[7:12])
Input Datasets
The bind_rows() function combine two datasets with rows. So combined dataset would contain 12 rows (6+6) and 2 columns.
xy = bind_rows(df1,df2)
It is equivalent to base R function rbind.
xy = rbind(df1,df2)
The bind_cols() function combine two datasets with columns. So combined dataset would contain 4 columns and 6 rows.
xy = bind_cols(x,y)
or
xy = cbind(x,y)
The output is shown below-
cbind Output

Example 41 : Calculate Percentile Values

The quantile() function is used to determine Nth percentile value. In this example, we are computing percentile values by variable Index.
mydata %>% group_by(Index) %>%
  summarise(Pecentile_25=quantile(Y2015, probs=0.25),
            Pecentile_50=quantile(Y2015, probs=0.5),
            Pecentile_75=quantile(Y2015, probs=0.75),
            Pecentile_99=quantile(Y2015, probs=0.99))

The ntile() function is used to divide the data into N bins.
x= data.frame(N= 1:10)
x = mutate(x, pos = ntile(x$N,5))

Example 42 : Automate Model Building

This example explains the advanced usage of do() function. In this example, we are building linear regression model for each level of a categorical variable. There are 3 levels in variable cyl of dataset mtcars.
length(unique(mtcars$cyl))
Result : 3
by_cyl <- group_by(mtcars, cyl)
models <- by_cyl %>% do(mod = lm(mpg ~ disp, data = .))
summarise(models, rsq = summary(mod)$r.squared)
models %>% do(data.frame(
  var = names(coef(.$mod)),
  coef(summary(.$mod)))
)
Output : R-Squared Values

if() Family of Functions

It includes functions like select_if, mutate_if, summarise_if. They come into action only when logical condition meets. See examples below.

Example 43 : Select only numeric columns

The select_if() function returns only those columns where logical condition is TRUE. The is.numeric refers to retain only numeric variables.
mydata2 = select_if(mydata, is.numeric)
Similarly, you can use the following code for selecting factor columns - 
mydata3 = select_if(mydata, is.factor)

Example 44 : Number of levels in factor variables

Like select_if() function, summarise_if() function lets you to summarise only for variables where logical condition holds.
summarise_if(mydata, is.factor, funs(nlevels(.)))
It returns 19 levels for variable Index and 51 levels for variable State.

Example 45 : Multiply by 1000 to numeric variables

mydata11 = mutate_if(mydata, is.numeric, funs("new" = .* 1000))

Example 46 : Convert value to NA

In this example, we are converting "" to NA using na_if() function.
k <- c("a", "b", "", "d")
na_if(k, "")
Result : "a" "b" NA  "d"

Endnotes

There are hundreds of packages that are dependent on this package. The main benefit it offers is to take off fear of R programming and make coding effortless and lower processing time. However, some R programmers prefer data.table package for its speed. I would recommend learn both the packages. Check out data.table tutorial. The data.table package wins over dplyr in terms of speed if data size greater than 1 GB.

R Tutorials : 75 Free R Tutorials

About Author:

Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has close to 7 years of experience in data science and predictive modeling. During his tenure, he has worked with global clients in various domains like retail and commercial banking, Telecom, HR and Automotive.


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24 Responses to "dplyr Tutorial (With 50 Examples)"

  1. Thanks for share, great stuff and examples.

    ReplyDelete
  2. This is the best tutorial out there

    ReplyDelete
  3. Having searched many sites and lectures I am bookmarking your site after looking at this page. Its the simplicity of your presentation. Thanks.

    ReplyDelete
    Replies
    1. Thank you for stopping by my blog. Glad you found it useful. Cheers!

      Delete
  4. Thank you, this indeed very helpful and precise. Great Job!

    ReplyDelete
  5. I followed along your script step by step and got a warning message
    in Example 29 : Multiply all the variables by 1000 as follows:
    1: In Ops.factor(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 4L, 5L, 6L, :
    ‘*’ not meaningful for factors
    2: In Ops.factor(1:51, 1000) : ‘*’ not meaningful for factors
    What did it mean? Could you please give me some explanation. Thanks.

    ReplyDelete
    Replies
    1. This error says 'multiplying 1000 on factor(string) variables' does not make sense. Run this command - str(mydata[,1:2])
      First two variables in the dataframe mydata are strings that are stored as factor variables.

      Delete
    2. I got it. Thanks. I think I'm ready to go next to your another tutorial - data.table. It's quite interesting to learn R from your blog posts.

      Delete
  6. Example#22 - gives incorrect number of levels as 0 - fix is below
    Why doesn't nlevels() work?

    > summarise_all(dt["Index"], funs(nlevels(.), sum(is.na(.))))
    # A tibble: 1 × 2
    nlevels sum

    1 0 0
    ===============
    The fix is change nlevels() to length(unique(.)) as below

    summarise_all(dt["Index"], funs(length(unique(.)), sum(is.na(.))))
    # A tibble: 1 × 2
    length sum

    1 19 0

    ReplyDelete
    Replies
    1. It works fine at my end. Check out the code below -
      library(dplyr)
      mydata = read.csv("C:\\Users\\Deepanshu\\Documents\\sampledata.csv")
      summarise_all(mydata["Index"], funs(nlevels(.), sum(is.na(.))))

      Delete
  7. Example #21

    Alternatively, we can use the following:

    mydata %>% summarise_if(is.numeric, funs(n(),mean,median))

    ReplyDelete
    Replies
    1. Thank you for posting alternative method. I have added it to the tutorial. Cheers!

      Delete
  8. This is a great tutorial. A doubt that crept to me when I tries to mix multiple functions. Any reason why the following is not working:

    DF <- mutate_if(mydata, is.numeric & contains('Y2015'), funs('new' = *.100));

    but this works:
    DF <- mutate_if(mydata, is.numeric, funs('new' = *.100));


    what if I want to mutate to add only a column for Y2015?

    Thanks

    ReplyDelete
  9. Great tutorials. Took too much time to found this tutorial.

    ReplyDelete
  10. Wonderfull for a newcomer to R !

    ReplyDelete
  11. Thank you for this great presentation.

    ReplyDelete
  12. Example 39 is wrong.

    ReplyDelete
  13. W. r. t. chapter 'SQL-Style CASE WHEN Statement': The workaround .$ is not necessary anymore from dplyr version 0.7.0

    ReplyDelete

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