Data cleaning project
Muhammad Nashrur
2022-11-20
Data cleaning project- smarthphone activity prediction
Cleaning the data
- The next step is to load the data into R. The following code assumes the data is already downloaded and unzipped into the working directory.
# Load train data
trainData <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/X_train.txt")
trainLabel <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/y_train.txt")
trainSubject <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/subject_train.txt")
# Load test data
testData <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/X_test.txt")
testLabel <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/y_test.txt")
testSubject <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/subject_test.txt")Merging the training and the test sets
joinData <- rbind(trainData, testData)
# remove no longer required data sets
remove(trainData); remove(testData)
# Show dimensions of new data table
dim(joinData)## [1] 10299 561The train and test labels are also joined and finally the train and test subject identifiers.
joinLabel <- rbind(trainLabel, testLabel)
joinSubject <- rbind(trainSubject, testSubject)
remove(trainLabel); remove(testLabel); remove(trainSubject); remove(testSubject)
head(joinData,5)[,c('V1', 'V2', 'V3', 'V4', 'V5')]## V1 V2 V3 V4 V5
## 1 0.2885845 -0.02029417 -0.1329051 -0.9952786 -0.9831106
## 2 0.2784188 -0.01641057 -0.1235202 -0.9982453 -0.9753002
## 3 0.2796531 -0.01946716 -0.1134617 -0.9953796 -0.9671870
## 4 0.2791739 -0.02620065 -0.1232826 -0.9960915 -0.9834027
## 5 0.2766288 -0.01656965 -0.1153619 -0.9981386 -0.9808173Extract mean and standard deviation for each measurement
features <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/features.txt")
head(features)## V1 V2
## 1 1 tBodyAcc-mean()-X
## 2 2 tBodyAcc-mean()-Y
## 3 3 tBodyAcc-mean()-Z
## 4 4 tBodyAcc-std()-X
## 5 5 tBodyAcc-std()-Y
## 6 6 tBodyAcc-std()-ZmeanSD <- grep("mean\\(\\)|std\\(\\)", features[, 2])
joinData <- joinData[, meanSD]
dim(joinData)## [1] 10299 66- This has reduced the number of columns to 66. The columns can also be renamed using the features list with some additional cleaning.
names(joinData) <- gsub("\\(\\)", "", features[meanSD, 2]) # remove "()"
names(joinData) <- gsub("mean", "Mean", names(joinData)) # capitalise M
names(joinData) <- gsub("std", "Std", names(joinData)) # change "std" to "SD"
names(joinData) <- gsub("-", "", names(joinData)) # remove "-"
remove(features)
# preview data
head(joinData,5)[,c('tBodyAccMeanX', 'tBodyAccMeanY', 'tBodyAccMeanZ', 'tBodyAccStdX',
'tBodyAccStdY')]## tBodyAccMeanX tBodyAccMeanY tBodyAccMeanZ tBodyAccStdX tBodyAccStdY
## 1 0.2885845 -0.02029417 -0.1329051 -0.9952786 -0.9831106
## 2 0.2784188 -0.01641057 -0.1235202 -0.9982453 -0.9753002
## 3 0.2796531 -0.01946716 -0.1134617 -0.9953796 -0.9671870
## 4 0.2791739 -0.02620065 -0.1232826 -0.9960915 -0.9834027
## 5 0.2766288 -0.01656965 -0.1153619 -0.9981386 -0.9808173Use descriptive activity names to name the activities
activity <- read.table("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/activity_labels.txt")
activity## V1 V2
## 1 1 WALKING
## 2 2 WALKING_UPSTAIRS
## 3 3 WALKING_DOWNSTAIRS
## 4 4 SITTING
## 5 5 STANDING
## 6 6 LAYING- These labels will be cleaned up by changing to lower case and removing the underscore
activity[, 2] <- tolower(gsub("_", "", activity[, 2]))
substr(activity[2, 2], 8, 8) <- toupper(substr(activity[2, 2], 8, 8))
substr(activity[3, 2], 8, 8) <- toupper(substr(activity[3, 2], 8, 8))- These labels can then be joined to the label data that was loaded previously.
activityLabel <- activity[joinLabel[, 1], 2]
joinLabel[, 1] <- activityLabel
names(joinLabel) <- "activity"
head(joinLabel)## activity
## 1 standing
## 2 standing
## 3 standing
## 4 standing
## 5 standing
## 6 standingAppropriately label the data set with the activity names
Finally the full data will be binded with the subject and activity labels. The activity labels will become the dependent variable for the statistical modelling task in the next section of the project.
names(joinSubject) <- "subject"
cleanData <- cbind(joinSubject, joinLabel, joinData)
remove(joinData); remove(activity)
remove(joinLabel); remove(joinSubject)
remove(activityLabel); remove(meanSD)
head(cleanData,5)[,c('subject', 'activity', 'tBodyAccMeanX', 'tBodyAccMeanY',
'tBodyAccMeanZ')]## subject activity tBodyAccMeanX tBodyAccMeanY tBodyAccMeanZ
## 1 1 standing 0.2885845 -0.02029417 -0.1329051
## 2 1 standing 0.2784188 -0.01641057 -0.1235202
## 3 1 standing 0.2796531 -0.01946716 -0.1134617
## 4 1 standing 0.2791739 -0.02620065 -0.1232826
## 5 1 standing 0.2766288 -0.01656965 -0.1153619Split data by subject into train and test sets
First the clean data will be re-split into train and test sets using a 70/30 split. The data will be split on subject in order to get an even distribution of activities in each set.
set.seed(123)
# Convert to factors BEFORE splitting (preserves all levels)
cleanData$activity <- factor(cleanData$activity)
cleanData$subject <- factor(cleanData$subject)
# Get unique subjects
unique_subjects <- unique(cleanData$subject)
# Randomly select 70% of subjects for training
train_subjects <- sample(unique_subjects,
size = floor(0.7 * length(unique_subjects)))
# Split data
cleanData.train <- cleanData[cleanData$subject %in% train_subjects, ]
cleanData.test <- cleanData[!cleanData$subject %in% train_subjects, ]
# Verify factor levels match
identical(levels(cleanData.train$activity), levels(cleanData.test$activity))## [1] TRUE# Should return TRUE- Next the random forest model can be performed on the training data with activity as outcome.
activity.rf <- randomForest(as.factor(cleanData.train$activity)~., data=cleanData.train)- The confusion matrix for the training set shows a 100% accuracy using the random forest model.
training.cm <- confusionMatrix(cleanData.train$activity,
predict(activity.rf, cleanData.train, type="class"))
training.cm[3]## $overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.0000000 1.0000000 0.9994805 1.0000000 0.1887590
## AccuracyPValue McnemarPValue
## 0.0000000 NaNpar(mfrow=c(1,1))
varImpPlot(activity.rf, pch=1, main="Random Forest Model Variables Importance")
- Finally the model is run on the test set to further determine its accuracy.
test.cm <- confusionMatrix(cleanData.test$activity,
predict(activity.rf, cleanData.test,type="class"))
test.cm[3]## $overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9118750 0.8939579 0.9015165 0.9214732 0.1928125
## AccuracyPValue McnemarPValue
## 0.0000000 NaNThe accuracy of the random forests model on the test set is 91.15%. This indicates that it is a good model for predicting activities based on data collected from the smart phones.
Conclusion
This project aimed to demonstrate the importance of create a clean and structured data set in order to perform meaningful analysis. There are a number of other important cleaning techniques that were not required as part of this project but should be considered in any data cleaning task. This includes filling in missing values, correcting erroneous values and standardising.
The clean data set allowed statistic analysis to be performed in a few simple steps with meaningful results. Further steps in this analysis could be to assess the random forests model against other statistical methods.
2. Cleaning data hotel booking company
bookings_df <- read_csv("C:/Users/shafi/OneDrive/Desktop/resume project/project 1/data project 1/hotel_bookings.csv")## Rows: 119390 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (15): hotel, is_canceled, arrival_date_month, meal, country, market_segm...
## dbl (17): lead_time, arrival_date_year, arrival_date_week_number, arrival_da...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(bookings_df) # Using base R## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <chr> <chr> <dbl> <dbl> <chr>
## 1 Resort Hotel no 342 2015 July
## 2 Resort Hotel no 737 2015 July
## 3 Resort Hotel no 7 2015 July
## 4 Resort Hotel no 13 2015 July
## 5 Resort Hotel no 14 2015 July
## 6 Resort Hotel no 14 2015 July
## # ℹ 27 more variables: arrival_date_week_number <dbl>,
## # arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
## # stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
## # meal <chr>, country <chr>, market_segment <chr>,
## # distribution_channel <chr>, is_repeated_guest <dbl>,
## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
## # reserved_room_type <chr>, assigned_room_type <chr>, …bookings_df$is_canceled <- ifelse(bookings_df$is_canceled == "yes", 1, 0)
bookings_df$is_canceled <- as.numeric(bookings_df$is_canceled)
head(bookings_df)## # A tibble: 6 × 32
## hotel is_canceled lead_time arrival_date_year arrival_date_month
## <chr> <dbl> <dbl> <dbl> <chr>
## 1 Resort Hotel 0 342 2015 July
## 2 Resort Hotel 0 737 2015 July
## 3 Resort Hotel 0 7 2015 July
## 4 Resort Hotel 0 13 2015 July
## 5 Resort Hotel 0 14 2015 July
## 6 Resort Hotel 0 14 2015 July
## # ℹ 27 more variables: arrival_date_week_number <dbl>,
## # arrival_date_day_of_month <dbl>, stays_in_weekend_nights <dbl>,
## # stays_in_week_nights <dbl>, adults <dbl>, children <dbl>, babies <dbl>,
## # meal <chr>, country <chr>, market_segment <chr>,
## # distribution_channel <chr>, is_repeated_guest <dbl>,
## # previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
## # reserved_room_type <chr>, assigned_room_type <chr>, …- You can also summarize or preview the data with the str() and glimpse() functions to get a better understanding of the data by running the code chunks below
str(bookings_df)## spc_tbl_ [119,390 × 32] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ hotel : chr [1:119390] "Resort Hotel" "Resort Hotel" "Resort Hotel" "Resort Hotel" ...
## $ is_canceled : num [1:119390] 0 0 0 0 0 0 0 0 1 1 ...
## $ lead_time : num [1:119390] 342 737 7 13 14 14 0 9 85 75 ...
## $ arrival_date_year : num [1:119390] 2015 2015 2015 2015 2015 ...
## $ arrival_date_month : chr [1:119390] "July" "July" "July" "July" ...
## $ arrival_date_week_number : num [1:119390] 27 27 27 27 27 27 27 27 27 27 ...
## $ arrival_date_day_of_month : num [1:119390] 1 1 1 1 1 1 1 1 1 1 ...
## $ stays_in_weekend_nights : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ stays_in_week_nights : num [1:119390] 0 0 1 1 2 2 2 2 3 3 ...
## $ adults : num [1:119390] 2 2 1 1 2 2 2 2 2 2 ...
## $ children : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ babies : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ meal : chr [1:119390] "BB" "BB" "BB" "BB" ...
## $ country : chr [1:119390] "PRT" "PRT" "GBR" "GBR" ...
## $ market_segment : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
## $ distribution_channel : chr [1:119390] "Direct" "Direct" "Direct" "Corporate" ...
## $ is_repeated_guest : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_cancellations : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ previous_bookings_not_canceled: num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ reserved_room_type : chr [1:119390] "C" "C" "A" "A" ...
## $ assigned_room_type : chr [1:119390] "C" "C" "C" "A" ...
## $ booking_changes : num [1:119390] 3 4 0 0 0 0 0 0 0 0 ...
## $ deposit_type : chr [1:119390] "No Deposit" "No Deposit" "No Deposit" "No Deposit" ...
## $ agent : chr [1:119390] "NULL" "NULL" "NULL" "304" ...
## $ company : chr [1:119390] "NULL" "NULL" "NULL" "NULL" ...
## $ days_in_waiting_list : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ customer_type : chr [1:119390] "Transient" "Transient" "Transient" "Transient" ...
## $ adr : num [1:119390] 0 0 75 75 98 ...
## $ required_car_parking_spaces : num [1:119390] 0 0 0 0 0 0 0 0 0 0 ...
## $ total_of_special_requests : num [1:119390] 0 0 0 0 1 1 0 1 1 0 ...
## $ reservation_status : chr [1:119390] "Check-Out" "Check-Out" "Check-Out" "Check-Out" ...
## $ reservation_status_date : chr [1:119390] "7/1/2015" "7/1/2015" "7/2/2015" "7/2/2015" ...
## - attr(*, "spec")=
## .. cols(
## .. hotel = col_character(),
## .. is_canceled = col_character(),
## .. lead_time = col_double(),
## .. arrival_date_year = col_double(),
## .. arrival_date_month = col_character(),
## .. arrival_date_week_number = col_double(),
## .. arrival_date_day_of_month = col_double(),
## .. stays_in_weekend_nights = col_double(),
## .. stays_in_week_nights = col_double(),
## .. adults = col_double(),
## .. children = col_double(),
## .. babies = col_double(),
## .. meal = col_character(),
## .. country = col_character(),
## .. market_segment = col_character(),
## .. distribution_channel = col_character(),
## .. is_repeated_guest = col_double(),
## .. previous_cancellations = col_double(),
## .. previous_bookings_not_canceled = col_double(),
## .. reserved_room_type = col_character(),
## .. assigned_room_type = col_character(),
## .. booking_changes = col_double(),
## .. deposit_type = col_character(),
## .. agent = col_character(),
## .. company = col_character(),
## .. days_in_waiting_list = col_double(),
## .. customer_type = col_character(),
## .. adr = col_double(),
## .. required_car_parking_spaces = col_double(),
## .. total_of_special_requests = col_double(),
## .. reservation_status = col_character(),
## .. reservation_status_date = col_character()
## .. )
## - attr(*, "problems")=<externalptr># You can also use colnames() to check the names of the columns in your data set. Run the code chunk below to find out the column names in this data set:
colnames(bookings_df)## [1] "hotel" "is_canceled"
## [3] "lead_time" "arrival_date_year"
## [5] "arrival_date_month" "arrival_date_week_number"
## [7] "arrival_date_day_of_month" "stays_in_weekend_nights"
## [9] "stays_in_week_nights" "adults"
## [11] "children" "babies"
## [13] "meal" "country"
## [15] "market_segment" "distribution_channel"
## [17] "is_repeated_guest" "previous_cancellations"
## [19] "previous_bookings_not_canceled" "reserved_room_type"
## [21] "assigned_room_type" "booking_changes"
## [23] "deposit_type" "agent"
## [25] "company" "days_in_waiting_list"
## [27] "customer_type" "adr"
## [29] "required_car_parking_spaces" "total_of_special_requests"
## [31] "reservation_status" "reservation_status_date"- Some packages contain more advanced functions for summarizing and exploring your data. One example is the skimr package, which has a number of functions for this purpose. For example, the skim_without_charts() function provides a detailed summary of the data. Try running the code below:
skim_without_charts(bookings_df)| Name | bookings_df |
| Number of rows | 119390 |
| Number of columns | 32 |
| _______________________ | |
| Column type frequency: | |
| character | 14 |
| numeric | 18 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| hotel | 0 | 1 | 10 | 12 | 0 | 2 | 0 |
| arrival_date_month | 0 | 1 | 3 | 9 | 0 | 12 | 0 |
| meal | 0 | 1 | 2 | 9 | 0 | 5 | 0 |
| country | 0 | 1 | 2 | 4 | 0 | 178 | 0 |
| market_segment | 0 | 1 | 6 | 13 | 0 | 8 | 0 |
| distribution_channel | 0 | 1 | 3 | 9 | 0 | 5 | 0 |
| reserved_room_type | 0 | 1 | 1 | 1 | 0 | 10 | 0 |
| assigned_room_type | 0 | 1 | 1 | 1 | 0 | 12 | 0 |
| deposit_type | 0 | 1 | 10 | 10 | 0 | 3 | 0 |
| agent | 0 | 1 | 1 | 4 | 0 | 334 | 0 |
| company | 0 | 1 | 1 | 4 | 0 | 353 | 0 |
| customer_type | 0 | 1 | 5 | 15 | 0 | 4 | 0 |
| reservation_status | 0 | 1 | 7 | 9 | 0 | 3 | 0 |
| reservation_status_date | 0 | 1 | 8 | 10 | 0 | 926 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 |
|---|---|---|---|---|---|---|---|---|---|
| is_canceled | 0 | 1 | 0.37 | 0.48 | 0.00 | 0.00 | 0.00 | 1 | 1 |
| lead_time | 0 | 1 | 104.01 | 106.86 | 0.00 | 18.00 | 69.00 | 160 | 737 |
| arrival_date_year | 0 | 1 | 2016.16 | 0.71 | 2015.00 | 2016.00 | 2016.00 | 2017 | 2017 |
| arrival_date_week_number | 0 | 1 | 27.17 | 13.61 | 1.00 | 16.00 | 28.00 | 38 | 53 |
| arrival_date_day_of_month | 0 | 1 | 15.80 | 8.78 | 1.00 | 8.00 | 16.00 | 23 | 31 |
| stays_in_weekend_nights | 0 | 1 | 0.93 | 1.00 | 0.00 | 0.00 | 1.00 | 2 | 19 |
| stays_in_week_nights | 0 | 1 | 2.50 | 1.91 | 0.00 | 1.00 | 2.00 | 3 | 50 |
| adults | 0 | 1 | 1.86 | 0.58 | 0.00 | 2.00 | 2.00 | 2 | 55 |
| children | 4 | 1 | 0.10 | 0.40 | 0.00 | 0.00 | 0.00 | 0 | 10 |
| babies | 0 | 1 | 0.01 | 0.10 | 0.00 | 0.00 | 0.00 | 0 | 10 |
| is_repeated_guest | 0 | 1 | 0.03 | 0.18 | 0.00 | 0.00 | 0.00 | 0 | 1 |
| previous_cancellations | 0 | 1 | 0.09 | 0.84 | 0.00 | 0.00 | 0.00 | 0 | 26 |
| previous_bookings_not_canceled | 0 | 1 | 0.14 | 1.50 | 0.00 | 0.00 | 0.00 | 0 | 72 |
| booking_changes | 0 | 1 | 0.22 | 0.65 | 0.00 | 0.00 | 0.00 | 0 | 21 |
| days_in_waiting_list | 0 | 1 | 2.32 | 17.59 | 0.00 | 0.00 | 0.00 | 0 | 391 |
| adr | 0 | 1 | 101.83 | 50.54 | -6.38 | 69.29 | 94.58 | 126 | 5400 |
| required_car_parking_spaces | 0 | 1 | 0.06 | 0.25 | 0.00 | 0.00 | 0.00 | 0 | 8 |
| total_of_special_requests | 0 | 1 | 0.57 | 0.79 | 0.00 | 0.00 | 0.00 | 1 | 5 |
# Check total missing values
sum(is.na(bookings_df))## [1] 4# Check missing per column
colSums(is.na(bookings_df))## hotel is_canceled
## 0 0
## lead_time arrival_date_year
## 0 0
## arrival_date_month arrival_date_week_number
## 0 0
## arrival_date_day_of_month stays_in_weekend_nights
## 0 0
## stays_in_week_nights adults
## 0 0
## children babies
## 4 0
## meal country
## 0 0
## market_segment distribution_channel
## 0 0
## is_repeated_guest previous_cancellations
## 0 0
## previous_bookings_not_canceled reserved_room_type
## 0 0
## assigned_room_type booking_changes
## 0 0
## deposit_type agent
## 0 0
## company days_in_waiting_list
## 0 0
## customer_type adr
## 0 0
## required_car_parking_spaces total_of_special_requests
## 0 0
## reservation_status reservation_status_date
## 0 0# Check missing percentage per column
missing_pct <- colSums(is.na(bookings_df)) / nrow(bookings_df) * 100
print(missing_pct)## hotel is_canceled
## 0.000000000 0.000000000
## lead_time arrival_date_year
## 0.000000000 0.000000000
## arrival_date_month arrival_date_week_number
## 0.000000000 0.000000000
## arrival_date_day_of_month stays_in_weekend_nights
## 0.000000000 0.000000000
## stays_in_week_nights adults
## 0.000000000 0.000000000
## children babies
## 0.003350364 0.000000000
## meal country
## 0.000000000 0.000000000
## market_segment distribution_channel
## 0.000000000 0.000000000
## is_repeated_guest previous_cancellations
## 0.000000000 0.000000000
## previous_bookings_not_canceled reserved_room_type
## 0.000000000 0.000000000
## assigned_room_type booking_changes
## 0.000000000 0.000000000
## deposit_type agent
## 0.000000000 0.000000000
## company days_in_waiting_list
## 0.000000000 0.000000000
## customer_type adr
## 0.000000000 0.000000000
## required_car_parking_spaces total_of_special_requests
## 0.000000000 0.000000000
## reservation_status reservation_status_date
## 0.000000000 0.000000000# Remove ALL rows with any NA
cleanData_complete <- na.omit(bookings_df)
colSums(is.na(cleanData_complete))## hotel is_canceled
## 0 0
## lead_time arrival_date_year
## 0 0
## arrival_date_month arrival_date_week_number
## 0 0
## arrival_date_day_of_month stays_in_weekend_nights
## 0 0
## stays_in_week_nights adults
## 0 0
## children babies
## 0 0
## meal country
## 0 0
## market_segment distribution_channel
## 0 0
## is_repeated_guest previous_cancellations
## 0 0
## previous_bookings_not_canceled reserved_room_type
## 0 0
## assigned_room_type booking_changes
## 0 0
## deposit_type agent
## 0 0
## company days_in_waiting_list
## 0 0
## customer_type adr
## 0 0
## required_car_parking_spaces total_of_special_requests
## 0 0
## reservation_status reservation_status_date
## 0 0Now, let’s say you are primarily interested in the following variables: ‘hotel’, ‘is_canceled’, and ‘lead_time’. Create a new data frame with just those columns, calling it trimmed_df by adding the variable names to this code chunk:
trimmed_df <- cleanData_complete %>%
select('hotel','is_canceled','lead_time')
trimmed_df %>%
select(hotel, is_canceled, lead_time) %>%
rename('hotel_type' = hotel)## # A tibble: 119,386 × 3
## hotel_type is_canceled lead_time
## <chr> <dbl> <dbl>
## 1 Resort Hotel 0 342
## 2 Resort Hotel 0 737
## 3 Resort Hotel 0 7
## 4 Resort Hotel 0 13
## 5 Resort Hotel 0 14
## 6 Resort Hotel 0 14
## 7 Resort Hotel 0 0
## 8 Resort Hotel 0 9
## 9 Resort Hotel 1 85
## 10 Resort Hotel 1 75
## # ℹ 119,376 more rows- Another common task is to either split or combine data in different columns. In this example, you can combine the arrival month and year into one column using the unite() function:
example_df <- cleanData_complete %>%
select(arrival_date_year, arrival_date_month) %>%
unite(arrival_month_year, c("arrival_date_month", "arrival_date_year"), sep = " ")