R: changing certain quiz category answers (values) to a string in a DF based on the number of incorrect answers given in that category

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If anyone can help; thanks. Say I had a data frame of players answers to quiz questions regarding a certain area (countries in the table): enter image description here

The 0 means a wrong answer, the 1 means a right answer. Also, lets say I had 40 country questions and 100 players. How would I loop and change certain scores for each player to a string like "disc" when they had answered a certain number of questions incorrectly?

That is, when a player has answered say 3/4 questions incorrectly on the countries category within the first 4 questions (as player one has), how would I then change each of his subsequent answers to "disc" (i.e., they would not count regardless of a correct or incorrect answer as 3 out of the first 4 questions were incorrect?) I would like to do this over the whole DF for each player in the specific "countries" category (so not the flag category).

loops r
1 Answers

Here is an example on how you can achieve this, in the future it would be best to attach a reproducible example.

Data

set.seed(34)
df1 = data.frame(player=c(1:10), replicate(9,sample(0:1,10,rep=TRUE)), Flag = sample(0:1,10,rep=TRUE))
names(df1)[2:10] = paste0("Countries", names(df1)[2:10])
> df1
   player CountriesX1 CountriesX2 CountriesX3 CountriesX4 CountriesX5 CountriesX6 CountriesX7 CountriesX8 CountriesX9 Flag
1       1           0           0           0           0           1           1           1           1           0    0
2       2           0           1           0           0           1           0           1           0           1    0
3       3           0           1           1           0           1           0           1           0           1    1
4       4           1           1           0           0           0           1           1           1           0    0
5       5           1           1           1           1           1           1           1           0           1    1
6       6           1           1           0           0           1           1           1           0           1    1
7       7           1           0           1           0           1           0           0           0           1    1
8       8           1           0           0           1           0           0           1           0           0    0
9       9           1           0           1           0           1           1           0           0           0    0
10     10           0           0           0           1           1           0           1           0           1    1

Code

A option to transform the data is to create a intermediate data frame that is the cumulative sum of the 'fail':

df2 = t(df1) %>% #transpose df1
  row_to_names(row_number = 1) %>% #names the columns with row 1
  as.data.frame() %>% # transform as data frame
  filter(grepl("Countries",rownames(.)))%>% # filter to have only "Countries" rows
  mutate(across(everything(), ~ifelse(.x==1, 0,1))) %>% #Invert 0 and 1
  mutate(across(everything(), ~cumsum(.x))) %>% #Calculate cumulative sum
  t() #transpose the new data frame

> df2
   CountriesX1 CountriesX2 CountriesX3 CountriesX4 CountriesX5 CountriesX6 CountriesX7 CountriesX8 CountriesX9
1            1           2           3           4           4           4           4           4           5
2            1           1           2           3           3           4           4           5           5
3            1           1           1           2           2           3           3           4           4
4            0           0           1           2           3           3           3           3           4
5            0           0           0           0           0           0           0           1           1
6            0           0           1           2           2           2           2           3           3
7            0           1           1           2           2           3           4           5           5
8            0           1           2           2           3           4           4           5           6
9            0           1           1           2           2           2           3           4           5
10           1           2           3           3           3           4           4           5           5

Then the initial data frame (df1) can be filtered by the intermediate data frame (df2). For the columns of interest, here "Countries", a threshold can be made to change values to "Dis" if more that 2 errors were made:

df1[2:10][df2>2]="Dis" 

> df1
   player CountriesX1 CountriesX2 CountriesX3 CountriesX4 CountriesX5 CountriesX6 CountriesX7 CountriesX8 CountriesX9 Flag
1       1           0           0         Dis         Dis         Dis         Dis         Dis         Dis         Dis    0
2       2           0           1           0         Dis         Dis         Dis         Dis         Dis         Dis    0
3       3           0           1           1           0           1         Dis         Dis         Dis         Dis    1
4       4           1           1           0           0         Dis         Dis         Dis         Dis         Dis    0
5       5           1           1           1           1           1           1           1           0           1    1
6       6           1           1           0           0           1           1           1         Dis         Dis    1
7       7           1           0           1           0           1         Dis         Dis         Dis         Dis    1
8       8           1           0           0           1         Dis         Dis         Dis         Dis         Dis    0
9       9           1           0           1           0           1           1         Dis         Dis         Dis    0
10     10           0           0         Dis         Dis         Dis         Dis         Dis         Dis         Dis    1
Bushidov0
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