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From python to R: Nested lists
How do I check if a list is empty?Calling an external command in PythonWhat are metaclasses in Python?Finding the index of an item given a list containing it in PythonDifference between append vs. extend list methods in PythonHow can I safely create a nested directory in Python?Does Python have a ternary conditional operator?How to make a flat list out of list of lists?How do I list all files of a directory?Does Python have a string 'contains' substring method?
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I have spatial coordinate data from python which looks something like this:
[[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]]
I am analyzing this data in R, but I don't know how to grab individual coordinates i.e. how to get to [179.0, -79.0]. I have tried different indexing options but had no luck. Just for context, my dataframe has a column with python list of lists.
Any help would be much appreciated. Thanks!
Clarification:
I was wondering if I can extract individual coordinates from '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]] ' in R.
This is what the data looks like in R. I loaded a csv file into a dataframe and each observation in df$click_pos has a value like '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]'
So can I extract [-183.0, -30.0] from the above ?
dfgood['click_pos'][[1]][1] gives
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
Note that typeof( dfgood['click_pos'][[1]][1]) gives type int
R data when I do dfgood['click_pos'][[1]]:
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
[2] [[-12.0, 78.0], [4.0, -186.0], [170.0, -138.0], [45.0, 254.0], [-153.0, 232.0], [-140.0, -37.0], [-227.0, -28.0], [-62.0, -12.0]]
.
.
.
[24] [[-14.0, 270.0], [-28.0, -164.0], [-302.0, -119.0], [-324.0, -278.0], [22.0, -259.0], [-316.0, -106.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
dput(head(df))--trimmed
structure(list(X = 0:5, subjID = c(2, 2, 2, 2, 2, 2), block = c(2,
2, 2, 2, 2, 2), order_ntrials = c(1, 2, 3, 4, 5, 6), ntrials = c(13,
4, 8, 16, 1, 21), routelen = c(5, 7, 4, 7, 6, 7), ideal_x = c(-183,
4, -92, 19, -193, -133), ideal_y = c(-30, -186, 310, -26, 361,
138), subj_x = c(-115, -62, -114, -107, -418, -199), subj_y = c(-8,
-12, 323, 273, 305, 152), trial_rt = c(31.430577373947, 40.6340158929816,
26.0440646470524, 50.8890219930327, 34.8192964689806, 37.9083978550043
), home_rt = c(5.36384105798788, 3.99772735999431, 3.22768635000102,
5.48362414108124, 5.97196817304939, 5.93354129395448), all_pos = structure(c(8L,
3L, 19L, 15L, 16L, 5L), .Label = c("[[-102.0, 348.0], [-101.0, 352.0], [-101.0, 352.0], [-101.0, 352.0], [-102.0, 352.0],...,[118.0, -41.0]]",
"[[-11.0, 11.0], [-12.0, 14.0], [-12.0, 14.0],..., [6.0, 394.0]]",
"[[-12.0, 78.0], [-12.0, 78.0], [-12.0, 78.0],..., [6.0, 394.0]]",
), class = "factor"), all_pos_times = structure(c(19L, 10L, 17L,
6L, 12L, 9L), .Label = c("[15.970820963033475, 19.385453240014613, 19.89702452905476], ... "[2.4681653099833056, 5.662667237920687, 6.189063199912198]"
), class = "factor"), test_pos_idx = c(2, 2, 3, 5, 4, 6), mid_locs = structure(c(6L,
24L, 15L, 3L, 18L, 4L), .Label = c("[[-100.0, 163.0], [54.0, 149.0], [7.0, -355.0], [-244.0, -363.0]]",
"[[-104.0, -37.0], [258.0, 87.0], [234.0, 418.0]]", ...,"[[-122.0, 372.0], [-294.0, 372.0], [-235.0, -88.0], [19.0, -26.0], [-26.0, 277.0]]"), class = "factor"), test_pos = structure(c(4L,
23L, 14L, 17L, 5L, 2L), .Label = c("[-100.0, 163.0]", "[-133.0, 138.0]",
"[-150.0, -98.0]", "[-183.0, -30.0]", "[-193.0, 361.0]", "[-194.0, 213.0]",
"[-302.0, -119.0]", "[-308.0, -254.0]", ... , "[80.0, -210.0]"), class = "factor"),
exclude = c(0, 0, 0, 0, 0, 0)), .Names = c("X", "subjID",
"block", "order_ntrials", "ntrials", "routelen", "ideal_x", "ideal_y",
"subj_x", "subj_y", "trial_rt", "home_rt", "all_pos", "all_pos_times",
"click_pos", "click_pos_times", "test_pos_idx", "mid_locs", "test_pos",
"exclude"), row.names = c(NA, 6L), class = "data.frame")
python r
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
add a comment |
I have spatial coordinate data from python which looks something like this:
[[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]]
I am analyzing this data in R, but I don't know how to grab individual coordinates i.e. how to get to [179.0, -79.0]. I have tried different indexing options but had no luck. Just for context, my dataframe has a column with python list of lists.
Any help would be much appreciated. Thanks!
Clarification:
I was wondering if I can extract individual coordinates from '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]] ' in R.
This is what the data looks like in R. I loaded a csv file into a dataframe and each observation in df$click_pos has a value like '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]'
So can I extract [-183.0, -30.0] from the above ?
dfgood['click_pos'][[1]][1] gives
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
Note that typeof( dfgood['click_pos'][[1]][1]) gives type int
R data when I do dfgood['click_pos'][[1]]:
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
[2] [[-12.0, 78.0], [4.0, -186.0], [170.0, -138.0], [45.0, 254.0], [-153.0, 232.0], [-140.0, -37.0], [-227.0, -28.0], [-62.0, -12.0]]
.
.
.
[24] [[-14.0, 270.0], [-28.0, -164.0], [-302.0, -119.0], [-324.0, -278.0], [22.0, -259.0], [-316.0, -106.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
dput(head(df))--trimmed
structure(list(X = 0:5, subjID = c(2, 2, 2, 2, 2, 2), block = c(2,
2, 2, 2, 2, 2), order_ntrials = c(1, 2, 3, 4, 5, 6), ntrials = c(13,
4, 8, 16, 1, 21), routelen = c(5, 7, 4, 7, 6, 7), ideal_x = c(-183,
4, -92, 19, -193, -133), ideal_y = c(-30, -186, 310, -26, 361,
138), subj_x = c(-115, -62, -114, -107, -418, -199), subj_y = c(-8,
-12, 323, 273, 305, 152), trial_rt = c(31.430577373947, 40.6340158929816,
26.0440646470524, 50.8890219930327, 34.8192964689806, 37.9083978550043
), home_rt = c(5.36384105798788, 3.99772735999431, 3.22768635000102,
5.48362414108124, 5.97196817304939, 5.93354129395448), all_pos = structure(c(8L,
3L, 19L, 15L, 16L, 5L), .Label = c("[[-102.0, 348.0], [-101.0, 352.0], [-101.0, 352.0], [-101.0, 352.0], [-102.0, 352.0],...,[118.0, -41.0]]",
"[[-11.0, 11.0], [-12.0, 14.0], [-12.0, 14.0],..., [6.0, 394.0]]",
"[[-12.0, 78.0], [-12.0, 78.0], [-12.0, 78.0],..., [6.0, 394.0]]",
), class = "factor"), all_pos_times = structure(c(19L, 10L, 17L,
6L, 12L, 9L), .Label = c("[15.970820963033475, 19.385453240014613, 19.89702452905476], ... "[2.4681653099833056, 5.662667237920687, 6.189063199912198]"
), class = "factor"), test_pos_idx = c(2, 2, 3, 5, 4, 6), mid_locs = structure(c(6L,
24L, 15L, 3L, 18L, 4L), .Label = c("[[-100.0, 163.0], [54.0, 149.0], [7.0, -355.0], [-244.0, -363.0]]",
"[[-104.0, -37.0], [258.0, 87.0], [234.0, 418.0]]", ...,"[[-122.0, 372.0], [-294.0, 372.0], [-235.0, -88.0], [19.0, -26.0], [-26.0, 277.0]]"), class = "factor"), test_pos = structure(c(4L,
23L, 14L, 17L, 5L, 2L), .Label = c("[-100.0, 163.0]", "[-133.0, 138.0]",
"[-150.0, -98.0]", "[-183.0, -30.0]", "[-193.0, 361.0]", "[-194.0, 213.0]",
"[-302.0, -119.0]", "[-308.0, -254.0]", ... , "[80.0, -210.0]"), class = "factor"),
exclude = c(0, 0, 0, 0, 0, 0)), .Names = c("X", "subjID",
"block", "order_ntrials", "ntrials", "routelen", "ideal_x", "ideal_y",
"subj_x", "subj_y", "trial_rt", "home_rt", "all_pos", "all_pos_times",
"click_pos", "click_pos_times", "test_pos_idx", "mid_locs", "test_pos",
"exclude"), row.names = c(NA, 6L), class = "data.frame")
python r
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
Welcome to stackoverflow! Please remember to always indent your code. You can do this by highlighting the code, then pressing CTRL+K or CMD+K, or by using 4 spaces. Alternatively you can wrap your code using ` ` ` , which will cause the code to appearlike this.
– Jane
Mar 8 at 23:59
1
Ah, thank you! @Jane
– Anisha Khosla
Mar 9 at 0:06
1
You have shown us what your data looks like in python, but to help you, we need what you have in R. Please usedputto create a text version of your R data and paste it into your question. If your data is too long, you could use something likedput(head(MyData))to show us a small sample in the format that you have.
– G5W
Mar 9 at 0:33
Sorry about that. The data set is quite big (or poorly managed). I tried to trim it down so it fits here. Seems quite messy to me. Let me know if there is another way I can give more information about what the R data looks like. This is from a csv file (python output). Thanks!
– Anisha Khosla
Mar 9 at 1:17
add a comment |
I have spatial coordinate data from python which looks something like this:
[[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]]
I am analyzing this data in R, but I don't know how to grab individual coordinates i.e. how to get to [179.0, -79.0]. I have tried different indexing options but had no luck. Just for context, my dataframe has a column with python list of lists.
Any help would be much appreciated. Thanks!
Clarification:
I was wondering if I can extract individual coordinates from '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]] ' in R.
This is what the data looks like in R. I loaded a csv file into a dataframe and each observation in df$click_pos has a value like '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]'
So can I extract [-183.0, -30.0] from the above ?
dfgood['click_pos'][[1]][1] gives
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
Note that typeof( dfgood['click_pos'][[1]][1]) gives type int
R data when I do dfgood['click_pos'][[1]]:
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
[2] [[-12.0, 78.0], [4.0, -186.0], [170.0, -138.0], [45.0, 254.0], [-153.0, 232.0], [-140.0, -37.0], [-227.0, -28.0], [-62.0, -12.0]]
.
.
.
[24] [[-14.0, 270.0], [-28.0, -164.0], [-302.0, -119.0], [-324.0, -278.0], [22.0, -259.0], [-316.0, -106.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
dput(head(df))--trimmed
structure(list(X = 0:5, subjID = c(2, 2, 2, 2, 2, 2), block = c(2,
2, 2, 2, 2, 2), order_ntrials = c(1, 2, 3, 4, 5, 6), ntrials = c(13,
4, 8, 16, 1, 21), routelen = c(5, 7, 4, 7, 6, 7), ideal_x = c(-183,
4, -92, 19, -193, -133), ideal_y = c(-30, -186, 310, -26, 361,
138), subj_x = c(-115, -62, -114, -107, -418, -199), subj_y = c(-8,
-12, 323, 273, 305, 152), trial_rt = c(31.430577373947, 40.6340158929816,
26.0440646470524, 50.8890219930327, 34.8192964689806, 37.9083978550043
), home_rt = c(5.36384105798788, 3.99772735999431, 3.22768635000102,
5.48362414108124, 5.97196817304939, 5.93354129395448), all_pos = structure(c(8L,
3L, 19L, 15L, 16L, 5L), .Label = c("[[-102.0, 348.0], [-101.0, 352.0], [-101.0, 352.0], [-101.0, 352.0], [-102.0, 352.0],...,[118.0, -41.0]]",
"[[-11.0, 11.0], [-12.0, 14.0], [-12.0, 14.0],..., [6.0, 394.0]]",
"[[-12.0, 78.0], [-12.0, 78.0], [-12.0, 78.0],..., [6.0, 394.0]]",
), class = "factor"), all_pos_times = structure(c(19L, 10L, 17L,
6L, 12L, 9L), .Label = c("[15.970820963033475, 19.385453240014613, 19.89702452905476], ... "[2.4681653099833056, 5.662667237920687, 6.189063199912198]"
), class = "factor"), test_pos_idx = c(2, 2, 3, 5, 4, 6), mid_locs = structure(c(6L,
24L, 15L, 3L, 18L, 4L), .Label = c("[[-100.0, 163.0], [54.0, 149.0], [7.0, -355.0], [-244.0, -363.0]]",
"[[-104.0, -37.0], [258.0, 87.0], [234.0, 418.0]]", ...,"[[-122.0, 372.0], [-294.0, 372.0], [-235.0, -88.0], [19.0, -26.0], [-26.0, 277.0]]"), class = "factor"), test_pos = structure(c(4L,
23L, 14L, 17L, 5L, 2L), .Label = c("[-100.0, 163.0]", "[-133.0, 138.0]",
"[-150.0, -98.0]", "[-183.0, -30.0]", "[-193.0, 361.0]", "[-194.0, 213.0]",
"[-302.0, -119.0]", "[-308.0, -254.0]", ... , "[80.0, -210.0]"), class = "factor"),
exclude = c(0, 0, 0, 0, 0, 0)), .Names = c("X", "subjID",
"block", "order_ntrials", "ntrials", "routelen", "ideal_x", "ideal_y",
"subj_x", "subj_y", "trial_rt", "home_rt", "all_pos", "all_pos_times",
"click_pos", "click_pos_times", "test_pos_idx", "mid_locs", "test_pos",
"exclude"), row.names = c(NA, 6L), class = "data.frame")
python r
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
I have spatial coordinate data from python which looks something like this:
[[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]]
I am analyzing this data in R, but I don't know how to grab individual coordinates i.e. how to get to [179.0, -79.0]. I have tried different indexing options but had no luck. Just for context, my dataframe has a column with python list of lists.
Any help would be much appreciated. Thanks!
Clarification:
I was wondering if I can extract individual coordinates from '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]] ' in R.
This is what the data looks like in R. I loaded a csv file into a dataframe and each observation in df$click_pos has a value like '[[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]'
So can I extract [-183.0, -30.0] from the above ?
dfgood['click_pos'][[1]][1] gives
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
Note that typeof( dfgood['click_pos'][[1]][1]) gives type int
R data when I do dfgood['click_pos'][[1]]:
[1] [[-188.0, -261.0], [-183.0, -30.0], [88.0, -26.0], [11.0, 339.0], [-198.0, 323.0], [-115.0, -8.0]]
[2] [[-12.0, 78.0], [4.0, -186.0], [170.0, -138.0], [45.0, 254.0], [-153.0, 232.0], [-140.0, -37.0], [-227.0, -28.0], [-62.0, -12.0]]
.
.
.
[24] [[-14.0, 270.0], [-28.0, -164.0], [-302.0, -119.0], [-324.0, -278.0], [22.0, -259.0], [-316.0, -106.0]]
25 Levels: [[-102.0, 348.0], [-104.0, -37.0], [258.0, 87.0], [234.0, 418.0], [-93.0, 312.0], [118.0, -41.0]] ...
dput(head(df))--trimmed
structure(list(X = 0:5, subjID = c(2, 2, 2, 2, 2, 2), block = c(2,
2, 2, 2, 2, 2), order_ntrials = c(1, 2, 3, 4, 5, 6), ntrials = c(13,
4, 8, 16, 1, 21), routelen = c(5, 7, 4, 7, 6, 7), ideal_x = c(-183,
4, -92, 19, -193, -133), ideal_y = c(-30, -186, 310, -26, 361,
138), subj_x = c(-115, -62, -114, -107, -418, -199), subj_y = c(-8,
-12, 323, 273, 305, 152), trial_rt = c(31.430577373947, 40.6340158929816,
26.0440646470524, 50.8890219930327, 34.8192964689806, 37.9083978550043
), home_rt = c(5.36384105798788, 3.99772735999431, 3.22768635000102,
5.48362414108124, 5.97196817304939, 5.93354129395448), all_pos = structure(c(8L,
3L, 19L, 15L, 16L, 5L), .Label = c("[[-102.0, 348.0], [-101.0, 352.0], [-101.0, 352.0], [-101.0, 352.0], [-102.0, 352.0],...,[118.0, -41.0]]",
"[[-11.0, 11.0], [-12.0, 14.0], [-12.0, 14.0],..., [6.0, 394.0]]",
"[[-12.0, 78.0], [-12.0, 78.0], [-12.0, 78.0],..., [6.0, 394.0]]",
), class = "factor"), all_pos_times = structure(c(19L, 10L, 17L,
6L, 12L, 9L), .Label = c("[15.970820963033475, 19.385453240014613, 19.89702452905476], ... "[2.4681653099833056, 5.662667237920687, 6.189063199912198]"
), class = "factor"), test_pos_idx = c(2, 2, 3, 5, 4, 6), mid_locs = structure(c(6L,
24L, 15L, 3L, 18L, 4L), .Label = c("[[-100.0, 163.0], [54.0, 149.0], [7.0, -355.0], [-244.0, -363.0]]",
"[[-104.0, -37.0], [258.0, 87.0], [234.0, 418.0]]", ...,"[[-122.0, 372.0], [-294.0, 372.0], [-235.0, -88.0], [19.0, -26.0], [-26.0, 277.0]]"), class = "factor"), test_pos = structure(c(4L,
23L, 14L, 17L, 5L, 2L), .Label = c("[-100.0, 163.0]", "[-133.0, 138.0]",
"[-150.0, -98.0]", "[-183.0, -30.0]", "[-193.0, 361.0]", "[-194.0, 213.0]",
"[-302.0, -119.0]", "[-308.0, -254.0]", ... , "[80.0, -210.0]"), class = "factor"),
exclude = c(0, 0, 0, 0, 0, 0)), .Names = c("X", "subjID",
"block", "order_ntrials", "ntrials", "routelen", "ideal_x", "ideal_y",
"subj_x", "subj_y", "trial_rt", "home_rt", "all_pos", "all_pos_times",
"click_pos", "click_pos_times", "test_pos_idx", "mid_locs", "test_pos",
"exclude"), row.names = c(NA, 6L), class = "data.frame")
python r
python r
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
edited Mar 9 at 17:29
Anisha Khosla
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
asked Mar 8 at 23:55
Anisha KhoslaAnisha Khosla
63
63
Welcome to stackoverflow! Please remember to always indent your code. You can do this by highlighting the code, then pressing CTRL+K or CMD+K, or by using 4 spaces. Alternatively you can wrap your code using ` ` ` , which will cause the code to appearlike this.
– Jane
Mar 8 at 23:59
1
Ah, thank you! @Jane
– Anisha Khosla
Mar 9 at 0:06
1
You have shown us what your data looks like in python, but to help you, we need what you have in R. Please usedputto create a text version of your R data and paste it into your question. If your data is too long, you could use something likedput(head(MyData))to show us a small sample in the format that you have.
– G5W
Mar 9 at 0:33
Sorry about that. The data set is quite big (or poorly managed). I tried to trim it down so it fits here. Seems quite messy to me. Let me know if there is another way I can give more information about what the R data looks like. This is from a csv file (python output). Thanks!
– Anisha Khosla
Mar 9 at 1:17
add a comment |
Welcome to stackoverflow! Please remember to always indent your code. You can do this by highlighting the code, then pressing CTRL+K or CMD+K, or by using 4 spaces. Alternatively you can wrap your code using ` ` ` , which will cause the code to appearlike this.
– Jane
Mar 8 at 23:59
1
Ah, thank you! @Jane
– Anisha Khosla
Mar 9 at 0:06
1
You have shown us what your data looks like in python, but to help you, we need what you have in R. Please usedputto create a text version of your R data and paste it into your question. If your data is too long, you could use something likedput(head(MyData))to show us a small sample in the format that you have.
– G5W
Mar 9 at 0:33
Sorry about that. The data set is quite big (or poorly managed). I tried to trim it down so it fits here. Seems quite messy to me. Let me know if there is another way I can give more information about what the R data looks like. This is from a csv file (python output). Thanks!
– Anisha Khosla
Mar 9 at 1:17
Welcome to stackoverflow! Please remember to always indent your code. You can do this by highlighting the code, then pressing CTRL+K or CMD+K, or by using 4 spaces. Alternatively you can wrap your code using ` ` ` , which will cause the code to appear
like this.– Jane
Mar 8 at 23:59
Welcome to stackoverflow! Please remember to always indent your code. You can do this by highlighting the code, then pressing CTRL+K or CMD+K, or by using 4 spaces. Alternatively you can wrap your code using ` ` ` , which will cause the code to appear
like this.– Jane
Mar 8 at 23:59
1
1
Ah, thank you! @Jane
– Anisha Khosla
Mar 9 at 0:06
Ah, thank you! @Jane
– Anisha Khosla
Mar 9 at 0:06
1
1
You have shown us what your data looks like in python, but to help you, we need what you have in R. Please use
dput to create a text version of your R data and paste it into your question. If your data is too long, you could use something like dput(head(MyData)) to show us a small sample in the format that you have.– G5W
Mar 9 at 0:33
You have shown us what your data looks like in python, but to help you, we need what you have in R. Please use
dput to create a text version of your R data and paste it into your question. If your data is too long, you could use something like dput(head(MyData)) to show us a small sample in the format that you have.– G5W
Mar 9 at 0:33
Sorry about that. The data set is quite big (or poorly managed). I tried to trim it down so it fits here. Seems quite messy to me. Let me know if there is another way I can give more information about what the R data looks like. This is from a csv file (python output). Thanks!
– Anisha Khosla
Mar 9 at 1:17
Sorry about that. The data set is quite big (or poorly managed). I tried to trim it down so it fits here. Seems quite messy to me. Let me know if there is another way I can give more information about what the R data looks like. This is from a csv file (python output). Thanks!
– Anisha Khosla
Mar 9 at 1:17
add a comment |
1 Answer
1
active
oldest
votes
I had problems recreating your data, so I assumed you're coming from python and your data looked like this: [[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]] - python style.
Recreating it using R code:
df <- list(list(179.0, -79.0),
list(127.0, 301.0),
list(-92.0, 310.0))
What you have here is a list of lists, and if you'd like to get an item using indexing you need to use [] .
First, in R it's always nice to use the str() function to understand your variable.
List of 3
$ :List of 2
..$ : num 179
..$ : num -79
$ :List of 2
..$ : num 127
..$ : num 301
$ :List of 2
..$ : num -92
..$ : num 310
We can see that we have a list with 3 list within it.
Next, we can start to get each list/item from the list using [] and indexing (don't forget that unlike python, R uses 1 indexing):
If will type df[1] will get to our first list:
[[1]]
[[1]][[1]]
[1] 179
[[1]][[2]]
[1] -79
But if will do df[[1]] will get the first items from our first list:
df[[1]]
[[1]]
[1] 179
[[2]]
[1] -79
If we'll use a second index like this df[[1]][1], we'll get our first item in our list:
[[1]]
[1] 179
I think that the picture from here and here is very useful: 
answered Mar 9 at 8:17
DJVDJV
1,7391519
Thanks so much for the explanation! I think my problem is a little different though. I tried clarifying it more in my question. In short,typeof( dfgood['click_pos'][[1]][1]) gives type int
– Anisha Khosla
Mar 9 at 17:31
It looks likeclick_posis a factor. So when you access an element, it gives you its value and not its label. Try to usevarhandle::unfactor()oras.character(dfgood$click_pos)to unfactor/change the variable to a character vector.
– DJV
Mar 9 at 21:48
add a comment |
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1 Answer
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1 Answer
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votes
I had problems recreating your data, so I assumed you're coming from python and your data looked like this: [[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]] - python style.
Recreating it using R code:
df <- list(list(179.0, -79.0),
list(127.0, 301.0),
list(-92.0, 310.0))
What you have here is a list of lists, and if you'd like to get an item using indexing you need to use [] .
First, in R it's always nice to use the str() function to understand your variable.
List of 3
$ :List of 2
..$ : num 179
..$ : num -79
$ :List of 2
..$ : num 127
..$ : num 301
$ :List of 2
..$ : num -92
..$ : num 310
We can see that we have a list with 3 list within it.
Next, we can start to get each list/item from the list using [] and indexing (don't forget that unlike python, R uses 1 indexing):
If will type df[1] will get to our first list:
[[1]]
[[1]][[1]]
[1] 179
[[1]][[2]]
[1] -79
But if will do df[[1]] will get the first items from our first list:
df[[1]]
[[1]]
[1] 179
[[2]]
[1] -79
If we'll use a second index like this df[[1]][1], we'll get our first item in our list:
[[1]]
[1] 179
I think that the picture from here and here is very useful:
answered Mar 9 at 8:17
DJVDJV
1,7391519
Thanks so much for the explanation! I think my problem is a little different though. I tried clarifying it more in my question. In short,typeof( dfgood['click_pos'][[1]][1]) gives type int
– Anisha Khosla
Mar 9 at 17:31
It looks likeclick_posis a factor. So when you access an element, it gives you its value and not its label. Try to usevarhandle::unfactor()oras.character(dfgood$click_pos)to unfactor/change the variable to a character vector.
– DJV
Mar 9 at 21:48
add a comment |
I had problems recreating your data, so I assumed you're coming from python and your data looked like this: [[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]] - python style.
Recreating it using R code:
df <- list(list(179.0, -79.0),
list(127.0, 301.0),
list(-92.0, 310.0))
What you have here is a list of lists, and if you'd like to get an item using indexing you need to use [] .
First, in R it's always nice to use the str() function to understand your variable.
List of 3
$ :List of 2
..$ : num 179
..$ : num -79
$ :List of 2
..$ : num 127
..$ : num 301
$ :List of 2
..$ : num -92
..$ : num 310
We can see that we have a list with 3 list within it.
Next, we can start to get each list/item from the list using [] and indexing (don't forget that unlike python, R uses 1 indexing):
If will type df[1] will get to our first list:
[[1]]
[[1]][[1]]
[1] 179
[[1]][[2]]
[1] -79
But if will do df[[1]] will get the first items from our first list:
df[[1]]
[[1]]
[1] 179
[[2]]
[1] -79
If we'll use a second index like this df[[1]][1], we'll get our first item in our list:
[[1]]
[1] 179
I think that the picture from here and here is very useful:
answered Mar 9 at 8:17
DJVDJV
1,7391519
Thanks so much for the explanation! I think my problem is a little different though. I tried clarifying it more in my question. In short,typeof( dfgood['click_pos'][[1]][1]) gives type int
– Anisha Khosla
Mar 9 at 17:31
It looks likeclick_posis a factor. So when you access an element, it gives you its value and not its label. Try to usevarhandle::unfactor()oras.character(dfgood$click_pos)to unfactor/change the variable to a character vector.
– DJV
Mar 9 at 21:48
add a comment |
I had problems recreating your data, so I assumed you're coming from python and your data looked like this: [[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]] - python style.
Recreating it using R code:
df <- list(list(179.0, -79.0),
list(127.0, 301.0),
list(-92.0, 310.0))
What you have here is a list of lists, and if you'd like to get an item using indexing you need to use [] .
First, in R it's always nice to use the str() function to understand your variable.
List of 3
$ :List of 2
..$ : num 179
..$ : num -79
$ :List of 2
..$ : num 127
..$ : num 301
$ :List of 2
..$ : num -92
..$ : num 310
We can see that we have a list with 3 list within it.
Next, we can start to get each list/item from the list using [] and indexing (don't forget that unlike python, R uses 1 indexing):
If will type df[1] will get to our first list:
[[1]]
[[1]][[1]]
[1] 179
[[1]][[2]]
[1] -79
But if will do df[[1]] will get the first items from our first list:
df[[1]]
[[1]]
[1] 179
[[2]]
[1] -79
If we'll use a second index like this df[[1]][1], we'll get our first item in our list:
[[1]]
[1] 179
I think that the picture from here and here is very useful:
answered Mar 9 at 8:17
DJVDJV
1,7391519
I had problems recreating your data, so I assumed you're coming from python and your data looked like this: [[179.0, -79.0], [127.0, 301.0], [-92.0, 310.0]] - python style.
Recreating it using R code:
df <- list(list(179.0, -79.0),
list(127.0, 301.0),
list(-92.0, 310.0))
What you have here is a list of lists, and if you'd like to get an item using indexing you need to use [] .
First, in R it's always nice to use the str() function to understand your variable.
List of 3
$ :List of 2
..$ : num 179
..$ : num -79
$ :List of 2
..$ : num 127
..$ : num 301
$ :List of 2
..$ : num -92
..$ : num 310
We can see that we have a list with 3 list within it.
Next, we can start to get each list/item from the list using [] and indexing (don't forget that unlike python, R uses 1 indexing):
If will type df[1] will get to our first list:
[[1]]
[[1]][[1]]
[1] 179
[[1]][[2]]
[1] -79
But if will do df[[1]] will get the first items from our first list:
df[[1]]
[[1]]
[1] 179
[[2]]
[1] -79
If we'll use a second index like this df[[1]][1], we'll get our first item in our list:
[[1]]
[1] 179
I think that the picture from here and here is very useful:
answered Mar 9 at 8:17
DJVDJV
1,7391519
edited Mar 9 at 8:41
answered Mar 9 at 8:17
DJVDJV
1,7391519
answered Mar 9 at 8:17
DJVDJV
1,7391519
answered Mar 9 at 8:17
DJVDJV
1,7391519
1,7391519
Thanks so much for the explanation! I think my problem is a little different though. I tried clarifying it more in my question. In short,typeof( dfgood['click_pos'][[1]][1]) gives type int
– Anisha Khosla
Mar 9 at 17:31
It looks likeclick_posis a factor. So when you access an element, it gives you its value and not its label. Try to usevarhandle::unfactor()oras.character(dfgood$click_pos)to unfactor/change the variable to a character vector.
– DJV
Mar 9 at 21:48
add a comment |
Thanks so much for the explanation! I think my problem is a little different though. I tried clarifying it more in my question. In short,typeof( dfgood['click_pos'][[1]][1]) gives type int
– Anisha Khosla
Mar 9 at 17:31
It looks likeclick_posis a factor. So when you access an element, it gives you its value and not its label. Try to usevarhandle::unfactor()oras.character(dfgood$click_pos)to unfactor/change the variable to a character vector.
– DJV
Mar 9 at 21:48
Thanks so much for the explanation! I think my problem is a little different though. I tried clarifying it more in my question. In short,
typeof( dfgood['click_pos'][[1]][1]) gives type int– Anisha Khosla
Mar 9 at 17:31
Thanks so much for the explanation! I think my problem is a little different though. I tried clarifying it more in my question. In short,
typeof( dfgood['click_pos'][[1]][1]) gives type int– Anisha Khosla
Mar 9 at 17:31
It looks like
click_pos is a factor. So when you access an element, it gives you its value and not its label. Try to use varhandle::unfactor() or as.character(dfgood$click_pos) to unfactor/change the variable to a character vector.– DJV
Mar 9 at 21:48
It looks like
click_pos is a factor. So when you access an element, it gives you its value and not its label. Try to use varhandle::unfactor() or as.character(dfgood$click_pos) to unfactor/change the variable to a character vector.– DJV
Mar 9 at 21:48
add a comment |
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Welcome to stackoverflow! Please remember to always indent your code. You can do this by highlighting the code, then pressing CTRL+K or CMD+K, or by using 4 spaces. Alternatively you can wrap your code using ` ` ` , which will cause the code to appear
like this.– Jane
Mar 8 at 23:59
1
Ah, thank you! @Jane
– Anisha Khosla
Mar 9 at 0:06
1
You have shown us what your data looks like in python, but to help you, we need what you have in R. Please use
dputto create a text version of your R data and paste it into your question. If your data is too long, you could use something likedput(head(MyData))to show us a small sample in the format that you have.– G5W
Mar 9 at 0:33
Sorry about that. The data set is quite big (or poorly managed). I tried to trim it down so it fits here. Seems quite messy to me. Let me know if there is another way I can give more information about what the R data looks like. This is from a csv file (python output). Thanks!
– Anisha Khosla
Mar 9 at 1:17