KoBo Collect - KoBoToolbox - Google Sheet - TSSFL Stack - Data Analysis

[Eli]

KoBoToolbox is a free and open-source suite of tools for field data collection, very suitable for use in challenging environments. This post intends to teach you how to use KoBoToolbox to collect and submit the data to the KoBo platform using an android device and web-based data collection forms. KoBoToolbox offers two major options for data collection, they are, collecting data using KoBoCollect Android App and data collection using Enketo web forms.

Today we are focusing on Collecting Data with KoBoCollect on Android, and also include all available options for which KoBo forms can be used to collect data, and finally import data from KoBoToolbox into a Google spreadsheet. We further show how to use koboextractor Python module to read data directly from KoBoToolbox and visualize them with various charts.

The procedure to achieve data collection using KoBoCollect Android App goes as follows:

1. Go to KoBoToolbox website and create an account. Confirm/activate the account you created by clicking the link sent to your email. Clicking the link will automatically log you in, otherwise, log in here.

2. After logging in, click the New button on the top left corner to create a new form for your project. You will be presented with four options, choose "Build from scratch". Next, fill in the form details as appropriate.



3. Click the plus (+) sign on the left, write a question, and then click "+ADD QUESTION" on the right to choose a response type from the menu. Let's create a simple form that collects a person's name, age, and height.







4. Select settings to choose whether the question is mandatory or not, or to add skip logic -- that's the condition whether to display the next question or not depending on the previous response. Questions can be grouped and skip logic added to them depending on relevance. You can drag and rearrange questions.

5. Apply validation: a condition for which a certain question/response is valid. Include an error message if the response is not acceptable.

6. Finally, deploy your form. There are multiple options to deploy the KoBo form: we will choose the android application option, and follow these instructions:

1. Install KoboCollect on your Android device.
2. Click on three vertical dots (...) to open settings.
3. Enter the server URL https://kc.kobotoolbox.org and your username and password
4. Open "Get Blank Form" and select this project.
5. Open "Enter Data."

Android Application: Configure Kobo Collect, Upload Form, and Collect Data

The whole process is shown pictorially as follows: General Settings -> Server -> Type -> KoBoToolbox, then Get Blank Form, and finally Fill Blank Form:


























Other Options to Collect and Submit Data to KoBoToolbox

A. Online-Offline (multiple submission)

This allows online and offline submissions and is the best option for collecting data in the field:




B. Online-Only (multiple submission)

This is the best option when entering many records at once on a computer, e.g. for transcribing paper records.




C. Online-Only (single submission)

This allows a single submission, and can be paired with the "return_url" parameter to redirect the user to a URL of your choice after the form has been submitted




D. Online-Only (once per respondent)

This allows your web form to only be submitted once per user, using basic protection to prevent the same user (on the same browser & device) from submitting more than once




E. Embeddable version





F. View Only





Importing Data From KoBoToolbox Into Google Sheet

We first need to install API Connector: https://gsuite.google.com/marketplace/a ... 5804724197 in order to be able to connect and import data from APIs to Google Sheets. After that, then we can follow these steps:

1. Get your KoBo API key by navigating to https://kf.kobotoolbox.org/token/. This is a string in a double quote that follows after "token":

2. Create your KoBo API request URL, currently the URL is: https://kf.kobotoolbox.org/api/v2/assets.json


3. Pull KoBo API Data Into Google Sheet:

- Open up Google Sheets and click Add-ons > API Connector > Open.
- In the Create screen, enter the API Request URL we just created in 2 above



4. Under Headers, enter two sets of key-value pair like this:

- Authorization: Token Your_API_Token
- Accept: application/json

Only replace YOUR_API_TOKEN with the token you got in 1 above.




5. We don’t need OAuth2 authentication so just leave that set to None. Create a new tab and click ‘Set current’ to use that tab as your data destination.

6. Name your request and click Run. A moment later you’ll see a list of your Kobo assets (forms/projects, questions, blocks, templates, collections) populate your sheet.



We named our request Import From KOBO and this is what we get:




See reference for importing data into Google sheet. See also discussion on KoBo Community.

We can download our data from KoBoToolbox, for example in CSV format, upload it to DropBox, and then retrieve the data for analysis with TSSFL Stack.

[Eli]

See how the deployed forms look like:

[Eli]

We can read KoBo data saved in DropBox as below:

Code: [Select all] [Expand/Collapse]

1. import urllib.request
2. import pandas as pd
3.  
4. urllib.request.urlretrieve('https://www.dropbox.com/s/ydvnsh33sf9rq5e/KoBo.csv?dl=1', 'KoBo.csv')
5.  
6. df = pd.read_csv("KoBo.csv")
7. #Renaming columns: https://stackoverflow.com/questions/11346283/renaming-column-names-in-pandas
8. #df2 = df.rename({'What is your name?': 'Name', 'How old are you?': 'Age', 'How tall are you?': 'Height'}, axis=1)  # new method
9. #df.columns = df.columns.str.replace('What is your name?', 'Name')
10. print(df)

[Eli]

Let's visualize the Google Sheet data below imported from KoBoToolbox:

Code: [Select all] [Expand/Collapse]

1. #Plot some graph
2. #Import required libraries
3. import gspread
4. import urllib.request
5. import numpy as np
6. import matplotlib.pyplot as plt
7. import seaborn as sns
8. import pandas as pd
9.  
10. """
11. urllib.request.urlretrieve("https://www.dropbox.com/s/mqsyfuetv8potvd/credentials.json?dl=1", "credentials.json")
12.  
13. gc = gspread.service_account(filename="credentials.json")
14. sh = gc.open_by_key("1BC48PKPZW71AC6hOn1SNesvZk1PoBjE4wXwl1YlWFpY") #Open spreadsheet,
15. #the spreadsheet ID starts with 1019ke.... between "" in the line above
16. """
17. #Alternative
18. #If your file only has one sheet, replace sheet_url
19. sheet_url = "https://docs.google.com/spreadsheets/d/1GQk7WzixWPs7iyEwAEm63cMcW6dDX_MzEq0eUKucgEA/edit#gid=0"
20. url_1 = sheet_url.replace('/edit#gid=', '/export?format=csv&gid=')
21.  
22. data = pd.read_csv(url_1)
23.  
24. #worksheet = sh.sheet1
25.  
26. #Define variables
27. var1 = "results.Name"
28. var2 = "results.Age"
29. var3 = "results.Height"
30.  
31. #age = worksheet.col_values(3)[1:]
32. age = data[var2]
33. print("Ages:", age)
34. #height = worksheet.col_values(4)[1:]
35. height = data[var3]
36. print("Heights:", height)
37.  
38. #Pandas is extremely very useful for Google spreadsheets
39. #Convert the json to Pandas dataframe
40. #Get all data records as dictionary
41. #data = worksheet.get_all_records()
42. #df = pd.DataFrame.from_dict(data)
43.  
44. #Let's get some statistics
45. #age_arr = np.array(age)
46. #age_array = age_arr.astype(float)
47. #h_arr = np.array(height)
48. #h_array = h_arr.astype(float)
49.  
50. print("Average Age:", np.mean(age))
51. print("Mean Height:", np.mean(height))
52. print("Minimum and Maximum Age:", np.min(age), np.max(age))
53. print("Minimum and Maximum Height:", np.min(height), np.max(height))
54.  
55. #Let's visualize
56. #Graph styles and font size
57. sns.set_style('darkgrid') # darkgrid, white grid, dark, white and ticks
58. plt.rc('axes', titlesize=18)     # fontsize of the axes title
59. plt.rc('axes', labelsize=14)    # fontsize of the x and y labels
60. plt.rc('xtick', labelsize=13)    # fontsize of the tick labels
61. plt.rc('ytick', labelsize=13)    # fontsize of the tick labels
62. plt.rc('legend', fontsize=13)    # legend fontsize
63. plt.rc('font', size=13)          # controls default text sizes
64.  
65. #sns list of color plettes
66. #print(sns.color_palette('deep'), sns.color_palette("pastel"), sns.color_palette("Set2"))
67.  
68. #Let's Read Data from Google Sheets into Pandas without the Google Sheets API
69. #Useful for multiple sheets
70. sheet_id = "1GQk7WzixWPs7iyEwAEm63cMcW6dDX_MzEq0eUKucgEA"
71. sheet_name = "Sheet1"
72. url = f"https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}"
73.  
74. #If your file only has one sheet, replace sheet_url
75. #sheet_url = “https://docs.google.com/spreadsheets/d/1XqOtPkiE_Q0dfGSoyxrH730RkwrTczcRbDeJJpqRByQ/edit#gid=0"
76. #url_1 = sheet_url.replace(‘/edit#gid=’, ‘/export?format=csv&gid=’)
77.  
78. #Get Pandas dataframe
79. dataset = pd.read_csv(url)
80. #print(dataset)
81.  
82. #Names = worksheet.col_values(2)[1:]
83. #Names = data[var1]
84. #print(Names)
85.  
86. df_names = dataset[var1]
87. df_ages = dataset[var2]
88. df_heights = dataset[var3]
89. print(df_names)
90.  
91. #Preprocessing
92. plots = dataset.groupby([var1], as_index=False).mean()
93. #print(plots)
94.  
95. #Bar Plot in MatplotLib with plt.bar()
96. #Names vs Age
97. plt.figure(figsize=(10,5), tight_layout=True)
98. colors = sns.color_palette('pastel')
99. plt.bar(dataset[var1], dataset[var2], color=colors[:5])
100. plt.xlabel(var1)
101. plt.xticks(rotation=90)
102. plt.ylabel('Age')
103. plt.title('Barplot')
104. plt.show()
105.  
106. #Name Vs Height
107. plt.figure()
108. plt.figure(figsize=(10,5), tight_layout=True)
109. colors = sns.color_palette('deep')
110. plt.bar(dataset[var1], dataset[var3], color=colors[:6])
111. plt.xlabel(var1)
112. plt.xticks(rotation=90)
113. plt.ylabel('Height')
114. plt.title('Barplot')
115. plt.show()
116.  
117. #Bar Plot in Seaborn with sns.barplot()
118. plt.figure(figsize=(10,5), tight_layout=True)
119. ax = sns.barplot(x=dataset[var1], y=dataset[var2], palette='pastel', ci=None)
120. ax.set(title='Barplot with Seaborn', xlabel='Names', ylabel='Age')
121. plt.xticks(rotation=90)
122. plt.show()
123.  
124. #Barplot grouped data by "n" variables
125. plt.figure(figsize=(12, 6), tight_layout=True)
126. ax = sns.barplot(x=dataset[var2], y=dataset[var3], hue=dataset[var1], palette='pastel')
127. ax.set(title='Age vs Height' ,xlabel='Age', ylabel='Height')
128. ax.legend(title='Names', title_fontsize='13', loc='upper right')
129. plt.show()
130.  
131. #Histograms with plt.hist() or sns.histplot()
132. plt.figure(figsize=(10,6), tight_layout=True)
133. bins = [160, 165, 170, 175, 180, 185, 190, 195, 200]
134. # matplotlib
135. plt.hist(dataset[var3], bins=bins, color=sns.color_palette('Set2')[2], linewidth=2)
136. plt.title('Histogram')
137. plt.xlabel('Height (cm)')
138. plt.ylabel('Count')
139. # seaborn
140. ax = sns.histplot(data=dataset, x=var3, bins=bins, color=sns.color_palette('Set2')[2], linewidth=2)
141. ax.set(title='Histogram', xlabel='Height (cm)', ylabel='Count')
142. plt.show()
143.  
144. #Boxplot
145. plt.figure(figsize=(10,6), tight_layout=True)
146. ax = sns.boxplot(data=dataset, x=var1, y=var2, palette='Set2', linewidth=2.5)
147. ax.set(title='Boxplot', xlabel='Names', ylabel='Age (Years)')
148. plt.xticks(rotation=90)
149. plt.show()
150.  
151. #Scatter plot
152. plt.figure(figsize=(10,6), tight_layout=True)
153. ax = sns.scatterplot(data=dataset, x=var2, y=var3,   hue=var1, palette='Set2', s=60)
154. ax.set(xlabel='Age (Years)', ylabel='Height (cm)')
155. ax.legend(title='People', title_fontsize = 12)
156. plt.show()
157.  
158. #Something else
159. pivot = dataset.groupby([var1], as_index=False).mean()
160. relationship = pivot.loc[:,var2:var3]
161. print(relationship)
162.  
163. #Plot some graph
164. charts = ["bar", "line", "barh", "hist", "box", "kde", "density", "area"]
165. for chart_type in charts:
166.     relationship.plot(kind="%s" % chart_type) #Replace bar with line, barh, hist, box, kde, density, area
167.     plt.title("%s plot" % chart_type)
168.     plt.show()
169.  
170. #Seaborn
171. plt.figure()
172. sns.set_style("darkgrid")
173. sns.lineplot(data = dataset, x = var2, y = var3)
174. plt.show()
175.  
176. plt.figure()
177. sns.set_style("whitegrid")
178. sns.lineplot(data = dataset, x = var2, y = var3)
179. plt.show()
180.  
181. #Hexbin
182. #Split the plotting window into 20 hexbins
183. plt.figure()
184. nbins = 20
185. plt.title('Hexbin')
186. plt.hexbin(dataset[var2], dataset[var3], gridsize=nbins, color=colors[:6])
187. plt.show()
188.  
189. #2-D Hist
190. plt.figure()
191. plt.title('2-D Histogram')
192. plt.hist2d(dataset[var2], dataset[var3], bins=nbins, color=colors[:5])
193. plt.show()
194.  
195. #Set variables
196. x = dataset[var2]
197. y = dataset[var3]
198. z = dataset[var1]
199.  
200. #Linear Regression
201. plt.figure()
202. sns.regplot(x = x, y = y, data=dataset);
203. plt.show()
204.  
205. plt.figure()
206. sns.jointplot(x=x, y=y, data=dataset, kind="reg");
207. plt.show()
208.  
209. #Set seaborn style
210. sns.set_style("white")
211.  
212. # Basic 2D density plot
213. plt.figure()
214. sns.kdeplot(x=x, y=y)
215. plt.show()
216.  
217. # Custom the color, add shade and bandwidth
218. plt.figure()
219. sns.kdeplot(x=x, y=y, cmap="Reds", shade=True, bw_adjust=.5)
220. plt.show()
221.  
222. # Add thresh parameter
223. plt.figure()
224. sns.kdeplot(x=x, y=y, cmap="Blues", shade=True, thresh=0)
225. plt.show()
226.  
227. #Joint plot
228. plt.figure()
229. sns.jointplot(x = x,y = y,data = dataset,kind = 'hex')
230. plt.show()

[Eli]

Get specific data of the ith asset in your KoBoToolbox account by creating a KoBo API request using a URL of the form https://kf.kobotoolbox.org/api/v2/assets/xxx/data.json where xxx is some string id with the API Connector:

[Eli]

We use koboextract Python module to extract the list of assests and data from KoBotoolbox, and convert kobo data into Pandas DataFrame. We can then process, analyze and visualize the data. This only requires to replace "KoBo_Token" with a valid KoBotoolbox API Key.

Code: [Select all] [Expand/Collapse]

1. from koboextractor import KoboExtractor #https://pypi.org/project/koboextractor/
2. kobo = KoboExtractor("KoBo_Token", 'https://kf.kobotoolbox.org/api/v2', debug=True)
3.  
4. #Get the unique ID of the ith asset in your KoBoToolbox account:
5. i = 1
6. assets = kobo.list_assets()
7. asset_uid = assets['results'][i]['uid']
8.  
9. #Get the list of assets
10. asset = kobo.get_asset(asset_uid)
11. #choice_lists = kobo.get_choices(asset)
12. #questions = kobo.get_questions(asset=asset, unpack_multiples=True)
13.  
14. #Download all responses submitted after a certain point in time:
15. #new_data = kobo.get_data(asset_uid, submitted_after='2020-05-20T17:29:30')
16. #Get data
17. new_data = kobo.get_data(asset_uid)
18.  
19. #new_data will be an unordered list of form submissions
20. #We can sort this list by submission time by calling:
21.  
22. new_results = kobo.sort_results_by_time(new_data['results'])
23. #print(new_results)
24. import pandas as pd
25. # Create DataFrame
26. df = pd.DataFrame(new_results)
27. print("DataFrame:", df.head())
28. print("KoBo Data Column Names:", df.columns)

[Eli]

The code below does the following: Read data directly from KoBotoolbox, convert the data into pandas dataframe, select a subset of pandas dataframe and upload/paste it into the "imported_from_kobo" sheet of the Kobo_Android_Data Google Spreadsheet below, plot and visualize charts for a select dataset:

Code: [Select all] [Expand/Collapse]

1. import gspread
2. import numpy as np
3. from koboextractor import KoboExtractor #https://pypi.org/project/koboextractor/
4. kobo = KoboExtractor("KoBo_Token", 'https://kf.kobotoolbox.org/api/v2', debug=True)
5.  
6. #Get the unique ID of the ith asset in your KoBoToolbox account:
7. i = 1
8. assets = kobo.list_assets()
9. asset_uid = assets['results'][i]['uid']
10.  
11. #Get the list of assets
12. asset = kobo.get_asset(asset_uid)
13. #choice_lists = kobo.get_choices(asset)
14. #questions = kobo.get_questions(asset=asset, unpack_multiples=True)
15.  
16. #Download all responses submitted after a certain point in time:
17. #new_data = kobo.get_data(asset_uid, submitted_after='2020-05-20T17:29:30')
18. #Get data
19. new_data = kobo.get_data(asset_uid)
20.  
21. #new_data will be an unordered list of form submissions
22. #We can sort this list by submission time by calling:
23.  
24. new_results = kobo.sort_results_by_time(new_data['results'])
25. #print(new_results)
26. import pandas as pd
27. # Create DataFrame
28. df = pd.DataFrame(new_results)
29. print("DataFrame:", df.head())
30. print("KoBo Data Column Names:", df.columns)
31.  
32. #Slice a dataframe
33. dataset = df[["Name", "Age", "Height"]]
34. #print("df:", df["Age"]=="40")
35.  
36. #Convert just columns "Age" and "Height" data types to numerics
37. df[["Age", "Height"]] = df[["Age", "Height"]].apply(pd.to_numeric)
38. print("df:", df["Age"]==40)
39. print("df:", df)
40.  
41. #df = df.iloc[:, 2:9]
42.  
43. #Select multiple ranges of columns in Pandas DataFrame
44. df = df.iloc[:, np.r_[2:9, 12:13, 14, 16]]
45.  
46. import urllib.request
47. urllib.request.urlretrieve("https://www.dropbox.com/s/m728v370159b2xm/credentials.json?dl=1", "credentials.json")
48.  
49. gc = gspread.service_account(filename="credentials.json")
50.  
51. wb = gc.open_by_key("1GQk7WzixWPs7iyEwAEm63cMcW6dDX_MzEq0eUKucgEA")
52.  
53. new_sheet = wb.worksheet("imported_from_kobo")
54.  
55. new_sheet.update([df.columns.values.tolist()] + df.values.tolist())
56.  
57.  
58. var1 = "Name"
59. var2 = "Age"
60. var3 = "Height"
61.  
62. #Run code
63. #Plot some graph
64. #Import required libraries
65. import gspread
66. import urllib.request
67. import numpy as np
68. import matplotlib.pyplot as plt
69. import seaborn as sns
70. import pandas as pd
71.  
72. age = df[var2]
73. print("Ages:", age)
74. height = df[var3]
75. print("Heights:", height)
76.  
77. print("Average Age:", np.mean(age))
78. print("Mean Height:", np.mean(height))
79. print("Minimum and Maximum Age:", np.min(age), np.max(age))
80. print("Minimum and Maximum Height:", np.min(height), np.max(height))
81.  
82. #Let's visualize
83. #Graph styles and font size
84. sns.set_style('darkgrid') # darkgrid, white grid, dark, white and ticks
85. plt.rc('axes', titlesize=18)     # fontsize of the axes title
86. plt.rc('axes', labelsize=14)    # fontsize of the x and y labels
87. plt.rc('xtick', labelsize=13)    # fontsize of the tick labels
88. plt.rc('ytick', labelsize=13)    # fontsize of the tick labels
89. plt.rc('legend', fontsize=13)    # legend fontsize
90. plt.rc('font', size=13)          # controls default text sizes
91.  
92. #sns list of color plettes
93. #print(sns.color_palette('deep'), sns.color_palette("pastel"), sns.color_palette("Set2"))
94.  
95. df_names = df[var1]
96. df_ages = df[var2]
97. df_heights = df[var3]
98. print(df_names)
99.  
100. #Preprocessing
101. plots = df.groupby([var1], as_index=False).mean()
102. #print(plots)
103.  
104. #Bar Plot in MatplotLib with plt.bar()
105. #Names vs Age
106. plt.figure(figsize=(10,5), tight_layout=True)
107. colors = sns.color_palette('pastel')
108. plt.bar(df[var1], df[var2], color=colors[:5])
109. plt.xlabel(var1)
110. plt.xticks(rotation=90)
111. plt.ylabel('Age')
112. plt.title('Barplot')
113. plt.show()
114.  
115. #Name Vs Height
116. plt.figure()
117. plt.figure(figsize=(10,5), tight_layout=True)
118. colors = sns.color_palette('deep')
119. plt.bar(df[var1], df[var3], color=colors[:6])
120. plt.xlabel(var1)
121. plt.xticks(rotation=90)
122. plt.ylabel('Height')
123. plt.title('Barplot')
124. plt.show()
125.  
126. print(df[var1], df[var2])
127.  
128. #Bar Plot in Seaborn with sns.barplot()
129. plt.figure(figsize=(10,5), tight_layout=True)
130. ax = sns.barplot(x=df[var1].astype("category"), y=df[var2].astype(np.float32), palette='pastel', ci=None)
131. ax.set(title='Barplot with Seaborn', xlabel='Names', ylabel='Age')
132. plt.xticks(rotation=90)
133. plt.show()
134.  
135. #Barplot grouped data by "n" variables
136. plt.figure(figsize=(12, 6), tight_layout=True)
137. ax = sns.barplot(x=df[var2].astype(np.float32), y=df[var3].astype(np.float32), hue=df[var1].astype("category"), palette='pastel')
138. ax.set(title='Age vs Height' ,xlabel='Age', ylabel='Height')
139. ax.legend(title='Names', title_fontsize='13', loc='upper right')
140. plt.show()
141. plt.clf()
142.  
143. #Histograms with plt.hist() or sns.histplot()
144. plt.figure(figsize=(10,6), tight_layout=True)
145. bins = [160, 165, 170, 175, 180, 185, 190, 195, 200]
146.  
147. # matplotlib
148. plt.hist(df[var3], bins=bins, color=sns.color_palette('Set2')[2], linewidth=2)
149. plt.title('Histogram')
150. plt.xlabel('Height (cm)')
151. plt.ylabel('Count')
152. # seaborn
153. ax = sns.histplot(data=df, x=var3, bins=bins, color=sns.color_palette('Set2')[2], linewidth=2)
154. ax.set(title='Histogram', xlabel='Height (cm)', ylabel='Count')
155. plt.show()
156. plt.clf()
157.  
158. #Boxplot
159. plt.figure(figsize=(10,6), tight_layout=True)
160. ax = sns.boxplot(data=df, x=var1, y=var2, palette='Set2', linewidth=2.5)
161. ax.set(title='Boxplot', xlabel='Names', ylabel='Age (Years)')
162. plt.xticks(rotation=90)
163. plt.show()
164.  
165. #Scatter plot
166. plt.figure(figsize=(10,6), tight_layout=True)
167. ax = sns.scatterplot(data=df, x=var2, y=var3,   hue=var1, palette='Set2', s=60)
168. ax.set(xlabel='Age (Years)', ylabel='Height (cm)')
169. ax.legend(title='People', title_fontsize = 12)
170. plt.show()
171.  
172. #Something else
173. pivot = df.groupby([var1], as_index=False).mean()
174. relationship = pivot.loc[:,var2:var3]
175. print(relationship)
176.  
177. #Plot some graph
178. charts = ["bar", "line", "barh", "hist", "box", "kde", "density", "area"]
179. for chart_type in charts:
180.     relationship.plot(kind="%s" % chart_type) #Replace bar with line, barh, hist, box, kde, density, area
181.     plt.title("%s plot" % chart_type)
182.     plt.show()
183.  
184. #Seaborn
185. plt.figure()
186. sns.set_style("darkgrid")
187. sns.lineplot(data = df, x = var2, y = var3)
188. plt.show()
189.  
190. plt.figure()
191. sns.set_style("whitegrid")
192. sns.lineplot(data = df, x = var2, y = var3)
193. plt.show()
194.  
195. #Hexbin
196. #Split the plotting window into 20 hexbins
197. plt.figure()
198. nbins = 20
199. plt.title('Hexbin')
200. plt.hexbin(df[var2], df[var3], gridsize=nbins, color=colors[:6])
201. plt.show()
202.  
203. #2-D Hist
204. plt.figure()
205. plt.title('2-D Histogram')
206. plt.hist2d(df[var2], df[var3], bins=nbins, color=colors[:5])
207. plt.show()
208.  
209. #Set variables
210. x = df[var2]
211. y = df[var3]
212. z = df[var1]
213.  
214. #Linear Regression
215. plt.figure()
216. sns.regplot(x = x, y = y, data=df);
217. plt.show()
218.  
219. plt.figure()
220. sns.jointplot(x=x, y=y, data=df, kind="reg");
221. plt.show()
222.  
223. #Set seaborn style
224. sns.set_style("white")
225.  
226. # Basic 2D density plot
227. plt.figure()
228. sns.kdeplot(x=x, y=y)
229. plt.show()
230.  
231. # Custom the color, add shade and bandwidth
232. plt.figure()
233. sns.kdeplot(x=x, y=y, cmap="Reds", shade=True, bw_adjust=.5)
234. plt.show()
235.  
236. # Add thresh parameter
237. plt.figure()
238. sns.kdeplot(x=x, y=y, cmap="Blues", shade=True, thresh=0)
239. plt.show()
240.  
241. #Joint plot
242. plt.figure()
243. sns.jointplot(x = x,y = y,data = df,kind = 'hex')
244. plt.show()

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Mobile data collection using KoBo Collect and KoBoToolbox platform

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