Solutions for Tutorial 1, Advanced Crime Analysis, BSc Security and Crime Science, UCL

## Problem 1: Reading a full year of police data

### Problem outline

When you’re working with open-source data from the police.uk data repository, you will be provided with data per month. In the data/tutorial1/police_data folder, you will find data for each police-recorded crime for Greater Manchester Police from Dec. 2017 - Nov. 2018 as a csv file.

The problem is that each month is a separate file (e.g. 2017-12-... for Dec. 2017, etc.). You want to work with all crimes in one file (e.g. do check for temporal effects) so you’d need to ‘paste’ them together somehow.

One option would be to read all 12 files. However, this is not very useful if the data were to grow (e.g. 100,000 files), so a more ‘generative’ approach is needed.

Read the 12 months of crime data in a way that is extendable to 100,000 files (assuming the same structure). Use only core R functions for this (no packages).

all_files = list.files(path = './data/tutorial1/police_data'
, full.names = T)

# this shows all files in the folder
#We can use a solution with the following parts:
#1. reading all files form the all_files vector
#2. binding these input data.frames together row-wise

#Solution for part 1:

#Do not run this because it will print all dataframes to your screen
# lapply(X = all_files
#        , FUN = function(x){
#          )
#          }
#        )
#Solution for part 2:

?do.call

#We use this function to call the rbind function (binding data row-wise)
#We will rbind the output from the args argument.

big_data_frame = do.call(what = rbind
, args = lapply(X = all_files
, FUN = function(x){
)
}
)
)

head(big_data_frame)
dim(big_data_frame)
## [1] 394017     12

Example: Suppose your new problem is to read all .txt files (from a folder with many different file formats) and bind them together column-wise (i.e. adding a column for each new file).

You can solve this problem by re-using the code since the structure of the problem is the same with a few minor additions:

1. reading a .csv file from a specific path reading a .txt file from a specific path
1. NEW: select files by file format (select only .txt files)
2. repeating that read operation and ‘binding’ the 12 .csv files repeating that read operation for all .txt files and ‘binding’ them
1. finding all files
2. getting the relative paths to the files
3. combining the files column-wise

You find a folder with files where each contains a column of 100 variables at ./data/tutorial1/mixed_file_formats. Read and column-bind only the .txt files.

all_files = list.files(path = './data/tutorial1/mixed_file_formats'
, full.names = T
, pattern = "*.txt")

# this shows all files in the folder
big_data_frame_2 = do.call(what = cbind
, args = lapply(X = all_files
, FUN = function(x){
)
}
)
)

head(big_data_frame_2)
dim(big_data_frame_2)
## [1] 100  26

## Problem 2: Calculation with dates

### Problem outline

In some cases, you might be interested in temporal effects (e.g. how language use develops over time) which might require you to do arithmetic operations with dates (e.g. calculating the difference in minutes between two dates).

In the folder ./data/tutorial1/vlogs_data you can find an .RData file called vlogs_data.RData. This file contains a dataframe with four columns expressing YouTube metadata (the YouTuber’s name, the video URL, number of views and date of the video posting) from the alt-right controverse YouTuber Milos Yiannopulos and popular vlogger Caset Neistat. Suppose you’re interested in comparing the view count: a problem you’d encounter is that view count might be highly correlated to the days the video is on the platform.

Your task is to calculate a new variable (column) that is called view_count_corrected and is equal to the original view count divided by the number of days the video is active. All videos were scraped on the 30th of November 2018 (use this date as the reference date).

#1: load the RData file

head(vlogs_data)
dim(vlogs_data)
## [1] 1520    4
#2: calculate the difference between the data_posted column and the reference date
#Let's creata a variable with the reference date as a "Date" variable in R
reference_date = as.Date('2018-11-30')

#Let's create a new column: days_until_reference
vlogs_data$days_until_reference = as.numeric(reference_date - vlogs_data$date_posted)
## Error in unclass(as.Date(e1)) - e2: non-numeric argument to binary operator

You’ll see that R does return an error here: if you read the error message carefully, you see that it has trouble with the substraction due to different types of variables.

We need to convert the vlogs_data$date_posted column to the Date type too. vlogs_data$date_posted = as.Date(vlogs_data$date_posted) Now try again: vlogs_data$days_until_reference = as.numeric(reference_date - vlogs_data$date_posted) head(vlogs_data) #3. correct the view count vlogs_data$view_count_corrected = vlogs_data$view_count/vlogs_data$days_until_reference
head(vlogs_data)

## Problem 3: Cleaning text data

### Problem outline

Later in this module, you will work with text data. This source of data is one of the most exciting ones but because it is very messy and unstructured. This means that you often have to spend a considerable amount of time to clean the data.

Read the .csv file called messy_text_data.csv from the folder ./data/tutorial1/messy_text. You can see that each row contains two variables: the original string (names of the top 5 most wanted terrorists by the FBI) and the cleaned string. Your task is to reproduce the cleaned string from the original one. You can check your work against then cleaned_string column. Name your output variable cleaned_string_check.

#1. read the file

text_data
#2. identify the "messy characters"

#you can see that it is here only the < and > that are problematic

There are ways that automatically find “non-text”" characters (e.g. in this answer).

#3. remove the < and >
# Install the stringr package!
library(stringr)
text_data$cleaned_string_check = str_replace_all(text_data$original_string, "[><]", "")

#an alternative with base R
#text_data$cleaned_string_check = gsub('[><]', '', text_data$original_string)

#4. test whether the two cleaned columns are identical

text_data$cleaned_string == text_data$cleaned_string_check
## [1] TRUE TRUE TRUE TRUE TRUE

Note: the stringr package is very useful for dealing with text data and we will use it later in this module.

Problem 4: Writing data to individual files

### Problem outline

Another issue you could encounter when dealing with text data (e.g. when sharing text data) is that of writing data “in reverse” (i.e. writing from a dataframe to individual files).

In the folder ./data/tutorial1/vlogs_data_2 you can find the .RData file vlogs_data_with_text.RData. This file loads the dataframe called vlogs_with_text which contains a vlog ID (channel_vlog_id), the video URL (url), and the actual transcript from the vlog. Your task is to write each vlog transcript to a separate .txt file. Each file should contain only the transcript and should have as a file name the channel_vlog_id.

Tip: you’d need to loop through the dataframe and then use the standard function to write a table (write.table(...)).

#1. load the data

#2. find the way to write one text to a file

#this will write the 10th text to a file
write.table(vlogs_with_text$text[10] , file = vlogs_with_text$channel_vlog_id[10]
, col.names = FALSE
, row.names = FALSE
, append=F
)

For more on data output from R, have a look at this neat illustration.

#3. build a loop around the write.table function from above

lapply(seq(nrow(vlogs_with_text)), function(i){
write.table(vlogs_with_text$text[i] , file = vlogs_with_text$channel_vlog_id[i]
, col.names = FALSE
, row.names = FALSE
, append=F
)
})

Note how the code above uses lapply to loop through each row. You could also re-write this with a for loop but lapply generally outperforms the for ... in way on larger datasets.

Problem 5: Transforming wide data to long data

### Problem outline

A common data transformation problem is that of converting wide data to long data (brief explainer). Often, you want to analyse the data using factors instead of different columns. For example, rather than having one column for burglaries and one for violent crimes per city, you’d rather have one column indicating the crime type (which is either burglaries or violent crimes) and another indicating the count. This kind of dataframe representation is in line with Wickhams (2014) idea of tidy data.

Read the .txt file called crime_data.txt from the folder ./data/tutorial1/wide_data. You will notice that the data is in the wide format (i.e. having different columns for burglaries and violent crimes). Your task is to convert this wide data frame to a long dataframe where:

• a new ‘key’ column is created called crime_type
• a new ‘value’ column is created called count
#1. read the data

crime_data
#2. use the tidyr package
library(tidyr)

crime_data_long = gather(data = crime_data
, key = crime_type
, value = count
, burglaries:violentcrimes
, factor_key=TRUE
)

crime_data_long

Note: you will likely use the tidyr package throughout this module, so it’s good to have it installed from the start.

More details on the gather function and its complement spread can be found in this Cookbook for R page.