1
$\begingroup$

Performing some calculations on a dataframe and stuck trying to calculate a few percentages. Trying to append 3 additional columns added for %POS/NEG/NEU. E.g., the sum of amount col for all observations w/ POS Direction in both Drew & A/total sum of all amounts for Drew **

Name     Rating   Amount    Price    Rate   Type    Direction
Drew     A        455       99.54    4.5    white   POS
Drew     A        655       88.44    5.3    white   NEG
Drew     B        454       54.43    3.4    blue    NEU
Drew     B        654       33.54    5.4    blue    POS
Drew     C        754       54.43    4.3    green   POS
Jon      A        454       65.23    3.4    blue    NEG
Jon      B        954       86.34    4.3    blue    NEG
Jon      B        545       34.54    4.4    green   NEG
Jon      C        454       65.45    3.4    green   POS
Jon      C        544       65.55    4.4    blue    NEU
Nick     A        675       54.33    3.4    white   POS
Nick     A        565       65.33    3.4    white   POS
Nick     B        343       54.44    6.4    blue    POS
Nick     C        656       65.33    4.3    green   NEG
Nick     C        655       94.44    3.3    green   NEU

To Get the Following Output Calculation for POS/NEG/NEU columns

Name   Rating   sum  count percent wm_price wm_rate mode_type POS NEG NEU
Drew    A                                                    .3735
Drew    B                                                    .3728
Drew    C
Jon     A
Jon     B
Jon     C
Nick    A
Nick    B
Nick    C

Here's what i got so far but im stuck implementing the pivot_wider for calculating/appending the % direction (POS/NEG/NEU) for each rating category of Each Name Any feedback appreciated!

df <- df %>% group_by(Name, Rating) %>% summarize(sum_rating = sum(Amount), count = n(), wm_Price = weighted.mean(Price,Amount), wm_Rate = weighted.mean(Rate,Amount), mode_Type = mode(Type)) %>% mutate(pct_rating = sum_rating/sum(sum_rating)) %>% pivot_wider(names_from = Direction, values_from = Amount/sum Amount)

$\endgroup$
0
$\begingroup$

I cannot get pivot_wider to work here either, but I can replicate your described operation without it.

df %>% group_by(Name) %>%                                      # Group only by Name first
  mutate(Total = sum(Amount)) %>%                              # Total Amount by Name
  group_by(Name, Rating) %>%                                   # Now perform your calculations 
  summarize(sum_rating = sum(Amount),
        count = n(),
        wm_Price = weighted.mean(Price, Amount),
        wm_Rate = weighted.mean(Rate, Amount),
        mode_Type = mode(Type),
        POS = sum(Amount * (Direction == "POS")) / max(Total), # This is % POS
        NEU = sum(Amount * (Direction == "NEU")) / max(Total), # This is % NEU
        NEG = sum(Amount * (Direction == "NEG")) / max(Total)  # This is % NEG
        ) %>%
   mutate(pct_rating = sum_rating / sum(sum_rating)) 

Here is the output

# A tibble: 9 x 11
# Groups:   Name [3]
  Name  Rating sum_rating count wm_Price wm_Rate mode_Type   POS   NEU   NEG
  <fct> <fct>       <int> <int>    <dbl>   <dbl> <fct>     <dbl> <dbl> <dbl>
1 Drew  A            1110     2     93.0    4.97 white     0.153 0     0.220
2 Drew  B            1108     2     42.1    4.58 blue      0.220 0.153 0    
3 Drew  C             754     1     54.4    4.3  green     0.254 0     0    
4 Jon   A             454     1     65.2    3.4  blue      0     0     0.154
5 Jon   B            1499     2     67.5    4.34 blue      0     0     0.508
6 Jon   C             998     2     65.5    3.95 green     0.154 0.184 0    
7 Nick  A            1240     2     59.3    3.4  white     0.428 0     0    
8 Nick  B             343     1     54.4    6.4  blue      0.119 0     0    
9 Nick  C            1311     2     79.9    3.80 green     0     0.226 0.227
# ... with 1 more variable: pct_rating <dbl>

This method works because r can do math on logical values, treating them as TRUE = 1 and FALSE = 0. Thus, if you want to count the number of occurrences of POS in the Direction column, you could sum a logical vector:

sum(Direction == 'POS')

If you want to know the percent of the rows that contain POS, then use the mean:

mean(Direction == 'POS')

You can even weight that mean by another vector:

weighted.mean(Direction == 'POS', Amount)

The results of my calculation do not match your sample output, so I want to check to make sure I understand what you want the column POS to contain for each row. I understand your desired calculation to be as follows: the Amount of POS for Drew and A (455) divided by the total Amount for Drew (455 + 655 + 454 + 654 + 754 = 2972). Thus, 455 / 2972 = 0.153. I cannot replicate the 0.3735 value you have in your sample output.

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.