1.2.1 What is RStudio?

RStudio is an integrated development environment (IDE) for R which works with the standard version of R available from CRAN. RStudio includes a wide range of productivity enhancing features and runs on all major platforms. There are several related IDE products: RStudio Workbench (previously RStudio Server Pro), RStudio Server Open Source, and RStudio Desktop. RStudio Workbench and RStudio Server enable you to provide a browser-based interface to a server running on a remote Linux system.

1.2.2 What versions of R is RStudio compatible with?

RStudio requires an installation of R 4.2.3 or higher. You can download the most recent version of R for your environment from CRAN.

1.2.3 What is the difference between RStudio Desktop, RStudio Server, and RStudio Workbench?

RStudio Desktop is an IDE that works with the version of R you have installed on your local Windows, Mac OS X, or Linux workstation. Version 1.3 and later of RStudio Desktop can also be used as a client of RStudio Workbench (previously RStudio Server Pro).

RStudio Workbench (previously RStudio Server Pro) and RStudio Server Open Source are Linux server applications that provide a web-browser-based interface to a server running on a remote Linux system. For more on why you might want to deploy an RStudio Server instead of RStudio Desktop, see the server documentation.

Check POSIT to see more on FAQs.

3.4.1 Basic formatting

Use the following simple rules for writing readable codes:

• Use spaces between variables and operators
• Break up long line of codes. You can set up a threshold max line by going to Tools -> Global Options -> Code -> Display -> Show Margin -> set up to 110
• Use meaningful variable names taking into consideration that R is case sensitive.

See the difference between:

malDiff1=lm(y~grp+grpTim,df,subset=sext1=="m")

and:

male_difference = lm(score ~ group + group_time_interaction,
                     data = interview_data,
                     subset = gender == "male")

Both codes are the same, but the second is better 😊.

3.4.2 Be Consistent

Make sure to stay consistent across your whole script as you and the nexst person using the script will get used to the format in use and will be able to identify variables and data frames.

3.4.3 The most important: Include Comments

It is the best feeling in the world when I open someone’s else code and find comments in them. Comments are used to either explain what you are trying to do or to even show how to use the script the right way. Comments in R are written using #.

# Here I am adding two variables

variable1 <- 1 # first variable
variable2 <- 4 # second variable

sum <- variable1 + variable2

Output:

## [1] 5

5.1.1 Numbers

Numbers in R can be called either numerics or integers.

## Numeric variable
x <- 28.5
class(x)

Output:

## [1] "numeric"

## Integer variable
y <- 28L
class(y)

Output:

## [1] "integer"

5.1.2 Logical

Logical is a Boolean value with a binary operators: TRUE or FALSE

## Logical variable
z <- TRUE
class(z)

Output:

## [1] "logical"

## Logical variable
logical <- 2 < 1
class(logical)

## [1] "logical"

5.1.3 Characters

Anything that is put inside " " or' ' are considered a text (string).

## String variable
my_string <- "I love this training"
class(my_string)

Output:

## [1] "character"

5.2.1 Arithmetic

Mainly used to perform common arithmetic operations.

5.2.2 Assignment

These operators are used to assign values to variables.

my_var <- "value"
my_var <<- "value"
my_var = "value"
"value" -> my_var
"value" ->> my_var
my_var ## to print my_var

Output:

## [1] "value"

5.2.3 Comparison

Mainly used to compare two values.

5.2.4 Logical

Mainly used to combine conditional statements

5.2.5 Miscellaneous

Mainly used to manipulate data. We will see them a lot in use in coming chapters.

5.3.1 Variables

Variables are objects for storing data values. There is no commend to create a variable and it is set the moment a value is assigned. We saw in the previous operators chapters an example of assigning a value to a variable.

name <- "Abraham"
age <- 30
name
age

Output:

## [1] "Abraham"

## [1] 30

Variables can be concatenate together using the paste() function. Use a comma (,) between the variables inside the function.

name <- "Abraham"
age <- 30
paste(name, " is ", age, " years old.")

Output:

## [1] "Abraham  is  30  years old."

For numbers, you can use the arithmetic operator (+) but it will perform the actual addition. If you try to use it between a numeric and character types variable, R will give you an error.

name <- "Abraham"
age <- 30
name + age

Output:

## Error in name + age: non-numeric argument to binary operator

5.3.2 Vectors

Vectors are a list of items that are from the same type. You will be using them a lot to identify and assign lists to be iterated or used to be compared with other lists.

To combine items in a vector list, you can use the c() function and use the comma to separate the items.

names <- c("Abraham","Maksym","Oleksandr","Karyna")
names

Output:

## [1] "Abraham"   "Maksym"    "Oleksandr" "Karyna"

You can use the : operator if you want to create a list of vectors with a series of numbers.

numeric_list <- c(1:10)
numeric_list

Output:

##  [1]  1  2  3  4  5  6  7  8  9 10

The following is some features used in vectors.

names <- c("Abraham","Maksym","Oleksandr","Karyna")
names[1]

## [1] "Abraham"

names <- c("Abraham","Maksym","Oleksandr","Karyna")
names[1] <- "Anastasiia"
names

## [1] "Anastasiia" "Maksym"     "Oleksandr"  "Karyna"

names <- c("Abraham","Maksym","Oleksandr","Karyna")
length(names)

## [1] 4

5.3.3 Factors

Factors exists to categorize data and the categories inside a factor are considered levels in R. To create a factor, you can use the factor() function.

units <- factor(c("ISU","CEU","Sectors","ISU","ISU","CASH","CEU"))
units

Output:

## [1] ISU     CEU     Sectors ISU     ISU     CASH    CEU    
## Levels: CASH CEU ISU Sectors

You can set the levels inside the factor function by calling the parameter levels.

Same as vectors, you can Access, Change, and check the lenght.

5.3.4 Data frames

Data frames are the most commonly used data structure in R. It is a data displayed in the format of a table. Data frames can store different types of data in every column. It can be characters, numeric, or logical. Remember, each complumn should have the same type.

You can use data.frame() function to create a data frame.

units_df <- data.frame(
  units = c("ISU","CEU","CASH","Sectors"),
  num_of_employess = c(10,45,20,1),
  success = c(T,T,T,T)
)
units_df

Output:

##     units num_of_employess success
## 1     ISU               10    TRUE
## 2     CEU               45    TRUE
## 3    CASH               20    TRUE
## 4 Sectors                1    TRUE

You can use the summary() function to summarise your data frame.

summary(units_df)

Output:

##     units           num_of_employess success       
##  Length:4           Min.   : 1.00    Mode:logical  
##  Class :character   1st Qu.: 7.75    TRUE:4        
##  Mode  :character   Median :15.00                  
##                     Mean   :19.00                  
##                     3rd Qu.:26.25                  
##                     Max.   :45.00

units_df[1]

units_df[["units"]]

units_df$units

##     units
## 1     ISU
## 2     CEU
## 3    CASH
## 4 Sectors

## [1] "ISU"     "CEU"     "CASH"    "Sectors"

## [1] "ISU"     "CEU"     "CASH"    "Sectors"

new_units_df <- data.frame(
  units = c("ISU","CEU","Sectors"),
  num_of_employess = c(10,45,1),
  success = c(T,T,T)
)

combined_df <- rbind(units_df, new_units_df)

combined_df

##     units num_of_employess success
## 1     ISU               10    TRUE
## 2     CEU               45    TRUE
## 3    CASH               20    TRUE
## 4 Sectors                1    TRUE
## 5     ISU               10    TRUE
## 6     CEU               45    TRUE
## 7 Sectors                1    TRUE

ncol(combined_df)
nrow(combined_df)

## [1] 3

## [1] 7

5.4.1 If Statement

The “if statement” use if keyword and have the following syntax to be executed.

num_one <- 20
num_two <- 30

if (num_one < num_two) {
  print("num_two is greater than num_one")
}

Output:

## [1] "num_two is greater than num_one"

5.4.2 Else If

This statement use else if keyword and is used if the previous conditions is not true, then use this condition. You can use as many else if as you want.

num_one <- 20
num_two <- 20

if (num_one < num_two) {
  print("num_two is greater than num_one")
} else if (num_two == num_one){
  print("num_one and num_two are equal")
}

Output:

## [1] "num_one and num_two are equal"

5.4.3 If Else

else is a keyword that is used to catch anything that is not meeting any of the previous conditions.

num_one <- 30
num_two <- 20

if (num_one < num_two) {
  print("num_two is greater than num_one")
} else if (num_two == num_one){
  print("num_one and num_two are equal")
} else {
  print("num_one is greater than num_two")
}

Output:

## [1] "num_one is greater than num_two"

5.4.4 Use of AND or OR in IF

Remembering from the operators section, we learned about the logical ones. They can be used in defining two conditions.

num_one <- 30
num_two <- 20
num_three <- 100

if (num_one > num_two & num_three > num_two) {
  print("Both conditions TRUE.")
}

Output:

## [1] "Both conditions TRUE."

num_one <- 30
num_two <- 20
num_three <- 100

if (num_one > num_two | num_two > num_three) {
  print("At least one condition is TRUE.")
}

Output:

## [1] "At least one condition is TRUE."

5.4.5 For Loop

for loops are used to iterate over a sequence, vector, list, or rows/columns of data frames.

for(i in 1:10){
  print(i)
}

Output:

## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10

Another example with the units list from before:

for(i in units){
  print(i)
}

Output:

## [1] "ISU"
## [1] "CEU"
## [1] "Sectors"
## [1] "ISU"
## [1] "ISU"
## [1] "CASH"
## [1] "CEU"

7.2.1 What is dplyr?

Dplyr is a powerful R package to manipulate, clean and summarize unstructured data. It makes data manipulation very fast. It compromises of many functions that perform mostly used data manipulation operations such as filtering, selecting specific columns, sorting data, adding or deleting columns, aggregating data.

To install and load dplyr package into your RStudio, follow the steps

install.packages(‘dplyr’)

library(dplyr)

7.2.2 Most important functions

The most common used functions in the dplyr package are:

• select(): picks columns based on their names or type
• filter(): picks rows based on their values
• group_by(): group the rows together depending on conditions
• summarise(): summarize or aggregate the data together
• arrange(): sort and order the rows by values of the column
• mutate(): create new variables by mutating existing ones
• join(): joining data frames together
• rename(): rename column names in your dataset

7.2.3 Pipes

You will most likely come across this symbol %>%. It is used to emphasise the sequence of actions and chain together different actions that are performed together.

For example:

• We will filter our data to keep only ages above 18
• then, we want to mutate the sum of income in last month and this month
• then, we want to group_by the hromada together
• finally, summarise the average income per hromada.

Here is some documentation on %>%

7.3.1 select() function

Let’s say we would like to select only the uuid and the A_2_respondent_sex.

age_respondent_df <- data %>% 
  select(uuid,A2_respondent_sex)

head(age_respondent_df)

Output:

## # A tibble: 6 × 2
##   uuid                                 A_2_respondent_sex
##   <chr>                                <chr>             
## 1 af204813-5e1f-4040-bc71-6b0061bf4773 female            
## 2 6a5fe753-984c-42ec-8fbe-28d79f6d0ca3 female            
## 3 acc9eac0-bf27-4216-b6d3-1c48919f5999 male              
## 4 e516a220-ba49-4736-ab1f-bd409459d1a0 female            
## 5 c0b55200-d706-46a8-bf13-7d274f03ac4e female            
## 6 9c61d1f0-edc2-4558-ae4d-e56321ca58b2 female

Let’s breakdown a bit the code above. As you see, We started by assigning a new data frame to age_respondent_df. If you start with data <- data %>%, you will be overwriting the actual data with the new actions. We also used a new function called head() which shows only the first few rows of you data.

You can perform many different actions with the select() function.

To select all columns except specific ones, you can use the -subtraction operator.

df_without_uuid <- data %>% 
  select(-uuid)

To select the first five columns, you can use the : colon operator with indexes or the names of the columns.

first_five_col_df <- data %>% 
  select(1:5)

To select all columns that start with a specific character string like "A_", you can use the function starts_with().

all_A_columns_df <- data %>% 
  select(starts_with("A_"))

Here are some additional options to select columns based on a specific criteria:

• ends_with(): Select columns that end with a specific character string
• contatins(): Select columns that contains a specific character string
• matches(): Select columns that matches a specific character string or a REGEX

Here is some documentation on select()

7.3.2 filter() function

Filter is used to select specific rows from the datasets. Let’s say we would like to only see the HHs that are above 60 years old.

elderly_df <- data %>% 
  select(uuid,A_1_respondent_age) %>% 
  filter(as.numeric(A_1_respondent_age) > 60)

head(elderly_df)

Output:

## # A tibble: 6 × 2
##   uuid                                 A_1_respondent_age
##   <chr>                                <chr>             
## 1 5656517f-c389-4d20-8f6d-c0f02b588257 65                
## 2 571f52a6-6370-46d3-b1a7-f27ca5658d0d 68                
## 3 d42104cc-8d0b-4024-b230-45ece957c687 61                
## 4 b8aec8d4-fb84-47d6-9104-16d8f6a9c797 63                
## 5 00bb7e1a-7ff2-4e80-9077-d24f7b1fdb06 61                
## 6 c80714a5-e964-488c-ac68-ddf8428b6771 64

You can see that we also used a new function called as.numeric(). If you recall, while we imported the data to R, we did read all columns as characters. Hence why we are transforming it to numeric to be able to conductr some mathematical computations.

You can use all the logical operators here for combining conditions.

elderly_male_df <- data %>% 
  select(uuid,A_1_respondent_age, A_2_respondent_sex) %>% 
  filter(as.numeric(A_1_respondent_age) > 60 & A_2_respondent_sex =="male")

head(elderly_male_df)

Output:

## # A tibble: 6 × 3
##   uuid                                 A_1_respondent_age A_2_respondent_sex
##   <chr>                                <chr>              <chr>             
## 1 5656517f-c389-4d20-8f6d-c0f02b588257 65                 male              
## 2 d42104cc-8d0b-4024-b230-45ece957c687 61                 male              
## 3 00bb7e1a-7ff2-4e80-9077-d24f7b1fdb06 61                 male              
## 4 def21a5c-e1d8-4487-9b83-427a8c261bbd 82                 male              
## 5 80f9b3dd-bd1d-4e03-9119-04151d1edb06 70                 male              
## 6 6b1b34bf-b537-4ba4-a066-40688780c6b3 71                 male

You can use the %in% operator to filter for specific group of values in a column.

shelter_type <- data %>% 
  select(uuid,D_1_shelter_type) %>% 
  filter(D_1_shelter_type %in% c("detached_house","apartment_in_apartment_block"))

head(shelter_type)

Output:

## # A tibble: 6 × 2
##   uuid                                 D_1_shelter_type            
##   <chr>                                <chr>                       
## 1 af204813-5e1f-4040-bc71-6b0061bf4773 apartment_in_apartment_block
## 2 6a5fe753-984c-42ec-8fbe-28d79f6d0ca3 detached_house              
## 3 acc9eac0-bf27-4216-b6d3-1c48919f5999 apartment_in_apartment_block
## 4 c0b55200-d706-46a8-bf13-7d274f03ac4e apartment_in_apartment_block
## 5 9c61d1f0-edc2-4558-ae4d-e56321ca58b2 apartment_in_apartment_block
## 6 04ffcbcb-b936-4cd0-a24e-67643346674f apartment_in_apartment_block

Here is some documentation on filter()

7.3.3 arrange() function

Arrange is used to re-order rows following a particular order. You will only need to provide the name of the column. If it is of a character type, it will arrange alphabetically. If it is numeric, then it will arrange ascendant or descendant depending on your specification.

age <- data %>% 
  select(uuid, A_1_respondent_age) %>%
  arrange(A_1_respondent_age)

head(age)

Output:

## # A tibble: 6 × 2
##   uuid                                 A_1_respondent_age
##   <chr>                                <chr>             
## 1 021e6806-d708-4dd0-aae3-99bd10a14ac4 18                
## 2 ce753961-14ff-427c-b0f3-8b0c9b7dc534 18                
## 3 8e8970ac-86b3-48c3-ace5-91f0bbf785ac 18                
## 4 24165246-800a-471e-a2b7-1cfe7ad2506a 18                
## 5 ec942b56-39ce-4feb-8590-eb0b4dc53475 18                
## 6 ef05a1ee-8bb7-4fbe-a3f2-b406032d4e09 18

You can also add multiple columns to arrange the rows respectively. You should add desc() to the column inside arrange() if you are aiming for a descendant order.

age_desc <- data %>% 
  select(uuid, A_1_respondent_age) %>%
  arrange(desc(A_1_respondent_age))

head(age_desc)

Output:

## # A tibble: 6 × 2
##   uuid                                 A_1_respondent_age
##   <chr>                                <chr>             
## 1 a7ed3939-589c-4700-b64a-6b9ce1ff6161 92                
## 2 c9615d0a-0d24-4376-9b6a-4531eafbfe08 87                
## 3 20a5ecb8-ecc1-4ddc-8de7-c7eb5a67dd07 85                
## 4 0027d58b-b67c-4e7b-87e5-f9da508dd3a7 85                
## 5 075042a7-370f-420b-8fd9-1d76eb4ec261 85                
## 6 ff08380c-6aa7-426a-9777-c735595e5de7 85

Here is some documentation on arrange()

7.3.4 mutate() function

This function will add a new column to your data frame. Here is where the fun begins and sky is your limit. I will be giving here many examples of what you can use inside a mutate() function.

If I want to fill a new column with a single string

single_string_col <- data %>% 
  mutate(test_column = "test") %>% 
  select(uuid,test_column)


head(single_string_col)

Output:

## # A tibble: 6 × 2
##   uuid                                 test_column
##   <chr>                                <chr>      
## 1 af204813-5e1f-4040-bc71-6b0061bf4773 test       
## 2 6a5fe753-984c-42ec-8fbe-28d79f6d0ca3 test       
## 3 acc9eac0-bf27-4216-b6d3-1c48919f5999 test       
## 4 e516a220-ba49-4736-ab1f-bd409459d1a0 test       
## 5 c0b55200-d706-46a8-bf13-7d274f03ac4e test       
## 6 9c61d1f0-edc2-4558-ae4d-e56321ca58b2 test

We can also do some calculations

# creating the set of columns with only shelter_issues
shelter_columns <- data %>% 
  select(starts_with("D_7_shelter_issues/"),-c("D_7_shelter_issues/none","D_7_shelter_issues/dont_know","D_7_shelter_issues/prefer_not_to_answer")) %>% 
  colnames

## the actual new data frame
shelter_issues <- data %>% 
  mutate(new_column_one = rowSums(across(shelter_columns, .fns = as.numeric), na.rm =T)) %>% 
  select(uuid,new_column_one) %>% 
  arrange(desc(new_column_one))

head(shelter_issues)

Output:

## # A tibble: 6 × 2
##   uuid                                 new_column_one
##   <chr>                                         <dbl>
## 1 2c2d1c15-9eb6-4533-aa89-7f365354d931              7
## 2 a1c5d05a-4464-4a97-b09c-42799e6f5d59              6
## 3 db2602b7-8298-4513-8aad-6d7292330a1c              6
## 4 50bd09f7-f001-4a46-8fab-0689a3547735              6
## 5 65d71b90-26c9-4e82-90d0-6091d2d4eaa9              5
## 6 630f4e79-0382-45a4-ad13-47cd27b580df              5

Here we targeted all shelter issues and calculated the sum of shelter issues the HH is facing. We can see there is new function called rowSums() which is a base function that let you add multiple columns values in a row wise manner. Also, across() function is a dplyr function that calls many columns at the same time and you can provide a .fns parameter that force a as.numeric() function on all columns. starts_with() also is a dplyr function, only used inside select() function to target columns starting with specific pattern.

We can include some conditions with ifelse() or case_when()

# male HoHH or female HoHH
hoHH_genderifelse <- data %>% 
  mutate(gender_head_household = ifelse(A_3_respondent_hohh == "yes" & A_2_respondent_sex == "male", "male_hoHH",
                                        ifelse(A_3_respondent_hohh == "yes" & A_2_respondent_sex == "female", "female_hoHH", "other"))) %>% 
  select(uuid, gender_head_household)

head(hoHH_genderifelse)

Output:

## # A tibble: 6 × 2
##   uuid                                 gender_head_household
##   <chr>                                <chr>                
## 1 af204813-5e1f-4040-bc71-6b0061bf4773 female_hoHH          
## 2 6a5fe753-984c-42ec-8fbe-28d79f6d0ca3 female_hoHH          
## 3 acc9eac0-bf27-4216-b6d3-1c48919f5999 male_hoHH            
## 4 e516a220-ba49-4736-ab1f-bd409459d1a0 female_hoHH          
## 5 c0b55200-d706-46a8-bf13-7d274f03ac4e female_hoHH          
## 6 9c61d1f0-edc2-4558-ae4d-e56321ca58b2 other

As you can see, I used ifelse() many times to target every single options in my conditions. In case we have NA in our data, it is better to handle them with the first condition being is.na(...), NA, ....

# male HoHH or female HoHH
hoHH_gendercasewhen <- data %>% 
  mutate(gender_head_household = case_when(A_3_respondent_hohh == "yes" & A_2_respondent_sex == "male" ~ "male_hoHH",
                                           A_3_respondent_hohh == "yes" & A_2_respondent_sex == "female" ~ "female_hoHH",
                                           TRUE ~ "other")) %>% 
  select(uuid, gender_head_household)

head(hoHH_gendercasewhen)

Output:

## # A tibble: 6 × 2
##   uuid                                 gender_head_household
##   <chr>                                <chr>                
## 1 af204813-5e1f-4040-bc71-6b0061bf4773 female_hoHH          
## 2 6a5fe753-984c-42ec-8fbe-28d79f6d0ca3 female_hoHH          
## 3 acc9eac0-bf27-4216-b6d3-1c48919f5999 male_hoHH            
## 4 e516a220-ba49-4736-ab1f-bd409459d1a0 female_hoHH          
## 5 c0b55200-d706-46a8-bf13-7d274f03ac4e female_hoHH          
## 6 9c61d1f0-edc2-4558-ae4d-e56321ca58b2 other

Here is some documentation on mutate()

7.3.5 summarise() function

Summarise function is used to create summary for a column in the data frame, such as finding the mean, min, or max.

# average age
age_average <- data %>% 
  summarise(average_age = mean(as.numeric(A_1_respondent_age)))

age_average

Output:

## # A tibble: 1 × 1
##   average_age
##         <dbl>
## 1        50.4

You can also perform multiple summary of a column:

# age stats
age_stats <- data %>% 
  summarise(average_age = mean(as.numeric(A_1_respondent_age)),
            min_age = min(as.numeric(A_1_respondent_age)),
            max_age = max(as.numeric(A_1_respondent_age)),
            total_num_submission = n())

age_stats

Output:

## # A tibble: 1 × 4
##   average_age min_age max_age total_num_submission
##         <dbl>   <dbl>   <dbl>                <int>
## 1        50.4      18      92                 1000

Here are the different summary statistics you can perform:

• sd(): Standard deviation
• min(): Minimum value
• max(): Maximum value
• median(): Median
• mean(): Mean
• sum(): Sum
• n(): Length of the vector (count of all rows)
• first(): First value in the vector
• last(): Last value in the vector
• n_distinct(): Number of distinct values in vector

Here is some documentation on summarise()

7.3.6 group_by() function

This function is very important to know if you are aiming to create disaggregations in your data and combine groups/strata together. It is usually always followed by a summarise as the main goal of using it is to split and apply a computation.

Let’s say we want to know the number of males vs female in our data.

# gender disaggregation
gender <- data %>% 
  group_by(A_2_respondent_sex) %>% 
  summarise(count = n())

gender

Output:

## # A tibble: 2 × 2
##   A_2_respondent_sex count
##   <chr>              <int>
## 1 female               626
## 2 male                 374

We can actually calculate and then ungroup using the following dplyr function ungroup(), and the column will remain providing the value for each group respectively. However, instead of summarise() we should use mutate().

# gender age average ungrouped
gender_age_average_ungrouped <- data %>% 
  group_by(A_2_respondent_sex) %>% 
  mutate(age_average = mean(as.numeric(A_1_respondent_age))) %>% 
  ungroup() %>% 
  select(uuid, A_2_respondent_sex, age_average)

head(gender_age_average_ungrouped)

Output:

## # A tibble: 6 × 3
##   uuid                                 A_2_respondent_sex age_average
##   <chr>                                <chr>                    <dbl>
## 1 af204813-5e1f-4040-bc71-6b0061bf4773 female                    51.8
## 2 6a5fe753-984c-42ec-8fbe-28d79f6d0ca3 female                    51.8
## 3 acc9eac0-bf27-4216-b6d3-1c48919f5999 male                      48.2
## 4 e516a220-ba49-4736-ab1f-bd409459d1a0 female                    51.8
## 5 c0b55200-d706-46a8-bf13-7d274f03ac4e female                    51.8
## 6 9c61d1f0-edc2-4558-ae4d-e56321ca58b2 female                    51.8

Here is some documentation on group_by()

7.3.7 join() function

We can use the join function to merge multiple data frames together. The most used function that you might run through is the left_join().

Here are the different join functions you might encounter as well:

• inner_join(): return all rows from dataframe_1 where there are matching values in dataframe_2 and all the columns in both dataframes. All matches are returned.
• left_join(): return all rows from dataframe_1 and all columns from both dataframes. Rows that are in dataframe_1 and not in dataframe_2 will include NA values in the column from dataframe_2. All matches are returned.
• right_join(): return all rows from dataframe_2 and all columns from both dataframes. Rows that are in dataframe_2 and not in dataframe_1 will include NA values in the column from dataframe_1. All matches are returned.
• full_join(): return all rows and columns from both dataframes. if there is no match, NA is filled in the missing values.

Here is another Excel file including some data for all 1000 submission about the the total number of days with piped water disruptions. Below is the button to upload the data.

# join pipe_water_data with original data
joined_data <- data %>% 
  left_join(pipe_water_data, by=c("uuid")) %>% 
  select(uuid, A_2_respondent_sex, F_2_piped_water_disruptions)

head(joined_data)

Output:

## # A tibble: 6 × 3
##   uuid                                 A_2_respondent_sex F_2_piped_water_disr…¹
##   <chr>                                <chr>              <chr>                 
## 1 af204813-5e1f-4040-bc71-6b0061bf4773 female             <NA>                  
## 2 6a5fe753-984c-42ec-8fbe-28d79f6d0ca3 female             7                     
## 3 acc9eac0-bf27-4216-b6d3-1c48919f5999 male               7                     
## 4 e516a220-ba49-4736-ab1f-bd409459d1a0 female             <NA>                  
## 5 c0b55200-d706-46a8-bf13-7d274f03ac4e female             <NA>                  
## 6 9c61d1f0-edc2-4558-ae4d-e56321ca58b2 female             7                     
## # ℹ abbreviated name: ¹​F_2_piped_water_disruptions

Here is some documentation on join()