## Introduction

At the end of Part 1 of Episode 3 on my series on Ghanaian elections, I performed a quick sum of the percentage values obtained by the political parties grouping them by region and some had 101%. This figure was exclusive of the percentage of rejected ballots. Sum of Percentage Values Per Region for Parties

This did not sit well with me, so I decided to enquire from people whom I believe know much about elections than myself.I learnt that the percentage vote for each party is calculated as:

`(Number of Party's Votes/**Total Number of Votes**)*100%`.

This was right by me as the sum of all percentage values for each party and the percent of rejected ballots grouped by region should add up to 100%. But I realized the percentage values from the original dataset was calculated as:

`(Number of Party's Votes/**Total Number of Valid Votes**)*100%`

Hence the difference in both calculations is the denominator.

NB:

So I got my Disqus up and running. Please let me know what you think about the method of calculating the percentage values for each political party. Come on let us discuss!! (no pun intended)

With this idea in mind, I will have to update the final dataset from Episode 3. Join me on this journey of data updating

## Importing Library and Reading In Data

``````library(tidyverse)

vote_data <- vote_data[, -1]
``````## # A tibble: 6 x 12
##   Region `J.J Rawlings` `NDC%Vote` `J.A Kufuor` `NPP%Vote` `E.N Mahama`
##   <chr>           <dbl>      <dbl>        <dbl>      <dbl>        <dbl>
## 1 WESTE~         405992       57.3       289730      40.9         12862
## 2 CENTR~         330841       55.2       259555      43.3          8715
## 3 GT AC~         658626       54.0       528484      43.3         32723
## 4 VOLTA~         690421       94.5        34538       4.73         5292
## 5 EASTE~         459092       53.8       384597      45.0         10251
## 6 ASHAN~         412474       32.8       827804      43.8         17736
## # ... with 6 more variables: `PNC%Vote` <dbl>, Valid_Votes <dbl>,
## #   Rejected_Ballots <dbl>, Tt_Votes_Cast <dbl>, Tt_Reg_Voters <dbl>,
## #   `Turnout%` <dbl>``````

So I will be using my favorite R library tidyverse only as it contains all the functions I will need. Next, I am going to read in the data, it is available for download here. This is a cleaned up but not final version of the data downloaded from the Electoral Commission of Ghana’s website. Below are a few actions I performed on it

1. Removed few rows.
2. Renamed the columns.
3. Removed all row and sum totals.

And if you would like to know the details on the above steps,check the sections from Importing Libraries and Data Extraction to More data cleaning in Episode 3 of the blog series. In the final line of the code above, first column which is an auto-increment value from 1.

## Structure of the dataset

``dim(vote_data)``
``##  10 12``
``summary(vote_data)``
``````##     Region           J.J Rawlings       NDC%Vote       J.A Kufuor
##  Length:10          Min.   :145812   Min.   :32.79   Min.   : 21871
##  Class :character   1st Qu.:340638   1st Qu.:54.30   1st Qu.: 93481
##  Mode  :character   Median :400687   Median :59.21   Median :245006
##                     Mean   :409946   Mean   :61.41   Mean   :283488
##                     3rd Qu.:447438   3rd Qu.:67.18   3rd Qu.:360880
##                     Max.   :690421   Max.   :94.55   Max.   :827804
##     NPP%Vote       E.N Mahama       PNC%Vote        Valid_Votes
##  Min.   : 4.73   Min.   : 5292   Min.   : 0.7247   Min.   : 195437
##  1st Qu.:21.26   1st Qu.:10904   1st Qu.: 1.4210   1st Qu.: 600659
##  Median :38.44   Median :16186   Median : 2.0501   Median : 674529
##  Mean   :31.86   Mean   :21114   Mean   : 4.5293   Mean   : 714547
##  3rd Qu.:43.32   3rd Qu.:31481   3rd Qu.: 5.0658   3rd Qu.: 823018
##  Max.   :45.04   Max.   :45696   Max.   :14.2010   Max.   :1258014
##  Min.   :    0    Min.   : 206200   Min.   : 272015   Min.   : 0.00
##  1st Qu.: 4518    1st Qu.: 615029   1st Qu.: 783210   1st Qu.:72.59
##  Median :10384    Median : 685494   Median : 900378   Median :78.76
##  Mean   : 9327    Mean   : 725658   Mean   : 927961   Mean   :62.72
##  3rd Qu.:12024    3rd Qu.: 825604   3rd Qu.:1034002   3rd Qu.:80.41
##  Max.   :22387    Max.   :1270071   Max.   :1592854   Max.   :81.84``````

Have you noticed some columns have 0 as a value, let me give you a clue. Check the minimum values. Time is up, the image below shows the columns which have this value. Columns with 0

1. Number of Rejected Ballots for the NORTHERN REGION.
2. % turnout for the NORTHERN REGION.
3. % turnout for the UPPER WEST REGION.

Fortunately, these are values can be calculated in a few steps:

• For 1: Subtract the number of valid votes from total registered votes.
• For 2 & 3: Calculate the Turnout percent for points 2 and 3

These calculations will be done along the line.

## Storing Non-Percentage Columns

``````non_percent <- vote_data[, !grepl("%", colnames(vote_data))]
colnames(non_percent)``````
``````##  "Region"           "J.J Rawlings"     "J.A Kufuor"
##  "E.N Mahama"       "Valid_Votes"      "Rejected_Ballots"
``head(non_percent, 2)``
``````## # A tibble: 2 x 8
##   Region `J.J Rawlings` `J.A Kufuor` `E.N Mahama` Valid_Votes
##   <chr>           <dbl>        <dbl>        <dbl>       <dbl>
## 1 WESTE~         405992       289730        12862      708584
## 2 CENTR~         330841       259555         8715      599111
## # ... with 3 more variables: Rejected_Ballots <dbl>, Tt_Votes_Cast <dbl>,
## #   Tt_Reg_Voters <dbl>``````

Next, a dataframe of columns whose values are not percentages: that are of the integer data type are separated since the rest of the calculation is based on them. In creating the dataframe, I used regular expressions to extract all columns without the % sign in its name.

## Creating the calc_percent function and Calling it

``````calc_percent <- function(col1, col2){
return ((col1/col2) * 100)
}``````

Using the formula stated in Introduction, the function calc_percent is created. The details for the function are as follows:

• The function takes 2 arguments:
1. col1: column whose percentage value we are interested in. Example: % of NDC’s votes in the Northern Region
2. col2: column that has the total value. Example: Total Number of Votes in the Northern Region.
• col1 is divided by col2 and the result is multiplied by 100% to return the percentage value of col1.
• The result above is returned to the variable that called the function.
• Also, note that the result of this function will be a Series of percentage value for each of the 10 regions.

Moving on, I called this function to calculate the percentage of turnout for each region, the result was stored under the name Percent_Turnout and assigned back to the non_percent dataframe.

``````non_percent["Percent_Turnout"] <- calc_percent(non_percent\$Tt_Votes_Cast,non_percent\$Tt_Reg_Voters)

non_percent[, c("Region", "Percent_Turnout")]``````
``````## # A tibble: 10 x 2
##    Region              Percent_Turnout
##    <chr>                         <dbl>
##  1 WESTERN REGION                 74.5
##  2 CENTRAL REGION                 79.1
##  3 GT ACCRA  REGION               78.4
##  4 VOLTA  REGION                  81.8
##  5 EASTERN  REGION                81.1
##  6 ASHANTI  REGION                79.7
##  7 BRONG AHAFO  REGION            72.0
##  8 NORTHERN  REGION               77.0
##  9 UPPER EAST  REGION             80.6
## 10 UPPER WEST REGION              75.8``````

The output shows usually more than 2/3rds of the registered voters come out to vote for the presidential elections.

Rightaway, I question I am interested in answering after the data extraction for all the Ghanaian elections will be How much have this value changed since the 1992 elections till 2016 elections?

#### Remember, the 0 values?

Fortunately, 2 & 3: the Northern and Upper West Region have values for the column Turnout%. The lone ranger now is 1: Number of Rejected Ballots in the Northern Region. For this value only, I will re-calculate the number of rejected votes. This can be found in the column Rejected_Ballots.

The formula is: `Rejected_Ballots = Tt_Votes_Cast - Valid_Votes`

``````# number of rejected votes

# confirm Northern Region now has a value
non_percent[8, c("Region","Rejected_Ballots")]``````
``````## # A tibble: 1 x 2
##   Region           Rejected_Ballots
##   <chr>                       <dbl>
## 1 NORTHERN  REGION            17840``````

About 17,000 of rejected ballots, this is a huge figure. TO put this into perspective, in 2016, the only Independent candidate; Jacob Osei Yeboah pulled 15,889 votes from all the regions.

Going back to the function calc_percent, it is used to find percentage values per region for the details below. All of these would be assigned back to the non_percent dataframe as columns

``````# confirm the number of columns before calculation
dim(non_percent)``````
``##  10  9``
``````# percentage of valid votes

# percentage of rejected ballots

# percentage_ndc

# percentage_npp

# percentage_pnc

# validate an increase in number of columns
dim(non_percent)``````
``##  10 14``
``head(select(non_percent, c("Region", "Percent_Turnout":"Percent_Reject_Ballots")),5)``
``````## # A tibble: 5 x 4
##   <chr>                     <dbl>               <dbl>                 <dbl>
## 1 WESTERN REGION             74.5                98.3                 1.66
## 2 CENTRAL REGION             79.1                97.8                 2.16
## 3 GT ACCRA  REGI~            78.4                99.4                 0.571
## 4 VOLTA  REGION              81.8                99.5                 0.502
## 5 EASTERN  REGION            81.1                99.7                 0.259``````
``head(select(non_percent, c("Region", "Percentage_NDC":"Percentage_PNC")),5)``
``````## # A tibble: 5 x 4
##   Region           Percentage_NDC Percentage_NPP Percentage_PNC
##   <chr>                     <dbl>          <dbl>          <dbl>
## 1 WESTERN REGION             56.3          40.2           1.79
## 2 CENTRAL REGION             54.0          42.4           1.42
## 3 GT ACCRA  REGION           53.7          43.1           2.67
## 4 VOLTA  REGION              94.1           4.71          0.721
## 5 EASTERN  REGION            53.6          44.9           1.20``````

Using select from the dplyr package, I was able to show subsets of the columns. One can see that the values have been calculated successfully.

## Verifying the Sum of Percentages Per Region

This whole point of this article was to calculate using a different formula, the percentage values for political parties and other relevant columns. Next to group them by region and sum up the calculated values. Confirm the result is 100%.

To cut the very long story short, the procedures below were used to verify the calculated results.

1. From the dataframe non_percent, assign all columns with ‘Percentage_’ in their names to a new dataframe party_percent_cols. The list of affected columns are: Percentage_NDC, Percentage_NPP & Percentage_PNC.
2. In the new dataframe, party_percent_cols:
• Add the columns: Region and Rejected_Ballots from the dataframe non_percent.
• Reorder the columns so the Region column comes first then it is followed by the other numerical columns.
• Convert the dataframe from wide to long format.
• Separate a column to get the party name.
• Group by the Region column and add the percentages.
• Keep the relevant rows, take off all rows with rejected votes

### 1. Keep Columns with ‘Percentage_’

``````party_percent_cols <- non_percent[,grepl("Percentage_", colnames(non_percent))]
colnames(party_percent_cols)``````
``##  "Percentage_NDC" "Percentage_NPP" "Percentage_PNC"``
``head(party_percent_cols)``
``````## # A tibble: 6 x 3
##   Percentage_NDC Percentage_NPP Percentage_PNC
##            <dbl>          <dbl>          <dbl>
## 1           56.3          40.2           1.79
## 2           54.0          42.4           1.42
## 3           53.7          43.1           2.67
## 4           94.1           4.71          0.721
## 5           53.6          44.9           1.20
## 6           32.5          65.2           1.40``````

### 2.1 Add columns: Region & Percent_Reject_Ballots

``````party_percent_cols[, c("Region","Percent_Reject_Ballots")] <- non_percent[, c("Region", "Percent_Reject_Ballots")]

# verify the column names have been added
colnames(party_percent_cols)``````
``````##  "Percentage_NDC"         "Percentage_NPP"
##  "Percentage_PNC"         "Region"
##  "Percent_Reject_Ballots"``````
``````# view the first 5 rows
``````## # A tibble: 5 x 5
##   Percentage_NDC Percentage_NPP Percentage_PNC Region     Percent_Reject_B~
##            <dbl>          <dbl>          <dbl> <chr>                  <dbl>
## 1           56.3          40.2           1.79  WESTERN R~             1.66
## 2           54.0          42.4           1.42  CENTRAL R~             2.16
## 3           53.7          43.1           2.67  GT ACCRA ~             0.571
## 4           94.1           4.71          0.721 VOLTA  RE~             0.502
## 5           53.6          44.9           1.20  EASTERN  ~             0.259``````

### 2.2. Reorder columns in party_percent_cols

``````party_percent_cols <- party_percent_cols[, c(4,1,2,3,5)]
``````## # A tibble: 5 x 5
##   Region     Percentage_NDC Percentage_NPP Percentage_PNC Percent_Reject_B~
##   <chr>               <dbl>          <dbl>          <dbl>             <dbl>
## 1 WESTERN R~           56.3          40.2           1.79              1.66
## 2 CENTRAL R~           54.0          42.4           1.42              2.16
## 3 GT ACCRA ~           53.7          43.1           2.67              0.571
## 4 VOLTA  RE~           94.1           4.71          0.721             0.502
## 5 EASTERN  ~           53.6          44.9           1.20              0.259``````

### 2.3. Convert dataframe from wide to long format.

``````gather_percent_parties <- party_percent_cols %>%
gather("Percentage","PercentVote",-1)

# the columns have reduced and the rows have .......
dim(gather_percent_parties)``````
``##  40  3``
``head(gather_percent_parties)``
``````## # A tibble: 6 x 3
##   Region           Percentage     PercentVote
##   <chr>            <chr>                <dbl>
## 1 WESTERN REGION   Percentage_NDC        56.3
## 2 CENTRAL REGION   Percentage_NDC        54.0
## 3 GT ACCRA  REGION Percentage_NDC        53.7
## 4 VOLTA  REGION    Percentage_NDC        94.1
## 5 EASTERN  REGION  Percentage_NDC        53.6
## 6 ASHANTI  REGION  Percentage_NDC        32.5``````

### 2.4 Separate the Percentage column into 2 to get name of political party

``````gather_percent_parties <- gather_percent_parties %>%
separate(Percentage, c("Percentage","PartyName"))

# remove the Percentage column
gather_percent_parties\$Percentage <- NULL

# the Percentage column has truly been taken off
``````## # A tibble: 6 x 3
##   Region           PartyName PercentVote
##   <chr>            <chr>           <dbl>
## 1 WESTERN REGION   NDC              56.3
## 2 CENTRAL REGION   NDC              54.0
## 3 GT ACCRA  REGION NDC              53.7
## 4 VOLTA  REGION    NDC              94.1
## 5 EASTERN  REGION  NDC              53.6
## 6 ASHANTI  REGION  NDC              32.5``````

### 2.5. Group by Region column and add the percentages

``````gather_percent_parties %>%
group_by(Region) %>%
summarise(Reg_Percent = sum(PercentVote))``````
``````## # A tibble: 10 x 2
##    Region              Reg_Percent
##    <chr>                     <dbl>
##  1 ASHANTI  REGION             100
##  2 BRONG AHAFO  REGION         100
##  3 CENTRAL REGION              100
##  4 EASTERN  REGION             100
##  5 GT ACCRA  REGION            100
##  6 NORTHERN  REGION            100
##  7 UPPER EAST  REGION          100
##  8 UPPER WEST REGION           100
##  9 VOLTA  REGION               100
## 10 WESTERN REGION              100``````

### 2.6 Keep only relevant rows

``````# advisable to check the dimensions before and after
dim(gather_percent_parties)``````
``##  40  3``
``````percent_parties <- gather_percent_parties %>%
filter(PartyName != "Reject")

dim(percent_parties)``````
``##  30  3``

## Updating the PercentVote column in the earlier dataset

Now to the final part of this enterprise,

1. Read in the final dataset from Part 1, of Episode 3
2. Calculate the sum of percentages per Region before the update
3. Replace the PercentVote column with the same from the percent_parties dataframe.
4. To confirm the changes, calculate the sum of percentages per Region which should now be lower than 100%.
``````full_1996 <- read_csv("presidential_results_1996.csv")

# Sum of values grouped by Region
full_1996 %>%
group_by(Region) %>%
summarise(Regional_Percent = sum(PercentVote))``````
``````## # A tibble: 10 x 2
##    Region              Regional_Percent
##    <chr>                          <dbl>
##  1 ASHANTI  REGION                   78
##  2 BRONG AHAFO  REGION              100
##  3 CENTRAL REGION                    99
##  4 EASTERN  REGION                  100
##  5 GT ACCRA  REGION                 100
##  6 NORTHERN  REGION                 100
##  7 UPPER EAST  REGION               100
##  8 UPPER WEST REGION                100
##  9 VOLTA  REGION                    101
## 10 WESTERN REGION                   100``````
``````full_1996\$PercentVote <- percent_parties\$PercentVote

# save the new data as a .csv file
write.csv(full_1996, "final_presidential_results_1996.csv")

full_1996 %>%
group_by(Region) %>%
summarise(Regional_Percent = sum(PercentVote))``````
``````## # A tibble: 10 x 2
##    Region              Regional_Percent
##    <chr>                          <dbl>
##  1 ASHANTI  REGION                 99.1
##  2 BRONG AHAFO  REGION             98.5
##  3 CENTRAL REGION                  97.8
##  4 EASTERN  REGION                 99.7
##  5 GT ACCRA  REGION                99.4
##  6 NORTHERN  REGION                97.1
##  7 UPPER EAST  REGION              93.7
##  8 UPPER WEST REGION               94.8
##  9 VOLTA  REGION                   99.5
## 10 WESTERN REGION                  98.3``````

This has been a very long blog post on an update of a single column. I could have easily updated the values and put a one-liner in Episode 2 informing everyone about the change.

But I wanted to go through the whole process with you and I am glad I did. The presidential results for 1996 Ghanaian elections is available here. So until the next blog post, thank you and bye bye.