Logistic models
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Exercise 1: Using mutate()
Reminder:
\[
yoy\_growth = \frac{level_t - level_{t-4}}{level_{t-4}}
\]
{"language":"r","pre_exercise_code":"df <- data.frame(tic=rep(\"WMT\",194),\n                 fyearq=c(1970,1970,1970,1970,1971,1971,1971,1971,1972,1972,1972,1972,1973,1973,1973,1973,1974,1974,1974,1974,1975,1975,1975,1975,1976,1976,1976,1976,1977,1977,1977,1977,1978,1978,1978,1978,1979,1979,1979,1979,1980,1980,1980,1980,1981,1981,1981,1981,1982,1982,1982,1982,1983,1983,1983,1983,1984,1984,1984,1984,1985,1985,1985,1985,1986,1986,1986,1986,1987,1987,1987,1987,1988,1988,1988,1988,1989,1989,1989,1989,1990,1990,1990,1990,1991,1991,1991,1991,1992,1992,1992,1992,1993,1993,1993,1993,1994,1994,1994,1994,1995,1995,1995,1995,1996,1996,1996,1996,1997,1997,1997,1997,1998,1998,1998,1998,1999,1999,1999,1999,2000,2000,2000,2000,2001,2001,2001,2001,2002,2002,2002,2002,2003,2003,2003,2003,2004,2004,2004,2004,2005,2005,2005,2005,2006,2006,2006,2006,2007,2007,2007,2007,2008,2008,2008,2008,2009,2009,2009,2009,2010,2010,2010,2010,2011,2011,2011,2011,2012,2012,2012,2012,2013,2013,2013,2013,2014,2014,2014,2014,2015,2015,2015,2015,2016,2016,2016,2016,2017,2017,2017,2017,2018,2018),\n                 fqtr=c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2),\n                 revtq=c(7.267,9.939,11.47,15.61,12.758,18.207,19.205,27.844,21.011,29.918,32.683,41.277,32.737,37.655,42.098,55.071,46.355,56.177,59.462,74.215,64.65,78.852,86.108,110.721,93.971,115.186,121.875,147.775,122.655,152.381,172.637,230.783,167.274,204.085,221.982,306.957,238.569,291.685,300.747,417.175,314.575,372.598,401.842,554.184,434.251,533.653,634.476,842.617,665.229,788.705,827.767,1094.551,854.734,1098.942,1166.594,1546.639,1234.768,1508.533,1583.573,2073.983,1655.661,1934.358,2087.533,2773.936,2344.253,2770.276,2949.008,3845.557,3211.761,3732.237,4015.017,5000.238,4282.012,4845.133,4991.113,6530.734,5373.258,6046.41,6283.496,8107.484,6768.191,7543.508,7930.949,10358.898,9280.566,10339.898,10627.5,13638.797,11649.398,13028.398,13683.797,17122,13920.398,16236.5,16826.898,20360.5,17686.098,19942.297,20417.699,24447.797,20440,22723,22914,27550,22772,25587,25644,30856,25409,28386,28777,35386,29819,33521,33509,40785,35129,38913,40899,51868,43447,46588,46181,57079,48565,53187,53185,64735,52126,56781,55765,66905,57224,63231,63036,75190,65434,70516,69282,82900,70755,76697,75397,89252,79675,85430,84467,99078,86378,92827,91794,107289,94940,102342,98345,108747,94214,100876,99373,113622,99811,103726,101952,116360,104189,109366,110226,123169,113010,114065,113800,127776,114070,116830,115688,129706,114960,120125,119001,131565,114826,120229,117408,129667,115904,120319,118179,130936,117542,122968,123179,136267,122690,128028),\n                 recession=c(1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))","sample":"# The dataframe below contains historical revenue for Walmart since 1970\n# All data is quarterly\n# Recession data comes from the US Federal Reserve\n# The file uses Compustat's nomenclature:\n#     - revenue is revtq\n#     - ticker is tic\n#     - fyearq is year\n#     - fqtr is quarter\n#     - recession is an indicator for US recessions\ndf[1,5]\n\nlibrary(dplyr) # loads dplyr for data manipulation\n\n# Create a variable for year over year revenue growth by quarter\n# Store the result in a data frame column named revtq_yoy\n\n\n# Create an indicator equal to 1 if revtq_yoy is less than 0, and 0 otherwise\n# Using ifelse makes this easy to do\n# Store the result in a data frame column named revtq_down\n\n\n# Create a lag of recession, call it recession_lag\n\n\n#View the output\nhead(df)\n\n#END","solution":"# The dataframe below contains historical revenue for Walmart since 1970\n# All data is quarterly\n# Recession data comes from the US Federal Reserve\n# The file uses Compustat's nomenclature:\n#     - revenue is revtq\n#     - ticker is tic\n#     - fyearq is year\n#     - fqtr is quarter\n#     - recession is an indicator for US recessions\ndf[1,5]\n\nlibrary(dplyr) # loads dplyr for data manipulation\n\n# Create a variable for year over year revenue growth by quarter\n# Store the result in a data frame column named revtq_yoy\ndf <- df %>% mutate(revtq_yoy = (revtq - lag(revtq, 4)) / lag(revtq, 4))\n\n# Create an indicator equal to 1 if revtq_yoy is less than 0, and 0 otherwise\n# Store the result in a data frame column named revtq_down\ndf <- df %>% mutate(revtq_down = ifelse(revtq_yoy<0,1,0))\n\n# Create a 1 quarter lag of recession, call it recession_lag\ndf <- df %>% mutate(recession_lag = lag(recession))\n\n#View the output\nhead(df)\n\n#END","sct":"# Template based on https://www.rdocumentation.org/packages/testwhat/versions/4.1.1\n# Check if something is explicitly typed\n\ntest_student_typed(\"<-\", not_typed_msg='Did you remember to store the results back into the data frame?')\ntest_expression_output(\"'revtq_yoy' %in% colnames(df)\", incorrect_msg=\"Did you remember to save the output to the dataframe, and to name the column `revtq_yoy`?\")\ntest_expression_output(\"df$revtq_yoy\", incorrect_msg=\"It looks like either the column name is wrong or the calculation is wrong for `revtq_yoy`.\")\n\ntest_expression_output(\"'revtq_down' %in% colnames(df)\", incorrect_msg=\"Did you remember to save the output to the dataframe, and to name the column `revtq_down`?\")\ntest_expression_output(\"df$revtq_down\", incorrect_msg=\"It looks like either the column name is wrong or the calculation is wrong for `revtq_down`.\")\n\ntest_expression_output(\"'recession_lag' %in% colnames(df)\", incorrect_msg=\"Did you remember to save the output to the dataframe, and to name the column `recession_lag`?\")\ntest_expression_output(\"df$recession_lag\", incorrect_msg=\"It looks like either the column name is wrong or the calculation is wrong for `recession_lag`.\")\ntest_student_typed('head(df)', not_typed_msg='Don\\'t remove the code `head(df)`.')\n\n# test_student_typed('x <- 2', not_typed_msg='')\n\n# Check if function was used in input code\n# test_function('c',incorrect_msg='')  \n\n# Requires an object `x` to have the same value as the solution\n# test_object(\"x\",incorrect_msg = \"\",undefined_msg = \"\")  \n\n# Requires an onject with the same value of `x` in the solution\n# test_an_object(\"x\",undefined_msg=\"\")\n\n# Checks if output of student's code contains given evaluated expression\n# test_output_contains(\"x\",incorrect_msg = \"\")\n\n# Check if a vector of predefined objects are unchanged\n# test_predefined_objects(c('x','y'),incorrect_msg=\"Don't onverwrite the predefined variables\")\n\n# Checks for a regex pattern in trhe output\n# test_output_regex(pattern,fixed=F, times=1, incorrect_msg='')\n\n# Can check an arbitrary expression across both solution and student code\n#test_expression_output(\"typeof(company_name)\", incorrect_msg=\"Did you store textual data in `company_name`?\")\n\ntest_error()\nsuccess_msg(\"Awesome!\")\n\n# Other functions to note:\n#     - test_or(a,b) -- checks if either test a or test b pass\n#     - test_ggplot() -- can check if plots are correct\n#     - test_function() -- can also check included parameters\n#     - test_loop() -- checking for and while loops\n#     - test_library_function('package', not_called_msg='',incorrect_msg='')\n#     - test_if_else() -- checking if statements\n#     - test_expression_error() -- can check if functions are properly defined\n#     - test_operator('operator',), not_called_msg='',incorrect_msg='')\n#     - test_function_definition() -- rigorously check defined function\n#     - test_data_frame() -- check if dataframe [columns] are equivalent\n#     - test_function_result, test_expression_result"}
Exercise 2: Running a logit regression
{"language":"r","pre_exercise_code":"library(dplyr) # loads dplyr for data manipulation\ndf <- data.frame(tic=rep(\"WMT\",194),\n                 fyearq=c(1970,1970,1970,1970,1971,1971,1971,1971,1972,1972,1972,1972,1973,1973,1973,1973,1974,1974,1974,1974,1975,1975,1975,1975,1976,1976,1976,1976,1977,1977,1977,1977,1978,1978,1978,1978,1979,1979,1979,1979,1980,1980,1980,1980,1981,1981,1981,1981,1982,1982,1982,1982,1983,1983,1983,1983,1984,1984,1984,1984,1985,1985,1985,1985,1986,1986,1986,1986,1987,1987,1987,1987,1988,1988,1988,1988,1989,1989,1989,1989,1990,1990,1990,1990,1991,1991,1991,1991,1992,1992,1992,1992,1993,1993,1993,1993,1994,1994,1994,1994,1995,1995,1995,1995,1996,1996,1996,1996,1997,1997,1997,1997,1998,1998,1998,1998,1999,1999,1999,1999,2000,2000,2000,2000,2001,2001,2001,2001,2002,2002,2002,2002,2003,2003,2003,2003,2004,2004,2004,2004,2005,2005,2005,2005,2006,2006,2006,2006,2007,2007,2007,2007,2008,2008,2008,2008,2009,2009,2009,2009,2010,2010,2010,2010,2011,2011,2011,2011,2012,2012,2012,2012,2013,2013,2013,2013,2014,2014,2014,2014,2015,2015,2015,2015,2016,2016,2016,2016,2017,2017,2017,2017,2018,2018),\n                 fqtr=c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2),\n                 revtq=c(7.267,9.939,11.47,15.61,12.758,18.207,19.205,27.844,21.011,29.918,32.683,41.277,32.737,37.655,42.098,55.071,46.355,56.177,59.462,74.215,64.65,78.852,86.108,110.721,93.971,115.186,121.875,147.775,122.655,152.381,172.637,230.783,167.274,204.085,221.982,306.957,238.569,291.685,300.747,417.175,314.575,372.598,401.842,554.184,434.251,533.653,634.476,842.617,665.229,788.705,827.767,1094.551,854.734,1098.942,1166.594,1546.639,1234.768,1508.533,1583.573,2073.983,1655.661,1934.358,2087.533,2773.936,2344.253,2770.276,2949.008,3845.557,3211.761,3732.237,4015.017,5000.238,4282.012,4845.133,4991.113,6530.734,5373.258,6046.41,6283.496,8107.484,6768.191,7543.508,7930.949,10358.898,9280.566,10339.898,10627.5,13638.797,11649.398,13028.398,13683.797,17122,13920.398,16236.5,16826.898,20360.5,17686.098,19942.297,20417.699,24447.797,20440,22723,22914,27550,22772,25587,25644,30856,25409,28386,28777,35386,29819,33521,33509,40785,35129,38913,40899,51868,43447,46588,46181,57079,48565,53187,53185,64735,52126,56781,55765,66905,57224,63231,63036,75190,65434,70516,69282,82900,70755,76697,75397,89252,79675,85430,84467,99078,86378,92827,91794,107289,94940,102342,98345,108747,94214,100876,99373,113622,99811,103726,101952,116360,104189,109366,110226,123169,113010,114065,113800,127776,114070,116830,115688,129706,114960,120125,119001,131565,114826,120229,117408,129667,115904,120319,118179,130936,117542,122968,123179,136267,122690,128028),\n                 recession=c(1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))\ndf <- df %>% mutate(revtq_yoy = (revtq - lag(revtq, 4)) / lag(revtq, 4))\ndf <- df %>% mutate(recession_lag = lag(recession))\ndf <- df %>% mutate(revtq_down = ifelse(revtq_yoy<0,1,0))","sample":"# The dataframe below contains historical revenue for Walmart since 1970\n# All data is quarterly\n# Recession data comes from the US Federal Reserve\n# The file uses Compustat's nomenclature:\n#     - revenue is revtq\n#     - ticker is tic\n#     - fyearq is year\n#     - fqtr is quarter\n#     - recession is an indicator for US recessions\n# It also contains revtq_yoy, revtq_down, and recession_lag from the first exercise\ndf[1:5,]\n\n# Calculate a logistic model of a decrease in year over year quarterly revenue\n# on the presence of a recession last quarter, store it in mod1\nmod1 <- glm( ~ , data=, family=)\n\n# Print out a summary of the model using summary\n\n\n#END","solution":"# The dataframe below contains historical revenue for Walmart since 1970\n# All data is quarterly\n# Recession data comes from the US Federal Reserve\n# The file uses Compustat's nomenclature:\n#     - revenue is revtq\n#     - ticker is tic\n#     - fyearq is year\n#     - fqtr is quarter\n#     - recession is an indicator for US recessions\n# It also contains revtq_yoy, revtq_down, and recession_lag from the first exercise\ndf[1:5,]\n\n# Calculate a logistic model of a decrease in year over year quarterly revenue\n# on the presence of a recession last quarter, store it in mod1\nmod1 <- glm(revtq_down ~ recession_lag, data=df, family=binomial)\n\n# Print out a summary of the model using summary\nsummary(mod1)\n\n#END","sct":"# Template based on https://www.rdocumentation.org/packages/testwhat/versions/4.1.1\n# Check if something is explicitly typed\n\ntest_student_typed('revtq_down ~ recession_lag',not_typed_msg='Make sure to regress `revtq_down` on `recession_lag`.')\ntest_student_typed('data=df',not_typed_msg='Make sure to using the `df` data frame for the test.')\ntest_student_typed('family=binomial',not_typed_msg='Make sure to set family to be `binomial`, so as to run a logistic regression.')\ntest_expression_output(\"mod1\", incorrect_msg=\"Your regression model isn't quite right.\")\ntest_student_typed('summary(mod1)',not_typed_msg='Your model is correct, but did you remember to summarize it with `summary()`?')\n\n# test_student_typed('x <- 2', not_typed_msg='')\n\n# Check if function was used in input code\n# test_function('c',incorrect_msg='')  \n\n# Requires an object `x` to have the same value as the solution\n# test_object(\"x\",incorrect_msg = \"\",undefined_msg = \"\")  \n\n# Requires an onject with the same value of `x` in the solution\n# test_an_object(\"x\",undefined_msg=\"\")\n\n# Checks if output of student's code contains given evaluated expression\n# test_output_contains(\"x\",incorrect_msg = \"\")\n\n# Check if a vector of predefined objects are unchanged\n# test_predefined_objects(c('x','y'),incorrect_msg=\"Don't onverwrite the predefined variables\")\n\n# Checks for a regex pattern in trhe output\n# test_output_regex(pattern,fixed=F, times=1, incorrect_msg='')\n\n# Can check an arbitrary expression across both solution and student code\n#test_expression_output(\"typeof(company_name)\", incorrect_msg=\"Did you store textual data in `company_name`?\")\n\ntest_error()\nsuccess_msg(\"Awesome!\")\n\n# Other functions to note:\n#     - test_or(a,b) -- checks if either test a or test b pass\n#     - test_ggplot() -- can check if plots are correct\n#     - test_function() -- can also check included parameters\n#     - test_loop() -- checking for and while loops\n#     - test_library_function('package', not_called_msg='',incorrect_msg='')\n#     - test_if_else() -- checking if statements\n#     - test_expression_error() -- can check if functions are properly defined\n#     - test_operator('operator',), not_called_msg='',incorrect_msg='')\n#     - test_function_definition() -- rigorously check defined function\n#     - test_data_frame() -- check if dataframe [columns] are equivalent\n#     - test_function_result, test_expression_result"}
Visualization with maps
Exercise 3: Using Plotly for maps
NOTE: Unforetunately, though DataCamp light has plotly installed, it doesn’t work at present (plots don’t open). As such, we can’t run this here and see any output. Instead, you can download this file, load the file into R with load.csv(), and then run the code given below. Alternatively, you can download this Rmd file containing this exercise’s code. The output of the code is available here.
You will need plotly installed to run the code. If you do not have it installed, run install.packages("plotly").
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Exercise 4: Plotting with leaflet…
NOTE: Unforetunately, DataCamp light doesn’t have leaflet installed. As such, we can’t run this here. If you would like to see a nice graph of this in leaflet, you can download this file, load the file into R with load.csv(), and then run the code given below. Alternatively, you can download this Rmd file containing this exercise’s code. The output of the code is available here.
You will need leaflet installed to run the code. If you do not have it installed, run install.packages("leaflet")
The third map also requires leaflet.extras installed. If you do not have it installed, run install.packages("leaflet.extras")
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