• summary(x): quantile information
• count(x): what unique values do you have?
  • distinct(): what are the distinct values?
  • n_distinct() with pull(): how many distinct values?
• group_by(): changes all subsequent functions
  • combine with summarize() to get statistics per group
  • combine with mutate() to add column
• summarize() with n() gives the count (NAs included)

📃Cheatsheet

• There are two types of number class objects: integer and double

• Logic class objects only have TRUE or FALSE (without quotes)

• class() can be used to test the class of an object x

• as.CLASS_NAME(x) can be used to change the class of an object x

• Factors are a special character class that has levels - more on that soon!

• tibbles show column classes!

• two dimensional object classes include: data frames, tibbles, matrices, and lists

• Dates can be handled with the lubridate package

• Make sure you choose the right function for the way the date is formatted!

  📃Cheatsheet

One of the most important aspects of data cleaning is missing values.

Types of “missing” data:

• NA - general missing data
• NaN - stands for “Not a Number”, happens when you do 0/0.
• Inf and -Inf - Infinity, happens when you divide a positive number (or negative number) by 0.

any() can help you check if there are any NA values in a vector

test

[1]  NaN   NA -Inf

any(is.na(test))

[1] TRUE

count() is a great option because it helps you check if rare values make sense.

bike <- read_csv(file =
    "http://jhudatascience.org/intro_to_r/data/Bike_Lanes.csv")
bike %>% count(subType)

# A tibble: 4 × 2
  subType     n
  <chr>   <int>
1 STCLN       1
2 STRALY      3
3 STRPRD   1623
4 <NA>        4

Sometimes you need to look at lots of data though… the naniar package is a good option.

#install.packages("naniar")
library(naniar)

“Artwork by @allison_horst”. https://allisonhorst.com/

This can tell you if there are missing values in the dataset.

pct_complete(bike)

[1] 89.21589

Or for a particular variable:

bike%>% select(route) %>%
pct_complete()

[1] 22.19497

To get the percent missing (and counts) for each variable as a table, use this function.

miss_var_summary(bike)

# A tibble: 9 × 3
  variable      n_miss pct_miss
  <chr>          <int>    <num>
1 route           1269   77.8  
2 block            215   13.2  
3 project           74    4.54 
4 name              12    0.736
5 type               9    0.552
6 subType            4    0.245
7 numLanes           0    0    
8 length             0    0    
9 dateInstalled      0    0    

The gg_miss_var() function creates a nice plot about the number of missing values for each variable, (need a data frame).

gg_miss_var(bike)

Recall that mathematical operations with NA often result in NAs.

sum(c(1,2,3,NA))

[1] NA

mean(c(1,2,3,NA))

[1] NA

median(c(1,2,3,NA))

[1] NA

This is also true for logical data. Recall that TRUE is evaluated as 1 and FALSE is evaluated as 0.

x <- c(TRUE, TRUE, TRUE, TRUE, FALSE, NA)
sum(x)

[1] NA

sum(x, na.rm = TRUE)

[1] 4

Be careful with missing data using subsetting!

filter() removes missing values by default. Because R can’t tell for sure if an NA value meets the condition. To keep them need to add is.na() conditional.

Think about if this is OK or not - it depends on your data!

is.na() can help us keep them.

bike %>% filter(subType == "STCLN" | is.na(subType))

# A tibble: 5 × 9
  subType name           block type  numLanes project route length dateInstalled
  <chr>   <chr>          <chr> <chr>    <dbl> <chr>   <chr>  <dbl>         <dbl>
1 <NA>    <NA>           <NA>  BIKE…        1 GUILFO… <NA>    436.             0
2 <NA>    <NA>           <NA>  SIDE…        1 <NA>    NORT…  1025.          2010
3 <NA>    <NA>           <NA>  SIGN…        1 SOUTHE… <NA>   3749.          2010
4 <NA>    HUNTINGDON PA… <NA>  SIDE…        1 <NA>    <NA>      0              0
5 STCLN   EDMONDSON AVE  5300… BIKE…        1 OPERAT… <NA>    181.          2011

A function from the tidyr package. (Need a data frame to start!)

Disclaimer: Don’t do this unless you have thought about if dropping NA values makes sense based on knowing what these values mean in your data. Also consider if you need those rows for values for other variables.

dim(bike)

[1] 1631    9

bike_drop <- bike %>% drop_na(route)
dim(bike_drop)

[1] 362   9

Can still have NAs for other columns

bike_drop 

# A tibble: 362 × 9
   subType name          block type  numLanes project route length dateInstalled
   <chr>   <chr>         <chr> <chr>    <dbl> <chr>   <chr>  <dbl>         <dbl>
 1 <NA>    <NA>          <NA>  SIDE…        1 <NA>    NORT… 1025.           2010
 2 STRALY  WINSTON-ROSS… 1200… SIGN…        2 COLLEG… COLL…  148.           2007
 3 STRALY  WINSTON-ROSS… 1200… SIGN…        2 COLLEG… COLL…  366.           2007
 4 STRALY  WINSTON-STON… 1200… SIGN…        2 COLLEG… COLL…  262.           2007
 5 STRPRD  <NA>          <NA>  SIGN…        1 COLLEG… COLL…   49.3          2007
 6 STRPRD  <NA>          <NA>  SIGN…        1 COLLEG… COLL…   70.0          2007
 7 STRPRD  <NA>          <NA>  SIGN…        1 COLLEG… COLL…  765.           2007
 8 STRPRD  <NA>          <NA>  SIGN…        2 COLLEG… COLL…  170.           2007
 9 STRPRD  <NA>          <NA>  SIGN…        2 COLLEG… COLL… 1724.           2007
10 STRPRD  ALBEMARLE ST  100 … SIGN…        1 SOUTHE… LITT…  250.           2011
# ℹ 352 more rows

This function of the tidyr package drops rows with any missing data in any column when used on a df.

bike_drop <- bike %>% drop_na()
bike_drop

# A tibble: 257 × 9
   subType name          block type  numLanes project route length dateInstalled
   <chr>   <chr>         <chr> <chr>    <dbl> <chr>   <chr>  <dbl>         <dbl>
 1 STRALY  WINSTON-ROSS… 1200… SIGN…        2 COLLEG… COLL…   148.          2007
 2 STRALY  WINSTON-ROSS… 1200… SIGN…        2 COLLEG… COLL…   366.          2007
 3 STRALY  WINSTON-STON… 1200… SIGN…        2 COLLEG… COLL…   262.          2007
 4 STRPRD  ALBEMARLE ST  100 … SIGN…        1 SOUTHE… LITT…   250.          2011
 5 STRPRD  ALBEMARLE ST  100 … SIGN…        1 SOUTHE… LITT…   257.          2011
 6 STRPRD  ALBEMARLE ST  200 … SIGN…        1 SOUTHE… LITT…   251.          2011
 7 STRPRD  ALBEMARLE ST  200 … SIGN…        1 SOUTHE… LITT…   252.          2011
 8 STRPRD  ALBEMARLE ST  300 … SIGN…        1 SOUTHE… LITT…   252.          2011
 9 STRPRD  ALBEMARLE ST  UNIT… SIGN…        1 SOUTHE… LITT…   130.          2011
10 STRPRD  ALBEMARLE ST  UNIT… SIGN…        1 SOUTHE… LITT…   143.          2011
# ℹ 247 more rows

Use the miss_var_which() function from naniar

miss_var_which(bike)# which columns have missing values

[1] "subType" "name"    "block"   "type"    "project" "route"  

Let’s say we think that all 0 values should be NA.

count(bike, dateInstalled)

# A tibble: 9 × 2
  dateInstalled     n
          <dbl> <int>
1             0   126
2          2006     2
3          2007   368
4          2008   206
5          2009    86
6          2010   625
7          2011   101
8          2012   107
9          2013    10

The na_if() function of dplyr can be helpful for changing all 0 values to NA.

bike <- bike %>% 
  mutate(dateInstalled = na_if(dateInstalled, 0))
count(bike, dateInstalled)

# A tibble: 9 × 2
  dateInstalled     n
          <dbl> <int>
1          2006     2
2          2007   368
3          2008   206
4          2009    86
5          2010   625
6          2011   101
7          2012   107
8          2013    10
9            NA   126

⚠️ Sometimes removing NA values leads to distorted math - be careful!

⚠️ Think about what your NA means for your data (are you sure ?).

• Is an NA for values so low they could not be reported?

• Or is it if it was too low and also if there was a different issue (like no one reported)?

If it is something more like a zero then you might want it included in your data like a zero instead of an NA.

Example: - survey reports NA if student has never tried cigarettes - survey reports 0 if student has tried cigarettes but did not smoke that week

⚠️ You might want to keep the NA values so that you know the original sample size.

count(bike, dateInstalled) %>% mutate(percent = (n/(sum(n)) *100))

# A tibble: 9 × 3
  dateInstalled     n percent
          <dbl> <int>   <dbl>
1          2006     2   0.123
2          2007   368  22.6  
3          2008   206  12.6  
4          2009    86   5.27 
5          2010   625  38.3  
6          2011   101   6.19 
7          2012   107   6.56 
8          2013    10   0.613
9            NA   126   7.73 

bike %>% drop_na(dateInstalled) %>% 
  count(dateInstalled) %>% mutate(percent = (n/(sum(n)) *100))

# A tibble: 8 × 3
  dateInstalled     n percent
          <dbl> <int>   <dbl>
1          2006     2   0.133
2          2007   368  24.5  
3          2008   206  13.7  
4          2009    86   5.71 
5          2010   625  41.5  
6          2011   101   6.71 
7          2012   107   7.11 
8          2013    10   0.664

Should you be dividing by the total count with NA values included?
It depends on your data and what NA might mean.
Pay attention to your data and your NA values!

• Extra or Missing commas
  
• Extra or Missing parentheses
  
• Case sensitivity
  
• Spelling

• is.na(),any(is.na()), all(is.na()),count(), and functions from naniar like gg_miss_var() and miss_var_summary can help determine if we have NA values
• miss_var_which() can help you drop columns that have any missing values.
• filter() automatically removes NA values - can’t confirm or deny if condition is met (need | is.na() to keep them)
• drop_na() can help you remove NA values from a variable or an entire data frame
• NA values can change your calculation results
• think about what NA values represent - don’t drop them if you shouldn’t
• na_if() will make NA values for a particular value
• replace_na() will replace `NA values with a particular value

🏠 Class Website
💻Lab

set.seed(124)
data_ginger_mint <- tibble(Group = rep(c("A", "B", "B"),
                               times = 4), 
                      Treatment = c("Ginger", 
                                  "Ginger",
                                  "Other",
                                  "peppermint", 
                                  "peppermint",
                                  "Ginger", 
                                  "Mint",
                                  "O", 
                                  "Ginger",
                                  "mint",
                                  "Mint",
                                  "O"), 
                    Measurement_start = sample(70:250, size = 12),
                    Measurement_change = sample(-20:40, size = 12))

Data is also here:

http://jhudatascience.org/intro_to_r/data/cleaning_data.xlsx

library(readxl)
data_ginger_mint<- read_excel(here::here("data", "cleaning_data.xlsx"))

data_ginger_mint

# A tibble: 12 × 4
   Group Treatment  Measurement_start Measurement_change
   <chr> <chr>                  <int>              <int>
 1 A     Ginger                   134                 39
 2 B     Ginger                   236                 17
 3 B     Other                    224                 13
 4 A     peppermint                74                 36
 5 B     peppermint               203                -19
 6 B     Ginger                   242                -14
 7 A     Mint                     143                 29
 8 B     O                        230                -16
 9 B     Ginger                   212                  5
10 A     mint                     160                  9
11 B     Mint                      89                -13
12 B     O                         91                  6

This needs lots of recoding.

data_ginger_mint %>%
  count(Treatment)

# A tibble: 6 × 2
  Treatment      n
  <chr>      <int>
1 Ginger         4
2 Mint           2
3 O              2
4 Other          1
5 mint           1
6 peppermint     2

The case_when() function of dplyr can help us to do this as well.

It is more flexible and powerful.

data_ginger_mint %>% 
  mutate(Treatment_recoded = case_when(
                          Treatment == "O" ~ "Other",
                          Treatment == "Mint" ~ "Peppermint",
                          Treatment == "mint" ~ "Peppermint",
                          Treatment == "peppermint" ~ "Peppermint"))  %>% 
  count(Treatment, Treatment_recoded)

# A tibble: 6 × 3
  Treatment  Treatment_recoded     n
  <chr>      <chr>             <int>
1 Ginger     <NA>                  4
2 Mint       Peppermint            2
3 O          Other                 2
4 Other      <NA>                  1
5 mint       Peppermint            1
6 peppermint Peppermint            2

Note that automatically values not reassigned explicitly by case_when() will be NA unless otherwise specified.

# General Format - this is not code!
{data_input} %>%
  mutate({variable_to_fix} = case_when({Variable_fixing}   
             /some condition/ ~ {value_for_con},
                         TRUE ~ {value_for_not_meeting_condition})

{value_for_not_meeting_condition} could be something new or it can be the original values of the column

data_ginger_mint %>% 
  mutate(Treatment_recoded = case_when(
                               Treatment == "O" ~ "Other",
                               Treatment == "Mint" ~ "Peppermint",
                               Treatment == "mint" ~ "Peppermint",
                               Treatment == "peppermint" ~ "Peppermint",
                                TRUE ~ Treatment)) %>%
  count(Treatment, Treatment_recoded)

# A tibble: 6 × 3
  Treatment  Treatment_recoded     n
  <chr>      <chr>             <int>
1 Ginger     Ginger                4
2 Mint       Peppermint            2
3 O          Other                 2
4 Other      Other                 1
5 mint       Peppermint            1
6 peppermint Peppermint            2

You never know if you might be missing something - and if a value already was an NA it will stay that way.

data_ginger_mint %>% 
  mutate(Treatment_recoded = case_when(
                               Treatment == "O" ~ "Other",
                               Treatment == "Mint" ~ "Peppermint",
                               Treatment == "mint" ~ "Peppermint",
                               Treatment == "peppermint" ~ "Peppermint",
                               TRUE ~ Treatment)) %>%
  count(Treatment, Treatment_recoded)

Perhaps we want values that are O or Other to actually be NA, then case_when can be helpful for this. We simply specify everything else.

data_ginger_mint %>% 
  mutate(Treatment_recoded = case_when(
                        Treatment == "Ginger" ~ "Ginger", 
                        Treatment == "Mint" ~ "Peppermint",
                        Treatment == "mint" ~ "Peppermint",
                        Treatment == "peppermint" ~ "Peppermint")) %>%
  count(Treatment, Treatment_recoded)

# A tibble: 6 × 3
  Treatment  Treatment_recoded     n
  <chr>      <chr>             <int>
1 Ginger     Ginger                4
2 Mint       Peppermint            2
3 O          <NA>                  2
4 Other      <NA>                  1
5 mint       Peppermint            1
6 peppermint Peppermint            2

You need to specify what we want in the first part of mutate.

data_ginger_mint %>% 
  mutate(Treatment = case_when(
                          Treatment == "Ginger" ~ "Ginger", 
                          Treatment == "Mint" ~ "Peppermint",
                          Treatment == "mint" ~ "Peppermint",
                          Treatment == "peppermint" ~ "Peppermint")) %>%
  count(Treatment)

# A tibble: 3 × 2
  Treatment      n
  <chr>      <int>
1 Ginger         4
2 Peppermint     5
3 <NA>           3

case_when can do complicated statements and can match many patterns at a time.

data_ginger_mint %>% 
  mutate(Treatment_recoded = case_when(
    Treatment == "Ginger" ~ "Ginger", # keep it the same!
    Treatment %in% 
c("Mint", "mint", "Peppermint", "peppermint") ~ "Peppermint",
    Treatment %in% c("O", "Other") ~ "Other")) %>%

  count(Treatment, Treatment_recoded)

# A tibble: 6 × 3
  Treatment  Treatment_recoded     n
  <chr>      <chr>             <int>
1 Ginger     Ginger                4
2 Mint       Peppermint            2
3 O          Other                 2
4 Other      Other                 1
5 mint       Peppermint            1
6 peppermint Peppermint            2

case_when can do very sophisticated comparisons!

Here we create a new variable called Effect.

data_ginger_mint <- data_ginger_mint  %>% 
      mutate(Effect = case_when(Measurement_change > 0 ~ "Increase",
                                Measurement_change == 0 ~ "Same",
                                Measurement_change < 0 ~ "Decrease"))

head(data_ginger_mint)

# A tibble: 6 × 5
  Group Treatment  Measurement_start Measurement_change Effect  
  <chr> <chr>                  <int>              <int> <chr>   
1 A     Ginger                   134                 39 Increase
2 B     Ginger                   236                 17 Increase
3 B     Other                    224                 13 Increase
4 A     peppermint                74                 36 Increase
5 B     peppermint               203                -19 Decrease
6 B     Ginger                   242                -14 Decrease

data_ginger_mint %>% 
  count(Group, Effect)

# A tibble: 3 × 3
  Group Effect       n
  <chr> <chr>    <int>
1 A     Increase     4
2 B     Decrease     4
3 B     Increase     4

• R can do much more than find exact matches for a whole string!

The stringr package:

• Modifying or finding part or all of a character string
• We will not cover grep or gsub - base R functions
  • are used on forums for answers
• Almost all functions start with str_*

str_detect, and str_replace search for matches to argument pattern within each element of a character vector (not data frame or tibble!).

• str_detect - returns TRUE if pattern is found
• str_replace - replaces pattern with replacement

The string argument specifies what to check
The pattern argument specifies what to check for (case sensitive)

Effect <- pull(data_ginger_mint) %>% head(n = 6)
Effect

[1] "Increase" "Increase" "Increase" "Increase" "Decrease" "Decrease"

str_detect(string = Effect, pattern = "d")

[1] FALSE FALSE FALSE FALSE FALSE FALSE

str_detect(string = Effect, pattern = "D")

[1] FALSE FALSE FALSE FALSE  TRUE  TRUE

The string argument specifies what to check
The pattern argument specifies what to check for
The replacement argument specifies what to replace the pattern with

str_replace(string = Effect, pattern = "D", replacement = "d")

[1] "Increase" "Increase" "Increase" "Increase" "decrease" "decrease"

str_replace_all() can be used to replace all instances within each value

str_replace(string = Effect, pattern = "e", replacement = "E")

[1] "IncrEase" "IncrEase" "IncrEase" "IncrEase" "DEcrease" "DEcrease"

str_replace_all(string = Effect, pattern = "e", replacement = "E")

[1] "IncrEasE" "IncrEasE" "IncrEasE" "IncrEasE" "DEcrEasE" "DEcrEasE"

str_sub() allows you to subset part of a string
The string argument specifies what strings to work with
The start argument specifies position of where to start
The end argument specifies position of where to end

str_sub(string = Effect, start = 1, end = 3)

[1] "Inc" "Inc" "Inc" "Inc" "Dec" "Dec"

head(data_ginger_mint, n = 4)

# A tibble: 4 × 5
  Group Treatment  Measurement_start Measurement_change Effect  
  <chr> <chr>                  <int>              <int> <chr>   
1 A     Ginger                   134                 39 Increase
2 B     Ginger                   236                 17 Increase
3 B     Other                    224                 13 Increase
4 A     peppermint                74                 36 Increase

data_ginger_mint %>% 
  filter(str_detect(string = Treatment,
                    pattern = "int"))

# A tibble: 5 × 5
  Group Treatment  Measurement_start Measurement_change Effect  
  <chr> <chr>                  <int>              <int> <chr>   
1 A     peppermint                74                 36 Increase
2 B     peppermint               203                -19 Decrease
3 A     Mint                     143                 29 Increase
4 A     mint                     160                  9 Increase
5 B     Mint                      89                -13 Decrease

count(data_ginger_mint, Treatment)

# A tibble: 6 × 2
  Treatment      n
  <chr>      <int>
1 Ginger         4
2 Mint           2
3 O              2
4 Other          1
5 mint           1
6 peppermint     2

But we still might miss a strange value

data_ginger_mint %>% 
  mutate(Treatment_recoded = case_when(
    Treatment %in% c("G", "g", "Ginger", "ginger") ~ "Ginger",
    Treatment %in% 
     c("Mint", "mint", "Peppermint", "peppermint") ~ "Peppermint",
    Treatment %in% c("O", "Other") ~ "Other",
    TRUE ~ Treatment))

^ indicates the beginning of a character string $ indicates the end

data_ginger_mint %>% 
  mutate(Treatment_recoded = case_when(
    str_detect(string = Treatment, pattern = "int") ~ "Peppermint",
    str_detect(string = Treatment, pattern = "^o|^O") ~ "Other",
    TRUE ~ Treatment)) %>%
  count(Treatment, Treatment_recoded)

# A tibble: 6 × 3
  Treatment  Treatment_recoded     n
  <chr>      <chr>             <int>
1 Ginger     Ginger                4
2 Mint       Peppermint            2
3 O          Other                 2
4 Other      Other                 1
5 mint       Peppermint            1
6 peppermint Peppermint            2

This is a more robust solution! It will catch typos as long as first letter is correct or there is part of the word mint.

The unite() function can help combine columns
The col argument specifies new column name
The sep argument specifies what separator to use when combining -default is “_” The remove argument specifies if you want to drop the old columns

data_comb <- data_ginger_mint %>% 
  unite(Group, Effect, col = "change", remove = TRUE)

data_comb

# A tibble: 12 × 4
   change     Treatment  Measurement_start Measurement_change
   <chr>      <chr>                  <int>              <int>
 1 A_Increase Ginger                   134                 39
 2 B_Increase Ginger                   236                 17
 3 B_Increase Other                    224                 13
 4 A_Increase peppermint                74                 36
 5 B_Decrease peppermint               203                -19
 6 B_Decrease Ginger                   242                -14
 7 A_Increase Mint                     143                 29
 8 B_Decrease O                        230                -16
 9 B_Increase Ginger                   212                  5
10 A_Increase mint                     160                  9
11 B_Decrease Mint                      89                -13
12 B_Increase O                         91                  6

The separate() function from tidyr can split a column into multiple columns.
The col argument specifies what column to work with
The into argument specifies names of new columns
The sep argument specifies what to separate by

data_comb <- data_comb %>% 
  separate(col = change, into = c("Group", "Change"), sep = "_" )
data_comb

# A tibble: 12 × 5
   Group Change   Treatment  Measurement_start Measurement_change
   <chr> <chr>    <chr>                  <int>              <int>
 1 A     Increase Ginger                   134                 39
 2 B     Increase Ginger                   236                 17
 3 B     Increase Other                    224                 13
 4 A     Increase peppermint                74                 36
 5 B     Decrease peppermint               203                -19
 6 B     Decrease Ginger                   242                -14
 7 A     Increase Mint                     143                 29
 8 B     Decrease O                        230                -16
 9 B     Increase Ginger                   212                  5
10 A     Increase mint                     160                  9
11 B     Decrease Mint                      89                -13
12 B     Increase O                         91                  6

• case_when() requires mutate() when working with dataframes/tibbles
• case_when() can recode entire values based on conditions (need quotes for conditions and new values)
  • remember case_when() needs TRUE ~ varaible to keep values that aren’t specified by conditions, otherwise will be NA

Note: you might see the recode() function, it only does some of what case_when() can do, so we skipped it, but it is in the extra slides at the end.

“Artwork by @allison_horst”. https://allisonhorst.com/

• stringr package has great functions for looking for specific parts of values especially filter() and str_detect() combined
• stringr also has other useful string functions like str_detect() (finding patterns in a column or vector), str_subset() (parsing text), str_replace() (replacing the first instance in values), str_replace_all() (replacing all instances in each value) and more!
• separate() can split columns into additional columns
• unite() can combine columns
• : can indicate when you want to start and end with columns next to one another

🏠 Class Website
💻Lab

Image by Gerd Altmann from Pixabay

This is similar to case_when() but it can’t do as much.

# General Format - this is not code!
{data_input} %>%
  mutate({variable_to_fix_or_new} = recode({Variable_fixing}, {old_value} = {new_value},
                                       {another_old_value} = {new_value}))

data_ginger_mint %>% 
  mutate(Treatment_recoded = recode(Treatment,  
                                       O = "Other",
                                    Mint = "Peppermint",
                                    mint = "Peppermint",
                              peppermint = "Peppermint")) %>%
  count(Treatment, Treatment_recoded)

data_ginger_mint %>% 
  mutate(Treatment_recoded = recode(Treatment,
                                       O = "Other",
                                    Mint = "Peppermint",
                                    mint = "Peppermint",
                              peppermint = "Peppermint")) %>%
  count(Treatment, Treatment_recoded)

# A tibble: 6 × 3
  Treatment  Treatment_recoded     n
  <chr>      <chr>             <int>
1 Ginger     Ginger                4
2 Mint       Peppermint            2
3 O          Other                 2
4 Other      Other                 1
5 mint       Peppermint            1
6 peppermint Peppermint            2

Just use the same variable name to change the variable within mutate.

data_ginger_mint %>% 
  mutate(Treatment= recode(Treatment,
                                       O = "Other",
                                    Mint = "Peppermint",
                                    mint = "Peppermint",
                              peppermint = "Peppermint")) %>%
  count(Treatment)

# A tibble: 3 × 2
  Treatment      n
  <chr>      <int>
1 Ginger         4
2 Other          3
3 Peppermint     5

• str_split(string, pattern) - splits strings up - returns list!

library(stringr)
x <- c("I really like writing R code")
df <- tibble(x = c("I really", "like writing", "R code programs"))
y <- unlist(str_split(x, " "))
y

[1] "I"       "really"  "like"    "writing" "R"       "code"   

length(y)

[1] 6

• http://www.regular-expressions.info/reference.html
• They can use to match a large number of strings in one statement
• . matches any single character
• * means repeat as many (even if 0) more times the last character
• ? makes the last thing optional
• ^ matches start of vector ^a - starts with “a”
• $ matches end of vector b$ - ends with “b”

?modifiers

• fixed - match everything exactly
• ignore_case is an option to not have to use tolower

One example case is when you want to split on a period “.”. In regular expressions . means ANY character, so we need to specify that we want R to interpret “.” as simply a period.

str_split("I.like.strings", ".")

[[1]]
 [1] "" "" "" "" "" "" "" "" "" "" "" "" "" "" ""

str_split("I.like.strings", fixed("."))

[[1]]
[1] "I"       "like"    "strings"

str_split("I.like.strings", "\\.")

[[1]]
[1] "I"       "like"    "strings"

Paste can be very useful for joining vectors together:

paste("Visit", 1:5, sep = "_")

[1] "Visit_1" "Visit_2" "Visit_3" "Visit_4" "Visit_5"

paste("Visit", 1:5, sep = "_", collapse = "_")

[1] "Visit_1_Visit_2_Visit_3_Visit_4_Visit_5"

# and paste0 can be even simpler see ?paste0 
paste0("Visit",1:5) # no space!

[1] "Visit1" "Visit2" "Visit3" "Visit4" "Visit5"

stringr

• str_split(string, pattern) - splits strings up - returns list!

In stringr, str_split splits a vector on a string into a list

library(stringr)
x <- c("I really", "like writing", "R code programs")
y <- str_split(x, pattern =  " ") # returns a list
y

[[1]]
[1] "I"      "really"

[[2]]
[1] "like"    "writing"

[[3]]
[1] "R"        "code"     "programs"

Base R does not use these functions. Here is a “translator” of the stringr function to base R functions

• str_detect - similar to grepl (return logical)
• grep(value = FALSE) is similar to which(str_detect())
• str_subset - similar to grep(value = TRUE) - return value of matched
• str_replace - similar to sub - replace one time
• str_replace_all - similar to gsub - replace many times

Base R:

• Argument order is (pattern, x)
• Uses option (fixed = TRUE)

stringr

• Argument order is (string, pattern) aka (x, pattern)
• Uses function fixed(pattern)

library(jhur)
Sal = read_salaries() # or

str_extract extracts just the matched string

ss = str_extract(Sal$Name, "Rawling")

Warning: Unknown or uninitialised column: `Name`.

head(ss)

character(0)

ss[ !is.na(ss)]

character(0)

str_extract_all extracts all the matched strings

head(str_extract(Sal$AgencyID, "\\d"))

[1] "0" "2" "6" "9" "4" "9"

head(str_extract_all(Sal$AgencyID, "\\d"), 2)

[[1]]
[1] "0" "3" "0" "3" "1"

[[2]]
[1] "2" "9" "0" "4" "5"

• Look for any name that starts with:
  • Payne at the beginning,
  • Leonard and then an S
  • Spence then capital C

head(grep("^Payne.*", x = Sal$name, value = TRUE), 3)

[1] "Payne El,Boaz L"         "Payne El,Jackie"        
[3] "Payne Johnson,Nickole A"

head(grep("Leonard.?S", x = Sal$name, value = TRUE))

[1] "Payne,Leonard S"      "Szumlanski,Leonard S"

head(grep("Spence.*C.*", x = Sal$name, value = TRUE))

[1] "Spencer,Charles A"  "Spencer,Clarence W" "Spencer,Michael C" 

head(str_subset( Sal$name, "^Payne.*"), 3)

[1] "Payne El,Boaz L"         "Payne El,Jackie"        
[3] "Payne Johnson,Nickole A"

head(str_subset( Sal$name, "Leonard.?S"))

[1] "Payne,Leonard S"      "Szumlanski,Leonard S"

head(str_subset( Sal$name, "Spence.*C.*"))

[1] "Spencer,Charles A"  "Spencer,Clarence W" "Spencer,Michael C" 

Let’s say we wanted to sort the data set by Annual Salary:

class(Sal$AnnualSalary)

[1] "character"

sort(c("1", "2", "10")) #  not sort correctly (order simply ranks the data)

[1] "1"  "10" "2" 

order(c("1", "2", "10"))

[1] 1 3 2

So we must change the annual pay into a numeric:

head(Sal$AnnualSalary, 4)

[1] "$55314.00" "$74000.00" "$64500.00" "$46309.00"

head(as.numeric(Sal$AnnualSalary), 4)

Warning in head(as.numeric(Sal$AnnualSalary), 4): NAs introduced by coercion

[1] NA NA NA NA

R didn’t like the $ so it thought turned them all to NA.

sub() and gsub() can do the replacing part in base R.

Now we can replace the $ with nothing (used fixed=TRUE because $ means ending):

Sal$AnnualSalary <- as.numeric(gsub(pattern = "$", replacement="", 
                              Sal$AnnualSalary, fixed=TRUE))
Sal <- Sal[order(Sal$AnnualSalary, decreasing=TRUE), ] 
Sal[1:5, c("name", "AnnualSalary", "JobTitle")]

# A tibble: 5 × 3
  name            AnnualSalary JobTitle              
  <chr>                  <dbl> <chr>                 
1 Mosby,Marilyn J       238772 STATE'S ATTORNEY      
2 Batts,Anthony W       211785 Police Commissioner   
3 Wen,Leana             200000 Executive Director III
4 Raymond,Henry J       192500 Executive Director III
5 Swift,Michael         187200 CONTRACT SERV SPEC II 

We can do the same thing (with 2 piping operations!) in dplyr

dplyr_sal = Sal
dplyr_sal = dplyr_sal %>% mutate( 
  AnnualSalary = AnnualSalary %>%
    str_replace(
      fixed("$"), 
      "") %>%
    as.numeric) %>%
  arrange(desc(AnnualSalary))
check_Sal = Sal
rownames(check_Sal) = NULL
all.equal(check_Sal, dplyr_sal)

[1] TRUE

A two-way table. If you pass in 2 vectors, table creates a 2-dimensional table.

tab <- table(c(0, 1, 2, 3, 2, 3, 3, 2,2, 3),
             c(0, 1, 2, 3, 2, 3, 3, 4, 4, 3),
              useNA = "always")
tab

      
       0 1 2 3 4 <NA>
  0    1 0 0 0 0    0
  1    0 1 0 0 0    0
  2    0 0 2 0 2    0
  3    0 0 0 4 0    0
  <NA> 0 0 0 0 0    0

tab_df = tibble(x = c(0, 1, 2, 3, 2, 3, 3, 2,2, 3),
             y = c(0, 1, 2, 3, 2, 3, 3, 4, 4, 3))
tab_df %>% count(x, y)

# A tibble: 5 × 3
      x     y     n
  <dbl> <dbl> <int>
1     0     0     1
2     1     1     1
3     2     2     2
4     2     4     2
5     3     3     4

tab_df %>% 
  count(x, y) %>% 
  group_by(x) %>% mutate(pct_x = n / sum(n))

# A tibble: 5 × 4
# Groups:   x [4]
      x     y     n pct_x
  <dbl> <dbl> <int> <dbl>
1     0     0     1   1  
2     1     1     1   1  
3     2     2     2   0.5
4     2     4     2   0.5
5     3     3     4   1  

library(scales)
tab_df %>% 
  count(x, y) %>% 
  group_by(x) %>% mutate(pct_x = percent(n / sum(n)))

# A tibble: 5 × 4
# Groups:   x [4]
      x     y     n pct_x
  <dbl> <dbl> <int> <chr>
1     0     0     1 100% 
2     1     1     1 100% 
3     2     2     2 50%  
4     2     4     2 50%  
5     3     3     4 100%