INTRODUCTION
India is a country of 28 states and 7 Union Territories (UTs). Universal and compulsory education is a cherished dream of the republic of India. Primary Education is a basic right of every citizen, but recent study on the dropouts from schools has stated that every year 62% of students have been dropping out. This is raising concern in the country. Education enriches people’s understanding of their own country and the world. It improves the quality of life, promotes creativity, productivity and entrepreneurship leading to the economic development of the country. As a nation with second most highest population in the world, India needs to have access to quality education to its citizens.
This motivated me to perform my analysis on the Indian Education System, specifically on the enrollment and dropout data. I have found 7 data sets from data.gov.in the official Indian government run website for data. As this is my first project in the field of data analysis I really wanted to work on a topic which I am always passionate about - Education. I am very proud and happy with the findings of my study.
In this project I have worked on 7 data sets related to the Indian Education System from 2013-2016. First two data sets talk about the Gross Enrollment Ratio and Dropout Ratio, remaining 5 talk about the availability of basic facilities (Water, Electricity, Boys & Girls Toilets and Computers) in Schools. Every data set has State/Union Territory, Year and Percentage data across various levels of the Education - Primary, Upper Primary, Secondary and Higher Secondary.
Lower Primary/ Primary - Nursery to class 1st Upper Primary - Class 1st to 5th Secondary - Class 6th to 8th Higher Secondary/Higher Secondary - Class 9th and 10th
The aim of this project is to perform Exploratory Data Analysis(EDA) of the 7 data sets to:
- Analyze the Gross Enrollment Ratio and Dropout Ratio in the above mentioned classes All over India & across states and understand the,
- Correlation between GER and Dropout Ratio
- Trends of boys and girls enrollment and dropout
- What are the states with lowest dropout and enrollment percentage
and provide few recommendations to the Indian Government based on the Analysis.
Compare the states with lowest dropout ratio with the available facilities data sets.
To find out the impact of non-availability of these facilities on the dropout ratio.
To analyze the trends of available facilities data sets across India.
Loading the packages
Dataset-1 | Gross Enrollment Ratio from 2013-2016 across all Indian States
Gross Enrollment Ratio (GER) or Gross Enrollment Index (GEI) is a statistical measure used in the education sector, to determine the number of students enrolled in school at several different grade levels (like elementary, middle school and high school), and use it to show the ratio of the number of students who live in that country to those who qualify for the particular grade level.
The GER can be over 100% as it includes students who may be older or younger than the official age group.
For instance, in India it improved from 25.8 to 26.3, the GER includes students who are repeating a grade, those who enrolled late and are older than their classmates, or those who have advanced quickly and are younger than their classmates. This allows the total enrollment to exceed the population that corresponds to that level of education.
Calculation Method
a = number of students enrolled in a given level b = population of the age group corresponds to given level of education India
GER=a/b×100
Reading Dataset-1
gross_enrollment_ratio <- read_csv("601 Major Project/gross-enrollment-ratio.csv")
dim(gross_enrollment_ratio)
[1] 110 14head(gross_enrollment_ratio)
# A tibble: 6 x 14
State_UT Year Primary_Boys Primary_Girls Primary_Total
<chr> <chr> <dbl> <dbl> <dbl>
1 Andaman & Nicobar Is~ 2013~ 95.9 92.0 93.9
2 Andhra Pradesh 2013~ 96.6 96.9 96.7
3 Arunachal Pradesh 2013~ 129. 128. 128.
4 Assam 2013~ 112. 115. 113.
5 Bihar 2013~ 95.0 101. 98.0
6 Chandigarh 2013~ 88.4 96.1 91.8
# ... with 9 more variables: Upper_Primary_Boys <dbl>,
# Upper_Primary_Girls <dbl>, Upper_Primary_Total <dbl>,
# Secondary_Boys <dbl>, Secondary_Girls <dbl>,
# Secondary_Total <dbl>, Higher_Secondary_Boys <chr>,
# Higher_Secondary_Girls <chr>, Higher_Secondary_Total <chr>colnames(gross_enrollment_ratio)
[1] "State_UT" "Year"
[3] "Primary_Boys" "Primary_Girls"
[5] "Primary_Total" "Upper_Primary_Boys"
[7] "Upper_Primary_Girls" "Upper_Primary_Total"
[9] "Secondary_Boys" "Secondary_Girls"
[11] "Secondary_Total" "Higher_Secondary_Boys"
[13] "Higher_Secondary_Girls" "Higher_Secondary_Total"str(gross_enrollment_ratio)
spec_tbl_df [110 x 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ State_UT : chr [1:110] "Andaman & Nicobar Islands" "Andhra Pradesh" "Arunachal Pradesh" "Assam" ...
$ Year : chr [1:110] "2013-14" "2013-14" "2013-14" "2013-14" ...
$ Primary_Boys : num [1:110] 95.9 96.6 129.1 111.8 95 ...
$ Primary_Girls : num [1:110] 92 96.9 127.8 115.2 101.2 ...
$ Primary_Total : num [1:110] 93.9 96.7 128.5 113.4 98 ...
$ Upper_Primary_Boys : num [1:110] 94.7 82.8 112.6 87.8 80.6 ...
$ Upper_Primary_Girls : num [1:110] 89 84.4 115.3 98.7 94.9 ...
$ Upper_Primary_Total : num [1:110] 91.8 83.6 113.9 93.1 87.2 ...
$ Secondary_Boys : num [1:110] 102.9 73.8 88.4 65.6 57.7 ...
$ Secondary_Girls : num [1:110] 97.4 76.8 84.9 77.2 63 ...
$ Secondary_Total : num [1:110] 100.2 75.2 86.7 71.2 60.1 ...
$ Higher_Secondary_Boys : chr [1:110] "105.4" "59.83" "65.16" "31.78" ...
$ Higher_Secondary_Girls: chr [1:110] "96.61" "60.83" "65.38" "34.27" ...
$ Higher_Secondary_Total: chr [1:110] "101.28" "60.3" "65.27" "32.94" ...
- attr(*, "spec")=
.. cols(
.. State_UT = col_character(),
.. Year = col_character(),
.. Primary_Boys = col_double(),
.. Primary_Girls = col_double(),
.. Primary_Total = col_double(),
.. Upper_Primary_Boys = col_double(),
.. Upper_Primary_Girls = col_double(),
.. Upper_Primary_Total = col_double(),
.. Secondary_Boys = col_double(),
.. Secondary_Girls = col_double(),
.. Secondary_Total = col_double(),
.. Higher_Secondary_Boys = col_character(),
.. Higher_Secondary_Girls = col_character(),
.. Higher_Secondary_Total = col_character()
.. )
- attr(*, "problems")=<externalptr> As you can see, 3 columns (Higher_Secondary_Boys, Higher_Secondary_Girls, Higher_Secondary_Total) are character instead of double. They have NR, @ in the observations. The data needs to be cleaned.
Data Wrangling
gross_enrollment_ratio[ gross_enrollment_ratio == "NR" ] <- NA
gross_enrollment_ratio[ gross_enrollment_ratio == "@" ] <- NA
ger1 <- data.frame(gross_enrollment_ratio)
ger <- na.exclude(ger1)
ger$Higher_Secondary_Boys = as.numeric(ger$Higher_Secondary_Boys)
ger$Higher_Secondary_Girls = as.numeric(ger$Higher_Secondary_Girls)
ger$Higher_Secondary_Total = as.numeric(ger$Higher_Secondary_Total)
str(ger)
'data.frame': 108 obs. of 14 variables:
$ State_UT : chr "Andaman & Nicobar Islands" "Andhra Pradesh" "Arunachal Pradesh" "Assam" ...
$ Year : chr "2013-14" "2013-14" "2013-14" "2013-14" ...
$ Primary_Boys : num 95.9 96.6 129.1 111.8 95 ...
$ Primary_Girls : num 92 96.9 127.8 115.2 101.2 ...
$ Primary_Total : num 93.9 96.7 128.5 113.4 98 ...
$ Upper_Primary_Boys : num 94.7 82.8 112.6 87.8 80.6 ...
$ Upper_Primary_Girls : num 89 84.4 115.3 98.7 94.9 ...
$ Upper_Primary_Total : num 91.8 83.6 113.9 93.1 87.2 ...
$ Secondary_Boys : num 102.9 73.8 88.4 65.6 57.7 ...
$ Secondary_Girls : num 97.4 76.8 84.9 77.2 63 ...
$ Secondary_Total : num 100.2 75.2 86.7 71.2 60.1 ...
$ Higher_Secondary_Boys : num 105.4 59.8 65.2 31.8 23.3 ...
$ Higher_Secondary_Girls: num 96.6 60.8 65.4 34.3 24.2 ...
$ Higher_Secondary_Total: num 101.3 60.3 65.3 32.9 23.7 ...
- attr(*, "na.action")= 'exclude' Named int [1:2] 26 99
..- attr(*, "names")= chr [1:2] "26" "99"plotting All India girls enrollment ratio:
Year Primary_Girls Upper_Primary_Girls Secondary_Girls
1 2013-14 102.65 92.75 76.47
2 2014-15 101.43 95.29 78.94
3 2015-16 100.69 97.57 80.97
Higher_Secondary_Girls
1 51.58
2 53.81
3 56.41 fig1 <- pivot_longer(all_india_ger_girls, c(Primary_Girls, Upper_Primary_Girls, Secondary_Girls, Higher_Secondary_Girls), names_to = "Education_Level", values_to = "GER")
ggplot(fig1, aes(x=Year, y=GER, fill=Education_Level)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-1: Gross Enrollment Ratio of Girls in India") + geom_text(aes(label=GER), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + theme_classic()
Findings:
Girls who join Primary School have exceeded 100 GER which means the students also include who are repeating a grade, or those who enrolled late and are older than their classmates, or those who have advanced quickly and are younger than their classmates. This is a positive sign in a developing country like India.
There’s a considerable increase in GER from 2013-2016 for girls who are joining Upper Primary, Secondary, and Higher Secondary Schools. This is again a very positive sign.
Lowest is the Higher Secondary Girls enrollment with 51.58, 53.81, 56.41. The Indian Government should take measures to increase this.
Plotting All India boys enrollment ratio:
Year Primary_Boys Upper_Primary_Boys Secondary_Boys
1 2013-14 100.20 86.31 76.80
2 2014-15 98.85 87.71 78.13
3 2015-16 97.87 88.72 79.16
Higher_Secondary_Boys
1 52.77
2 54.57
3 55.95 fig2 <- pivot_longer(all_india_ger_boys, c(Primary_Boys, Upper_Primary_Boys, Secondary_Boys, Higher_Secondary_Boys), names_to = "Education_Level", values_to = "GER")
ggplot(fig2, aes(x=Year, y=GER, fill=Education_Level)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-2: Gross Enrollment Ratio of boys in India") + geom_text(aes(label=GER), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + theme_classic()
Findings:
- The trends for Primary Schools are the same as girls. Again, if we could look further into data about the GER, we could gather some important information as to why there’s a decrease in GER for Girls and Boys.
- Compared to Girls, the GER of Boys in Upper Primary is very low. This is a negative sign because this might affect the enrollments of Secondary and Higher Secondary Schools among boys.
- There is not much difference between the enrollment of girls and boys in secondary level.
- Again the enrollement ratio of boys is the lowest in Higher Secondary same as girls.
fig3 <- pivot_longer(all_india_ger, c(Primary_Boys, Primary_Girls, Primary_Total, Upper_Primary_Boys, Upper_Primary_Girls, Upper_Primary_Total, Secondary_Boys, Secondary_Girls, Secondary_Total, Higher_Secondary_Girls, Higher_Secondary_Boys, Higher_Secondary_Total), names_to = "Education_Level")
ggplot(fig3, aes(x=value, y=Education_Level)) + geom_boxplot(color="red") + geom_text(aes(label=value), size=4, vjust=0) + labs(title = "Fig-3: All India Enrollment Ratio - Education Wise ", x="Enrollment Percentage", y="Education Level") + facet_wrap(~Year)
Finding:
- The lowest enrollment is in Higher Secondary level.
State-Wise Gross Enrollment Analysis
State_UT Year Primary_Boys Primary_Girls
1 Andaman & Nicobar Islands 2013-14 95.88 91.97
2 Andhra Pradesh 2013-14 96.62 96.87
3 Arunachal Pradesh 2013-14 129.12 127.77
4 Assam 2013-14 111.77 115.16
5 Bihar 2013-14 95.03 101.15
6 Chandigarh 2013-14 88.42 96.09
Primary_Total Upper_Primary_Boys Upper_Primary_Girls
1 93.93 94.70 88.98
2 96.74 82.81 84.38
3 128.46 112.64 115.27
4 113.43 87.85 98.69
5 97.96 80.60 94.92
6 91.85 99.93 103.02
Upper_Primary_Total Secondary_Boys Secondary_Girls Secondary_Total
1 91.83 102.89 97.36 100.16
2 83.57 73.76 76.77 75.20
3 113.94 88.37 84.89 86.65
4 93.13 65.60 77.20 71.21
5 87.24 57.66 62.96 60.08
6 101.27 92.08 92.16 92.11
Higher_Secondary_Boys Higher_Secondary_Girls Higher_Secondary_Total
1 105.40 96.61 101.28
2 59.83 60.83 60.30
3 65.16 65.38 65.27
4 31.78 34.27 32.94
5 23.33 24.17 23.70
6 90.50 92.88 91.49I used google maps access key to get the Indian map and to get latitude and longitude coordinates for the states. I merged the coordinates data with my existing dataset.
library(ggmap)
register_google(key = "AIzaSyDc2lDTQRLgvlGtdiZM6hkShq0fW_wv4-0")
coordinates <- geocode(states_ger$State_UT)
plot <- merge(states_ger,coordinates)
head(plot)
State_UT Year Primary_Boys Primary_Girls
1 Andaman & Nicobar Islands 2013-14 95.88 91.97
2 Andhra Pradesh 2013-14 96.62 96.87
3 Arunachal Pradesh 2013-14 129.12 127.77
4 Assam 2013-14 111.77 115.16
5 Bihar 2013-14 95.03 101.15
6 Chandigarh 2013-14 88.42 96.09
Primary_Total Upper_Primary_Boys Upper_Primary_Girls
1 93.93 94.70 88.98
2 96.74 82.81 84.38
3 128.46 112.64 115.27
4 113.43 87.85 98.69
5 97.96 80.60 94.92
6 91.85 99.93 103.02
Upper_Primary_Total Secondary_Boys Secondary_Girls Secondary_Total
1 91.83 102.89 97.36 100.16
2 83.57 73.76 76.77 75.20
3 113.94 88.37 84.89 86.65
4 93.13 65.60 77.20 71.21
5 87.24 57.66 62.96 60.08
6 101.27 92.08 92.16 92.11
Higher_Secondary_Boys Higher_Secondary_Girls Higher_Secondary_Total
1 105.40 96.61 101.28
2 59.83 60.83 60.30
3 65.16 65.38 65.27
4 31.78 34.27 32.94
5 23.33 24.17 23.70
6 90.50 92.88 91.49
lon lat
1 92.65864 11.74009
2 92.65864 11.74009
3 92.65864 11.74009
4 92.65864 11.74009
5 92.65864 11.74009
6 92.65864 11.74009The below map is a terrain style map of India. I wanted to integrate my data with a choropleth map, however i understood that R-Studio has pre-existing choropleth map for world and USA but not for other countries and ggmap supports very few map types - “terrain”, “satellite”, “hybrid” and “roadmap” but not choropleth. I feel this is a drawback for R-Studio as well as ggmaps.
map <- get_map(location = 'India', zoom = 5, maptype= 'terrain', scale = "auto")
Plotting the Education Level - wise GER data in the map
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size= Primary_Boys, colour=Primary_Boys, alpha=0.5))
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Upper_Primary_Boys, colour=Upper_Primary_Boys, alpha=0.5))
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Secondary_Boys, color=Secondary_Boys, alpha=0.5 ))
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Higher_Secondary_Boys, color=Higher_Secondary_Boys, alpha=0.5))
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Primary_Girls, color=Primary_Girls, alpha=0.5))
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Upper_Primary_Girls,color=Upper_Primary_Girls, alpha=0.5))
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Secondary_Girls,color=Secondary_Girls, alpha=0.5))
ggmap(map) + geom_point(data=plot, aes(x =lon, y =lat, size=Higher_Secondary_Girls,color=Higher_Secondary_Girls, alpha=0.5))
Findings from the maps:
- The Primary girls Gross Enrollment Ratio is the highest for Eastern states like Assam, Mizoram, etc and it’s the lowest for Andhra Pradesh and J & K.
- Upper_Primary girls Gross Enrollment Ratio is the lowest in Jammu & Kashmir and that too at an alarming number! AP also has a lot of room for improvement in its GER.
- Secondary girls GER top 3 lowest states are J & K, Uttar Pradesh, and Gujarat. This is really concerning.
The analysis would have been much more clear if the map is choropleth, I would take this as a scope for improvement in my next projects.
states_ger %>%
select(Year, State_UT, Primary_Boys,Upper_Primary_Boys, Secondary_Boys, Higher_Secondary_Boys) %>%
group_by(Year) %>%
summarise(avg_pb=mean(Primary_Boys), avg_upb=mean(Upper_Primary_Boys), avg_sb=mean(Secondary_Boys), avg_hsb=mean(Higher_Secondary_Boys))
# A tibble: 3 x 5
Year avg_pb avg_upb avg_sb avg_hsb
<chr> <dbl> <dbl> <dbl> <dbl>
1 2013-14 105. 96.9 87.2 60.0
2 2014-15 102. 97.0 88.0 60.4
3 2015-16 100. 98.1 86.9 58.2states_ger %>%
select(Year, Primary_Girls,Upper_Primary_Girls, Secondary_Girls, Higher_Secondary_Girls) %>%
group_by(Year) %>%
summarise(avg_pb=mean(Primary_Girls), avg_upb=mean(Upper_Primary_Girls), avg_sb=mean(Secondary_Girls), avg_hsb=mean(Higher_Secondary_Girls))
# A tibble: 3 x 5
Year avg_pb avg_upb avg_sb avg_hsb
<chr> <dbl> <dbl> <dbl> <dbl>
1 2013-14 106. 99.8 88.0 60.5
2 2014-15 103. 102. 89.6 62.2
3 2015-16 101. 104. 89.4 61.8Findings:
- The overall mean enrollment percentage in Primary and Upper Primary levels is greater than or equal to 100% which is very good sign.
- But, the overall mean percentage in Secondary and Higher Secondary is almost same in all the three years, this is where the government has to pitch in and take adequate measures.
My further analysis will focus on analyzing the dropout percentage and finding if we can get any correlation between the Gross Enrollment and Dropout.
Data Set-2 | Dropout Ratio/Percentage across all Indian States from 2013-2016
There are varying definitions on the web for Dropout Ratio. I will keep it simple here. Dropout Ratio simply means any student who leaves school for any reason before graduation or completion of a program of studies without transferring to another school.
Reading Dataset-2
# A tibble: 6 x 14
State_UT year Primary_Boys Primary_Girls Primary_Total
<chr> <chr> <chr> <chr> <chr>
1 A & N Islands 2012-13 0.83 0.51 0.68
2 A & N Islands 2013-14 1.35 1.06 1.21
3 A & N Islands 2014-15 0.47 0.55 0.51
4 Andhra Pradesh 2012-13 3.3 3.05 3.18
5 Andhra Pradesh 2013-14 4.31 4.39 4.35
6 Andhra Pradesh 2014-15 6.57 6.89 6.72
# ... with 9 more variables: `Upper Primary_Boys` <chr>,
# `Upper Primary_Girls` <chr>, `Upper Primary_Total` <chr>,
# `Secondary _Boys` <chr>, `Secondary _Girls` <chr>,
# `Secondary _Total` <chr>, HrSecondary_Boys <chr>,
# HrSecondary_Girls <chr>, HrSecondary_Total <chr>colnames(dropout_ratio)
[1] "State_UT" "year" "Primary_Boys"
[4] "Primary_Girls" "Primary_Total" "Upper Primary_Boys"
[7] "Upper Primary_Girls" "Upper Primary_Total" "Secondary _Boys"
[10] "Secondary _Girls" "Secondary _Total" "HrSecondary_Boys"
[13] "HrSecondary_Girls" "HrSecondary_Total" Datatype of each column
str(dropout_ratio)
spec_tbl_df [110 x 14] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ State_UT : chr [1:110] "A & N Islands" "A & N Islands" "A & N Islands" "Andhra Pradesh" ...
$ year : chr [1:110] "2012-13" "2013-14" "2014-15" "2012-13" ...
$ Primary_Boys : chr [1:110] "0.83" "1.35" "0.47" "3.3" ...
$ Primary_Girls : chr [1:110] "0.51" "1.06" "0.55" "3.05" ...
$ Primary_Total : chr [1:110] "0.68" "1.21" "0.51" "3.18" ...
$ Upper Primary_Boys : chr [1:110] "Uppe_r_Primary" "NR" "1.44" "3.21" ...
$ Upper Primary_Girls: chr [1:110] "1.09" "1.54" "1.95" "3.51" ...
$ Upper Primary_Total: chr [1:110] "1.23" "0.51" "1.69" "3.36" ...
$ Secondary _Boys : chr [1:110] "5.57" "8.36" "11.47" "12.21" ...
$ Secondary _Girls : chr [1:110] "5.55" "5.98" "8.16" "13.25" ...
$ Secondary _Total : chr [1:110] "5.56" "7.2" "9.87" "12.72" ...
$ HrSecondary_Boys : chr [1:110] "17.66" "18.94" "21.05" "2.66" ...
$ HrSecondary_Girls : chr [1:110] "10.15" "12.2" "12.21" "NR" ...
$ HrSecondary_Total : chr [1:110] "14.14" "15.87" "16.93" "0.35" ...
- attr(*, "spec")=
.. cols(
.. State_UT = col_character(),
.. year = col_character(),
.. Primary_Boys = col_character(),
.. Primary_Girls = col_character(),
.. Primary_Total = col_character(),
.. `Upper Primary_Boys` = col_character(),
.. `Upper Primary_Girls` = col_character(),
.. `Upper Primary_Total` = col_character(),
.. `Secondary _Boys` = col_character(),
.. `Secondary _Girls` = col_character(),
.. `Secondary _Total` = col_character(),
.. HrSecondary_Boys = col_character(),
.. HrSecondary_Girls = col_character(),
.. HrSecondary_Total = col_character()
.. )
- attr(*, "problems")=<externalptr> Data Wrangling
library(janitor)
dropout_ratio <- clean_names(dropout_ratio)
dim(dropout_ratio)
[1] 110 14dropout_ratio[ dropout_ratio == "NR" ] <- NA
#dropout_ratio[ dropout_ratio == "upper_primary_boys" ] <- NA
dropout_ratio[ dropout_ratio == "Uppe_r_Primary" ] <- NA
dropout_ratio <- data.frame(dropout_ratio)
dropout_ratio <- na.exclude(dropout_ratio)
dim(dropout_ratio)
[1] 55 14dropout_ratio$primary_boys = as.numeric(dropout_ratio$primary_boys)
dropout_ratio$primary_girls = as.numeric(dropout_ratio$primary_girls)
dropout_ratio$primary_total = as.numeric(dropout_ratio$primary_total)
dropout_ratio$upper_primary_boys = as.numeric(dropout_ratio$upper_primary_boys)
dropout_ratio$upper_primary_girls = as.numeric(dropout_ratio$upper_primary_girls)
dropout_ratio$upper_primary_total = as.numeric(dropout_ratio$upper_primary_total)
dropout_ratio$secondary_boys = as.numeric(dropout_ratio$secondary_boys)
dropout_ratio$secondary_girls = as.numeric(dropout_ratio$secondary_girls)
dropout_ratio$secondary_total = as.numeric(dropout_ratio$secondary_total)
dropout_ratio$hr_secondary_boys = as.numeric(dropout_ratio$hr_secondary_boys)
dropout_ratio$hr_secondary_girls = as.numeric(dropout_ratio$hr_secondary_girls)
dropout_ratio$hr_secondary_total = as.numeric(dropout_ratio$hr_secondary_total)
str(dropout_ratio)
'data.frame': 55 obs. of 14 variables:
$ state_ut : chr "A & N Islands" "Andhra Pradesh" "Arunachal Pradesh" "Arunachal Pradesh" ...
$ year : chr "2014-15" "2013-14" "2013-14" "2012-13" ...
$ primary_boys : num 0.47 4.31 11.54 15.84 11.51 ...
$ primary_girls : num 0.55 4.39 10.22 14.44 10.09 ...
$ primary_total : num 0.51 4.35 10.89 15.16 10.82 ...
$ upper_primary_boys : num 1.44 3.46 4.44 5.86 5.31 7.89 7.6 6.47 3.31 3.7 ...
$ upper_primary_girls: num 1.95 4.12 6.74 9.06 8.08 6.55 6.54 5.22 5.09 4.4 ...
$ upper_primary_total: num 1.69 3.78 5.59 7.47 6.71 7.2 7.05 5.85 4.13 4.02 ...
$ secondary_boys : num 11.5 11.9 16.1 14 18.3 ...
$ secondary_girls : num 8.16 13.37 12.75 11.77 15.81 ...
$ secondary_total : num 9.87 12.65 14.49 12.93 17.11 ...
$ hr_secondary_boys : num 21.05 12.65 18.57 7.85 19.37 ...
$ hr_secondary_girls : num 12.21 10.85 15.49 2.14 17.44 ...
$ hr_secondary_total : num 16.93 11.79 17.07 5.11 18.42 ...
- attr(*, "na.action")= 'exclude' Named int [1:55] 1 2 4 6 12 13 14 15 16 17 ...
..- attr(*, "names")= chr [1:55] "1" "2" "4" "6" ...Plotting Education-Wise All India dropout percentage:
[1] 1 14fig4 <- pivot_longer(all_india_drop, c(primary_boys, primary_girls, primary_total, upper_primary_boys, upper_primary_girls, upper_primary_total, secondary_boys, secondary_girls, secondary_total, hr_secondary_girls, hr_secondary_boys, hr_secondary_total), names_to = "EducationLevel")
ggplot(fig4, aes(x=value, y=EducationLevel)) + geom_boxplot(color="red") + geom_text(aes(label=value), size=4) + labs(title = "Fig-4: All India Dropout Ratio - Education Wise ", x="Dropout Percentage", y="Education Level") + facet_wrap(~year)
Findings:
- The highest dropout ratio is in the secondary level with 17.79 for girls and 17.93 for boys.
Correlation between Gross Enrollment Ratio and Dropout Ratios:
- Comparing Fig-3 and Fig-4, Higher Secondary level has the lowest Enrollment and Secondary Level has the highest dropout.
- We can draw a conclusion from this that, the highest dropouts in Secondary is leading to the lowest enrollment in the Higher secondary schools. Indian Government needs to take measures and implement schemes or improve facilities in these two levels.
Filtering out the State-Wise Dropouts data:
state_ut year primary_boys primary_girls primary_total
1 A & N Islands 2014-15 0.47 0.55 0.51
2 Andhra Pradesh 2013-14 4.31 4.39 4.35
3 Arunachal Pradesh 2013-14 11.54 10.22 10.89
4 Arunachal Pradesh 2012-13 15.84 14.44 15.16
5 Arunachal Pradesh 2014-15 11.51 10.09 10.82
6 Assam 2012-13 7.02 5.46 6.24
upper_primary_boys upper_primary_girls upper_primary_total
1 1.44 1.95 1.69
2 3.46 4.12 3.78
3 4.44 6.74 5.59
4 5.86 9.06 7.47
5 5.31 8.08 6.71
6 7.89 6.55 7.20
secondary_boys secondary_girls secondary_total hr_secondary_boys
1 11.47 8.16 9.87 21.05
2 11.95 13.37 12.65 12.65
3 16.08 12.75 14.49 18.57
4 13.99 11.77 12.93 7.85
5 18.33 15.81 17.11 19.37
6 25.65 27.79 26.77 4.87
hr_secondary_girls hr_secondary_total
1 12.21 16.93
2 10.85 11.79
3 15.49 17.07
4 2.14 5.11
5 17.44 18.42
6 4.50 4.69Analysis of the dropout ratio of Primary Boys:
primary_boys_drop <- states_drop[c("state_ut", "year", "primary_boys")]
slice_min(primary_boys_drop, primary_boys)
state_ut year primary_boys
1 Gujarat 2012-13 0.21top10 <- arrange(primary_boys_drop, desc(primary_boys))
top10 <- slice_head(top10, n=10)
top10
state_ut year primary_boys
1 Nagaland 2013-14 19.09
2 Manipur 2013-14 17.27
3 Arunachal Pradesh 2012-13 15.84
4 Arunachal Pradesh 2013-14 11.54
5 Arunachal Pradesh 2014-15 11.51
6 Manipur 2012-13 10.24
7 Mizoram 2014-15 10.17
8 Madhya Pradesh 2013-14 9.91
9 Uttar Pradesh 2014-15 9.08
10 Assam 2013-14 8.19ggplot(top10, aes(x=year, y=primary_boys, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-5: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - Primary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=primary_boys), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut)
Analysis of the dropout ratio of Primary Girls:
primary_girls_drop <- states_drop[c("state_ut", "year", "primary_girls")]
slice_min(primary_girls_drop, primary_girls)
state_ut year primary_girls
1 Daman & Diu 2014-15 0.29top10 <- arrange(primary_girls_drop, desc(primary_girls))
top10 <- slice_head(top10, n=10)
top10
state_ut year primary_girls
1 Nagaland 2013-14 19.74
2 Manipur 2013-14 18.74
3 Arunachal Pradesh 2012-13 14.44
4 Madhya Pradesh 2013-14 10.40
5 Arunachal Pradesh 2013-14 10.22
6 Arunachal Pradesh 2014-15 10.09
7 Mizoram 2014-15 10.03
8 Manipur 2012-13 9.48
9 Uttar Pradesh 2014-15 8.04
10 Nagaland 2012-13 7.03ggplot(top10, aes(x=year, y=primary_girls, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-6: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - Primary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=primary_girls), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut)
Analysis of dropout ratio of Upper Primary Boys:
upper_primary_boys_drop <- states_drop[c("state_ut", "year", "upper_primary_boys")]
slice_min(upper_primary_boys_drop, upper_primary_boys)
state_ut year upper_primary_boys
1 Puducherry 2012-13 0.33top10 <- arrange(upper_primary_boys_drop, desc(upper_primary_boys))
top10 <- slice_head(top10, n=10)
top10
state_ut year upper_primary_boys
1 Nagaland 2013-14 18.08
2 Nagaland 2012-13 10.15
3 Madhya Pradesh 2013-14 9.88
4 Jharkhand 2014-15 9.01
5 Assam 2012-13 7.89
6 Nagaland 2014-15 7.87
7 Assam 2013-14 7.60
8 Manipur 2013-14 7.48
9 Chhattisgarh 2014-15 6.47
10 Sikkim 2013-14 6.35ggplot(top10, aes(x=year, y=upper_primary_boys, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-7: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - Upper Primary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=upper_primary_boys), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut)
Analysis of dropout ratio of upper primary girls:
upper_primary_girls_drop <- states_drop[c("state_ut", "year", "upper_primary_girls")]
slice_min(upper_primary_girls_drop, upper_primary_girls)
state_ut year upper_primary_girls
1 Himachal Pradesh 2012-13 0.49top10 <- arrange(upper_primary_girls_drop, desc(upper_primary_girls))
top10 <- slice_head(top10, n=10)
top10
state_ut year upper_primary_girls
1 Nagaland 2013-14 17.63
2 Madhya Pradesh 2013-14 13.57
3 Nagaland 2012-13 9.51
4 Arunachal Pradesh 2012-13 9.06
5 Jharkhand 2014-15 8.96
6 Gujarat 2014-15 8.54
7 Gujarat 2012-13 8.19
8 Arunachal Pradesh 2014-15 8.08
9 Gujarat 2013-14 8.04
10 Nagaland 2014-15 7.97ggplot(top10, aes(x=year, y=upper_primary_girls, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-8: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - Upper Primary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=upper_primary_girls), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut)
Analysis of dropout ratio of Secondary boys:
secondary_boys_drop <- states_drop[c("state_ut", "year", "secondary_boys")]
slice_min(secondary_boys_drop, secondary_boys)
state_ut year secondary_boys
1 Himachal Pradesh 2014-15 6.31top10 <- arrange(secondary_boys_drop, desc(secondary_boys))
top10 <- slice_head(top10, n=10)
top10
state_ut year secondary_boys
1 Karnataka 2012-13 40.70
2 Daman & Diu 2014-15 34.45
3 Nagaland 2013-14 34.14
4 Dadra & Nagar Haveli 2013-14 30.02
5 Assam 2013-14 28.59
6 Tripura 2014-15 28.03
7 Nagaland 2012-13 26.70
8 Gujarat 2014-15 26.29
9 Assam 2012-13 25.65
10 Madhya Pradesh 2013-14 25.21ggplot(top10, aes(x=year, y=secondary_boys, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-9: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - secondary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=secondary_boys), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut)
Analysis of dropout ratio of Secondary Girls:
secondary_girls_drop <- states_drop[c("state_ut", "year", "secondary_girls")]
slice_min(secondary_girls_drop, secondary_girls)
state_ut year secondary_girls
1 Himachal Pradesh 2014-15 5.8top10 <- arrange(secondary_girls_drop, desc(secondary_girls))
top10 <- slice_head(top10, n=10)
top10
state_ut year secondary_girls
1 Karnataka 2012-13 39.07
2 Nagaland 2013-14 36.08
3 Assam 2013-14 32.10
4 Daman & Diu 2014-15 29.73
5 Tripura 2014-15 28.83
6 Madhya Pradesh 2013-14 27.91
7 Assam 2012-13 27.79
8 Tripura 2012-13 26.99
9 Dadra & Nagar Haveli 2013-14 26.83
10 Nagaland 2012-13 26.33ggplot(top10, aes(x=year, y=secondary_girls, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-10: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - secondary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=secondary_girls), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut)
Analysis of dropout ratio of Higher-Secondary boys:
hr_secondary_boys_drop <- states_drop[c("state_ut", "year", "hr_secondary_boys")]
slice_min(hr_secondary_boys_drop, hr_secondary_boys)
state_ut year hr_secondary_boys
1 Madhya Pradesh 2013-14 0.52top10 <- arrange(hr_secondary_boys_drop, desc(hr_secondary_boys))
top10 <- slice_head(top10, n=10)
top10
state_ut year hr_secondary_boys
1 Daman & Diu 2014-15 44.38
2 A & N Islands 2014-15 21.05
3 Karnataka 2012-13 19.47
4 Arunachal Pradesh 2014-15 19.37
5 Nagaland 2012-13 18.67
6 Arunachal Pradesh 2013-14 18.57
7 Nagaland 2013-14 15.36
8 Daman & Diu 2013-14 14.48
9 Sikkim 2013-14 14.11
10 Jammu & Kashmir 2014-15 13.85ggplot(top10, aes(x=year, y=hr_secondary_boys, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-11: top-10 states with highest dropout rate of boys in India ", subtitle = "Education Level - Higher secondary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=hr_secondary_boys), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut)
Analysis of dropout ratio of Higher Secondary Girls:
hr_secondary_girls_drop <- states_drop[c("state_ut", "year", "hr_secondary_girls")]
slice_min(hr_secondary_girls_drop, hr_secondary_girls)
state_ut year hr_secondary_girls
1 Gujarat 2012-13 0.3top10 <- arrange(hr_secondary_girls_drop, desc(hr_secondary_girls))
top10 <- slice_head(top10, n=10)
top10
state_ut year hr_secondary_girls
1 Daman & Diu 2014-15 36.05
2 Nagaland 2012-13 17.87
3 Arunachal Pradesh 2014-15 17.44
4 Arunachal Pradesh 2013-14 15.49
5 Telangana 2013-14 13.20
6 Nagaland 2013-14 12.96
7 A & N Islands 2014-15 12.21
8 Sikkim 2013-14 11.92
9 Karnataka 2012-13 11.26
10 Jammu & Kashmir 2014-15 11.20ggplot(top10, aes(x=year, y=hr_secondary_girls, fill=year)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-12: top-10 states with highest dropout rate of girls in India ", subtitle = "Education Level - Higher secondary ", y="dropout percentage", caption="data from the Government of India") + geom_text(aes(label=hr_secondary_girls), size = 4, position = position_dodge(width = .9), vjust = 1, color = "white") + theme_dark() + facet_wrap(~state_ut, scales = "free_y")
Findings:
My key findings from the analysis of dropout percentage of boys and girls across all the 4 education levels are as follows:
Boys:
- Gujarat has the lowest(0.21) and Nagaland has the highest(19.09) dropout percentage in Primary level
- Puducherry had the lowest(0.33) and Nagaland has the highest(18.08) dropout percentage in Upper Primary level
- Himachal pradesh has the lowest(6.31) and Karnataka has the highest(40.70) dropout percentage in Secondary level
- Madhya Pradesh has the lowest(0.52) and Daman & Diu has the highest (44.38) dropout percentage in Higher Secondary level
Girls:
- Daman & Diu has the lowest(0.29) and Nagaland has the highest(19.74) dropout percentage in Primary level
- Himachal Pradesh has the lowest(0.49) and Nagaland has the highest(17.63) dropout percentage in upper primary level
- Himachal Pradesh has the lowest(5.89) and Karnaaka has the highest(39.09) dropout percentage in secondary level
- Gujarat has the lowest(0.3) and Daman & Diu is the highest(36.05) dropout percentage in higher secondary level
My analysis shows that Nagaland, Karnataka and Daman & Diu has the highest dropout rates for boys and girls in primary & upper primary levels, Secondary and Higher Secondary levels respectively.
Gujarat is doing well in terms of the dropout percentage and similar to the Gross Enrollment Ratio the dropout rates are good for girls rather than boys.
My further analysis will be focused on the states of Nagaland, Karnataka and Daman & Diu to findout whether the availability of facilities in schools is effecting the dropout percentage. The study will also focus on those states having less or no facilities at all.
Dataset - 3 | Percentage of Schools with access to computers in India
Reading Dataset-3
schools_with_comps <- read_csv("601 Major Project/percentage-of-schools-with-comps.csv")
colnames(schools_with_comps)
[1] "State_UT"
[2] "year"
[3] "Primary_Only"
[4] "Primary_with_U_Primary"
[5] "Primary_with_U_Primary_Sec_HrSec"
[6] "U_Primary_Only"
[7] "U_Primary_With_Sec_HrSec"
[8] "Primary_with_U_Primary_Sec"
[9] "U_Primary_With_Sec"
[10] "Sec_Only"
[11] "Sec_with_HrSec."
[12] "HrSec_Only"
[13] "All Schools" schools_with_comps <- rename(schools_with_comps, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")
Plotting Education-Level wise percentage of All India access to computers:
All_India <- filter(schools_with_comps, State_UT=="All India") %>%
select(year, primary, upper_primary, secondary, hr_secondary)
All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage")
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-13: Percentage of Schools with access to Computer facility all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 1, color = "black") + theme_classic() + facet_wrap(~Education_Level)
Findings:
- It is really concerning to know that the highest percentage of schools with access to computers all over india is 55.72 only. This says that in every 100 schools, only 56 of them are having access to computers.
- A good sign is that the percentage has a growth every year from 2013-2016, but there is a huge scope for growth.
- Primary schools are the ones having lowest percentage of access to computers and higher secondary schools are the highest. Hence I further analyzed the states having lowest access to computers
Plotting State-Wise Primary level percentage with access to computers:
library(tidytext)
primary_wise<- select(schools_with_comps, State_UT, year, primary) %>%
filter(State_UT != "All India")
primary_wise <- arrange(primary_wise, primary)
primary_wise <- slice_head(primary_wise, n=50)
ggplot(primary_wise, aes(x=primary, y=State_UT, fill=State_UT))+geom_bar(stat="identity")+facet_wrap(~year) + labs(title = "Fig-14: States with lowest percentage of Computer Facility", subtitle = "Education Level - Primary", x="percentage", y="State name") + geom_text(aes(label=primary), size = 3, position = position_dodge(width = .9), vjust = 0, color = "black")
Findings:
- All the 18 states plotted here are the northern states of India, this shows South Indian governments are more supportive towards providing computer access to the schools.
- Assam is the lowest with less than 2% access to computers. But it has shown an increase from 2013-2016 which is a good sign.
Nagaland is among this data, which can be related to the lowest percentage of dropouts in that area. Least access to computers might be one of the reasons for their highest dropout percentage in Primary schools.
library(kableExtra)
upper_primary_wise<- select(schools_with_comps, State_UT, year, upper_primary) %>%
filter(State_UT != "All India") %>%
arrange(upper_primary) %>%
slice(1:20)
kable(upper_primary_wise, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table1 : State-wise Percentage of Upper Primary Schools having lowest access to computers", color="black") %>%
kable_styling(font_size = 15) %>%
row_spec(c(1,1,1))
| State/Union Territory | Year | Percentage |
|---|---|---|
| Andaman & Nicobar Islands | 2013-14 | 0.00 |
| Andaman & Nicobar Islands | 2015-16 | 0.00 |
| Chandigarh | 2013-14 | 0.00 |
| Chandigarh | 2014-15 | 0.00 |
| Chandigarh | 2015-16 | 0.00 |
| Puducherry | 2013-14 | 0.00 |
| Sikkim | 2015-16 | 0.00 |
| Telangana | 2015-16 | 0.00 |
| West Bengal | 2013-14 | 7.93 |
| Odisha | 2013-14 | 8.24 |
| Jammu And Kashmir | 2013-14 | 8.82 |
| Odisha | 2014-15 | 9.19 |
| Odisha | 2015-16 | 9.28 |
| West Bengal | 2014-15 | 9.32 |
| West Bengal | 2015-16 | 9.97 |
| Bihar | 2013-14 | 10.79 |
| Jammu And Kashmir | 2014-15 | 11.19 |
| Jammu And Kashmir | 2015-16 | 11.28 |
| Bihar | 2015-16 | 11.64 |
| Bihar | 2014-15 | 11.65 |
Findings:
- Andaman & Nicobar Islands, Chandigarh, Sikkim and Telangana are the states with no access to computers in Upper primary schools. Class 5-7 is where children need this access the most so that they have required computer knowledge in further classes.
Analyzing Primary and Upper-Primary data of Nagaland
nagaland <- filter(schools_with_comps, State_UT=="Nagaland")
nagaland <- select(nagaland, State_UT, year, primary, upper_primary)
nagaland
# A tibble: 3 x 4
State_UT year primary upper_primary
<chr> <chr> <dbl> <dbl>
1 Nagaland 2013-14 4.98 59.1
2 Nagaland 2014-15 4.97 68.8
3 Nagaland 2015-16 5.53 76.9ggplot() + geom_line(data=nagaland, mapping=aes(x=year, y=primary, group=State_UT), size=1, color="red") + geom_point(data=nagaland, mapping=aes(x=year, y=primary, group=State_UT), color="black") +
geom_line(data=nagaland, mapping=aes(x=year, y=upper_primary, group=State_UT), color="blue", size=1) + geom_point(data=nagaland, mapping=aes(x=year, y=upper_primary, group=State_UT), color="black") + labs(title = "Fig-15: Percentage of Schools with access to Computers in Nagaland State", subtitle = "Education Level - Primary and Upper Primary (Red-Primary, Blue-Upper Primary)", x="year", y="dropout percentage")
Plotting States with high access to computer facility:
secondary_wise<- select(schools_with_comps, State_UT, year, secondary) %>%
filter(State_UT != "All India")
secondary_wise <- arrange(secondary_wise, desc(secondary))
secondary_wise
# A tibble: 107 x 3
State_UT year secondary
<chr> <chr> <dbl>
1 Daman & Diu 2013-14 100
2 Himachal Pradesh 2013-14 100
3 Kerala 2014-15 100
4 Kerala 2015-16 100
5 Nagaland 2015-16 100
6 Punjab 2013-14 100
7 Daman & Diu 2014-15 92.3
8 Daman & Diu 2015-16 92.3
9 Maharashtra 2015-16 90.5
10 Maharashtra 2014-15 88.4
# ... with 97 more rowssecondary_wise <- slice_head(secondary_wise, n=40)
ggplot(secondary_wise, aes(x=secondary, y=State_UT, fill=State_UT))+geom_bar(stat="identity")+facet_wrap(~year) + labs(title = "Fig-16: States with highest percentage of Computer Facility", subtitle = "Education Level - secondary", x="percentage", y="State name") + geom_text(aes(label=secondary), size = 3, position = position_dodge(width = .1), vjust = 0, color = "black")
Plotting Secondary level access to computers in the state of Karnataka
karnataka <- filter(schools_with_comps, State_UT=="Karnataka")
karnataka <- select(karnataka, State_UT, year, secondary)
karnataka
# A tibble: 3 x 3
State_UT year secondary
<chr> <chr> <dbl>
1 Karnataka 2013-14 67.0
2 Karnataka 2014-15 69.9
3 Karnataka 2015-16 69.3ggplot(karnataka, aes(x=year, y=secondary, group=State_UT)) + geom_line(size=1, color="purple") + geom_point() + geom_text(aes(label=secondary), size = 5) + labs(title = "Fig-17: Percentage of Schools with access to Computers in the state of Karnataka", subtitle = "Education Level - Secondary", x="year", y="Percentage")
Plotting States with no access to computers in the Higher Secondary Schools
hr_secondary_wise<- select(schools_with_comps, State_UT, year, hr_secondary) %>%
filter(State_UT != "All India") %>%
arrange(hr_secondary) %>%
slice(1:20)
kable(hr_secondary_wise, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table2 : State-wise percentage of Higher Secondary Schools having zero access to computers") %>%
kable_styling(font_size = 15) %>%
row_spec(c(1,1,1))
| State/Union Territory | Year | Percentage |
|---|---|---|
| Andaman & Nicobar Islands | 2013-14 | 0 |
| Andaman & Nicobar Islands | 2014-15 | 0 |
| Andaman & Nicobar Islands | 2015-16 | 0 |
| Arunachal Pradesh | 2014-15 | 0 |
| Arunachal Pradesh | 2015-16 | 0 |
| Chandigarh | 2013-14 | 0 |
| Chandigarh | 2014-15 | 0 |
| Chandigarh | 2015-16 | 0 |
| Chhattisgarh | 2013-14 | 0 |
| Dadra & Nagar Haveli | 2013-14 | 0 |
| Dadra & Nagar Haveli | 2014-15 | 0 |
| Dadra & Nagar Haveli | 2015-16 | 0 |
| Delhi | 2013-14 | 0 |
| Delhi | 2014-15 | 0 |
| Haryana | 2013-14 | 0 |
| Lakshadweep | 2013-14 | 0 |
| Lakshadweep | 2014-15 | 0 |
| Lakshadweep | 2015-16 | 0 |
| Odisha | 2013-14 | 0 |
| Odisha | 2014-15 | 0 |
Findings:
- Higher Secondary schools of 8 states of India have zero access to computer facility. It is concerning to know that the situation has remained same across 3 years in these states.
- Delhi being capital of India is among this list.
Dataset-4 | Percentage of Schools with access to Electricity in India
Reading Dataset-4
schools_with_electricity <- read_csv("601 Major Project/percentage-of-schools-with-electricity.csv")
head(schools_with_electricity)
# A tibble: 6 x 13
State_UT year Primary_Only Primary_with_U_~ Primary_with_U_~
<chr> <chr> <dbl> <dbl> <dbl>
1 Andaman & Nico~ 2013~ 82.4 96.0 100
2 Andaman & Nico~ 2014~ 80.7 96.3 100
3 Andaman & Nico~ 2015~ 82.1 97.6 100
4 Andhra Pradesh 2013~ 87.7 93.6 99.3
5 Andhra Pradesh 2014~ 91.1 94.7 100
6 Andhra Pradesh 2015~ 91.6 95.6 100
# ... with 8 more variables: U_Primary_Only <dbl>,
# U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
# U_Primary_With_Sec <dbl>, Sec_Only <dbl>, Sec_with_HrSec. <dbl>,
# HrSec_Only <dbl>, `All Schools` <dbl>colnames(schools_with_electricity)
[1] "State_UT"
[2] "year"
[3] "Primary_Only"
[4] "Primary_with_U_Primary"
[5] "Primary_with_U_Primary_Sec_HrSec"
[6] "U_Primary_Only"
[7] "U_Primary_With_Sec_HrSec"
[8] "Primary_with_U_Primary_Sec"
[9] "U_Primary_With_Sec"
[10] "Sec_Only"
[11] "Sec_with_HrSec."
[12] "HrSec_Only"
[13] "All Schools" schools_with_electricity <- rename(schools_with_electricity, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")
Datatype of each column
str(schools_with_electricity)
spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ State_UT : chr [1:110] "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andhra Pradesh" ...
$ year : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
$ primary : num [1:110] 82.4 80.7 82.1 87.7 91.1 ...
$ Primary_with_U_Primary : num [1:110] 96 96.3 97.6 93.6 94.7 ...
$ Primary_with_U_Primary_Sec_HrSec: num [1:110] 100 100 100 99.3 100 ...
$ upper_primary : num [1:110] 0 100 0 100 100 ...
$ U_Primary_With_Sec_HrSec : num [1:110] 100 100 100 67.5 86.1 ...
$ Primary_with_U_Primary_Sec : num [1:110] 100 100 100 96.2 97.6 ...
$ U_Primary_With_Sec : num [1:110] 0 0 0 96.2 97.1 ...
$ secondary : num [1:110] 0 0 0 97.5 93.5 ...
$ Sec_with_HrSec. : num [1:110] 100 100 100 100 83.3 ...
$ hr_secondary : num [1:110] 0 0 0 91.3 93.2 ...
$ All Schools : num [1:110] 88.9 88.9 90.1 90.3 92.8 ...
- attr(*, "spec")=
.. cols(
.. State_UT = col_character(),
.. year = col_character(),
.. Primary_Only = col_double(),
.. Primary_with_U_Primary = col_double(),
.. Primary_with_U_Primary_Sec_HrSec = col_double(),
.. U_Primary_Only = col_double(),
.. U_Primary_With_Sec_HrSec = col_double(),
.. Primary_with_U_Primary_Sec = col_double(),
.. U_Primary_With_Sec = col_double(),
.. Sec_Only = col_double(),
.. Sec_with_HrSec. = col_double(),
.. HrSec_Only = col_double(),
.. `All Schools` = col_double()
.. )
- attr(*, "problems")=<externalptr> Plotting Education Level wise percentage with access to Electricity all over India
All_India <- filter(schools_with_electricity, State_UT=="All India") %>%
select(year, primary, upper_primary, secondary, hr_secondary)
All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage")
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-18: Percentage of Schools with access to Electricity all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 1, color = "black") + theme_classic() + facet_wrap(~Education_Level)
Findings:
- The numbers for Secondary and Higher Secondary are looking good.
- Nearly 50% of Primary and Upper Primary schools don’t have access to electricity in India.
states_electricity <- filter(schools_with_electricity, State_UT != "All India")
nagaland <- filter(states_electricity, State_UT == "Nagaland") %>%
select(year, primary, upper_primary )
nagaland <- pivot_longer(nagaland, c(primary,upper_primary), names_to = "Education_Level", values_to = "Percentage")
ggplot(nagaland, aes(x=year, y=Percentage, fill=Education_Level)) + geom_bar(position="dodge", stat="identity") + coord_polar() + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + labs(title = "Fig-19: Percentage of Schools with access to Electricity in the state of Nagaland", subtitle = "Education Level - Primary and Upper Primary" )
karnataka <- filter(states_electricity, State_UT == "Karnataka") %>%
select(year, secondary, State_UT )
ggplot(karnataka, aes(x=year, y=secondary, group=State_UT)) + geom_line(color="red", size=1) + geom_point() + geom_text(aes(label=secondary), size = 4, position = position_dodge(width = .6), vjust = 0, color = "black") + labs(title = "Fig-20: Percentage of Schools with access to Electricity in the state of Karnataka", subtitle = "Education Level - Secondary" )
states_electricity
# A tibble: 107 x 13
State_UT year primary Primary_with_U_~ Primary_with_U_~
<chr> <chr> <dbl> <dbl> <dbl>
1 Andaman & Nicobar ~ 2013~ 82.4 96.0 100
2 Andaman & Nicobar ~ 2014~ 80.7 96.3 100
3 Andaman & Nicobar ~ 2015~ 82.1 97.6 100
4 Andhra Pradesh 2013~ 87.7 93.6 99.3
5 Andhra Pradesh 2014~ 91.1 94.7 100
6 Andhra Pradesh 2015~ 91.6 95.6 100
7 Arunachal Pradesh 2013~ 19.7 53.6 92.2
8 Arunachal Pradesh 2014~ 21.5 55.0 96.8
9 Arunachal Pradesh 2015~ 22.6 53.9 95.5
10 Assam 2013~ 9.51 51.1 81.2
# ... with 97 more rows, and 8 more variables: upper_primary <dbl>,
# U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
# U_Primary_With_Sec <dbl>, secondary <dbl>, Sec_with_HrSec. <dbl>,
# hr_secondary <dbl>, `All Schools` <dbl>Daman_Diu <- filter(states_electricity, State_UT == "Daman & Diu") %>%
select(State_UT, year, hr_secondary )
kable(Daman_Diu, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table4 : Daman & Diu Percentage of Schools with Electricity") %>%
kable_styling(font_size = 15) %>%
row_spec(c(1,1,1))
| State/Union Territory | Year | Percentage |
|---|---|---|
| Daman & Diu | 2013-14 | 100 |
| Daman & Diu | 2014-15 | 100 |
| Daman & Diu | 2015-16 | 100 |
Findings:
- All the three states are doing good with providing access to electricity in schools expect in the primary level.
Dataset-5 | Percentage of Schools with water faciliity in India
Reading Dataset-5
schools_with_water <- read_csv("601 Major Project/percentage-of-schools-with-water-facility.csv")
head(schools_with_water)
# A tibble: 6 x 13
`State/UT` Year Primary_Only Primary_with_U_~ Primary_with_U_~
<chr> <chr> <dbl> <dbl> <dbl>
1 Andaman & Nico~ 2013~ 98.2 98.7 100
2 Andaman & Nico~ 2014~ 99.6 98.8 100
3 Andaman & Nico~ 2015~ 100 100 100
4 Andhra Pradesh 2013~ 86.9 94.5 99.7
5 Andhra Pradesh 2014~ 91.8 96.1 100
6 Andhra Pradesh 2015~ 93.9 97.0 100
# ... with 8 more variables: U_Primary_Only <dbl>,
# U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
# U_Primary_With_Sec <dbl>, Sec_Only <dbl>, Sec_with_HrSec. <dbl>,
# HrSec_Only <dbl>, `All Schools` <dbl>colnames(schools_with_water)
[1] "State/UT"
[2] "Year"
[3] "Primary_Only"
[4] "Primary_with_U_Primary"
[5] "Primary_with_U_Primary_Sec_HrSec"
[6] "U_Primary_Only"
[7] "U_Primary_With_Sec_HrSec"
[8] "Primary_with_U_Primary_Sec"
[9] "U_Primary_With_Sec"
[10] "Sec_Only"
[11] "Sec_with_HrSec."
[12] "HrSec_Only"
[13] "All Schools" schools_with_water <- rename(schools_with_water, State_UT="State/UT", primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only" )
Datatype of each column
str(schools_with_water)
spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ State_UT : chr [1:110] "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andhra Pradesh" ...
$ Year : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
$ primary : num [1:110] 98.2 99.5 100 86.9 91.8 ...
$ Primary_with_U_Primary : num [1:110] 98.7 98.8 100 94.5 96.1 ...
$ Primary_with_U_Primary_Sec_HrSec: num [1:110] 100 100 100 99.7 100 ...
$ upper_primary : num [1:110] 0 100 0 90.9 100 ...
$ U_Primary_With_Sec_HrSec : num [1:110] 100 100 100 87.3 90 ...
$ Primary_with_U_Primary_Sec : num [1:110] 100 100 100 98.8 99.6 ...
$ U_Primary_With_Sec : num [1:110] 0 0 0 96 97.5 ...
$ secondary : num [1:110] 0 0 0 97.5 100 100 0 0 0 88.3 ...
$ Sec_with_HrSec. : num [1:110] 100 100 100 100 100 ...
$ hr_secondary : num [1:110] 0 0 0 97.5 98.4 ...
$ All Schools : num [1:110] 98.7 99.5 100 90.3 93.7 ...
- attr(*, "spec")=
.. cols(
.. `State/UT` = col_character(),
.. Year = col_character(),
.. Primary_Only = col_double(),
.. Primary_with_U_Primary = col_double(),
.. Primary_with_U_Primary_Sec_HrSec = col_double(),
.. U_Primary_Only = col_double(),
.. U_Primary_With_Sec_HrSec = col_double(),
.. Primary_with_U_Primary_Sec = col_double(),
.. U_Primary_With_Sec = col_double(),
.. Sec_Only = col_double(),
.. Sec_with_HrSec. = col_double(),
.. HrSec_Only = col_double(),
.. `All Schools` = col_double()
.. )
- attr(*, "problems")=<externalptr> plotting Education-Wise percentage of schools with water faciltity in India
All_India <- filter(schools_with_water, State_UT=="All India") %>%
select(Year, primary, upper_primary, secondary, hr_secondary)
All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage")
ggplot(All_India, aes(x=Year, y=Percentage, fill=Education_Level)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-21: Percentage of Schools Water Facility all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .98), vjust = 1, color = "black") + theme_classic() + facet_wrap(~Education_Level)
Finding: It is really glad to know that schools at every education level have more than 90% access to water facility, but still it is not 100%.
states_water <- filter(schools_with_water, State_UT != "All India")
nagaland <- filter(states_water, State_UT == "Nagaland") %>%
select(Year, primary, upper_primary )
nagaland <- pivot_longer(nagaland, c(primary,upper_primary), names_to = "Education_Level", values_to = "Percentage")
ggplot(nagaland, aes(x=Year, y=Percentage, fill=Education_Level)) + geom_bar(position="dodge", stat="identity") + coord_polar() + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .9), vjust = 0, color = "black") + labs(title = "Fig-22: Percentage of Schools with access to Drinking Water in the state of Nagaland", subtitle = "Education Level - Primary and Upper Primary" )
karnataka <- filter(schools_with_water, State_UT == "Karnataka") %>%
select(Year, secondary, State_UT )
ggplot(karnataka, aes(x=Year, y=secondary, group=State_UT)) + geom_line(color="red", size=1) + geom_point() + geom_text(aes(label=secondary), size = 4, position = position_dodge(width = .6), vjust = 0, color = "black") + labs(title = "Fig-23: Percentage of Schools with access to Drinking Water in the state of Karnataka", subtitle = "Education Level - Secondary" )
Daman_Diu <- filter(schools_with_water, State_UT == "Daman & Diu") %>%
select(State_UT, Year, hr_secondary )
kable(Daman_Diu, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Percentage"), caption = "Table4 : Daman & Diu Percentage of Schools with Drinking Water Facility") %>%
kable_styling(font_size = 15) %>%
row_spec(c(1,1,1))
| State/Union Territory | Year | Percentage |
|---|---|---|
| Daman & Diu | 2013-14 | 100 |
| Daman & Diu | 2014-15 | 100 |
| Daman & Diu | 2015-16 | 100 |
Dataset-6 | Percentage of Schools with boys toilet all over India
Reading Dataset-6
schools_with_boys_toilet <- read_csv("601 Major Project/schools-with-boys-toilet.csv")
head(schools_with_boys_toilet)
# A tibble: 6 x 13
State_UT year Primary_Only Primary_with_U_~ Primary_with_U_~
<chr> <chr> <dbl> <dbl> <dbl>
1 Andaman & Nico~ 2013~ 91.6 97.4 100
2 Andaman & Nico~ 2014~ 100 100 100
3 Andaman & Nico~ 2015~ 100 100 100
4 Andhra Pradesh 2013~ 53.0 62.6 82.0
5 Andhra Pradesh 2014~ 57.9 76.5 96
6 Andhra Pradesh 2015~ 99.6 99.9 99.0
# ... with 8 more variables: U_Primary_Only <dbl>,
# U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
# U_Primary_With_Sec <dbl>, Sec_Only <dbl>, Sec_with_HrSec. <dbl>,
# HrSec_Only <dbl>, `All Schools` <dbl>colnames(schools_with_boys_toilet)
[1] "State_UT"
[2] "year"
[3] "Primary_Only"
[4] "Primary_with_U_Primary"
[5] "Primary_with_U_Primary_Sec_HrSec"
[6] "U_Primary_Only"
[7] "U_Primary_With_Sec_HrSec"
[8] "Primary_with_U_Primary_Sec"
[9] "U_Primary_With_Sec"
[10] "Sec_Only"
[11] "Sec_with_HrSec."
[12] "HrSec_Only"
[13] "All Schools" schools_with_boys_toilet <- rename(schools_with_boys_toilet, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")
Datatype of each column
str(schools_with_boys_toilet)
spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ State_UT : chr [1:110] "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andaman & Nicobar Islands" "Andhra Pradesh" ...
$ year : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
$ primary : num [1:110] 91.6 100 100 53 57.9 ...
$ Primary_with_U_Primary : num [1:110] 97.4 100 100 62.6 76.5 ...
$ Primary_with_U_Primary_Sec_HrSec: num [1:110] 100 100 100 82 96 ...
$ upper_primary : num [1:110] 0 100 0 45.5 75 ...
$ U_Primary_With_Sec_HrSec : num [1:110] 100 100 100 64.1 93.3 ...
$ Primary_with_U_Primary_Sec : num [1:110] 100 100 100 76.2 91.4 ...
$ U_Primary_With_Sec : num [1:110] 0 0 0 60.6 78 ...
$ secondary : num [1:110] 0 0 0 59.3 80.7 ...
$ Sec_with_HrSec. : num [1:110] 100 100 100 85.7 60 ...
$ hr_secondary : num [1:110] 0 0 0 73.4 86.5 ...
$ All Schools : num [1:110] 94.5 100 100 56.9 65.3 ...
- attr(*, "spec")=
.. cols(
.. State_UT = col_character(),
.. year = col_character(),
.. Primary_Only = col_double(),
.. Primary_with_U_Primary = col_double(),
.. Primary_with_U_Primary_Sec_HrSec = col_double(),
.. U_Primary_Only = col_double(),
.. U_Primary_With_Sec_HrSec = col_double(),
.. Primary_with_U_Primary_Sec = col_double(),
.. U_Primary_With_Sec = col_double(),
.. Sec_Only = col_double(),
.. Sec_with_HrSec. = col_double(),
.. HrSec_Only = col_double(),
.. `All Schools` = col_double()
.. )
- attr(*, "problems")=<externalptr> Plotting Education Level wise percentage of schools with boys toilet in India
All_India <- filter(schools_with_boys_toilet, State_UT=="All India") %>%
select(year, primary, upper_primary, secondary, hr_secondary)
All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage")
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-24: Percentage of Schools with Boys toilet all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .98), vjust = 1, color = "black") + theme_classic() + facet_wrap(~Education_Level)
Findings:
- The percentage has increased to greater than or equal to 95 among all levels in 2015-2016 which is a good sign of improvement.
states <- c("Nagaland", "Karnataka", "Daman & Diu")
states_with_boys_toilet <- filter(schools_with_boys_toilet, State_UT == states) %>%
pivot_longer(c(primary,upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>%
select(State_UT, year, Education_Level, Percentage)
ggplot(states_with_boys_toilet, aes(x=year, y=Percentage, fill=Education_Level)) + geom_bar(position = "dodge", stat = "identity") + facet_wrap(~State_UT) + theme_dark() + labs(title = "Fig-25: Percentage of Schools with Boys toilet", subtitle = "Daman & Diu, Karnataka, Nagaland")
States with no boys toilets in India
states_with_no_boys_toilet <- filter(schools_with_boys_toilet, State_UT != "All India", upper_primary==0, secondary==0, hr_secondary==0) %>%
pivot_longer(c(upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>%
select(State_UT, year, Education_Level, Percentage)
kable(states_with_no_boys_toilet, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Education Level", "Percentage"), caption = "Table4 : States with no boys toilet") %>%
kable_styling(font_size = 15) %>%
row_spec(c(1,1,1))
| State/Union Territory | Year | Education Level | Percentage |
|---|---|---|---|
| Andaman & Nicobar Islands | 2013-14 | upper_primary | 0 |
| Andaman & Nicobar Islands | 2013-14 | secondary | 0 |
| Andaman & Nicobar Islands | 2013-14 | hr_secondary | 0 |
| Andaman & Nicobar Islands | 2015-16 | upper_primary | 0 |
| Andaman & Nicobar Islands | 2015-16 | secondary | 0 |
| Andaman & Nicobar Islands | 2015-16 | hr_secondary | 0 |
| Arunachal Pradesh | 2013-14 | upper_primary | 0 |
| Arunachal Pradesh | 2013-14 | secondary | 0 |
| Arunachal Pradesh | 2013-14 | hr_secondary | 0 |
| Chandigarh | 2013-14 | upper_primary | 0 |
| Chandigarh | 2013-14 | secondary | 0 |
| Chandigarh | 2013-14 | hr_secondary | 0 |
| Chandigarh | 2014-15 | upper_primary | 0 |
| Chandigarh | 2014-15 | secondary | 0 |
| Chandigarh | 2014-15 | hr_secondary | 0 |
| Chandigarh | 2015-16 | upper_primary | 0 |
| Chandigarh | 2015-16 | secondary | 0 |
| Chandigarh | 2015-16 | hr_secondary | 0 |
| Dadra & Nagar Haveli | 2013-14 | upper_primary | 0 |
| Dadra & Nagar Haveli | 2013-14 | secondary | 0 |
| Dadra & Nagar Haveli | 2013-14 | hr_secondary | 0 |
Dataset-7 | Percentage of Schools with girls toilets in India
Reading Dataset-7
schools_with_girls_toilet <- read_csv("601 Major Project/schools-with-girls-toilet.csv")
schools_with_girls_toilet <- rename(schools_with_girls_toilet, primary="Primary_Only", upper_primary="U_Primary_Only", secondary="Sec_Only", hr_secondary="HrSec_Only")
head(schools_with_girls_toilet)
# A tibble: 6 x 13
State_UT year primary Primary_with_U_~ Primary_with_U_~
<chr> <chr> <dbl> <dbl> <dbl>
1 All India 2013~ 88.7 96.0 98.8
2 All India 2014~ 91.2 96.9 99.5
3 All India 2015~ 97.0 99.0 99.7
4 Andaman & Nicobar I~ 2013~ 89.7 97.4 100
5 Andaman & Nicobar I~ 2014~ 100 100 100
6 Andaman & Nicobar I~ 2015~ 100 100 100
# ... with 8 more variables: upper_primary <dbl>,
# U_Primary_With_Sec_HrSec <dbl>, Primary_with_U_Primary_Sec <dbl>,
# U_Primary_With_Sec <dbl>, secondary <dbl>, Sec_with_HrSec. <dbl>,
# hr_secondary <dbl>, `All Schools` <dbl>Datatype of each column
str(schools_with_girls_toilet)
spec_tbl_df [110 x 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
$ State_UT : chr [1:110] "All India" "All India" "All India" "Andaman & Nicobar Islands" ...
$ year : chr [1:110] "2013-14" "2014-15" "2015-16" "2013-14" ...
$ primary : num [1:110] 88.7 91.2 97 89.7 100 ...
$ Primary_with_U_Primary : num [1:110] 96 96.9 99 97.4 100 ...
$ Primary_with_U_Primary_Sec_HrSec: num [1:110] 98.8 99.5 99.7 100 100 ...
$ upper_primary : num [1:110] 91.4 91.4 96.3 0 100 ...
$ U_Primary_With_Sec_HrSec : num [1:110] 98.2 99.2 99.6 100 100 ...
$ Primary_with_U_Primary_Sec : num [1:110] 97.3 98.2 99.3 100 100 ...
$ U_Primary_With_Sec : num [1:110] 94.4 96.6 98.8 0 0 ...
$ secondary : num [1:110] 99.1 90.3 95.2 0 0 ...
$ Sec_with_HrSec. : num [1:110] 98.4 94 98.3 100 100 ...
$ hr_secondary : num [1:110] 76.1 90.9 96.2 0 0 ...
$ All Schools : num [1:110] 91.2 93.1 97.5 93.4 100 ...
- attr(*, "spec")=
.. cols(
.. State_UT = col_character(),
.. year = col_character(),
.. Primary_Only = col_double(),
.. Primary_with_U_Primary = col_double(),
.. Primary_with_U_Primary_Sec_HrSec = col_double(),
.. U_Primary_Only = col_double(),
.. U_Primary_With_Sec_HrSec = col_double(),
.. Primary_with_U_Primary_Sec = col_double(),
.. U_Primary_With_Sec = col_double(),
.. Sec_Only = col_double(),
.. Sec_with_HrSec. = col_double(),
.. HrSec_Only = col_double(),
.. `All Schools` = col_double()
.. )
- attr(*, "problems")=<externalptr> Plotting Education-Wise percentage of schools with girls toilets in India
All_India <- filter(schools_with_girls_toilet, State_UT=="All India") %>%
select(year, primary, upper_primary, secondary, hr_secondary)
All_India <- pivot_longer(All_India, c(primary, upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage")
ggplot(All_India, aes(x=year, y=Percentage, fill=Education_Level)) +
geom_bar(position = "dodge", stat = "identity") + labs(title = "Fig-26: Percentage of Schools with Girls toilet all over india") + geom_text(aes(label=Percentage), size = 4, position = position_dodge(width = .98), vjust = 1, color = "black") + theme_classic() + facet_wrap(~Education_Level)
states <- c("Nagaland", "Karnataka", "Daman & Diu")
states_with_girls_toilet <- filter(schools_with_girls_toilet, State_UT == states) %>%
pivot_longer(c(primary,upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>%
select(State_UT, year, Education_Level, Percentage)
ggplot(states_with_girls_toilet, aes(x=year, y=Percentage, fill=Education_Level)) + geom_bar(position = "dodge", stat = "identity") + facet_wrap(~State_UT) + theme_dark() + labs(title = "Fig-27: Percentage of Schools with Girls toilet", subtitle = "Daman & Diu, Karnataka, Nagaland")
States with no girls toilets in India
states_with_no_girls_toilet <- filter(schools_with_girls_toilet, State_UT != "All India", upper_primary==0, secondary==0, hr_secondary==0) %>%
pivot_longer(c(upper_primary, secondary, hr_secondary), names_to = "Education_Level", values_to = "Percentage") %>%
select(State_UT, year, Education_Level, Percentage)
kable(states_with_no_girls_toilet, digits = 4, align = "ccccccc", col.names = c("State/Union Territory", "Year", "Education Level", "Percentage"), caption = "Table4 : States with no girls toilet") %>%
kable_styling(font_size = 15) %>%
row_spec(c(1,1,1))
| State/Union Territory | Year | Education Level | Percentage |
|---|---|---|---|
| Andaman & Nicobar Islands | 2013-14 | upper_primary | 0 |
| Andaman & Nicobar Islands | 2013-14 | secondary | 0 |
| Andaman & Nicobar Islands | 2013-14 | hr_secondary | 0 |
| Andaman & Nicobar Islands | 2015-16 | upper_primary | 0 |
| Andaman & Nicobar Islands | 2015-16 | secondary | 0 |
| Andaman & Nicobar Islands | 2015-16 | hr_secondary | 0 |
| Chandigarh | 2013-14 | upper_primary | 0 |
| Chandigarh | 2013-14 | secondary | 0 |
| Chandigarh | 2013-14 | hr_secondary | 0 |
| Chandigarh | 2014-15 | upper_primary | 0 |
| Chandigarh | 2014-15 | secondary | 0 |
| Chandigarh | 2014-15 | hr_secondary | 0 |
| Chandigarh | 2015-16 | upper_primary | 0 |
| Chandigarh | 2015-16 | secondary | 0 |
| Chandigarh | 2015-16 | hr_secondary | 0 |
Conclusion:
Summary of my key Findings:
Gross Enrollment Ratio:
The Gross Enrollment Ratio of girls and boys is high in primary schools with 100% and low in Higher secondary schools with around 50%. Comparatively girls enrollment is higher and showed an increasing pattern than the boys.
The Primary girls Gross Enrollment Ratio is the highest for Eastern states like Assam, Mizoram, etc and it’s the lowest for Andhra Pradesh (AP) and Jammu & Kashmir.
Upper_Primary girls Gross Enrollment Ratio is the lowest in Jammu & Kashmir and that too at an alarming number! AP also has a lot of room for improvement in its GER.
Secondary girls GER top 3 lowest states are J & K, Uttar Pradesh, and Gujarat. This is really concerning.
Overall the lowest of the GER belongs to the Higher Secondary Level.
Dropout Ratio:
- The highest dropout ratio is in secondary level among boys and girls, which is major reason why there is less GER in Higher Secondary Level.
Boys:
- Gujarat has the lowest(0.21) and Nagaland has the highest(19.09) dropout percentage in Primary level
- Puducherry had the lowest(0.33) and Nagaland has the highest(18.08) dropout percentage in Upper Primary level
- Himachal pradesh has the lowest(6.31) and Karnataka has the highest(40.70) dropout percentage in Secondary level
- Madhya Pradesh has the lowest(0.52) and Daman & Diu has the highest (44.38) dropout percentage in Higher Secondary level
Girls:
- Daman & Diu has the lowest(0.29) and Nagaland has the highest(19.74) dropout percentage in Primary level
- Himachal Pradesh has the lowest(0.49) and Nagaland has the highest(17.63) dropout percentage in upper primary level
- Himachal Pradesh has the lowest(5.89) and Karnaaka has the highest(39.09) dropout percentage in secondary level
- Gujarat has the lowest(0.3) and Daman & Diu is the highest(36.05) dropout percentage in higher secondary level
My analysis shows that Nagaland, Karnataka and Daman & Diu has the highest dropout rates for boys and girls in primary & upper primary levels, Secondary and Higher Secondary levels respectively.
Gujarat is doing well in terms of the dropout percentage and similar to the Gross Enrollment Ratio the dropout rates are good for girls rather than boys.
Access to basic facilities in schools (Electricity, Water, Toilets and Computers):
Nearly 50% of Primary and Upper Primary schools don’t have access to electricity in India.
Schools of every education level have more than 90% access to water facility, but still it is not 100%.
Higher secondary schools are having the lowest access to boys toilets.
Andaman & Nicobar islands, Arunachal Pradesh, Chandigarh and Dadra & Nagar Haveli are the states and union territories with no boys toiets in upper primary, secondary and higher secondary levels.
Andaman & Nicobar islands and Chandigarh are the states with no access to girls toilets in upper primary, secondary and higher secondary levels.
The highest percentage of schools with access to computers all over india is 55.72 only. This says that in every 100 schools, only 56 of them are having access to computers.
Primary schools are the ones having lowest percentage of access to computers and higher secondary schools are the highest.
References
data.gov.in - (https://data.gov.in/search?title=Percentage%20of%20Schools%20with%20Drinking%20Water%20Facility%20from%202013-14%20to%202015-16)
The Education System in India, Dr. V. Sasi Kumar - (https://www.gnu.org/education/edu-system-india.en.html#:~:text=The%20school%20system%20in%20India,and%20higher%20secondary%20into%20two.)
